Uncovering Key Predictors of Statistics Achievement among Postgraduate Students: A Stepwise Regression Model
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Abstract
The primary objective of this study was to identify the factors that predict academic performance in statistics among postgraduate students at higher education institutions. This quantitative study utilized a descriptive correlational design and focused on three predictors: level of satisfaction with the statistics class, basic computer literacy, and prior performance in statistics. The academic performance in the statistics course served as the outcome variable. The study involved a sample of 587 postgraduate students who took the statistics course during the 2021-2023 academic years. Using the Pearson Product-Moment correlation coefficient, correlation analysis was conducted to investigate the associations between the predictors and the outcome variable. A model that explains the relationship between the predictors and learning outcomes in the statistics course was also developed using stepwise multiple linear regression analysis. Both the model fit and the coefficient of determination as measured by the R squared value were assessed. The results revealed a significant correlation between the predictors and the outcome variable. Furthermore, the predictive linear model for academic performance in statistics indicated significant model adequacy, despite a minimal coefficient of determination. Notably, the level of satisfaction with the statistics class emerged as the strongest predictor of academic outcomes in statistics.
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