which is part of many different types of analyses such as mediation analysis, factor analysis, Item Response Theory analysis, growth modeling, mixture modeling, and multilevel modeling. In addition to regression with a continuous dependent variable, this involves building blocks for binary, ordinal, nominal, count, and censored dependent variable using logistic, probit, multinomial logistic, Poisson, zero-inflated Poisson, negative binomial, censored-normal (Tobit), censored-inflated, Heckman, and two-part modeling.

The course covers the modern treatment of mediation analysis using counterfactually-defined causal effects. A unique feature is an emphasis on mediation with binary, ordinal, nominal, count, and censored mediators and outcomes, avoiding shortcomings of traditional effect definitions.

The course gives an introduction to Bayesian analysis as implemented in Mplus. Examples of Bayesian advantages over maximum-likelihood estimation are discussed. An introduction is also given to missing data analysis under MCAR, MAR, and NMAR assumptions using maximum-likelihood and Bayesian analysis. A particular emphasis is on missing data for covariates including covariates that are binary.

Mplus inputs and outputs are discussed for a multitude of real-data examples as well as for Monte Carlo simulations.

nested within individuals. Individual differences in level-1 parameters such as the mean, variance, and autocorrelation are represented as random effects that are modeled on level 2 in a two-level analysis.

Mplus Version 8, released April 20, 2017, offers two-level, cross-classified, as well as single-level (N=1) time series analysis. In cross-classified analysis the random effects are allowed to vary not only across individuals but also across time to represent time-varying effects.

Mplus can estimate a variety of N=1, two-level and cross-classified time series models. These include univariate autoregressive, regression, cross-lagged, confirmatory factor analysis, Item Response Theory, and structural equation models for continuous, binary, ordered categorical (ordinal), or combinations of these variable types. Bayesian analysis is used in the estimation using a flexible latent variable modeling framework referred to as dynamic structural equation modeling (DSEM).