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Regression And Mediation Analysis Using Mplus 

With the purchase of a single copy of the book Regression and Mediation Analysis Using Mplus, all international customers  are offered a special, subsidized shipping  rate of US$20.00 due to the high cost of shipping internationally.   When ordering more than one copy of the book at a time, the subsidized shipping rate of US$20.00 is not available.

Chapter 1 covers linear regression analysis including regression with an interaction, multiple-group analysis, missing data on covariates, and heteroscedasticity modeling. Chapter 2 covers mediation analysis with a continuous mediator and a continuous outcome including moderated mediation. Chapter 3 covers special topics in mediation analysis that are not normally found in books on mediation analysis. These include Monte Carlo simulation studies of mediation and moderated mediation, model misspecification due to omitted variables and confounders, instrumental variable estimation, sensitivity analysis, multiple group analysis of moderated mediation, and measurement error. Chapter 4 covers causal inference based on counterfactuals for mediation analysis with a continuous mediator and a continuous outcome. Chapter 5 covers regression analysis for categorical dependent variables including binary, ordinal, and nominal variables. Chapter 6 covers regression analysis for a count dependent variable including the following models: Poisson, Poisson with a random intercept, zero-inflated Poisson, negative binomial, zero-inflated negative binomial, two-part (hurdle) with zero-truncation, and varying-exposure. Chapter 7 covers regression analysis for a censored dependent variable including the following models: censored-normal (tobit), censored-inflated, sample selection (Heckman), two-part, and switching regressions. Chapter 8 covers causal inference for mediation analysis with a binary outcome and a continuous mediator, a count outcome and a continuous mediator, a two-part outcome and a continuous mediator, a binary and an ordinal mediator, a nominal mediator, and a mediator with measurement error. Chapter 9 discusses Bayesian analysis and uses it to estimate several mediation examples which show how it can be used as an alternative to maximum likelihood estimation. Chapter 10 discusses several approaches to missing data modelling including missing completely at random (MCAR), missing at random (MAR), and not missing at random (NMAR) including selection modeling.

A more detailed description including the table of contents is available.