Message/Author 

Anonymous posted on Thursday, February 03, 2000  12:16 pm



Studies based on small samples are very common in social and behavioral sciences. One seldom knows the power of those findings. Could it be possible to incorporate Monte Carlo procedures in a study with a small sample? Has anyone done so in "content" studies rather than methodological studies? Can you give me some guideline, especially on using Mplus? 


I think it is a very good idea to include a small Monte Carlo study also in "content" studies. This would give great insights into the quality of the results. Often, parameter estimates are good even at small samples but the quality of the standard errors, parameter coverage, the power of detecting effects, and the quality of overall tests of fit may be in question. When the parameter estimates are likely to be dependable they can be taken as rough population values for a Monte Carlo simulation. The population mean vector and covariance matrix can be computed for any model by fixing each parameter at its population value and requesting RESIDUAL (see estimated mean vector and covariance matrix). I have not, however, seen Monte Carlo approaches taken in content studies, but it is possible that this idea has been used. In my 1997 Psych Methods article with Curran, we did something akin to this to study power in our realdata application (see also the McCallum article referred to in that article). In that article, power was computed both via Monte Carlo simulation and using population values (SatorraSaris approach). Mplus allows Monte Carlo simulations in an automated fashion (data are generated, analyzed, and result summaries presented by Mplus) for several analysis types. See Chapter 29 of the User's Guide. Exceptions include twolevel and mixture analysis and for such cases, Monte Carlo simulated data can be generated outside Mplus as my research group often does. It would be good if articles including Monte Carlo were published to show the usefulness of the approach. 

Anonymous posted on Tuesday, October 03, 2000  3:15 pm



I try the Monde Carlo examples in the Chapter 29 of the User's Guide,but it comes out error messageInsufficient data in "monte.dat".Why?How can I fix it? 


You must add a line to the data for the means of the dependent variables. This was left out in the first printing of the User's Guide. See the description of the FILE statement for Monte Carlo in Chapter 12. 

Subert Wu posted on Friday, October 06, 2000  2:52 pm



I use Mplus to do Monte Carlo simulation study.I want to generate 1000 replications data,but SAVE command just allows me to save the first one.Please tell me how to save the rest 999 replications data. 


You can save data from only the first replication. There is no way to save the other replications. 


I have previous conducted a 1factor model with about 20 dichotomous indicators. I am finding that the item difficulty values (threshold/loading) are spotty in the lower portion of the factor score continuum. In order to create viable factor scores outside the context of Mplus, I have composited the items into 4 continuous level indicators by averaging over a set of dichotomous items with widely varying item difficulty levels. This way, I can use the factor score coefficient matrix to estimate the factor scores in a straightforward way without iterative procedures. This model fit the data very well but the constructed factor scores using the factor score coefficient matrix are very susceptible to the underidentification in the lower end of the factor  the distribution is very skewed. I plan to collect data on the original indicators as well as a number of new dichotomous indicators I hope will adequately measure the same factor and fill in the needed item difficulty levels. I am thinking I need to construct the "testlets" as before and set the measurement model parameters of these testlets to equal the results I obtained in my current dataset, then estimate the new indicators' measurement parameters freely. This is a very longwinded way of saying that I am trying the Monte Carlo feature of Mplus for the first time to try to determine the sample size needed to obtain stable and unbiased measurement parameter estimates for new dichotomous items with a variety of factor loading and threshold values, holding the rest of the factor model to the original obtained values from the continuous indicator model. How do I generate a set of data of this sort, where population parameters drive the generation of continuous and dichotomous data? Must I try to construct a reasonable variance/covariance matrix (out of thin air?) of the 4 continuous and 4 hypothetical dichotomous indicators in order to generate the data or can I do something akin to the approach in the Monte Carlo examples in the most recent addendum? My concern is that the mixture modeling approach used in the addendum example will not allow me to use the CUTPOINT and CATEGORICAL options I need. As always, I am very grateful for your help and amazed with the attention you give to us Mplus groupies. 

bmuthen posted on Thursday, August 29, 2002  9:49 am



Yes, to do Monte Carlo with categorical outcomes and continuous latent variable, the current Mplus requires you to go through the older Monte Carlo track (not the mixture track) and therefore construct a population covariance matrix form which the data are drawn. But not constructed out of thin air. The covariance matrix is for the y* variables, the continuous outcomes before categorization. The covariance matrix elements are obtained from the parameter values you hypothesize for the loadings, the factor (co)variances, and the residual variances. You can get this matrix in a run where you pretend you have continuous outcomes, inputting say an identity covariance matrix as your "sample matrix", and fixing all parameters at the values you desire. The RESIDUAL output then gives you the estimated covariance matrix. Note that this older Monte Carlo track does not give you the same output as the mixture Monte Carlo track. You don't get power information. But you do get chisquare information. 


