Learning statistics among undergraduate tourism students: between attitude and academic achievement

Learning statistics among undergraduate tourism students: between attitude and academic achievement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Learning statistics among undergraduate tourism students: between attitude and academic achievement Herlan Suherlan, R Kusherdyana, Anwari Masatip, Mohamad Ridwan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9111399/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract This study aims to determine the attitudes of tourism students towards statistics, the relationship between students' attitudes towards statistics and learning achievement, and the relationship between attitudes towards statistics and academic achievement based on student demographics. A total of 435 students were asked to fill out a questionnaire for the Survey of Attitude Towards Statistics (SATS). Descriptive statistical data analysis, correlational analysis, and covariance analysis were performed with SPSS 26.0. The results showed that students' attitudes toward statistics before and after the learning process tended to be positive. Student attitudes towards statistics before and after the learning process were not influenced by the demographic background of students, except for the grade level variable. Simultaneously, student attitudes toward statistics and student background variables affect student academic achievements. This study's findings have implications for the development of tourism education curricula, as students in this field are increasingly expected to possess statistical literacy. Social science/Education Biological sciences/Psychology Social science/Psychology Attitudes toward statistics Academic achievements Learning process Tourism education Higher education Figures Figure 1 Introduction Modern education in social sciences and humanities, including tourism, necessitates extensive training in research methods and statistics, fostering critical thinking, problem-solving, and decision-making skills for professional development (Berndt et al., 2021 ; Cladera et al., 2019a , 2021). Statistical knowledge, literacy, and skills are crucial for undergraduate students' academic and professional careers, and students' attitudes toward statistics play a crucial role in their understanding of its importance (Chu, 2025a ), therefore, it is not surprising that statistics courses are required for tourism graduates to collect, analyze, and interpret tourism-related data, such as tourist numbers, hotel occupancy rates, and length of stay, to support research, development, and strategic decision-making in the tourism industry (Suherlan, 2022 ; Sulaiman & Kusherdyana, 2016 ). However, many tourism students do not realize that their program of study includes multiple statistics courses, so it is not uncommon to cause anxiety (Khavenson et al., 2012 ; Kiekkas et al., 2015 ; MacArthur & Santo, 2023a ) there is even a tendency to show a negative attitude that can be an obstacle to learning statistical concepts and effective learning (Cladera et al., 2019a ; Lavidas et al., 2021 ; MacArthur & Santo, 2023a ). A positive attitude enhances statistical thinking skills, practical application, pleasant lecture experiences, and significantly impacts academic achievement (Asare, 2023 ; Ashaari et al., 2011 ; Cujba & Pifarré, 2024a ; Shida et al., 2024a ). Studies show that attitudes towards statistics in higher education, particularly in nursing, medicine, psychology, and social science courses, are often linked to anxiety (Hagen et al., 2013 ; Heretick & Tanguma, 2021 ; Jazayeri et al., 2024 ; Khavenson et al., 2012 ; Nielsen & Kreiner, 2018 ), demographic profile background, previous bad experiences in similar subjects with statistics (Chiesi & Bruno, 2021 ; Da Silva & Moura, 2020 ; Guo et al., 2024 ; Hunt et al., 2023 ; Prayoga & Abraham, 2017 ), perceptions, motivations, and personality (Chiesi & Bruno, 2021 ; O’Bryant et al., 2021a ; Opstad, 2020 ; Schwerter & Brahm, 2024 ), and academic achievement (Eshet et al., 2021a ; Levpušček & Cukon, 2022 ). Studies that discuss attitudes toward student statistics at tourism universities are still very limited. A study conducted by Cladera et al., (2021) examines the analysis of factors that underlie tourism students' attitudes towards statistics associated with demographic characteristics and student academic achievement. Prior research examining student attitudes towards statistics has typically focused on various variables, including demographic characteristics and academic achievement, in isolation across distinct studies. However, a comprehensive investigation encompassing these factors within the context of tourism higher education remains unaddressed. Thus, the aim of this research is to determine students' attitudes towards statistics before and after the learning process, the relationship between students' attitudes towards statistics and learning achievement, and the differences between students' attitudes towards statistics and academic achievement based on student demographics. The specific questions are: RQ1. Are there differences in student attitudes towards statistics before and after the learning process? RQ2. Is there a relationship between students' attitudes towards statistics before and after the learning process and their academic achievements? RQ3. Is there a relationship between attitudes towards statistics and academic achievement based on student demographics? Theoretical framework Attitude Towards Statistics Statistics is a common subject in many college programs. Programs leading to undergraduate and postgraduate degrees, particularly in the social sciences, often require successful completion of statistics and research courses (Levpušček & Cukon, 2022 ). In addition, there are efforts in universities to develop undergraduate-practitioner training models that will encourage the use of research techniques to solve real-world problems (Heretick & Tanguma, 2021 ). However, some emotional difficulties, such as negative attitudes and anxiety, were identified in various literatures (Lethbridge et al., 2024a ). A person's attitude can be defined as mental and emotional strength, which motivates them to act in a certain way towards certain items or problems, dispositions, emotions, or mind conditioning towards something (Peiró-Signes et al., 2021a ). The concept of attitude is at the core of many research studies and theories (Tubaishat et al., 2016 ). There is no agreement among scholars on how to define attitudes (Chew & Dillon, 2014 ; Cujba & Pifarré, 2024a ). Many studies say that attitude is just a feeling, but others say that it has more than one meaning (Chiesi & Bruno, 2021 ; Steinberger, 2020 ). Attitude toward statistics is a person's tendency to react positively or negatively to things, situations, or people that have to do with statistical learning. It is made up of emotional, cognitive, and behavioral parts (Chiesi & Bruno, 2021 ; Peiró-Signes et al., 2021b ; Steinberger, 2020 ). Attitudes toward statistics can be viewed as "a learned tendency to respond positively or negatively to various items, circumstances, concepts, or personalities." (Mcintee et al., 2022 ). A positive attitude towards statistics can motivate students to understand and apply statistics in their personal and professional lives, enhancing their understanding and application of the subject (Li et al., 2024 ). On the other hand, a negative outlook has been shown to increase statistical anxiety (Hunt et al., 2023 ). This can make it harder for students to learn statistics or use what they learn in the real world (O’Bryant et al., 2021b ). The study of student attitudes toward statistics has started to attract researchers attention in recent years. In addition to the purported impact of attitudes on learning (Lethbridge et al., 2024b ) other factors that may contribute to this interest include the need for statistics as a tool for everyone in today's society, where numerical data is more common than ever (van Dijck et al., 2022 ). Research on student attitudes towards statistics has been carried out in various fields of higher education because of the importance of attitudes towards academic achievement during and after statistics lectures (Peiró-Signes et al., 2020 ). Previous research has shown that students' academic backgrounds consist of several dimensions of individual achievement and contextual resources. Along with family and social ties, secondary schools play an important role in shaping students' pathways to higher education, where demographic background and psychosocial disposition have a major effect on their academic achievement (MacArthur & Santo, 2023a ) except for gender. A study conducted by (Menon et al., 2022 ) did not find a significant gender difference in academic achievement. The specific context of the study In this study, attitudes toward statistics refer to the feelings, beliefs, and behaviors that students develop in relation to learning statistics (Pascual et al., 2025 ). One of the best illustrations of this description can be found in models that define attitude as a construct consisting of six subjective component dimensions: affect, cognitive appraisal, value, difficulty, interest, and effort (Lethbridge et al., 2024b ; O’Bryant et al., 2021b ; Ruiz-Jiménez et al., 2022 ). Affection refers to students' subjective emotions and feelings towards statistics, influencing their enthusiasm, comfort, and happiness. Positive effects make students enjoy learning, feel safe, and pay more attention, while negative effects lead to boredom, anxiety, and dislike (Cujba & Pifarré, 2024b ; Levpušček & Cukon, 2022 ). However, there are many factors that can influence the learning of statistics. Individual demographic and academic factors and learning backgrounds can influence students' attitudes towards statistics (Zhang et al., 2012 ). Statistics courses in higher education should teach data analysis, presentation methods, and professional insights, guiding resource allocation and program development for first-year students (Al-Sheeb et al., 2019 ). The learning process in statistics class is expected to be improved by good student attitudes towards statistics, and this is also expected to be correlated with better performance on course exams (Mcintee et al., 2022 ). There is a positive and high relationship between attitudes towards statistics and how well graduate and undergraduate students perform on exams in statistics courses (Eshet et al., 2021b ). A positive student attitude will contribute significantly to the achievement of learning outcomes (Dean et al., 2025 ; Deti et al., 2023 ). Thus, at the end of the lesson, the statistics lecturer must be able to determine whether this goal has been achieved; using an attitude measurement scale for that purpose would be very helpful. If it is determined that goals are not being achieved, this failure highlights the need to implement educational strategies and activities that will change students' perceptions of statistics (Cladera et al., 2019b , b ). Method Sample The data used in this study were obtained from the NHI Bandung Tourism Polytechnic, the oldest tourism college in Indonesia and structurally under the Ministry of Tourism of the Republic of Indonesia. The sample consisted of 435 students from the NHI Bandung Tourism Polytechnic in the 2021/2022 