Self-efficacy and Other Predictors in GPA among College Students, Kathmandu, Nepal

Self-efficacy and Other Predictors in GPA among College Students, Kathmandu, Nepal | 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 Self-efficacy and Other Predictors in GPA among College Students, Kathmandu, Nepal Dev Bandhu Poudel, Samjhana Acharya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4098484/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 We observed General Self-efficacy in 527 student participants from Kathmandu valley, studying in different levels, faculties, years and institutions. The students participated from different ages, genders, ethnicities, religious affiliations, marital status, residence, education levels, faculties, varieties of institutions, year of study, employment status of students. Our objective was to analyse the level of general self-efficacy among demographic groups and observe the variables that predicts GPA. We also aimed to find the reliability scores of the items in the scale. We found no relationship of demographic variables with general self-efficacy except the religious affiliations where Buddhists had significantly lower general self-efficacy than Hindu. The general self-efficacy was found to have no impact on GPA. We observed age, gender, marital status, residential location, year of study, employment status as non-predictors of GPA; however, ethnicity (i.e., Janajati), religion (i.e., Buddhists), education level (i.e., high school), education faculty (i.e., mamagement), type of institution (i.e., private) can predict GPA. The reliability score was observed to have been acceptable and the tool demonstrate moderate livel of convergent validity. Future studies must investigate discrepancy in self-efficacy among religious groups to enhance relevant educational strategies. Educational Psychology General Self-efficacy grade point average correlation reliability validity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Itroduction Individuals affect their psychological well-being through personal agency, believing in their ability to accomplish tasks being a central driver that shapes their actions and decisions in life (Bandura, 1997 ). Self-efficacy views are intriically subjective and often remain latent or hidden due to their internalized nature, making them challenging to observe or measure externally (Ritchie, 2016). Self-efficacy sizes individuals' actions and success across diverse domains, empowering them to overcome fears, achieve lifelong success, and excel academically (Zajacova et al., 2005 ). Self-efficacy theory and research are concerned with people’s ability to engage in successful self-regulation (Maddux, 2016). Students with higher self-efficacy are more capable of adapting to the challenges and pressures of life (Axford, 2007 ). Additionally, self-efficacy is essential in developing student personalities to facilitate their studying process (Fan & Williams, 2010 ). Many studies show that self-efficacy assist students in their academic performance. However, the current state of research in Nepal examining the relationship between self-efficacy and academic factors reveals notable gaps that warrant further investigation. Among the existing studies, three relevant works were found. Mahat and Pradhan ( 2012 ) explored self-efficacy in relation to HIV/AIDS knowledge, but the study's main limitation lies in its exclusive focus on genders and academic levels. Shrestha & Tuladhar ( 2021 ) reported that most nursing students exhibited an average level of self-efficacy; however, the generalizability of the findings is limited due to the small sample size of 209 participants from a singular academic institution and a specific academic stream. Bhusal ( 2023 ) highlighted the crucial roles of self-efficacy, self-regulation, and self-efficacy for self-regulation as determinants affecting academic procrastination among undergraduate students in Kathmandu Valley. Nevertheless, this study lacks essential information, such as the potential contribution of self-efficacy to academic performance and GPA. Addressing research gaps is crucial in enhancing our understanding of the intricate relationship between self-efficacy and academic outcomes among diverse student populations in Nepal. Firstly, current studies lack inclusivity in demographic characteristics, such as faculties, types of institutions, academic years, and working status. Secondly, there is a significant gap in exploring the interactions between self-efficacy and other factors beyond self-efficacy in predicting academic performance, including GPA, to enhance understanding of their influence on educational outcomes. Thirdly, the reliability and validity of self-efficacy measurement tools has not been systematically assessed, posing concerns about knowledge reliability and validity. This research aims to address gaps by examining self-efficacy variations among Nepalese students based on demographics. We also explore the relationship between self-efficacy and GPA, identifying predictors influencing educational outcomes. Additionally, we plan to systematically assess the reliability and convergent validity of a commonly used generalized self-efficacy measurement tool in the context of Nepalese academic research. This study, exploring demographics' impact on self-efficacy and GPA, holds promise for understanding academic dynamics. By identifying prevalent high self-efficacy levels and revealing the non-significant role of general self-efficacy in predicting GPA, the research offers valuable insights into the complex interplay between personal beliefs and academic performance. The examination of demographic influences, ranging from age and gender to ethnicity and religion, contributes nuanced findings that could guide tailored educational interventions. Despite limitations, the study signals the importance of further exploring the mechanisms shaping self-efficacy among diverse student groups, suggesting avenues for future research that integrates longitudinal and qualitative approaches. Methods Participants A total of 527 participants, aged between 18 and 24 ( M = 21.46, SD = 3.72), were included in this study. The sample included 296 females (56.17%) and 231 males (43.83%). The ethnic distribution was as follows: Brahmin/Kshetri (48.77%), Janajati (31.69%), Newar (12.14%), and unspecified (7.40%). Regarding religious affiliation, Hindu participants constituted the majority (75.52%), followed by Buddhist (14.99%) and unspecified (9.49%). Marital status revealed that single participants formed the largest group (88.24%), while married participants accounted for 9.30%. In terms of residence location, non-valley residents (55.98%) outnumbered valley residents (38.90%). Educational levels included high school degree (33.02%), bachelor’s degree (58.44%), and master’s degree (6.26%). The faculties represented were arts (28.65%), management (55.79%), and natural science (6.45%). Institution types comprised community college (25.24%), governmental college (33.97%), and private college (37.19%). The majority of participants were pure students (70.21%), with employed students accounting for 29.41%. Academic years were distributed across first year (39.28%), second year (29.03%), third year (8.54%), fourth year (14.61%), and session completed (back paper) (7.02%). Materials General Self-Efficacy Scale (GES). Reliability. Previous studies confirmed high reliability, stability, and construct validity of the GSE scale (Schwarzer & Greenglass, 1999 ). Internal consistencies typically ranged between alpha = 0.75 and 0.91 (Scholz, Doña, Sud, & Schwarzer, 2002 ). The Cronbach alphas, showing internal consistency, diversified across different groups: 0.94 for patients with cardiovascular diseases in Germany, 0.89 for patients with cancer in Germany, 0.90 for students in Poland, 0.87 for patients with gastrointestinal diseases in Poland, 0.87 for swimmers in Poland, and 0.86 for participants from South Korea. (Luszczynska, Scholz, & Schwarzer (2005). Validity . In Germany, a large-scale study including 3514 high-school students and 302 teachers found evidence supporting the validity of the GSE scale (Schwarzer & Jerusalem, 1999, as cited in (Scholz, Doña, Sud, & Schwarzer, 2002 ). The GSE scale revealed correlations of 0.49 with optimism and 0.45 with the perception of challenge in stressful situations for students whereas for teachers, strong correlations were observed with proactive coping (0.55), self-regulation (0.58), and negative correlations with procrastination (-0.56). Moreover, significant relationships were observed with all three dimensions of teacher burnout: emotional exhaustion (− 0.47), depersonalization (− 0.44), and lack of accomplishment (-0.75). Validity evidence was found alike for teachers in Hong Kong (Schwarzer, Schmitz, & Tang, 2000 ). Cross-cultural Relevance . The GSE scale was observed to demonstrate equivalence across 28 nations, constituting a single global dimension (Leganger, Kraft, & Røysamb, 2000 ). Procedure The study employed an online cross-sectional design and utilized a customized Google form questionnaire. The questionnaire consisted of three sections – the first section addressed informed consent with information concerning privacy, confidentiality, participant rights, task duration, data security, commitments, and study benefits; the second section included demographic information, and the third section included the Generalized Self-Efficacy Scale. An opportunity sampling was employed for reaching out to individuals from different colleges. The participants were invited via various online platforms (Facebook, LinkedIn, Instagram, and email) between June 26 and October 24, 2023. The online survey design tried to minimize biases by ensuring comprehensiveness of the questionnaire by providing clarifications. A reliable and valid tool was employed in the study. The tool was translated into Nepali backing tool translation protocols (forward translation, back translation, and expert consultation). The translation procedure also involved the Think-Aloud protocol, where five different language experts commented on the translated version before its administration to minimize language bias. The tool was examined within the context of Nepal to mitigate cross-cultural biases. Reliability and validity assessments were conducted as part of this process. To alleviate the response biases, participants were requested to carefully read the questionnaire and choose the best options provided. We utilized nonparametric tests (e.g., Mann-Whitney U and Kruskal-Wallis) instead of Welch’s t-test and ANOVA due to violations of normality (Shapiro-Wilk test) and homogeneity of variance (Levene's test) assumptions in our data for comparing the means between or among the groups. For post hoc analysis, James-Howell test was applied. Pearson’s correlation was calculated to observe the relationship between variables. Regression analysis was utilized to identify the predictors on GPA. For effect size, Fisher’s z , Cohen’s d were used and for analyzing the distribution of variance, R 2 was used. Cronbach’s alpha was calculated to observe the reliability of the scale and its indicators. The subgroups with fewer than 30 participants were either merged into the "others" category or excluded from the analysis. Data cleaning and analysis were conducted using Google Sheets and Jeffreys's Amazing Statistics Program (JASP) (an open source software). The table, charts and graphs were designed by using JASP and MS Excel (online 365 version). Results We conducted the analysis using the JASP version 0.18.1.0 for PCs. We also screened the data for the normality of the distributions and for outliers. We found the skewness value − 0.59 with a standard error of 0.11 and the Kurtosis value 0.66 with a standard error of 0.21. The score ranged from 10 to 40 ( M = 31.28, SD = 5.25). Demographic Composition We identified 527 participants after data cleaning. The participants’ ages ranged from 18–49 years. In sex category, 56.17% were female particapants, while 43.83% were male. The study consisted of different ethnicities, including 48.77% Brahmin/Kshetri, 31.69% Janajati, 12.14% Newar, and 7.4% Unspecified ethnic group. Religious groups comprised 14.99% Buddhists, 75.52% Hindus, and 9.49% unspecified. In terms of marital status, 49.30% participants were married, while 88.24% were single. Based on residential location, 55.98% were non-valley residents, and 38.90% were valley residents. The educational background of participants comprised 58.44% with bachelor’s degree, 33.02% with high school degree, and 6.26% with master’s degree. Participants were distributed across educational faculties, including 28.65% in Arts, 55.79% in mgnt., 6.45% in science, and 8.73% in an unspecified faculty. Regarding the type of educational institutions, 25.24% were from community colleges, 33.97% from governmental colleges, and 37.19% from private colleges. Among the participants, 29.41% were employed students, while 70.21% were pure