Cognitive Distortions as Hidden Barriers to Student Success: Evidence from University Students’ Psychological Well-being and Academic Achievement

Cognitive Distortions as Hidden Barriers to Student Success: Evidence from University Students’ Psychological Well-being 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 Research Article Cognitive Distortions as Hidden Barriers to Student Success: Evidence from University Students’ Psychological Well-being and Academic Achievement Mah Noor Haider, Dr. Nazir Haider Shah, Dr. Ghulam Muhammad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9413489/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cognitive distortions are maladaptive thought patterns that may undermine students’ psychological well-being and academic functioning. This study was conducted to examine the relationship among cognitive distortions, psychological well-being and academic achievement of university students. A cross-sectional survey method with stratified random sampling technique was used to collect the data from N = 347 participants from sciences and social sciences faculties of the University of Sargodha, Pakistan. Data were collected from undergraduate students through two standardized adapted instruments Cognitive Distortion Scale (Briere, 2000 ), Psychological Well-Being Scale (Ryff, 1989 ) and self-reported academic performance. The Urdu versions of instruments were also used to measure the cognitive distortions and psychological well-being of students. The results revealed that cognitive distortions were negatively associated with psychological well-being and academic achievement while psychological well-being positively correlated with Academic Achievement. The results further indicated that psychological well-being partially mediate the relationship between cognitive distortions and academic achievement of students. Cognitive Distortions Student Success Psychological Well-being Academic Achievement Figures Figure 1 Figure 2 1. Introduction Higher education is characterized as a period of change in a student’s life involving both personal, academic, and social stressors (López, 2023 ). It brings both intellectual development and autonomy for many students but also increased stress, pressure and vulnerability (Conley et al., 2013 ). Academic pressure, social change, and the need to excel academically may all contribute to psychological stress, which can take numerous forms, including anxiety, depression, and stress (Beiter et al., 2015 ). These psychological problems can significantly affect the academic and the health of the student (Eisenberg et al., 2009 ). The cognitive distortions have become one of the most popular psychological factors influencing the emotional dysfunction and academic problems of individuals (Buğa & Kaya, 2022a ).Cognitive distortions are irrational, biased patterns of thinking that negatively influence how individuals perceive themselves, others, and life events. These distorted thoughts tend to exaggerate negative experiences, minimize positive outcomes, and contribute to maladaptive emotions and behaviors (Dozois & Beck, 2023 ). Common distortions include all-or-nothing thinking, overgeneralization, catastrophizing, and personalization. For example, a student who fails one test may irrationally conclude that they are incapable of academic success overall (Abirami & Murugesan, 2025 ). Such distorted patterns often arise automatically and have been linked to anxiety, depression, and academic stress among university students (Tauscher et al., 2023 ). Recent studies highlight that cognitive distortions are extremely common in students with academic stress, and the results indicate that over 60% of undergraduates often commit academic distortions, including mental filtering and should-statements (Kawamoto et al., 2023 ). These forms of errors in thinking hinder academic motivations, self-efficacy, and reduce academic performance (Parikh et al., 2024 ). Besides, cognitive distortions interfere with emotional regulation, make a person more susceptible to psychological distress, and perpetuate procrastination and avoidance cycles (Deperrois & Combalbert, 2022 ). Treating the distorted thinking patterns is thus at the center of enhancing resilience, good coping, and academic persistence (Shengyao et al., 2024 ). Psychological well-being (PWB) is a general state of emotional, cognitive, and social functioning of an individual, which involves positive mental health, life satisfaction, and resilience (Ryff, 2022 ). PWB, as opposed to no indication of mental illness, is the indicator of the capacity to prosper, grow personally, and be balanced in the presence of stressors (Suldo & Shaffer, 2008 ). According to Ryff, the multidimensional model, there are six central dimensions of PWB, including autonomy, environmental mastery, personal growth, positive relations, purpose in life, or self-acceptance. These factors are especially important to students who have to overcome their academic needs, social adaptation, and identity development. It has been established that students whose PWB is high report more academic resilience, higher motivation, and positive coping mechanisms than students with lower well-being (Abdullah et al., 2024 ). Conversely, individuals with low PWB have a higher probability of depression, anxiety, and disengagement, which impedes personal and academic performance (Li et al., 2025 ). The recent longitudinal studies prove that PWB is an important predictor of GPA, attendance at classes, and participation in co-curricular activities, which is why this resource can play an essential role in academic success (Tape et al., 2021 ). Besides, mindfulness and resilience training have been identified to positively influence the well-being of the students and indirectly their academic performance (Yuan & Hu, 2025 ). Therefore, PWB is a stress-protective factor and also an academic success factor. Academic achievement represents the measurable outcomes of students’ learning, including GPA, test scores, and other performance indicators (York et al., 2015 ). It remains one of the primary markers of success in higher education, directly influencing future career opportunities, social mobility, and psychological adjustment (Wang & Yang, 2025 ; Zhang & Huang, 2025 ). Academic performance is shaped by multiple internal factors such as self-regulation, cognitive ability, and motivation, as well as external factors like family support, financial stress, and institutional resources (Manwa & Sithole, 2022 ; Shi & Qu, 2022 ). Recent findings emphasize that that cognitive and emotional factors have a greater impact on achievement. As an example, the procrastination, time mismanagement, and decreased persistence can be observed in the students with maladaptive cognitive distortion, which leads to the decreased academic performance (Lourenço & Paiva, 2025 ). On the other hand, rational thinking and adaptive thinking styles are related to better persistence and success (Coşkun, 2018 ). Also, students whose mental health is good experience more engagement and resilience against academic stress, and are better equipped to face academic stress, thus attaining better academic performance (Mohamed et al., 2025 ). In that regard, academic success is not merely a sign of intellectual potential, but also represents psychological well-being and cognitive tendencies of students. 2. Review of Literature and Hypothesis Development 2.1 Cognitive Distortion Cognitive distortions are regular and habitual ways of thinking that are biased or irrational and distort the reality and support negative feelings and behavior (Ryum & Nikolaos Kazantzis, 2024). All of them are all-or-nothing thinking, catastrophizing, overgeneralization, and personalization that affect the ability of students to overcome the challenges (Hershey H Friedman, 2023). Recent literature proves that cognitive distortions occur among university students, especially when they are under academic pressure, in the competitive atmosphere, and face the problem of identity (Buğa & Kaya, 2022) These skewed perceptions contribute to the development of dysfunctional coping mechanisms like avoidance and procrastination that deteriorate psychological health and educational activities (Sirois, 2023 ). Moreover, the studies indicate that there is a positive correlation between high levels of cognitive distortions and anxiety, depressive symptoms, and low resilience among higher education students (Sapmaz, 2023 ). Cognitive-behavioral training and mindfulness interventions have been demonstrated to help decrease these distortions significantly, leading to greater control over emotions and functional academic performance (McBride & Greeson, 2023 ). Therefore, the knowledge of cognitive distortions can be instrumental in the treatment of psychological as well as academic issues in university students. 2.2 Cognitive Distortion and Academic Achievement Cognitive distortions negatively influence students’ academic performance by lowering motivation, diminishing self-efficacy, and impairing self-regulation skills (Sirois, 2016 ). When students adopt maladaptive beliefs such as “I will always fail,” they are less likely to engage in effective study behaviors, which reduces persistence and achievement (Pekin & Güme, 2025 ). Recent evidence suggests that students with higher distortion scores report significantly lower GPA and poorer study habits (Aljaffer et al., 2024 ). Moreover, irrational thought patterns increase test anxiety and academic stress, which further deteriorate academic performance (Al-Tameemi et al., 2023 ). In contrast, rational and adaptive thinking styles are linked to greater persistence, improved academic engagement, and better grades (Grimm & Richter, 2025 ). These findings suggest that cognitive distortions play a central role in shaping academic achievement outcomes, especially in competitive higher education contexts. H1: Cognitive distortion is a positive predictor of Academic Achievement. On the basis of the above literature, it is proposed that cognitive distortions significantly influence students’ academic performance. 2.3 Cognitive Distortion and Psychological Well-being Cognitive distortions are closely related to the poor psychological well-being, since they support maladaptive emotions, including feelings of hopelessness, worthlessness, and anxiety (Ryff & Singer, 2008 ). University students who practice high rates of cognitive distortions are less satisfied with their lives, have a reduced capacity to feel resilience, and experience depression and stress levels (Charan et al., 2025 ). As an illustration, persistent negative moods have been associated with overgeneralization and catastrophizing, which consequently lowers the overall well-being (Raes et al., 2023 ). Recent research proves that distorted thought is a predictor of emotional dysregulation and the withdrawal, thus undermining the mental health of students further (Keskiner et al., 2024 ). Conversely, distorted cognition, which is addressed through interventions (CBT and mindfulness), has been shown to improve self-acceptance, emotional stability, and well-being among students (Fattah Katamjani et al., 2024 ). Therefore, it is necessary that cognitive distortions are minimized so that overall psychological functioning of students can be improved. H2: Cognitive distortion is a significant positive predictor of psychological well-being. On the basis of the above literature, it is proposed that cognitive distortions significantly affect students’ mental health and overall well-being. 2.4 Psychological Well-being and Academic Achievement Psychological well-being (PWB) plays a pivotal role in shaping students’ academic outcomes. Better well-being is associated with increased academic motivation, resilience, and self-regulation, which lead to higher performance among students (Sarzhanova & Nurgabdeshov, 2025 ). Extensive research indicates that students with high PWB have higher GPA, attend classes more, and engage more in co-curricular activities than their low well-being counterparts (Pervaiz et al., 2025 ). On the other hand, low academic persistence and engagement occur due to poor well-being, stress, anxiety, and low self-esteem (Chu et al., 2023 ). According to the recent interventions, PWB can be enhanced with the help of mindfulness and resilience training that can greatly enhance the academic functioning of students (Oh et al., 2022 ). These results highlight the fact that PWB has a protective effect on academic performance. H3: Psychological well-being is a positive predictor of academic achievement. On the basis of the above literature, it is proposed that well-being significantly contributes to students’ academic success. 