This chapter introduces the conceptual and statistical basics of mediation analysis in the context of experimental research. Adopting the respective terminology, mediation analysis can be referred to as an array of quantitative methods developed to investigate the causal mechanism(s) through which an independent variable influences a dependent variable. The chapter takes a regression-based approach to mediation analysis and focuses on mediation models likely to be tested in experiments (i.e., the single mediator model, parallel and serial multiple mediator models, and conditional process models). Yet, the scope of mediation analysis beyond an experimental setting will also be touched upon. Furthermore, the chapter addresses the question how to strengthen causal inference in mediation analysis through design, the collection of additional evidence, and statistical methods. It closes with a discussion of common topics of relevance when implementing mediation analysis such as sample size and power, mean centering in conditional process analysis, coding of categorical independent variables, advantages and disadvantages of a regression-based approach to mediation analysis, and software options to perform mediation analysis.

Following needs to be significant in model 8 if hypotheses need approval.

1. Interactions

2. Index of moderated-mediation

3. Conditional effects (either 1+sd or 1-sd) in case interaction is insignificant -- for that you need to have outcome(s) being predicted at higher or lower levels of predictor(s)