REGRESSION ANALYSIS IN PSYCHOLOGICAL RESEARCH: METHODOLOGICAL CHALLENGES AND CONTEMPORARY ISSUES

Abstract

Regression analysis, an essential statistical technique in psychological research, is used to examine relationships among variables and to predict outcomes. However, its application is often hindered by various methodological challenges that undermine the reliability of results. These include issues with model specification, assumption violations, multicollinearity, overfitting, and inadequate interpretation. This paper highlights critical gaps in the current use of regression analysis, particularly in psychological research. The research underscores the importance of carefully considering model assumptions and diagnostic tests to avoid flawed conclusions. It also discusses the implications of these issues for psychological research, especially regarding the interpretation of causal relationships and the generalizability of findings. The paper proposes several innovations and recommendations to mitigate these challenges, including improving model specification through theoretical knowledge and exploratory data analysis, conducting rigorous assumption checks, employing advanced techniques such as bootstrapping for model validation, and ensuring clear and accurate reporting of results. These steps are essential for enhancing the validity and reliability of regression-based findings in psychology.