2.5 Cognitive Distortion and Academic Achievement: Mediating Role of Psychological Well-being Recent studies indicate that academic performance is mediated by psychological well-being between cognitive distortions and academic performance. The distorted thinking decreases the PWB as it leads to the development of stress, hopelessness and anxiety, the latter, in turn, decreases the academic motivation and persistence (Buğa & Kaya, 2022). As an example, catastrophizing students complain not only of worse well-being but also of poorer GPA (Sinval et al., 2025 ). Likewise, students who have stronger resilience and self-acceptance as important dimensions of PWB can overcome the adverse impact of cognitive distortions and maintain academic engagement (Craig et al., 2025 ). It has been confirmed using longitudinal studies that PWB is a partial explanation of the effects of cognitive distortions on educational outcomes (Şahin & Türk, 2021 ). Cognitive distortions and well-being-reducing interventions have been observed to improve academic performance simultaneously (Nashtban et al., 2025 ). Therefore, PWB is an intermediate that connects the maladaptive thinking and educational achievement. H4: Psychological well-being serially mediates the relationship between cognitive distortion and academic achievement. On the basis of the above literature, it is proposed that well-being explains how distorted thinking influences academic performance. 3. Methodology 3.1 Research Design The research design was a quantitative one. A survey instrument was designed using a structured questionnaire, which served as the primary tool for data collection. This approach was chosen because it could be measured objectively and statistically analyze the correlation of cognitive distortions, psychological well-being and academic achievement. Such a design enables the researcher to test hypotheses, establish correlations, and evaluate the mediation effect of psychological well-being (Creswell & Creswell, 2017 ). 3.2 Population The target population in this study was undergraduate and graduate (11,833) students in the faculties of Sciences and Social Sciences in the University of Sargodha. The demographic profile of the respondents are diversified, which makes the study findings more generalizable. Table 1 Distribution of the Study Population Faculty Undergraduate Students Graduate Students Total Sciences 6,100 1,320 7,420 Social Sciences 3,250 1,163 4,413 Total 9,350 2,483 11,833 3.3 Sampling Technique and Sample Size The stratified random sampling technique was employed to guarantee the demographic variety in the selection of the participants as per the recommendation of (Cochran, 1977 ; Lohr, 2021 ; Creswell & Creswell, 2017 ). They all assert that they are all represented in the sample which makes the results accurate and increases the generalizability of the results. A total sample of 347 students were selected. The sample was chosen based on the recommendation of (Cochran, 1977 ); Krejci and Morgan, 1970; Israel, 2013 ; Creswell & Creswell, 2017 ). To them, the sample size is adequate to perform inferential statistics analysis, and also representativeness of the target population. In the table 3.2, the sample is presented: Table 2 Sampling Distribution (N = 347) University Faculty No. of Students Sargodha University Social Sciences Sciences 215 132 Total 347 3.4 Research Instruments This study employed two standardized and adapted instruments along with students’ self-reported academic records. The Cognitive Distortion Scale (CDS) developed by Briere, ( 2000 ), the Psychological Well-Being Scale (PWB) developed by Ryff, ( 1989 ), and students’ Grade Point Average (GPA) as a measure of academic achievement. 3.4.1 Cognitive Distortion Scale (CDS) The Cognitive Distortion Scale (CDS), developed by Briere, ( 2000 ), to assess maladaptive cognitive patterns that may contribute to emotional and behavioral difficulties. It consisted of 40 items along with five subscales self-blame, helplessness, hopelessness, Worthlessness and, Expectations of Negative Outcomes. For this study, a 20-items adapted version was employed, covering five subscales. Self-Blame (Items 1 to 4), Hopelessness (Items 5 to 8), Helplessness (Items 9 to 12), Worthlessness (Items 13 to 16), Expectation of Negative Outcomes (Items 17 to 20). All items were rated on a 5-point Likert scale ranging from 1 = Strongly Disagree (SDA) to 5 = Strongly Agree (SA). Some modifications (items removed or altered) were made to ensure clarity for university students in the Pakistani context. 3.4.2 Psychological Well-Being Scale (PWB) The Psychological Well-Being Scale (PWB) developed by Ryff, ( 1989 ), to measure positive psychological functioning. It consisted of 42 items along with six subscales. Autonomy, environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance. For this study, a 24-items adapted version was employed, covering six subscales. Autonomy (Items 1 to 4), Environmental Mastery (Items 5 to 8), Personal Growth (Items 9 to 12), Positive Relations with Others (Items 13 to 16), Purpose in Life (Items 17 to 20), Self-Acceptance (Items 21 to 24). All items were rated on the 5-point Likert scale 1 = Strongly Disagree (SDA) to 5 = Strongly Agree (SA ). Some modifications (items removed or altered) were made to ensure clarity for university students in the Pakistani context. 3.4.3 Academic Achievement (GPA) Students’ self-reported Grade Point Average (GPA) was recorded as an indicator of academic achievement. GPA provides a standardized measure of academic performance and is widely used in educational research. Although self-reported, prior studies suggest that students’ GPA reports are generally accurate and reliable according to (Kuncel et al., 2005 ). 3.5 Data Collection Data collection is one of the most critical stages of any research project. Following the successful completion of the pilot study and finalization of the questionnaire, the main data collection phase commenced. The validated instrument was distributed to university students, who completed it either in person or via an online Google Form. During both modes of administration, the purpose and significance of the study were clearly explained to the participants to ensure informed responses. Despite the procedural steps involved, the study achieved a high response rate. A total of 347 students from various departments of university of Sargodha participated in the data collection process, representing diverse academic programs and backgrounds. This process ensured the inclusion of a broad and representative sample aligned with the study’s objectives. 3.6 Data Analysis Data analysis was implemented utilizing two software tools i.e., SmartPLS and SPSS. Data were analyzed using both descriptive and inferential statistics. Descriptive statistics (frequencies, means, and standard deviations) were used to summarize the demographic characteristics and overall levels of study variables. Inferential statistics, including Pearson’s correlation were applied to examine the relationships among cognitive distortions, psychological well-being, and academic achievement. SmartPLS was used to test the mediating role of psychological well-being and to examine the impact among cognitive distortions, psychological well-being, and academic achievement. Having received a total of 347 valid and usable responses from university students enrolled mainly in sciences and social science programs in University of Sargodha, which were used to make conclusions about the connections between the variables being studied i.e., Cognitive Distortions (CDS) as the independent variable, Psychological Well-Being (PWB) as the mediator and Academic Achievement as the dependent variable. 4. Data Analysis 4.1 Introduction For this study, data analysis was implemented utilizing two software tools i.e., SmartPLS and SPSS. Having received a total of 347 valid and usable responses from university students enrolled mainly in sciences and social science programs in University of Sargodha, which were used to make conclusions about the connections between the variables being studied i.e., Cognitive Distortions (CDS) as the independent variable, Psychological Well-Being (PWB) as the mediator and Academic Achievement as the dependent variable. This chapter is divided in into four major sections, first of those is demographic profile, second section bears the details on measures of central tendency and bivariate correlations, third section is about evaluation of measurement model in terms of reliability and validity, while fourth and last section deal with hypothesis testing using SEM and group comparison method. 4.2 Demographic Analysis Demographic analysis deal with the identification of study population as the different groups within the population, such as gender as grouped in males and females. This analysis tells the researcher how much these groups differ from each other based on their grouping (Bougie & Sekaran, 2019 ). The details on each demography is given in Table 4.1, which reveals that majority of the population was Female (n = 196, 56.5%), who were enrolled in Undergraduate study programs (n = 214, 61.7%) in Social Sciences faculty (n = 183, 52.7%), they were Day Scholars (n = 204, 58.8%), hailed from Urban areas (n = 183, 52.7%), they had a CGPA range of 3.1–3.5 (n = 148, 42.7%) while they were studying the Chemistry (n = 41, 11.8%), they had progressed to 3rd Semester (n = 65, 18.7%). Table 3 Demographic Profile Characteristic Frequency Percentage Gender Male 151 43.5% Female 196 56.5% Faculty Social Science 183 52.7% Sciences 164 47.3% Study Program Undergraduate 214 61.7% Graduate 133 38.3% Accommodation Hosteller 143 41.2% Day Scholar 204 58.8% Residential Area Urban 189 54.5% Rural 158 45.5% CGPA 2.00–2.50 0 0% 2.51–3.00 88 25.4% 3.10–3.50 148 42.7% 3.51–4.0 111 32.0% Department Education 50 14.4% Psychology 36 10.4% Social work 40 11.5% Economics 37 10.7% Statistics 35 10.1% Chemistry 41 11.8% Botany 37 10.7% Physics 29 8.4% Math 22 6.3% Pakistan Studies 20 5.8% Semester 1st 52 15.0% 2nd 60 17.3% 3rd 65 18.7% 4th 41 11.8% 5th 41 11.8% 6th 34 9.8% 7th 35 10.1% 8th 19 5.5% n = 347 4.3 Descriptive Statistics and Correlations This analysis mainly covers evaluation of central tendency i.e., mean (M) and standard deviation (SD), along with bivariate correlations among the study variables. Furthermore, analyzing M and SD for the study variables provides useful clues on how any respondent has replied to the details in the questionnaires and how useful the scales and corresponding entries are to draw on the related theories (Bougie & Sekaran, 2019 ), see Table 4 for details on the central tendency of the study variables. Table 4 Mean, SD and Correlations Variable Minimum Maximum Mean SD CDS PWB CGPA CDS 1.00 5.00 3.44 1.09 1 − .282 ** − .304 ** PWB 1.00 5.00 3.34 1.12 − .282 ** 1 .307 ** CGPA 2.00 4.00 3.07 0.76 − .304 ** .307 ** 1 n = 347, SD = standard deviation, **p < .01 , CDS = Cognitive Distortions, PWB = Psychological Well-Being (PWB), CGPA = Academic Achievement Correlations among research variables give hints on how good the variables are related to one another i.e., what linear association ‘if any’ is present stuck among the variables (Field, 2024). To ascertain in what way the variables are correlated to one another, correlations among the study variables were calculated which can be reviewed in Table 4 . CDS had a negative and significant correlation with PWB (r = − .282, p < .05), similarly CDS had a negative and significant correlation with CGPA (r = − .304, p < .05). While PWB had a positive and significant correlation with CGPA (r = .307, p < .05). These correlation were ranging from mildly moderate correlations on the strength scale (Cohen, 1988 ). 4.4 Measurement Model Assessment In the context of Partial Least Squares Structural Equation Modeling (PLS-SEM), the measurement model, also referred to as the outer model, plays a crucial role in assessing the reliability and validity of the constructs under investigation. Pertinent to mention that this study utilized SmartPLS software because the dependent variable was categorical which required non-parametric method of analysis (Becker et al., 2023 ). This evaluation involves several key measures, including indicator reliability, composite reliability (CR), average variance extracted (AVE) which collectively contribute to establishing the convergent validity of the constructs, discriminant validity with the help of HTMT ratios, and predictive relevance of the measurement model (Hair et al., 2019 ). 