students. In terms of academic year distribution, 39.28% were in the first year, 29.03% in the second year, 8.54% in the third year, 14.61% in the fourth year, and 7.02% had retake (back paper). Cut-off Scores The determined cut-off scores to categorize participants' general self-efficacy levels were established at distinct percentiles: 27 for the 20th percentile, 30 for the 40th percentile, 33 for the 60th percentile, and 36 for the 80th percentile. Utilizing these benchmarks, participants were classified into different self-efficacy categories. Those scoring below the 20th percentile were categorized as having 'very low self-efficacy. Participants falling between the 20th and below the 40th percentile were labeled as exhibiting 'low self-efficacy.' Individuals whose scores ranged from the 40th percentile to below the 60th percentile were considered to possess 'average self-efficacy.' Those scoring between the 60th percentile to below the 80th percentile were classified as having 'high self-efficacy.' Finally, participants scoring above the 80th percentile were identified as demonstrating 'very high self-efficacy.' We found 82 (15.56%) participants with very low self-efficacy level; 92 (18.41%) participants with low level of self efficacy; 119 (22.58%) participants with high level of self efficacy and 108 (20.49%) participants with very high level of self efficacy. The rest of the participants, 121 (22.96%) participants, had average level of self efficacy. Overall, college students in our context had higher level of self efficacy. General Self-efficacy and Grade Point Average General self-efficacy ( N = 527) did not significantly predict of GPA, β = .003, t (166) = .62, p = .615. General self-efficacy non-significantly explained 0.2% of variance in GPA scores, R² = .002, F (1, 166) = 0.38, p = .539. We found the the inclusion of general self-efficacy total did not significantly improve the GPA scores. In the following sections, we have not observed moderational effect of general self-efficacy because of it’s minimal impact carried out from prior analysis. Age We found non-significant relationship between age and general self-efficacy, r (525) = .02, p = .66. No relationship between age and GPA, r (162) = .12, p = .14 was observed. However, significant weak negative relationship was observed between age and percentage ( N = 47), r (45) = − .46, p = .001, Fisher's ( z ) = − .50, 95% CI [-0.66, -0.20]. The regression analysis further explored the relationship between percentage and age, statistically significant, R² = .212, F (1, 45) = 12.09, p = .001, explaining 21.2% of the variance, indicating that age was a significant predictor of percentage, β = -1.32, t (45) = − 3.48, p = .001. Figure 1 shows the trend of general self-efficacy increment in relation to age. Gender The Shapiro-Wilk test indicated non-normality in both female ( W = 0.96, p < .001) and male ( W = 0.97, p < .001) groups. However, Levene's test for equality of variances was non-significant, F (1, 525) = .025, p = .875. Given the large sample size, Welch's t-test was conducted. No significant difference in GSE was found between females ( N = 296, M = 31.16, SD = 5.26) and males ( N = 231, M = 31.45, SD = 5.24), F (1, 525) = .025, p = .875. In this study, we analysed a large sample of male ( N = 83) and female ( N = 85). We observed that male as a gender did not significantly predict the GPA, β = .034, t (166) = 0.51, p = .612. Gender as a whole explained only 0.2% of the variance in GPA scores, R² = .002, F (1, 166) = 0.26, p = .612. Figure 2 shows some of the other significant descriptive statistics in sex varaibles. Ethnicity The Shapiro-Wilk test indicated non-normality in Brahmin/Kshetri ( W = 0.96, p < .001), Janajati ( W = 98, p = .006) and Other ( W = 0.96, p = .002) groups. Levene's test for equality of variances showed non-significant results, F (2, 524) = 0.83, p = .437. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis H test was used to compare GSE scores across ethnicity. The test showed non-significant difference in general self-efficacy among Brahmin/Kshetri ( N = 257, Mdn = 32), Janajati ( N = 167, Mdn = 31), Newar ( N = 64, Mdn = 32), and unspecified ( N = 39, Mdn = 31), H (2) = 4.27, p = .234. Due to data not meeting assumptions for GPA in other ethnic groups, only Brahmin/Kshetri ( N = 84) and Janajati groups ( N = 85) were analysed in the regression analysis. Janajati emerged as a significant predictor of GPA, β = − .342, t (140) = − 5.08 p < .001. Overll ethnicity explained 15.6% of the variance, R² = .156, F (1, 140) = 25.84, p < .001, indicating a significant negative relationship between being Janajati and GPA. Religion The Shapiro-Wilk test indicated non-normality in Buddhist ( W = 0.97, p = .028), Hindu ( W = 97, p < .001) and Unspecified ( W = 0.94, p = .018) groups. Levene's test for equality of variances showed non-significant results, F (2, 524) = 2.52, p = .082. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis H test was used to compare general self-efficacy scores across ethnicity. Subsequent Kruskal-Wallis H test revealed a statistically significant difference in compare general self-efficacy among Buddhist ( N = 79, Mdn = 31), Hindu ( N = 398, Mdn = 32, and unspecified religious groups ( N = 50, Mdn = 31), H (2) = 9.88, p = .007, η² = .026. Further post-hoc analysis (Tukey's HSD) identified a significant difference between Buddhists and Hindus groups ( p = .002, 95% CI [-3.68, -0.68] in compare general self-efficacy, with a medium effect size, Cohen's d = -0.42, 95% CI [-0.72, -0.12], suggesting that the Hindu have significantly higher level of general self-efficacy compared to Buddhist. We included only Buddhist ( N = 34) and Hindu ( N = 121). Hindu as a religious group was found as a significant predictor of GPA, β = .345, t (153) = 4.38, p < .001. Overall model accounted for 11.1% of variance in GPA scores, R² = 0.111, F (2, 153) = 19.14, p < .001. Marital Status The Shapiro-Wilk test indicated normality in married ( W = 0.96, p = .06) and non-normality in single ( W = 0.97, p < .001) groups. Levene's test for equality of variances was non-significant, F (1, 512) = 0.025, p = .182. Given the large sample size, Welch's t -test was conducted. No significant difference in GSE was found between married ( N = 49, M = 31.25, SD = 5.6) and single ( N = 465, M = 31.48, SD = 5.04), t (56.52) = − 0. 28, p = .781. We did not observed GPA scores based on marital status due to very limited observations. Valley versus Non-valley Residents The Shapiro-Wilk test indicated non-normality in both groups: non-valley residents ( W = 0.98, p < .001) and valley residents ( W = 0.95, p < .001). Levene's test for equality of variances revealed a non-significant difference, F (1, 498) = 3.80, p = .052. Due to non-normality in both groups, a Mann-Whitney U test was conducted to compare general self-efficacy between non-valley residents ( N = 295, Mdn = 31) and valley residents ( N = 205, Mdn = 32). The test revealed no significant difference in general self-efficacy, U = 29684.50, p = 728. We included both valley residence ( N = 63) and non-valley residence ( N = 93) in our study. The valley residence as a member of participant's residence was not found to be significant predictor of GPA scores, β = .088, t (154) = 1.27, p = .205. It explained only 1% of variance, R² = .01, F (1, 154) = 1.62, p = .205. Education Level The Shapiro-Wilk test revealed significant deviations from normality in all three education level groups: bachelor's degree ( W = 0.97, p < .001), high school degree ( W = 0.97, p = .002), and master's degree ( W = 0.95, p = .100). Levene's test indicated no statistically significant difference in variances between the groups, F (2, 512) = 0.71, p = .491. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis H test was used to compare general self-efficacy scores across education levels. The test did not reveal a statistically significant difference between the groups: bachelor’s degree ( N = 308, M = 31.56, SD = 5.19), high school degree ( N = 174, M = 31.11, SD = 5.29) and master’s degree ( N = 33, M = 31.15, SD = 4.18), H (2) = 1.60, p = .450, suggesting no meaningful differences in general self-efficacy among bachelor’s degree, high school degree, and master’s degree groups. We analysed only bachelor’s degree ( N = 91) and high school degree ( N = 61) in regression analysis because master degree had insufficient data points. High school degree significantly predicated GPA scores, β = − .289, t (150) = -4.40, p < .001. Academic level accounted for 11.4% of variance in GPA scores, R² = .114, F (1, 150) = 19.33, p < .001. It indicates that higher education level were associated with lower overall GPAs by 0.289 units, on average, after controlling for other variables in the model. Faculties of Education The Shapiro-Wilk test indicated non-normality arts ( W = 0.96, p < .001), management ( W = 98, p < .001) and Other ( W = 0.94, p = .022) groups, but non-normality in science ( W = 0.97, p = .541). The homogeneity test for equality of variance (Levene’s test) showed significant result, F (3, 521) = 3.43, p = .017. Therefore, we employed a non-parametric test (e.g., Kruskal-Wallis test) to compare the medians. We found statistically a non-significant difference in the level of general self-efficacy among arts ( N = 151, Mdn = 32), mgnt. ( N = 294, Mdn = 32), science ( N = 34, Mdn = 30) and unspecified ( N = 46, Mdn = 31), H (3) = 2.16, p = .54. We excluded science due to limited data points and analysed arts ( N = 39), management ( N = 79) and unspecified ( N = 30) in regression analysis. Our study found that the management faculty significantly predicted GPA scores. The faculty of management had statistically significant effect on GPA, β = .194, t (145) = 2.39, p = .018, whereas unspecified faculty did not ( β = .097, t (145) = 0.96, p = .338). The overall model accounted for 3.9% of the variance in GPA scores, R² = .039, F (2, 145) = 2.918, p = .057. Varieties of Institution The Shapiro-Wilk test revealed significant deviations from normality in all three institutional type groups: community ( W = 0.93, p < .001), government ( W = 0.97, p < .001), and private ( W = 0.97, p = .001). Levene's test indicated no statistically significant difference in variances between the groups, F (2, 505) = 2.36, p = .095. Due to non-normality, a non-parametric Kruskal-Wallis H test compared general self-efficacy scores across institution types. The test did not reveal a statistically significant difference between community college ( N = 133, Mdn = 31.33, SD = 4.71), government college ( N = 179, Mdn = 31.78, SD = 5.14), and private college ( N = 196, Mdn = 31.20, SD = 5.30), H (2) = 1.71, p = .425. This suggests no meaningful differences in general self-efficacy among the groups. Further analysis focused on private ( N = 72) and government colleges ( N = 70) due to the limited data for community colleges. Our study revealed revealed private college as a significant predictor of GPA, β = .209, t (121) = 2.41, p = .003. The overall institution type contributed 6% of the variance in GPA, R² = .06, F (1, 121) = 8.957, p = .003. This finding suggests that students attending private colleges tend to have higher GPAs compared to students from government colleges. Year of Study The Shapiro-Wilk test indicated non-normality first year ( W = 0.98, p = .001), second year ( W = 96, p < .001), fourth year ( W = 0.92, p < .001), but non-normality in third year ( W = 0.97, p = .290) and retake ( W = 0.95, p = .088). The equality of variance susing (Levene’s test) found statistically non-significant results, F (4, 514) = 2.15, p = .074. Therefore, we employed Kruskal-Wallis Test. The test showed significant difference among first year ( N = 207, Mdn = 31), second year ( N = 153, Mdn = 32), third year ( N = 45, Mdn = 30) and fourth year ( N = 77, Mdn = 33) and Retake ( N = 37, Mdn = 31), H (4) = 10.44, p = .034. in general self-efficacy level. However, we did not observe any pariwise difference in post-hoc analysis, except that first year and fourth year had significance level appraoacing to the significance level, p = .86, 95% CI [− 3.76, 0. 15], Cohen’s d = − 0. 