academic year, and data collection took place between February and August 2023. Table 1 below provides an illustration that, based on the demographics above, the characteristics of the sample are quite varied. Table 1 Demographic description of the sample Student Profile: n Percent Department: Hospitality 156 35,9% Travel 124 28,5% Tourism 155 35,6% Class Level: Lower 105 24,1% Midle 220 50,6% Upper 110 25,3% Gender: Male 169 38,9% Female 266 61,1% Origin of high school: Senior High School 353 81,1% Vocational High School 82 18,9% Study Program in High School: Natural Science 234 53,8% Social Sciences 201 46,2% Work Experience: Yes 186 42,8% No 249 57,2% Samples were taken randomly and proportionally, adjusted to the study program (hospitality, travel, and tourism); class levels; gender; origin of high school; study program in high school; and work experience. Instruments The questionnaires filled out by students were taken from instruments adapted from the Survey of Attitude Towards Statistics – SATS (Berndt et al., 2021 ; Chew & Dillon, 2014 ; Prayoga & Abraham, 2017 ), so that 28 items are used, which are grouped into four attitudinal components: cognitive (6 items); affective (6 items); conative (7 items); and value (9 items); The SATS uses a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) and has been tested for reliability and validity (Tables 2 and 3 ). We chose the SATS-28 instrument because it is a subset of the SATS-36 instrument (excluding the effort and interest constructs), research on the SATS-28 remains directly relevant to the corresponding SATS-36 subset (Whitaker et al., 2022 ) and “the SATS-28 appears to be the strongest attitude measure available (Cladera et al., 2019a , c ). Table 2 Statistics Reliability Test Variable/Dimension Cronbach's Alpha n of Items Attitudes .921 28 Cognitive .764 6 Affective .781 6 Conative .705 7 Value .874 9 To ensure the SATS-28 is easily understood by respondents, we adapted it to Indonesian language and culture and then tested its validity and reliability. The reliability analysis of the survey instrument yielded high scores, with a Cronbach's alpha of 0.921 for all items. Each dimension had a high reliability index: cognitive (0.764), affective (0.781), difficulty (0.705), and value (0.874). The survey instrument reliability analysis gave a high score, with a Cronbach alpha of 0.921 for all items. Each dimension has a high reliability index: cognitive (0.764), affective (0.781), conative (0.705), and value (0.874). Table 3 , Validity Test Result, presenting Pearson items, refers to the use of the Pearson product moment correlation coefficient to determine the validity of an item in a questionnaire or scale by measuring its correlation to the total score of the instrument, where an item is considered valid if it shows a significant positive correlation, indicating that the item measures the same basic construct as the entire scale (Hidayati et al., 2023 ). Table 3 Item Validity Test Results Items No. Pearson Correlation Sig. 1 .705** .000 2 .623** .000 3 .426* .019 4 .657** .000 5 .458* .011 6 .712** .000 7 .405* .026 8 .588** .001 9 .602** .000 10 .684** .000 11 .635** .000 12 .542** .002 13 .598** .000 14 .416* .022 15 .705** .000 16 .433* .017 17 .592** .001 18 .488** .006 19 .623** .000 20 .587** .001 21 .501** .005 22 .536** .002 23 .707** .000 24 .623** .000 25 .636** .000 26 .475** .008 27 .438* .016 28 .623** .000 Table 3 above indicates that all 28 items are appropriate to be used to measure student attitudes towards statistics. Data collection Data collection was conducted after obtaining approval from the institution's leadership through the Head of the Centre for Research and Community Service at the NHI Bandung Tourism Polytechnic. Technically, students selected as random samples were provided with a letter of consent to complete a questionnaire, ensuring their anonymity. Before filling out the questionnaire, students were given an explanation of the purpose of the study and the procedure for filling it out. The time allotted for filling out the questionnaire (self-administered) is about 20–30 minutes. After students complete the statistical learning process for one semester, they are asked to fill out the same questionnaire (post-SATS) as well as report on academic achievement in the form of a final score for the statistics course. After the learning process took place, all the pre-and post-SATS answers were collected, and an administrative completeness check of the questionnaire was carried out. It turned out that 435 respondents filled it out completely and validly. Analysis All analyses in this study were conducted using the Statistical Package for Social Science (SPSS, version 26.0). First, descriptive statistics were generated to explain the mean and standard deviation of students' attitudes toward Statistics scores and compare them with their academic achievement scores. Then, the mean (M) for pre- and post-learning, standard deviation (SD), skewness, and kurtosis of the SATS items were calculated. A t-test for correlated data was applied to determine whether the obtained mean was significant or not regarding the relationship between students' attitudes toward statistics before and after the learning process and their academic achievement. Furthermore, Analysis of covariance (ANCOVA) was used to analyze the data of the relationship between attitudes toward statistics and academic achievement based on student demographics. All data processing was carried out using IBM SPSS Statistics software version 26.0. The univariate analysis of variance test was carried out by first eliminating the influence of differences in student backgrounds (major, college level, gender, school origin, high school study program, and work experience) from the model. Next, testing was carried out to determine the effect of student background on the scores obtained by students. This test was carried out by eliminating the influence of student background from the model. Result Differences in student attitudes towards statistics before and after the learning process The following Table 4 provides an illustration of students' attitudes towards statistics before and after the learning process. Overall, it can be said that students' attitudes towards statistics before and after the learning process tend to be positive, as evidenced by the average score for students' attitudes toward statistics before they began learning, which changed from 3.35 to 3.52, representing an increase of 0.17 units (on a scale of 1–5). This finding also shows that the learning process of statistics courses contributes to increasing students' attitude scores towards statistics. Table 4 Mean of Attitude towards Statistics before and after the learning process Attitude Score N Mean STD Std. Error Mean Before Learning Process 435 3.35 .48 .474 After Learning Process 435 3.52 .51 .496 Table 5 below presents the results of statistical tests of student attitudes before and after learning. One of the pieces of information obtained from Table 5 about Levene's Test for Equality of Variances is the assumption requirement for using the t-test, showing a result of 1.349 (P > 0.05), which means that the scores of students' attitudes toward statistics before and after the learning process have a variance that is directed (homogeneous), which means that the t-test can be used to test the differences in students' attitudes toward statistics before and after the learning process. The paired-sample t-test for equality of means showed a result of -4.669 (P < 0.05), which means that there is a significant difference between the scores of students' attitudes towards statistics before and after the learning process. Changes in students' attitudes towards statistics, if traced in more detail, show a comparison of differences in attitudes towards statistics before and after the learning process based on attitude components. Table 5 Testing Students' Attitudes to Statistics before and after the learning process Levene's Test for Equality of Variances t-test for Equality of Means Components Mean F Before After 1.349 * -4.669 ** Cognitive 32.62 34.63 30.891 ** Affective 33.22 33.96 2.786 * Difficulty 31.39 32.95 49.227 ** Value 36.63 37.15 2.288 * ** P 0,05 Table 5 also shows the results of the F-test, which shows significant changes in attitudes in the cognitive and difficulty components, as indicated by a significance of p 0.05. Figure 1 below shows an increase or decrease in students' attitude scores towards statistics on each item, before and after the learning process. The items that showed a significant change were: (1) I'm having trouble understanding statistical concepts; (2) I'm having a hard time understanding statistics because of the way I think; (6) I don't know what happened in this statistics course; (17) Statistical formulas are easy to understand; (18) Statistics is a subject that is easy for most people to learn; and (24) I am able to use statistics in my daily life. Meanwhile, there were 10 items that did not show a (fixed) change, including: (4) I can understand statistical equations; (7) I'm afraid of statistics; (12) I like statistics; (14) Most people have to learn a new way of thinking to work on statistics; (15) Statistics are very technical; (19) Statistics require a lot of ability to count; (22) I'm not going to apply statistics in my profession; (26) Statistics should be a must in my professional training; (27) Statistical skills will make me more employable; and (28) Statistics are rarely used in daily life. There are even items whose changes are negative (down), namely items (3) I made a lot of math mistakes doing statistics. The relationship between student attitudes towards statistics before and after the learning process and academic achievement. Before conducting statistical tests, first examine the distribution of the data with univariate items in SATS. Table 6 shows that the skewness and kurtosis indices are between − 1 and + 1, except for two items that are slightly outside the normality range, but the differences between some items and their norms can be considered small (Mohd Ibrahim, 2024 ). Table 6 Means (M), standard deviations (SDs), skewness, and kurtosis of the fourteen items of the Attitude Toward Statistic Item M SD Skewness Kurtosis 1 2.83 .840 − .031 − .832 2 3.09 .935 − .315 − .831 3 3.11 .937 − .289 − .630 4 3.58 .686 − .473 .244 5 3.96 .657 − .594 1.723 6 3.00 .980 − .128 − .843 7 3.46 .970 − .523 − .294 8 3.44 .920 − .487 − .100 9 3.02 .994 − .103 − .698 10 3.28 .977 − .517 − .336 11 3.44 .717 − .282 .493 12 3.29 .720 − .232 .814 13 2.60 .854 .161 − .729 14 3.54 .845 − .499 − .178 15 2.29 .744 .510 .324 16 3.90 .705 − .970 2.255 17 2.94 .715 .084 .472 18 2.78 .809 .156 − .200 19 3.92 .808 − .824 .848 20 3.44 .833 − .289 .052 21 3.66 .790 − .341 .201 22 3.99 .771 − .560 .482 23 3.55 .803 − .269 .000 24 3.17 .845 − .313 − .277 25 4.23 .673 − .716 .523 26 3.75 .738 − .460 .536 27 3.89 .704 − .327 .104 28 3.28 .909 − .204 − .361 Thus, statistical testing can be done to prove the correlation between student attitudes towards statistics before and after the learning process and academic achievement. The results show that students' attitudes towards statistics before and after the learning process are significantly correlated (Table 7 ). Table 7 Correlation Between Students' Attitudes towards Statistics and Academic Achievements Correlation between