Chapter 7: Censored dependent variable view examples Chapter 8: Mediation non-continuous variables view examples Chapter 9: Bayesian analysis view examples Chapter 10: Missing data view examples * Note: The examples in chapter 4 do not include any Mplus runs....
All this is explained and exemplified in Chapter 7 of our book Regression And Mediation Analysis Using Mplus: http://www.statmodel.com/Mplus_Book.shtml John B. Nezlek posted on Saturday, September 02, 2017 - 9:41 pm Thank you Bengt for the quick reply. At the risk of overstaying...
Otherwise, the result of the multiple regression and the corresponding effect size △R2 should be reported. We exemplify how to conduct the proposed procedure by using Mplus. It is noteworthy that, with this software, a two-level regression model could be built via a "trick" for 2-level ...
The text covers both MR and SEM using MR as starting point for understanding SEM. The MR portion includes simultaneous and sequential regression, the analysis of interactions and curves, and mediation analysis. The SEM portion includes path analysis, confirmatory factor analysis, latent variable SEM,...
I am running a multilevel regression analysis using Mplus (with random intercepts and slopes). In my model, I've entered three continuous level 2 independent variables, two dummy-coded level 1 independent variables and a continuous dependent variable. My level 2 sample size is only 10 (lev...
Mediation Analysis Missing Data Mixture Modeling Multilevel Modeling Randomized Trials RI-CLPM RI-LTA Structural Equation Modeling Survival AnalysisHow-To Using Mplus via R - MplusAutomation Mplus plotting using R H5 results Chi-Square Difference Test for MLM and MLR Power Calculation Monte Carlo Uti...
I obtain from the model a set of WITHIN and BETWEEN parameter estimates (“Beta”s), plus a BETWEEN cluster Tau-1 and Tau-2 estimates. The residual variance of I is non-zero. I’m interested in using the Mplus parameter estimates to predict values of CAT (CAT-HAT) and seeing how we...
I am using mplus 4.2. Thanks! Title: mixture Data: a.dta.dat; Variable: Names are a1 a2 a3; Categorical are a1 a2 Classes = c(4); Analysis: Type=mixture; Model: %OVERALL% a3 ON a1 a2; c ON a1 a2; Output: tech1 tech8; *** ERROR in Model command...