4.4.1 Indicator Reliability / Factor Loading This was the first criterion examined was the reliability of the measurement model. Individual indicator reliability is an aspect considered in evaluating the measurement model. It involves examining the factor loadings of the indicators, which represent the strength of the relationship between the indicators and the construct. The measure is said to be reliable when its factor loadings (FL) are above 0.50 (Hair et al., 2019 ). All of the study scales factor loadings were above 0.50, (see Table 5 ). Table 5 Factor Loadings Indicator Cognitive Distortions Psychological Well -Being Academic Achievement CDS1 0.815 - - CDS10 0.747 - - CDS11 0.740 - - CDS12 0.744 - - CDS13 0.746 - - CDS14 0.689 - - CDS15 0.774 - - CDS16 0.789 - - CDS17 0.829 - - CDS18 0.767 - - CDS19 0.758 - - CDS2 0.748 - - CDS20 0.815 - - CDS3 0.825 - - CDS4 0.784 - - CDS5 0.804 - - CDS6 0.795 - - CDS7 0.813 - - CDS8 0.545 - - CDS9 0.860 - - PWB1 - 0.787 - PWB10 - 0.783 - PWB11 - 0.667 - PWB12 - 0.775 - PWB13 - 0.739 - PWB14 - 0.733 - PWB15 - 0.818 - PWB16 - 0.852 - PWB17 - 0.800 - PWB18 - 0.697 - PWB19 - 0.818 - PWB2 - 0.839 - PWB20 - 0.744 - PWB21 - 0.774 - PWB22 - 0.843 - PWB23 - 0.730 - PWB24 - 0.753 - PWB3 - 0.790 - PWB4 - 0.871 - PWB5 - 0.746 - PWB6 - 0.737 - PWB7 - 0.727 - PWB8 - 0.756 - PWB9 - 0.789 - CGPA - - 1.000 4.4.2 Internal Consistency As a second criterion of the measurement model assessment, Cronbach’s alpha is the measure of internal consistency of a measuring scale. In what way closely related a set of items are as a group. The threshold for reliability of the measure is > 0.7 scores of the Cronbach’s alpha (CA) for each of the measure (Hair et al., 2019 ), our estimations met this criteria very well for all studied constructs (see Table 6 ). 4.4.3 Composite Reliability (CR) The third criterion involved assessing the internal consistency and stability of the construct by analyzing measures such as composite reliability (CR). Due to the underestimation problem with Cronbach’s α there is a need of greater estimation of true reliability (Garson, 2012 ). As shown in Table 6 , study’s measurement model met the acceptable values of CR i.e., > 0.7 for confirmatory purposes (Hair et al., 2019 ). 4.4.4 Convergent Validity The fourth criterion evaluated was the convergent validity of the measurement model. It describes how a measure is relatable or different from the items of the same variable. Degree to which an item is positively associated with the other items of the same variable is convergent validity. Average variance extracted (AVE) was used to assess convergent validity. For convergent validity, the AVE (average variance extracted) should be greater than 0.5 (Garson, 2012 ). The AVE values in Table 4.4 are well above the defined criteria to prove the convergent validity of the constructs. 4.4.5 Predictive Relevance (H 2 ) The measurement model employed in this study ensured its predictive relevance through the assessment of its predictive validity. Predictive validity refers to the model's ability to accurately predict future outcomes or behaviors based on the measured variables or the indicators used for data collection. To assess predictive validity, the values of communality (H 2 ) were calculated for each block in the measurement model, see (Table 6 ). The consistently positive H 2 or > 0 values throughout all blocks effectively bolsters the predictive significance inherent within the measurement model (Sarstedt et al., 2019 ). Table 6 Reliability, Validity and Quality of the Measurement Model Variable CA CR AVE H 2 Academic Achievement 1.000 1.000 1.000 1.000 Cognitive Distortions 0.964 0.967 0.596 0.554 Psychological Well-Being 0.971 0.973 0.601 0.568 Discriminant Validity It’s defined as the degree to which a particular latent construct is dissimilar with other latent variables in any given measurement model (Duarte et al., 2010 ). We concluded discriminant validity-based HTMT < 0.85 (Hair et al., 2019 ), as shown in Table 4.5 that all of HTMT values are below 0.85, so it was concluded that study constructs had a sufficient discriminant validity. Table 7 Discriminant Validity – HTMT Ratios Variable Academic Achievement Cognitive Distortions Psychological Well-Being Academic Achievement - - - Cognitive Distortions 0.310 - - Psychological Well-Being 0.313 0.291 - Another way to ensure discriminant validity is to evaluate the squared AVE values of the study variable in comparison with the correlations among study variables. This is known as Forenell-Larcker criteria (Fornell & Larcker, 1981 ). The Forenell-Larcker values are required to greater than all of the corresponding correlations to establish the discriminant validity. As shown in Table 8 that this study met this criteria as well to fulfill the discriminant validity requirement. Table 8 Discriminant Validity – Fornell-Larcker Criterion Variable Academic Achievement Cognitive Distortions Psychological Well-Being Academic Achievement 1.000 - - Cognitive Distortions -0.309 0.772 - Psychological Well-Being 0.311 -0.294 0.775 Similarly, another criteria to establish the discriminant validity is to examine the cross loadings, generally a difference of 0.2, between principal variable and all other variables in the model, is the minimum requirement to establish the discriminant validity (Hair et al., 2019 ). As shown in Table 9 that none of the cross loadings had any difference less than 0.2, so again the assumption of discriminant validity was fulfilled. Table 9 Discriminant Validity – Cross Loadings Indicator Cognitive Distortions Psychological Well-Being Academic Achievement CDS1 0.815 -0.237 -0.220 CDS2 0.748 -0.219 -0.199 CDS3 0.825 -0.254 -0.225 CDS4 0.784 -0.264 -0.264 CDS5 0.804 -0.247 -0.266 CDS6 0.795 -0.213 -0.230 CDS7 0.813 -0.192 -0.247 CDS8 0.545 -0.222 -0.238 CDS9 0.860 -0.205 -0.236 CDS10 0.747 -0.254 -0.240 CDS11 0.740 -0.199 -0.188 CDS12 0.744 -0.195 -0.164 CDS13 0.746 -0.155 -0.198 CDS14 0.689 -0.159 -0.170 CDS15 0.774 -0.168 -0.234 CDS16 0.789 -0.235 -0.256 CDS17 0.829 -0.269 -0.247 CDS18 0.767 -0.307 -0.281 CDS19 0.758 -0.249 -0.291 CDS20 0.815 -0.200 -0.285 PWB1 -0.171 0.787 0.197 PWB10 -0.291 0.783 0.257 PWB11 -0.240 0.667 0.163 PWB12 -0.220 0.775 0.277 PWB13 -0.172 0.739 0.203 PWB14 -0.146 0.733 0.207 PWB15 -0.253 0.818 0.231 PWB16 -0.241 0.852 0.245 PWB17 -0.287 0.800 0.262 PWB18 -0.257 0.697 0.190 PWB19 -0.304 0.818 0.246 PWB2 -0.205 0.839 0.250 PWB20 -0.329 0.744 0.258 PWB21 -0.268 0.774 0.250 PWB22 -0.191 0.843 0.267 PWB23 -0.212 0.730 0.207 PWB24 -0.258 0.753 0.258 PWB3 -0.152 0.790 0.254 PWB4 -0.216 0.871 0.292 PWB5 -0.163 0.746 0.220 PWB6 -0.161 0.737 0.209 PWB7 -0.162 0.727 0.234 PWB8 -0.234 0.756 0.289 PWB9 -0.200 0.789 0.257 GPA_CGPA -0.309 0.311 1.000 4.5 Structural Model Assessment Upon the completion of the measurement model assessment in terms of establishing validity and reliability, the assessment of the structural model also termed as the inner model, was initiated. This step aimed to explore the interconnections among the constructs of both exogenous and endogenous variables within the scope of partial least squares structural equation modeling (PLS-SEM). 4.5.1 Direct Effect Hypotheses Their testing was carried out by bootstrapping technique by taking 2000 subsamples, results were determined by using path coefficients, p-values and t-statistics. H01 There is no significant impact of cognitive distortions on students' psychological well-being. H1A There is a negative significant impact of cognitive distortions on students' psychological well-being. CDS had a negative and significant impact on PWB (B = − .294, t = 6.641, p < .001). Which meant that H1A was supported while H01 was rejected, see Table 4.8 and Fig. 2 as well. H02 There is no significant impact of cognitive distortions on students' academic achievement. H2A There is a negative significant impact of cognitive distortions on students' academic achievement. Similarly, CDS had a negative and significant impact on CGPA (B = − .238, t = 5.037, p < .001). Which meant that H2A was supported while H02 was rejected, see Table 4.8 and Fig. 2 too. Table 10 Direct Effect Hypotheses Results Path Estimate T Value P Value Cognitive Distortions ◊ Psychological Well-Being -0.294 6.641 0.000 Cognitive Distortions ◊ Academic Achievement -0.238 5.037 0.000 Psychological Well-Being ◊ Academic Achievement 0.241 4.761 0.000 4.5.2 Mediation Analysis H03 Psychological well-being does not mediate the relationship between cognitive distortions and academic achievement. H3A Psychological well-being mediates the relationship between cognitive distortions and academic achievement. As shown in Table 4.9 that CDS had a negative and significant impact on CGPA via PWB (B = − .071, t = 3.660, p < .001). So, PWB mediation was occurred, which provided an obvious support to the H3A instead of H03. The variance accounted for (VAF) revealed that only 22.98% of the impact of CDS on CGPA could be upheld when PWB was introduced as mediator (indirect effect / total effect = − .071 / − .238 + − .071 = .2298). In other words 77.02% of the negative impact of CDS on CGPA was direct which was the major one, since the VAF was between 20–80% thus it was a partial mediation, this provided further support to H3A. Table 11 Mediation Effect Hypothesis Path Estimate T Value P Value CDS ◊ PWB ◊ Academic Achievement -0.071 3.660 0.000 4.5.3 Effect Size or F 2 The F 2 effect size provides an estimation of the variation in the R 2 value when a particular exogenous variable is removed from the research model. It helps to quantify the effect of a specific predictor latent variable on a particular endogenous variable. Table 12 presents the F 2 effect sizes in the model, showcasing that all the effect sizes were small (Cohen, 1988 ). The F 2 effect size is a valuable metric as it allows researchers to gauge the magnitude of the impact that each exogenous variable has on the endogenous variable of interest. Table 12 Effect Size – F 2 Predictor Academic Achievement Psychological Well-Being Academic Achievement - - Cognitive Distortions 0.061 0.095 Psychological Well-Being 0.062 - 4.5.4 Coefficient of Determination or R 2 In order to assess the predictive accuracy of the structural model, the coefficient of determination (R 2 ) was evaluated. R 2 measures the squared correlation between the predicted and actual values of the specific endogenous constructs. The R 2 values in the structural model indicate the degree to which the exogenous variables collectively contribute to the variation in the endogenous variables. Higher R 2 values suggest a stronger influence of the exogenous variables on the endogenous constructs (Hair et al., 2019 ). As shown in Table 4.13 that all CDS and PWB jointly explained a variance of 14.9% in CGPA, while CDS explained 8.9% variance in PWB. 4.5.5 Predictive Relevance or Q 2 By executing this blindfolding technique, cross-validated redundancy Q 2 values were obtained for all endogenous variables. The Q 2 value functions as a metric for assessing the predictive pertinence of the structural model. It indicates how well the model can predict the endogenous variables based on the exogenous variables. A positive Q 2 value suggests that the model has predictive relevance and can accurately predict the outcomes of interest, so was the case for this study as all Q 2 value for outcome variable was positive indeed (Table 13 ). By considering the Q 2 values, the researchers can cultivate an appreciation for the reliability and dependability of the structural model's predictions regarding the endogenous variables (Tenenhaus et al., 2005 ). 4.5.6 Collinearity Issues To ascertain the integrity and dependability of the structural model, the examination of VIF values was undertaken to estimate the potential presence of perturbing collinearity, given that elevated VIF values can signal collinearity concerns (Kock, 2015 ). The VIF values obtained were well within an acceptable threshold, specifically < 3.3, as clearly presented in Table 11 . This outcome affirms that neither path contamination nor collinearity had influence on the model, thereby underlining its elevated level of quality. Table 13 Quality Features of Structural Model Outcome Variable R Square (R²) Q Square (Q²) VIF Academic Achievement 0.149 0.135 1.095 Psychological Well-Being 0.087 0.048 1.000 Table 14 Hypotheses results No. Statement Status H 0 1 There is no significant impact of cognitive distortions on students' psychological well-being. Rejected H1A There is a negative significant impact of cognitive distortions on students' psychological well-being. Supported H 0 2 There is no significant impact of cognitive distortions on students' academic achievement. Rejected H2A There is a negative significant impact of cognitive distortions on students' academic achievement. Supported H 0 3 Psychological well-being doesn’t mediate the relationship between cognitive distortions and academic achievement. Rejected H3A Psychological well-being mediates the relationship between cognitive distortions and academic achievement. Supported Discussion The major purpose of the study was to investigate the impact of cognitive distortions on the academic achievement of university students through psychological well-being and comparing the influence of demographic factors on these variables. The results revealed that cognitive distortions significantly and negatively influenced students’ psychological well-being (H1). This implies that students who experience distorted patterns of thinking (e.g., catastrophizing, overgeneralization, or self-blame) are more likely to report