Am I correct in thinking that Mplus Monte Carlo can be useful in assessing if a particular pattern of partial measurement equivalence permits reasonable estimation of parameters for two group MACS analysis? That is, if I find a particular pattern of partial measurement equivalence, I could make that pattern the population values in Monte Carlo. Then I could run Mplus specifying a model that has that partial invariance pattern across the groups. Then I could see in the output the coverage and the reasonableness of the estimated standard errors. This seems to me at first look to be useful, but maybe its not really informative? Perhaps it will always look good no matter how weak the partial invariance? 

bmuthen posted on Thursday, February 13, 2003  8:08 am



I think you are right in expecting the Monte Carlo results to come out looking good even with only partial invariance. This is probably true for factor means for instance  because you have good information on the means even with only partial invariance. The deterioration of the statistical qualities happens much later  as more and more items are noninvariant  than the deterioration of the plausibility that you measure the same construct. The only parameters vulnerable to noninvariance are those that are not invariant since they are only estimated from one group. But even here, a large enough sample will give good results. 


Thanks for your very helpful response. I would like to followup with an example of my situation. I am working with, say, accommodated math test scores where the accommodation is reading of questions due to low English reading skills. The content specialists/cognitive psychologists endorse that the items, even though read, still engage math ability. My preliminary results indicate well over half of the items are invariant with standard administration math items in a two group run. Given this and the endorsement of content specialists, would it be reasonable to say that there is a strong argument that the same construct is being measured? If that is the case, would the Monte Carlo analysis then give me support that the statistical qualities are there to estimate the non invariant parameters for the accommodated students? To complicate it further, to be true to the real world scenario, I should probably run the standard administration students as a single group and determine the estimated item parameters (as test scoring would really be done). Then in the two group run wire those in as fixed values for the nonaccommodated folks and then determine which items are noninvariant with those values in the accommodated group. Is that correct? Thanks so much. I am hopeful this type of analysis will be useful in the study of these important testing issues. 

bmuthen posted on Thursday, February 13, 2003  9:33 am



Yes to your question in the first paragraph. Regarding the second paragraph, you certainly want to do a separate analysis of each group. But to test the (non) invariance I think a better way is to analyze the two groups jointly, either with accomodation status as a covariate or in a 2group run  you can then study/test (non) invariance of each item or sets of items. 

Tao Xin posted on Friday, August 29, 2003  10:22 am



I want to use Mplus to do a simulation study. The populaion model is a CFA model that include 9 binary indicators and three latent variables (continuous). I saw a similar mplus code made by Linda and Bengt on the paper named as " sample size and power". I tried to modify that code to match my research situation, but it didn't work after I added up the commands (cutpoints & categorical) related to categoriacal variable. I am wondering if the Mplus can generate the binary data directly for the simulation purpose, or should I generate the binary data using other software first? Thanks in advance, 


Yes, Mplus can generate such data but not using the approach that was given in the paper. If you are generating such data to get informaton on power for categorical outcomes, the power information is not printed. The current version of Mplus has two approaches to Monte Carlo simulation. Verson 3 will have only one and you will be able to easily do what you want. See pages 141142 for a brief description of the current Monte Carlo facilities in Mplus. See Example 29.1A for an example of how to generate categorical outcomes in the current version of Mplus. 

Tao Xin posted on Friday, August 29, 2003  1:14 pm



Hi Linda, Thank you very much for your quick response. I saw Example 29.1A, but I still have some questions for that example. In my study, I surpose that each of three latent variables can been measured by three binary indictors, and residuals of indictors are correlated in some way. So it's easy for my to propose the population parameters in this situation, but hard to make a correlation matrix among indictors. Example 29.1A requires a correlation matrix among observed variables. I am wondering if there is a way to decide the correlation matrix based on the population parameters. Thanks in advance again, Tao 

bmuthen posted on Friday, August 29, 2003  6:56 pm



If you don't want to compute the population covariance/corr matrix elements by usual expectation rules, you can use Mplus to generate the population covariance matrix and then simply get the correlation matrix in the usual way by dividing by the standard deviations. To get the population covariance matrix, do an ML run assuming continuous outcomes, where the input is a covariance matrix  for simplicity the unity matrix: 1 0 1 0 0 1 etc In this run you fix all the parameters at the values you decide. So there is no free parameter. Ask for RESIDUAL  this will give you the "estimated" covariance matrix which is the population cov matrix. 

