Multiple Regression Analysis in Educational Research

Multiple regression is a statistical technique for examining relationships among multiple variables. It extends simple linear regression by allowing researchers to include more than one predictor variable, making it ideal for complex educational technology data. For example, in online learning environments, an instructor may want to identify which early indicators best predict a student’s final exam score. Does early academic performance, such as a student’s score on their first assessment, matter more, or do background characteristics such as highest education level or number of credit hours play a larger role? Using multiple regression, researchers can systematically control for demographic factors while estimating the unique contribution of early performance metrics. This chapter focuses on applying Ordinary Least Squares (OLS) multiple regression to educational data to examine how various factors jointly influence student outcomes. Multiple regression is a powerful tool for leveraging educational data to improve student outcomes and provides the foundational groundwork for more advanced statistical models (e.g., multilevel modeling, structural equation modeling).