lower levels of mental health and life satisfaction (Hossain, 2025 ). This result is consistent with cognitive-behavioral theory (Beck, 1979 ), which posits that maladaptive thinking patterns contribute to psychological distress. Similar findings were reported by Yurica & DiTomasso, ( 2005 ), who also emphasized that cognitive distortions are closely related to depression, anxiety, and poor psychological resilience. Recent research (Kausar et al., 2025 ; Bingöl & Batık, 2018 ; Garcia et al., 2014 ; Shorey et al., 2012 ) also indicates the statement that maladaptive thinking patterns reduce the capacity of individuals to feel a sense of self-acceptance, positive relationships, and autonomy. The finding also revealed that the negative significant effect of cognitive distortions on the academic achievement of students was negative (H2). This hypothesis was also confirmed by the results wherein cognitive distortions are a major cause of poor academic performance. The students possessing more distorted thinking recorded lower CGPA, which means that bad cognitive patterns disrupt their concentration, motivation and good learning strategies. This finding aligns with previous research suggesting that negative thought patterns undermine self-efficacy, academic engagement, and overall performance (Putwain & Daly, 2014 ). Therefore, reducing cognitive distortions may directly enhance academic outcomes. Similarly, research by (Ameer et al., 2023 ) found that cognitive errors predict lower academic outcomes in Pakistani university students, as they create barriers to effective time management and problem-solving. These results are also consistent with Lega & Ellis, ( 2001 ) rational emotive behavior theory, which suggests that irrational beliefs directly impair learning efficiency. The results indicated that psychological well-being mediates the relationship between cognitive distortions and academic achievement (H3). The mediation analysis confirmed this hypothesis, showing that psychological well-being partially mediates the link between cognitive distortions and academic performance. This suggests that cognitive distortions not only directly affect academic success but also indirectly influence it by lowering students' well-being. Although the effect sizes were small, the model demonstrated meaningful explanatory power. This supports the findings of Shengyao et al., ( 2024 ), who reported that well-being significantly influences learning engagement and GPA among Chinese undergraduates. Likewise, Srem-Sai et al., ( 2025 ) demonstrated that well-being serves as a protective factor, helping students manage academic challenges more effectively despite the presence of cognitive difficulties These findings are consistent with the broaden-and-build theory of positive emotions (Fredrickson, 2001 ), which emphasizes that psychological well-being expands students’ coping resources, leading to better academic performance. Therefore, the adverse impacts of distorted thinking can be counteracted by interventions that could improve psychological well-being. Conclusion Findings show that students reported a moderate level of Cognitive Distortions and Psychological Well-Being. Moreover, correlations reveal that Cognitive Distortions were negatively associated with Psychological Well-Being and Academic Achievement while Psychological Well-Being positively correlated with Academic Achievement. These results suggest that higher distortions are linked with poorer well-being and lower academic achievement, whereas greater well-being enhances performance. Moreover, Cognitive Distortions had a significant negative effect on Psychological Well-Being and Academic Achievement. Psychological Well-Being had a significant positive effect on Academic Achievement. These findings confirm that distortions harm both well-being and academic outcomes, while strong well-being fosters higher achievement. The results also demonstrated that psychological well-being plays a partial mediating role in the relationship between cognitive distortions and academic achievement. 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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-9413489","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625613614,"identity":"6cff3c52-d110-42be-8c80-79ff027913da","order_by":0,"name":"Mah Noor Haider","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Mah","middleName":"Noor","lastName":"Haider","suffix":""},{"id":625613615,"identity":"c8a6bfa5-17a4-491a-975a-70329d08a3af","order_by":1,"name":"Dr. Nazir Haider Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYJCCAyDExpDAwPABImAAxAnEaWGcQawWiC6gImYeYrSYsx9/eLiC4Y48H3vysc+2bTb2DOzN2yQYd6Th1GLZk5Bw8AzDM8M2nmfJs3Pb0hIbeI6VSTCeycGpxeBAwoGDDQyHGdskcoyZc9sOJzBI5JhJMLZV4NZy/mEDSIt9m0T+Z2bLtsP2DPJvCGi5kcwA0pIItIWZmbHtMGODBA9ICx6H3XgG1GJwOBnoF2PGnnNpiW08acUWiWdwe9/gfPrjjw0Vh23ntyc/ZvhRZmPPz354442PO5JxaoFqRGKzgYjEBgI6MAEj6VpGwSgYBaNg+AIAIcZUyU9blEQAAAAASUVORK5CYII=","orcid":"","institution":"University of Sargodha","correspondingAuthor":true,"prefix":"Dr.","firstName":"Nazir","middleName":"Haider","lastName":"Shah","suffix":""},{"id":625613616,"identity":"29c4d654-9000-4d35-b16c-552066c3d9cd","order_by":2,"name":"Dr. Ghulam Muhammad","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"Dr.","firstName":"Ghulam","middleName":"","lastName":"Muhammad","suffix":""}],"badges":[],"createdAt":"2026-04-14 09:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9413489/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9413489/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108411097,"identity":"e5af8d09-1c92-49d8-9e4f-200c23b927d8","added_by":"auto","created_at":"2026-05-04 10:20:19","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":484413,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9413489/v1/1397642dfa74a1f1c1f7c38b.png"},{"id":108493818,"identity":"f0e3dd6e-7d5f-4595-afa3-0875391d813b","added_by":"auto","created_at":"2026-05-05 10:01:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural / Path Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9413489/v1/b9362193d7aa62a0f7ac065a.png"},{"id":108803962,"identity":"5130c235-c894-4e70-92a6-0cbbba5b676d","added_by":"auto","created_at":"2026-05-08 15:12:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1371899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9413489/v1/502359db-49ff-4a7e-95e8-3258a2f64df0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Distortions as Hidden Barriers to Student Success: Evidence from University Students’ Psychological Well-being and Academic Achievement","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHigher education is characterized as a period of change in a student\u0026rsquo;s life involving both personal, academic, and social stressors (L\u0026oacute;pez, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It brings both intellectual development and autonomy for many students but also increased stress, pressure and vulnerability (Conley et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Academic pressure, social change, and the need to excel academically may all contribute to psychological stress, which can take numerous forms, including anxiety, depression, and stress (Beiter et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These psychological problems can significantly affect the academic and the health of the student (Eisenberg et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The cognitive distortions have become one of the most popular psychological factors influencing the emotional dysfunction and academic problems of individuals (Buğa \u0026amp; Kaya, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).Cognitive distortions are irrational, biased patterns of thinking that negatively influence how individuals perceive themselves, others, and life events. These distorted thoughts tend to exaggerate negative experiences, minimize positive outcomes, and contribute to maladaptive emotions and behaviors (Dozois \u0026amp; Beck, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Common distortions include all-or-nothing thinking, overgeneralization, catastrophizing, and personalization. For example, a student who fails one test may irrationally conclude that they are incapable of academic success overall (Abirami \u0026amp; Murugesan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Such distorted patterns often arise automatically and have been linked to anxiety, depression, and academic stress among university students (Tauscher et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies highlight that cognitive distortions are extremely common in students with academic stress, and the results indicate that over 60% of undergraduates often commit academic distortions, including mental filtering and should-statements (Kawamoto et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These forms of errors in thinking hinder academic motivations, self-efficacy, and reduce academic performance (Parikh et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Besides, cognitive distortions interfere with emotional regulation, make a person more susceptible to psychological distress, and perpetuate procrastination and avoidance cycles (Deperrois \u0026amp; Combalbert, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Treating the distorted thinking patterns is thus at the center of enhancing resilience, good coping, and academic persistence (Shengyao et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePsychological well-being (PWB) is a general state of emotional, cognitive, and social functioning of an individual, which involves positive mental health, life satisfaction, and resilience (Ryff, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). PWB, as opposed to no indication of mental illness, is the indicator of the capacity to prosper, grow personally, and be balanced in the presence of stressors (Suldo \u0026amp; Shaffer, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). According to Ryff, the multidimensional model, there are six central dimensions of PWB, including autonomy, environmental mastery, personal growth, positive relations, purpose in life, or self-acceptance. These factors are especially important to students who have to overcome their academic needs, social adaptation, and identity development.\u003c/p\u003e \u003cp\u003eIt has been established that students whose PWB is high report more academic resilience, higher motivation, and positive coping mechanisms than students with lower well-being (Abdullah et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, individuals with low PWB have a higher probability of depression, anxiety, and disengagement, which impedes personal and academic performance (Li et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The recent longitudinal studies prove that PWB is an important predictor of GPA, attendance at classes, and participation in co-curricular activities, which is why this resource can play an essential role in academic success (Tape et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Besides, mindfulness and resilience training have been identified to positively influence the well-being of the students and indirectly their academic performance (Yuan \u0026amp; Hu, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, PWB is a stress-protective factor and also an academic success factor.\u003c/p\u003e \u003cp\u003eAcademic achievement represents the measurable outcomes of students\u0026rsquo; learning, including GPA, test scores, and other performance indicators (York et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It remains one of the primary markers of success in higher education, directly influencing future career opportunities, social mobility, and psychological adjustment (Wang \u0026amp; Yang, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang \u0026amp; Huang, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Academic performance is shaped by multiple internal factors such as self-regulation, cognitive ability, and motivation, as well as external factors like family support, financial stress, and institutional resources (Manwa \u0026amp; Sithole, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Shi \u0026amp; Qu, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent findings emphasize that that cognitive and emotional factors have a greater impact on achievement. As an example, the procrastination, time mismanagement, and decreased persistence can be observed in the students with maladaptive cognitive distortion, which leads to the decreased academic performance (Louren\u0026ccedil;o \u0026amp; Paiva, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, rational thinking and adaptive thinking styles are related to better persistence and success (Coşkun, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Also, students whose mental health is good experience more engagement and resilience against academic stress, and are better equipped to face academic stress, thus attaining better academic performance (Mohamed et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In that regard, academic success is not merely a sign of intellectual potential, but also represents psychological well-being and cognitive tendencies of students.