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Uncovering Key Predictors of Statistics Achievement among Postgraduate Students: A Stepwise Regression Model |
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| รหัสดีโอไอ | |
| Creator | Deo G. Indunan |
| Title | Uncovering Key Predictors of Statistics Achievement among Postgraduate Students: A Stepwise Regression Model |
| Contributor | Jennifer Madonna G. Dait, Arri Steven P. Dulnuan |
| Publisher | Phetchaburi Rajabhat University |
| Publication Year | 2568 |
| Journal Title | Interdisciplinary Research Review (IRR) |
| Journal Vol. | 20 |
| Journal No. | 4 |
| Page no. | 1-9 |
| Keyword | Academic performance, class satisfaction, computer literacy, statistics |
| URL Website | https://ph02.tci-thaijo.org/index.php/jtir |
| Website title | Interdisciplinary Research Review (IRR) |
| ISSN | 2697-536X |
| Abstract | Academic performance in statistics remains a significant challenge for many postgraduate students, despite its vital role in fostering research competence and overall academic success. This study aimed to identify key predictors of academic performance in statistics among postgraduate students enrolled in higher education institutions. Employing a quantitative approach with a descriptive correlational design, the study examined three potential predictors: satisfaction with the statistics class, basic computer literacy, and prior academic performance in statistics. The outcome variable was students’ final performance in the statistics course. Data were collected through a complete enumeration of all 587 postgraduate students who enrolled in the course during the academic years 2021-2023. As the entire target population was included, no sampling technique was applied. Pearson’s product-moment correlation was used to assess the associations among variables, while stepwise multiple linear regression was employed to develop a predictive model for academic performance in statistics. Model adequacy and the proportion of variance explained were evaluated using standard fit indices and the coefficient of determination (R²). The findings revealed statistically significant correlations between the predictors and academic performance. Moreover, the regression model demonstrated acceptable fit, although the explained variance was relatively low. Among the predictors, satisfaction with the statistics class emerged as the most influential factor affecting academic performance. |
