Message/Author 

Erin P posted on Monday, July 09, 2012  7:59 pm



I'm hoping you can help me figure out where I'm going wrong... VARIABLE: ... MISSING ARE .; CENSORED ARE outcome (b); MODEL: F1 BY v1v3 F2 BY v4; F3 BY v5; F4 BY v6; F5BY v7; v4@0; v5@0; v6@0; v7@0; F6 BY v8; F6 ON F1F5; F7 BY outcome; F7 ON f6; outcome@0; v8@0;  This is the error message I get: *** FATAL ERROR THE STARTING VALUE FOR THE VARIANCE OF A CENSORED VARIABLE outcome IS NOT POSITIVE. 


I would imagine it is for setting the residual variance of outcome at zero. It is not necessary to put a latent variable behind an observed variable in Mplus. Simply specify your model using the observed variables. MODEL: F1 BY v1v3 v8 ON f1 v4 v5 v6 v7; outcome ON v8; 

Barbara O. posted on Tuesday, January 08, 2013  6:06 pm



I'm conducting a path analysis using a censored outcome variable (time 3 variable), 3 continuous mediators (time 2 variables), and 3 initial (time 1) predictors. The analysis requires montecarlo integration, and I was able to use the constraint command to estimate the indirect effects. I'm wondering: 1) Is there a way to use bootstrapping to test for sig mediation (rather than testing the indirect effects)? I get an error message when I try. 2) The output provides AIC, BIC, and my loglikelihood value, but not RMSEA or GIF is there a way to get these values using path analysis with a censored outcome variable? 


1. What is the message? 2. Chisquare and related fit statistics are available only when means, variances, and covariances are sufficient statistics for model estimation. This is not the case when numerical integration is needed. 

Barbara O. posted on Tuesday, January 08, 2013  6:46 pm



1. The error message I receive is: "BOOTSTRAP is not allowed with ALGORITHM=INTEGRATION." I'm concerned reviewers might ask for RMSEA. If so, is a response similar to yours above something appropriate to give? Thank you for your time! 


1. BOOTSTRAP has not yet been developed for numerical integration. 2. RMSEA does not exist when means, variances, and covariances are not sufficient statistics for model estimation. 

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