Examination for Professional Practice of Psychology (EPPP) 2025 – 400 Free Practice Questions to Pass the Exam

The primary goal of a researcher using stepwise multiple regression is to minimize the number of predictors in the model while still maintaining an adequate level of predictive power. This method aids in systematically selecting variables that contribute the most to the variance in the dependent variable. By evaluating the significance of each predictor, the researcher can add or remove predictors step by step, leading to a more parsimonious model.

In this context, minimizing predictors is crucial as it reduces complexity and enhances the interpretability of the model. It also helps in avoiding issues related to overfitting, where a model may perform well on training data but poorly on new, unseen data due to the inclusion of too many irrelevant or redundant variables. Stepwise regression therefore aims to strike a balance between a model that is simple and one that adequately describes the relationships in the data.

While enhancing the validity of the model is an important concern in research, the stepwise approach is more focused on the selection process rather than direct validation. The option regarding maximizing the exposure of variables doesn’t align with the intention of stepwise regression. Additionally, identifying interacting effects can involve more complex modeling techniques, such as moderated regression analysis, rather than the direct application of stepwise multiple regression.