Successful factors in statistics learning for non-STEM courses students: A PLS-PM approach

The paper focuses on factors affecting students’ Statistics performance in non-STEM (Science, Technology, Engineering, and Mathematics) degree courses. Specifically, this study examines the effect of students’ math knowledge, amotivation, self-efficacy, attitude toward Statistics and statistical anxiety on performance in higher education. Data were collected from 201 Italian psychology students enrolled in an undergraduate introductory Statistics course. The partial least squares path modelling (PLS-PM) was used to test our hypothesis. Overall, our findings show the potential role of math knowledge, selfefficacy and attitude toward Statistics as predictors of Statistics performance. Instead, statistical anxiety is not significantly related to students’ performance. Finally, directions for future research and practical implications of the findings are also discussed.