Tao Xin posted on Saturday, August 30, 2003  9:55 am



I tried to practice the monte carlo analysis using Mplus. So I typed the Mplus code and data file listed in Example 29.1A, but it didn't work. The output showed a error message: *** ERROR in Montecarlo command (Err#: 59) Invalid data in file C:\Mplus\monte\monte.dat 07060 I rechecked my Mplus code and data file many times, and I didn't find any difference between my typed code and data file and those listed in the Mplus manual. Would please tell me if there is a bug in Mplus software? how should I fix it? thank you very much. 


You need to send the files to support@statmodel.com for me to see what is wrong. 

Tao Xin posted on Monday, September 08, 2003  10:44 am



I used the Mplus to simulate the CFA models with categorical observed variables. In the mplus code I fixed all parameters with the values used to generate covariance matrix. The estimated parameters were very close to the population values. The only thing I feel confused is that the average of standard error and 95% covers are all zero. I am wondering if this is acceptable, and what kind of model fit indices could be used to assess the effect of sample size in this situation? Thanks very much in advance. 


The problem is that you have the following statement: f1 BY y1y4*.8; It should be y1 y2y4*.8; With your statement, the y1 factor loading is a free parameter and the model is not identified so you get no standard errors. You need to also change your other BY statements in the same way. 

Tao Xin posted on Thursday, October 23, 2003  9:16 am



Dear Linda, As you know, I used the Mplus to simulate the CFA models with categorical observed variables. It seems that Mplus doesn't provide the modelfit indices for this type of simulations, such as chisquare, CFI, NFI and MRMEA. Would you please tell me how to get these indices? Thank you very much. Tao 


You should get a table for chisquare. The other fit indices will be available for Monte Carlo in Version 3. 


Per bmuthen's message of 2/9/2000 3:34 pm, "twolevel and mixture analysis . . . in such cases, monte carlo simulated data can be generated outside mplus" Can you suggest a reference that would indicate how to get started on this? I want to generate multilevel data using parameters of an actual multilevel data set that I have. 


In the current version of Mplus, you can generate multilevel data inside of Mplus. See the Addendum to the Mplus User's Guide at www.statmodel.com under Product Support. 


Hello, I am using Mplus v3 and would like to use the Monte Carlo command to generate a data file containing categorical variables with 5 categories. I am using the program MCEX5.2.INP as a starting point. Following the example on p 477 I change "generate = u1u6(1);" to "generate = u1u6(4);" which produces data with values of 0 or 4. What am I missing? Sincerely, John 

bmuthen posted on Thursday, June 17, 2004  3:31 pm



You are on the right track, but you have probably not given population threshold values in Model Population (Model montecarlo). You need to give values for each of the 4 thresholds: u$1, u$2, u$3, u$4. The example you mention draws on the default of a zero threshold and there is only one in that example so it works without mentioning them. 


Hello, INITIAL QUESTION: I am using Mplus v3 and would like to use the Monte Carlo command to generate a data file containing categorical variables with 5 categories. I am using the program MCEX5.2.INP as a starting point. Following the example on p 477 I change "generate = u1u6(1);" to "generate = u1u6(4);" which produces data with values of 0 or 4. RESPONSE FROM JUNE 17:You are on the right track, but you have probably not given population threshold values in Model Population (Model montecarlo). You need to give values for each of the 4 thresholds: u$1, u$2, u$3, u$4. The example you mention draws on the default of a zero threshold and there is only one in that example so it works without mentioning them. ***** NEW QUESTION How can I implement the solution described above in the syntax provided below? Sincerely, JOhn montecarlo: names = u1u4; generate = u1u4(2); categorical = u1u4; nobs = 500; nreps = 1; SAVE = C:\MyDocuments\1research\factor\f3\F1CAT100L6_*.DAT ; model population: f1 by u1u4*.7; u1u4*.51 ; !V*u*) = 1 so that the parameter metric matches !that of the Delta parameterization f1@1; model: f1 by u1u4*.7; f1@1; output: tech9; 

bmuthen posted on Friday, August 20, 2004  1:21 pm



In Model Population you should include statements such as [u1$1*.... ]; where * should be followed by a value that you choose. See the User's Guide for more information about threshold parameters. 

Istvan posted on Tuesday, February 01, 2005  9:57 am



Dear Linda & Bengt, I would like to carry out a simulation study using MPLUS. My data are generated with R. The problem that I would like to save the output (estimates, fit indices etc.) for each data set, and, as far as to my understanding, it cannot be done in MPLUS using the MONTECARLO command (as it gives only averaged values, not separate ones for each data set). Is my interpretation correct? Is there a way to do it anyhow? Thank you very much in advance. All the best, Istvan 


You can save the results for each replication of a Monte Carlo study in Version 3. You can also save each data set. See the MONTECARLO command in the Mplus User's Guide. 


