Exploring the Role of School and Classroom Climate in Shaping Mathematics Achievement and Self-Concept: A Multilevel Analysis of Indonesian Students Using TIMSS 2011

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However, achieving these objectives can be influenced by several factors, with school climate being a significant one. This study focuses on exploring students' mathematics achievement and self-concept using data from the TIMSS 2011 assessment. Methods This study utilized TIMSS 2011 data, comprising a sample of 5,795 eighth-grade students from Indonesia. The data was analyzed using three-level multilevel models with MLWin. The students were nested within 174 classes and 153 schools, and this hierarchical structure was incorporated into the analysis through the three-level multilevel modeling approach. Results The study reveals a complex relationship between school climate and student outcomes. Our analysis shows that active student participation in mathematics lessons and classroom discipline positively impacted academic achievement, even after accounting for various student, teacher, and school-level factors. Surprisingly, student safety exhibited a negative association with academic performance. Furthermore, in relation to student mathematics self-concept, five school climate elements were significant predictors: students' connection to their school, teachers' perceived safety, teachers' confidence in their mathematics instruction, the availability of adequate school physical resources, and student safety. In contrast, interactions among teachers and students' engagement in mathematics lessons were negatively linked to academic achievement. It's important to note these findings with caution, as the overall goodness of fit for the models was relatively modest. Conclusion The multilevel analysis offers a detailed and insightful understanding of the Indonesian school climate. It not only provides a valuable framework for interpreting diverse school practices but also powerfully illustrates how school climate serves as a critical protective factor for improving student outcomes, irrespective of the school's type. This research strongly reinforces the idea that school climate is a highly malleable aspect of education, one that schools and local governments can effectively shape and improve. Mathematics performance Self-efficacy Self-concept School climate Classroom climate TIMSS Multilevel analysis INTRODUCTION According to the World Bank ( 2014c ), Indonesia's education system is the fourth biggest in the world, behind China, India, and the United States, based on population. It is huge and diverse, with over 250,000 schools, 50 million pupils, and over 2.6 million teachers scattered among 34 provinces and 514 districts (Ministry of Education and Culture, 2017a). In terms of education authorities, Indonesia has a unique education system since it has two school systems running in parallel. Furthermore, the systems are governed by two separate ministries, the Ministry of Education and Culture (MOEC) and the Ministry of Religious Affairs (MORA). Overall, MOEC is in charge of public and private general schools, whereas MORA is in charge of Islamic schools, both public and private. The MOEC supervises 81% of primary and secondary schools, while the MORA supervises the other 19% (Ministry of Education and Culture, 2017a). Due to the dual education system in Indonesia, lower secondary school students, such as those in the TIMSS sample, can enroll in one of two pathways: Sekolah Menengah Pertama (SMP), a regular secondary school offering standard education, or Madrasah Tsanawiyah (MTs), an Islamic secondary school providing Islamic education (Republic of Indonesia, 2003 ; 2010 ). The regular education pathway follows a six-year curriculum that includes subjects such as civics education, religious and moral education, Bahasa Indonesia (the Indonesian language), mathematics, art, and physical education. Starting in Year 4, students study additional subjects—science and social studies (Ministry of Education and Culture, 2013b). These curricula are implemented in both regular and Islamic schools. However, Islamic schools or madrasahs adopt a more specialized approach to religious and moral education. Here, the subject is divided into distinct areas, including Quran and Hadith studies, Islamic theology (aqidah), Islamic jurisprudence (fiqh), Arabic language, and, from Year 4, Islamic history (Indonesia Ministry of Religious Affairs, 2014). Consequently, madrasahs allocate 4 to 6 additional hours of study each week compared to regular schools. The differing educational orientations of regular and Islamic schools can shape the school climate, creating unique challenges in their management. Madrasahs are often regarded as having lower academic standards, largely because they are predominantly located in rural areas (Ali et al., 2011 ). As such, investigating the school climate, as proposed by the current study, could provide valuable insights for the Government of Indonesia. This research would support efforts to evaluate and enhance the effectiveness of education by examining school climate across various types of schools. According to the results of the Trends in International Mathematics and Science Study (TIMSS), Indonesia consistently ranks as the country with the lowest mathematics performance scores (Martin et al., 2008 ; Martin et al., 2004). Across TIMSS assessments conducted in 1999, 2003, 2007, and 2011, the average mathematics achievement of Indonesian eighth-graders was consistently below the international mean. In addition to assessing mathematics and science achievement, TIMSS also examines non-cognitive aspects of student learning, such as self-concept and self-efficacy. The emphasis on outcomes beyond academic achievement has long been a topic of discussion, underscoring the need for schools to address both cognitive and non-cognitive areas of development. Schools should strive to foster students' affective and psychomotor growth—encompassing their emotions, thoughts, behaviors, beliefs, and the processes they experience when engaging with specific subjects. This perspective aligns with UNESCO's (2004) recommendation that education should emphasize both academic and affective outcomes to help students reach their full potential. It also mirrors the goals of Indonesia's education system, which aims to improve outcomes in both domains. Furthermore, UNESCO ( 2014 ) highlights the importance of non-academic learning outcomes as part of the Post-2015 Education Indicators to support student achievement. Some academics critique the traditional focus on cognitive outcomes in school effectiveness studies, describing this approach as mechanistic (Elliott, 1996 ). Such a focus may fail to ensure that students achieve their educational goals (Creemers & Kyriakides, 2010; Knuver & Brandsma, 1993 ; Mortimore, 1988 ; Thomas et al., 2000 ). Since student achievement is a multidimensional construct that includes both cognitive and non-cognitive aspects, this mechanistic view is paradoxical (Guskey, 2012 ). Mortimore and colleagues' ( 1989 ) influential study revealed that 77% of teachers from 50 schools in London prioritized social-affective goals for their students. Therefore, assessing the emotional and social aspects of education is just as critical as evaluating its cognitive outcomes (Reynolds et al., 2014 ). The present study aims to investigate the role of school climate in enhancing student achievement, including its non-cognitive dimensions, such as self-concept. The Indonesia Education Act (Indonesia, 2003 ) also emphasizes the importance of nurturing students to achieve their fullest potential, making it essential to establish a constructive and positive learning environment. Schools should prioritize creating a setting that centres on the aspirations and goals of students and their parents. Teachers and school staff should be motivated to perform at their best, while fostering mutual respect and emotional connections within the school community. Consequently, research on school climate holds significant relevance in the Indonesian context. Each school has its own unique traits, challenges, and issues, which contribute to its distinct character, setting, and culture (Freiberg, 1999 ). The concept of school climate encapsulates the overall character of a school, reflecting the essence of its organizational personality (Hoy, 2012 ). It can be likened to the personality of an individual within an organizational framework (Hoy et al., 1991 ). School climate is further described as the character and quality of school life, shaped by the emotional dynamics and lived experiences of students within the school environment (Cohen et al., 2009 ). These aspects collectively influence the educational experience and outcomes, highlighting the profound impact of school climate on the overall effectiveness of a school. In the early development of the School Effectiveness Research (SER) field, school climate emerged as a vital indicator for evaluating school effectiveness (Brault et al., 2014 ). Brookover et al. ( 1978 ), for instance, highlighted the role of school climate in facilitating student learning. Consequently, Edmonds' (1979) foundational model of effective schools recognized the significance of school climate. He identified key elements that contribute to an environment fostering academic achievement, including strong school leadership, high academic performance standards, safe and organized settings, a focus on essential academic skills, and a system for monitoring student progress. Hoy et al. ( 1991 ) similarly acknowledged that a positive and favorable school climate significantly impacts overall school performance. School climate is often defined as the "quality and character of school life" (Cohen et al., 2009 ), encompassing both the social and physical dimensions of the school environment. It is widely regarded as a crucial factor in promoting diverse student outcomes, both academic and personal. Since the early 20th century, educators, policymakers, and researchers have explored the impact of school climate. Studies have demonstrated its meaningful influence on students' emotional development, self-esteem, self-concept, psychological well-being, and reduced absenteeism (Aldridge et al., 2016 ; Anderson, 1982 ; Creemers & Reezigt, 1999 ; Mortimore, 1988 ; Scheerens, 1992 ; Scheerens & Bosker, 1996 ; Thapa et al., 2013 ). A positive school climate is essential for a successful educational experience, as emotions and relationships profoundly affect learning. How students are treated at school, home, and within their communities plays a critical role in shaping these outcomes. Supportive environments and stress management skills are necessary for children to thrive academically and socially. Conversely, fear, trauma, and emotional distress hinder learning. Therefore, schools must cultivate a nurturing climate that prioritizes both academic success and social-emotional development. Thapa et al. ( 2013 ) identified four key areas when evaluating and researching school climate: institutional environment, connections, teaching and learning, and safety. Safety encompasses students' emotional, intellectual, and physical well-being. Connections reflect students’ relationships with their school, teachers, and peers. Teaching and learning refer to educators’ efforts to establish the standards, objectives, and values that define the learning environment. Meanwhile, the institutional environment relates to the physical school surroundings, emphasizing connectedness and participation within the school community. Despite the importance of school climate for enhancing student learning outcomes, limited research has been conducted in Asian contexts, particularly in Indonesia. Further exploration is needed, as school climate in Indonesia may differ from that in Western nations where it is more commonly studied. In Western settings, such as the United States, school climate research has been prominent. In Asia, its popularity is growing—especially in China. For example, Yang et al. ( 2013 ) examined Chinese and American students' perceptions of school climate, while Jia et al. ( 2009 ) explored the relationship between school climate and students' academic and emotional adjustment in both countries. Bear et al. ( 2018 ) analyzed differences in school climate perceptions and student engagement between Chinese and American students. These studies revealed that Chinese students experienced greater support from instructors and peers, along with more opportunities for self-autonomy in the classroom compared to their American counterparts. Such findings underscore the distinct emphasis placed on certain school climate elements by students from Western and Eastern contexts. However, it remains necessary to clarify how these insights translate to Indonesia's educational setting. Indonesia’s cultural characteristics play a vital role in shaping school climate. The country is known for its collectivist values, which prioritize communal interests over individual preferences (Hofstede, 1993 ; Hofstede & Minkov, 2010 ). For instance, one prominent ethnic group in North Sumatra, the Batak Toba, places a high value on education compared to other major ethnic groups in the region (Irmawati, 2007 ). Even under financial constraints, parents often prioritize schooling for their children over other needs. Given the uniqueness of Indonesian culture, it is reasonable to expect these cultural traits to influence the school climate and its relationship with educational outcomes. Indonesia’s cultural diversity further complicates the school climate profile. The nation is home to a wide variety of ethnicities, languages, religions, and local government structures (Novera, 2004 ), indicating that cultural differences within the country could shape school climates in unique ways. This study analyzed data on mathematics scores and self-concept among eighth-grade secondary school students from the TIMSS 2015 dataset. TIMSS 2019, the latest dataset, was not used as it does not include data on secondary school students. The focus on secondary school students stems from their developmental stage: they are young enough to be influenced by environmental factors and daily experiences, yet mature enough to understand and report on their surroundings, including school climate (Zysberg & Schwabsky, 2021 ). The study aims to address a knowledge gap by investigating and explaining school climate in the Indonesian context. It also examines the relationships between school climate and various educational outcomes, contributing to research on school effectiveness. Research Questions How does Indonesian student mathematics achievement and self-concept vary within classrooms, between classrooms, and between schools? After accounting for student, teacher, and school characteristics, what trends emerge in Indonesian students' mathematics achievement and self-concept? Which aspects of the school and classroom climate have the most significant influence on Indonesian students' mathematics achievement and self-concept, after adjusting for student, teacher, and school factors? Do students in regular schools outperform those in madrasahs in mathematics achievement, both before and after adjusting for school climate and other relevant factors? METHODS Participants The data for this study was obtained from the TIMSS 2011 assessment. In Indonesia, information was collected from 5,795 eighth-grade students (aged 13–14) across 153 schools. The sample consisted of 2,823 male students and 2,972 female students, representing schools from 31 of Indonesia's 33 provinces. To ensure a representative distribution, the students were divided into strata during the sampling process. The stratification explicitly categorized schools into public versus private schools and general versus Islamic schools. Additionally, it is important to note that the quality of education in Indonesian schools varies widely. Due to this variability, TIMSS implicitly stratified schools not only by province but also by performance. Schools were classified into three performance categories: high, medium, or low (Joncas & Foy, 2012 ). This implicit stratification was nested within the explicit categories of regular and madrasah schools. It served as the foundation for arranging the sampling frame before systematically selecting schools for inclusion in the study (please refer to Table 1 for more details). Table 1 Explicit and implicit stratum of the sample school Explicit Stratum Public General Public Madrasah Private General Private Madrasah Total Implicit Stratum High 26 3 10 9 48 Medium 51 4 16 13 84 Low 11 1 6 3 21 Total 88 8 32 25 153 Source: Methods and Procedures in TIMSS & PIRLS, 2011 (Joncas & Foy, 2012 ). Furthermore, TIMSS collected data on school location from principals in order to determine the population size of the city, town, or area in which their schools were located. The school location revealed that the sample schools were divided into various classifications, as shown in Table 2 . The sample schools were largely located in the suburbs (56%) and small towns (24%). However, a minor percentage of the sample comes from urban and rural areas. Table 2 School location School’s Location Frequency Percentage Urban 10 7% Sub-urban 85 56% Moderate sized city 15 10% Small town 36 24% Rural area 7 5% Total 153 100% Measures The predicted or outcome variable is the TIMSS five plausible values of maths score, which are an estimate of how a student would have achieved if the entire items had been administered (Hastedt, 2006 ). For self-concept, school and classroom climate as perceived by students, and other student level variables were extracted from the student questionnaire and utilised to explain the student-level variance in maths achievement. The mathematics teacher's characteristics (gender, teaching experience, level of education, and major of study) and how teacher perceived school and classroom climate were acquired from the teacher's questionnaire and used to explain the classroom-level variance in maths achievement. School climate as perceived by principals were obtained from the principal's questionnaire and utilised to explain school-level variance in maths. Data analysis strategy It is difficult to conduct multilevel analysis without encountering the problem of missing data, particularly at the group or upper levels (Gibson & Olejnik, 2003 ; McCoach, 2010 ). This is due to the fact that any group-level unit with missing data excludes all individual units nested within the group-level unit from the analyses. MLWin was used to analyse three-level hierarchical linear modelling. TIMSS used multistage cluster sampling; it has unequal sample unit selection likelihood (Asparouhov, 2005; Rabe-Hesketh & Skrondal, 2006 ). Sample weights—TOTWGT (student sample weight), MATWGT (maths teacher weight), and SCHWGT (school weight)—were applied at the student, classroom, and school levels to reduce bias in parameter estimates and provide nationally representative conclusions (Rutkowski et al., 2010 ). Centering or scaling a predictor or explanatory variable in hierarchical linear modelling involves subtracting the mean or some other constant value from each individual raw value of the predictor variable and subtracting the raw metric from the mean or constant value. (Tabachnick & Fidell, 2007 ; Wu & Wooldridge, 2005 ). This helps change the interpretation of the intercept (Kreft & De Leeuw, 1998 ). This is because in regression, the intercept is defined as the expected score of someone's outcome variable with a score of zero for all predictors in the model (Raudenbush & Bruk, 2002). Because social science attributes have few meaningful or real zeros (Kreft & de Leeuw, 1998 ), predictor variables need to be transferred. The intercept is therefore interpreted as the expected score of someone's outcome variable whose score for a particular predictor variable equals the group mean or the overall mean, depending on the type of centering approach the analyst is using. All predictors focused on overall averages at both student and classroom levels (Enders & Tofighi, 2007 ; Hofmann & Gavin, 1998 ; Hox, 2002 ; Kreft, De Leeuw, & Aiken, 1995 ). RESULTS Research question 1 The initial step in multilevel data analysis involves the estimation of the unconditional or null model, as stated by Raudenbush and Bryk ( 2002 ). The present model does not explain any variance observed in the outcome variable. Instead, its purpose is to allocate the overall variance present in the outcome variable across the various levels present in the data. The null model was estimated as a response to the first research question “How does Indonesian student mathematics achievement and self-concept vary within classrooms, between classrooms, and between schools?” To answer this question, the researcher investigated the variances in mathematics achievement and self-concept based on school-level and classroom-level. Specifically, by employing the variance components model for a three-levels model (school, classroom, student levels). Mathematics achievement was evaluated using students' mathematics scores from the TIMSS 2011 dataset, while self-concept was assessed based on students’ self-concept data from the same dataset. Model 0 (see Table 3 ) describes school performance of the Indonesian secondary schools in terms of mathematics score and self-concept in mathematics, which is indicated by the proportion of variance explained by each level. The results showed that Indonesian students’ mathematics achievement varied among schools. Specifically, the school-level explained about 35% of the variance of mathematics score. The classroom-level had lesser predictive power. Specifically, the classroom-level accounted for only about 9% of the variance of mathematics score. This finding indicated that Indonesian students’ mathematics achievement varied less among classrooms. Caution is warranted to apply these results because of the small sample of classes in the TIMSS dataset, which is from the 74 schools that are available in the TIMSS dataset, only 20 schools with more than one classroom. As such, there might not have been enough power to conclude the differences in mathematics achievement at the classroom-level. In terms of self-concept the school-level accounted for only about 5% of the variance. Similarly, the classroom-level accounted for only about 4% of the variance of self-concept. These results were consistent with past studies that have shown a small variation of non-cognitive outcomes when comparing one school to another (Gray, 2004 ; Thomas et al., 2000 ). The variation of mathematics score was best accounted for at the student-level. The student-level predicted more than 56% of the variance of mathematics score. The student-level also accounted for 91% of the variance of self-concept. As expected, these results were consistent with past studies that showed individual student-level played a major role in predicting students' performance (Gray et al., 2001 ; Thiele et al., 2016 ). A comparison of the empty models of the two outcome measures revealed that the differences between schools were more noticeable on academic achievement than of affective one. This findings are in line with the results of studies conducted in Belgium (Opdenakker & Van Damme, 2000 ), the UK (Gray, 2004 ; Thomas, 2001 ), and Cyprus (Creemers & Kyriakides, 2010a ) that also found that differences between schools in terms of affective outcomes were smaller in comparison to the results of the academic outcomes. Table 3 The null model for mathematics score and self-concept Responds Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 393.73 4.52 10.46 0.26 Random Part school variance 2416.22 420.32 4.47 1.69 class variance 599.66 240.22 3.55 1.50 Student variance 3893.84 99.36 82.20 1.99 VPCschool 0.35 0.05 VPCclassroom 0.09 0.04 VPCstudent 0.56 0.91 Deviance 64924.58 42240.44 Research question 2 RQ2 asked, “After accounting for student, teacher, and school characteristics, what trends emerge in Indonesian students' mathematics achievement and self-concept?” To answer this question, Model 1, 2, 3, and 4 were conducted. Each model was aimed to verify the relationship between student characteristics (Model 1), teachers (Model 2), and school (Model 3), and learning outcomes. Then, Model 4 combined Model 1 to 3 that includes all statistically significant explanatory variables at students, teachers, and school levels for the two learning outcomes (mathematics score & self-concept). Model 1: Student characteristics Model 1 (Table 4 ) was utilized to investigate whether differences in mathematics achievement and self-concept existed across schools and classrooms within schools, after accounting for student characteristics and backgrounds. The study considered three background variables related to students' socio-cultural contexts: gender, language (used as an indicator of ethnicity), and socioeconomic status (SES). In the TIMSS 2011 study, SES was measured through: (a) parents' education level, (b) the availability of study support, and (c) the number of books in the household. Additionally, students' self-concept was incorporated into the model, as self-belief have a reciprocal relationship with academic achievement. Therefore, when examining self-concept outcomes, mathematics achievement was included as an explanatory variable for this non-academic measure. Table 4 Model 1 with student’s backgrounds variables Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 417.45 8.16 9.90 0.66 Gender Boy -4.69 2.92 0.34 0.28 Parents’ education level Post-secondary but not university -10.23 6.16 0.30 0.62 Upper secondary -9.47 4.04 0.55 0.42 Lower secondary -13.59 3.59 0.93 0.48 Some primary, lower secondary or no school -12.98 4.98 0.30 0.48 Study support Possession of either a private room or electronic devices -8.32 3.92 0.11 0.32 Possession of both private room and electronic devices -7.81 4.80 0.53 0.56 Number of books 11–25 pieces -6.04 2.67 0.49 0.32 26–100 pieces 2.89 2.69 0.62 0.40 101–200 pieces -5.30 6.93 1.98 0.97 More than 200 pieces -3.06 11.57 3.63 0.91 Language Sometimes -1.49 3.55 -0.99 0.30 Never -6.39 4.81 -2.59 0.50 Self belief Self-concept 1.39 0.17 Achievement NA NA Mathematics score 0.03 0.00 Random Part School variance 2310.22 402.13 4.44 1.68 Class variance 577.47 236.26 4.05 1.45 Student variance 3675.27 98.74 77.92 1.80 VPCschool 0.35 0.05 VPCclassroom 0.09 0.05 VPCstudent 0.56 0.90 Deviance 64561.60 41945.31 School variance explained 4% 1% Class variance explained 4% -14% Student variance explained 6% 5% Total variance explained 5% 4% After accounting for student background variables (Table 4 ), 35% of the variance in mathematics score was attributed to differences between schools, a proportion similar to that observed in Model 0. Additionally, the variation between classes within schools showed comparable results. For self-belief outcomes, the proportion of school-level differences in self-concept closely mirrored those found in Model 0. This study revealed no significant associations between students' gender, mathematics achievement, and self-concept. In general, students with parents who have lower levels of education tend to achieve lower mathematics scores. However, parental education level was not associated with students' self-belief. Regarding study support, students with fewer home resources, such as a dedicated study space and other educational materials, performed worse in mathematics compared to those with better home study support. Nevertheless, no relationship was observed between self-concept and home study resources. An empirical study by Filmer and Pritchett ( 1999 ) also identified varying patterns in the relationship between wealth disparities and academic performance across different countries. Consequently, the relationship between home resources and outcomes in this study appeared inconsistent as well. The association between the number of books in a student’s home and mathematics achievement appears inconsistent. For example, possessing fewer books, as opposed to none, showed a negative correlation with mathematics score. Conversely, a positive trend was identified for self-concept, indicating that students with a greater number of books generally reported higher self-concept scores (refer to Table 4 for detailed results). Another socio-cultural background variable considered in the study is language, specifically how frequently students use the language of