Estimation of multiple mediator model

Models for mediation are widely used in psychology, behavior science and education because they help researchers understand how a causal effect happens through one or several mediating variables. And more complex mediation models that incorporate multiple mediators are increasingly being assessed. This report uses a generated dataset to provide an overview of the assessment of direct effects and indirect effects in multiple mediator models. Use of a multiple comparison-based procedure for testing a set of hypotheses simultaneously while controlling the experiment-wise type I error rate is used to calculate a confidence interval for each pairwise contrast of mediated effects. Three approaches will be used to test hypotheses concerning the contrast between pairs of mediator effects. These approaches include 1) an assumption of zero covariance between parameters from different models, 2) assumption of a non-zero covariance between parameters from different models and 3) use of bootstrapping. Results are provided and discussed.