Introduction.- Comparing differences across groups.- Assessing (innocuous) relationships.- Models with latent concepts and multiple relationships: structural equation modeling.- Nested data and multilevel models: hierarchical linear models.- Analyzing longitudinal and panel data.- Causality: Endogeneity biases and possible remedies.- How to start analyzing, test assumptions, and deal with that pesky p-value.- Keeping track and staying sane.