344. We analysed first year ( N = 51), second year ( N = 33), third year ( N = 35) and fourth year ( N = 28, nearly 30). We observed an interesting result here. The result found at least one variable being significant predictor of GPA as explained by over all model; however, the coefficients for academic year (2nd year), β = − .12, t (143) = − 1.32, p = .188, academic year (3rd year), β = .129, t (143) = 1.45, p = .15), and academic year (4th year), β = .18, t (143) = 1.88, p = .062, did not reach statistical significance. The overall model accounted for 6.9% of variance in GPA scores, R² = .069, F (3, 143) = 3.537, p = .016. Employed versus Pure students We evaluated the assumption of normality using the Shapiro-Wilk test. It indicated significant deviations from normality for both groups: employed students ( W = 0.97, p < .001) and pure students ( W = 0.97, p < .001). Mann-Whitney U test compared employed students ( N = 155, Mdn = 31) with pure students ( N = 370, Mdn = 32) concerning their general self-efficacy level. The analysis revealed a statistically non-significant difference between the groups, U = 57350, p = .835. We analysed a large sample for pure student ( N = 104) and employed student ( N = 64)The linear regression explored that the group of pure student was not a significant predictor of GPA scores, R² = .00, β = .013, t (166) = 0.19, p = .848. The overall model accounting for 0% of variance in GPA scores. Reliability and Validity of the Tool Since the tool was properly translated in the Nepalese language with proper consideration of translation protocols, we onserved reliability and corelations of items to find convergent validity. The was found to be reliable with higher cronbach alpha ( α = .78). Frequentist individual item reliability statistics showed reliability score over than required, ( α = .75 to α = .77), indiating the tool as an acceptable measure. Similarly, the corelation of individual item with total scale item was found to range from r (525) = .53 (item 1) to r (525) = .67 (item 10), indicating moderate level of convergent validity. Discussion The demographic characteristics of the 527 participants encompassed various factors such as age, gender, ethnicity, religion, marital status, residential location, academic level, educational background, institution types, academic year and students types (i.e., employed vs pure studetns). Cut-off scores were established to categorize participants' self-efficacy levels, ranging from 'very low' to 'very high,' providing valuable insights into the distribution of self-efficacy within the sample. In our sample, we found a very low self efficacy (15.56%), low self-efficacy (18.41%), average self efficacy (22.96%), high level of self efficacy (22.58%) and very high level of self efficacy (20.49%). Notably, a substantial proportion of participants exhibited 'high' and 'very high' levels of self-efficacy, indicating a predominantly positive perception of one's abilities among college students in our study context. In our study, genaral self-efficacy served as a non-significant predictor of GPA, indicating minimal contribution to academic achievements. Consistently, in another study with college students. Fenning and May ( 2013 ) found no significant interrelationships between general self-efficacy and college GPA. However, they found that general self-efficacay was associated with high school GPA. Similarly, another study also revealed no significant relationship between self efficacy and GPA in university students (Ramos-sánchez & Nichols, 2007 ). Conversely, Yip ( 2012 ) found the self efficacy as a significant predictor of students’ GPA, indicating that high academic achievers differed significantly from low academic achievers in the level of self-efficacy. Galyon et al. ( 2012 ) found that academic self-efficacy was the second strongest factor explaining college GPA following closely behind standardized test scores, while combining predictors. Self-efficacy demonstrated a positive correlation with academic motivation, suggesting that higher self-efficacy is associated with elevated levels of academic motivation (Shrestha et.al, 2021). In a study, motivation was found to be significant predictor of academic performance (Yip, 2012 ). This suggests that self-efficacy can generate academic motivation which in tern contributes to academic performance. Furthermore, students with elevated levels of self-regulation, self-efficacy, and self-efficacy for self-regulation were less likely to displaying procastination in academic behavior; however, this relationship is very weak (Bhusal, 2023 ). We found no significant relationship between age and general self-efficacy. The correlation between age and GPA was also non-significant. However, a significant weak negative relationship was observed between age and percentage. The regression analysis confirmed the significance and identified age as a predictor of percentage. However, the casual factor in this relationship was unknown. Our study revealed no significant difference in general self-efficacy between male and female participants. In line with our findings, other studies also found the difference was not statistically significant between male female (Lindley & Borgen, 2002 ; Mahat & Pradhan, 2012 ). Consistently, D’Lima et al. ( 2014 ) also reported that there was no significant gender-by-ethnicity interaction observed for academic self-efficacy. We observed gender as non-predictor of GPA which is consistent with finding that no significant gender differences were observed in academic performance (Busch, 2006 ). According to Busch ( 2006 ), female students outperform their male counterparts except for statistics. They also reported that the gender differences in self-efficacy, a construct central to the study of business administration, were found to be small. They further explained that female students exhibited significantly lower self-efficacy in computing and marketing, while demonstrating higher self-efficacy in statistics compared to their male counterparts (Busch, 2006 ). It is noteworthy that possessing higher self-efficacy in statistics does not seem to be correlated with obtaining higher scores compared to males. Inconsistently, a main effect for gender was observed, indicating that female university students had higher GPAs than male students (D’Lima et al., 2014 ). We observed ethnicity showing no significant influence on self-efficacy levels. DeFreitas ( 2012 ) also found that there was no significant relation between ethnicity and self-efficacy. However, D’Lima et al. ( 2014 ) mentioned that African Americans and Caucasians reported significantly higher levels of self-efficacy compared to Asian American students. Our study revealed Janajati ethnicity as a significant predictor of GPA which is consistent again with the finding that ethnicity had significant effect on GPA (DeFreitas, 2012 ). Furthermore, they demonstrated that individuals with higher self-efficacy, particularly among European Americans, exhibited higher GPAs (DeFreitas, 2012 ). We discovered that religion was as potential predictors of both general self-efficacy and GPA. Religion exhibited a significant association with both general self-efficacy and GPA, implying a potential link between religious affiliation, self-efficacy, and academic achievement. Marital status did not significantly influence either general self-efficacy or GPA, suggesting its limited role in determining self-efficacy or academic performance among college students. Residential location comparison between valley and non-valley residents revealed no significant difference in the level of general self-efficacy or GPA scores, indicating geographical location's negligible impact on both general self-efficacy and academic performance. Analysis of education levels showed no significant differences in general self-efficacy level. However, a significant predictive relationship was found between education level and overall GPA, suggesting that academic performance in higher education level were associated with lower overall GPAs. A study in college student found that the high school GPA was best predicted by general self-efficacy, while college GPA was most strongly associated with self-efficacy for learning, but not with general self efficacy (Fenning & May, 2013 ). The correlation results revealed a significant positive correlation between general self-efficacy and high school GPA. However, no significant interrelationships were observed between general self-efficacy and college GPA (Fenning & May, 2013 ). Furthermore, no significant differences were observed in the level of general self-efficacy among students in various educational faculties, although faculty of management showed a significant association with higher GPA. Lastly, no significant differences in the level of general self-efficacy was observed among students attending different types of institutions, while students attending private colleges tended to have higher GPAs compared to those in government colleges. We found statistically non-significant difference among year of studies in the level of general self-efficacy. In line with our findings, a study reported that each ethnic group showed consistent self-efficacy across the semester (D’Lima et al., 2014 ). Similarly, we also found that academic year distribution did not significantly predict GPA. Consistently, a study reported that self-efficacy did not significantly change over time (Ramos-sánchez & Nichols, 2007 ). We observed being employed or a pure student did not influence on general self-efficacy. A study found no significant differences in self-efficacy levels based on the income (Ramos-sánchez & Nichols, 2007 ). We also found income as a non-predictor of GPA outcomes significantly. However, a study found that individuals with higher income levels exhibited higher SAT math scores (DeFreitas, 2012 ). The reliability analysis demonstrated a robust internal consistency with a high Cronbach's alpha coefficient ( α = 0.78). Schwarzer et al. (1995) reported the internal consistency ratings for each of the five samples examined indicated high reliability, with alpha values ranging from 0.82 to 0.93. In a sample of 991 migrants from what was then Germany, the retest reliability over a two-year period was 0.47 for men and 0.63 for women (Schwarzer et al., 1995). The generalised self efficacy scale demonstrated strong internal consistency with a Cronbach's alpha of 0.83 in a Colombian Sample (Juarez & Torres, 2008 ). Additionally, the correlation of individual items with the total scale ranged from r = 0.53 to r = 0.67, indicating a moderate level of convergent validity. Schwarzer et al. (1995) assert that concurrent validity is supported by significant correlations with other tests. Positive correlations were observed with self-esteem (0.52), internal control belief (0.40), and optimism (0.49). Conversely, negative correlations were identified with general anxiety (-0.54), performance anxiety (− 0.42), shyness (− 0.58), and pessimism (− 0.28). Predictive validity was assessed in a one-year follow-up of East German migrants. For women, self-efficacy positively correlated with later self-esteem (0.40) and optimism (0.56). However, men showed less impressive correlations (0.20 and 0.30) over a two-year period (Schwarzer et al., 1995). Additionally, a study reported correlations between items and the total scale ranging from 0.3 to 0.66 (Juarez & Torres, 2008 ). Limitations The survey research, while insightful, has limitations. The sample size of 527 participants and the specific context may limit generalizability. The cross-sectional design restricts causal inference, and reliance on self-report measures introduces potential bias. The authors focus on GPA as the sole academic measure oversimplifies student success. Cultural nuances and unknown moderating factors could impact the observed relationships. Opportunity sampling may potentially introduce selection bias. Clarification of causal links and consideration of additional variables could enhance the study's practical implications. Conclusion In conclusion, cut-off scores categorized self-efficacy levels, revealing predominance of high and very high self-efficacy. No direct, moderational and mediational effect of general self-efficacy on demographic variables and GPA was observed. Age, gender, ethnicity, marital status, location of residence, educational levels, faculties, institution types, and employment status of the students were not found to be related to self-efficacy. Religion, and year of study were associated to differential self-efficacy. GPA was not associated with self-efficacy. Ethnicity, religion, academic level, faculties, institution types, year of study were associated with differential GPA where Brahmin/Kshetri, Hindu, high school degree, management, private college and fourth year had higher GPA than their comparision groups, while age, gender and location of residence, and employment status had no effect on GPA. Interestingly, age was found to be negatively associated with percentage. The overall scale showed its reliability and validity in our cross-cultural context of Nepal with potential questions for further tool validation study. Future Research These research avenues aim to enhance our comprehensive understanding of the interplay between self-efficacy, demographic variables, and academic performance, providing practical implications for education. Future research is needed to investigate specific factors within demographic categories affecting self-efficacy. The reason behid the higher self-eeficacy in Hindu compared to Buddhist should be explored. Understanding the reasons behind GPA disparities based on ethnicities, religions, academic levels, faculties, institution types, and year of studies is crucial. Examining the dynamic relationship between self-efficacy and GPA over time through longitudinal studies is necessary. Contextual factors influencing the negative association between age and percentage need further exploration. The validation of self-efficacy measurement tools in diverse educational settings is necessary. Additionally, exploring non-demographic variables that impact self-efficacy levels will contribute valuable insights. Finally, incorporating qualitative methods such as interviews or focus groups could offer a deeper understanding of students' experiences and perceptions, thereby enhancing the effectiveness of educational interventions aimed at improving self-efficacy and academic performance. Longitudinal studies tracking self-efficacy levels over time throughout students' academic journeys could provide valuable insights into the development and fluctuation of self-efficacy beliefs. Declarations The authors received approval from the Administration Board of G.P. Koirala Memorial College, Sifal, Kathmandu. Author Note Dev Bandhu Poudel https://orcid.org/0000-0002-3672-185X Correspondence concerning this article should be addressed to Dev Bandhu Poudel, G.P. Koirala Community College affiliated to Tribhuvan University, Sifal, or Brooklyn International College, Sukedhara, Kathmandu, [email protected] Dev Bandhu Poudel is a lecturer of Psychology at G. P. Koirala Community College at Sifal in Kathmandu. Samjhana Acharya was the past student at Central Department of Rural Development, Tribhuvan University. We declare no conflict of interest in the publication of this article. 