the TIMSS test (Indonesian) in their daily lives. Language use served as a proxy for measuring ethnic differences. The findings indicated that students who never used the Indonesian language exhibited a significant negative correlation with self-concept outcomes, whereas no such relationship was observed with mathematics achievement. With regard to students' psychological background variables, the study identified a significant positive correlation between self-concept and mathematics achievement. This finding aligns with the majority of prior research in the field (Marsh, 1990a ; Marsh & Martin, 2011 ; O'Mara et al., 2006 ; Pajares & Urdan, 2002 ; Parker et al., 2014 ). Moreover, the study reinforces the reciprocal relationship between academic achievement and self-beliefs as reported in previous studies (Huang, 2011 ; Marsh & O'Mara, 2008 ; O'Mara et al., 2006 ; Seaton et al., 2014 ). Concerning the model's overall goodness of fit, Model 1 accounted for only 5% of the total variance in mathematics score (refer to Table 4 ) and 4% of the total variance in self-concept. These findings indicate that including students' characteristic factors in the model provided limited predictive power for their learning outcomes. Model 2: Teacher characteristics Model 2 incorporated four teacher-level variables: gender, age, educational background, and teaching experience. Table 5 illustrates the variability in mathematics score attributed to schools and classrooms, considering these teacher background variables. The analysis revealed statistically significant associations between mathematics achievement and all teacher characteristics, except for gender. However, none of the teacher variables showed a relationship with students' self-concept in terms of self-belief outcomes. Previous research has shown inconsistent findings regarding the relationship between teacher gender and student achievement. For instance, Antecol et al. ( 2015 ) identified teacher gender as a non-significant factor in their randomized experiment examining its impact on primary students' achievement. Conversely, in other contexts such as Pakistan, Warwick and Jatoi ( 1994 ) reported a strong correlation between teacher gender and student achievement. A significant negative relationship was identified between teachers' age and mathematics score, with older teachers being associated with lower student achievement in mathematics. Conversely, teaching experience showed a significant positive relationship, as less experienced teachers tended to have students with lower achievement levels. Similarly, teachers' educational background was positively associated with mathematics score; students taught by teachers without formal education beyond upper-secondary school generally scored lower in math. In contrast, higher levels of education and specialization in mathematics among teachers were linked to better students’ math achievement. Overall, these trends indicate that students' mathematics achievement is influenced by various teacher demographic factors, including age, experience, educational background, and subject specialization. While teacher age demonstrated a significant negative relationship with mathematics achievement, positive significant relationships were observed with teaching experience, educational background, and subject specialization in mathematics. These findings align with much of the existing research, which highlights the positive impact of teacher experience and qualifications on student achievement (Croninger et al., 2007 ; Darling-Hammond, 2000a , 2000b ). Interestingly, older teachers were negatively associated with students' mathematics score. This may be attributed to older teachers being less familiar with contemporary teaching methods or challenges in bridging the generational gap with students. Nonetheless, further investigation is required to better understand how teacher age influences mathematics instruction. The variability in mathematics scores between schools decreased slightly, dropping from 35% in Model 0 to 30%, representing a 5% reduction. For classroom-level variability, the trend remained consistent with Model 0. In contrast, for self-concept outcomes, the variability between schools and classrooms showed almost no change compared to Model 0. Table 5 Model 2 with teacher’s backgrounds variables Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 422.48 22.48 10.35 1.55 Gender Male -2.02 8.26 0.06 0.49 Age 25–29 years 24.15 17.05 -1.36 1.26 30–39 years -30.59 18.58 -1.00 1.32 40–49 years -13.44 20.33 -0.65 1.43 50–59 years -8.62 24.93 -0.51 1.66 60 years or more -84.26 25.20 0.66 1.57 Experiences Spanning a minimum of 10 but not exceeding 20 years 5.20 13.58 0.55 0.78 Spanning a minimum of 5 but not exceeding 10 years -10.39 16.16 1.79 1.00 Less than 5 years -42.97 18.11 1.47 1.19 Education All other majors 8.20 11.41 0.68 0.77 No formal education beyond upper-secondary -60.97 16.77 -0.58 1.27 Random Part School variance 1962.41 362.45 4.09 1.57 Classroom variance 565.16 236.90 3.49 1.42 Student variance 3906.80 102.24 82.20 1.99 VPCschool 0.30 0.05 VPCclassroom 0.09 0.04 VPCstudent 0.61 0.92 Deviance 64898.14 42233.70 School variance explained 19% 9% Class variance explained 0% 2% Student variance explained 6% 0% Total variance explained 7% 0% Regarding the overall goodness of fit, Model 2 explained a small proportion of the total variance in mathematics score, ranging from approximately 5–7%, which represents a slight improvement over Model 1 (refer to Table 5 ). However, for self-concept outcomes, the explained variance decreased further. Even after incorporating teacher background variables, the overall explained variance remained low, indicating that teacher characteristics alone are insufficient to effectively predict students' learning outcomes. Model 3: School Characteristics Model 3 (Table 6 ) was utilized to analyze school-level variables influencing mathematics score and self-concept variance at both the school and classroom levels. The model incorporated factors such as the school’s socioeconomic background (aggregated from students’ SES), school location, and school type (general private school, general public school, private madrasah, and public madrasah). Table 6 revealed that, as anticipated, schools with a higher average socioeconomic status (SES) demonstrated significantly better mathematics score. Regarding school location, students enrolled in schools located in small towns scored considerably lower compared to their peers in remote rural areas. Lastly, concerning school type, as expected, students attending private schools, including both madrasahs and general private schools, exhibited significantly lower mathematics score. The average socioeconomic status (SES) of the school was not associated with students' self-concept. Regarding school location, students attending sub-urban schools exhibited a significant positive association with their self-concept. As for school types, no significant relationship was observed between school type and students' self-concept. Table 6 Model 3 with school’s backgrounds variables Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 414.20 8.80 9.21 0.47 Mean SES 32.06 5.59 -0.11 0.37 Location Sub-urban 0.46 10.32 1.37 0.57 Moderate sized city -6.66 13.53 1.61 0.90 Small city -24.02 11.92 1.39 0.77 Rural area -21.41 29.68 2.05 1.49 School Types Public madrasah -8.10 12.69 -0.64 1.11 Private general -20.79 8.21 -1.20 0.72 Private madrasah -34.05 12.19 0.3 3 0.83 Random Part School variance 1222.16 300.82 3.09 1.70 Classroom variance 659.29 242.43 3.88 1.56 Student variance 3907.00 102.21 82.21 2.00 VPCschool 0.21 0.03 VPCclassroom 0.11 0.04 VPCstudent 0.67 0.92 Deviance 64856.92 42225.30 School variance explained 49% 31% Classroom variance explained -10% -10% Student variance explained 0% 0% Total variance explained 16% 1% The variability in mathematics scores across schools decreased significantly by 14% compared to Model 0. Regarding classroom variability, the trend remained largely consistent with Model 0, showing a slight increase of up to 2%. This aligns with previous studies, which have generally found that accounting for school context diminishes the apparent school effect (Muijs & Reynolds, 2003 ; Opdenakker & Van Damme, 2000 ; Opdenakker et al., 2002 ). In contrast to mathematics scores, the variability in self-concept between schools and classrooms was nearly identical to that observed in Model 0. Incorporating school context into Model 3 resulted in a significant improvement in the model's overall "goodness of fit" compared to Model 1 and Model 2, accounting for 16% of the total variance in mathematics score. In contrast, the change for self-concept was minimal. This enhanced goodness of fit demonstrated a greater capacity to predict students' learning outcomes, suggesting that school-related factors provide a stronger explanation of student mathematics achievement than variables related to students' or teachers' backgrounds. Model 4: Student, teacher, and school characteristics Model 4 (Table 7 ) was employed to examine the scope and degree of school performance in mathematics score and self-concept among Indonesian lower secondary schools, while accounting for student, teacher, and school characteristics. For mathematics score, the variance across schools was comparable to Model 3, though it exhibited a slight reduction of up to 1%. Nonetheless, some notable changes emerged. For instance, teacher characteristics, including both teaching experience and educational background, were not found to have a statistically significant association with mathematics score. In contrast, for self-concept outcomes, teacher characteristics maintained a significant relationship, consistent with the findings from Model 2. The effects of student characteristic variables remained consistent with those observed in Model 1. Similarly, the effects of school context variables were comparable to those in Model 3. However, a notable change was identified in relation to school types. In Model 4, only private madrasahs showed a significant negative association with mathematics score. This finding suggests that, after accounting for student, teacher, and school background variables, students attending private madrasahs scored lower in mathematics compared to their peers in other school types, including public general, public madrasahs, and private general schools. Conversely, for self-concept, the effects of student, teacher, and school-level variables remained relatively similar across Models 1, 2, and 3. When all student, teacher, and school-level variables were included in the model, the overall "goodness of fit" showed substantial improvement compared to Model 3, accounting for 22% of the total variance in mathematics score across schools. With regard to school-level variability, Model 4 demonstrated a notable enhancement compared to Models 0, 1, and 2, although it was slightly higher than Model 3. Conversely, classroom-level variability did not exhibit significant improvement across any of the outcomes. Including all student, teacher, and school-level variables in the model, the overall "goodness of fit" was considerably improved compared to Model 3, explaining 22% of the total school variance in mathematics score. In terms of school classroom variability, compared to Model 0, 1, and 2, Model 4 had a remarkable improvement in school variability, but slightly higher compared to Model 3. On the other hand, classroom variability was not improved significantly for all outcomes. Table 7 Model 4 with student, teacher, and school’s backgrounds variables Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 448.93 23.76 9.60 1.71 Gender Boy -4.63 2.92 0.34 0.28 Parents’ education Post-secondary but not university -9.75 6.14 0.25 0.62 Upper secondary -8.92 4.03 0.50 0.42 Lower secondary -12.43 3.57 0.79 0.49 Some primary, lower secondary or no school -11.18 5.00 0.09 0.50 Study support Possession of either a private room or electronic devices -8.13 3.92 0.08 0.32 Possession of both private room and electronic devices -8.13 4.82 0.59 0.55 Number of books 11–25 pieces -6.12 2.68 0.50 0.32 26–100 pieces 2.70 2.71 0.67 0.40 101–200 pieces -5.72 6.95 2.02 0.97 More than 200 pieces -2.93 11.53 3.62 0.91 Language Sometimes -0.50 3.55 -1.06 0.30 Never -5.21 4.77 -2.68 0.51 Self belief Self-concept 1.40 0.17 NA Achievement Mathematics score NA 0.03 0.00 Teacher Level Age 25–29 years -22.47 17.36 -2.41 1.33 30–39 years -27.58 17.03 -1.95 1.33 40–49 years -17.39 19.22 -1.61 1.43 50–59 years -17.26 21.99 -1.62 1.68 60 years or more -37.42 24.64 0.81 1.83 Experiences Spanning a minimum of 10 but not exceeding 20 years 10.68 11.42 0.27 0.77 Spanning a minimum of 5 but not exceeding 10 years 0.22 14.71 1.72 0.97 Less than 5 years -22.44 17.01 0.94 1.21 Education Background All other majors 14.80 10.28 0.65 0.79 No formal education beyond upper-secondary -26.13 12.58 -1.08 1.16 School Level Mean SES 27.94 6.18 -0.56 0.42 Location Sub-urban 4.34 10.76 2.01 0.67 Moderate sized city -2.51 13.55 2.36 0.97 Small town -17.90 13.03 1.46 0.84 Remote rural -12.97 29.77 2.32 1.44 School Types Public madrasah 0.13 14.10 -1.00 1.12 Private general -13.64 9.49 -1.89 0.83 Private madrasah -30.45 12.14 0.08 0.83 Random Part School variance 1081.26 295.20 2.68 1.77 Class variance 663.39 245.27 4.12 1.54 Student variance 3675.29 98.69 77.92 1.80 VPCschool 0.20 0.03 VPCclassroom 0.12 0.05 VPCstudent 0.68 0.92 Deviance 64494.98 41918.28 School variance explained 55% 40% Class variance explained -11% -16% Student variance explained 6% 5% Total variance explained 22% 6% Research question 3 Research question 3 is as follows: “Which aspects of the school and classroom climate have the most significant influence on Indonesian students' mathematics achievement and self-concept, after adjusting for student, teacher, and school factors?” To address this question, Models 5 and 6 were utilized to compare the aspects of school climate that significantly explained the variance in classroom and school performance, both before and after adjusting for student, teacher, and school characteristics. Model 5: Key school climate factors prior to accounting for student, teacher, and school characteristics Model 5 (Table 8 ), which included only school climate factors, attributed 24% of the variance in mathematics score to differences between schools. This represents a significant improvement compared to Model 0. However, classroom-level differences remained nearly identical to those in Model 0, with a slight reduction of approximately 1%. As previously noted, this is likely due to the relatively small variability within the classroom sample, which may limit the model's ability to accurately detect differences. Table 8 Model 5 school climate factors Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 398.22 3.67 10.30 0.19 School Climate Student connected with school 0.35 0.14 0.04 0.02 Classroom Climate Student’s mathematics lesson engagement (teaching-learning) 0.04 0.21 -0.23 0.03 Student safety -0.67 0.29 0.15 0.03 Teacher safety 0.72 1.44 0.12 0.03 Teacher emphasis on academic success 2.76 2.42 0.01 0.07 Teacher-teacher interaction 0.48 2.31 -0.03 0.09 Teacher connected with school -1.16 1.55 -0.10 0.05 Teacher working condition 0.32 2.67 -0.10 0.09 Instructions to engage student (Teacher) -1.16 2.39 -0.06 0.05 Classroom disturbance (Teacher) -0.15 3.13 -0.01 0.08 Teaching confidence (Teacher) -0.01 2.58 0.35 0.08 Emphasis on academic success (School) 2.51 0.91 0.01 0.05 Discipline (School) 2.25 0.97 0.06 0.06 Technology resources (School) 0.15 0.62 0.03 0.00 Safety (School) -0.59 0.67 -0.02 0.05 General resources (School) -2.65 1.08 0.08 0.06 School leadership -0.99 0.61 -0.02 0.03 Student school connectedness (School mean) 1.94 1.81 -0.08 0.10 Student safety (School mean) -7.18 2.46 0.50 0.14 Teacher school connectedness (School mean) 0.91 0.67 -0.01 0.04 Teacher emphasis on academic success (School mean) 0.30 0.74 0.00 0.03 Teacher to teacher interaction (School mean) -0.61 0.70 -0.03 0.03 Teacher safety (Teacher safety) -0.72 0.31 -0.01 0.02 Teacher working condition (School mean) -2.78 1.52 -0.11 0.07 Teacher’s confidence in teaching (Class mean) 0.57 0.94 -0.02 0.05 Teacher engaging instruction (Class mean) -0.13 0.55 0.04 0.03 Classroom disturbance (Class mean) 0.18 0.71 0.01 0.04 Students’ mathematics lesson engagement (Class mean) 6.20 1.62 -0.78 0.08 Random Part School variance 1399.20 279.6 5 2.94 1.01 Class variance 472.98 170.7 3 0.30 0.89 Student variance 3888.48 102.2 1 79.56 1.99 VPCschool 0.24 0.04 VPCclass 0.08 0.00 VPCstudent 0.68 0.96 Deviance 64818.49 41947.56 School variance explained 42% 34% Class variance explained 21% 92% Student variance explained 0% 3% Total variance explained 17% 8% For self-concept, the variance across schools decreased by 1%, declining from 5% in Model 0 to 4% in Model 5. Conversely, classroom-level variability in self-concept outcomes showed a reduction of 4% compared to Model 0. The total variance explained (overall goodness of fit) for self-concept outcomes in Model 5 was notably higher compared to Models 1, 2, 3, and 4. This finding suggests that the school climate factor had a significant impact on students' self-concept outcomes. Regarding school climate factors, not all were significantly associated with mathematics achievement. At the student level, a positive and significant relationship was observed between students’ sense of connection to their school (e.g., enjoying being at school) and their mathematics score. Meanwhile, student engagement specifically in mathematics lessons was found to have a significant negative relationship with self-concept but showed no significant association with mathematics score. This finding contrasts with previous research, such as the study by Fung et al. ( 2018 ), which analyzed data from 295,416 15-year-old secondary school students across 34 countries using PISA 2012. Their study revealed that greater engagement in mathematics lessons was linked to higher academic achievement. In the Indonesian context, however, while students perceived themselves as engaged in school and classroom, their academic achievement was reported to be low. The results for student safety were unexpected, as a significant negative relationship was identified between student safety and mathematics score. However, student safety exhibited a significant positive association with self-concept. One possible explanation is that the relationship may not follow a linear pattern. At the teacher level, there was a significant positive association between teachers' confidence in teaching mathematics and students' self-concept. At the school level, a strong emphasis on academic success was significantly and positively associated with students' mathematics score, but it showed no connection to their self-concept. Surprisingly, the availability of general school resources had a significant negative association with mathematics score. Lastly, school leadership was found to have no significant relationship with mathematics score. The aggregated climate factors at the school and classroom levels were analyzed. The school-level mean of connectedness showed no significant association with mathematics score. Conversely, the school-level mean of student safety followed a pattern consistent with the non-aggregated factors, demonstrating a significant negative relationship with mathematics score and a significant positive relationship with self-concept. Based on the teacher questionnaire, the school-level mean of student safety also exhibited a significant negative relationship. Finally, the classroom-level mean of student engagement showed a significant positive association with mathematics score but a significant negative association with students' self-concept. Model 6 (the next model) incorporated all significant factors related to school and classroom climate, along with the relevant student, teacher, and school background variables. Model 6: The influence of school climate factors after accounting for student, teacher, and school characteristics. Model 6 was developed (Table 9 ) to examine whether differences in mathematics score, to measure mathematics achievement, and self-concept exist between schools and classrooms after incorporating all significant school climate factors from Model 5 and accounting for student, teacher, and school characteristics from Model 4. Table 9 Model 6 Final Model Response Mathematics Score Self-Concept Fixed Part Estimate SE Estimate SE Cons 436.65 13.17 10.26 1.05 Student Level Gender Boy -4.84 2.94 0.50 0.26 Parents’ education level Post-secondary but not university -9.58 6.19 -0.04 0.60 Upper secondary -8.88 4.05 0.35 0.42 Lower secondary -12.26 3.54 0.61 0.49 Some primary, lower secondary or no school -10.95 5.01 -0.11 0.49 Study support Possession of either a private room or electronic devices -7.43 3.82 -0.15 0.31 Possession of both private room and electronic devices -7.72 4.69 0.34 0.52 Number of books 11–25 pieces -6.16 2.66 0.42 0.31 26–100 pieces 2.69 2.70 0.43 0.39 101–200 pieces -6.08 6.95 1.62 0.93 More than 200 pieces -3.49 11.32 3.32 0.96 Language Sometimes -0.47 3.50 -0.92 0.29 Never -4.20 4.65 -2.51 0.51 Self belief Self-concept 1.45 0.17 NA Achievement Mathematics score NA 0.03 0.00 Age 25–29 years -24.98 12.78 -0.39 0.92 30–39 years -22.40 10.44 -0.43 0.83 40–49 years -8.95 10.97 -0.55 0.83 50–59 years -9.35 13.76 -0.79 1.11 60 years or more -30.92 18.20 0.68 1.09 Education All other majors 13.18 8.98 1.17 0.55 No formal education beyond upper-secondary -22.78 14.14 -1.13 0.76 School Level Mean SES 20.86 6.06 -0.12 0.38 Location Sub-urban 9.69 9.44 0.59 0.63 Moderate sized city 0.68 12.84 0.72 0.79 Small town -8.61 11.03 0.34 0.74 Rural area 0.05 28.44 0.89 1.12 School Types Public madrasah -6.40 15.35 -0.42 0.92 Private general -11.50 8.06 -0.76 0.55 Private madrasah -22.11 12.52 -0.12 0.67 School Climate Students’ school connectedness 0.23 0.14 0.03 0.02 Student safety -0.89 0.28 0.17 0.03 Teacher safety 0.43 0.98 0.04 0.03 Teacher-teacher interaction -0.76 1.18 -0.03 0.09 Teacher’s confidence in teaching mathematics -0.87 1.38 0.12 0.08 School’s emphasis on academic success 0.95 0.80 0.02 0.04 School discipline 1.39 0.55 0.03 0.03 School’s general resources -1.50 0.98 0.09 0.05 Student safe (school) -5.09 2.36 0.46 0.14 Teacher safe (school) -0.31 0.33 -0.01 0.02 Classroom Climate Students’ mathematics lesson engagement 0.51 0.21 -0.23 0.03 Random Part School variance 1006.21 275.83 2.41 1.02 Class variance 518.05 207.09 1.06 0.87 Student variance 3649.19 99.20 75.33 1.84 VPCschool 0.19 0.03 VPCclass 0.10 0.01 VPCstudent 0.71 0.96 Deviance 64431.66 41646.41 School variance explained 58% 46% Class variance explained 14% 70% Student variance explained 6% 8% Total variance explained 25% 13% School discipline demonstrated a significant positive association with mathematics score. Conversely, school safety exhibited a significant negative relationship with mathematics score. This finding contradicts previous research, such as Thapa et al. ( 2013 ), which generally identified a significant positive relationship between students' perception of safety and academic performance. However, the association between safety and self-concept outcomes differed, as safety was found to have a significant positive relationship with self-concept. DISCUSSION The variability and magnitude of mathematics score and self-concept among Indonesian year 8 students. The results of the null model indicate that 35% of the total variance in mathematics score can be attributed to differences between schools. In contrast, classroom-level differences within schools accounted for a relatively small portion of the variance, approximately 9%. These findings regarding classroom variability are inconsistent with previous studies, which have generally suggested that differences between classrooms are significantly more influential than variations between schools (Hill & Rowe, 1996 ; Rowe & Hill, 1998 ). In the context of Indonesia, it is worth noting that only 20 schools participating in the study had two classrooms, while the remaining 54 schools were limited to a single classroom. In contrast to mathematics score, the null model revealed that a smaller proportion of the variance in self-concept could be attributed to differences between schools. Specifically, school-level differences accounted for only 5% of the variance in self-concept. Similarly, classroom-level differences in self-concept outcomes were also minor compared to those in mathematics score, with classroom variance accounting for approximately 4% of the total variance in self-concept. These findings align with previous research, which has generally observed that differences between schools or classrooms have a more pronounced impact on academic achievement than on self-beliefs or affective outcomes. Examples include studies conducted in Belgium (Opdenakker & Van Damme, 2000 ), the United Kingdom (Gray, 2004 ; Thomas, 2001 ), and Cyprus (Creemers & Kyriakides, 2010). The proportion of variance at the school level highlights the achievement gap in students' mathematics achievement in Indonesia, resembling patterns observed in other developing countries (e.g., Zanzibar, as noted by Yu & Thomas, 2008 ). In contrast, the school-level variation in academic self-concept was relatively low. The Null Model revealed that 35% of the variance in mathematics score, as reported in the TIMSS 2011 results, could be attributed to school effects. This finding underscores a significant achievement gap within the Indonesian education system. Notably, this result is slightly higher than earlier estimates from research on primary school effectiveness in Indonesia two decades ago (Kaluge, 1998 ). For instance, Kaluge ( 1998 ) reported that 29.2% of the variance in mathematics at schools was attributable to school-level factors. The pattern of raw results was comparatively lower than in other developing countries, such as Brazil, Colombia, Honduras, Egypt, India, Jordan, Namibia, Pakistan, Thailand, Zimbabwe, Botswana, and Philippines. In these countries, the average school-level variation in performance was reported as 46% at the primary level and 41% at the secondary level (Riddell, 1997 ). Similarly, Yu and Thomas ( 2008 ) found that Zanzibar exhibited a comparable achievement gap, with 34% of the variance in mathematics performance attributed to schools. In contrast, in developed countries like the United Kingdom, only 14% of the total unadjusted variance was attributed to schools (Thomas & Mortimore, 1996 ). The variability and magnitude of school and classroom performance among Indonesian Year 8 students in mathematics score and self-concept after controlling for student characteristics. After accounting for student background variables, 35% of the variance in mathematics score could be attributed to differences between schools. Similarly, differences between classrooms within schools were found to be comparable. For self-belief outcomes, the proportion of variance in self-concept attributable to school-level differences was consistent with the variance component observed in the null model. These findings suggest that, in the Indonesian context, student background variables had a limited impact in explaining differences in student learning outcomes at the school, classroom, or individual levels. However, it is important to note that this analysis did not include students' prior achievement, and the findings should therefore be interpreted with caution. Prior attainment has consistently been identified in numerous School Effectiveness Research (SER) studies as one of the most influential student-level variables (Gray et al., 2001 ; Lenkeit, 2013 ; Muijs & Reynolds, 2003 ; Thomas, 1998 ; Timmermans & Thomas, 2015 ). Socio-cultural factors, such as parental education and the number of books at home, were found to have a significant association with both student mathematics achievement and self-belief. Generally, it can be concluded that lower levels of parental education correspond to lower student mathematics achievement. However, no significant relationship was observed between parental education and students' self-concept. Previous studies have highlighted the positive influence of parental education on students' academic performance. For instance, Gooding ( 2001 ) found that students whose parents had higher levels of education outperformed those whose parents had lower levels of education. Similarly, Khan et al. ( 2015 ) reported comparable findings