\u003c/p\u003e"},{"header":"2. Review of Literature and Hypothesis Development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cognitive Distortion\u003c/h2\u003e \u003cp\u003eCognitive distortions are regular and habitual ways of thinking that are biased or irrational and distort the reality and support negative feelings and behavior (Ryum \u0026amp; Nikolaos Kazantzis, 2024). All of them are all-or-nothing thinking, catastrophizing, overgeneralization, and personalization that affect the ability of students to overcome the challenges (Hershey H Friedman, 2023). Recent literature proves that cognitive distortions occur among university students, especially when they are under academic pressure, in the competitive atmosphere, and face the problem of identity (Buğa \u0026amp; Kaya, 2022) These skewed perceptions contribute to the development of dysfunctional coping mechanisms like avoidance and procrastination that deteriorate psychological health and educational activities (Sirois, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, the studies indicate that there is a positive correlation between high levels of cognitive distortions and anxiety, depressive symptoms, and low resilience among higher education students (Sapmaz, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cognitive-behavioral training and mindfulness interventions have been demonstrated to help decrease these distortions significantly, leading to greater control over emotions and functional academic performance (McBride \u0026amp; Greeson, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, the knowledge of cognitive distortions can be instrumental in the treatment of psychological as well as academic issues in university students.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cognitive Distortion and Academic Achievement\u003c/h2\u003e \u003cp\u003eCognitive distortions negatively influence students\u0026rsquo; academic performance by lowering motivation, diminishing self-efficacy, and impairing self-regulation skills (Sirois, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). When students adopt maladaptive beliefs such as \u0026ldquo;I will always fail,\u0026rdquo; they are less likely to engage in effective study behaviors, which reduces persistence and achievement (Pekin \u0026amp; G\u0026uuml;me, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Recent evidence suggests that students with higher distortion scores report significantly lower GPA and poorer study habits (Aljaffer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, irrational thought patterns increase test anxiety and academic stress, which further deteriorate academic performance (Al-Tameemi et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In contrast, rational and adaptive thinking styles are linked to greater persistence, improved academic engagement, and better grades (Grimm \u0026amp; Richter, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These findings suggest that cognitive distortions play a central role in shaping academic achievement outcomes, especially in competitive higher education contexts.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1: Cognitive distortion is a positive predictor of Academic Achievement.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOn the basis of the above literature, it is proposed that cognitive distortions significantly influence students\u0026rsquo; academic performance.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Cognitive Distortion and Psychological Well-being\u003c/h2\u003e \u003cp\u003eCognitive distortions are closely related to the poor psychological well-being, since they support maladaptive emotions, including feelings of hopelessness, worthlessness, and anxiety (Ryff \u0026amp; Singer, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). University students who practice high rates of cognitive distortions are less satisfied with their lives, have a reduced capacity to feel resilience, and experience depression and stress levels (Charan et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). As an illustration, persistent negative moods have been associated with overgeneralization and catastrophizing, which consequently lowers the overall well-being (Raes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent research proves that distorted thought is a predictor of emotional dysregulation and the withdrawal, thus undermining the mental health of students further (Keskiner et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Conversely, distorted cognition, which is addressed through interventions (CBT and mindfulness), has been shown to improve self-acceptance, emotional stability, and well-being among students (Fattah Katamjani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, it is necessary that cognitive distortions are minimized so that overall psychological functioning of students can be improved.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: Cognitive distortion is a significant positive predictor of psychological well-being.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOn the basis of the above literature, it is proposed that cognitive distortions significantly affect students\u0026rsquo; mental health and overall well-being.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Psychological Well-being and Academic Achievement\u003c/h2\u003e \u003cp\u003ePsychological well-being (PWB) plays a pivotal role in shaping students\u0026rsquo; academic outcomes. Better well-being is associated with increased academic motivation, resilience, and self-regulation, which lead to higher performance among students (Sarzhanova \u0026amp; Nurgabdeshov, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Extensive research indicates that students with high PWB have higher GPA, attend classes more, and engage more in co-curricular activities than their low well-being counterparts (Pervaiz et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, low academic persistence and engagement occur due to poor well-being, stress, anxiety, and low self-esteem (Chu et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to the recent interventions, PWB can be enhanced with the help of mindfulness and resilience training that can greatly enhance the academic functioning of students (Oh et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results highlight the fact that PWB has a protective effect on academic performance.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: Psychological well-being is a positive predictor of academic achievement.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOn the basis of the above literature, it is proposed that well-being significantly contributes to students\u0026rsquo; academic success.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Cognitive Distortion and Academic Achievement: Mediating Role of Psychological Well-being\u003c/h2\u003e \u003cp\u003eRecent studies indicate that academic performance is mediated by psychological well-being between cognitive distortions and academic performance. The distorted thinking decreases the PWB as it leads to the development of stress, hopelessness and anxiety, the latter, in turn, decreases the academic motivation and persistence (Buğa \u0026amp; Kaya, 2022). As an example, catastrophizing students complain not only of worse well-being but also of poorer GPA (Sinval et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Likewise, students who have stronger resilience and self-acceptance as important dimensions of PWB can overcome the adverse impact of cognitive distortions and maintain academic engagement (Craig et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). It has been confirmed using longitudinal studies that PWB is a partial explanation of the effects of cognitive distortions on educational outcomes (Şahin \u0026amp; T\u0026uuml;rk, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cognitive distortions and well-being-reducing interventions have been observed to improve academic performance simultaneously (Nashtban et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, PWB is an intermediate that connects the maladaptive thinking and educational achievement.\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4: Psychological well-being serially mediates the relationship between cognitive distortion and academic achievement.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eOn the basis of the above literature, it is proposed that well-being explains how distorted thinking influences academic performance.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThe research design was a quantitative one. A survey instrument was designed using a structured questionnaire, which served as the primary tool for data collection. This approach was chosen because it could be measured objectively and statistically analyze the correlation of cognitive distortions, psychological well-being and academic achievement. Such a design enables the researcher to test hypotheses, establish correlations, and evaluate the mediation effect of psychological well-being (Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Population\u003c/h2\u003e \u003cp\u003eThe target population in this study was undergraduate and graduate (11,833) students in the faculties of Sciences and Social Sciences in the University of Sargodha. The demographic profile of the respondents are diversified, which makes the study findings more generalizable.\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\u003eDistribution of the Study Population\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate Students\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGraduate Students\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,420\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,833\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Sampling Technique and Sample Size\u003c/h2\u003e \u003cp\u003eThe stratified random sampling technique was employed to guarantee the demographic variety in the selection of the participants as per the recommendation of (Cochran, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Lohr, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). They all assert that they are all represented in the sample which makes the results accurate and increases the generalizability of the results. A total sample of 347 students were selected. The sample was chosen based on the recommendation of (Cochran, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1977\u003c/span\u003e); Krejci and Morgan, 1970; Israel, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Creswell \u0026amp; Creswell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To them, the sample size is adequate to perform inferential statistics analysis, and also representativeness of the target population. In the table 3.2, the sample is presented:\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\u003eSampling Distribution (N\u0026thinsp;=\u0026thinsp;347)\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\u003eUniversity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. of Students\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSargodha University\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003cp\u003eSciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e347\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 \u003ch2\u003e3.4 Research Instruments\u003c/h2\u003e \u003cp\u003eThis study employed two standardized and adapted instruments along with students\u0026rsquo; self-reported academic records. The Cognitive Distortion Scale (CDS) developed by Briere, (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), the Psychological Well-Being Scale (PWB) developed by Ryff, (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and students\u0026rsquo; Grade Point Average (GPA) as a measure of academic achievement.