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Quality in Higher Education , 18 (1), 23–34. https://doi.org/10.1080/13538322.2012.667263 Zajacova, A., Lynch, S., & Espenshade, T. (2005). Self-efcacy, stress, and academic success in college. Research in Higher Education, 46 (6), 677-706. Zhang, J., Xiaonan, Y. N., Zhang, J., & Zhou, M. (2017). Age stereotypes, flexible goal adjustment, and well-being among Chinese older adults. Psychology, Health & Medicine , 1-6. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4098484","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279496055,"identity":"303f354f-96fe-4b4e-93c1-5c3c549b5b86","order_by":0,"name":"Dev Bandhu Poudel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYDCCAzwgkpmBj70BSBtYkKCFjecASIsEKVokEkAMIrTw3cg99uBjm7Ucm+Tzqxt+FEgw8Ld3J+DVInkjL91wZlu6MZt0TtnNHqDDJM6c3YBXi8GZM2bSvG2HE9ukc9Ju8AC1GEjkEqelvk3yTNrNP0RpOd4D1pLAJsF+7DZRtkge70s3nHEu3bCNJ4fttoyBBA9Bv/Ad5j324EOZtTw/+/FnN9/8sZHjb+/FrwUI2KA0jwGYJKQcWQv7A2JUj4JRMApGwQgEAAjdRTOViYxkAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3672-185X","institution":"G.P. 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3","display":"","copyAsset":false,"role":"figure","size":63411,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in GSE and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Boxplot showing five different parameters in gender variables, where GSE refers to generalized self-efficacy and GPA refers to grade point average.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/d53195e739bba414bc883d39.png"},{"id":53117092,"identity":"78d81a1b-1370-4bdb-8c42-9810b07dfcf5","added_by":"auto","created_at":"2024-03-20 19:47:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":80592,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in Ethnicity and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eBoxplot showing five different parameters in ethnicity variables, where GSE 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7","display":"","copyAsset":false,"role":"figure","size":83749,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in Residence and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003eBoxplot showing five different parameters in residence variables, where GSE refers to generalized self-efficacy and GPA refers to grade point average.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/ffbf536bc2d311b4a5827bb5.png"},{"id":53117094,"identity":"00ebfbfa-97f9-4a36-8dba-9b029b0fcd45","added_by":"auto","created_at":"2024-03-20 19:47:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":83171,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in Education Level and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e: Boxplot showing five different parameters in educational levels, where GSE refers to generalized 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19:47:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":77051,"visible":true,"origin":"","legend":"\u003cp\u003eGSE \u0026amp; GPA with Institution Types\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eBoxplot showing five different parameters in institution types, where GSE refers to generalized self-efficacy and GPA refers to grade point average.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/12f8119a9492b060268a629a.png"},{"id":53117101,"identity":"b2242b06-cac2-4e91-aeeb-59169212228b","added_by":"auto","created_at":"2024-03-20 19:47:32","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":116542,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in Year of Study and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eBoxplot showing five different parameters in year of study, where GSE refers to generalized self-efficacy and GPA refers to grade point average.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/1606e7702ff8061ae3497490.png"},{"id":53118323,"identity":"bf1d258e-e3e3-48b4-9dd1-c8bb80e061e2","added_by":"auto","created_at":"2024-03-20 19:55:32","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":80526,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive Values in Employment Status and GPA\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote:\u003c/em\u003eBoxplot showing five different parameters in employment status, where GSE refers to generalized self-efficacy and GPA refers to grade point average.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/27b7fca3b31c6e3837e249f3.png"},{"id":53118716,"identity":"ee310232-7a9e-4193-9652-889fc94e5602","added_by":"auto","created_at":"2024-03-20 20:03:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1137151,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4098484/v1/15bdab8e-e249-4686-8d23-d7f756781fda.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSelf-efficacy and Other Predictors in GPA among College Students, Kathmandu, Nepal\u003c/p\u003e","fulltext":[{"header":"Itroduction","content":"\u003cp\u003eIndividuals affect their psychological well-being through personal agency, believing in their ability to accomplish tasks being a central driver that shapes their actions and decisions in life (Bandura, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Self-efficacy views are intriically subjective and often remain latent or hidden due to their internalized nature, making them challenging to observe or measure externally (Ritchie, 2016). Self-efficacy sizes individuals' actions and success across diverse domains, empowering them to overcome fears, achieve lifelong success, and excel academically (Zajacova et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Self-efficacy theory and research are concerned with people\u0026rsquo;s ability to engage in successful self-regulation (Maddux, 2016).\u003c/p\u003e \u003cp\u003eStudents with higher self-efficacy are more capable of adapting to the challenges and pressures of life (Axford, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Additionally, self-efficacy is essential in developing student personalities to facilitate their studying process (Fan \u0026amp; Williams, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies show that self-efficacy assist students in their academic performance. However, the current state of research in Nepal examining the relationship between self-efficacy and academic factors reveals notable gaps that warrant further investigation. Among the existing studies, three relevant works were found. Mahat and Pradhan (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) explored self-efficacy in relation to HIV/AIDS knowledge, but the study's main limitation lies in its exclusive focus on genders and academic levels. Shrestha \u0026amp; Tuladhar (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported that most nursing students exhibited an average level of self-efficacy; however, the generalizability of the findings is limited due to the small sample size of 209 participants from a singular academic institution and a specific academic stream. Bhusal (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) highlighted the crucial roles of self-efficacy, self-regulation, and self-efficacy for self-regulation as determinants affecting academic procrastination among undergraduate students in Kathmandu Valley. Nevertheless, this study lacks essential information, such as the potential contribution of self-efficacy to academic performance and GPA.\u003c/p\u003e \u003cp\u003eAddressing research gaps is crucial in enhancing our understanding of the intricate relationship between self-efficacy and academic outcomes among diverse student populations in Nepal. Firstly, current studies lack inclusivity in demographic characteristics, such as faculties, types of institutions, academic years, and working status. Secondly, there is a significant gap in exploring the interactions between self-efficacy and other factors beyond self-efficacy in predicting academic performance, including GPA, to enhance understanding of their influence on educational outcomes. Thirdly, the reliability and validity of self-efficacy measurement tools has not been systematically assessed, posing concerns about knowledge reliability and validity.\u003c/p\u003e \u003cp\u003eThis research aims to address gaps by examining self-efficacy variations among Nepalese students based on demographics. We also explore the relationship between self-efficacy and GPA, identifying predictors influencing educational outcomes. Additionally, we plan to systematically assess the reliability and convergent validity of a commonly used generalized self-efficacy measurement tool in the context of Nepalese academic research.\u003c/p\u003e \u003cp\u003eThis study, exploring demographics' impact on self-efficacy and GPA, holds promise for understanding academic dynamics. By identifying prevalent high self-efficacy levels and revealing the non-significant role of general self-efficacy in predicting GPA, the research offers valuable insights into the complex interplay between personal beliefs and academic performance. The examination of demographic influences, ranging from age and gender to ethnicity and religion, contributes nuanced findings that could guide tailored educational interventions. Despite limitations, the study signals the importance of further exploring the mechanisms shaping self-efficacy among diverse student groups, suggesting avenues for future research that integrates longitudinal and qualitative approaches.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eA total of 527 participants, aged between 18 and 24 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21.46, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.72), were included in this study. The sample included 296 females (56.17%) and 231 males (43.83%). The ethnic distribution was as follows: Brahmin/Kshetri (48.77%), Janajati (31.69%), Newar (12.14%), and unspecified (7.40%). Regarding religious affiliation, Hindu participants constituted the majority (75.52%), followed by Buddhist (14.99%) and unspecified (9.49%). Marital status revealed that single participants formed the largest group (88.24%), while married participants accounted for 9.30%. In terms of residence location, non-valley residents (55.98%) outnumbered valley residents (38.90%). Educational levels included high school degree (33.02%), bachelor\u0026rsquo;s degree (58.44%), and master\u0026rsquo;s degree (6.26%). The faculties represented were arts (28.65%), management (55.79%), and natural science (6.45%). Institution types comprised community college (25.24%), governmental college (33.97%), and private college (37.19%). The majority of participants were pure students (70.21%), with employed students accounting for 29.41%. Academic years were distributed across first year (39.28%), second year (29.03%), third year (8.54%), fourth year (14.61%), and session completed (back paper) (7.02%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003e \u003cb\u003eGeneral Self-Efficacy Scale (GES).\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003eReliability.\u003c/b\u003e Previous studies confirmed high reliability, stability, and construct validity of the GSE scale (Schwarzer \u0026amp; Greenglass, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Internal consistencies typically ranged between alpha\u0026thinsp;=\u0026thinsp;0.75 and 0.91 (Scholz, Do\u0026ntilde;a, Sud, \u0026amp; Schwarzer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Cronbach alphas, showing internal consistency, diversified across different groups: 0.94 for patients with cardiovascular diseases in Germany, 0.89 for patients with cancer in Germany, 0.90 for students in Poland, 0.87 for patients with gastrointestinal diseases in Poland, 0.87 for swimmers in Poland, and 0.86 for participants from South Korea. (Luszczynska, Scholz, \u0026amp; Schwarzer (2005).