How to study social phenomena through multiple regression analysis?

Multiple regression analysis allows researchers to investigate how multiple independent variables simultaneously relate to a single dependent variable representing a social phenomenon. It quantitatively models complex social relationships and predicts outcomes. Researchers must carefully define their research question and select appropriate variables based on theory or prior evidence. Key assumptions requiring verification include linearity, independence of errors, homoscedasticity, normality of residuals, and absence of severe multicollinearity. Meeting these conditions is essential for valid coefficient estimates and significance tests. The method requires interval or ratio-level data, typically collected through surveys, administrative records, or experiments. Interpretation centers on coefficients indicating the effect size and direction of each predictor while controlling for others. To implement it, formulate clear hypotheses linking predictors to the social outcome. Collect high-quality data ensuring adequate variation. Specify the regression model statistically. Analyze using standard software to obtain estimates. Interpret the unstandardized and standardized coefficients (beta weights), p-values assessing statistical significance, and the model's overall fit (e.g., R-squared). Results help identify key social drivers, predict trends, and guide policy by quantifying the relative importance of factors after accounting for others.