in India after analyzing the academic achievement of 100 secondary school student in relation to their parents’ educational level. One possible explanation is that parents acquire knowledge and skills during their own education, which may influence how they support their children's learning at home and facilitate modeling behaviors (Eccles, 2005 ). With regard to self-belief, this study found no significant relationship between parental education levels and students' self-concept. A possible explanation, as suggested by Eccles ( 2005 ), is that parents with higher levels of education may exercise strong or excessive parental control, which could negatively impact students' confidence. In terms of study support, students with better access to resources at home, such as a dedicated study space and other materials, performed better in mathematics compared to those with limited study support. Similar findings were reported by Chudgar et al. ( 2015 ), who demonstrated a positive relationship between children's out-of-school resources and their mathematics achievement. However, no significant relationship was found between home study support and self-concept. Similarly, Filmer and Pritchett ( 1999 ) conducted an empirical analysis on home study support, revealing varied trends between disparities in student wealth and academic performance across different countries. Consequently, the relationship between household resources and academic outcomes was also inconsistent in the present study. Regarding the number of books at home, having fewer books, as opposed to none, showed a negative association with mathematics score. However, the findings for self-concept indicated a positive relationship between the number of books at home and students' self-belief (refer to Table 4 for detailed scores). Turning to another socio-cultural background variable, this study utilized language use (frequency of using the language of the TIMSS test, i.e., Indonesian, in daily life) as a proxy for measuring ethnic differences. The findings revealed that students who never used the test language did not exhibit a significant relationship with mathematics score. However, the analysis of self-belief outcomes identified a negative relationship between self-concept and language use. Previous research on ethnicity and learning outcomes has also yielded inconclusive results (Strand, 2016 ; Worrell, 2007 ). The study found a significant positive association between self-concept and mathematics achievement within the psychological context variables of students. This finding aligns with the majority of prior research in the field (Marsh, 1990a ; Marsh & Martin, 2011 ; O'Mara et al., 2006 ; Pajares & Urdan, 2002 ; Parker et al., 2014 ). Consequently, the study supports the reciprocal relationship between academic achievement and self-belief, as highlighted in earlier studies (Huang, 2011 ; Marsh & O'Mara, 2008 ; Seaton et al., 2014 ). The proportion of total variance explained by student background variables was relatively small, accounting for only 5% of the variance in mathematics score (refer to Table 2 ). For self-concept, the total variance explained was 4%. These findings indicate that while student characteristic variables were statistically significant, their contribution to predicting students’ learning outcomes was minimal due to the low percentage. The variability and magnitude of school and classroom performance among Indonesian Year 8 students in math score and self-concept after controlling for teacher’s characteristics. The relationship between mathematics score and all teacher characteristics was statistically significant, with the exception of teacher gender. However, when examining self-belief outcomes, none of the teacher variables showed a statistically significant association with students' self-concept. Previous studies have reported mixed findings regarding the influence of teacher gender on student achievement. For instance, Antecol et al. ( 2015 ) found that teacher gender was not a significant variable in their randomized experiment analyzing the effect of teacher gender on primary school students' achievement in the United States. Conversely, in the context of developing countries such as Pakistan, teacher gender demonstrated a strong association with student performance (Warwick & Jatoi, 1994 ). Teacher age was found to have a significant negative association with mathematics score, indicating that as teacher age increases, students' mathematics performance decreases. A similar pattern was observed with teaching experience, where teachers with less experience tended to have students with lower mathematics scores. These findings align with existing research, which consistently highlights the significant positive effects of teacher experience and qualifications on student performance (Croninger et al., 2007 ; Darling-Hammond, 2000a , 2000b ). This trend also extends to teachers' educational backgrounds, as students taught by teachers with no formal education beyond upper-secondary level generally performed worse in mathematics. Notably, the higher the teacher's level of education and their specialization in mathematics, the better students' mathematics achievement tended to be. The range and extent of school and classroom performance among Indonesian Year 8 students in math score and self-concept after controlling for school characteristics. After accounting for school characteristics, including social and economic background (aggregated from students’ SES), school location, school size, and school type (general private school, general public school, private madrasah, and public madrasah), this study found that schools with a higher average SES, as anticipated, tended to achieve significantly better results in mathematics. These findings align with previous studies that have emphasized the critical role of school SES in influencing academic performance (De Fraine et al., 2002 ; López et al., 2023 ; Berkowitz et al., 2017; Opdenakker & Van Damme, 2005; Opdenakker et al., 2002 ; Sammons et al., 1994 ; Timmermans & Thomas, 2014). In terms of school location, students attending schools in small towns performed significantly worse in mathematics compared to their peers in rural areas, medium-sized cities, sub-urban, and urban. This finding aligns with earlier studies, such as those by Young ( 1998 ) and Burger ( 2011 ), which highlighted achievement differences between urban and rural students. However, the observed trend is somewhat unusual, as students in remote rural areas outperformed those in small towns in mathematics. This result is also somewhat consistent with Tayyaba's (2012) research in Pakistan, which demonstrated comparable academic success among rural and urban students in certain provinces. In contrast, a study conducted in Malaysia by Othman and Muijs ( 2013 ) found no significant differences in school performance between rural and urban schools. Turning to school types, as anticipated, students attending private schools—both madrasahs and general private schools—demonstrated significantly lower mathematics achievement. This finding aligns with prior research by Hendajany ( 2016 ), which utilized data from the Indonesian Family Life Survey (IFLS) and concluded that students in public schools exhibited higher achievement levels compared to those in private schools. When comparing madrasahs and general schools, the current study also supports the conclusion of Newhouse and Beegle ( 2006 ), who analyzed IFLS data and found no significant differences in achievement between private madrasahs and private general schools. The school’s average socioeconomic status (SES) showed no significant association with self-concept. Regarding school location, students in sub-urban areas demonstrated a significant positive relationship with their self-concept. In terms of school types, no significant relationship was found between self-concept and school type. Notably, there is limited research examining the relationship between self-beliefs and the school context. The variability in mathematics achievement between schools decreased significantly by 14% compared to Model 0. In contrast, classroom-level variability remained similar to that in Model 0, with a slight increase of 2%. This finding aligns with previous research, which has frequently observed that accounting for school context diminishes the apparent school effect (Muijs & Reynolds, 2003 ). However, unlike the outcomes for mathematics score, the variability in self-concept between schools and classrooms was nearly identical to that observed in the variance component model. Incorporating the school context significantly enhanced the model's goodness of fit, accounting for 16% of the total variance in mathematics score. However, the change observed for self-concept was minimal. This improved goodness of fit demonstrates a greater capacity to predict students’ learning outcomes. It implies that school-related variables are more effective than student and teacher background variables in explaining mathematics achievement. The variability and magnitude of school and classroom performance among Indonesian Year 8 students in mathematics score and self-concept after including school climate factors and controlling for student, teacher, and school characteristics. When accounting for all critical factors related to school and classroom climates, as well as controlling for relevant characteristics of students, teachers, and schools, the proportion of variance in mathematics score that could be attributed to differences between schools decreased substantially to 19%. In contrast, the variance in self-concept between schools did not exhibit a statistically significant difference. The influence of school and classroom climate on student learning improved significantly after incorporating school climate variables, compared to models that solely included student, teacher, and school background characteristics. The overall model fit, represented by the percentage of total variance explained, increased and accounted for approximately 25% of the total variance in mathematics score. For self-concept, the explained variance also showed notable improvement, accounting for 13%. Although the total variance explained is relatively modest, it underscores the impact of school and classroom climate on students' mathematics achievement and self-concept. The 25% variance explained is considered acceptable, given that this model did not incorporate prior student achievement, which is widely recognized as one of the most significant predictors (Muijs & Reynolds, 2003 ; Muñoz-Chereau, 2013; Salim, 2011 ; Timmermans & Thomas, 2015 ). For context, Muñoz-Chereau (2013), in their quest for a more equitable model of school effectiveness in Chile, demonstrated that including prior attainment substantially enhanced the model's fit, with explained variance increasing from 16–63% when compared to a model limited to student background variables. The model accounts for 58% of the variance in mathematics scores at the school level and 46% for self-concept. Notably, self-concept demonstrates the highest explained classroom-level variance, approximately 65%, after controlling for student, teacher, and background variables. This substantial classroom-level variance (65%) highlights the pivotal role of teachers in the classroom, which can be interpreted through Bandura's social learning theory (Bandura, 1997 ; Bandura & Walters, 1963 ). According to this theory, teachers serve as references or models, influencing students who then emulate their behavior. This interpretation is further supported by Cheng's (2016) research, which revealed that students’ non-cognitive outcomes are shaped through modeling their teachers. In terms of student characteristics, gender does not have a significant association with mathematics score. However, it is significantly related to self-concept, with boys exhibiting higher self-concept scores compared to girls. This finding aligns with previous studies, Goldman and Penner ( 2016 ) reported that girls’ self-concept in mathematics was lower than that of boys the majority of the 49 countries they studied. Additionally, Hergovich et al. ( 2004 ) also reported the same findings. They further noted that girls’ self-concept is heavily influenced by the judgments of teachers and parents, a factor that does not similarly affect boys. Moreover, the persistence of gender inequality in Indonesia may heighten the risk of female students developing a weaker self-concept compared to their male counterparts. Teacher age was found to have a significant relationship with students' mathematics score; however, the relationship was non-linear. Students tended to demonstrate lower mathematics score when taught by younger teachers (under 40 years old) or older teachers (over 60 years old). This finding suggests that teachers within the average age range of 40 to 59 possess the necessary experience to effectively manage their classrooms. In contrast, teachers under the age of 30 may lack sufficient experience and, being relatively young, might have a limited understanding of students' behavior. On the other hand, teachers aged 40 to 59 benefit from more extensive professional experience. With regard to self-belief outcomes, teacher age was found to be an insignificant factor. However, the teacher's academic major appeared to play a role, as students taught by non-mathematics teachers tended to exhibit higher self-concept scores. In terms of school-level characteristics or context, average SES was found to have a significant association with students' mathematics score but not with their self-concept. This finding aligns with prior research, which has often shown that schools serving low-SES student populations tend to be under-resourced, thereby impacting student performance (Aikens & Barbarin, 2008 ; Timmermans & Thomas, 2015 ). Additionally, other studies have demonstrated that the socioeconomic composition of students has a significant influence on academic achievement (Chudgar et al., 2015 ; Muijs et al., 2004 ). Other school-level factors, such as school location and school type, no longer show significant relationships with any of the outcomes. This finding is both unexpected and intriguing, as the inclusion of school and classroom climate factors renders the impact of school type—particularly being a private madrasah—insignificant. This result suggests that school and classroom climate factors play a crucial role in mitigating the negative effects associated with private madrasahs. In previous studies, madrasahs were often found to have significantly lower academic performance compared to other school types (Ali et al., 2011 ; ADB, 2014 ; Ghozali et al., 2013 ). Among the school and classroom climate factors significantly associated with mathematics achievement were student safety (freedom from bullying), school discipline, and active student engagement in mathematics lessons. Interestingly, the relationship between student safety and mathematics score was found to be negative. This result contrasts with most prior studies, which have generally reported that a safe and orderly environment exerts a significant positive impact (e.g., Sammons et al., 1997 ). Nonetheless, this study also revealed that school discipline, as an indicator of a school's orderliness, had a significant positive correlation with mathematics score. Thus, the findings are not entirely inconsistent with previous research. Additionally, the correlation between school discipline and mathematics score was stronger than the correlation between student safety (defined as freedom from bullying) and mathematics score. Among the significant school climate factors, student engagement in mathematics lessons exhibited the strongest correlation coefficient. This highlights its critical role in accounting for the variance in mathematics score. CONCLUSION The multilevel analysis conducted in this study offers valuable evidence and a comprehensive understanding of school climate within the Indonesian context. It can be used to interpret school practices and illustrate how school climate serves as a protective factor in enhancing student outcomes, regardless of school type. These findings support the perspective that school climate is a flexible aspect of education that can be shaped by schools or local governments (Voight et al., 2013 ). Moreover, interventions can be implemented promptly without the need for extensive policy changes. Nonetheless, the design approach implemented in this study should be interpreted with caution, as the explanations offered are not causal in nature, primarily due to the cross-sectional nature of the data utilized. Abbreviations IFLS Indonesian Family Life Survey MOEC Ministry of Education and Culture MORA Ministry of Religious Affairs MTs Madrasah Tsanawiyah PIRLS Progress in International Reading Literacy Study PISA Programme for International Student Assessment SES Sosioeconomic Status SER School Effectiveness Research SMP Sekolah Menengah Pertama TIMSS Trends in Mathematics and Science Study UK United Kingdom UNESCO United Nations Educational, Scientific, and Cultural Organization Declarations Author Contribution This research was conducted by TD, who served as the first author and led the data analysis, model development, and manuscript writing. The study was supervised by ST, who provided critical guidance throughout the research process and offered substantial feedback to ensure academic rigor and relevance. References ADB. (2014). Indonesia: Madrasah education development project. Retrieved from https://www.adb.org/documents/indonesia-madrasah-education-development-project Aldridge, J. M., Fraser, B. J., Fozdar, F., Ala’i, K., Earnest, J., & Afari, E. (2016). Students’ perceptions of school climate as determinants of wellbeing, resilience and identity. Improving schools , 19 (1), 5-26. https://doi.org/10.1177/1365480215612616 Ali, M., Kos, J., Lietz, P., Nugroho, D., Furqon, Zainul, A., & Emilia, E. (2011). Quality of Education in Madrasah: Main Study . Retrieved from Washington, DC: http://documents.worldbank.org/curated/en/2011/02/14048827/quality-educationmadrasah-main-study Aikens, N., & Barbarin, O. (2008). Socioeconomic differences in reading trajectories: The contribution of family, neighborhood, and school contexts. Journal of Educational Psychology , 100 (2), 235. https://doi.org/10.1037/0022-0663.100.2.235 Anderson, C. (1982). The search for school climate: A review of the research. Review of Educational Research , 52 (3), 368-420. https://doi.org/10.2307/1170423 Antecol, H., Eren, O., & Ozbeklik, S. (2015). The effect of teacher gender on student achievement in primary school. Journal of Labor Economics , 33 , 63-89. https://doi.org/10.2139/ssrn.2039639 Asparouhov, T. (2006). General multi-level modeling with sampling weights. Communications in Statistics—Theory Methods , 35 (3), 439-460. https://doi.org/10.1080/03610920500476598 Bandura, A. (1997). Self-efficacy: the exercise of control . Basingstoke: W. H. Freeman Bandura, A., & Walters, R. H. (1963). Social learning and personality development . London: Holt, Rinehart and Winston. Bear, G. G., Yang, C., Chen, D., He, X., Xie, J.-S., & Huang, X. (2018). Differences in school climate and student engagement in China and the United States. School Psychology Quarterly, 33 (2), 323–335. https://doi.org/10.1037/spq0000247 Brault, M. C., Janosz, M., & Archambault, I. (2014). Effects of school composition and school climate on teacher expectations of students: A multilevel analysis. Teaching and Teacher Education , 44 , 148-159. https://doi.org/10.1016/j.tate.2014.08.008 Brookover, W. B., Schweitzer, J. H., Schneider, J. M., Beady, C. H., Flood, P. K., & Wisenbaker, J. M. (1978). Elementary school social climate and school achievement. American Educational Research Journal , 15 (2), 301-318. https://doi.org/10.3102/00028312015002301 Burger, R. (2011). School effectiveness in Zambia: The origins of differences between rural and urban outcomes. Development Southern Africa , 28 (2), 157-176. https://doi.org/10.1080/0376835x.2011.570064 Cheng, A. (2016). Teachers and the Development of Student Noncognitive Skills . (PhD Thesis). University of Arkansas, Fayetteville, Arkansas Chudgar, A., Chandra, M., Iyengar, R., & Shanker, R. (2015). School resources and student achievement: Data from rural India. Prospects , 45 (4), 515-531. https://doi.org/10.1007/s11125-015-9360-3 Cohen, J., McCabe, L., Michelli, N. M., & Pickeral, T. (2009). School climate: Research, policy, practice, and teacher education. Teachers College Record , 111 (1), 180-213. https://doi.org/10.1177/016146810911100108 Creemers, B., & Kyriakides, L. (2010a). Explaining stability and changes in school effectiveness by looking at changes in the functioning of school factors. School Effectiveness and School Improvement , 21 (4), 409-427. https://doi.org/10.1080/09243453.2010.512795 Creemers, B., & Kyriakides, L. (2010b). School factors explaining achievement on cognitive and affective outcomes: Establishing a dynamic model of educational effectiveness. Scandinavian Journal of Educational Research , 54 (3), 263-294. https://doi.org/10.1080/00313831003764529 Creemers, B., & Reezigt, G. J. (1999). The role of school and classroom climate in elementary school learning environments. In J. Freiberg (Ed.), School Climate: Measuring, Improving and Sustaining Healthy Learning Environments . London: Falmer Press Croninger, R., Rice, J., Rathbun, A., & Nishio, M. (2007). Teacher qualifications and early learning: Effects of certification, degree, and experience on first-grade student achievement. Economics of Education Review, 26(3), 312-324. https://doi.org/10.1016/j.econedurev.2005.05.008 Darling-Hammond, L. (2000a). How teacher education matters. Journal of Teacher Education, 51 (3) , 166-173. https://doi.org/10.1177/0022487100051003002 Darling-Hammond, L. (2000b). Teacher quality and student achievement. Education policy analysis archives , 8, 1. https://doi.org/10.14507/epaa.v8n1.2000 De Fraine, B., Van Damme, J., & Onghena, P. (2002). Accountability of schools and teachers: What should be taken into account? European Educational Research Journal , 1 (3), 403-428. https://doi.org/10.2304/eerj.2002.1.3.2 Edmonds, R. (1979). Effective schools for the urban poor. Educational Leadership , 37 (1). 15-24 Eccles, J. (2005). Influences of parents' education on their children's educational attainments: The role of parent and child perceptions. London review of education , 3 (3), 191-204. https://doi.org/10.1080/14748460500372309 Elliott, J. (1996). School effectiveness research and its critics: alternative visions of schooling. Cambridge Journal of Education , 26 (2), 199-224. https://doi.org/10.1080/0305764960260205 Enders, C., & Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. Psychological Methods , 12 (2), 121-138. https://doi.org/10.1037/1082-989x.12.2.121.supp Filmer, D., & Pritchett, L. (1999). The effect of household wealth on educational attainment: Evidence from 35 countries. Population and Development Review , 25(1), 85-120. https://doi.org/10.1111/j.1728-4457.1999.00085.x Freiberg, H. (1999). School climate: measuring, improving and sustaining healthy learning environments . London; Philadelphia: Falmer Press. Fung, F., Tan, C., & Chen, G. (2018). Student engagement and mathematics achievement: Unraveling main and interactive effects. Psychology in the Schools , 55 (7), 815-831. https://doi.org/10.1002/pits.22139 Ghozali, A., Mudjahid, A. K., & Hayati, M. (2013). Madrasah education financing study: The education sector analytical and capacity development partnership [Press release] Gibson, N., & Olejnik, S. (2003). Treatment of missing data at the second level of hierarchical linear models. Educational and Psychological Measurement , 63 (2). https://doi.org/10.1177/0013164402250987. Goldman, A. D., & Penner, A. M. (2016). Exploring international gender differences in mathematics self-concept. International Journal of Adolescence and Youth , 21 (4), 403-418. https://doi.org/10.1080/02673843.2013.847850 Gooding, Y. (2001). The relationship between parental educational level and academic success of college freshmen . (PhD thesis). Iowa State University, Ames, Iowa. https://doi.org/10.31274/rtd-180813-12012 Gray, J. (2004). School effectiveness and the ‘other outcomes’ of secondary schooling: a reassessment of three decades of British research. Improving Schools , 7 (2), 185-198. https://doi.org/10.1177/1365480204047348 Gray, J., Goldstein, H., & Thomas, S. (2001). Predicting the future: the role of past performance in determining trends in institutional effectiveness at A level. British Educational Research Journal , 27 (4), 391-405. https://doi.org/10.1080/01411920125622 Guskey, T. (2012). Defining Student Achievement. In J. Hattie & E. M. Anderman (Eds.), International Guide to Student Achievement . New York: Routledge. Hastedt, D. (2006). Inconsistent student responses to questions related to their mathematics lessons. In Contexts of Learning Mathematics and Science (pp. 77-96). Routledge. Hendajany, N. (2016). The Effectiveness of Public Vs Private Schools in Indonesia. Journal of Indonesian Applied Economics , 6 (1), 66-89. https://doi.org/10.21776/ub.jiae.2016.006.01.4 Hergovich, A., Sirsch, U., & Felinger, M. (2004). Gender differences in the self-concept of preadolescent children. School Psychology International , 25 (2), 207-222. https://doi.org/10.1177/0143034304043688 Hill, P., & Rowe, K. (1996). Multilevel modelling in school effectiveness research. School Effectiveness and School Improvement , 7 (1), 1-34. https://doi.org/10.1080/0924345960070101 Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management , 24 (5), 623-641. https://doi.org/10.1177/014920639802400504 Hofstede, G. (1993). Cultural constraints in management theories. The Executive , 7 (1), 81- 94. https://doi.org/10.5465/ame.1993.9409142061 Hofstede, G., & Minkov, M. (2010). Cultures and organizations: Software of the mind, (3rd ed) . New York: McGraw-Hill Education. Hox, J. (2002). Multilevel analysis: techniques and applications . Mahwah: Lawrence Erlbaum Publishers. Hoy, W. (2012). School characteristics that make a difference for the achievement of all students. Journal of Educational Administration , 50 (1), 76-97. https://doi.org/10.1108/09578231211196078 Hoy, W., Tarter, C., & Kottkamp, R. (1991). Open schools, healthy schools: measuring organizational climate . California: Sage Publications. Huang, C. (2011). Self-concept and academic achievement: A meta-analysis of longitudinal relations. Journal of School Psychology , 49 (5), 505-528. https://doi.org/10.1016/j.jsp.2011.07.001 Indonesia, Republic of. (2003). Undang-Undang No 20 Tahun 2003 tentang sistem pendidikan indonesia Jakarta. Retrieved from http://peraturan.go.id/common/dokumen/ln/2003/uu20-2003.pdf Indonesia, Republic of. (2010). Peraturan Pemerintah Republik Indonesia Nomor 17 Tahun 2010 tentang pengelolaan dan penyelenggaraan pendidikan Jakarta. Retrieved from http://peraturan.go.id/common/dokumen/ln/2010/pp17-2010bt.pdf Irmawati. (2007). Nilai-nilai yang mendasari motif-motif penentu keberhasilan suku batak toba . (PhD Thesis). University of Indonesia, Jakarta. Jia, Y., Way, N., Ling, G., Yoshikawa, H., Chen, X., Hughes, D., Ke, X., & Lu, Z. (2009). The influence of student perceptions of school climate on socioemotional and academic adjustment: A comparison of chinese and american adolescents. Child Development , 80(5), 1514-1530. https://doi.org/10.1111/j.1467-8624.2009.01348.x Joncas, M., & Foy, P. (2012). Sample Design in TIMSS and PIRLS. In M. O. Martin & I. V. S. Mullis (Eds.), Methods and procedures in TIMSS and PIRLS 2011 . Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Boston College. Kaluge, L. (1998). Some factors related to educational attainment in Indonesian primary schools . (PhD Thesis). University of London, London. Khan, R. M. A., Iqbal, N., & Tasneem, S. (2015). The Influence of Parents Educational Level on Secondary School Students Academic Achievements in District Rajanpur. Journal of education and Practice , 6 (16), 76-79. Retrieved from https://eric.ed.gov/?id=EJ1079955 Knuver, A., & Brandsma, H. (1993). Cognitive and affective outcomes in school effectiveness research. School Effectiveness and School Improvement , 4 (3), 189-204. https://doi.org/10.1080/0924345930040302 Kreft, I., & De Leeuw, J. (1998). Introducing multilevel modeling . London: Sage. Kreft, I., De Leeuw, J., & Aiken, L. