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I am working on a multilevel analysis and I am interested in the groupspecific betas. So I had the idea to save the grouplevel residuals. As far as I read in some above posted threads this was not possible in earlier versions of Mplus. Is this still true of Mplus 5.2 (5.21)? 


Betweenlevel factors scores can be estimated and saved. 


Thank you for your reply! I had already tried the FSCORESoption but had difficulties in understanding what the datfile shows. Now I see what you mean. 


hello, i am running twolevelregressionmodels w/ manifest variables using the complex option. i've been wondering whether it is possible to check for model assumptions (i. e. normality of residuals) while or after running the analyses? if so, could you please let me know how this can be done? is there a way to save level 1 and level 2residuals? thank you very much for your help! 


Type=Complex uses the MLR estimator which is already robust to deviations from normality. There is no option for saving or checking normality of residuals. 


thank you very much for your prompt reply. that helps! 


dear professors, i realize my questions are kind of naive and i apologize  i'm only getting started working with mplus. since my last post, i've come to realize that it's actually the "twolevel"option i'm using in my analyses. am i using the mlr estimator all the same and how about the fiml algorithm? i was told it was working automatically if i wrote nothing but "type = twolevel" in my analysiscommand. is that true? the reason i'm asking is that i get the following warning: *** WARNING Data set contains cases with missing on xvariables. These cases were not included in the analysis. Number of cases with missing on xvariables: 20 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS I get a similar warning concerning the dependent variable. is fiml replacing the missing values still or are they in fact excluded? again  sorry to bother you if this seems all too stupid to ask. Thank you very much for your time! 


Observations with missing values on the observed exogenous variables in the model are excluded because the model is estimated conditioned on these variables. Missing data theory applies only to observed endogenous variables. If you want the observations with missing on the observed exogenous variables to be included, mention their variances in the MODEL command. They will then be treated as endogenous variables and distributional assumptions will be made about them. Observations will missing on all dependent variables are also excluded because they contribute no information. 


i'm sorry, but i still don't seem to understand. it's a multilevel regression model with manifest variables and i'm not specifying anything but "type = twolevel" in the model command. in this case  am i using mlr (because it's the default) and is fiml operating, too although i get the warning menttioned above, that variables with missings on xvariables are excluded from the analysis? i mean are they estimated by fiml despite the warning or are they really not included? thank you!! 


They are not included as stated in the message. FIML is used for estimating the model. Means, variances, and covariances of observed exogenous variables are not parameters in the model. 


thank you very much for your answer! but why is it that i don't find fiml in the "twolevel"section of the "estimator"list in the user's guide (p. 483)? i still don't seem to get that straight: with type = twolevel and nothing else changed  am i using mlr as stated in the user's guide or am i using fiml? or are they in fact the same? where can i find a reference to better understand what fiml is doing? how can i best describe to a reader how my models are being estimated? 


The current Version 6 user's guide has the information on page 532. The estimators ML, MLR, and MLF are fullinfomration maximum likelihood estimators (FIML). See the Little and Rubin book on the reference list in the user's guide for a description of how Mplus handles missing data estimation. 

Jak posted on Tuesday, May 24, 2011  5:49 am



Dear prof. Muthen, Does FIML (with MLR estimation for example), mean that the parameters are estimated by optimizing the likelihood of the raw data? Or the likelihood of observed covariances and means? Thanks in advance! 


Raw data. 

Jak posted on Wednesday, May 25, 2011  2:54 am



Thank you very much for your quick reply. Is this the case for both continuous and categorical variables? I am wondering because computational time is so much shorter for continuous data. 


Yes. With categorical outcomes and maximum likelihood estimation, some models require numerical integration. This is computationally very demanding. There is a brief description of this in the user's guide. 

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