Hi Linda and Bengt, I am wondering how Mplus can compare the expected and observed chisquare pvalues in a simulation study using the WLSMV option with only one table. As I learned from the Muthén, duToit, and Spisic paper the df may change from replication to replication depending partly on sample properties. How may I compare different chisquare values and their pvalues to their theoretical counterparts in one analysis if there are more than one theoretical distribution involved? 

bmuthen posted on Wednesday, May 25, 2005  11:01 am



There are 4 columns in the chisquare output (see also the Mplus User's Guide discussing these 4 columns). The second column is the observed proportions column, which for WLSMV is based on the p values for the replications (proportion of p values above a certain value is recorded). The 3rd column is the expected percentile, which for WLSMV is based on the acrossreplication average percentile. Hope that helps. 

bmuthen posted on Wednesday, May 25, 2005  6:44 pm



Actually, after some more investigation, the 3rd column is based on the expected percentiles of the chisquare when using the average df across the replications. 

Anonymous posted on Tuesday, September 27, 2005  5:26 pm



Hi, I have N=124 with three different treatment groups and am trying to estimate power for a path model (x=treatment group status, y1, y2, and y3). Since there are not much studies out there on the subject we're trying to study, we don't really know about the effect sizes. Is it okay to assume moderate effect sizes for the paths (.2) and do the MC runs like below? I'm especially not sure about the error variances for the variables. Thank you very much! MODEL MONTECARLO: %OVERALL% [x1@0]; x1@1; [x2@0]; x2@1; [y1y3@0]; y1*.12; y2*.12; y3*.2; y1 ON x1 *.2 x2 *.2; y2 ON x1 *.2 x2 *.2; Y3 on x1 *.2; Y3 on x2 *.2; y3 on y1 *.32; y3 on y2 *.32; %C#1% [y1*0 y2*0 y3*0]; 


You might want to consider using your data to generate the population values for data generation. See Example 11.7 in the Mplus User's Guide. 

bmuthen posted on Tuesday, September 27, 2005  6:14 pm



Regarding the residual variances, it looks like you are getting an Rsquare greater than 50%, which may be high depending on the application area. If so, you might want to reduce the residual variances. 


I'm trying to generate a sample of likert data with five points. This data will then be used as input into a markov model. The output is below What does the eror mean? How do I get the data TITLE: MONTECARLO DATA GENERATION FOR 5 POINT LIKERT SCALE FOR A CATEGORICAL LATENT VARIABLE MONTECARLO: names = u1u6; generate = u1u6(2); categorical = u1u6; nobs = 1000; nreps = 1; SAVE = C:\MONTELIKERT.DAT ; OUTPUT:TECH9; *** WARNING in Model command All variables are uncorrelated with all other variables in the model. Check that this is what is intended. *** ERROR in Model Population command No MODEL statements for MODEL POPULATION. True values must be specified. 


That is not a full Monte Carlo input. Most of the examples in the user's guide come with a Monte Carlo example also. Find the example in the user's guide closest to the model you want to estimate and use the Monte Carlo counterpart input as a start. Also, see Chapter 11 and the MONTECARLO command in the user's guide. 


The examples in the users guide all specify the Analysis: and the Model: as well as the Model Population: commands. If I just want to create the data so that I can analyze it in a number of different ways in Mplus, can I just stop after the Model Population is specified. For example, if I find a published paper that specifies a specific LV SEM model and I want to create a dataset based on the published parameters so that I can look at different ways of specify the model, can I stop at Model Population, then use the saved data set as I would normally with any dataset? Also, if I want to create clustered data does specifying the MODEL: differently than is specified in MODEL POPULATION command when using TYPE = TWOLEVEL, change the data that is generated? 


Example 11.6 shows how to save data for a subsequent external Monte Carlo. You don't need the MODEL command if you are only saving the data. You do need the ANALYSIS command. Nothing in the MODEL command affects data generation. 


I'm a little fuzzy when you refer to it as an external monte carlo. Is it termed external becuase it is outside of the original data generation simulation? If I create a data set using say for example: MONTECARLO: NAMES ARE Y1Y10; NOBSERVATIONS = 250; NREPS = 1000; SAVE = CFA1.DAT; ANALYSIS: TYPE = GENERAL; ESTIMATOR = ML; MODEL MONTECARLO: F1 BY Y1Y5*.60 F2 BY Y6Y10*.60 Y1Y10*.36 F1F2@1; F1 WITH F2@.25; CAN I THEN USE THE SAVED DATA AS NORMAL IN A DIFFERENT ANALYSIS SUCH AS THE FOLLOWING OR IS THERE A COMMAND I NEED TO TELL IT THAT IT WAS GENERATED VIA MONTE CARLO. DATA: FILE IS CFA1.DAT NAMES ARE Y1Y10; ANALYSIS: TYPE = GENERAL; ESTIMATOR = ML; MODEL: F1 BY Y1Y3; F2 BY Y4Y7; F3 BY Y8Y10; 


Yes, you can do this. 