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Cognitive Distortion Scale (CDS)\u003c/h2\u003e \u003cp\u003eThe Cognitive Distortion Scale (CDS), developed by Briere, (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), to assess maladaptive cognitive patterns that may contribute to emotional and behavioral difficulties. It consisted of 40 items along with five subscales self-blame, helplessness, hopelessness, Worthlessness and, Expectations of Negative Outcomes. For this study, a 20-items adapted version was employed, covering five subscales. Self-Blame (Items 1 to 4), Hopelessness (Items 5 to 8), Helplessness (Items 9 to 12), Worthlessness (Items 13 to 16), Expectation of Negative Outcomes (Items 17 to 20). All items were rated on a 5-point Likert scale ranging from 1\u0026thinsp;=\u0026thinsp;Strongly Disagree (SDA) to 5\u0026thinsp;=\u0026thinsp;Strongly Agree (SA). Some modifications (items removed or altered) were made to ensure clarity for university students in the Pakistani context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Psychological Well-Being Scale (PWB)\u003c/h2\u003e \u003cp\u003eThe Psychological Well-Being Scale (PWB) developed by Ryff, (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), to measure positive psychological functioning. It consisted of 42 items along with six subscales. Autonomy, environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance. For this study, a 24-items adapted version was employed, covering six subscales. Autonomy (Items 1 to 4), Environmental Mastery (Items 5 to 8), Personal Growth (Items 9 to 12), Positive Relations with Others (Items 13 to 16), Purpose in Life (Items 17 to 20), Self-Acceptance (Items 21 to 24). All items were rated on the 5-point Likert scale 1\u0026thinsp;=\u0026thinsp;Strongly Disagree (SDA) to 5\u0026thinsp;=\u0026thinsp;Strongly Agree (SA\u003cb\u003e).\u003c/b\u003e Some modifications (items removed or altered) were made to ensure clarity for university students in the Pakistani context.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Academic Achievement (GPA)\u003c/h2\u003e \u003cp\u003eStudents\u0026rsquo; self-reported Grade Point Average (GPA) was recorded as an indicator of academic achievement. GPA provides a standardized measure of academic performance and is widely used in educational research. Although self-reported, prior studies suggest that students\u0026rsquo; GPA reports are generally accurate and reliable according to (Kuncel et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Data Collection\u003c/h2\u003e \u003cp\u003eData collection is one of the most critical stages of any research project. Following the successful completion of the pilot study and finalization of the questionnaire, the main data collection phase commenced. The validated instrument was distributed to university students, who completed it either in person or via an online Google Form. During both modes of administration, the purpose and significance of the study were clearly explained to the participants to ensure informed responses. Despite the procedural steps involved, the study achieved a high response rate. A total of 347 students from various departments of university of Sargodha participated in the data collection process, representing diverse academic programs and backgrounds. This process ensured the inclusion of a broad and representative sample aligned with the study\u0026rsquo;s objectives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Data Analysis\u003c/h2\u003e \u003cp\u003eData analysis was implemented utilizing two software tools i.e., SmartPLS and SPSS. Data were analyzed using both descriptive and inferential statistics. Descriptive statistics (frequencies, means, and standard deviations) were used to summarize the demographic characteristics and overall levels of study variables. Inferential statistics, including Pearson\u0026rsquo;s correlation were applied to examine the relationships among cognitive distortions, psychological well-being, and academic achievement. SmartPLS was used to test the mediating role of psychological well-being and to examine the impact among cognitive distortions, psychological well-being, and academic achievement. Having received a total of 347 valid and usable responses from university students enrolled mainly in sciences and social science programs in University of Sargodha, which were used to make conclusions about the connections between the variables being studied i.e., Cognitive Distortions (CDS) as the independent variable, Psychological Well-Being (PWB) as the mediator and Academic Achievement as the dependent variable.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data Analysis","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Introduction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFor this study, data analysis was implemented utilizing two software tools i.e., SmartPLS and SPSS. Having received a total of 347 valid and usable responses from university students enrolled mainly in sciences and social science programs in University of Sargodha, which were used to make conclusions about the connections between the variables being studied i.e., Cognitive Distortions (CDS) as the independent variable, Psychological Well-Being (PWB) as the mediator and Academic Achievement as the dependent variable.\u003c/p\u003e \u003cp\u003eThis chapter is divided in into four major sections, first of those is demographic profile, second section bears the details on measures of central tendency and bivariate correlations, third section is about evaluation of measurement model in terms of reliability and validity, while fourth and last section deal with hypothesis testing using SEM and group comparison method.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Demographic Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDemographic analysis deal with the identification of study population as the different groups within the population, such as gender as grouped in males and females. This analysis tells the researcher how much these groups differ from each other based on their grouping (Bougie \u0026amp; Sekaran, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe details on each demography is given in Table\u0026nbsp;4.1, which reveals that majority of the population was Female (n\u0026thinsp;=\u0026thinsp;196, 56.5%), who were enrolled in Undergraduate study programs (n\u0026thinsp;=\u0026thinsp;214, 61.7%) in Social Sciences faculty (n\u0026thinsp;=\u0026thinsp;183, 52.7%), they were Day Scholars (n\u0026thinsp;=\u0026thinsp;204, 58.8%), hailed from Urban areas (n\u0026thinsp;=\u0026thinsp;183, 52.7%), they had a CGPA range of 3.1\u0026ndash;3.5 (n\u0026thinsp;=\u0026thinsp;148, 42.7%) while they were studying the Chemistry (n\u0026thinsp;=\u0026thinsp;41, 11.8%), they had progressed to 3rd Semester (n\u0026thinsp;=\u0026thinsp;65, 18.7%).\u003c/p\u003e \u003c/div\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\u003eDemographic Profile\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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFaculty\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eStudy Program\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAccommodation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHosteller\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay Scholar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eResidential Area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCGPA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.00\u0026ndash;2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.51\u0026ndash;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.10\u0026ndash;3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.51\u0026ndash;4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDepartment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBotany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePakistan Studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSemester\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;347\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Descriptive Statistics and Correlations\u003c/h2\u003e \u003cp\u003eThis analysis mainly covers evaluation of central tendency i.e., mean (M) and standard deviation (SD), along with bivariate correlations among the study variables. Furthermore, analyzing M and SD for the study variables provides useful clues on how any respondent has replied to the details in the questionnaires and how useful the scales and corresponding entries are to draw on the related theories (Bougie \u0026amp; Sekaran, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for details on the central tendency of the study variables.\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, SD and Correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCDS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCGPA\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.282\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.304\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.282\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.307\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.304\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.307\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cem\u003en\u0026thinsp;=\u0026thinsp;347, SD\u0026thinsp;=\u0026thinsp;standard deviation, **p \u0026lt; .01\u003c/em\u003e, CDS\u0026thinsp;=\u0026thinsp;Cognitive Distortions, PWB\u0026thinsp;=\u0026thinsp;Psychological Well-Being (PWB), CGPA\u0026thinsp;=\u0026thinsp;Academic Achievement\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrelations among research variables give hints on how good the variables are related to one another i.e., what linear association \u0026lsquo;if any\u0026rsquo; is present stuck among the variables (Field, 2024). To ascertain in what way the variables are correlated to one another, correlations among the study variables were calculated which can be reviewed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eCDS had a negative and significant correlation with PWB (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.282, p \u0026lt; .05), similarly CDS had a negative and significant correlation with CGPA (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.304, p \u0026lt; .05). While PWB had a positive and significant correlation with CGPA (r = .307, p \u0026lt; .05). These correlation were ranging from mildly moderate correlations on the strength scale (Cohen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Measurement Model Assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the context of Partial Least Squares Structural Equation Modeling (PLS-SEM), the measurement model, also referred to as the outer model, plays a crucial role in assessing the reliability and validity of the constructs under investigation. Pertinent to mention that this study utilized SmartPLS software because the dependent variable was categorical which required non-parametric method of analysis (Becker et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This evaluation involves several key measures, including indicator reliability, composite reliability (CR), average variance extracted (AVE) which collectively contribute to establishing the convergent validity of the constructs, discriminant validity with the help of HTMT ratios, and predictive relevance of the measurement model (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Indicator Reliability / Factor Loading\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis was the first criterion examined was the reliability of the measurement model. Individual indicator reliability is an aspect considered in evaluating the measurement model. It involves examining the factor loadings of the indicators, which represent the strength of the relationship between the indicators and the construct. The measure is said to be reliable when its factor loadings (FL) are above 0.50 (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). All of the study scales factor loadings were above 0.50, (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \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\u003eFactor Loadings\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=\"left\" 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\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePsychological Well -Being\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Internal Consistency\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs a second criterion of the measurement model assessment, Cronbach\u0026rsquo;s alpha is the measure of internal consistency of a measuring scale. In what way closely related a set of items are as a group. The threshold for reliability of the measure is \u0026gt;\u0026thinsp;0.7 scores of the Cronbach\u0026rsquo;s alpha (CA) for each of the measure (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), our estimations met this criteria very well for all studied constructs (see Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e4.4.3 Composite Reliability (CR)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe third criterion involved assessing the internal consistency and stability of the construct by analyzing measures such as composite reliability (CR). Due to the underestimation problem with Cronbach\u0026rsquo;s α there is a need of greater estimation of true reliability (Garson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, study\u0026rsquo;s measurement model met the acceptable values of CR i.e., \u0026gt; 0.7 for confirmatory purposes (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e4.4.4 Convergent Validity\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe fourth criterion evaluated was the convergent validity of the measurement model. It describes how a measure is relatable or different from the items of the same variable. Degree to which an item is positively associated with the other items of the same variable is convergent validity. Average variance extracted (AVE) was used to assess convergent validity. For convergent validity, the AVE (average variance extracted) should be greater than 0.5 (Garson, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The AVE values in Table\u0026nbsp;4.4 are well above the defined criteria to prove the convergent validity of the constructs.