Statistical control and regression analysis (simpler)

Описание: In social science research, establishing causal relationships is crucial. However, merely observing a correlation between two variables doesn't confirm causality. The gold standard for determining causality is the experiment, where subjects are randomized into treatment and control groups. However, many situations don't allow for experimental setups, so researchers rely on observational data and statistical techniques to control for alternative explanations.

Regression analysis is a primary statistical method used in social sciences. It examines how one variable (e.g., CEO gender) affects another (e.g., company profitability) while controlling for other factors (e.g., company size or industry). The idea is to eliminate spurious correlations, which are misleading relationships that might be caused by a third variable.

Two key assumptions in regression analysis are that all relevant controls are included in the model and that relationships are linear. If these assumptions aren't met, the regression results might not be trustworthy.

In essence, statistical controlling allows researchers to approximate experimental conditions by statistically adjusting for potential confounding variables. This helps in making more accurate causal claims based on observational data. Understanding regression analysis provides a solid foundation for grasping more complex statistical techniques in social science research.

Link to slides: https://osf.io/wyjmb