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research , 30 (1), 1-21. https://doi.org/10.1207/s15327906mbr3001_1 Lenkeit, J. (2013). Effectiveness measures for cross-sectional studies: a comparison of valueadded models and contextualised attainment models. School Effectiveness and School Improvement , 24 (1), 1-25. https://doi.org/10.1080/09243453.2012.680892 López, V., Salgado, M., & Berkowitz, R. (2023). The contributions of school and classroom climate to mathematics test scores: a three-level analysis. School Effectiveness and School Improvement , 34 (1), 43-64. https://doi.org/10.1080/09243453.2022.2096645 Marsh, H. (1990a). Causal ordering of academic self-concept and academic achievement: A multiwave, longitudinal panle analysis. Journal of Educational Psychology , 82 (4), 646-656. https://doi.org/10.1037//0022-0663.82.4.646 Marsh, H., & Martin, A. (2011). Academic self-concept and academic achievement: relations and causal ordering. British Journal Educational Psychology, 81 (1), 59-77. https://doi.org/10.1348/000709910X503501 Marsh, H., & O'Mara, A. (2008). Reciprocal effects between academic self-concept, selfesteem, achievement, and attainment over seven adolescent years: Unidimensional and multidimensional perspectives of self concept. Personality and Social Psychology Bulletin , 34 (4), 542-552. https://doi.org/10.1177/0146167207312313 Martin, M., Mullis, I., & Foy, P. (2008). TIMSS 2007 international science report . Chestnut Hill, MA: TIMSS & PIRLS International Study Center. McCoach, D. B. (2010). Hierarchical linear modeling. The reviewer’s guide to quantitative methods in the social sciences , 123-140. Ministry of Education and Culture, Republic of Indonesia. (2013b). Peraturan Menteri Pendidikan Dan Kebudayaan Republik Indonesia Nomor 67 Tahun 2013 tentang kerangka dasar dan struktur kurikulum sekolah dasar/madrasah ibtidaiyah Jakarta Retrieved from http://simpuh.kemenag.go.id/regulasi/permendikbud_67_13_lampiran.pdf Ministry of Education and Culture, Republic of Indonesia. (2017a). Indonesia Educational Statistic in Brief 2016/2017. In Yearly . Jakarta: Center for educational data and statistics and culture. Ministry of Religious Affairs, Republic of Indonesia. (2014a). Keputusan Menteri Agama Republik Indonesia Nomor 207 Tahun 2014 tentang kurikulum madrasah . Jakarta Retrieved from http://simpuh.kemenag.go.id/regulasi/kma_207_14.pdf Mortimore, P. (1988). School matters: The junior years . Wells: Open Books. Mortimore, P., Sammons, P., Stoll, L., Lewis, D., & Ecob, R. (1989). A study of effective junior schools. International Journal of Educational Research , 13 (7), 753-768. https://doi.org/10.1016/0883-0355(89)90026-8 Muijs, D., Harris, A., Chapman, C., Stoll, L., & Russ, J. (2004). Improving schools in socioeconomically disadvantaged areas – A review of research evidence. School Effectiveness and School Improvement , 15 (2), 149–175. https://doi.org/10.1076/sesi.15.2.149.30433 Muijs, D., & Reynolds, D. (2003). Student background and teacher effects on achievement and attainment in mathematics: A longitudinal study. Educational Research and Evaluation , 9 (3), 289-314. https://doi.org/10.1076/edre.9.3.289.15571 Mullis, I. V., Martin, M. O., Gonzalez, E. J., & Chrostowski, S. J. (2004). TIMSS 2003 International Mathematics Report: Findings from IEA's Trends in International Mathematics and Science Study at the Fourth and Eighth Grades . International Association for the Evaluation of Educational Achievement. Herengracht 487, Amsterdam, 1017 BT, The Netherlands. Muñoz-Chereau, B. (2019). Exploring gender gap and school differential effects in mathematics in Chilean primary schools. School Effectiveness and School Improvement , 30 (2), 83-103. https://doi.org/10.1080/09243453.2018.1503604 Newhouse, D., & Beegle, K. (2006). The effect of school type on academic achievement evidence from Indonesia. Journal of Human Resources , 41 (3), 529-557. https://doi.org/10.1037/e515652013-001 Novera, I. (2004). Indonesian postgraduate students studying in Australia: An examination of their academic, social and cultural experiences. International Education Journal , 5 (4), 475-487. O'Mara, A., Marsh, H., Craven, R., & Debus, R. (2006). Do self-concept interventions make a difference? A synergistic blend of construct validation and meta-analysis. Educational Psychologist , 41 (3). https://doi.org/10.1207/s15326985ep4103_4 Opdenakker, M., & Van Damme, J. (2000). Effects of schools, teaching staff and classes on achievement and well-being in secondary education: Similarities and differences between school outcomes. School Effectiveness and School Improvement , 11 (2), 165-196. https://doi.org/10.1076/0924-3453(200006)11:2;1-q;ft165 Opdenakker, M., Van Damme, J., De Fraine, B., Van Landeghem, G., & Onghena, P. (2002). The effect of schools and classes on mathematics achievement. School Effectiveness and School Improvement , 13 (4), 399-427. https://doi.org/10.1076/sesi.13.4.399.10283 Othman, M., & Muijs, D. (2013). Educational quality differences in a middle-income country: the urban-rural gap in Malaysian primary schools. School Effectiveness and School Improvement , 24 (1), 1-18. https://doi.org/10.1080/09243453.2012.691425 Pajares, F., & Urdan, T. (2002). Academic motivation of adolescents . Greenwich: Information Age Pub Parker, P., Marsh, H., Ciarrochi, J., Marshall, S., & Abduljabbar, A. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. Educational Psychology , 34 (1), 29-48. https://doi.org/10.1080/01443410.2013.797339 Rabe-Hesketh, S., & Skrondal, A. (2006). Multilevel modelling of complex survey data. Journal of the Royal Statistical Society Series A: Statistics in Society , 169 (4), 805-827. https://doi.org/10.1111/j.1467-985X.2006.00426.x Raudenbush, S., & Bryk, A. (2002). Hierarchical linear models: applications and data analysis methods (2nd ed.) . Thousand Oaks: Sage. Reynolds, D., Sammons, P., De Fraine, B., Van Damme, J., Townsend, T., Teddlie, C., & Stringfield, S. (2014). Educational effectiveness research (EER): a state-of-the-art review. School Effectiveness and School Improvement , 25 (2), 197-230. https://doi.org/10.1080/09243453.2014.885450 Riddell, A. (1997). Assessing designs for school effectiveness research and school improvement in developing countries. Comparative Education Review , 41 (2), 178- 204. https://doi.org/10.1086/447429 Rowe, K., & Hill, P. (1998). Modeling educational effectiveness in classrooms: The use of multi-level structural equations to model students’ progress. Educational Research and Evaluation , 4 (4), 307-347. https://doi.org/10.1076/edre.4.4.307.6953 Rutkowski, L., Gonzalez, E., Joncas, M., & von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. Educational Researcher , 39 (2), 142-151. https://doi.org/10.3102/0013189x10363170 Salim, M. (2011). Exploring issues of school effectiveness and self-evaluation at the system and school levels in the context of Zanzibar . (PhD thesis). University of Bristol, Bristol, UK Sammons, P., Thomas, S., & Mortimore, P. (1997). Forging links: effective schools and effective departments . London: Paul Chapman. Sammons, P., Thomas, S., Mortimore, P., Owen, C., & Pennell, H. (1994). Assessing school effectiveness : Developing measures to put school performance in context . London: Office for Standards in Education [OFSTED] Scheerens, J. (1992). Effective schooling: research, theory and practice . London: Cassell. Scheerens, J., & Bosker, R. J. (1996). The foundations of educational effectiveness . Oxford; New York: Pergamon Elsevier Seaton, M., Parker, P., Marsh, H., Craven, R., & Yeung, A. (2014). The reciprocal relations between self-concept, motivation and achievement: juxtaposing academic selfconcept and achievement goal orientations for mathematics success. Educational Psychology , 34 (1), 49-72. https://doi.org/10.1080/01443410.2013.825232 Strand, S. (2016). Do some schools narrow the gap? Differential school effectiveness revisited. Review of Education , 4 (2), 107-144. https://doi.org/10.1002/rev3.3054 Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics (5th ed.) . Boston: Allyn & Bacon/Pearson Education. Tayyaba, S. (2012). Rural-urban gaps in academic achievement, schooling conditions, student, and teachers' characteristics in Pakistan. International Journal of Educational Management , 26 (1), 6-26. https://doi.org/10.1108/09513541211194356 Thapa, A., Cohen, J., Guffey, S., & Higgins-D’Alessandro, A. (2013). A review of school climate research. Review of Educational Research , 83 (3), 357-385. https://doi.org/10.3102/0034654313483907 Thiele, T., Singleton, A., Pope, D., & Stanistreet, D. (2016). Predicting students' academic performance based on school and socio-demographic characteristics. Studies in Higher Education , 41 (8), 1424-1446. https://doi.org/10.1080/03075079.2014.974528 Thomas, S. (2001). Dimensions of secondary school effectiveness: Comparative analyses across regions. School Effectiveness and School Improvement , 12 (3), 285-322. https://doi.org/10.1076/sesi.12.3.285.3448 Thomas, S. (1998). Value-added measures of school effectiveness in the United Kingdom. Prospects , 28 (1), 91-108. https://doi.org/10.1007/bf02737782 Thomas, S., & Mortimore, P. (1996). Comparison of value‐added models for secondary school effectiveness. Research Papers in Education , 11 (1), 5-33. https://doi.org/10.1080/0267152960110103 Thomas, S., Smees, R., MacBeath, J., Robertson, P., & Boyd, B. (2000). Valuing pupils’ views in scottish schools. Educational Research and Evaluation , 6 (4), 281-316. https://doi.org/10.1076/edre.6.4.281.6934 Timmermans, A., & Thomas, S. (2015). The impact of student composition on schools’ value-added performance: a comparison of seven empirical studies. School Effectiveness and School Improvement , 26 (3), 487-498. https://doi.org/10.1080/09243453.2014.957328 UNESCO. (2004). EFA Global Monitoring Report 2005 . Retrieved from http://unesdoc.unesco.org/images/0013/001373/137333e.pdf UNESCO. (2014). Teaching and Leraning: Achieving Quality for all . Paper presented at the EFA Global Monitoring Report 2013/14, Paris, France. Voight, A., Austin, G., & Hanson, T. (2013). A climate for academic success: How school climate distinguishes schools that are beating the achievement odds . San Francisco: WestEd. Warwick, D., & Jatoi, H. (1994). Teacher gender and student achievement in Pakistan. Comparative Education Review , 38 (3), 377-399. https://doi.org/10.1086/447257 Worrell, F. (2007). Ethnic identity, academic achievement, and global self-concept in four groups of academically talented adolescents. Gifted Child Quarterly , 51 (1), 23-38. https://doi.org/10.1177/0016986206296655 World Bank. (2014c). World Bank and Education in Indonesia. Retrieved from http://www.worldbank.org/en/country/indonesia/brief/world-bank-and-education-inindonesia Wu, Y. W. B., & Wooldridge, P. J. (2005). The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis. Nursing research , 54 (3), 212-216. https://doi.org/10.1097/00006199-200505000-00009 Yang, C., Bear, G., Chen, F., Zhang, W., Blank, J., & Huang, X. (2013). Students' perceptions of school climate in the U.S. and China. School Psychology Quarterly , 28 (1), 7-24. https://doi.org/10.1037/spq0000002.supp Yu, G., & Thomas, S. (2008). Exploring school effects across southern and eastern African school systems and in Tanzania. Assessment in Education: Principles, Policy & Practice , 15 (3), 283-305. https://doi.org/10.1080/09695940802417525 Young, D. (1998). Rural and urban differences in student achievement in science and mathematics: A multilevel analysis. School Effectiveness and School Improvement , 9 (4), 386-418. https://doi.org/10.1080/0924345980090403 Zysberg, L., & Schwabsky, N. (2021). School climate, academic self-efficacy and student achievement. Educational Psychology , 41 (4), 467-482. https://doi.org/10.1080/01443410.2020.1813690 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7140999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535447629,"identity":"dbc3f08d-f67a-4f45-94f3-23d97c95a307","order_by":0,"name":"Tarmidi Dadeh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYLCCBDYJORibh+EAAdU8UC3GJGphYGNIbIALEdJiz3726YYHZRbpG86fMWD4UcMgw0fQFp50sxsJ5yRyN9zIMWDsOcbAI0nYYWlsNxLbQFp4DBh4Gxh4DAhq4X8G1pJuAHQY41+itEhAbEkwOJBjwEycLTeAtgD9YjjzRlrBYZljEoT9wt6fxnbzR1mdPN/5wxsfvqmxsScYYigAqFiCFPWjYBSMglEwCnABAD//PQJ3XVC/AAAAAElFTkSuQmCC","orcid":"","institution":"Universitas Sumatera Utara","correspondingAuthor":true,"prefix":"","firstName":"Tarmidi","middleName":"","lastName":"Dadeh","suffix":""},{"id":535447630,"identity":"d1ed1e3e-1ddd-427a-8924-fe95b38ebd34","order_by":1,"name":"Sally Thomas","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Sally","middleName":"","lastName":"Thomas","suffix":""}],"badges":[],"createdAt":"2025-07-16 14:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7140999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7140999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95241697,"identity":"0a0d3766-5b0e-43e4-9a29-2750440c888b","added_by":"auto","created_at":"2025-11-05 19:35:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":110533,"visible":true,"origin":"","legend":"","description":"","filename":"BlindedDadehLSAEJournal.docx","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/7e0e8a42425a1f72fab2fd87.docx"},{"id":95241698,"identity":"4624a062-4a3f-4c82-ad9a-478d1e8e1681","added_by":"auto","created_at":"2025-11-05 19:35:10","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3811,"visible":true,"origin":"","legend":"","description":"","filename":"f2c21b44a66f4eedb077a035fb67316f.json","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/8f5509e2acba20b6386e08aa.json"},{"id":95241699,"identity":"e1893b3a-7d04-4797-a697-c5f845dd1842","added_by":"auto","created_at":"2025-11-05 19:35:10","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":331561,"visible":true,"origin":"","legend":"","description":"","filename":"f2c21b44a66f4eedb077a035fb67316f1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/725d858ee9952e965c6ba210.xml"},{"id":95312572,"identity":"99b8e5c6-0e9b-45bd-a3f9-9aad02938e0e","added_by":"auto","created_at":"2025-11-06 15:49:45","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":330322,"visible":true,"origin":"","legend":"","description":"","filename":"f2c21b44a66f4eedb077a035fb67316f1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/43462fe7bfd0ef9f6d9f72d0.xml"},{"id":95241701,"identity":"7bafb013-3e41-4962-86a1-f11739bcd678","added_by":"auto","created_at":"2025-11-05 19:35:10","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":341380,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/7fa7213ec93d4d932e0291a2.html"},{"id":95315629,"identity":"cce0f06a-a8df-4f9a-bb88-8aefc2f5dc6d","added_by":"auto","created_at":"2025-11-06 15:56:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2602248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7140999/v1/41f5984c-ef1c-43a0-bc50-201bec50b719.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Role of School and Classroom Climate in Shaping Mathematics Achievement and Self-Concept: A Multilevel Analysis of Indonesian Students Using TIMSS 2011","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAccording to the World Bank (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2014c\u003c/span\u003e), Indonesia's education system is the fourth biggest in the world, behind China, India, and the United States, based on population. It is huge and diverse, with over 250,000 schools, 50\u0026nbsp;million pupils, and over 2.6\u0026nbsp;million teachers scattered among 34 provinces and 514 districts (Ministry of Education and Culture, 2017a). In terms of education authorities, Indonesia has a unique education system since it has two school systems running in parallel. Furthermore, the systems are governed by two separate ministries, the Ministry of Education and Culture (MOEC) and the Ministry of Religious Affairs (MORA). Overall, MOEC is in charge of public and private general schools, whereas MORA is in charge of Islamic schools, both public and private. The MOEC supervises 81% of primary and secondary schools, while the MORA supervises the other 19% (Ministry of Education and Culture, 2017a).\u003c/p\u003e\u003cp\u003eDue to the dual education system in Indonesia, lower secondary school students, such as those in the TIMSS sample, can enroll in one of two pathways: Sekolah Menengah Pertama (SMP), a regular secondary school offering standard education, or Madrasah Tsanawiyah (MTs), an Islamic secondary school providing Islamic education (Republic of Indonesia, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe regular education pathway follows a six-year curriculum that includes subjects such as civics education, religious and moral education, Bahasa Indonesia (the Indonesian language), mathematics, art, and physical education. Starting in Year 4, students study additional subjects—science and social studies (Ministry of Education and Culture, 2013b). These curricula are implemented in both regular and Islamic schools.\u003c/p\u003e\u003cp\u003eHowever, Islamic schools or madrasahs adopt a more specialized approach to religious and moral education. Here, the subject is divided into distinct areas, including Quran and Hadith studies, Islamic theology (aqidah), Islamic jurisprudence (fiqh), Arabic language, and, from Year 4, Islamic history (Indonesia Ministry of Religious Affairs, 2014). Consequently, madrasahs allocate 4 to 6 additional hours of study each week compared to regular schools.\u003c/p\u003e\u003cp\u003eThe differing educational orientations of regular and Islamic schools can shape the school climate, creating unique challenges in their management. Madrasahs are often regarded as having lower academic standards, largely because they are predominantly located in rural areas (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As such, investigating the school climate, as proposed by the current study, could provide valuable insights for the Government of Indonesia. This research would support efforts to evaluate and enhance the effectiveness of education by examining school climate across various types of schools.\u003c/p\u003e\u003cp\u003eAccording to the results of the Trends in International Mathematics and Science Study (TIMSS), Indonesia consistently ranks as the country with the lowest mathematics performance scores (Martin et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Martin et al., 2004). Across TIMSS assessments conducted in 1999, 2003, 2007, and 2011, the average mathematics achievement of Indonesian eighth-graders was consistently below the international mean.\u003c/p\u003e\u003cp\u003eIn addition to assessing mathematics and science achievement, TIMSS also examines non-cognitive aspects of student learning, such as self-concept and self-efficacy. The emphasis on outcomes beyond academic achievement has long been a topic of discussion, underscoring the need for schools to address both cognitive and non-cognitive areas of development. Schools should strive to foster students' affective and psychomotor growth—encompassing their emotions, thoughts, behaviors, beliefs, and the processes they experience when engaging with specific subjects.\u003c/p\u003e\u003cp\u003eThis perspective aligns with UNESCO's (2004) recommendation that education should emphasize both academic and affective outcomes to help students reach their full potential. It also mirrors the goals of Indonesia's education system, which aims to improve outcomes in both domains. Furthermore, UNESCO (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) highlights the importance of non-academic learning outcomes as part of the Post-2015 Education Indicators to support student achievement.\u003c/p\u003e\u003cp\u003eSome academics critique the traditional focus on cognitive outcomes in school effectiveness studies, describing this approach as mechanistic (Elliott, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Such a focus may fail to ensure that students achieve their educational goals (Creemers \u0026amp; Kyriakides, 2010; Knuver \u0026amp; Brandsma, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Mortimore, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Since student achievement is a multidimensional construct that includes both cognitive and non-cognitive aspects, this mechanistic view is paradoxical (Guskey, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMortimore and colleagues' (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) influential study revealed that 77% of teachers from 50 schools in London prioritized social-affective goals for their students. Therefore, assessing the emotional and social aspects of education is just as critical as evaluating its cognitive outcomes (Reynolds et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The present study aims to investigate the role of school climate in enhancing student achievement, including its non-cognitive dimensions, such as self-concept.\u003c/p\u003e\u003cp\u003eThe Indonesia Education Act (Indonesia, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) also emphasizes the importance of nurturing students to achieve their fullest potential, making it essential to establish a constructive and positive learning environment. Schools should prioritize creating a setting that centres on the aspirations and goals of students and their parents. Teachers and school staff should be motivated to perform at their best, while fostering mutual respect and emotional connections within the school community. Consequently, research on school climate holds significant relevance in the Indonesian context.\u003c/p\u003e\u003cp\u003eEach school has its own unique traits, challenges, and issues, which contribute to its distinct character, setting, and culture (Freiberg, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The concept of school climate encapsulates the overall character of a school, reflecting the essence of its organizational personality (Hoy, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It can be likened to the personality of an individual within an organizational framework (Hoy et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSchool climate is further described as the character and quality of school life, shaped by the emotional dynamics and lived experiences of students within the school environment (Cohen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These aspects collectively influence the educational experience and outcomes, highlighting the profound impact of school climate on the overall effectiveness of a school.\u003c/p\u003e\u003cp\u003eIn the early development of the School Effectiveness Research (SER) field, school climate emerged as a vital indicator for evaluating school effectiveness (Brault et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Brookover et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), for instance, highlighted the role of school climate in facilitating student learning. Consequently, Edmonds' (1979) foundational model of effective schools recognized the significance of school climate. He identified key elements that contribute to an environment fostering academic achievement, including strong school leadership, high academic performance standards, safe and organized settings, a focus on essential academic skills, and a system for monitoring student progress.\u003c/p\u003e\u003cp\u003eHoy et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) similarly acknowledged that a positive and favorable school climate significantly impacts overall school performance. School climate is often defined as the \"quality and character of school life\" (Cohen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), encompassing both the social and physical dimensions of the school environment. It is widely regarded as a crucial factor in promoting diverse student outcomes, both academic and personal.\u003c/p\u003e\u003cp\u003eSince the early 20th century, educators, policymakers, and researchers have explored the impact of school climate. Studies have demonstrated its meaningful influence on students' emotional development, self-esteem, self-concept, psychological well-being, and reduced absenteeism (Aldridge et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Anderson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Creemers \u0026amp; Reezigt, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Mortimore, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Scheerens, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Scheerens \u0026amp; Bosker, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Thapa et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA positive school climate is essential for a successful educational experience, as emotions and relationships profoundly affect learning. How students are treated at school, home, and within their communities plays a critical role in shaping these outcomes. Supportive environments and stress management skills are necessary for children to thrive academically and socially. Conversely, fear, trauma, and emotional distress hinder learning. Therefore, schools must cultivate a nurturing climate that prioritizes both academic success and social-emotional development.\u003c/p\u003e\u003cp\u003eThapa et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) identified four key areas when evaluating and researching school climate: institutional environment, connections, teaching and learning, and safety. \u003cem\u003eSafety\u003c/em\u003e encompasses students' emotional, intellectual, and physical well-being. \u003cem\u003eConnections\u003c/em\u003e reflect students’ relationships with their school, teachers, and peers. \u003cem\u003eTeaching and learning\u003c/em\u003e refer to educators’ efforts to establish the standards, objectives, and values that define the learning environment. Meanwhile, the \u003cem\u003einstitutional environment\u003c/em\u003e relates to the physical school surroundings, emphasizing connectedness and participation within the school community.\u003c/p\u003e\u003cp\u003eDespite the importance of school climate for enhancing student learning outcomes, limited research has been conducted in Asian contexts, particularly in Indonesia. Further exploration is needed, as school climate in Indonesia may differ from that in Western nations where it is more commonly studied. In Western settings, such as the United States, school climate research has been prominent. In Asia, its popularity is growing—especially in China.\u003c/p\u003e\u003cp\u003eFor example, Yang et al. (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) examined Chinese and American students' perceptions of school climate, while Jia et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) explored the relationship between school climate and students' academic and emotional adjustment in both countries. Bear et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) analyzed differences in school climate perceptions and student engagement between Chinese and American students. These studies revealed that Chinese students experienced greater support from instructors and peers, along with more opportunities for self-autonomy in the classroom compared to their American counterparts. Such findings underscore the distinct emphasis placed on certain school climate elements by students from Western and Eastern contexts. However, it remains necessary to clarify how these insights translate to Indonesia's educational setting.\u003c/p\u003e\u003cp\u003eIndonesia’s cultural characteristics play a vital role in shaping school climate. The country is known for its collectivist values, which prioritize communal interests over individual preferences (Hofstede, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Hofstede \u0026amp; Minkov, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). For instance, one prominent ethnic group in North Sumatra, the Batak Toba, places a high value on education compared to other major ethnic groups in the region (Irmawati, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Even under financial constraints, parents often prioritize schooling for their children over other needs. Given the uniqueness of Indonesian culture, it is reasonable to expect these cultural traits to influence the school climate and its relationship with educational outcomes.\u003c/p\u003e\u003cp\u003eIndonesia’s cultural diversity further complicates the school climate profile. The nation is home to a wide variety of ethnicities, languages, religions, and local government structures (Novera, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), indicating that cultural differences within the country could shape school climates in unique ways.