\u003c/p\u003e \u003cp\u003e \u003cb\u003eValidity\u003c/b\u003e. In Germany, a large-scale study including 3514 high-school students and 302 teachers found evidence supporting the validity of the GSE scale (Schwarzer \u0026amp; Jerusalem, 1999, as cited in (Scholz, Do\u0026ntilde;a, Sud, \u0026amp; Schwarzer, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The GSE scale revealed correlations of 0.49 with optimism and 0.45 with the perception of challenge in stressful situations for students whereas for teachers, strong correlations were observed with proactive coping (0.55), self-regulation (0.58), and negative correlations with procrastination (-0.56). Moreover, significant relationships were observed with all three dimensions of teacher burnout: emotional exhaustion (\u0026minus;\u0026thinsp;0.47), depersonalization (\u0026minus;\u0026thinsp;0.44), and lack of accomplishment (-0.75). Validity evidence was found alike for teachers in Hong Kong (Schwarzer, Schmitz, \u0026amp; Tang, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCross-cultural Relevance\u003c/b\u003e. The GSE scale was observed to demonstrate equivalence across 28 nations, constituting a single global dimension (Leganger, Kraft, \u0026amp; R\u0026oslash;ysamb, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eThe study employed an online cross-sectional design and utilized a customized Google form questionnaire. The questionnaire consisted of three sections \u0026ndash; the first section addressed informed consent with information concerning privacy, confidentiality, participant rights, task duration, data security, commitments, and study benefits; the second section included demographic information, and the third section included the Generalized Self-Efficacy Scale. An opportunity sampling was employed for reaching out to individuals from different colleges. The participants were invited via various online platforms (Facebook, LinkedIn, Instagram, and email) between June 26 and October 24, 2023.\u003c/p\u003e \u003cp\u003eThe online survey design tried to minimize biases by ensuring comprehensiveness of the questionnaire by providing clarifications. A reliable and valid tool was employed in the study. The tool was translated into Nepali backing tool translation protocols (forward translation, back translation, and expert consultation). The translation procedure also involved the Think-Aloud protocol, where five different language experts commented on the translated version before its administration to minimize language bias. The tool was examined within the context of Nepal to mitigate cross-cultural biases. Reliability and validity assessments were conducted as part of this process. To alleviate the response biases, participants were requested to carefully read the questionnaire and choose the best options provided.\u003c/p\u003e \u003cp\u003eWe utilized nonparametric tests (e.g., Mann-Whitney \u003cem\u003eU\u003c/em\u003e and Kruskal-Wallis) instead of Welch\u0026rsquo;s t-test and ANOVA due to violations of normality (Shapiro-Wilk test) and homogeneity of variance (Levene's test) assumptions in our data for comparing the means between or among the groups. For post hoc analysis, James-Howell test was applied. Pearson\u0026rsquo;s correlation was calculated to observe the relationship between variables. Regression analysis was utilized to identify the predictors on GPA. For effect size, Fisher\u0026rsquo;s \u003cem\u003ez\u003c/em\u003e, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e were used and for analyzing the distribution of variance, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e was used. Cronbach\u0026rsquo;s alpha was calculated to observe the reliability of the scale and its indicators. The subgroups with fewer than 30 participants were either merged into the \"others\" category or excluded from the analysis.\u003c/p\u003e \u003cp\u003eData cleaning and analysis were conducted using Google Sheets and Jeffreys's Amazing Statistics Program (JASP) (an open source software). The table, charts and graphs were designed by using JASP and MS Excel (online 365 version).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWe conducted the analysis using the JASP version 0.18.1.0 for PCs. We also screened the data for the normality of the distributions and for outliers. We found the skewness value \u0026minus;\u0026thinsp;0.59 with a standard error of 0.11 and the Kurtosis value 0.66 with a standard error of 0.21. The score ranged from 10 to 40 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.28, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.25).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic Composition\u003c/h2\u003e\n \u003cp\u003eWe identified 527 participants after data cleaning. The participants\u0026rsquo; ages ranged from 18\u0026ndash;49 years. In sex category, 56.17% were female particapants, while 43.83% were male. The study consisted of different ethnicities, including 48.77% Brahmin/Kshetri, 31.69% Janajati, 12.14% Newar, and 7.4% Unspecified ethnic group. Religious groups comprised 14.99% Buddhists, 75.52% Hindus, and 9.49% unspecified. In terms of marital status, 49.30% participants were married, while 88.24% were single. Based on residential location, 55.98% were non-valley residents, and 38.90% were valley residents. The educational background of participants comprised 58.44% with bachelor\u0026rsquo;s degree, 33.02% with high school degree, and 6.26% with master\u0026rsquo;s degree. Participants were distributed across educational faculties, including 28.65% in Arts, 55.79% in mgnt., 6.45% in science, and 8.73% in an unspecified faculty. Regarding the type of educational institutions, 25.24% were from community colleges, 33.97% from governmental colleges, and 37.19% from private colleges. Among the participants, 29.41% were employed students, while 70.21% were pure students. In terms of academic year distribution, 39.28% were in the first year, 29.03% in the second year, 8.54% in the third year, 14.61% in the fourth year, and 7.02% had retake (back paper).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eCut-off Scores\u003c/h2\u003e\n \u003cp\u003eThe determined cut-off scores to categorize participants\u0026apos; general self-efficacy levels were established at distinct percentiles: 27 for the 20th percentile, 30 for the 40th percentile, 33 for the 60th percentile, and 36 for the 80th percentile. Utilizing these benchmarks, participants were classified into different self-efficacy categories.\u003c/p\u003e\n \u003cp\u003eThose scoring below the 20th percentile were categorized as having \u0026apos;very low self-efficacy. Participants falling between the 20th and below the 40th percentile were labeled as exhibiting \u0026apos;low self-efficacy.\u0026apos; Individuals whose scores ranged from the 40th percentile to below the 60th percentile were considered to possess \u0026apos;average self-efficacy.\u0026apos; Those scoring between the 60th percentile to below the 80th percentile were classified as having \u0026apos;high self-efficacy.\u0026apos; Finally, participants scoring above the 80th percentile were identified as demonstrating \u0026apos;very high self-efficacy.\u0026apos;\u003c/p\u003e\n \u003cp\u003eWe found 82 (15.56%) participants with very low self-efficacy level; 92 (18.41%) participants with low level of self efficacy; 119 (22.58%) participants with high level of self efficacy and 108 (20.49%) participants with very high level of self efficacy. The rest of the participants, 121 (22.96%) participants, had average level of self efficacy. Overall, college students in our context had higher level of self efficacy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneral Self-efficacy and Grade Point Average\u003c/h2\u003e\n \u003cp\u003eGeneral self-efficacy (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;527) did not significantly predict of GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003, \u003cem\u003et\u003c/em\u003e (166)\u0026thinsp;=\u0026thinsp;.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.615. General self-efficacy non-significantly explained 0.2% of variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .002, \u003cem\u003eF\u003c/em\u003e(1, 166)\u0026thinsp;=\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.539. We found the the inclusion of general self-efficacy total did not significantly improve the GPA scores.\u003c/p\u003e\n \u003cp\u003eIn the following sections, we have not observed moderational effect of general self-efficacy because of it\u0026rsquo;s minimal impact carried out from prior analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAge\u003c/h2\u003e\n \u003cp\u003eWe found non-significant relationship between age and general self-efficacy, \u003cem\u003er\u003c/em\u003e (525)\u0026thinsp;=\u0026thinsp;.02, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.66. No relationship between age and GPA, \u003cem\u003er\u003c/em\u003e (162)\u0026thinsp;=\u0026thinsp;.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14 was observed. However, significant weak negative relationship was observed between age and percentage (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;47), \u003cem\u003er\u003c/em\u003e (45)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.46, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, Fisher\u0026apos;s (\u003cem\u003ez\u003c/em\u003e)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.50, 95% CI [-0.66, -0.20]. The regression analysis further explored the relationship between percentage and age, statistically significant, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .212, \u003cem\u003eF\u003c/em\u003e(1, 45)\u0026thinsp;=\u0026thinsp;12.09, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001, explaining 21.2% of the variance, indicating that age was a significant predictor of percentage, \u003cem\u003e\u0026beta;\u003c/em\u003e = -1.32, \u003cem\u003et\u003c/em\u003e (45)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001. Figure 1 shows the trend of general self-efficacy increment in relation to age.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eGender\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality in both female (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and male (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) groups. However, Levene\u0026apos;s test for equality of variances was non-significant, \u003cem\u003eF\u003c/em\u003e(1, 525)\u0026thinsp;=\u0026thinsp;.025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.875. Given the large sample size, Welch\u0026apos;s t-test was conducted. No significant difference in GSE was found between females (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;296, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.16, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.26) and males (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;231, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.45, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.24), \u003cem\u003eF\u003c/em\u003e(1, 525)\u0026thinsp;=\u0026thinsp;.025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.875.\u003c/p\u003e\n \u003cp\u003eIn this study, we analysed a large sample of male (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;83) and female (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;85). We observed that male as a gender did not significantly predict the GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034, \u003cem\u003et\u003c/em\u003e (166)\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.612. Gender as a whole explained only 0.2% of the variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .002, \u003cem\u003eF\u003c/em\u003e (1, 166)\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.612. Figure 2 shows some of the other significant descriptive statistics in sex varaibles.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eEthnicity\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality in Brahmin/Kshetri (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), Janajati (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.006) and Other (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002) groups. Levene\u0026apos;s test for equality of variances showed non-significant results, \u003cem\u003eF\u003c/em\u003e(2, 524)\u0026thinsp;=\u0026thinsp;0.83, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.437. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test was used to compare GSE scores across ethnicity. The test showed non-significant difference in general self-efficacy among Brahmin/Kshetri (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;257, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32), Janajati (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;167, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), Newar (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;64, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32), and unspecified (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), \u003cem\u003eH\u003c/em\u003e(2)\u0026thinsp;=\u0026thinsp;4.