I am looking for references (ie: book or journal) on: (a) assessing model mispecification using monte carlo simulations (b) specifying the model to match exactly (or close to it) the parameters of an existing published study. Do you know of any? Thanks, Scott 


I am afraid that none comes to mind. 


Hi, Bengt and Linda. I am planning to build a Monte Carlo program to examine the power associated with testing direct and indirect effects in a structural equation model containing both continuous and ordered categorical indicators as well as an interaction between two latent factors. The structure of the model is quite similar to that depicted in Example 5.13 of the user's guide, except that indicators y10y12 would be binary rather than continuous. I have at my disposal scale alphas from prior literature that I can use as reliability inputs to the Monte Carlo program for indicators y1y9. I also have oneway frequency tables available for the binary indicators y10y12 (I'm guessing I'd need their bivariate/crosstabular information to be able to fully specify the Monte Carlo model, however). On page 601 of the Muthen and Muthen SEM Journal (2002) article on sample size planning via Monte Carlo simulation, you compute the factor loadings and residual variance values based on expected reliability values (or vice versa) using formula (1), which expresses the reliability as variance explained divided by [variance explained + residual variance]. To provide a concrete example, if I knew that the previously published alpha value of indicator y1 was .70, I'd set the factor variance to 1.00, the F1y1 loading to sqrt(.70) = .49 and the residual variance of y1 to .30. Is this a correct understanding of your recommended procedure for continuous indicators? I have two other questions. My first question is whether it is OK for me to use this same method to set the factor loading and residual variance values for my continuous indicators in my Monte Carlo program given that some of the other indicators will be categorical? My second question is even more basic, but pragmatic. What is the syntax I would need to change in the Monte Carlo version of example 5.13 to alter indicators y10y12 from continuous to binary or continuous to ordered categorical with three or more levels? Perhaps you have another user's guide example or Web note example you'd recommend that I look at to locate the relevant syntax? With best wishes and many thanks, Tor Neilands 


Regarding your concrete example, the variance of the indicator is 0.49+0.30=0.80 so the reliability is 0.5/0.8=0.63, right? Regarding using this formula for categorical outcomes, that is probably less well motivated. You would have had to obtain your reliability by such a factor model. On the other hand, working off reliability as estimated by alpha, is rather approximate as it is (see the lit on alpha in an SEM framework), so maybe this is ok as a rough approximation. Regarding the syntax, have a look at the Monte Carlo version of User's Guide example 5.2, which are on the Mplus CD. 


Thanks, Bengt. Your comment on the example showed me that my calculation was wrong: I'd written that sqrt(.7) = .49. Actually, the square of .7 is .49. The square root is instead ~= .837, so .837*.837 + .30 ~= 1.00, which is what I'd intended. The loading would therefore be set to .837 with the residual variance equal to .30 to yield an approximate unit variance of the continuous indicator. I hope I got it correct this time. Thanks also for pointing me to example 5.2 and for your comments regarding the usefulness (or lack thereof) in using alpha for continuous and categorical indicators for Monte Carlo simulations, especially w/respect to the categorical indicators. I've read the Raykov and Hancock articles on reliability estimation within the SEM framework vs. alpha. As well, your comments in the Mplus Discussion forum to a previous question of mine regarding computing optimal reliability for categorical y variables vs. underlying latent y* variables have been helpful as well. The purpose of this particular simulation is to estimate the minimum detectable effect size for structural direct and indirect effects given a specific, known N (567). The investigator is writing a grant proposal to analyze secondary data, so the N of the parent data set is known. As well, she knows the previously published alphas for the continuous scale scores that will appear in her model. Unforatunately, she does not have access to the data itself, so we must make educated guesses regarding correlations among the categorical indicators in the model. In your work, when you contemplate establishing values for categorical indicators, what criteria (aside from substantive area knowledge) do you use to set the values of categorical indicators' factor loadings and residual variances? Are there typical ranges you select for factor laodings and residual variances? Do those criteria shift depending on whether you're performing simulations with WLMSV vs. ML estimators? Regards and thanks, Tor 


With categorical indicators and working in the probit metric of WLSMV, I find that a binary item with relatively high reliability has around lambda=0.7 when the factor variance = 1. That's then 50% reliability in the "u* metric" (underlying continuous response variable). With a single binary item, I don't think one should expect higher reliability than that. I just looked at the classic LSAT6 and 7 results (Bock's classic example) and a more common loading there is around 0.4  the highest was 0.7. In logit metric you multiply the loadings by about 1.8. 