\u003c/p\u003e\u003cp\u003eThis study analyzed data on mathematics scores and self-concept among eighth-grade secondary school students from the TIMSS 2015 dataset. TIMSS 2019, the latest dataset, was not used as it does not include data on secondary school students. The focus on secondary school students stems from their developmental stage: they are young enough to be influenced by environmental factors and daily experiences, yet mature enough to understand and report on their surroundings, including school climate (Zysberg \u0026amp; Schwabsky, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study aims to address a knowledge gap by investigating and explaining school climate in the Indonesian context. It also examines the relationships between school climate and various educational outcomes, contributing to research on school effectiveness.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch Questions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow does Indonesian student mathematics achievement and self-concept vary within classrooms, between classrooms, and between schools?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eAfter accounting for student, teacher, and school characteristics, what trends emerge in Indonesian students' mathematics achievement and self-concept?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhich aspects of the school and classroom climate have the most significant influence on Indonesian students' mathematics achievement and self-concept, after adjusting for student, teacher, and school factors?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDo students in regular schools outperform those in \u003cem\u003emadrasahs\u003c/em\u003e in mathematics achievement, both before and after adjusting for school climate and other relevant factors?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cb\u003eParticipants\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data for this study was obtained from the TIMSS 2011 assessment. In Indonesia, information was collected from 5,795 eighth-grade students (aged 13–14) across 153 schools. The sample consisted of 2,823 male students and 2,972 female students, representing schools from 31 of Indonesia's 33 provinces.\u003c/p\u003e\u003cp\u003eTo ensure a representative distribution, the students were divided into strata during the sampling process. The stratification explicitly categorized schools into public versus private schools and general versus Islamic schools. Additionally, it is important to note that the quality of education in Indonesian schools varies widely. Due to this variability, TIMSS implicitly stratified schools not only by province but also by performance. Schools were classified into three performance categories: high, medium, or low (Joncas \u0026amp; Foy, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis implicit stratification was nested within the explicit categories of regular and madrasah schools. It served as the foundation for arranging the sampling frame before systematically selecting schools for inclusion in the study (please refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for more details).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExplicit and implicit stratum of the sample school\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eExplicit Stratum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePublic General\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePublic \u003cem\u003eMadrasah\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrivate General\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePrivate \u003cem\u003eMadrasah\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eImplicit Stratum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003e153\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eSource: Methods and Procedures in TIMSS \u0026amp; PIRLS, 2011 (Joncas \u0026amp; Foy, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, TIMSS collected data on school location from principals in order to determine the population size of the city, town, or area in which their schools were located. The school location revealed that the sample schools were divided into various classifications, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The sample schools were largely located in the suburbs (56%) and small towns (24%). However, a minor percentage of the sample comes from urban and rural areas.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSchool location\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool’s Location\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate sized city\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003eMeasures\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe predicted or outcome variable is the TIMSS five plausible values of maths score, which are an estimate of how a student would have achieved if the entire items had been administered (Hastedt, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). For self-concept, school and classroom climate as perceived by students, and other student level variables were extracted from the student questionnaire and utilised to explain the student-level variance in maths achievement. The mathematics teacher's characteristics (gender, teaching experience, level of education, and major of study) and how teacher perceived school and classroom climate were acquired from the teacher's questionnaire and used to explain the classroom-level variance in maths achievement. School climate as perceived by principals were obtained from the principal's questionnaire and utilised to explain school-level variance in maths.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData analysis strategy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIt is difficult to conduct multilevel analysis without encountering the problem of missing data, particularly at the group or upper levels (Gibson \u0026amp; Olejnik, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; McCoach, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This is due to the fact that any group-level unit with missing data excludes all individual units nested within the group-level unit from the analyses. MLWin was used to analyse three-level hierarchical linear modelling. TIMSS used multistage cluster sampling; it has unequal sample unit selection likelihood (Asparouhov, 2005; Rabe-Hesketh \u0026amp; Skrondal, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Sample weights—TOTWGT (student sample weight), MATWGT (maths teacher weight), and SCHWGT (school weight)—were applied at the student, classroom, and school levels to reduce bias in parameter estimates and provide nationally representative conclusions (Rutkowski et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCentering or scaling a predictor or explanatory variable in hierarchical linear modelling involves subtracting the mean or some other constant value from each individual raw value of the predictor variable and subtracting the raw metric from the mean or constant value. (Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wu \u0026amp; Wooldridge, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This helps change the interpretation of the intercept (Kreft \u0026amp; De Leeuw, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This is because in regression, the intercept is defined as the expected score of someone's outcome variable with a score of zero for all predictors in the model (Raudenbush \u0026amp; Bruk, 2002). Because social science attributes have few meaningful or real zeros (Kreft \u0026amp; de Leeuw, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), predictor variables need to be transferred. The intercept is therefore interpreted as the expected score of someone's outcome variable whose score for a particular predictor variable equals the group mean or the overall mean, depending on the type of centering approach the analyst is using. All predictors focused on overall averages at both student and classroom levels (Enders \u0026amp; Tofighi, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hofmann \u0026amp; Gavin, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Hox, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Kreft, De Leeuw, \u0026amp; Aiken, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eResearch question 1\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe initial step in multilevel data analysis involves the estimation of the unconditional or null model, as stated by Raudenbush and Bryk (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The present model does not explain any variance observed in the outcome variable. Instead, its purpose is to allocate the overall variance present in the outcome variable across the various levels present in the data. The null model was estimated as a response to the first research question \u0026ldquo;How does Indonesian student mathematics achievement and self-concept vary within classrooms, between classrooms, and between schools?\u0026rdquo; To answer this question, the researcher investigated the variances in mathematics achievement and self-concept based on school-level and classroom-level. Specifically, by employing the variance components model for a three-levels model (school, classroom, student levels). Mathematics achievement was evaluated using students' mathematics scores from the TIMSS 2011 dataset, while self-concept was assessed based on students\u0026rsquo; self-concept data from the same dataset. Model 0 (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) describes school performance of the Indonesian secondary schools in terms of mathematics score and self-concept in mathematics, which is indicated by the proportion of variance explained by each level.\u003c/p\u003e\u003cp\u003eThe results showed that Indonesian students\u0026rsquo; mathematics achievement varied among schools. Specifically, the school-level explained about 35% of the variance of mathematics score. The classroom-level had lesser predictive power. Specifically, the classroom-level accounted for only about 9% of the variance of mathematics score. This finding indicated that Indonesian students\u0026rsquo; mathematics achievement varied less among classrooms. Caution is warranted to apply these results because of the small sample of classes in the TIMSS dataset, which is from the 74 schools that are available in the TIMSS dataset, only 20 schools with more than one classroom. As such, there might not have been enough power to conclude the differences in mathematics achievement at the classroom-level.\u003c/p\u003e\u003cp\u003eIn terms of self-concept the school-level accounted for only about 5% of the variance. Similarly, the classroom-level accounted for only about 4% of the variance of self-concept. These results were consistent with past studies that have shown a small variation of non-cognitive outcomes when comparing one school to another (Gray, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Thomas et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe variation of mathematics score was best accounted for at the student-level. The student-level predicted more than 56% of the variance of mathematics score. The student-level also accounted for 91% of the variance of self-concept. As expected, these results were consistent with past studies that showed individual student-level played a major role in predicting students' performance (Gray et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Thiele et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA comparison of the empty models of the two outcome measures revealed that the differences between schools were more noticeable on academic achievement than of affective one. This findings are in line with the results of studies conducted in Belgium (Opdenakker \u0026amp; Van Damme, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the UK (Gray, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and Cyprus (Creemers \u0026amp; Kyriakides, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e) that also found that differences between schools in terms of affective outcomes were smaller in comparison to the results of the academic outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe null model for mathematics score and self-concept\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFixed Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e393.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eschool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2416.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e420.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclass variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e599.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3893.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclassroom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64924.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42240.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch question 2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRQ2 asked, \u0026ldquo;After accounting for student, teacher, and school characteristics, what trends emerge in Indonesian students' mathematics achievement and self-concept?\u0026rdquo; To answer this question, Model 1, 2, 3, and 4 were conducted. Each model was aimed to verify the relationship between student characteristics (Model 1), teachers (Model 2), and school (Model 3), and learning outcomes. Then, Model 4 combined Model 1 to 3 that includes all statistically significant explanatory variables at students, teachers, and school levels for the two learning outcomes (mathematics score \u0026amp; self-concept).\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 1: Student characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 1 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was utilized to investigate whether differences in mathematics achievement and self-concept existed across schools and classrooms within schools, after accounting for student characteristics and backgrounds. The study considered three background variables related to students' socio-cultural contexts: gender, language (used as an indicator of ethnicity), and socioeconomic status (SES). In the TIMSS 2011 study, SES was measured through: (a) parents' education level, (b) the availability of study support, and (c) the number of books in the household. Additionally, students' self-concept was incorporated into the model, as self-belief have a reciprocal relationship with academic achievement. Therefore, when examining self-concept outcomes, mathematics achievement was included as an explanatory variable for this non-academic measure.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 1 with student\u0026rsquo;s backgrounds variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e417.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParents\u0026rsquo; education level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary but not university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-13.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome primary, lower secondary or no school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStudy support\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of either a private room or electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of both private room and electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of books\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;25 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;100 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e101\u0026ndash;200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf belief\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-concept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAchievement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMathematics score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2310.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e577.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3675.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclassroom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64561.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e41945.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-14%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter accounting for student background variables (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), 35% of the variance in mathematics score was attributed to differences between schools, a proportion similar to that observed in Model 0. Additionally, the variation between classes within schools showed comparable results. For self-belief outcomes, the proportion of school-level differences in self-concept closely mirrored those found in Model 0.\u003c/p\u003e\u003cp\u003eThis study revealed no significant associations between students' gender, mathematics achievement, and self-concept. In general, students with parents who have lower levels of education tend to achieve lower mathematics scores. However, parental education level was not associated with students' self-belief. Regarding study support, students with fewer home resources, such as a dedicated study space and other educational materials, performed worse in mathematics compared to those with better home study support. Nevertheless, no relationship was observed between self-concept and home study resources. An empirical study by Filmer and Pritchett (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) also identified varying patterns in the relationship between wealth disparities and academic performance across different countries. Consequently, the relationship between home resources and outcomes in this study appeared inconsistent as well.\u003c/p\u003e\u003cp\u003eThe association between the number of books in a student\u0026rsquo;s home and mathematics achievement appears inconsistent. For example, possessing fewer books, as opposed to none, showed a negative correlation with mathematics score. Conversely, a positive trend was identified for self-concept, indicating that students with a greater number of books generally reported higher self-concept scores (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for detailed results).\u003c/p\u003e\u003cp\u003eAnother socio-cultural background variable considered in the study is language, specifically how frequently students use the language of the TIMSS test (Indonesian) in their daily lives. Language use served as a proxy for measuring ethnic differences. The findings indicated that students who never used the Indonesian language exhibited a significant negative correlation with self-concept outcomes, whereas no such relationship was observed with mathematics achievement.\u003c/p\u003e\u003cp\u003eWith regard to students' psychological background variables, the study identified a significant positive correlation between self-concept and mathematics achievement. This finding aligns with the majority of prior research in the field (Marsh, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1990a\u003c/span\u003e; Marsh \u0026amp; Martin, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; O'Mara et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pajares \u0026amp; Urdan, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, the study reinforces the reciprocal relationship between academic achievement and self-beliefs as reported in previous studies (Huang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Marsh \u0026amp; O'Mara, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; O'Mara et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Seaton et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eConcerning the model's overall goodness of fit, Model 1 accounted for only 5% of the total variance in mathematics score (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and 4% of the total variance in self-concept. These findings indicate that including students' characteristic factors in the model provided limited predictive power for their learning outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 2: Teacher characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 2 incorporated four teacher-level variables: gender, age, educational background, and teaching experience. Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the variability in mathematics score attributed to schools and classrooms, considering these teacher background variables. The analysis revealed statistically significant associations between mathematics achievement and all teacher characteristics, except for gender. However, none of the teacher variables showed a relationship with students' self-concept in terms of self-belief outcomes.\u003c/p\u003e\u003cp\u003ePrevious research has shown inconsistent findings regarding the relationship between teacher gender and student achievement. For instance, Antecol et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) identified teacher gender as a non-significant factor in their randomized experiment examining its impact on primary students' achievement. Conversely, in other contexts such as Pakistan, Warwick and Jatoi (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) reported a strong correlation between teacher gender and student achievement.\u003c/p\u003e\u003cp\u003eA significant negative relationship was identified between teachers' age and mathematics score, with older teachers being associated with lower student achievement in mathematics. Conversely, teaching experience showed a significant positive relationship, as less experienced teachers tended to have students with lower achievement levels. Similarly, teachers' educational background was positively associated with mathematics score; students taught by teachers without formal education beyond upper-secondary school generally scored lower in math. In contrast, higher levels of education and specialization in mathematics among teachers were linked to better students\u0026rsquo; math achievement. Overall, these trends indicate that students' mathematics achievement is influenced by various teacher demographic factors, including age, experience, educational background, and subject specialization.\u003c/p\u003e\u003cp\u003eWhile teacher age demonstrated a significant negative relationship with mathematics achievement, positive significant relationships were observed with teaching experience, educational background, and subject specialization in mathematics. These findings align with much of the existing research, which highlights the positive impact of teacher experience and qualifications on student achievement (Croninger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Darling-Hammond, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000b\u003c/span\u003e). Interestingly, older teachers were negatively associated with students' mathematics score. This may be attributed to older teachers being less familiar with contemporary teaching methods or challenges in bridging the generational gap with students. Nonetheless, further investigation is required to better understand how teacher age influences mathematics instruction.\u003c/p\u003e\u003cp\u003eThe variability in mathematics scores between schools decreased slightly, dropping from 35% in Model 0 to 30%, representing a 5% reduction. For classroom-level variability, the trend remained consistent with Model 0. In contrast, for self-concept outcomes, the variability between schools and classrooms showed almost no change compared to Model 0.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 2 with teacher\u0026rsquo;s backgrounds variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e422.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-13.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-84.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExperiences\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpanning a minimum of 10 but not exceeding 20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpanning a minimum of 5 but not exceeding 10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-42.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll other majors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education beyond upper-secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-60.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1962.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e565.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3906.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclassroom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64898.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42233.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eRegarding the overall goodness of fit, Model 2 explained a small proportion of the total variance in mathematics score, ranging from approximately 5\u0026ndash;7%, which represents a slight improvement over Model 1 (refer to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, for self-concept outcomes, the explained variance decreased further. Even after incorporating teacher background variables, the overall explained variance remained low, indicating that teacher characteristics alone are insufficient to effectively predict students' learning outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 3: School Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 3 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) was utilized to analyze school-level variables influencing mathematics score and self-concept variance at both the school and classroom levels. The model incorporated factors such as the school\u0026rsquo;s socioeconomic background (aggregated from students\u0026rsquo; SES), school location, and school type (general private school, general public school, private madrasah, and public madrasah).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e revealed that, as anticipated, schools with a higher average socioeconomic status (SES) demonstrated significantly better mathematics score. Regarding school location, students enrolled in schools located in small towns scored considerably lower compared to their peers in remote rural areas. Lastly, concerning school type, as expected, students attending private schools, including both madrasahs and general private schools, exhibited significantly lower mathematics score.\u003c/p\u003e\u003cp\u003eThe average socioeconomic status (SES) of the school was not associated with students' self-concept. Regarding school location, students attending sub-urban schools exhibited a significant positive association with their self-concept. As for school types, no significant relationship was observed between school type and students' self-concept.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 3 with school\u0026rsquo;s backgrounds variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e414.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate sized city\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall city\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-24.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-21.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Types\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate general\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-20.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-34.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1222.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e300.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e659.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3907.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclassroom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64856.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e42225.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-10%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe variability in mathematics scores across schools decreased significantly by 14% compared to Model 0. Regarding classroom variability, the trend remained largely consistent with Model 0, showing a slight increase of up to 2%. This aligns with previous studies, which have generally found that accounting for school context diminishes the apparent school effect (Muijs \u0026amp; Reynolds, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Opdenakker \u0026amp; Van Damme, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Opdenakker et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In contrast to mathematics scores, the variability in self-concept between schools and classrooms was nearly identical to that observed in Model 0.