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.234.\u003c/p\u003e\n \u003cp\u003eDue to data not meeting assumptions for GPA in other ethnic groups, only Brahmin/Kshetri (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;84) and Janajati groups (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;85) were analysed in the regression analysis. Janajati emerged as a significant predictor of GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.342, \u003cem\u003et\u003c/em\u003e (140)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.08 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Overll ethnicity explained 15.6% of the variance, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .156, \u003cem\u003eF\u003c/em\u003e (1, 140)\u0026thinsp;=\u0026thinsp;25.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, indicating a significant negative relationship between being Janajati and GPA.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eReligion\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality in Buddhist (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.028), Hindu (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Unspecified (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.018) groups. Levene\u0026apos;s test for equality of variances showed non-significant results, \u003cem\u003eF\u003c/em\u003e(2, 524)\u0026thinsp;=\u0026thinsp;2.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.082. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test was used to compare general self-efficacy scores across ethnicity. Subsequent Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test revealed a statistically significant difference in compare general self-efficacy among Buddhist (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;79, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), Hindu (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;398, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32, and unspecified religious groups (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;50, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), \u003cem\u003eH\u003c/em\u003e(2)\u0026thinsp;=\u0026thinsp;9.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.007, \u0026eta;\u0026sup2; = .026. Further post-hoc analysis (Tukey\u0026apos;s HSD) identified a significant difference between Buddhists and Hindus groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002, 95% CI [-3.68, -0.68] in compare general self-efficacy, with a medium effect size, Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e = -0.42, 95% CI [-0.72, -0.12], suggesting that the Hindu have significantly higher level of general self-efficacy compared to Buddhist.\u003c/p\u003e\n \u003cp\u003eWe included only Buddhist (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34) and Hindu (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;121). Hindu as a religious group was found as a significant predictor of GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.345, \u003cem\u003et\u003c/em\u003e (153)\u0026thinsp;=\u0026thinsp;4.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Overall model accounted for 11.1% of variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.111, \u003cem\u003eF\u003c/em\u003e(2, 153)\u0026thinsp;=\u0026thinsp;19.14, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eMarital Status\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated normality in married (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.06) and non-normality in single (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) groups. Levene\u0026apos;s test for equality of variances was non-significant, \u003cem\u003eF\u003c/em\u003e(1, 512)\u0026thinsp;=\u0026thinsp;0.025, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.182. Given the large sample size, Welch\u0026apos;s \u003cem\u003et\u003c/em\u003e-test was conducted. No significant difference in GSE was found between married (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;49, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.25, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.6) and single (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;465, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.48, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.04), \u003cem\u003et\u003c/em\u003e(56.52)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0. 28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.781. We did not observed GPA scores based on marital status due to very limited observations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eValley versus Non-valley Residents\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality in both groups: non-valley residents (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and valley residents (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Levene\u0026apos;s test for equality of variances revealed a non-significant difference, \u003cem\u003eF\u003c/em\u003e(1, 498)\u0026thinsp;=\u0026thinsp;3.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.052. Due to non-normality in both groups, a Mann-Whitney \u003cem\u003eU\u003c/em\u003e test was conducted to compare general self-efficacy between non-valley residents (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;295, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) and valley residents (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;205, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32). The test revealed no significant difference in general self-efficacy, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;29684.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;728.\u003c/p\u003e\n \u003cp\u003eWe included both valley residence (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63) and non-valley residence (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;93) in our study. The valley residence as a member of participant\u0026apos;s residence was not found to be significant predictor of GPA scores, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.088, \u003cem\u003et\u003c/em\u003e (154)\u0026thinsp;=\u0026thinsp;1.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.205. It explained only 1% of variance, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .01, \u003cem\u003eF\u003c/em\u003e(1, 154)\u0026thinsp;=\u0026thinsp;1.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.205.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\n \u003ch2\u003eEducation Level\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test revealed significant deviations from normality in all three education level groups: bachelor\u0026apos;s degree (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), high school degree (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.002), and master\u0026apos;s degree (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.100). Levene\u0026apos;s test indicated no statistically significant difference in variances between the groups, \u003cem\u003eF\u003c/em\u003e(2, 512)\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.491. Due to violation of normality assumptions, a non-parametric Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test was used to compare general self-efficacy scores across education levels. The test did not reveal a statistically significant difference between the groups: bachelor\u0026rsquo;s degree (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;308, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.56, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.19), high school degree (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;174, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.11, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.29) and master\u0026rsquo;s degree (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33, \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.15, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.18), \u003cem\u003eH\u003c/em\u003e(2)\u0026thinsp;=\u0026thinsp;1.60, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.450, suggesting no meaningful differences in general self-efficacy among bachelor\u0026rsquo;s degree, high school degree, and master\u0026rsquo;s degree groups.\u003c/p\u003e\n \u003cp\u003eWe analysed only bachelor\u0026rsquo;s degree (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;91) and high school degree (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;61) in regression analysis because master degree had insufficient data points. High school degree significantly predicated GPA scores, \u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.289, \u003cem\u003et\u003c/em\u003e (150) = -4.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. Academic level accounted for 11.4% of variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .114, \u003cem\u003eF\u003c/em\u003e(1, 150)\u0026thinsp;=\u0026thinsp;19.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. It indicates that higher education level were associated with lower overall GPAs by 0.289 units, on average, after controlling for other variables in the model.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eFaculties of Education\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality arts (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), management (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and Other (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.022) groups, but non-normality in science (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.541). The homogeneity test for equality of variance (Levene\u0026rsquo;s test) showed significant result, \u003cem\u003eF\u003c/em\u003e(3, 521)\u0026thinsp;=\u0026thinsp;3.43, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.017. Therefore, we employed a non-parametric test (e.g., Kruskal-Wallis test) to compare the medians. We found statistically a non-significant difference in the level of general self-efficacy among arts (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;151, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32), mgnt. (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;294, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32), science (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;34, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) and unspecified (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;46, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), \u003cem\u003eH\u003c/em\u003e (3)\u0026thinsp;=\u0026thinsp;2.16, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54.\u003c/p\u003e\n \u003cp\u003eWe excluded science due to limited data points and analysed arts (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;39), management (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;79) and unspecified (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) in regression analysis. Our study found that the management faculty significantly predicted GPA scores. The faculty of management had statistically significant effect on GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.194, \u003cem\u003et\u003c/em\u003e(145)\u0026thinsp;=\u0026thinsp;2.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.018, whereas unspecified faculty did not (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.097, \u003cem\u003et\u003c/em\u003e (145)\u0026thinsp;=\u0026thinsp;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.338). The overall model accounted for 3.9% of the variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .039, \u003cem\u003eF\u003c/em\u003e(2, 145)\u0026thinsp;=\u0026thinsp;2.918, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.057.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003eVarieties of Institution\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test revealed significant deviations from normality in all three institutional type groups: community (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), government (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and private (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001). Levene\u0026apos;s test indicated no statistically significant difference in variances between the groups, \u003cem\u003eF\u003c/em\u003e(2, 505)\u0026thinsp;=\u0026thinsp;2.