Attitudes Toward Statistics as Predictors of Learning Strategy in Statistics Education: A Logistic Regression Analysis

Statistics is a core competency in higher education; however, many undergraduates approach introductory statistics courses with anxiety, low confidence, and perceptions of difficulty that may influence their learning strategies. This study investigated whether multidimensional attitudes toward statistics predicted undergraduate students’ adoption of deep rather than surface learning strategies. A quantitative cross-sectional study was conducted among 306 undergraduates enrolled in introductory statistics courses at a Malaysian public university. Data were collected using adapted versions of the Survey of Attitudes Toward Statistics (SATS-36) and the Revised Two-Factor Study Process Questionnaire (R-SPQ-2F). Binary logistic regression was employed to estimate the probability of adopting a deep learning strategy based on six attitudinal dimensions: affect, cognitive competence, value, perceived difficulty, interest, and effort. The findings indicated that effort was the strongest positive predictor of deep learning strategy adoption (OR = 2.475, 95% CI [1.511, 4.051]), followed by interest (OR = 1.871, 95% CI [1.132, 3.093]). Conversely, perceived difficulty was negatively associated with the adoption of a deep learning strategy (OR = 0.493, 95% CI [0.258, 0.942]), indicating that students who perceived statistics as more difficult were less likely to adopt a deep learning strategy. The model achieved an overall classification accuracy of 67.0% and demonstrated greater accuracy in identifying deep learners than surface learners. These findings highlight the importance of strengthening students’ effort and interest while reducing perceived difficulty to promote deeper engagement with statistical learning in higher education.

Binary logistic regression; Deep learning; Learning strategies; SATS-36; Statistics attitude; Surface learning

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