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e4.4.5 Predictive Relevance (H\u003csup\u003e2\u003c/sup\u003e)\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe measurement model employed in this study ensured its predictive relevance through the assessment of its predictive validity. Predictive validity refers to the model's ability to accurately predict future outcomes or behaviors based on the measured variables or the indicators used for data collection. To assess predictive validity, the values of communality (H\u003csup\u003e2\u003c/sup\u003e) were calculated for each block in the measurement model, see (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The consistently positive H\u003csup\u003e2\u003c/sup\u003e or \u0026gt;\u0026thinsp;0 values throughout all blocks effectively bolsters the predictive significance inherent within the measurement model (Sarstedt et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\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\u003eReliability, Validity and Quality of the Measurement Model\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDiscriminant Validity\u003c/strong\u003e \u003cp\u003eIt\u0026rsquo;s defined as the degree to which a particular latent construct is dissimilar with other latent variables in any given measurement model (Duarte et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We concluded discriminant validity-based HTMT\u0026thinsp;\u0026lt;\u0026thinsp;0.85 (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as shown in Table\u0026nbsp;4.5 that all of HTMT values are below 0.85, so it was concluded that study constructs had a sufficient discriminant validity.\u003c/p\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\u003eDiscriminant Validity \u0026ndash; HTMT Ratios\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=\"left\" 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\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnother way to ensure discriminant validity is to evaluate the squared AVE values of the study variable in comparison with the correlations among study variables. This is known as Forenell-Larcker criteria (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). The Forenell-Larcker values are required to greater than all of the corresponding correlations to establish the discriminant validity. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e that this study met this criteria as well to fulfill the discriminant validity requirement.\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\u003eDiscriminant Validity \u0026ndash; Fornell-Larcker Criterion\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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.772\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.775\u003c/b\u003e\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\u003eSimilarly, another criteria to establish the discriminant validity is to examine the cross loadings, generally a difference of 0.2, between principal variable and all other variables in the model, is the minimum requirement to establish the discriminant validity (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e that none of the cross loadings had any difference less than 0.2, so again the assumption of discriminant validity was fulfilled.\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\u003eDiscriminant Validity \u0026ndash; Cross Loadings\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.815\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.748\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.825\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.784\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.804\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.795\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.813\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.545\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.860\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.747\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.740\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.744\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.746\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.689\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.774\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.789\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.829\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.767\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.758\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.815\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.787\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.783\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.775\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.739\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.733\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.818\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.852\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.800\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.697\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.818\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.839\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.744\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.774\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.843\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.730\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.753\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.790\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.871\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.746\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.737\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.727\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.756\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePWB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.789\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPA_CGPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.000\u003c/b\u003e\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 \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Structural Model Assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eUpon the completion of the measurement model assessment in terms of establishing validity and reliability, the assessment of the structural model also termed as the inner model, was initiated. This step aimed to explore the interconnections among the constructs of both exogenous and endogenous variables within the scope of partial least squares structural equation modeling (PLS-SEM).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e4.5.1 Direct Effect Hypotheses\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTheir testing was carried out by bootstrapping technique by taking 2000 subsamples, results were determined by using path coefficients, p-values and t-statistics.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH01\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThere is no significant impact of cognitive distortions on students' psychological well-being.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1A\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThere is a negative significant impact of cognitive distortions on students' psychological well-being.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eCDS had a negative and significant impact on PWB (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.294, t\u0026thinsp;=\u0026thinsp;6.641, p \u0026lt; .001). Which meant that H1A was supported while H01 was rejected, see Table\u0026nbsp;4.8 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as well.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH02\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThere is no significant impact of cognitive distortions on students' academic achievement.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2A\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThere is a negative significant impact of cognitive distortions on students' academic achievement.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSimilarly, CDS had a negative and significant impact on CGPA (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.238, t\u0026thinsp;=\u0026thinsp;5.037, p \u0026lt; .001). Which meant that H2A was supported while H02 was rejected, see Table\u0026nbsp;4.8 and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e too.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect Effect Hypotheses Results\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions \u0026loz; Psychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions \u0026loz; Academic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being \u0026loz; Academic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e4.5.2 Mediation Analysis\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eH03\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological well-being does not mediate the relationship between cognitive distortions and academic achievement.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3A\u003c/strong\u003e \u003cp\u003e \u003cem\u003ePsychological well-being mediates the relationship between cognitive distortions and academic achievement.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;4.9 that CDS had a negative and significant impact on CGPA via PWB (B\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.071, t\u0026thinsp;=\u0026thinsp;3.660, p \u0026lt; .001). So, PWB mediation was occurred, which provided an obvious support to the H3A instead of H03. The variance accounted for (VAF) revealed that only 22.98% of the impact of CDS on CGPA could be upheld when PWB was introduced as mediator (indirect effect / total effect\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.071 / \u0026minus;\u0026thinsp;.238\u0026thinsp;+\u0026thinsp;\u0026minus;\u0026thinsp;.071 = .2298). In other words 77.02% of the negative impact of CDS on CGPA was direct which was the major one, since the VAF was between 20\u0026ndash;80% thus it was a partial mediation, this provided further support to H3A.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation Effect Hypothesis\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=\"left\" 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\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDS \u0026loz; PWB \u0026loz; Academic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e4.5.3 Effect Size or F\u003csup\u003e2\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe F\u003csup\u003e2\u003c/sup\u003e effect size provides an estimation of the variation in the R\u003csup\u003e2\u003c/sup\u003e value when a particular exogenous variable is removed from the research model. It helps to quantify the effect of a specific predictor latent variable on a particular endogenous variable. Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e presents the F\u003csup\u003e2\u003c/sup\u003e effect sizes in the model, showcasing that all the effect sizes were small (Cohen, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The F\u003csup\u003e2\u003c/sup\u003e effect size is a valuable metric as it allows researchers to gauge the magnitude of the impact that each exogenous variable has on the endogenous variable of interest.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect Size \u0026ndash; F\u003csup\u003e2\u003c/sup\u003e\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\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Distortions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\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=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e4.5.4 Coefficient of Determination or R\u003csup\u003e2\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn order to assess the predictive accuracy of the structural model, the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e) was evaluated. R\u003csup\u003e2\u003c/sup\u003e measures the squared correlation between the predicted and actual values of the specific endogenous constructs. The R\u003csup\u003e2\u003c/sup\u003e values in the structural model indicate the degree to which the exogenous variables collectively contribute to the variation in the endogenous variables. Higher R\u003csup\u003e2\u003c/sup\u003e values suggest a stronger influence of the exogenous variables on the endogenous constructs (Hair et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As shown in Table\u0026nbsp;4.13 that all CDS and PWB jointly explained a variance of 14.9% in CGPA, while CDS explained 8.9% variance in PWB.