Randomized Response Analysis in Mplus

Continuous - Time Survival Analysis in Mplus

Here we will describe the basic continuous time survival model implemented in Mplus and will provide some details on the basic modeling options that are available. Introduction to continuous time survival modeling can be found in Singer & Willett (2003), Hougaard (2000) or Klein & Moeschberger (1997). Survival analysis: techniques for. The survival models implemented in Mplus includes many exte...

response articles: micro and macro analysis

the present study reports an analysis of response articles in four different disciplines in the social sciences, i.e., linguistics, english for specific purposes (esp), accounting, and psychology. the study has three phases: micro analysis, macro analysis, and e-mail interview. the results of the micro analysis indicate that a three-level linguistic pattern is used by the writers in order to cr...

Bayesian Analysis Using Mplus: Technical Implementation

IRT in Mplus

In this note we describe several of the IRT modeling features implemented in Mplus, namely the the item characteristic curves, the item information curves, the total information curve, item difficulty parameter and item discrimination parameter. Different estimators and parameterizations are considered. We also provide details on the Mplus implementation of the Partial Credit Model (PCM), the 3...

Standardized Residuals in Mplus

The fit of structural equation models with normally distributed observed and latent variables can be evaluated by examining the normalized and standardized residuals computed in Mplus. These residuals are available for the ML, MLR, and MLF estimators and can be obtained by the residual output command. Suppose that Y is the vector of dependent observed variables and η is the vector of latent var...