Message/Author 

Inga BEck posted on Thursday, August 14, 2008  3:05 am



Dear Authors I am planning to use Mplus 5.0 for a twolevel logistic analysis with a binary dependent variable and MLestimation. In my model, the predictor variables are situated either on the withinlevel (x) or the between level (w) only. All withinlevel effects are fixed (no random slopes). Mplus Manual p. 407 shows how to transform logistic regression coefficient in various ways. My question is whether these guidelines also apply for calculating odd ratios for betweenlevel (clusterlevel) predictors. Other than odd ratios for withinlevel predictors, such odd ratios are not automatically shown in the Mplusoutput. May I use Mplusexample ex9.3 for illustration. There is an unstandardised bvalue of 1.269 for the logistic regression of u on betweenlevel predictor w. W ould it be correct to transform this coefficient by exp(1.269) = 3.55, meaning that “between clusters, the odds of u = category ‘a’ vs. u = category ‘b’ increase by factor 3.55 for each unit increase in w? Many thanks for your comments Inga 


On the between level u is a random intercept. It is a continuous latent variable. The coefficient is a linear regression coefficient. It cannot be turned into an odds ratio. 

Inga BEck posted on Thursday, August 14, 2008  7:23 am



Ok, thank you. Yet now I have two follow up questions: Q 1. For the hypothetical model described above, in principle I could also use the stdyxstandardised coefficient of betweenlevel predictor w for interpretation? Q 2. If I would use WLSMV instead of ML and thus estimate a probit regression, the intercept (threshold)of u would still be considered to be continous and again parameter estimates of betweenlevel predictors would be linear regression coefficients, not probitcoefficients? Thanks for your help 


Q1. Yes. Q2. Yes. 

Inga BEck posted on Tuesday, August 19, 2008  3:07 am



Hello Yet I don't fully understand the Mplus approach to twolevel modeling for categorical data with maximum likelihood (keeping wlsmv aside for a moment). More specifically, Bryk and Raudenbush (2002, p.300f. ) give an example of a twolevel model with a dichotomous outcome including both level1 and level2 covariates. Here the effects of both level1 and level2 covariates are expressed in logit coeffecients and odd ratios. Obviously, while some statistical perspectives (and software programs used by Bry & Raudenbush, see also the example in the book by Joop Hox) use a logit link for both within (level 1) and between (level2) relations, Mplus proceeds in a way that allows for linear regression coefficients on level2 . I would be very grateful for more information just how (statistically, technically) Mplus treats varying intercepts (question 1) and slopes (question 2) in a twolevel framework with categorical dependent variable so that one can use linear regression (Not: logit) coefficients. Question 3: Is there any recommendable literature on this question for applied researchers (perhaps 'below' the level of psychometrika)? Or examples of published articles using the Mplus approach? Many thanks for your support! 


Mplus modeling is the same as other programs doing 2level ML for categorical data  and the interpretations are the same. Good that you gave a specific references to pages in the R & B book, which clarifies our discussion. Page 300 does talk about logodds, but note that this concerns regression coefficients that appear in level1. As an example gamma10=0.492. From equation 10.13, beta1j = gamma10 where beta1j is the fixed slope for SES in the level1 equation. When we say that level2 regression coefficients are those of linear regressions (because the dependent variables on level 2 are continuous), we refer to the coefficients gamma00, gamma01, gamma02, and gamma03 in eqn 10.13. In that equation, the dependent variable is beta0j which is a continuous dependent variable and therefore the gamma's are regular linear regression coefficients. Having clarified that, we see from eqn 10.12 that ultimately beta0j does affect etaij and therefore ultimately does affect the probability of the binary outcome. So, R & B is a good and sufficient reference for this. I can't remember seeing a less technical one. 


Just adding to the previous, when you insert beta0j into the level1 equation and therefore consider etaij (the logodds) as the dependent variable, then the gamma0's can be turned into logodds interpretations. R & B shows an example of that for gamma03. So it is a function of which dependent variable one considers, beta0j or etaij. 

Sarah Hall posted on Thursday, September 08, 2011  11:32 pm



Hello, I am modelling relationships between individual and group level predictors and a binary outcome (using TYPE=TWOLEVEL RANDOM). I would like to plot the significant crosslevel 2way and 3way interactions as logistic curves. Is it possible to do this in MPlus? I have tried each of the commands: “TYPE is plot1;” “TYPE is plot2;” and “TYPE is plot3;” but these only allow me to create scatterplots and histograms. Thank you Sarah 


With PLOT3 all available plot options are given. 


Can Mplus fit (or be tricked into fitting) a 2level random effects multinomial logistic regression model? I have multiple imputations and weights. I have been successful in fitting binary and ordinal models to the data. 


No trick needed. On the Between level, c#1 c#2, etc are random intercepts. 


Hi, I am trying to assess whether the level1 binary variable best friend stability (bfstab) varies across 9 schools (cluster variable) based on school policies regarding friendships (level2 binary variable frreq). I have opted to test this using the TWOLEVEL procedure, using the code below: USEVARIABLES ARE School bfstab frreq; MISSING ARE BLANK; IDVARIABLE = IDnum; CATEGORICAL = bfstab; CLUSTER IS SCHOOL; BETWEEN = frreq; ANALYSIS: TYPE IS TWOLEVEL; MODEL: %BETWEEN% bfstab ON frreq; I would be very grateful if you could help me regarding 3 questions. 1. Does this model code seem correct? 2. How do I interpret the unstandardised estimates for bfstab ON frreq? 3. I was confused as to why the unstandardised and SDYX pvalues differed so much, is this indicative of a modelling error on my behalf?  output below: MODEL RESULTS TwoTailed Estimate S.E. Est./S.E. PValue Between Level BFSTAB ON FRREQ 0.351 0.160 2.190 0.029 STDYX Standardization TwoTailed Estimate S.E. Est./S.E. PValue Between Level BFSTAB ON FRREQ 0.900 0.626 1.438 0.151 


You should not do twolevel modeling with only 9 clusters. There should be at least 3050. 

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