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.095. Due to non-normality, a non-parametric Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test compared general self-efficacy scores across institution types. The test did not reveal a statistically significant difference between community college (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;133, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.33, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.71), government college (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;179, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.78, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.14), and private college (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;196, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31.20, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.30), \u003cem\u003eH\u003c/em\u003e(2)\u0026thinsp;=\u0026thinsp;1.71, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.425. This suggests no meaningful differences in general self-efficacy among the groups.\u003c/p\u003e\n \u003cp\u003eFurther analysis focused on private (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;72) and government colleges (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;70) due to the limited data for community colleges. Our study revealed revealed private college as a significant predictor of GPA, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.209, \u003cem\u003et\u003c/em\u003e(121)\u0026thinsp;=\u0026thinsp;2.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003. The overall institution type contributed 6% of the variance in GPA, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .06, \u003cem\u003eF\u003c/em\u003e(1, 121)\u0026thinsp;=\u0026thinsp;8.957, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.003. This finding suggests that students attending private colleges tend to have higher GPAs compared to students from government colleges.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003eYear of Study\u003c/h2\u003e\n \u003cp\u003eThe Shapiro-Wilk test indicated non-normality first year (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.001), second year (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), fourth year (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.92, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), but non-normality in third year (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.290) and retake (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.95, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.088). The equality of variance susing (Levene\u0026rsquo;s test) found statistically non-significant results, \u003cem\u003eF\u003c/em\u003e(4, 514)\u0026thinsp;=\u0026thinsp;2.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.074. Therefore, we employed Kruskal-Wallis Test. The test showed significant difference among first year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;207, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), second year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;153, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32), third year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;45, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30) and fourth year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;77, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33) and Retake (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;37, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31), \u003cem\u003eH\u003c/em\u003e(4)\u0026thinsp;=\u0026thinsp;10.44, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.034. in general self-efficacy level. However, we did not observe any pariwise difference in post-hoc analysis, except that first year and fourth year had significance level appraoacing to the significance level, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.86, 95% CI [\u0026minus;\u0026thinsp;3.76, 0. 15], Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0. 344.\u003c/p\u003e\n \u003cp\u003eWe analysed first year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;51), second year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;33), third year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;35) and fourth year (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28, nearly 30). We observed an interesting result here. The result found at least one variable being significant predictor of GPA as explained by over all model; however, the coefficients for academic year (2nd year), \u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026minus;\u0026thinsp;.12, \u003cem\u003et\u003c/em\u003e (143)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.188, academic year (3rd year), \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.129, \u003cem\u003et\u003c/em\u003e(143)\u0026thinsp;=\u0026thinsp;1.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.15), and academic year (4th year), \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.18, \u003cem\u003et\u003c/em\u003e(143)\u0026thinsp;=\u0026thinsp;1.88, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.062, did not reach statistical significance. The overall model accounted for 6.9% of variance in GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .069, \u003cem\u003eF\u003c/em\u003e(3, 143)\u0026thinsp;=\u0026thinsp;3.537, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.016.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\n \u003ch2\u003eEmployed versus Pure students\u003c/h2\u003e\n \u003cp\u003eWe evaluated the assumption of normality using the Shapiro-Wilk test. It indicated significant deviations from normality for both groups: employed students (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and pure students (\u003cem\u003eW\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Mann-Whitney \u003cem\u003eU\u003c/em\u003e test compared employed students (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;155, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;31) with pure students (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;370, \u003cem\u003eMdn\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32) concerning their general self-efficacy level. The analysis revealed a statistically non-significant difference between the groups, \u003cem\u003eU\u003c/em\u003e\u0026thinsp;=\u0026thinsp;57350, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.835.\u003c/p\u003e\n \u003cp\u003eWe analysed a large sample for pure student (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104) and employed student (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;64)The linear regression explored that the group of pure student was not a significant predictor of GPA scores, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = .00, \u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.013, \u003cem\u003et\u003c/em\u003e (166)\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.848. The overall model accounting for 0% of variance in GPA scores.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e\n \u003ch2\u003eReliability and Validity of the Tool\u003c/h2\u003e\n \u003cp\u003eSince the tool was properly translated in the Nepalese language with proper consideration of translation protocols, we onserved reliability and corelations of items to find convergent validity. The was found to be reliable with higher cronbach alpha (\u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78). Frequentist individual item reliability statistics showed reliability score over than required, (\u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.75 to \u003cem\u003e\u0026alpha;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.77), indiating the tool as an acceptable measure. Similarly, the corelation of individual item with total scale item was found to range from \u003cem\u003er\u003c/em\u003e(525)\u0026thinsp;=\u0026thinsp;.53 (item 1) to \u003cem\u003er\u003c/em\u003e(525)\u0026thinsp;=\u0026thinsp;.67 (item 10), indicating moderate level of convergent validity.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe demographic characteristics of the 527 participants encompassed various factors such as age, gender, ethnicity, religion, marital status, residential location, academic level, educational background, institution types, academic year and students types (i.e., employed vs pure studetns).\u003c/p\u003e \u003cp\u003eCut-off scores were established to categorize participants' self-efficacy levels, ranging from 'very low' to 'very high,' providing valuable insights into the distribution of self-efficacy within the sample. In our sample, we found a very low self efficacy (15.56%), low self-efficacy (18.41%), average self efficacy (22.96%), high level of self efficacy (22.58%) and very high level of self efficacy (20.49%). Notably, a substantial proportion of participants exhibited 'high' and 'very high' levels of self-efficacy, indicating a predominantly positive perception of one's abilities among college students in our study context.\u003c/p\u003e \u003cp\u003eIn our study, genaral self-efficacy served as a non-significant predictor of GPA, indicating minimal contribution to academic achievements. Consistently, in another study with college students. Fenning and May (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) found no significant interrelationships between general self-efficacy and college GPA. However, they found that general self-efficacay was associated with high school GPA. Similarly, another study also revealed no significant relationship between self efficacy and GPA in university students (Ramos-s\u0026aacute;nchez \u0026amp; Nichols, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Conversely, Yip (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found the self efficacy as a significant predictor of students\u0026rsquo; GPA, indicating that high academic achievers differed significantly from low academic achievers in the level of self-efficacy. Galyon et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) found that academic self-efficacy was the second strongest factor explaining college GPA following closely behind standardized test scores, while combining predictors. Self-efficacy demonstrated a positive correlation with academic motivation, suggesting that higher self-efficacy is associated with elevated levels of academic motivation (Shrestha et.al, 2021). In a study, motivation was found to be significant predictor of academic performance (Yip, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This suggests that self-efficacy can generate academic motivation which in tern contributes to academic performance. Furthermore, students with elevated levels of self-regulation, self-efficacy, and self-efficacy for self-regulation were less likely to displaying procastination in academic behavior; however, this relationship is very weak (Bhusal, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe found no significant relationship between age and general self-efficacy. The correlation between age and GPA was also non-significant. However, a significant weak negative relationship was observed between age and percentage. The regression analysis confirmed the significance and identified age as a predictor of percentage. However, the casual factor in this relationship was unknown.\u003c/p\u003e \u003cp\u003eOur study revealed no significant difference in general self-efficacy between male and female participants. In line with our findings, other studies also found the difference was not statistically significant between male female (Lindley \u0026amp; Borgen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mahat \u0026amp; Pradhan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Consistently, D\u0026rsquo;Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also reported that there was no significant gender-by-ethnicity interaction observed for academic self-efficacy.\u003c/p\u003e \u003cp\u003eWe observed gender as non-predictor of GPA which is consistent with finding that no significant gender differences were observed in academic performance (Busch, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). According to Busch (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), female students outperform their male counterparts except for statistics. They also reported that the gender differences in self-efficacy, a construct central to the study of business administration, were found to be small. They further explained that female students exhibited significantly lower self-efficacy in computing and marketing, while demonstrating higher self-efficacy in statistics compared to their male counterparts (Busch, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). It is noteworthy that possessing higher self-efficacy in statistics does not seem to be correlated with obtaining higher scores compared to males. Inconsistently, a main effect for gender was observed, indicating that female university students had higher GPAs than male students (D\u0026rsquo;Lima et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe observed ethnicity showing no significant influence on self-efficacy levels. DeFreitas (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also found that there was no significant relation between ethnicity and self-efficacy. However, D\u0026rsquo;Lima et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) mentioned that African Americans and Caucasians reported significantly higher levels of self-efficacy compared to Asian American students.