\u003c/p\u003e\u003cp\u003eIncorporating school context into Model 3 resulted in a significant improvement in the model's overall \"goodness of fit\" compared to Model 1 and Model 2, accounting for 16% of the total variance in mathematics score. In contrast, the change for self-concept was minimal. This enhanced goodness of fit demonstrated a greater capacity to predict students' learning outcomes, suggesting that school-related factors provide a stronger explanation of student mathematics achievement than variables related to students' or teachers' backgrounds.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 4: Student, teacher, and school characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 4 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) was employed to examine the scope and degree of school performance in mathematics score and self-concept among Indonesian lower secondary schools, while accounting for student, teacher, and school characteristics. For mathematics score, the variance across schools was comparable to Model 3, though it exhibited a slight reduction of up to 1%. Nonetheless, some notable changes emerged. For instance, teacher characteristics, including both teaching experience and educational background, were not found to have a statistically significant association with mathematics score. In contrast, for self-concept outcomes, teacher characteristics maintained a significant relationship, consistent with the findings from Model 2.\u003c/p\u003e\u003cp\u003eThe effects of student characteristic variables remained consistent with those observed in Model 1. Similarly, the effects of school context variables were comparable to those in Model 3. However, a notable change was identified in relation to school types. In Model 4, only private madrasahs showed a significant negative association with mathematics score. This finding suggests that, after accounting for student, teacher, and school background variables, students attending private madrasahs scored lower in mathematics compared to their peers in other school types, including public general, public madrasahs, and private general schools. Conversely, for self-concept, the effects of student, teacher, and school-level variables remained relatively similar across Models 1, 2, and 3.\u003c/p\u003e\u003cp\u003eWhen all student, teacher, and school-level variables were included in the model, the overall \"goodness of fit\" showed substantial improvement compared to Model 3, accounting for 22% of the total variance in mathematics score across schools. With regard to school-level variability, Model 4 demonstrated a notable enhancement compared to Models 0, 1, and 2, although it was slightly higher than Model 3. Conversely, classroom-level variability did not exhibit significant improvement across any of the outcomes.\u003c/p\u003e\u003cp\u003eIncluding all student, teacher, and school-level variables in the model, the overall \"goodness of fit\" was considerably improved compared to Model 3, explaining 22% of the total school variance in mathematics score. In terms of school classroom variability, compared to Model 0, 1, and 2, Model 4 had a remarkable improvement in school variability, but slightly higher compared to Model 3. On the other hand, classroom variability was not improved significantly for all outcomes.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 4 with student, teacher, and school\u0026rsquo;s backgrounds variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e448.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParents\u0026rsquo; education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary but not university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome primary, lower secondary or no school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-11.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStudy support\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of either a private room or electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of both private room and electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of books\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;25 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;100 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e101\u0026ndash;200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf belief\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-concept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAchievement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMathematics score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTeacher Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-27.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-37.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExperiences\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpanning a minimum of 10 but not exceeding 20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpanning a minimum of 5 but not exceeding 10 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than 5 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation Background\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll other majors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education beyond upper-secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-26.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate sized city\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-17.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRemote rural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Types\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate general\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-13.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1081.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e663.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e245.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3675.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclassroom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64494.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e41918.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e40%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e-11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e-16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch question 3\u003c/b\u003e\u003c/p\u003e\u003cp\u003eResearch question 3 is as follows: \u0026ldquo;Which aspects of the school and classroom climate have the most significant influence on Indonesian students' mathematics achievement and self-concept, after adjusting for student, teacher, and school factors?\u0026rdquo; To address this question, Models 5 and 6 were utilized to compare the aspects of school climate that significantly explained the variance in classroom and school performance, both before and after adjusting for student, teacher, and school characteristics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 5: Key school climate factors prior to accounting for student, teacher, and school characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 5 (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), which included only school climate factors, attributed 24% of the variance in mathematics score to differences between schools. This represents a significant improvement compared to Model 0. However, classroom-level differences remained nearly identical to those in Model 0, with a slight reduction of approximately 1%. As previously noted, this is likely due to the relatively small variability within the classroom sample, which may limit the model's ability to accurately detect differences.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 5 school climate factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e398.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Climate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent connected with school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClassroom Climate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent\u0026rsquo;s mathematics lesson engagement (teaching-learning)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent safety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher safety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher emphasis on academic success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher-teacher interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher connected with school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher working condition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstructions to engage student (Teacher)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom disturbance (Teacher)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeaching confidence (Teacher)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmphasis on academic success (School)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiscipline (School)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology resources (School)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafety (School)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral resources (School)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool leadership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent school connectedness (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent safety (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher school connectedness (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher emphasis on academic success (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher to teacher interaction (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher safety (Teacher safety)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher working condition (School mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher\u0026rsquo;s confidence in teaching (Class mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher engaging instruction (Class mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClassroom disturbance (Class mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents\u0026rsquo; mathematics lesson engagement (Class mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1399.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e279.6 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e472.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.7 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3888.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.2 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e64818.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e41947.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e92%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor self-concept, the variance across schools decreased by 1%, declining from 5% in Model 0 to 4% in Model 5. Conversely, classroom-level variability in self-concept outcomes showed a reduction of 4% compared to Model 0.\u003c/p\u003e\u003cp\u003eThe total variance explained (overall goodness of fit) for self-concept outcomes in Model 5 was notably higher compared to Models 1, 2, 3, and 4. This finding suggests that the school climate factor had a significant impact on students' self-concept outcomes.\u003c/p\u003e\u003cp\u003eRegarding school climate factors, not all were significantly associated with mathematics achievement. At the student level, a positive and significant relationship was observed between students\u0026rsquo; sense of connection to their school (e.g., enjoying being at school) and their mathematics score. Meanwhile, student engagement specifically in mathematics lessons was found to have a significant negative relationship with self-concept but showed no significant association with mathematics score. This finding contrasts with previous research, such as the study by Fung et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which analyzed data from 295,416 15-year-old secondary school students across 34 countries using PISA 2012. Their study revealed that greater engagement in mathematics lessons was linked to higher academic achievement. In the Indonesian context, however, while students perceived themselves as engaged in school and classroom, their academic achievement was reported to be low.\u003c/p\u003e\u003cp\u003eThe results for student safety were unexpected, as a significant negative relationship was identified between student safety and mathematics score. However, student safety exhibited a significant positive association with self-concept. One possible explanation is that the relationship may not follow a linear pattern.\u003c/p\u003e\u003cp\u003eAt the teacher level, there was a significant positive association between teachers' confidence in teaching mathematics and students' self-concept. At the school level, a strong emphasis on academic success was significantly and positively associated with students' mathematics score, but it showed no connection to their self-concept. Surprisingly, the availability of general school resources had a significant negative association with mathematics score. Lastly, school leadership was found to have no significant relationship with mathematics score.\u003c/p\u003e\u003cp\u003eThe aggregated climate factors at the school and classroom levels were analyzed. The school-level mean of connectedness showed no significant association with mathematics score. Conversely, the school-level mean of student safety followed a pattern consistent with the non-aggregated factors, demonstrating a significant negative relationship with mathematics score and a significant positive relationship with self-concept. Based on the teacher questionnaire, the school-level mean of student safety also exhibited a significant negative relationship. Finally, the classroom-level mean of student engagement showed a significant positive association with mathematics score but a significant negative association with students' self-concept.\u003c/p\u003e\u003cp\u003eModel 6 (the next model) incorporated all significant factors related to school and classroom climate, along with the relevant student, teacher, and school background variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eModel 6: The influence of school climate factors after accounting for student, teacher, and school characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eModel 6 was developed (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) to examine whether differences in mathematics score, to measure mathematics achievement, and self-concept exist between schools and classrooms after incorporating all significant school climate factors from Model 5 and accounting for student, teacher, and school characteristics from Model 4.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel 6 Final Model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponse\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMathematics Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eSelf-Concept\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed Part\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e436.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e13.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStudent Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParents\u0026rsquo; education level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePost-secondary but not university\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUpper secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-12.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome primary, lower secondary or no school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-10.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e5.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStudy support\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of either a private room or electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePossession of both private room and electronic devices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of books\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u0026ndash;25 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e26\u0026ndash;100 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e101\u0026ndash;200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e6.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than 200 pieces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e11.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLanguage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf belief\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf-concept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAchievement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMathematics score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25\u0026ndash;29 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-24.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e12.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;39 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e10.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e40\u0026ndash;49 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e10.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e50\u0026ndash;59 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e13.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60 years or more\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-30.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e18.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAll other majors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e8.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal education beyond upper-secondary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e14.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean SES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e6.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSub-urban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e9.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate sized city\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e12.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall town\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e11.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e28.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Types\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-6.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e15.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate general\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-11.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e8.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrivate \u003cem\u003emadrasah\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-22.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e12.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSchool Climate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents\u0026rsquo; school connectedness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent safety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher safety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher-teacher interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher\u0026rsquo;s confidence in teaching mathematics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool\u0026rsquo;s emphasis on academic success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool discipline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool\u0026rsquo;s general resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent safe (school)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeacher safe (school)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClassroom Climate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudents\u0026rsquo; mathematics lesson engagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom Part\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e1006.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e275.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e518.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e207.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e3649.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e75.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCschool\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCclass\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVPCstudent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeviance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e64431.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e41646.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e46%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClass variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStudent variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal variance explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e13%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSchool discipline demonstrated a significant positive association with mathematics score. Conversely, school safety exhibited a significant negative relationship with mathematics score. This finding contradicts previous research, such as Thapa et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which generally identified a significant positive relationship between students' perception of safety and academic performance. However, the association between safety and self-concept outcomes differed, as safety was found to have a significant positive relationship with self-concept.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e\u003cb\u003eThe variability and magnitude of mathematics score and self-concept among Indonesian year 8 students.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe results of the null model indicate that 35% of the total variance in mathematics score can be attributed to differences between schools. In contrast, classroom-level differences within schools accounted for a relatively small portion of the variance, approximately 9%. These findings regarding classroom variability are inconsistent with previous studies, which have generally suggested that differences between classrooms are significantly more influential than variations between schools (Hill \u0026amp; Rowe, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Rowe \u0026amp; Hill, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). In the context of Indonesia, it is worth noting that only 20 schools participating in the study had two classrooms, while the remaining 54 schools were limited to a single classroom.\u003c/p\u003e\u003cp\u003eIn contrast to mathematics score, the null model revealed that a smaller proportion of the variance in self-concept could be attributed to differences between schools. Specifically, school-level differences accounted for only 5% of the variance in self-concept. Similarly, classroom-level differences in self-concept outcomes were also minor compared to those in mathematics score, with classroom variance accounting for approximately 4% of the total variance in self-concept. These findings align with previous research, which has generally observed that differences between schools or classrooms have a more pronounced impact on academic achievement than on self-beliefs or affective outcomes. Examples include studies conducted in Belgium (Opdenakker \u0026amp; Van Damme, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the United Kingdom (Gray, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and Cyprus (Creemers \u0026amp; Kyriakides, 2010).\u003c/p\u003e\u003cp\u003eThe proportion of variance at the school level highlights the achievement gap in students' mathematics achievement in Indonesia, resembling patterns observed in other developing countries (e.g., Zanzibar, as noted by Yu \u0026amp; Thomas, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In contrast, the school-level variation in academic self-concept was relatively low. The Null Model revealed that 35% of the variance in mathematics score, as reported in the TIMSS 2011 results, could be attributed to school effects. This finding underscores a significant achievement gap within the Indonesian education system. Notably, this result is slightly higher than earlier estimates from research on primary school effectiveness in Indonesia two decades ago (Kaluge, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). For instance, Kaluge (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) reported that 29.2% of the variance in mathematics at schools was attributable to school-level factors.\u003c/p\u003e\u003cp\u003eThe pattern of raw results was comparatively lower than in other developing countries, such as Brazil, Colombia, Honduras, Egypt, India, Jordan, Namibia, Pakistan, Thailand, Zimbabwe, Botswana, and Philippines. In these countries, the average school-level variation in performance was reported as 46% at the primary level and 41% at the secondary level (Riddell, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Similarly, Yu and Thomas (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) found that Zanzibar exhibited a comparable achievement gap, with 34% of the variance in mathematics performance attributed to schools. In contrast, in developed countries like the United Kingdom, only 14% of the total unadjusted variance was attributed to schools (Thomas \u0026amp; Mortimore, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe variability and magnitude of school and classroom performance among Indonesian Year 8 students in mathematics score and self-concept after controlling for student characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter accounting for student background variables, 35% of the variance in mathematics score could be attributed to differences between schools. Similarly, differences between classrooms within schools were found to be comparable. For self-belief outcomes, the proportion of variance in self-concept attributable to school-level differences was consistent with the variance component observed in the null model. These findings suggest that, in the Indonesian context, student background variables had a limited impact in explaining differences in student learning outcomes at the school, classroom, or individual levels. However, it is important to note that this analysis did not include students' prior achievement, and the findings should therefore be interpreted with caution. Prior attainment has consistently been identified in numerous School Effectiveness Research (SER) studies as one of the most influential student-level variables (Gray et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Lenkeit, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Muijs \u0026amp; Reynolds, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Timmermans \u0026amp; Thomas, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSocio-cultural factors, such as parental education and the number of books at home, were found to have a significant association with both student mathematics achievement and self-belief. Generally, it can be concluded that lower levels of parental education correspond to lower student mathematics achievement. However, no significant relationship was observed between parental education and students' self-concept. Previous studies have highlighted the positive influence of parental education on students' academic performance. For instance, Gooding (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) found that students whose parents had higher levels of education outperformed those whose parents had lower levels of education. Similarly, Khan et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported comparable findings in India after analyzing the academic achievement of 100 secondary school student in relation to their parents\u0026rsquo; educational level. One possible explanation is that parents acquire knowledge and skills during their own education, which may influence how they support their children's learning at home and facilitate modeling behaviors (Eccles, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). With regard to self-belief, this study found no significant relationship between parental education levels and students' self-concept. A possible explanation, as suggested by Eccles (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), is that parents with higher levels of education may exercise strong or excessive parental control, which could negatively impact students' confidence.