Academic Achievement, with: Pearson Correlation Attitude score Before Learning .186** Attitude score After Learning .223** **. Correlation is significant at the 0.01 level (2-tailed) Although the correlation coefficient between attitudes toward statistics before the learning process and academic achievement of 0.186 and 0.223 after the learning process is relatively weak (Ratner, 2009 ), this test is significant. This means that students' attitudes toward statistics before and after learning each contribute 3.5% and 5% to student academic achievement. The relationship between attitudes toward statistics and academic achievement based on student demographics. Table 8 shows that the significance figures for the attitude toward statistics score variables are all P < .05, indicating a linear relationship between attitudes toward statistics and students' Academic Achievements. This result indicates that the ANCOVA assumption has been fulfilled. Table 8 Summary of ANCOVA Processing Results for Testing the Influence of Attitude on Academic Achievement Based on Student Background Background Inter-Subject Effect Test Significance Adjusted R Squared Subjects Factors Intercept Attitude Score Corrected Model Department .000 .000 .000 .000 .130 College level .000 .000 .000 .000 .086 Gender .204 .000 .000 .000 .034 Origin of HS .025 .000 .000 .000 .041 Study program in HS .004 .000 .000 .000 .048 Experience .012 .000 .000 .000 .044 Source: SPSS Processing (2024) Table 8 also shows that the significance figures for the student background variables (department, college level, gender, origin of high school, study program in high school, and work experience) mostly show p .05. So it can be concluded that, without involving the attitude variable in statistics, at the 95% confidence level, demographic background has a significant effect on student academic achievements, except for the gender variable. To determine the effect of the attitude variable on statistics and student background variables on student academic achievements simultaneously, it can be seen from the significance number in the Corrected Model section. It can be seen that p < 0,05 at the 95% confidence level, it can be concluded that simultaneously, student attitudes towards statistics and student background variables affect student academic achievements. Furthermore, this study also found that learning outcomes based on demographic backgrounds (department, class levels, gender, type of school of origin, high school major, and work experience) of students differ significantly, as shown in Table 9 , where overall p .05. This means that the academic achievements of male and female students do not differ significantly. Table 9 Differences in Attitudes Towards Statistics and Academic Achievements based on Student Demographics Background Variable F Department Academic Achievements 25.916** Attitude-Before the Learning Process 1.326+ Attitude-After the Learning Process .195+ Class Levels Academic Achievements 11.430** Attitude-Before the Learning Process 2.365+ Attitude-After the Learning Process 11.064** Gender Academic Achievements 1.713+ Attitude-Before the Learning Process .093+ Attitude-After the Learning Process .060+ Type of School of Origin Academic Achievements 4.937* Attitude-Before the Learning Process .008+ Attitude-After the Learning Process .027+ High School Major Academic Achievements 9.273** Attitude-Before the Learning Process 1.381+ Attitude-After the Learning Process 1.524+ Work Experience Academic Achievements 5.749* Attitude-Before the Learning Process .131+ Attitude-After the Learning Process .170+ ** P < 0,01; * P 0,05 Table 9 also shows that, based on students' demographic background, attitudes toward statistics before and after the learning process did not differ significantly, except for the class level variable, where p < 0.05. This means that, based on class level, attitudes toward statistics before and after the learning process differed significantly. Meanwhile, students' attitudes toward statistics before and after the learning process, when viewed from a demographic background, did not differ significantly. Discussion This research investigates tourism students' attitudes towards statistics and their academic achievement, examining differences before and after learning, the relationship between attitudes and achievement, and demographic factors. Our results show that students' attitudes towards statistics before and after the learning process tend to be positive. For researchers, this is a surprise, because in general, students who are just learning about statistics tend to feel anxious and show negative attitudes (Cladera et al., 2019c ; Kiekkas et al., 2015 ). However, after the learning process, there was a significant change in attitude. These findings confirm that the positive effect makes students enjoy learning statistics, feel safe, and pay more attention to lessons, while the negative effect makes students more likely to be bored, anxious, and dislike statistics (Hunt et al., 2023 ; Lethbridge et al., 2024b ; Levpušček & Cukon, 2022 ; Trassi et al., 2022 ). The study reveals that statistical learning enhances students' understanding of concepts and formulas, but still reveals a fear among tourism students, suggesting mandatory statistics for professionals (Heretick & Tanguma, 2021 ), and a positive attitude towards statistics can help students realize the value of statistics in their personal and professional lives, which will motivate them to study and understand statistics in order to use the information in their daily lives (Trassi et al., 2022 ) and acquire practical statistical intuition and use it in the real world (Shida et al., 2024b ). Significant changes in attitude occurred in the cognitive and difficulty components, while for the affective and value components, the attitude change was not significant. In fact, affection describes students' subjective emotions and feelings about statistics, which are indicated by their enthusiasm, comfort, and happiness when studying statistics (Shida et al., 2024b ). The cognitive aspect of change is easier to implement than affect and values, as the emotional aspect, often rooted in attitude, is more resistant to change (Asare, 2023 ; MacArthur & Santo, 2023a ; Mohd Ibrahim, 2024 ; Van Dijck et al., 2022 ). The statistical testing revealed no significant impact of the statistical learning process on changes in tourism student attitudes for affective and value components. This finding is in line with the research of (Cladera et al., 2021b ), which showed that the level of fear and insecurity of students decreased after completing the lecture; on the contrary, the average value of the affective and self-confidence components worsened. The results of this study also showed that students' attitudes towards statistics before and after the learning process were significantly correlated, with attitudes contributing 5% to student academic achievement. These results are in line with the results of previous research on the importance of attitudes towards academic achievement during and after Statistics lectures (Eshet et al., 2021b ; Levpušček & Cukon, 2022 ; MacArthur & Santo, 2023a ; Van Dijck et al., 2022 ). This finding also confirms previous findings that a positive attitude makes it possible to develop statistical thinking skills, apply the acquired knowledge in daily life, have a pleasant experience during lectures, play an important role in academic achievement, and improve statistical skills in later professional life (Li et al., 2024 ). This finding is also consistent with previous literature observations that positive student attitudes will contribute significantly to the achievement of learning outcomes (MacArthur & Santo, 2023b ). Therefore, the development of a positive attitude towards the subject should be a desired outcome of the learning process, in addition to the acquisition of knowledge and skills (Getie, 2020 ). This study also shows that, simultaneously, student attitudes towards statistics and student background variables affect student academic achievements. In line with the research results of (Peiró-Signes et al., 2020 ), students' academic backgrounds consist of several dimensions of individual achievement and contextual resources. Secondary schools significantly influence students' higher education pathways, but demographic background, except for gender, does not influence academic achievements without considering attitude variables. This finding is in line with the results of research conducted by (MacArthur & Santo, 2023b ) which found that demographic factors such as gender and pre-university achievement do not affect student achievement. The results of other studies show that the academic achievement of male and female students is not significantly different, confirming a previous study conducted by (Menon et al., 2022 ), where there was no significant gender difference in actual achievement, as measured by grades and scores, among any of the academic domains assessed. Simultaneously, students' attitudes towards statistics and student background variables have an effect on their academic achievements. These results are consistent with previous literature that suggests the better attitude of students in flipped classroom learning has a positive effect on the students' perception of their academic outcomes (Ruiz-Jiménez et al., 2022 ). Attitudes towards statistics before and after learning do not significantly differ based on students' demographic background, except for class level variables, indicating that these attitudes affect academic achievement, consistent with previous research (Chu, 2025b ). This study confirmed earlier findings that students' demographic backgrounds did not significantly influence their attitudes toward statistics (Li et al., 2024 ; Zhang et al., 2012 ), which found that individual demographic and academic factors and learning backgrounds can affect students' attitudes towards statistics. The study results have theoretical and practical implications for scholars, organizations, and policy-making. To begin with the theoretical implications, the findings of this study have complemented the literature, especially on attitude to statistics research (Cladera et al., 2019c ; Hannigan et al., 2014 ; MacArthur & Santo, 2023b ) and the importance of attitudes towards academic achievement during and after statistics courses and the importance of motivating students to continue learning the quantitative skills they will need in their careers, so the role of attitudes towards academic achievement during and after courses in statistics. This study's practical implications for lecturers and policymakers include understanding attitudes towards statistical learning and its impact on academic achievement, enabling student-centered learning, motivating students, and ensuring the quality of the learning process. The study's findings have significant implications for tourism education curricula, as statistics literacy skills are increasingly required for students, encompassing knowledge of statistical analysis tools and reasoning, as these skills are crucial for their future careers. Some of the efforts that can be made are for example by integrating tourism industry case studies in statistical learning, using a project-based learning approach, or utilizing statistical software that is relevant to tourism research so that it can