Ilona posted on Saturday, February 17, 2007  6:35 am



Hi Drs. Muthen, I am attempting to do a MonteCarlo simulation by first generating the continuous data model, and then generating the corresponding categorical data model (in order to have categorical y values as well as the underlying y* values). In trying to generate both data sets, I am doing separate montecarlo programs, but using the same seed, and basically the same model (except residual variances are specified in the continuous model, but not in the categorical model (and these residual variances are=(1(loading)^2) so that the loadings are standardized.) So, I expected that the item response data generated in the continuous case would simply be categorized using the defined thresholds in my categorical model... but that does not appear to be the case. Is there some way to do this? (other than categorizing the continuous data in some other software?) Is rounding error in my standardized loadings/error variance causing the differences? Thanks you, Ilona 


Please send your inputs, outputs, and license number to support@statmodel.com. 


Hello, in an older post (from 2003) Linda wrote "See pages 141142 for a brief description of the current Monte Carlo facilities in Mplus. See Example 29.1A for an example of how to generate categorical outcomes in the current version of Mplus" How can I get example 29.1A. Have not found it on the website/cd etc. Thank you very much, Stephan 


This is a reference to an example in an old version of the user's guide. The closest thing we now have to that example is the Monte Carlo counterpart of Example 5.2. You can find this on the Mplus CD or the website. 


o.k., thank you for help. Stephan 

Erika Wolf posted on Wednesday, May 09, 2007  8:22 am



Hi, I'm running MPlus v. 3.11 and I am running a Monte Carlo simulation study for the purposes of power analysis. I am generating 10,000 datasets for an SEM model that includes 2 latent variable interaction terms. Mplus has been running for over a day and my task manager says that it is still actually running and using 50% of the CPU. Is this really possible? Should I let it run or restart the program? I recognize the interaction terms and the 10,000 datasets is a lot for the program to run, but when I ran a similar analysis in the past (without the interaction terms), it never took this long. Thanks for your help. 


Adding latent variable interactions requires numerical integration so this could definitely make the estimation more complex. You are also using an old version of Mplus. You would need to send your input and license number to support@statmodel.com but I doubt that your upgrade and support contract is current if you are using Version 3.11. 

Erika Wolf posted on Wednesday, May 09, 2007  9:57 am



Thanks for your fast reply. And yes, unfortunately our support contract is not current. I'll let the program continue to run. 


Hello, I am running an *external* Monte Carlo (MC) analysis (data sets were generated by an external program). I use Mplus to analyze the data sets and I would like to save the analysis results for each dataset in separate files. Can this be done? I am aware of the 'results' option in the 'montecarlo' command, but I do not think this option should be used in an *external* MC analysis. In an older post (from 2005), in reply to a similar question, Linda stated that it can be done, referring to the User's guide. I have not found a suitable example there, unfortunately. Could you perhaps shed some more light on this issue? Thank you in advance, Janke. 


See the RESULTS option of the SAVEDATA command. The results are saved in one file. 


That works. Thank you for clearing that up for me! Janke. 


Hello, I would like to run a Monte Carlo simulation with a misspecified model. The data are generated as categorical and analyzed as continuous with a linear factor model. Here is my code: montecarlo: names = v1v5; generate = v1v5(4 p); nobs = 200; nreps = 100; repsave = 1; save = mc1_generatedData.dat; seed = 4539; model population: f by v1v2@0.8 v3v4@0.5 v5@0.3; f@1.0; [v1$1v5$1@1.2816 v1$2v5$2@0.3853 v1$3v5$3@0.3853 v1$4v5$4@1.2816]; model: f by v1v5*; f@1; When I run this in Mplus I get an error: THE POPULATION COVARIANCE MATRIX THAT YOU GAVE AS INPUT IS NOT POSITIVE DEFINITE AS IT SHOULD BE. However, when I add the two lines: categorical = v1v5; ANALYSIS: ESTIMATOR = MLR; , it does run. When I subsequently analyze the saved generated dataset in Mplus with a linear factor analysis, treating the variables as continuous, I do not get an error. How can this be? Thanks in advance for any help, Janke. 


Please send the relevant files and your license number to support@statmodel.com. 

