In most social psychological studies, researchers conduct analyses that treat participants as a random effect. This means that inferential statistics about the effects of manipulated variables address the question whether one can generalize effects from the sample of participants included in the research to other participants that might have been used. In many research domains, experiments actually involve multiple random variables (e.g., stimuli or items to which participants respond, experimental accomplices, interacting partners, groups). If analyses in these studies treat participants as the only random factor, then conclusions cannot be generalized to other stimuli, items, accomplices, partners, or groups. What are required are mixed models that allow multiple random factors. For studies with single experimental manipulations, we consider alternative designs with multiple random factors, analytic models, and power considerations. Additionally, we discuss how random factors that vary between studies, rather than within them, may induce effect size heterogeneity, with implications for power and the conduct of replication studies.

Chapter 14 covers TWO-WAY ANALYSIS OF VARIANCE and includes the following specific topics, among others: Statistical Interaction, Balanced versus Unbalanced Factorial Designs, F-Ratio, Effect Size, Fixed Factors, Random Factors, Post-Hoc Multiple Comparison Tests, Simple Effects, and Power Analyses.

Chapter 14 covers two-way analysis of variance and includes the following specific topics, among others: statistical interaction, balanced versus unbalanced factorial designs, F-ratio, effect size, fixed factors, random factors, post hoc multiple comparison tests, simple effects, and power analysis.