## Introduction to statistical mediation analysisThis volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations. Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology.The book opens with a review of the types of research questions the mediation model addresses. Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions. Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate level research methods or statistics course is assumed. The overview of mediationanalysis and the guidelines for conducting a mediation analysis will be appreciated by all readers. |

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### Contents

Introduction | 1 |

Applications of the Mediation Model | 23 |

Single Mediator Model | 47 |

Copyright | |

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### Common terms and phrases

approach assess mediation assumptions autoregressive baseline behavior bootstrap causal effect causal relations compute confidence limits control group correlation covariance matrix data sets delta method dependent variable described earlier described in chapter difference score drug effect size effect size measures equal example experimental formula group level hypothesized independent instrumental variable interaction intercept intervention Kenny latent growth latent variable LISREL logistic regression MacKinnon medi mediated effect mediating variables mediation analysis mediation relations mediational processes Mplus multilevel model multiple mediator model normal distribution observed outcome Output for Equation Parameter Estimates participants path analysis predicted predictor prevention program probit probit regression proportion mediated random randomly assigned regression coefficients relations among variables represents resampling methods residual sample single mediator model slope social specific indirect effects specified standard error statistically significant structural equation models surrogate endpoints theory tion total indirect effects treatment two-mediator model types values variance zero