Behavioral Research: Statistical Methods

**Multicollinearity** — high correlation among *predictors* (not between predictors and outcome!) — causes coefficient SEs to balloon, signs to flip, and individual coefficients to become non-significant even when the model overall fits well. **Detect with VIF**. Fix by dropping a predictor, combining into composites, regularising (ridge), or collecting more data. **PCA** reduces dimensions by maximising variance with orthogonal linear combinations of observed variables — no latent model. **Factor Analysis** models *latent constructs* causing observed indicators + error. *PCA explains variance; FA explains shared variance with a generative model.*