Bill Dudley posted on Thursday, August 28, 2008  11:39 am



I need to estimate power of a mediation model in which the effect of X on u is mediated by Y. similar to example 3.17. e.g. VARIABLE: NAMES ARE u y x; CATEGORICAL IS u; MISSING IS y (999); ANALYSIS:ESTIMATOR = MLR; INTEGRATION = MONTECARLO; MODEL: y ON x; u ON y x; However this inp file does not include a INDIRECT command. I assumed that I could estimate the mediation using: MODEL: y ON x; u ON y x; Model INDIRECT: u IND y x; But I get an error indicating that MODEL INDIRECT is not available with ALGORITHM  INTEGRATION. If I eliminate the ANALYSIS command entirely the program runs. AND I see that the ESTIMATOR = WLSMV. If I then use the Monte Carlo counterpart, w/o an ANALYSIS Command BUT include the MODEL INDIRECT, I encounter a fatal error that the population covariance matrix is not positive definite my assumption is that I have not modeled the indirect effect in the MODEL population and or that I am making an error by excluding the ANALYSIS command QUESTIONS 1) In the modified 3.17 in which I have eliminated the ANALYSIS command, I wonder if the WLSMV estimates are appropriate or if I should model the data otherwise. 2) How should I specific the POPULATION parameters in the MODEL POPULATION section to reflect the indirect effect? (Hoping that this will eliminate the NPD error? Thanks Bill 


It looks like you have missing data on the mediator y. If that is not the case, things are more straightforward, but let's discuss as if you have missing on y. In this case I think the ML estimator is better than WLSMV because ML can do MAR. With ML you then need montecarlo integration and with montecarlo integration you don't get model indirect results. You can, however, always create your own indirect effects as a*b using Model Constraint and defining a "New" parameter ind, where ind = a*b; where a and b are parameter labels in the Model paragraph. Here, a is u on y and b is y on x. Regarding (2)  which you won't encounter by my approach  this happens with the WLSMV estimator when you don't give a residual variance in the population statement. See Monte Carlo input for such modeling in examples that mirror those of the UG examples (either on your Mplus CD, or on our web site). 

Bill Dudley posted on Friday, August 29, 2008  10:09 am



Thanks Bengt I will give this a try. Bill ps I greatly enjoyed the workshop in Charm City. 


Dear Linda or Bengt, I am using the Monte Carlo function to simulate CFA models with categorical variables. It seems that the output does not give results for CFI, which is my statistic of interest. In response to a similar question much earlier, you had said that the CFI stats in Monte Carlo would be available from Mplus version 3. I am using Mplus 4... is there a specific command I need to insert to call forth results on CFI? Thanks very much in advance, Yew Kwan 


We have not added CFI and TLI because they require the baseline model be estimated for each replication. You can save the Monte Carlo data sets and then run each one separately to obtain CFI. 

Ben Spycher posted on Tuesday, February 17, 2009  7:01 am



Dear Linda or Bengt, For a simulation study I generated replications externally and am fitting various models to this data in Mplus using "type is montecarlo" in the data command. Some of my models do not converge for all replications. However from my saved results I cannot find out which ones did converge. I know the results option in the Montecarlo command does this, but can I use it if I am not generating the data in Mplus. Thank you in advance Ben Spycher 


If you ask for TECH9 in the OUTPUT command, you will see which replications had problems. With external Monte Carlo, there is no way to tie a particular data set to a replication number as in internal Monte Carlo. 

Ben Spycher posted on Tuesday, February 17, 2009  9:09 am



Thanks for this help, it works. I will just have to write them out manually, but thats no big deal. Thanks Ben 


I am trying to run a Monte Carlo simulation to test the power I have to test a mediational model in my known sample size. It would be very helpful to be able to report the power of the indirect or total effects. I tried adding MODEL INDIRECT to the input and got the error saying that is not available. Does anyone know if you can use model indirect in a monte carlo simulation? I see that there is no MC example for the indirect example in chapter 3 of the UG. Is there an example somewhere else for the syntax? 


MODEL INDIRECT can be used in a Monte Carlo simulation. I can't remember when it was added but it is available now. 


When doing montecarlo simulation study, how to define a new variable based on the existing random variables? For example, x and y are existing variables, I want to have a new variable as z=x*y. 


The DEFINE command is not part of the MONTECARLO command. You would need to generate the data outside of Mplus if you want this feature. 

ywang posted on Tuesday, September 28, 2010  1:15 pm



Dear Drs. Muthen: For the corresponding Montecarlo simulation study for example 6.4, there is the following statement in the input file. Can you detail how you get the scale factors based on the output of example 6.4? Thanks! {u11@1 u12*.913 u13*.745 u14*.598}; ! this sets the scale factors at the inverted SDs for the u* variables, so that the estimates are in the metric of the Delta parametrizations 

ywang posted on Tuesday, September 28, 2010  2:06 pm



Dear Drs. Muthen: This is the followup for the montecarlo simulation. For the Mplus example 6.4, the ESTIMATED COVARIANCE MATRIX FOR PARAMETER ESTIMATES shows that the variance for u12 is 0.022, u13 is 0.037 and u14 is 0.024. Take u12 as an example, the scale for u12 should be 1/sqrt(0.022)=6.74. Why is it 0.913 as specified in the example 6.4 montecarlo counterpart? Thanks a lot for your patience! 