help students to increase their understanding and appreciation of statistics. Scholars face challenges in conducting in-depth exploratory research on affective components and values, as insignificant results indicate that these factors influence attitudes towards statistics, prompting further exploration. Limitations and further research There are several limitations to this study, including the fact that the sample was drawn from only one university. Furthermore, the adaptation of the SATS instrument into Indonesian and the limited time to complete the questionnaire may have affected the quality of respondents' responses. For further research, it is recommended to conduct research on the mechanisms of change in affective attitudes and values ​​using a longitudinal research design and a mixed-method approach. This effort will enhance understanding of how students' attitudes toward statistics can be shaped and transformed to support statistical understanding and literacy in tourism higher education. Conclusion This study examines tourism students' attitudes toward statistics and their relationship to academic achievement. Taking into account differences before and after the learning process, as well as demographic factors, the analysis shows that students generally have positive attitudes toward statistics both before and after the course, and they experienced significant improvements in cognitive aspects and perceived difficulty. Conversely, affective and grade-level aspects remained relatively stable, suggesting the need for more in-depth pedagogical interventions to alter students' emotional and grade-level perceptions. The study also demonstrated a positive, albeit weak, relationship between students' perceptions of statistics and their academic achievement. These findings confirm that attitude is an important predictor of student learning success. The demographic analysis shows that, in addition to gender, student background factors influence their academic achievement. Furthermore, demographic factors also play a role in determining tourism students' academic achievement. Declarations Ethical Approval This research was approved by the Ethics Committee, Politeknik Pariwisata NHI Bandung, on February 20, 2026, with approval reference number B/ND/35/PS.02.01/PTP.1.13/2026. Local ethical approval was obtained where required for multi-country research. All procedures performed involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards Informed consent The study was conducted between February and August, 2023. Informed consent was obtained from all individual participants included in the study. Participants received comprehensive information regarding the study's objectives, methodologies, associated risks, potential benefits, and their entitlements, including the right to withdraw at any moment without consequence. Informed consent was obtained from all participants included in the study before they participated in the survey. Author Contribution HS and RK devised the main conceptual ideas and wrote and revised the paper. MR worked out the technical details and performed data collection. HS, RK, MR, and AM conduct the data analysis and discussion section. HS and RK worked out inmanuscript incorporating revisions. The author(s) read and approved the final manuscript. Data Availability The online version contains supplementary material available at: [https://doi.org/10.5281/zenodo.19383101](https:/doi.org/10.5281/zenodo.19383101) References Al-Sheeb BA, Hamouda AM, Abdella GM (2019) Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors. J Appl Res High Educ 11(2):178–198. https://doi.org/10.1108/JARHE-10-2017-0120 Asare PY (2023) Profiling teacher pedagogical behaviours in plummeting postgraduate students’ anxiety in statistics. Cogent Educ 10(1):2222656. https://doi.org/10.1080/2331186X.2023.2222656 Ashaari NS, Judi HM, Mohamed H, Tengku, Wook MT (2011) Student’s Attitude towards Statistics Course. 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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-9111399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624740610,"identity":"7a56ff18-e91a-450d-b937-509be3629c7f","order_by":0,"name":"Herlan Suherlan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAwbmxgcMDBYMDDwQgQQ2iDgzHi2MzQYMDBISJGlpk0DRApXArcWcvbGtuqBCoo6f5+zBzxUMd/L4GJgffmAosMapxbLnYNvtGWckJCR7+5IlzzA8K2ZjYDOWYDBIx+2wG4ltt3nbJCQMzvMYSDb+O5zYxsBgBhQ/jFvL/YdtxSAt9ud5jH82MIC0sH/Dr+UGYxsz2BbeHjNJiBYeAracSWyWBvpFcsaZc2mWYC3MPMUSCfj8cvzwwc8FFTb8/D25h2+CtMxvb9/44cMf3CEGAtAo4EHiJuDVgKFlFIyCUTAKRgEaAACTcUwGLQcTOQAAAABJRU5ErkJggg==","orcid":"","institution":"Politeknik Pariwista NHI Bandung","correspondingAuthor":true,"prefix":"","firstName":"Herlan","middleName":"","lastName":"Suherlan","suffix":""},{"id":624740611,"identity":"c306eb34-7c94-45e4-9cda-1f51f8f45263","order_by":1,"name":"R Kusherdyana","email":"","orcid":"","institution":"Politeknik Pariwista NHI Bandung","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"","lastName":"Kusherdyana","suffix":""},{"id":624740612,"identity":"3c24aace-c096-41b9-8372-26998851cf65","order_by":2,"name":"Anwari Masatip","email":"","orcid":"","institution":"Politeknik Pariwista NHI Bandung","correspondingAuthor":false,"prefix":"","firstName":"Anwari","middleName":"","lastName":"Masatip","suffix":""},{"id":624740613,"identity":"d46a1645-d09a-4146-9d3c-8a9b058acaed","order_by":3,"name":"Mohamad Ridwan","email":"","orcid":"","institution":"Politeknik Pariwista NHI Bandung","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"","lastName":"Ridwan","suffix":""}],"badges":[],"createdAt":"2026-03-13 07:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9111399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9111399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107340307,"identity":"3892a202-f937-4d9b-bce8-b892eaf65436","added_by":"auto","created_at":"2026-04-20 14:15:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51666,"visible":true,"origin":"","legend":"\u003cp\u003eStudent Attitudes Towards Statistics Before and After the Learning Process\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9111399/v1/d38e89b1c1b5b5bc3538f1b5.png"},{"id":107340310,"identity":"faaafd3f-2c68-4a95-82c4-7d6d2fbc35ea","added_by":"auto","created_at":"2026-04-20 14:15:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854197,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9111399/v1/16b51a8f-fd03-4e80-862f-03aa7c98b247.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Learning statistics among undergraduate tourism students: between attitude and academic achievement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eModern education in social sciences and humanities, including tourism, necessitates extensive training in research methods and statistics, fostering critical thinking, problem-solving, and decision-making skills for professional development (Berndt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cladera et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, 2021). Statistical knowledge, literacy, and skills are crucial for undergraduate students' academic and professional careers, and students' attitudes toward statistics play a crucial role in their understanding of its importance (Chu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025a\u003c/span\u003e), therefore, it is not surprising that statistics courses are required for tourism graduates to collect, analyze, and interpret tourism-related data, such as tourist numbers, hotel occupancy rates, and length of stay, to support research, development, and strategic decision-making in the tourism industry (Suherlan, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sulaiman \u0026amp; Kusherdyana, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, many tourism students do not realize that their program of study includes multiple statistics courses, so it is not uncommon to cause anxiety (Khavenson et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kiekkas et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) there is even a tendency to show a negative attitude that can be an obstacle to learning statistical concepts and effective learning (Cladera et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e; Lavidas et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). A positive attitude enhances statistical thinking skills, practical application, pleasant lecture experiences, and significantly impacts academic achievement (Asare, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ashaari et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cujba \u0026amp; Pifarr\u0026eacute;, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e; Shida et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Studies show that attitudes towards statistics in higher education, particularly in nursing, medicine, psychology, and social science courses, are often linked to anxiety (Hagen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Heretick \u0026amp; Tanguma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jazayeri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Khavenson et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nielsen \u0026amp; Kreiner, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), demographic profile background, previous bad experiences in similar subjects with statistics (Chiesi \u0026amp; Bruno, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Da Silva \u0026amp; Moura, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Hunt et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Prayoga \u0026amp; Abraham, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), perceptions, motivations, and personality (Chiesi \u0026amp; Bruno, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; O\u0026rsquo;Bryant et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Opstad, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schwerter \u0026amp; Brahm, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and academic achievement (Eshet et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Levpušček \u0026amp; Cukon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies that discuss attitudes toward student statistics at tourism universities are still very limited. A study conducted by Cladera et al., (2021) examines the analysis of factors that underlie tourism students' attitudes towards statistics associated with demographic characteristics and student academic achievement.\u003c/p\u003e \u003cp\u003ePrior research examining student attitudes towards statistics has typically focused on various variables, including demographic characteristics and academic achievement, in isolation across distinct studies. However, a comprehensive investigation encompassing these factors within the context of tourism higher education remains unaddressed. Thus, the aim of this research is to determine students' attitudes towards statistics before and after the learning process, the relationship between students' attitudes towards statistics and learning achievement, and the differences between students' attitudes towards statistics and academic achievement based on student demographics. The specific questions are:\u003c/p\u003e \u003cp\u003eRQ1. Are there differences in student attitudes towards statistics before and after the learning process?\u003c/p\u003e \u003cp\u003eRQ2. Is there a relationship between students' attitudes towards statistics before and after the learning process and their academic achievements?