Exploring Suppression-Like Effects in Logistic Modeling of Stem Participation: Engagement and Self-Efficacy as Correlated Predictors

This study examines the joint statistical roles of STEM engagement and STEM self-efficacy in modeling collegiate students' participation in STEM disciplines. Survey data from students at Texas A&M International University were analyzed using exploratory factor analysis, reliability assessment, group comparisons, and a sequence of nested binary logistic regression models. Two reliable latent constructs, STEM engagement and STEM self-efficacy, were identified. In separate logistic models, both engagement and self-efficacy positively predicted STEM participation. However, the engagement coefficient changed from positive ($B=0.506$, $p=0.009$) in the engagement-only model to negative ($B=-0.568$, $p=0.047$) after adjustment for self-efficacy, while self-efficacy remained strongly positive ($B=1.593$, $p<0.001$). The predictors were substantially correlated ($r=0.698$), although variance inflation factors were only 1.95. Likelihood-ratio testing indicated that engagement provided statistically significant incremental information beyond self-efficacy ($chi^2=4.13$, $p=0.042$), whereas an engagement-by-self-efficacy interaction was not supported. Model comparison therefore suggests a suppression-like coefficient sign reversal rather than a simple interaction effect. These findings illustrate the importance of examining correlated latent predictors jointly and caution against interpreting marginal and adjusted associations as equivalent.

exploratory factor analysis, logistic regression, suppression effect, coefficient sign reversal, correlated predictors, STEM participation.

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