\u003c/p\u003e\u003cp\u003eIn terms of study support, students with better access to resources at home, such as a dedicated study space and other materials, performed better in mathematics compared to those with limited study support. Similar findings were reported by Chudgar et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who demonstrated a positive relationship between children's out-of-school resources and their mathematics achievement. However, no significant relationship was found between home study support and self-concept. Similarly, Filmer and Pritchett (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) conducted an empirical analysis on home study support, revealing varied trends between disparities in student wealth and academic performance across different countries. Consequently, the relationship between household resources and academic outcomes was also inconsistent in the present study.\u003c/p\u003e\u003cp\u003eRegarding the number of books at home, having fewer books, as opposed to none, showed a negative association with mathematics score. However, the findings for self-concept indicated a positive relationship between the number of books at home and students' self-belief (refer to Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for detailed scores).\u003c/p\u003e\u003cp\u003eTurning to another socio-cultural background variable, this study utilized language use (frequency of using the language of the TIMSS test, i.e., Indonesian, in daily life) as a proxy for measuring ethnic differences. The findings revealed that students who never used the test language did not exhibit a significant relationship with mathematics score. However, the analysis of self-belief outcomes identified a negative relationship between self-concept and language use. Previous research on ethnicity and learning outcomes has also yielded inconclusive results (Strand, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Worrell, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study found a significant positive association between self-concept and mathematics achievement within the psychological context variables of students. This finding aligns with the majority of prior research in the field (Marsh, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1990a\u003c/span\u003e; Marsh \u0026amp; Martin, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; O'Mara et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pajares \u0026amp; Urdan, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Parker et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, the study supports the reciprocal relationship between academic achievement and self-belief, as highlighted in earlier studies (Huang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Marsh \u0026amp; O'Mara, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Seaton et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe proportion of total variance explained by student background variables was relatively small, accounting for only 5% of the variance in mathematics score (refer to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For self-concept, the total variance explained was 4%. These findings indicate that while student characteristic variables were statistically significant, their contribution to predicting students\u0026rsquo; learning outcomes was minimal due to the low percentage.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe variability and magnitude of school and classroom performance among Indonesian Year 8 students in math score and self-concept after controlling for teacher\u0026rsquo;s characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe relationship between mathematics score and all teacher characteristics was statistically significant, with the exception of teacher gender. However, when examining self-belief outcomes, none of the teacher variables showed a statistically significant association with students' self-concept. Previous studies have reported mixed findings regarding the influence of teacher gender on student achievement. For instance, Antecol et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that teacher gender was not a significant variable in their randomized experiment analyzing the effect of teacher gender on primary school students' achievement in the United States. Conversely, in the context of developing countries such as Pakistan, teacher gender demonstrated a strong association with student performance (Warwick \u0026amp; Jatoi, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTeacher age was found to have a significant negative association with mathematics score, indicating that as teacher age increases, students' mathematics performance decreases. A similar pattern was observed with teaching experience, where teachers with less experience tended to have students with lower mathematics scores. These findings align with existing research, which consistently highlights the significant positive effects of teacher experience and qualifications on student performance (Croninger et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Darling-Hammond, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000b\u003c/span\u003e). This trend also extends to teachers' educational backgrounds, as students taught by teachers with no formal education beyond upper-secondary level generally performed worse in mathematics. Notably, the higher the teacher's level of education and their specialization in mathematics, the better students' mathematics achievement tended to be.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe range and extent of school and classroom performance among Indonesian Year 8 students in math score and self-concept after controlling for school characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter accounting for school characteristics, including social and economic background (aggregated from students\u0026rsquo; SES), school location, school size, and school type (general private school, general public school, private madrasah, and public madrasah), this study found that schools with a higher average SES, as anticipated, tended to achieve significantly better results in mathematics. These findings align with previous studies that have emphasized the critical role of school SES in influencing academic performance (De Fraine et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; L\u0026oacute;pez et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Berkowitz et al., 2017; Opdenakker \u0026amp; Van Damme, 2005; Opdenakker et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sammons et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Timmermans \u0026amp; Thomas, 2014).\u003c/p\u003e\u003cp\u003eIn terms of school location, students attending schools in small towns performed significantly worse in mathematics compared to their peers in rural areas, medium-sized cities, sub-urban, and urban. This finding aligns with earlier studies, such as those by Young (\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and Burger (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which highlighted achievement differences between urban and rural students. However, the observed trend is somewhat unusual, as students in remote rural areas outperformed those in small towns in mathematics. This result is also somewhat consistent with Tayyaba's (2012) research in Pakistan, which demonstrated comparable academic success among rural and urban students in certain provinces. In contrast, a study conducted in Malaysia by Othman and Muijs (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found no significant differences in school performance between rural and urban schools.\u003c/p\u003e\u003cp\u003eTurning to school types, as anticipated, students attending private schools\u0026mdash;both madrasahs and general private schools\u0026mdash;demonstrated significantly lower mathematics achievement. This finding aligns with prior research by Hendajany (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which utilized data from the Indonesian Family Life Survey (IFLS) and concluded that students in public schools exhibited higher achievement levels compared to those in private schools. When comparing madrasahs and general schools, the current study also supports the conclusion of Newhouse and Beegle (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), who analyzed IFLS data and found no significant differences in achievement between private madrasahs and private general schools.\u003c/p\u003e\u003cp\u003eThe school\u0026rsquo;s average socioeconomic status (SES) showed no significant association with self-concept. Regarding school location, students in sub-urban areas demonstrated a significant positive relationship with their self-concept. In terms of school types, no significant relationship was found between self-concept and school type. Notably, there is limited research examining the relationship between self-beliefs and the school context.\u003c/p\u003e\u003cp\u003eThe variability in mathematics achievement between schools decreased significantly by 14% compared to Model 0. In contrast, classroom-level variability remained similar to that in Model 0, with a slight increase of 2%. This finding aligns with previous research, which has frequently observed that accounting for school context diminishes the apparent school effect (Muijs \u0026amp; Reynolds, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, unlike the outcomes for mathematics score, the variability in self-concept between schools and classrooms was nearly identical to that observed in the variance component model.\u003c/p\u003e\u003cp\u003eIncorporating the school context significantly enhanced the model's goodness of fit, accounting for 16% of the total variance in mathematics score. However, the change observed for self-concept was minimal. This improved goodness of fit demonstrates a greater capacity to predict students\u0026rsquo; learning outcomes. It implies that school-related variables are more effective than student and teacher background variables in explaining mathematics achievement.\u003c/p\u003e\u003cp\u003e\u003cb\u003eThe variability and magnitude of school and classroom performance among Indonesian Year 8 students in mathematics score and self-concept after including school climate factors and controlling for student, teacher, and school characteristics.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWhen accounting for all critical factors related to school and classroom climates, as well as controlling for relevant characteristics of students, teachers, and schools, the proportion of variance in mathematics score that could be attributed to differences between schools decreased substantially to 19%. In contrast, the variance in self-concept between schools did not exhibit a statistically significant difference.\u003c/p\u003e\u003cp\u003eThe influence of school and classroom climate on student learning improved significantly after incorporating school climate variables, compared to models that solely included student, teacher, and school background characteristics. The overall model fit, represented by the percentage of total variance explained, increased and accounted for approximately 25% of the total variance in mathematics score. For self-concept, the explained variance also showed notable improvement, accounting for 13%. Although the total variance explained is relatively modest, it underscores the impact of school and classroom climate on students' mathematics achievement and self-concept. The 25% variance explained is considered acceptable, given that this model did not incorporate prior student achievement, which is widely recognized as one of the most significant predictors (Muijs \u0026amp; Reynolds, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mu\u0026ntilde;oz-Chereau, 2013; Salim, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Timmermans \u0026amp; Thomas, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For context, Mu\u0026ntilde;oz-Chereau (2013), in their quest for a more equitable model of school effectiveness in Chile, demonstrated that including prior attainment substantially enhanced the model's fit, with explained variance increasing from 16\u0026ndash;63% when compared to a model limited to student background variables.\u003c/p\u003e\u003cp\u003eThe model accounts for 58% of the variance in mathematics scores at the school level and 46% for self-concept. Notably, self-concept demonstrates the highest explained classroom-level variance, approximately 65%, after controlling for student, teacher, and background variables. This substantial classroom-level variance (65%) highlights the pivotal role of teachers in the classroom, which can be interpreted through Bandura's social learning theory (Bandura, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Bandura \u0026amp; Walters, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1963\u003c/span\u003e). According to this theory, teachers serve as references or models, influencing students who then emulate their behavior. This interpretation is further supported by Cheng's (2016) research, which revealed that students\u0026rsquo; non-cognitive outcomes are shaped through modeling their teachers.\u003c/p\u003e\u003cp\u003eIn terms of student characteristics, gender does not have a significant association with mathematics score. However, it is significantly related to self-concept, with boys exhibiting higher self-concept scores compared to girls. This finding aligns with previous studies, Goldman and Penner (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that girls\u0026rsquo; self-concept in mathematics was lower than that of boys the majority of the 49 countries they studied. Additionally, Hergovich et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) also reported the same findings. They further noted that girls\u0026rsquo; self-concept is heavily influenced by the judgments of teachers and parents, a factor that does not similarly affect boys. Moreover, the persistence of gender inequality in Indonesia may heighten the risk of female students developing a weaker self-concept compared to their male counterparts.\u003c/p\u003e\u003cp\u003eTeacher age was found to have a significant relationship with students' mathematics score; however, the relationship was non-linear. Students tended to demonstrate lower mathematics score when taught by younger teachers (under 40 years old) or older teachers (over 60 years old). This finding suggests that teachers within the average age range of 40 to 59 possess the necessary experience to effectively manage their classrooms. In contrast, teachers under the age of 30 may lack sufficient experience and, being relatively young, might have a limited understanding of students' behavior. On the other hand, teachers aged 40 to 59 benefit from more extensive professional experience.\u003c/p\u003e\u003cp\u003eWith regard to self-belief outcomes, teacher age was found to be an insignificant factor. However, the teacher's academic major appeared to play a role, as students taught by non-mathematics teachers tended to exhibit higher self-concept scores.\u003c/p\u003e\u003cp\u003eIn terms of school-level characteristics or context, average SES was found to have a significant association with students' mathematics score but not with their self-concept. This finding aligns with prior research, which has often shown that schools serving low-SES student populations tend to be under-resourced, thereby impacting student performance (Aikens \u0026amp; Barbarin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Timmermans \u0026amp; Thomas, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, other studies have demonstrated that the socioeconomic composition of students has a significant influence on academic achievement (Chudgar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Muijs et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther school-level factors, such as school location and school type, no longer show significant relationships with any of the outcomes. This finding is both unexpected and intriguing, as the inclusion of school and classroom climate factors renders the impact of school type\u0026mdash;particularly being a private madrasah\u0026mdash;insignificant. This result suggests that school and classroom climate factors play a crucial role in mitigating the negative effects associated with private madrasahs. In previous studies, madrasahs were often found to have significantly lower academic performance compared to other school types (Ali et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; ADB, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ghozali et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Among the school and classroom climate factors significantly associated with mathematics achievement were student safety (freedom from bullying), school discipline, and active student engagement in mathematics lessons.\u003c/p\u003e\u003cp\u003eInterestingly, the relationship between student safety and mathematics score was found to be negative. This result contrasts with most prior studies, which have generally reported that a safe and orderly environment exerts a significant positive impact (e.g., Sammons et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Nonetheless, this study also revealed that school discipline, as an indicator of a school's orderliness, had a significant positive correlation with mathematics score. Thus, the findings are not entirely inconsistent with previous research. Additionally, the correlation between school discipline and mathematics score was stronger than the correlation between student safety (defined as freedom from bullying) and mathematics score.\u003c/p\u003e\u003cp\u003eAmong the significant school climate factors, student engagement in mathematics lessons exhibited the strongest correlation coefficient. This highlights its critical role in accounting for the variance in mathematics score.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe multilevel analysis conducted in this study offers valuable evidence and a comprehensive understanding of school climate within the Indonesian context. It can be used to interpret school practices and illustrate how school climate serves as a protective factor in enhancing student outcomes, regardless of school type. These findings support the perspective that school climate is a flexible aspect of education that can be shaped by schools or local governments (Voight et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, interventions can be implemented promptly without the need for extensive policy changes.\u003c/p\u003e\u003cp\u003eNonetheless, the design approach implemented in this study should be interpreted with caution, as the explanations offered are not causal in nature, primarily due to the cross-sectional nature of the data utilized.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eIFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eIndonesian Family Life Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eMOEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eMinistry of Education and Culture\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eMORA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eMinistry of Religious Affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eMTs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eMadrasah Tsanawiyah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003ePIRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eProgress in International Reading Literacy Study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003ePISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eProgramme for International Student Assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eSES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eSosioeconomic Status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eSER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eSchool Effectiveness Research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eSMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eSekolah Menengah Pertama\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eTIMSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eTrends in Mathematics and Science Study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eUK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eUnited Kingdom\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5201%;\"\u003e\n \u003cp\u003eUNESCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87.4799%;\"\u003e\n \u003cp\u003eUnited Nations Educational, Scientific, and Cultural Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThis research was conducted by TD, who served as the first author and led the data analysis, model development, and manuscript writing. The study was supervised by ST, who provided critical guidance throughout the research process and offered substantial feedback to ensure academic rigor and relevance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eADB. (2014). Indonesia: Madrasah education development project. Retrieved from https://www.adb.org/documents/indonesia-madrasah-education-development-project\u003c/li\u003e\n\u003cli\u003eAldridge, J. M., Fraser, B. J., Fozdar, F., Ala\u0026rsquo;i, K., Earnest, J., \u0026amp; Afari, E. (2016). Students\u0026rsquo; perceptions of school climate as determinants of wellbeing, resilience and identity. \u003cem\u003eImproving schools\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 5-26. https://doi.org/10.1177/1365480215612616\u003c/li\u003e\n\u003cli\u003eAli, M., Kos, J., Lietz, P., Nugroho, D., Furqon, Zainul, A., \u0026amp; Emilia, E. (2011). \u003cem\u003eQuality of Education in Madrasah: Main Study\u003c/em\u003e. Retrieved from Washington, DC: http://documents.worldbank.org/curated/en/2011/02/14048827/quality-educationmadrasah-main-study\u003c/li\u003e\n\u003cli\u003eAikens, N., \u0026amp; Barbarin, O. (2008). Socioeconomic differences in reading trajectories: The contribution of family, neighborhood, and school contexts. \u003cem\u003eJournal of Educational Psychology\u003c/em\u003e, \u003cem\u003e100\u003c/em\u003e(2), 235. https://doi.org/10.1037/0022-0663.100.2.235\u003c/li\u003e\n\u003cli\u003eAnderson, C. (1982). The search for school climate: A review of the research. \u003cem\u003eReview of Educational Research\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(3), 368-420. https://doi.org/10.2307/1170423\u003c/li\u003e\n\u003cli\u003eAntecol, H., Eren, O., \u0026amp; Ozbeklik, S. (2015). The effect of teacher gender on student achievement in primary school. \u003cem\u003eJournal of Labor Economics\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 63-89. https://doi.org/10.2139/ssrn.2039639\u003c/li\u003e\n\u003cli\u003eAsparouhov, T. (2006). General multi-level modeling with sampling weights. \u003cem\u003eCommunications in Statistics\u0026mdash;Theory Methods\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 439-460. https://doi.org/10.1080/03610920500476598\u003c/li\u003e\n\u003cli\u003eBandura, A. (1997).\u003cem\u003e Self-efficacy: the exercise of control\u003c/em\u003e. Basingstoke: W. H. Freeman\u003c/li\u003e\n\u003cli\u003eBandura, A., \u0026amp; Walters, R. H. (1963). \u003cem\u003eSocial learning and personality development\u003c/em\u003e. London: Holt, Rinehart and Winston.\u003c/li\u003e\n\u003cli\u003eBear, G. G., Yang, C., Chen, D., He, X., Xie, J.-S., \u0026amp; Huang, X. (2018). Differences in school climate and student engagement in China and the United States. \u003cem\u003eSchool Psychology Quarterly, 33\u003c/em\u003e(2), 323\u0026ndash;335. https://doi.org/10.1037/spq0000247\u003c/li\u003e\n\u003cli\u003eBrault, M. C., Janosz, M., \u0026amp; Archambault, I. (2014). Effects of school composition and school climate on teacher expectations of students: A multilevel analysis. \u003cem\u003eTeaching and Teacher Education\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 148-159. https://doi.org/10.1016/j.tate.2014.08.008\u003c/li\u003e\n\u003cli\u003eBrookover, W. B., Schweitzer, J. H., Schneider, J. M., Beady, C. H., Flood, P. K., \u0026amp; Wisenbaker, J. M. (1978). Elementary school social climate and school achievement. \u003cem\u003eAmerican Educational Research Journal\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 301-318. https://doi.org/10.3102/00028312015002301\u003c/li\u003e\n\u003cli\u003eBurger, R. (2011). School effectiveness in Zambia: The origins of differences between rural and urban outcomes. \u003cem\u003eDevelopment Southern Africa\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(2), 157-176. https://doi.org/10.1080/0376835x.2011.570064\u003c/li\u003e\n\u003cli\u003eCheng, A. (2016). \u003cem\u003eTeachers and the Development of Student Noncognitive Skills\u003c/em\u003e. (PhD Thesis). University of Arkansas, Fayetteville, Arkansas\u003c/li\u003e\n\u003cli\u003eChudgar, A., Chandra, M., Iyengar, R., \u0026amp; Shanker, R. (2015). School resources and student achievement: Data from rural India. \u003cem\u003eProspects\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(4), 515-531. https://doi.org/10.1007/s11125-015-9360-3\u003c/li\u003e\n\u003cli\u003eCohen, J., McCabe, L., Michelli, N. M., \u0026amp; Pickeral, T. (2009). School climate: Research, policy, practice, and teacher education. \u003cem\u003eTeachers College Record\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e(1), 180-213. https://doi.org/10.1177/016146810911100108\u003c/li\u003e\n\u003cli\u003eCreemers, B., \u0026amp; Kyriakides, L. (2010a). Explaining stability and changes in school effectiveness by looking at changes in the functioning of school factors. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(4), 409-427. https://doi.org/10.1080/09243453.2010.512795\u003c/li\u003e\n\u003cli\u003eCreemers, B., \u0026amp; Kyriakides, L. (2010b). School factors explaining achievement on cognitive and affective outcomes: Establishing a dynamic model of educational effectiveness. \u003cem\u003eScandinavian Journal of Educational Research\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 263-294. https://doi.org/10.1080/00313831003764529\u003c/li\u003e\n\u003cli\u003eCreemers, B., \u0026amp; Reezigt, G. J. (1999). The role of school and classroom climate in elementary school learning environments. In J. Freiberg (Ed.), \u003cem\u003eSchool Climate: Measuring, Improving and Sustaining Healthy Learning Environments\u003c/em\u003e. London: Falmer Press\u003c/li\u003e\n\u003cli\u003eCroninger, R., Rice, J., Rathbun, A., \u0026amp; Nishio, M. (2007). Teacher qualifications and early learning: Effects of certification, degree, and experience on first-grade student achievement. Economics of Education Review, 26(3), 312-324. https://doi.org/10.1016/j.econedurev.2005.05.008\u003c/li\u003e\n\u003cli\u003eDarling-Hammond, L. (2000a). How teacher education matters. \u003cem\u003eJournal of Teacher Education, 51\u003c/em\u003e(3)\u003cem\u003e,\u003c/em\u003e 166-173. https://doi.org/10.1177/0022487100051003002\u003c/li\u003e\n\u003cli\u003eDarling-Hammond, L. (2000b). Teacher quality and student achievement. \u003cem\u003eEducation policy analysis archives\u003c/em\u003e, 8, 1. https://doi.org/10.14507/epaa.v8n1.2000\u003c/li\u003e\n\u003cli\u003eDe Fraine, B., Van Damme, J., \u0026amp; Onghena, P. (2002). Accountability of schools and teachers: What should be taken into account? \u003cem\u003eEuropean Educational Research Journal\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3), 403-428. https://doi.org/10.2304/eerj.2002.1.3.2\u003c/li\u003e\n\u003cli\u003eEdmonds, R. (1979). Effective schools for the urban poor. \u003cem\u003eEducational Leadership\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1). 15-24\u003c/li\u003e\n\u003cli\u003eEccles, J. (2005). Influences of parents\u0026apos; education on their children\u0026apos;s educational attainments: The role of parent and child perceptions. \u003cem\u003eLondon review of education\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), 191-204. https://doi.org/10.1080/14748460500372309\u003c/li\u003e\n\u003cli\u003eElliott, J. (1996). School effectiveness research and its critics: alternative visions of schooling. \u003cem\u003eCambridge Journal of Education\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(2), 199-224. https://doi.org/10.1080/0305764960260205\u003c/li\u003e\n\u003cli\u003eEnders, C., \u0026amp; Tofighi, D. (2007). Centering predictor variables in cross-sectional multilevel models: A new look at an old issue. \u003cem\u003ePsychological Methods\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(2), 121-138. https://doi.org/10.1037/1082-989x.12.2.121.supp\u003c/li\u003e\n\u003cli\u003eFilmer, D., \u0026amp; Pritchett, L. (1999). The effect of household wealth on educational attainment: Evidence from 35 countries. \u003cem\u003ePopulation and Development Review\u003c/em\u003e, 25(1), 85-120. https://doi.org/10.1111/j.1728-4457.1999.00085.x\u003c/li\u003e\n\u003cli\u003eFreiberg, H. (1999). \u003cem\u003eSchool climate: measuring, improving and sustaining healthy learning environments\u003c/em\u003e. London; Philadelphia: Falmer Press.