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hi all, could somebody tell me if MPLUS is able to combine poisson distributed count data variables (independent as well as dependent) and normal distributed variables in one path model with observed variables? many thanks in advance, joerg. 


In path analysis, observed outcome variables can be continuous, censored, binary, ordered categorical (ordinal), counts, or combinations of these variable types. In addition, for path analysis for nonmediating outcomes, observed outcomes variables can be unordered categorical (nominal). Observed independent variables can be binary or continuous. 


Hi, I have run a path analysis on a longitudinal dataset which combines continuous and count data, treated with Poisson regression and zeroinflated Poisson regression. After comparing a few alternative models, I have choosen the best model (the lowest BIC). Now, I would like to crossvalidate this model on a second dataset, but I have 2 doubts: 1) How can I evaluate the goodness of the model for the crossvalidation when Poisson regression is involved? I guess that I cannot use the traditional tests of model fits... 2) What is the syntax for the crossvalidation? Are there some specific aspects that I should take into account? The syntax for my model follows: VARIABLE: NAMES ARE y1y6 u1u3 x1x2; USEVARIABLES ARE y1y6 u1u2 x1 x2; COUNT IS u1; COUNT IS u2 (i); MODEL: y1 on x1 x2; y2 on x1 y1 u1; y3 on x1 y1 y2 u2; y4 on x1 x2; y5 on y4 x2 u1; y6 on y4 y5 x2 u2; u1 on x1 x2; u2 on x1 x2; u2#1 on x1 x2; y1 y2 y3 (3); y4 y5 y6 (4); y6 with y3@0; y6 with y1@0; y6 with y2@0; y1 with y3@0; y2 with y1@0; y2 with y3@0; Thank you very much for your time and support. michela 


I'm sure there is a crossvalidation literature out there. I am not familiar with it. With count variables, chisquare and related fit statistics are not available. Nested models can be compared using 2 times the loglikelihood difference which is distributed as chisquare. As far as crossvalidation, I would look at the pattern of signficance across the two data sets. You could also do a multiple group analysis. 


Dear Linda, Thank you very much. Best, michela 


We are running an SEM model in which we want to use zero inflated poisson regression to predict each of 4 count variable criteria. We have checked each count variable to ensure that each has only integer values. Each time we try to run the analysis, we get an error message saying "There is at least one observation in the data set where a count variable has negative or noninteger values." None of the 4 variables shows anything but 0, 1, 2, 3, 4, or 5. We cannot fix it and need help. Thank you. 


It sounds like you have blanks in the data set. This is not allowed with free format data. If you can't figure it out, please send the input, data, output, and your license number to support@statmodel.com. 

sahar shadi posted on Wednesday, August 22, 2012  11:21 pm



Dear all, I am now constructing a SEM model with 1 exogenous variable and 1 final endogenous variable . In between there are 5 key mediators (2 latent and 3 observed). The type of my endegenous variable is count. I used AMOS18 but I know I have to do this analysis with MPLUS Is my analysis completly wrong ? or is better to do with mplus? I am new begginer. please help me thank you very much 


You should not treat a count variable as a continuous variable. This will result in improper results. 

sahar shadi posted on Thursday, August 23, 2012  10:29 pm



Thanks for answer . I did this model with SMARTPLS also . Is it wrong in SMARTPLS also ? thank you in advance 


It would be incorrect if you do not treat the count variable as a count variable. I don't know anything about SMARTPLS so I can't say. 

sahar shadi posted on Friday, August 24, 2012  9:51 am



Thank you very much Linda 


Hello, I am running a model in which I have a count predictor (IV), and continuous mediators and outcomes. Can I use COUNT = IV in Mplus, when the predictor is count (and not the DV)? Would it be appropriate to specify a predictor as a count variable? Thank you so much 


The scale of predictors is not taken into account in regression. Only the scale of dependent variables matters. All predictors are treated as continuous variables. 

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