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e4.5.5 Predictive Relevance or Q\u003csup\u003e2\u003c/sup\u003e\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBy executing this blindfolding technique, cross-validated redundancy Q\u003csup\u003e2\u003c/sup\u003e values were obtained for all endogenous variables. The Q\u003csup\u003e2\u003c/sup\u003e value functions as a metric for assessing the predictive pertinence of the structural model. It indicates how well the model can predict the endogenous variables based on the exogenous variables. A positive Q\u003csup\u003e2\u003c/sup\u003e value suggests that the model has predictive relevance and can accurately predict the outcomes of interest, so was the case for this study as all Q\u003csup\u003e2\u003c/sup\u003e value for outcome variable was positive indeed (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e). By considering the Q\u003csup\u003e2\u003c/sup\u003e values, the researchers can cultivate an appreciation for the reliability and dependability of the structural model's predictions regarding the endogenous variables (Tenenhaus et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e4.5.6 Collinearity Issues\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo ascertain the integrity and dependability of the structural model, the examination of VIF values was undertaken to estimate the potential presence of perturbing collinearity, given that elevated VIF values can signal collinearity concerns (Kock, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The VIF values obtained were well within an acceptable threshold, specifically\u0026thinsp;\u0026lt;\u0026thinsp;3.3, as clearly presented in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e. This outcome affirms that neither path contamination nor collinearity had influence on the model, thereby underlining its elevated level of quality.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality Features of Structural Model\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR Square (R\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ Square (Q\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Achievement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Well-Being\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHypotheses 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\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e0\u003c/sub\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is no significant impact of cognitive distortions on students' psychological well-being.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a negative significant impact of cognitive distortions on students' psychological well-being.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e0\u003c/sub\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is no significant impact of cognitive distortions on students' academic achievement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere is a negative significant impact of cognitive distortions on students' academic achievement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003csub\u003e0\u003c/sub\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological well-being doesn\u0026rsquo;t mediate the relationship between cognitive distortions and academic achievement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRejected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological well-being mediates the relationship between cognitive distortions and academic achievement.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSupported\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 \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe major purpose of the study was to investigate the impact of cognitive distortions on the academic achievement of university students through psychological well-being and comparing the influence of demographic factors on these variables. The results revealed that cognitive distortions significantly and negatively influenced students\u0026rsquo; psychological well-being (H1). This implies that students who experience distorted patterns of thinking (e.g., catastrophizing, overgeneralization, or self-blame) are more likely to report lower levels of mental health and life satisfaction (Hossain, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This result is consistent with cognitive-behavioral theory (Beck, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1979\u003c/span\u003e), which posits that maladaptive thinking patterns contribute to psychological distress. Similar findings were reported by Yurica \u0026amp; DiTomasso, (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), who also emphasized that cognitive distortions are closely related to depression, anxiety, and poor psychological resilience. Recent research (Kausar et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Bing\u0026ouml;l \u0026amp; Batık, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Garcia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shorey et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also indicates the statement that maladaptive thinking patterns reduce the capacity of individuals to feel a sense of self-acceptance, positive relationships, and autonomy.\u003c/p\u003e\n\u003cp\u003eThe finding also revealed that the negative significant effect of cognitive distortions on the academic achievement of students was negative (H2). This hypothesis was also confirmed by the results wherein cognitive distortions are a major cause of poor academic performance. The students possessing more distorted thinking recorded lower CGPA, which means that bad cognitive patterns disrupt their concentration, motivation and good learning strategies. This finding aligns with previous research suggesting that negative thought patterns undermine self-efficacy, academic engagement, and overall performance (Putwain \u0026amp; Daly, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, reducing cognitive distortions may directly enhance academic outcomes. Similarly, research by (Ameer et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found that cognitive errors predict lower academic outcomes in Pakistani university students, as they create barriers to effective time management and problem-solving. These results are also consistent with Lega \u0026amp; Ellis, (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) rational emotive behavior theory, which suggests that irrational beliefs directly impair learning efficiency.\u003c/p\u003e\n\u003cp\u003eThe results indicated that psychological well-being mediates the relationship between cognitive distortions and academic achievement (H3). The mediation analysis confirmed this hypothesis, showing that psychological well-being partially mediates the link between cognitive distortions and academic performance. This suggests that cognitive distortions not only directly affect academic success but also indirectly influence it by lowering students\u0026apos; well-being. Although the effect sizes were small, the model demonstrated meaningful explanatory power. This supports the findings of Shengyao et al., (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who reported that well-being significantly influences learning engagement and GPA among Chinese undergraduates. Likewise, Srem-Sai et al., (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) demonstrated that well-being serves as a protective factor, helping students manage academic challenges more effectively despite the presence of cognitive difficulties These findings are consistent with the broaden-and-build theory of positive emotions (Fredrickson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which emphasizes that psychological well-being expands students\u0026rsquo; coping resources, leading to better academic performance. Therefore, the adverse impacts of distorted thinking can be counteracted by interventions that could improve psychological well-being.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFindings show that students reported a moderate level of Cognitive Distortions and Psychological Well-Being. Moreover, correlations reveal that Cognitive Distortions were negatively associated with Psychological Well-Being and Academic Achievement while Psychological Well-Being positively correlated with Academic Achievement. These results suggest that higher distortions are linked with poorer well-being and lower academic achievement, whereas greater well-being enhances performance. Moreover, Cognitive Distortions had a significant negative effect on Psychological Well-Being and Academic Achievement. Psychological Well-Being had a significant positive effect on Academic Achievement. These findings confirm that distortions harm both well-being and academic outcomes, while strong well-being fosters higher achievement. The results also demonstrated that psychological well-being plays a partial mediating role in the relationship between cognitive distortions and academic achievement. This implies that cognitive distortions do not only lower the academic performance of the students indirectly through their effects on their psychological well-being, but also directly through decreasing their academic performance. The model had a significant proportion of variance in the academic achievement and the psychological well-being despite the small effect sizes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthical Statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethical Review Board for Arts, Humanities, Social and Management Sciences Committee of the University of Sargodha, Pakistan Vide No. ERB/FS/480825, dated: 08-07-2025.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe data used in the current research are available from the correspondent author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declare no competing interests.\u003c/p\u003e\u003cp\u003eWritten informed consent was obtained from all the respondents before completing the questionnaire\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullah, N. 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Associations among social mobility beliefs, academic coping strategies and academic persistence in adolescents with lower family socioeconomic status. \u003cem\u003eSocial Psychology of Education\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 4.1, 4.4, 4.5, 4.8, 4.9, and 4.13 are not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive Distortions, Student Success, Psychological Well-being, Academic Achievement","lastPublishedDoi":"10.21203/rs.3.rs-9413489/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9413489/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCognitive distortions are maladaptive thought patterns that may undermine students\u0026rsquo; psychological well-being and academic functioning. This study was conducted to examine the relationship among cognitive distortions, psychological well-being and academic achievement of university students. A cross-sectional survey method with stratified random sampling technique was used to collect the data from N\u0026thinsp;=\u0026thinsp;347 participants from sciences and social sciences faculties of the University of Sargodha, Pakistan. Data were collected from undergraduate students through two standardized adapted instruments Cognitive Distortion Scale (Briere, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), Psychological Well-Being Scale (Ryff, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and self-reported academic performance. The Urdu versions of instruments were also used to measure the cognitive distortions and psychological well-being of students. The results revealed that cognitive distortions were negatively associated with psychological well-being and academic achievement while psychological well-being positively correlated with Academic Achievement. The results further indicated that psychological well-being partially mediate the relationship between cognitive distortions and academic achievement of students.\u003c/p\u003e","manuscriptTitle":"Cognitive Distortions as Hidden Barriers to Student Success: Evidence from University Students’ Psychological Well-being and Academic Achievement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 10:20:11","doi":"10.21203/rs.3.rs-9413489/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b017a09-391f-4a5c-ae54-5f29bb77658c","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T10:20:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 10:20:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9413489","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9413489","identity":"rs-9413489","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}