\u003c/li\u003e\n\u003cli\u003eFung, F., Tan, C., \u0026amp; Chen, G. (2018). Student engagement and mathematics achievement: Unraveling main and interactive effects. \u003cem\u003ePsychology in the Schools\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(7), 815-831. https://doi.org/10.1002/pits.22139\u003c/li\u003e\n\u003cli\u003eGhozali, A., Mudjahid, A. K., \u0026amp; Hayati, M. (2013). Madrasah education financing study: The education sector analytical and capacity development partnership [Press release]\u003c/li\u003e\n\u003cli\u003eGibson, N., \u0026amp; Olejnik, S. (2003). Treatment of missing data at the second level of hierarchical linear models. \u003cem\u003eEducational and Psychological Measurement\u003c/em\u003e, \u003cem\u003e63\u003c/em\u003e(2). https://doi.org/10.1177/0013164402250987.\u003c/li\u003e\n\u003cli\u003eGoldman, A. D., \u0026amp; Penner, A. M. (2016). Exploring international gender differences in mathematics self-concept. \u003cem\u003eInternational Journal of Adolescence and Youth\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(4), 403-418. https://doi.org/10.1080/02673843.2013.847850\u003c/li\u003e\n\u003cli\u003eGooding, Y. (2001). \u003cem\u003eThe relationship between parental educational level and academic success of college freshmen\u003c/em\u003e. (PhD thesis). Iowa State University, Ames, Iowa. https://doi.org/10.31274/rtd-180813-12012\u003c/li\u003e\n\u003cli\u003eGray, J. (2004). School effectiveness and the \u0026lsquo;other outcomes\u0026rsquo; of secondary schooling: a reassessment of three decades of British research. \u003cem\u003eImproving Schools\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 185-198. https://doi.org/10.1177/1365480204047348\u003c/li\u003e\n\u003cli\u003eGray, J., Goldstein, H., \u0026amp; Thomas, S. (2001). Predicting the future: the role of past performance in determining trends in institutional effectiveness at A level. \u003cem\u003eBritish Educational Research Journal\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 391-405. https://doi.org/10.1080/01411920125622\u003c/li\u003e\n\u003cli\u003eGuskey, T. (2012). Defining Student Achievement. In J. Hattie \u0026amp; E. M. Anderman (Eds.), \u003cem\u003eInternational Guide to Student Achievement\u003c/em\u003e. New York: Routledge.\u003c/li\u003e\n\u003cli\u003eHastedt, D. (2006). Inconsistent student responses to questions related to their mathematics lessons. In \u003cem\u003eContexts of Learning Mathematics and Science\u003c/em\u003e (pp. 77-96). Routledge.\u003c/li\u003e\n\u003cli\u003eHendajany, N. (2016). The Effectiveness of Public Vs Private Schools in Indonesia. \u003cem\u003eJournal of Indonesian Applied Economics\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 66-89. https://doi.org/10.21776/ub.jiae.2016.006.01.4\u003c/li\u003e\n\u003cli\u003eHergovich, A., Sirsch, U., \u0026amp; Felinger, M. (2004). Gender differences in the self-concept of preadolescent children. \u003cem\u003eSchool Psychology International\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 207-222. https://doi.org/10.1177/0143034304043688\u003c/li\u003e\n\u003cli\u003eHill, P., \u0026amp; Rowe, K. (1996). Multilevel modelling in school effectiveness research. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 1-34. https://doi.org/10.1080/0924345960070101\u003c/li\u003e\n\u003cli\u003eHofmann, D. A., \u0026amp; Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. \u003cem\u003eJournal of Management\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(5), 623-641. https://doi.org/10.1177/014920639802400504\u003c/li\u003e\n\u003cli\u003eHofstede, G. (1993). Cultural constraints in management theories. \u003cem\u003eThe Executive\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1), 81- 94. https://doi.org/10.5465/ame.1993.9409142061\u003c/li\u003e\n\u003cli\u003eHofstede, G., \u0026amp; Minkov, M. (2010).\u003cem\u003e Cultures and organizations: Software of the mind, (3rd ed)\u003c/em\u003e. New York: McGraw-Hill Education.\u003c/li\u003e\n\u003cli\u003eHox, J. (2002). \u003cem\u003eMultilevel analysis: techniques and applications\u003c/em\u003e. Mahwah: Lawrence Erlbaum Publishers.\u003c/li\u003e\n\u003cli\u003eHoy, W. (2012). School characteristics that make a difference for the achievement of all students. \u003cem\u003eJournal of Educational Administration\u003c/em\u003e, \u003cem\u003e50\u003c/em\u003e(1), 76-97. https://doi.org/10.1108/09578231211196078\u003c/li\u003e\n\u003cli\u003eHoy, W., Tarter, C., \u0026amp; Kottkamp, R. (1991). \u003cem\u003eOpen schools, healthy schools: measuring organizational climate\u003c/em\u003e. California: Sage Publications.\u003c/li\u003e\n\u003cli\u003eHuang, C. (2011). Self-concept and academic achievement: A meta-analysis of longitudinal relations. \u003cem\u003eJournal of School Psychology\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(5), 505-528. https://doi.org/10.1016/j.jsp.2011.07.001\u003c/li\u003e\n\u003cli\u003eIndonesia, Republic of. (2003). \u003cem\u003eUndang-Undang No 20 Tahun 2003 tentang sistem pendidikan indonesia \u003c/em\u003eJakarta. Retrieved from http://peraturan.go.id/common/dokumen/ln/2003/uu20-2003.pdf\u003c/li\u003e\n\u003cli\u003eIndonesia, Republic of. (2010). \u003cem\u003ePeraturan Pemerintah Republik Indonesia Nomor 17 Tahun 2010 tentang pengelolaan dan penyelenggaraan pendidikan \u003c/em\u003eJakarta. Retrieved from http://peraturan.go.id/common/dokumen/ln/2010/pp17-2010bt.pdf\u003c/li\u003e\n\u003cli\u003eIrmawati. (2007). \u003cem\u003eNilai-nilai yang mendasari motif-motif penentu keberhasilan suku batak toba\u003c/em\u003e. (PhD Thesis). University of Indonesia, Jakarta.\u003c/li\u003e\n\u003cli\u003eJia, Y., Way, N., Ling, G., Yoshikawa, H., Chen, X., Hughes, D., Ke, X., \u0026amp; Lu, Z. (2009). The influence of student perceptions of school climate on socioemotional and academic adjustment: A comparison of chinese and american adolescents. \u003cem\u003eChild Development\u003c/em\u003e, 80(5), 1514-1530. https://doi.org/10.1111/j.1467-8624.2009.01348.x\u003c/li\u003e\n\u003cli\u003eJoncas, M., \u0026amp; Foy, P. (2012). Sample Design in TIMSS and PIRLS. In M. O. Martin \u0026amp; I. V. S. Mullis (Eds.), \u003cem\u003eMethods and procedures in TIMSS and PIRLS 2011\u003c/em\u003e. Chestnut Hill, MA: TIMSS \u0026amp; PIRLS International Study Center, Boston College.\u003c/li\u003e\n\u003cli\u003eKaluge, L. (1998). \u003cem\u003eSome factors related to educational attainment in Indonesian primary schools\u003c/em\u003e. (PhD Thesis). University of London, London.\u003c/li\u003e\n\u003cli\u003eKhan, R. M. A., Iqbal, N., \u0026amp; Tasneem, S. (2015). The Influence of Parents Educational Level on Secondary School Students Academic Achievements in District Rajanpur. \u003cem\u003eJournal of education and Practice\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(16), 76-79. Retrieved from https://eric.ed.gov/?id=EJ1079955\u003c/li\u003e\n\u003cli\u003eKnuver, A., \u0026amp; Brandsma, H. (1993). Cognitive and affective outcomes in school effectiveness research. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(3), 189-204. https://doi.org/10.1080/0924345930040302\u003c/li\u003e\n\u003cli\u003eKreft, I., \u0026amp; De Leeuw, J. (1998). \u003cem\u003eIntroducing multilevel modeling\u003c/em\u003e. London: Sage.\u003c/li\u003e\n\u003cli\u003eKreft, I., De Leeuw, J., \u0026amp; Aiken, L. (1995). The effect of different forms of centering in hierarchical linear models. \u003cem\u003eMultivariate Behavioral Research\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(1), 1-21. https://doi.org/10.1207/s15327906mbr3001_1\u003c/li\u003e\n\u003cli\u003eLenkeit, J. (2013). Effectiveness measures for cross-sectional studies: a comparison of valueadded models and contextualised attainment models. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 1-25. https://doi.org/10.1080/09243453.2012.680892\u003c/li\u003e\n\u003cli\u003eL\u0026oacute;pez, V., Salgado, M., \u0026amp; Berkowitz, R. (2023). The contributions of school and classroom climate to mathematics test scores: a three-level analysis. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 43-64. https://doi.org/10.1080/09243453.2022.2096645\u003c/li\u003e\n\u003cli\u003eMarsh, H. (1990a). Causal ordering of academic self-concept and academic achievement: A multiwave, longitudinal panle analysis. \u003cem\u003eJournal of Educational Psychology\u003c/em\u003e, \u003cem\u003e82\u003c/em\u003e(4), 646-656. https://doi.org/10.1037//0022-0663.82.4.646\u003c/li\u003e\n\u003cli\u003eMarsh, H., \u0026amp; Martin, A. (2011). Academic self-concept and academic achievement: relations and causal ordering. British Journal Educational Psychology, \u003cem\u003e81\u003c/em\u003e(1), 59-77. https://doi.org/10.1348/000709910X503501\u003c/li\u003e\n\u003cli\u003eMarsh, H., \u0026amp; O\u0026apos;Mara, A. (2008). Reciprocal effects between academic self-concept, selfesteem, achievement, and attainment over seven adolescent years: Unidimensional and multidimensional perspectives of self concept. \u003cem\u003ePersonality and Social Psychology Bulletin\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 542-552. https://doi.org/10.1177/0146167207312313\u003c/li\u003e\n\u003cli\u003eMartin, M., Mullis, I., \u0026amp; Foy, P. (2008). \u003cem\u003eTIMSS 2007 international science report\u003c/em\u003e. Chestnut Hill, MA: TIMSS \u0026amp; PIRLS International Study Center.\u003c/li\u003e\n\u003cli\u003eMcCoach, D. B. (2010). Hierarchical linear modeling. \u003cem\u003eThe reviewer\u0026rsquo;s guide to quantitative methods in the social sciences\u003c/em\u003e, 123-140.\u003c/li\u003e\n\u003cli\u003eMinistry of Education and Culture, Republic of Indonesia. (2013b). \u003cem\u003ePeraturan Menteri Pendidikan Dan Kebudayaan Republik Indonesia Nomor 67 Tahun 2013 tentang kerangka dasar dan struktur kurikulum sekolah dasar/madrasah ibtidaiyah\u003c/em\u003e Jakarta Retrieved from http://simpuh.kemenag.go.id/regulasi/permendikbud_67_13_lampiran.pdf\u003c/li\u003e\n\u003cli\u003eMinistry of Education and Culture, Republic of Indonesia. (2017a). Indonesia Educational Statistic in Brief 2016/2017. In \u003cem\u003eYearly\u003c/em\u003e. Jakarta: Center for educational data and statistics and culture.\u003c/li\u003e\n\u003cli\u003eMinistry of Religious Affairs, Republic of Indonesia. (2014a). \u003cem\u003eKeputusan Menteri Agama Republik Indonesia Nomor 207 Tahun 2014 tentang kurikulum madrasah\u003c/em\u003e. Jakarta Retrieved from http://simpuh.kemenag.go.id/regulasi/kma_207_14.pdf\u003c/li\u003e\n\u003cli\u003eMortimore, P. (1988). \u003cem\u003eSchool matters: The junior years\u003c/em\u003e. Wells: Open Books.\u003c/li\u003e\n\u003cli\u003eMortimore, P., Sammons, P., Stoll, L., Lewis, D., \u0026amp; Ecob, R. (1989). A study of effective junior schools. \u003cem\u003eInternational Journal of Educational Research\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(7), 753-768. https://doi.org/10.1016/0883-0355(89)90026-8\u003c/li\u003e\n\u003cli\u003eMuijs, D., Harris, A., Chapman, C., Stoll, L., \u0026amp; Russ, J. (2004). Improving schools in socioeconomically disadvantaged areas \u0026ndash; A review of research evidence. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 149\u0026ndash;175. https://doi.org/10.1076/sesi.15.2.149.30433\u003c/li\u003e\n\u003cli\u003eMuijs, D., \u0026amp; Reynolds, D. (2003). Student background and teacher effects on achievement and attainment in mathematics: A longitudinal study. \u003cem\u003eEducational Research and Evaluation\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(3), 289-314. https://doi.org/10.1076/edre.9.3.289.15571\u003c/li\u003e\n\u003cli\u003eMullis, I. V., Martin, M. O., Gonzalez, E. J., \u0026amp; Chrostowski, S. J. (2004). \u003cem\u003eTIMSS 2003 International Mathematics Report: Findings from IEA\u0026apos;s Trends in International Mathematics and Science Study at the Fourth and Eighth Grades\u003c/em\u003e. International Association for the Evaluation of Educational Achievement. Herengracht 487, Amsterdam, 1017 BT, The Netherlands.\u003c/li\u003e\n\u003cli\u003eMu\u0026ntilde;oz-Chereau, B. (2019). Exploring gender gap and school differential effects in mathematics in Chilean primary schools. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(2), 83-103. https://doi.org/10.1080/09243453.2018.1503604\u003c/li\u003e\n\u003cli\u003eNewhouse, D., \u0026amp; Beegle, K. (2006). The effect of school type on academic achievement evidence from Indonesia. \u003cem\u003eJournal of Human Resources\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 529-557. https://doi.org/10.1037/e515652013-001\u003c/li\u003e\n\u003cli\u003eNovera, I. (2004). Indonesian postgraduate students studying in Australia: An examination of their academic, social and cultural experiences. \u003cem\u003eInternational Education Journal\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(4), 475-487. \u003c/li\u003e\n\u003cli\u003eO\u0026apos;Mara, A., Marsh, H., Craven, R., \u0026amp; Debus, R. (2006). Do self-concept interventions make a difference? A synergistic blend of construct validation and meta-analysis. \u003cem\u003eEducational Psychologist\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3). https://doi.org/10.1207/s15326985ep4103_4\u003c/li\u003e\n\u003cli\u003eOpdenakker, M., \u0026amp; Van Damme, J. (2000). Effects of schools, teaching staff and classes on achievement and well-being in secondary education: Similarities and differences between school outcomes. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 165-196. https://doi.org/10.1076/0924-3453(200006)11:2;1-q;ft165\u003c/li\u003e\n\u003cli\u003eOpdenakker, M., Van Damme, J., De Fraine, B., Van Landeghem, G., \u0026amp; Onghena, P. (2002). The effect of schools and classes on mathematics achievement. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 399-427. https://doi.org/10.1076/sesi.13.4.399.10283\u003c/li\u003e\n\u003cli\u003eOthman, M., \u0026amp; Muijs, D. (2013). Educational quality differences in a middle-income country: the urban-rural gap in Malaysian primary schools. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 1-18. https://doi.org/10.1080/09243453.2012.691425\u003c/li\u003e\n\u003cli\u003ePajares, F., \u0026amp; Urdan, T. (2002). \u003cem\u003eAcademic motivation of adolescents\u003c/em\u003e. Greenwich: Information Age Pub\u003c/li\u003e\n\u003cli\u003eParker, P., Marsh, H., Ciarrochi, J., Marshall, S., \u0026amp; Abduljabbar, A. (2014). Juxtaposing math self-efficacy and self-concept as predictors of long-term achievement outcomes. \u003cem\u003eEducational Psychology\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 29-48. https://doi.org/10.1080/01443410.2013.797339\u003c/li\u003e\n\u003cli\u003eRabe-Hesketh, S., \u0026amp; Skrondal, A. (2006). Multilevel modelling of complex survey data. \u003cem\u003eJournal of the Royal Statistical Society Series A: Statistics in Society\u003c/em\u003e, \u003cem\u003e169\u003c/em\u003e(4), 805-827. https://doi.org/10.1111/j.1467-985X.2006.00426.x\u003c/li\u003e\n\u003cli\u003eRaudenbush, S., \u0026amp; Bryk, A. (2002). \u003cem\u003eHierarchical linear models: applications and data analysis methods (2nd ed.)\u003c/em\u003e. Thousand Oaks: Sage.\u003c/li\u003e\n\u003cli\u003eReynolds, D., Sammons, P., De Fraine, B., Van Damme, J., Townsend, T., Teddlie, C., \u0026amp; Stringfield, S. (2014). Educational effectiveness research (EER): a state-of-the-art review. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(2), 197-230. https://doi.org/10.1080/09243453.2014.885450\u003c/li\u003e\n\u003cli\u003eRiddell, A. (1997). Assessing designs for school effectiveness research and school improvement in developing countries. \u003cem\u003eComparative Education Review\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 178- 204. https://doi.org/10.1086/447429\u003c/li\u003e\n\u003cli\u003eRowe, K., \u0026amp; Hill, P. (1998). Modeling educational effectiveness in classrooms: The use of multi-level structural equations to model students\u0026rsquo; progress. \u003cem\u003eEducational Research and Evaluation\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(4), 307-347. https://doi.org/10.1076/edre.4.4.307.6953\u003c/li\u003e\n\u003cli\u003eRutkowski, L., Gonzalez, E., Joncas, M., \u0026amp; von Davier, M. (2010). International large-scale assessment data: Issues in secondary analysis and reporting. \u003cem\u003eEducational Researcher\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 142-151. https://doi.org/10.3102/0013189x10363170\u003c/li\u003e\n\u003cli\u003eSalim, M. (2011). \u003cem\u003eExploring issues of school effectiveness and self-evaluation at the system and school levels in the context of Zanzibar\u003c/em\u003e. (PhD thesis). University of Bristol, Bristol, UK\u003c/li\u003e\n\u003cli\u003eSammons, P., Thomas, S., \u0026amp; Mortimore, P. (1997). \u003cem\u003eForging links: effective schools and effective departments\u003c/em\u003e. London: Paul Chapman.\u003c/li\u003e\n\u003cli\u003eSammons, P., Thomas, S., Mortimore, P., Owen, C., \u0026amp; Pennell, H. (1994). \u003cem\u003eAssessing school effectiveness : Developing measures to put school performance in context\u003c/em\u003e. London: Office for Standards in Education [OFSTED]\u003c/li\u003e\n\u003cli\u003eScheerens, J. (1992). \u003cem\u003eEffective schooling: research, theory and practice\u003c/em\u003e. London: Cassell.\u003c/li\u003e\n\u003cli\u003eScheerens, J., \u0026amp; Bosker, R. J. (1996). \u003cem\u003eThe foundations of educational effectiveness\u003c/em\u003e. Oxford; New York: Pergamon Elsevier\u003c/li\u003e\n\u003cli\u003eSeaton, M., Parker, P., Marsh, H., Craven, R., \u0026amp; Yeung, A. (2014). The reciprocal relations between self-concept, motivation and achievement: juxtaposing academic selfconcept and achievement goal orientations for mathematics success. \u003cem\u003eEducational Psychology\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(1), 49-72. https://doi.org/10.1080/01443410.2013.825232\u003c/li\u003e\n\u003cli\u003eStrand, S. (2016). Do some schools narrow the gap? Differential school effectiveness revisited. \u003cem\u003eReview of Education\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(2), 107-144. https://doi.org/10.1002/rev3.3054\u003c/li\u003e\n\u003cli\u003eTabachnick, B., \u0026amp; Fidell, L. (2007). \u003cem\u003eUsing multivariate statistics (5th ed.)\u003c/em\u003e. Boston: Allyn \u0026amp; Bacon/Pearson Education.\u003c/li\u003e\n\u003cli\u003eTayyaba, S. (2012). Rural-urban gaps in academic achievement, schooling conditions, student, and teachers\u0026apos; characteristics in Pakistan. \u003cem\u003eInternational Journal of Educational Management\u003c/em\u003e,\u003cem\u003e 26\u003c/em\u003e(1), 6-26. https://doi.org/10.1108/09513541211194356\u003c/li\u003e\n\u003cli\u003eThapa, A., Cohen, J., Guffey, S., \u0026amp; Higgins-D\u0026rsquo;Alessandro, A. (2013). A review of school climate research. \u003cem\u003eReview of Educational Research\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(3), 357-385. https://doi.org/10.3102/0034654313483907\u003c/li\u003e\n\u003cli\u003eThiele, T., Singleton, A., Pope, D., \u0026amp; Stanistreet, D. (2016). Predicting students\u0026apos; academic performance based on school and socio-demographic characteristics. \u003cem\u003eStudies in Higher Education\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(8), 1424-1446. https://doi.org/10.1080/03075079.2014.974528\u003c/li\u003e\n\u003cli\u003eThomas, S. (2001). Dimensions of secondary school effectiveness: Comparative analyses across regions. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(3), 285-322. https://doi.org/10.1076/sesi.12.3.285.3448\u003c/li\u003e\n\u003cli\u003eThomas, S. (1998). Value-added measures of school effectiveness in the United Kingdom. \u003cem\u003eProspects\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 91-108. https://doi.org/10.1007/bf02737782\u003c/li\u003e\n\u003cli\u003eThomas, S., \u0026amp; Mortimore, P. (1996). Comparison of value‐added models for secondary school effectiveness. \u003cem\u003eResearch Papers in Education\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1), 5-33. https://doi.org/10.1080/0267152960110103\u003c/li\u003e\n\u003cli\u003eThomas, S., Smees, R., MacBeath, J., Robertson, P., \u0026amp; Boyd, B. (2000). Valuing pupils\u0026rsquo; views in scottish schools. \u003cem\u003eEducational Research and Evaluation\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(4), 281-316. https://doi.org/10.1076/edre.6.4.281.6934\u003c/li\u003e\n\u003cli\u003eTimmermans, A., \u0026amp; Thomas, S. (2015). The impact of student composition on schools\u0026rsquo; value-added performance: a comparison of seven empirical studies. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(3), 487-498. https://doi.org/10.1080/09243453.2014.957328\u003c/li\u003e\n\u003cli\u003eUNESCO. (2004). EFA \u003cem\u003eGlobal Monitoring Report 2005\u003c/em\u003e. Retrieved from http://unesdoc.unesco.org/images/0013/001373/137333e.pdf\u003c/li\u003e\n\u003cli\u003eUNESCO. (2014). \u003cem\u003eTeaching and Leraning: Achieving Quality for all\u003c/em\u003e. Paper presented at the EFA Global Monitoring Report 2013/14, Paris, France.\u003c/li\u003e\n\u003cli\u003eVoight, A., Austin, G., \u0026amp; Hanson, T. (2013). \u003cem\u003eA climate for academic success: How school climate distinguishes schools that are beating the achievement odds\u003c/em\u003e. San Francisco: WestEd.\u003c/li\u003e\n\u003cli\u003eWarwick, D., \u0026amp; Jatoi, H. (1994). Teacher gender and student achievement in Pakistan. \u003cem\u003eComparative Education Review\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(3), 377-399. https://doi.org/10.1086/447257\u003c/li\u003e\n\u003cli\u003eWorrell, F. (2007). Ethnic identity, academic achievement, and global self-concept in four groups of academically talented adolescents. \u003cem\u003eGifted Child Quarterly\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 23-38. https://doi.org/10.1177/0016986206296655\u003c/li\u003e\n\u003cli\u003eWorld Bank. (2014c). World Bank and Education in Indonesia. Retrieved from http://www.worldbank.org/en/country/indonesia/brief/world-bank-and-education-inindonesia\u003c/li\u003e\n\u003cli\u003eWu, Y. W. B., \u0026amp; Wooldridge, P. J. (2005). The impact of centering first-level predictors on individual and contextual effects in multilevel data analysis. \u003cem\u003eNursing research\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(3), 212-216. https://doi.org/10.1097/00006199-200505000-00009\u003c/li\u003e\n\u003cli\u003eYang, C., Bear, G., Chen, F., Zhang, W., Blank, J., \u0026amp; Huang, X. (2013). Students\u0026apos; perceptions of school climate in the U.S. and China. \u003cem\u003eSchool Psychology Quarterly\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 7-24. https://doi.org/10.1037/spq0000002.supp\u003c/li\u003e\n\u003cli\u003eYu, G., \u0026amp; Thomas, S. (2008). Exploring school effects across southern and eastern African school systems and in Tanzania. \u003cem\u003eAssessment in Education: Principles, Policy \u0026amp; Practice\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(3), 283-305. https://doi.org/10.1080/09695940802417525\u003c/li\u003e\n\u003cli\u003eYoung, D. (1998). Rural and urban differences in student achievement in science and mathematics: A multilevel analysis. \u003cem\u003eSchool Effectiveness and School Improvement\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(4), 386-418. https://doi.org/10.1080/0924345980090403\u003c/li\u003e\n\u003cli\u003eZysberg, L., \u0026amp; Schwabsky, N. (2021). School climate, academic self-efficacy and student achievement. \u003cem\u003eEducational Psychology\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(4), 467-482. https://doi.org/10.1080/01443410.2020.1813690\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mathematics performance, Self-efficacy, Self-concept, School climate, Classroom climate, TIMSS, Multilevel analysis","lastPublishedDoi":"10.21203/rs.3.rs-7140999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7140999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary goal of education is to nurture \u0026nbsp;\u0026nbsp;children's cognitive and non-cognitive skills, preparing them to face future \u0026nbsp;\u0026nbsp;challenges. However, achieving these objectives can be influenced by several \u0026nbsp;\u0026nbsp;factors, with school climate being a significant one. This study focuses on \u0026nbsp;\u0026nbsp;exploring students' mathematics achievement and self-concept using data from \u0026nbsp;\u0026nbsp;the TIMSS 2011 assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized TIMSS 2011 data, \u0026nbsp;\u0026nbsp;comprising a sample of 5,795 eighth-grade students from Indonesia. The data \u0026nbsp;\u0026nbsp;was analyzed using three-level multilevel models with MLWin. The students \u0026nbsp;\u0026nbsp;were nested within 174 classes and 153 schools, and this hierarchical \u0026nbsp;\u0026nbsp;structure was incorporated into the analysis through the three-level \u0026nbsp;\u0026nbsp;multilevel modeling approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study reveals a complex relationship \u0026nbsp;\u0026nbsp;between school climate and student outcomes. Our analysis shows that active \u0026nbsp;\u0026nbsp;student participation in mathematics lessons and classroom discipline \u0026nbsp;\u0026nbsp;positively impacted academic achievement, even after accounting for various \u0026nbsp;\u0026nbsp;student, teacher, and school-level factors. Surprisingly, student safety \u0026nbsp;\u0026nbsp;exhibited a negative association with academic performance. Furthermore, in \u0026nbsp;\u0026nbsp;relation to student mathematics self-concept, five school climate elements \u0026nbsp;\u0026nbsp;were significant predictors: students' connection to their school, teachers' \u0026nbsp;\u0026nbsp;perceived safety, teachers' confidence in their mathematics instruction, the \u0026nbsp;\u0026nbsp;availability of adequate school physical resources, and student safety. In \u0026nbsp;\u0026nbsp;contrast, interactions among teachers and students' engagement in mathematics \u0026nbsp;\u0026nbsp;lessons were negatively linked to academic achievement. It's important to \u0026nbsp;\u0026nbsp;note these findings with caution, as the overall goodness of fit for the \u0026nbsp;\u0026nbsp;models was relatively modest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe multilevel analysis offers a detailed \u0026nbsp;\u0026nbsp;and insightful understanding of the Indonesian school climate. It not only \u0026nbsp;\u0026nbsp;provides a valuable framework for interpreting diverse school practices but \u0026nbsp;\u0026nbsp;also powerfully illustrates how school climate serves as a critical \u0026nbsp;\u0026nbsp;protective factor for improving student outcomes, irrespective of the \u0026nbsp;\u0026nbsp;school's type. This research strongly reinforces the idea that school climate \u0026nbsp;\u0026nbsp;is a highly malleable aspect of education, one that schools and local \u0026nbsp;\u0026nbsp;governments can effectively shape and improve.\u003c/p\u003e","manuscriptTitle":"Exploring the Role of School and Classroom Climate in Shaping Mathematics Achievement and Self-Concept: A Multilevel Analysis of Indonesian Students Using TIMSS 2011","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 19:35:05","doi":"10.21203/rs.3.rs-7140999/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"87abb3b7-4a3e-43f8-94f4-b3b96e3f770a","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T09:26:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 19:35:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7140999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7140999","identity":"rs-7140999","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}