\u003c/p\u003e \u003cp\u003eRQ3. Is there a relationship between attitudes towards statistics and academic achievement based on student demographics?\u003c/p\u003e\n\u003ch3\u003eTheoretical framework\u003c/h3\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAttitude Towards Statistics\u003c/h2\u003e \u003cp\u003eStatistics is a common subject in many college programs. Programs leading to undergraduate and postgraduate degrees, particularly in the social sciences, often require successful completion of statistics and research courses (Levpušček \u0026amp; Cukon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, there are efforts in universities to develop undergraduate-practitioner training models that will encourage the use of research techniques to solve real-world problems (Heretick \u0026amp; Tanguma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, some emotional difficulties, such as negative attitudes and anxiety, were identified in various literatures (Lethbridge et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). A person's attitude can be defined as mental and emotional strength, which motivates them to act in a certain way towards certain items or problems, dispositions, emotions, or mind conditioning towards something (Peir\u0026oacute;-Signes et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). The concept of attitude is at the core of many research studies and theories (Tubaishat et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). There is no agreement among scholars on how to define attitudes (Chew \u0026amp; Dillon, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cujba \u0026amp; Pifarr\u0026eacute;, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e). Many studies say that attitude is just a feeling, but others say that it has more than one meaning (Chiesi \u0026amp; Bruno, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Steinberger, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAttitude toward statistics is a person's tendency to react positively or negatively to things, situations, or people that have to do with statistical learning. It is made up of emotional, cognitive, and behavioral parts (Chiesi \u0026amp; Bruno, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Peir\u0026oacute;-Signes et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Steinberger, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Attitudes toward statistics can be viewed as \"a learned tendency to respond positively or negatively to various items, circumstances, concepts, or personalities.\" (Mcintee et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A positive attitude towards statistics can motivate students to understand and apply statistics in their personal and professional lives, enhancing their understanding and application of the subject (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, a negative outlook has been shown to increase statistical anxiety (Hunt et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This can make it harder for students to learn statistics or use what they learn in the real world (O\u0026rsquo;Bryant et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). The study of student attitudes toward statistics has started to attract researchers attention in recent years. In addition to the purported impact of attitudes on learning (Lethbridge et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e) other factors that may contribute to this interest include the need for statistics as a tool for everyone in today's society, where numerical data is more common than ever (van Dijck et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Research on student attitudes towards statistics has been carried out in various fields of higher education because of the importance of attitudes towards academic achievement during and after statistics lectures (Peir\u0026oacute;-Signes et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous research has shown that students' academic backgrounds consist of several dimensions of individual achievement and contextual resources. Along with family and social ties, secondary schools play an important role in shaping students' pathways to higher education, where demographic background and psychosocial disposition have a major effect on their academic achievement (MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) except for gender. A study conducted by (Menon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) did not find a significant gender difference in academic achievement.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe specific context of the study\u003c/h3\u003e\n\u003cp\u003eIn this study, attitudes toward statistics refer to the feelings, beliefs, and behaviors that students develop in relation to learning statistics (Pascual et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). One of the best illustrations of this description can be found in models that define attitude as a construct consisting of six subjective component dimensions: affect, cognitive appraisal, value, difficulty, interest, and effort (Lethbridge et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; O\u0026rsquo;Bryant et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Ruiz-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Affection refers to students' subjective emotions and feelings towards statistics, influencing their enthusiasm, comfort, and happiness. Positive effects make students enjoy learning, feel safe, and pay more attention, while negative effects lead to boredom, anxiety, and dislike (Cujba \u0026amp; Pifarr\u0026eacute;, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Levpušček \u0026amp; Cukon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, there are many factors that can influence the learning of statistics. Individual demographic and academic factors and learning backgrounds can influence students' attitudes towards statistics (Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Statistics courses in higher education should teach data analysis, presentation methods, and professional insights, guiding resource allocation and program development for first-year students (Al-Sheeb et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The learning process in statistics class is expected to be improved by good student attitudes towards statistics, and this is also expected to be correlated with better performance on course exams (Mcintee et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). There is a positive and high relationship between attitudes towards statistics and how well graduate and undergraduate students perform on exams in statistics courses (Eshet et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). A positive student attitude will contribute significantly to the achievement of learning outcomes (Dean et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Deti et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Thus, at the end of the lesson, the statistics lecturer must be able to determine whether this goal has been achieved; using an attitude measurement scale for that purpose would be very helpful. If it is determined that goals are not being achieved, this failure highlights the need to implement educational strategies and activities that will change students' perceptions of statistics (Cladera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019b\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003e The data used in this study were obtained from the NHI Bandung Tourism Polytechnic, the oldest tourism college in Indonesia and structurally under the Ministry of Tourism of the Republic of Indonesia. The sample consisted of 435 students from the NHI Bandung Tourism Polytechnic in the 2021/2022 academic year, and data collection took place between February and August 2023. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below provides an illustration that, based on the demographics above, the characteristics of the sample are quite varied.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eDemographic description of the sample\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStudent Profile:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHospitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTravel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28,5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass Level:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50,6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25,3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38,9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61,1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigin of high school:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81,1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVocational High School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy Program in High School:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNatural Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46,2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWork Experience:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57,2%\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\u003eSamples were taken randomly and proportionally, adjusted to the study program (hospitality, travel, and tourism); class levels; gender; origin of high school; study program in high school; and work experience.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments\u003c/h3\u003e\n\u003cp\u003eThe questionnaires filled out by students were taken from instruments adapted from the Survey of Attitude Towards Statistics \u0026ndash; SATS (Berndt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chew \u0026amp; Dillon, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Prayoga \u0026amp; Abraham, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), so that 28 items are used, which are grouped into four attitudinal components: cognitive (6 items); affective (6 items); conative (7 items); and value (9 items); The SATS uses a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree to 5\u0026thinsp;=\u0026thinsp;strongly agree) and has been tested for reliability and validity (Tables \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We chose the SATS-28 instrument because it is a subset of the SATS-36 instrument (excluding the effort and interest constructs), research on the SATS-28 remains directly relevant to the corresponding SATS-36 subset (Whitaker et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and \u0026ldquo;the SATS-28 appears to be the strongest attitude measure available (Cladera et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019a\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003ec\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eStatistics Reliability Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable/Dimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en of Items\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitudes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\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\u003eTo ensure the SATS-28 is easily understood by respondents, we adapted it to Indonesian language and culture and then tested its validity and reliability. The reliability analysis of the survey instrument yielded high scores, with a Cronbach's alpha of 0.921 for all items. Each dimension had a high reliability index: cognitive (0.764), affective (0.781), difficulty (0.705), and value (0.874). The survey instrument reliability analysis gave a high score, with a Cronbach alpha of 0.921 for all items. Each dimension has a high reliability index: cognitive (0.764), affective (0.781), conative (0.705), and value (0.874).