\u003c/p\u003e \u003cp\u003eOur study revealed Janajati ethnicity as a significant predictor of GPA which is consistent again with the finding that ethnicity had significant effect on GPA (DeFreitas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Furthermore, they demonstrated that individuals with higher self-efficacy, particularly among European Americans, exhibited higher GPAs (DeFreitas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe discovered that religion was as potential predictors of both general self-efficacy and GPA. Religion exhibited a significant association with both general self-efficacy and GPA, implying a potential link between religious affiliation, self-efficacy, and academic achievement.\u003c/p\u003e \u003cp\u003eMarital status did not significantly influence either general self-efficacy or GPA, suggesting its limited role in determining self-efficacy or academic performance among college students.\u003c/p\u003e \u003cp\u003eResidential location comparison between valley and non-valley residents revealed no significant difference in the level of general self-efficacy or GPA scores, indicating geographical location's negligible impact on both general self-efficacy and academic performance.\u003c/p\u003e \u003cp\u003eAnalysis of education levels showed no significant differences in general self-efficacy level. However, a significant predictive relationship was found between education level and overall GPA, suggesting that academic performance in higher education level were associated with lower overall GPAs. A study in college student found that the high school GPA was best predicted by general self-efficacy, while college GPA was most strongly associated with self-efficacy for learning, but not with general self efficacy (Fenning \u0026amp; May, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The correlation results revealed a significant positive correlation between general self-efficacy and high school GPA. However, no significant interrelationships were observed between general self-efficacy and college GPA (Fenning \u0026amp; May, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, no significant differences were observed in the level of general self-efficacy among students in various educational faculties, although faculty of management showed a significant association with higher GPA.\u003c/p\u003e \u003cp\u003eLastly, no significant differences in the level of general self-efficacy was observed among students attending different types of institutions, while students attending private colleges tended to have higher GPAs compared to those in government colleges.\u003c/p\u003e \u003cp\u003eWe found statistically non-significant difference among year of studies in the level of general self-efficacy. In line with our findings, a study reported that each ethnic group showed consistent self-efficacy across the semester (D\u0026rsquo;Lima et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, we also found that academic year distribution did not significantly predict GPA. Consistently, a study reported that self-efficacy did not significantly change over time (Ramos-s\u0026aacute;nchez \u0026amp; Nichols, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe observed being employed or a pure student did not influence on general self-efficacy. A study found no significant differences in self-efficacy levels based on the income (Ramos-s\u0026aacute;nchez \u0026amp; Nichols, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We also found income as a non-predictor of GPA outcomes significantly. However, a study found that individuals with higher income levels exhibited higher SAT math scores (DeFreitas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe reliability analysis demonstrated a robust internal consistency with a high Cronbach's alpha coefficient (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.78). Schwarzer et al. (1995) reported the internal consistency ratings for each of the five samples examined indicated high reliability, with alpha values ranging from 0.82 to 0.93. In a sample of 991 migrants from what was then Germany, the retest reliability over a two-year period was 0.47 for men and 0.63 for women (Schwarzer et al., 1995). The generalised self efficacy scale demonstrated strong internal consistency with a Cronbach's alpha of 0.83 in a Colombian Sample (Juarez \u0026amp; Torres, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the correlation of individual items with the total scale ranged from \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53 to \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.67, indicating a moderate level of convergent validity. Schwarzer et al. (1995) assert that concurrent validity is supported by significant correlations with other tests. Positive correlations were observed with self-esteem (0.52), internal control belief (0.40), and optimism (0.49). Conversely, negative correlations were identified with general anxiety (-0.54), performance anxiety (\u0026minus;\u0026thinsp;0.42), shyness (\u0026minus;\u0026thinsp;0.58), and pessimism (\u0026minus;\u0026thinsp;0.28). Predictive validity was assessed in a one-year follow-up of East German migrants. For women, self-efficacy positively correlated with later self-esteem (0.40) and optimism (0.56). However, men showed less impressive correlations (0.20 and 0.30) over a two-year period (Schwarzer et al., 1995). Additionally, a study reported correlations between items and the total scale ranging from 0.3 to 0.66 (Juarez \u0026amp; Torres, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThe survey research, while insightful, has limitations. The sample size of 527 participants and the specific context may limit generalizability. The cross-sectional design restricts causal inference, and reliance on self-report measures introduces potential bias. The authors focus on GPA as the sole academic measure oversimplifies student success. Cultural nuances and unknown moderating factors could impact the observed relationships. Opportunity sampling may potentially introduce selection bias. Clarification of causal links and consideration of additional variables could enhance the study's practical implications.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, cut-off scores categorized self-efficacy levels, revealing predominance of high and very high self-efficacy. No direct, moderational and mediational effect of general self-efficacy on demographic variables and GPA was observed. Age, gender, ethnicity, marital status, location of residence, educational levels, faculties, institution types, and employment status of the students were not found to be related to self-efficacy. Religion, and year of study were associated to differential self-efficacy. GPA was not associated with self-efficacy. Ethnicity, religion, academic level, faculties, institution types, year of study were associated with differential GPA where Brahmin/Kshetri, Hindu, high school degree, management, private college and fourth year had higher GPA than their comparision groups, while age, gender and location of residence, and employment status had no effect on GPA. Interestingly, age was found to be negatively associated with percentage. The overall scale showed its reliability and validity in our cross-cultural context of Nepal with potential questions for further tool validation study.\u003c/p\u003e "},{"header":"Future Research","content":"\u003cp\u003eThese research avenues aim to enhance our comprehensive understanding of the interplay between self-efficacy, demographic variables, and academic performance, providing practical implications for education. Future research is needed to investigate specific factors within demographic categories affecting self-efficacy. The reason behid the higher self-eeficacy in Hindu compared to Buddhist should be explored. Understanding the reasons behind GPA disparities based on ethnicities, religions, academic levels, faculties, institution types, and year of studies is crucial. Examining the dynamic relationship between self-efficacy and GPA over time through longitudinal studies is necessary. Contextual factors influencing the negative association between age and percentage need further exploration. The validation of self-efficacy measurement tools in diverse educational settings is necessary. Additionally, exploring non-demographic variables that impact self-efficacy levels will contribute valuable insights.\u003c/p\u003e\u003cp\u003eFinally, incorporating qualitative methods such as interviews or focus groups could offer a deeper understanding of students' experiences and perceptions, thereby enhancing the effectiveness of educational interventions aimed at improving self-efficacy and academic performance. Longitudinal studies tracking self-efficacy levels over time throughout students' academic journeys could provide valuable insights into the development and fluctuation of self-efficacy beliefs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors received approval from the Administration Board of G.P. Koirala Memorial College, Sifal, Kathmandu.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDev Bandhu Poudel\u0026nbsp;https://orcid.org/0000-0002-3672-185X\u003c/p\u003e\n\u003cp\u003eCorrespondence concerning this article should be addressed to Dev Bandhu Poudel, G.P. Koirala Community College affiliated to Tribhuvan University, Sifal, or Brooklyn International College, Sukedhara, Kathmandu, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDev Bandhu Poudel is a lecturer of Psychology at G. P. Koirala Community College at Sifal in Kathmandu.\u0026nbsp;Samjhana Acharya was\u0026nbsp;the past student at Central Department of Rural Development, Tribhuvan University. We declare no conflict of interest in the publication of this article.\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the various research studies and literature sources cited in this article that have contributed to our understanding of Students\u0026rsquo; anxiety or depression caused by academic overload. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis article represents the authors\u0026rsquo; independent research and does not necessarily reflect the views or opinions of any affiliated institutions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAxford, K. M. 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Age stereotypes, flexible goal adjustment, and well-being among Chinese older adults. \u003cem\u003ePsychology, Health \u0026amp; Medicine\u003c/em\u003e, 1-6. \u003c/li\u003e\n\u003c/ol\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":"General Self-efficacy, grade point average, correlation, reliability, validity","lastPublishedDoi":"10.21203/rs.3.rs-4098484/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4098484/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe observed General Self-efficacy in 527 student participants from Kathmandu valley, studying in different levels, faculties, years and institutions. The students participated from different ages, genders, ethnicities, religious affiliations, marital status, residence, education levels, faculties, varieties of institutions, year of study, employment status of students. Our objective was to analyse the level of general self-efficacy among demographic groups and observe the variables that predicts GPA. We also aimed to find the reliability scores of the items in the scale. We found no relationship of demographic variables with general self-efficacy except the religious affiliations where Buddhists had significantly lower general self-efficacy than Hindu. The general self-efficacy was found to have no impact on GPA. We observed age, gender, marital status, residential location, year of study, employment status as non-predictors of GPA; however, ethnicity (i.e., Janajati), religion (i.e., Buddhists), education level (i.e., high school), education faculty (i.e., mamagement), type of institution (i.e., private) can predict GPA. The reliability score was observed to have been acceptable and the tool demonstrate moderate livel of convergent validity. Future studies must investigate discrepancy in self-efficacy among religious groups to enhance relevant educational strategies.\u003c/p\u003e","manuscriptTitle":"Self-efficacy and Other Predictors in GPA among College Students, Kathmandu, Nepal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 19:47:27","doi":"10.21203/rs.3.rs-4098484/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1becb5ea-9378-4b1a-9bb2-b5f57fd9cafc","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29432903,"name":"Educational Psychology"}],"tags":[],"updatedAt":"2024-03-20T19:47:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 19:47:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4098484","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4098484","identity":"rs-4098484","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}