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Validity Test Result, presenting Pearson items, refers to the use of the Pearson product moment correlation coefficient to determine the validity of an item in a questionnaire or scale by measuring its correlation to the total score of the instrument, where an item is considered valid if it shows a significant positive correlation, indicating that the item measures the same basic construct as the entire scale (Hidayati et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\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\u003eItem Validity Test Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.705**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.426*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.657**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.458*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.712**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.405*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.588**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.602**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.684**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.635**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.542**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.598**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.416*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.705**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.433*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.592**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.488**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.587**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.501**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.536**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.707**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.636**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.475**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.438*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.623**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e above indicates that all 28 items are appropriate to be used to measure student attitudes towards statistics.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eData collection was conducted after obtaining approval from the institution's leadership through the Head of the Centre for Research and Community Service at the NHI Bandung Tourism Polytechnic. Technically, students selected as random samples were provided with a letter of consent to complete a questionnaire, ensuring their anonymity. Before filling out the questionnaire, students were given an explanation of the purpose of the study and the procedure for filling it out. The time allotted for filling out the questionnaire (self-administered) is about 20\u0026ndash;30 minutes. After students complete the statistical learning process for one semester, they are asked to fill out the same questionnaire (post-SATS) as well as report on academic achievement in the form of a final score for the statistics course. After the learning process took place, all the pre-and post-SATS answers were collected, and an administrative completeness check of the questionnaire was carried out. It turned out that 435 respondents filled it out completely and validly.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eAll analyses in this study were conducted using the Statistical Package for Social Science (SPSS, version 26.0). First, descriptive statistics were generated to explain the mean and standard deviation of students' attitudes toward Statistics scores and compare them with their academic achievement scores. Then, the mean (M) for pre- and post-learning, standard deviation (SD), skewness, and kurtosis of the SATS items were calculated. A t-test for correlated data was applied to determine whether the obtained mean was significant or not regarding the relationship between students' attitudes toward statistics before and after the learning process and their academic achievement. Furthermore, Analysis of covariance (ANCOVA) was used to analyze the data of the relationship between attitudes toward statistics and academic achievement based on student demographics. All data processing was carried out using IBM SPSS Statistics software version 26.0. The univariate analysis of variance test was carried out by first eliminating the influence of differences in student backgrounds (major, college level, gender, school origin, high school study program, and work experience) from the model. Next, testing was carried out to determine the effect of student background on the scores obtained by students. This test was carried out by eliminating the influence of student background from the model.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in student attitudes towards statistics before and after the learning process\u003c/h2\u003e \u003cp\u003eThe following Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides an illustration of students' attitudes towards statistics before and after the learning process. Overall, it can be said that students' attitudes towards statistics before and after the learning process tend to be positive, as evidenced by the average score for students' attitudes toward statistics before they began learning, which changed from 3.35 to 3.52, representing an increase of 0.17 units (on a scale of 1\u0026ndash;5). This finding also shows that the learning process of statistics courses contributes to increasing students' attitude scores towards statistics.\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\u003eMean of Attitude towards Statistics before and after the learning process\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eAttitude Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Error Mean\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.474\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.496\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\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below presents the results of statistical tests of student attitudes before and after learning. One of the pieces of information obtained from Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e about Levene's Test for Equality of Variances is the assumption requirement for using the t-test, showing a result of 1.349 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which means that the scores of students' attitudes toward statistics before and after the learning process have a variance that is directed (homogeneous), which means that the t-test can be used to test the differences in students' attitudes toward statistics before and after the learning process. The paired-sample t-test for equality of means showed a result of -4.669 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which means that there is a significant difference between the scores of students' attitudes towards statistics before and after the learning process. Changes in students' attitudes towards statistics, if traced in more detail, show a comparison of differences in attitudes towards statistics before and after the learning process based on attitude components.\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\u003eTesting Students' Attitudes to Statistics before and after the learning process\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevene's Test for Equality of Variances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et-test for Equality of Means\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eComponents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBefore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAfter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.349 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e-4.669 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.891 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAffective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.786 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDifficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.227 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.288 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e** P\u0026thinsp;\u0026lt;\u0026thinsp;0,05; * P\u0026thinsp;\u0026gt;\u0026thinsp;0,05\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e also shows the results of the F-test, which shows significant changes in attitudes in the cognitive and difficulty components, as indicated by a significance of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Meanwhile, in the affective and value components, changes in attitudes were not significant, as p\u0026thinsp;\u0026gt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below shows an increase or decrease in students' attitude scores towards statistics on each item, before and after the learning process. The items that showed a significant change were: (1) I'm having trouble understanding statistical concepts; (2) I'm having a hard time understanding statistics because of the way I think; (6) I don't know what happened in this statistics course; (17) Statistical formulas are easy to understand; (18) Statistics is a subject that is easy for most people to learn; and (24) I am able to use statistics in my daily life.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, there were 10 items that did not show a (fixed) change, including: (4) I can understand statistical equations; (7) I'm afraid of statistics; (12) I like statistics; (14) Most people have to learn a new way of thinking to work on statistics; (15) Statistics are very technical; (19) Statistics require a lot of ability to count; (22) I'm not going to apply statistics in my profession; (26) Statistics should be a must in my professional training; (27) Statistical skills will make me more employable; and (28) Statistics are rarely used in daily life. There are even items whose changes are negative (down), namely items (3) I made a lot of math mistakes doing statistics.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between student attitudes towards statistics before and after the learning process and academic achievement.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBefore conducting statistical tests, first examine the distribution of the data with univariate items in SATS. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that the skewness and kurtosis indices are between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1, except for two items that are slightly outside the normality range, but the differences between some items and their norms can be considered small (Mohd Ibrahim, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\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\u003eMeans (M), standard deviations (SDs), skewness, and kurtosis of the fourteen items of the Attitude Toward Statistic\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\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.832\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.630\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.814\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.472\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.361\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\u003eThus, statistical testing can be done to prove the correlation between student attitudes towards statistics before and after the learning process and academic achievement. The results show that students' attitudes towards statistics before and after the learning process are significantly correlated (Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\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\u003eCorrelation Between Students' Attitudes towards Statistics and Academic Achievements\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation between Academic Achievement, with:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePearson Correlation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude score Before Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.186**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude score After Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e.223**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003e**. Correlation is significant at the 0.01 level (2-tailed)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAlthough the correlation coefficient between attitudes toward statistics before the learning process and academic achievement of 0.186 and 0.223 after the learning process is relatively weak (Ratner, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), this test is significant. This means that students' attitudes toward statistics before and after learning each contribute 3.5% and 5% to student academic achievement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe relationship between attitudes toward statistics and academic achievement based on student demographics.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the significance figures for the attitude toward statistics score variables are all P \u0026lt; .05, indicating a linear relationship between attitudes toward statistics and students' Academic Achievements. This result indicates that the ANCOVA assumption has been fulfilled.\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\u003eSummary of ANCOVA Processing Results for Testing the Influence of Attitude on Academic Achievement Based on Student Background\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eInter-Subject Effect Test Significance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdjusted R Squared\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSubjects Factors\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAttitude Score\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCorrected Model\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e.204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrigin of HS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy program in HS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: SPSS Processing (2024)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e also shows that the significance figures for the student background variables (department, college level, gender, origin of high school, study program in high school, and work experience) mostly show p \u0026lt; .05, except for gender, which shows p \u0026gt; .05. So it can be concluded that, without involving the attitude variable in statistics, at the 95% confidence level, demographic background has a significant effect on student academic achievements, except for the gender variable. To determine the effect of the attitude variable on statistics and student background variables on student academic achievements simultaneously, it can be seen from the significance number in the Corrected Model section. It can be seen that p\u0026thinsp;\u0026lt;\u0026thinsp;0,05 at the 95% confidence level, it can be concluded that simultaneously, student attitudes towards statistics and student background variables affect student academic achievements.\u003c/p\u003e \u003cp\u003eFurthermore, this study also found that learning outcomes based on demographic backgrounds (department, class levels, gender, type of school of origin, high school major, and work experience) of students differ significantly, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, where overall p \u0026lt; .05 except for the gender variable, p \u0026gt; .05. This means that the academic achievements of male and female students do not differ significantly.\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\u003eDifferences in Attitudes Towards Statistics and Academic Achievements based on Student Demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackground\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\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\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.916**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.326+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.195+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClass Levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.430**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.365+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.064**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.713+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.093+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.060+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of School of Origin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.937*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.008+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.027+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHigh School Major\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.273**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.381+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.524+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eWork Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievements\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.749*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-Before the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.131+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttitude-After the Learning Process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.170+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003e** P\u0026thinsp;\u0026lt;\u0026thinsp;0,01; * P\u0026thinsp;\u0026lt;\u0026thinsp;0,05; + P\u0026thinsp;\u0026gt;\u0026thinsp;0,05\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e also shows that, based on students' demographic background, attitudes toward statistics before and after the learning process did not differ significantly, except for the class level variable, where p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This means that, based on class level, attitudes toward statistics before and after the learning process differed significantly. Meanwhile, students' attitudes toward statistics before and after the learning process, when viewed from a demographic background, did not differ significantly.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis research investigates tourism students' attitudes towards statistics and their academic achievement, examining differences before and after learning, the relationship between attitudes and achievement, and demographic factors. Our results show that students' attitudes towards statistics before and after the learning process tend to be positive. For researchers, this is a surprise, because in general, students who are just learning about statistics tend to feel anxious and show negative attitudes (Cladera et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019c\u003c/span\u003e; Kiekkas et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, after the learning process, there was a significant change in attitude. These findings confirm that the positive effect makes students enjoy learning statistics, feel safe, and pay more attention to lessons, while the negative effect makes students more likely to be bored, anxious, and dislike statistics (Hunt et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lethbridge et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e; Levpušček \u0026amp; Cukon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Trassi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study reveals that statistical learning enhances students' understanding of concepts and formulas, but still reveals a fear among tourism students, suggesting mandatory statistics for professionals (Heretick \u0026amp; Tanguma, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and a positive attitude towards statistics can help students realize the value of statistics in their personal and professional lives, which will motivate them to study and understand statistics in order to use the information in their daily lives (Trassi et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and acquire practical statistical intuition and use it in the real world (Shida et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). Significant changes in attitude occurred in the cognitive and difficulty components, while for the affective and value components, the attitude change was not significant. In fact, affection describes students' subjective emotions and feelings about statistics, which are indicated by their enthusiasm, comfort, and happiness when studying statistics (Shida et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024b\u003c/span\u003e). The cognitive aspect of change is easier to implement than affect and values, as the emotional aspect, often rooted in attitude, is more resistant to change (Asare, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Mohd Ibrahim, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Van Dijck et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The statistical testing revealed no significant impact of the statistical learning process on changes in tourism student attitudes for affective and value components. This finding is in line with the research of (Cladera et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e), which showed that the level of fear and insecurity of students decreased after completing the lecture; on the contrary, the average value of the affective and self-confidence components worsened.\u003c/p\u003e \u003cp\u003eThe results of this study also showed that students' attitudes towards statistics before and after the learning process were significantly correlated, with attitudes contributing 5% to student academic achievement. These results are in line with the results of previous research on the importance of attitudes towards academic achievement during and after Statistics lectures (Eshet et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e; Levpušček \u0026amp; Cukon, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e; Van Dijck et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding also confirms previous findings that a positive attitude makes it possible to develop statistical thinking skills, apply the acquired knowledge in daily life, have a pleasant experience during lectures, play an important role in academic achievement, and improve statistical skills in later professional life (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This finding is also consistent with previous literature observations that positive student attitudes will contribute significantly to the achievement of learning outcomes (MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). Therefore, the development of a positive attitude towards the subject should be a desired outcome of the learning process, in addition to the acquisition of knowledge and skills (Getie, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study also shows that, simultaneously, student attitudes towards statistics and student background variables affect student academic achievements. In line with the research results of (Peir\u0026oacute;-Signes et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), students' academic backgrounds consist of several dimensions of individual achievement and contextual resources. Secondary schools significantly influence students' higher education pathways, but demographic background, except for gender, does not influence academic achievements without considering attitude variables. This finding is in line with the results of research conducted by (MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) which found that demographic factors such as gender and pre-university achievement do not affect student achievement.\u003c/p\u003e \u003cp\u003eThe results of other studies show that the academic achievement of male and female students is not significantly different, confirming a previous study conducted by (Menon et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where there was no significant gender difference in actual achievement, as measured by grades and scores, among any of the academic domains assessed. Simultaneously, students' attitudes towards statistics and student background variables have an effect on their academic achievements. These results are consistent with previous literature that suggests the better attitude of students in flipped classroom learning has a positive effect on the students' perception of their academic outcomes (Ruiz-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Attitudes towards statistics before and after learning do not significantly differ based on students' demographic background, except for class level variables, indicating that these attitudes affect academic achievement, consistent with previous research (Chu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025b\u003c/span\u003e). This study confirmed earlier findings that students' demographic backgrounds did not significantly influence their attitudes toward statistics (Li et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which found that individual demographic and academic factors and learning backgrounds can affect students' attitudes towards statistics.\u003c/p\u003e \u003cp\u003eThe study results have theoretical and practical implications for scholars, organizations, and policy-making. To begin with the theoretical implications, the findings of this study have complemented the literature, especially on attitude to statistics research (Cladera et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019c\u003c/span\u003e; Hannigan et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; MacArthur \u0026amp; Santo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e) and the importance of attitudes towards academic achievement during and after statistics courses and the importance of motivating students to continue learning the quantitative skills they will need in their careers, so the role of attitudes towards academic achievement during and after courses in statistics. This study's practical implications for lecturers and policymakers include understanding attitudes towards statistical learning and its impact on academic achievement, enabling student-centered learning, motivating students, and ensuring the quality of the learning process. The study's findings have significant implications for tourism education curricula, as statistics literacy skills are increasingly required for students, encompassing knowledge of statistical analysis tools and reasoning, as these skills are crucial for their future careers. Some of the efforts that can be made are for example by integrating tourism industry case studies in statistical learning, using a project-based learning approach, or utilizing statistical software that is relevant to tourism research so that it can help students to increase their understanding and appreciation of statistics. Scholars face challenges in conducting in-depth exploratory research on affective components and values, as insignificant results indicate that these factors influence attitudes towards statistics, prompting further exploration.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and further research\u003c/h2\u003e \u003cp\u003eThere are several limitations to this study, including the fact that the sample was drawn from only one university. Furthermore, the adaptation of the SATS instrument into Indonesian and the limited time to complete the questionnaire may have affected the quality of respondents' responses.\u003c/p\u003e \u003cp\u003eFor further research, it is recommended to conduct research on the mechanisms of change in affective attitudes and values ​​using a longitudinal research design and a mixed-method approach. This effort will enhance understanding of how students' attitudes toward statistics can be shaped and transformed to support statistical understanding and literacy in tourism higher education.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examines tourism students' attitudes toward statistics and their relationship to academic achievement. Taking into account differences before and after the learning process, as well as demographic factors, the analysis shows that students generally have positive attitudes toward statistics both before and after the course, and they experienced significant improvements in cognitive aspects and perceived difficulty. Conversely, affective and grade-level aspects remained relatively stable, suggesting the need for more in-depth pedagogical interventions to alter students' emotional and grade-level perceptions. The study also demonstrated a positive, albeit weak, relationship between students' perceptions of statistics and their academic achievement. These findings confirm that attitude is an important predictor of student learning success. The demographic analysis shows that, in addition to gender, student background factors influence their academic achievement. Furthermore, demographic factors also play a role in determining tourism students' academic achievement.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval\u003c/h2\u003e \u003cp\u003e This research was approved by the Ethics Committee, Politeknik Pariwisata NHI Bandung, on February 20, 2026, with approval reference number B/ND/35/PS.02.01/PTP.1.13/2026. Local ethical approval was obtained where required for multi-country research. All procedures performed involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent\u003c/strong\u003e \u003cp\u003eThe study was conducted between February and August, 2023. Informed consent was obtained from all individual participants included in the study. Participants received comprehensive information regarding the study's objectives, methodologies, associated risks, potential benefits, and their entitlements, including the right to withdraw at any moment without consequence. Informed consent was obtained from all participants included in the study before they participated in the survey.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHS and RK devised the main conceptual ideas and wrote and revised the paper. MR worked out the technical details and performed data collection. HS, RK, MR, and AM conduct the data analysis and discussion section. HS and RK worked out inmanuscript incorporating revisions. The author(s) read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe online version contains supplementary material available at: [https://doi.org/10.5281/zenodo.19383101](https:/doi.org/10.5281/zenodo.19383101)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl-Sheeb BA, Hamouda AM, Abdella GM (2019) Modeling of student academic achievement in engineering education using cognitive and non-cognitive factors. 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BMC Med Educ 12(1):117. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1472-6920-12-117\u003c/span\u003e\u003cspan address=\"10.1186/1472-6920-12-117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Attitudes toward statistics, Academic achievements, Learning process, Tourism education, Higher education","lastPublishedDoi":"10.21203/rs.3.rs-9111399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9111399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to determine the attitudes of tourism students towards statistics, the relationship between students' attitudes towards statistics and learning achievement, and the relationship between attitudes towards statistics and academic achievement based on student demographics. A total of 435 students were asked to fill out a questionnaire for the Survey of Attitude Towards Statistics (SATS). Descriptive statistical data analysis, correlational analysis, and covariance analysis were performed with SPSS 26.0. The results showed that students' attitudes toward statistics before and after the learning process tended to be positive. Student attitudes towards statistics before and after the learning process were not influenced by the demographic background of students, except for the grade level variable. Simultaneously, student attitudes toward statistics and student background variables affect student academic achievements. This study's findings have implications for the development of tourism education curricula, as students in this field are increasingly expected to possess statistical literacy.\u003c/p\u003e","manuscriptTitle":"Learning statistics among undergraduate tourism students: between attitude and academic achievement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 14:15:15","doi":"10.21203/rs.3.rs-9111399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T04:52:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T20:52:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T08:24:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64299533918426761126520172044658633380","date":"2026-04-21T07:48:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246616060926596672999465175394161911585","date":"2026-04-14T19:15:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-13T07:58:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-10T13:12:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-08T10:45:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T02:21:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-08T02:15:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ec175050-0010-46d0-9404-bd580465f880","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T04:52:56+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":66497335,"name":"Social science/Education"},{"id":66497336,"name":"Biological sciences/Psychology"},{"id":66497337,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-08T05:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 14:15:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9111399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9111399","identity":"rs-9111399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}