The scale factors are not based on the output for ex6.4, but are based on the Model Population values from mcex6.4. You first figure out the u* population variance at each time point. For example, for the second time point you have u*_2 = i + 1*s + epsilon_2, V(u*_2) = V(i) + V(s) + 2cov(i,s) + V(epsilon_2). Model population gives V(i) = 0.5 V(s) = 0.1 Cov(i,s)= 0 V(epsilon_2) = 0.6. So, V(u*_2) = 1.2 and therefore the scale factor is 0.913. This is then given as a starting value in the Model statement so you get the correct population value for coverage reporting. 

ywang posted on Wednesday, September 29, 2010  11:55 am



Thank you very much for the detailed instruction. Now I am clear about how the scale factors were calculated. However, when I ran the ex6.4 using the dataset generated from the corresponding montecarlo simulation. The scale factors shown in the output file are not the same as specified in the montecarlo simulation {u11@1 u12*.913 u13*.745 u14*.598}. Instead they are as follows {u11@1 u12*1.060 u13*1.012 u14*0.772}. Why are the differences? Scales U11 1.000 0.000 999.000 99.000 U12 1.060 0.149 7.133 0.000 U13 1.012 0.192 5.259 0.000 U14 0.772 0.153 5.029 0.000 


They are estimates and therefore have a sampling distribution. If you run many replications, the average value should get close to the population value. 


Hello. I am trying to run a Monte Carlo simulation to estimate power for a path model that contains 5 continuous variables and 1 binary (categorical) variable. I used the code: CUTPOINTS = y3(0); to indicate that y3 is the binary variable. The % Sig Coeff, or estimates of power, are much lower for y3, the binary variable, than for the continuous variables. Have I used the correct code to indicate that y3 is a binary variable? 


For dependent variables in the model, use the GENERATE option to indicate that a variable is binary. For independent variables, use the CUTPOINTS option. 

Jak posted on Friday, May 13, 2011  7:03 am



Dear Linda or Bengt, I would like to generate data and then analyze it using both MLR estimation and WLSMV estimation on each dataset. Is this possible without saving the generated datasets to file? Thanks in advance! 


No, you can't do 2 analyses in one run (yet). 

Jak posted on Friday, May 20, 2011  7:28 am



Dear Linda or Bengt, I am saving the results and datasets of an internal monte carlo run, and then I evaluate a second model in an external run on the saved datasets. In the second run, the correction factors for MLR estimation are saved, but in the first run they are not. Is there a way to save the correction factors for the first model as well? Thanks in advance! 


There is no way to save anything that is not saved automatically. If you send the files and your license number to support@statmodel.com, I can look into this further. 

Xu, Man posted on Thursday, June 09, 2011  4:35 pm



Dear Dr. Muthen, I would like to do a power analysis for a MIMIC model with a latent variable outcome. The items for the latent variable are binary, therefore I guess the data need to be generated to be like this. 12.7 example looks the prefect for me, but it is for continuous measures of the items. I need syntax for ordinal CFA analysis. I was wondering if you could give me some suggestions in order to get started please? Thanks a lot! Kate 


Each example comes with a Monte Carlo counterpart where the data for the example are generated. Look at Chapter 5 and find the closest thing to what you want and start from there. The Monte Carlo counterpart for Example 5.2 is mcex5.2.inp. 

Xu, Man posted on Wednesday, July 06, 2011  11:00 am



Dear Linda, Thank you very much for your guide. I have had a look at the relevant examples. My model is simple. It has got six binary indicators forming a continuous factor. The factor is predicted by a continuous predictor. It is a 2 group analysis, and following the example in Mplus, I use delta parameterization. I would like to use unstandardised empricial value for the predictor, but stanardised, emprical values for the factor loading, threshhold, and ridisual variance. I think this is easier for me to vary the effect size (regression path coefficient from the predictor) in the simulation study. I have not figured out how to calculate the residual variance of the items as I suspect it is not the same as the situation in continuous items. In the later, to get residual variance of an item, I just need to substract the square fo the standadrdised factor loading from one. And similarly for factor residual variance and path coefficient. but I don' think it is done the same way for binary items. It would be great if you could give some advice as to how to set item related parameter values for the standardised factor soluation. thanks! Kate 


You will need a statistical consultant to help you with this. 

WJCAO posted on Wednesday, October 12, 2011  11:51 pm



Dear Linda, I didnot know why the below procedure cannot work. It says that: Number of replications Requested 10 Completed 0 Title: a monte carlo simulation study for an factor analysis with categorical indicators Montecarlo: names are y1y6; nobservations = 500; nreps = 10; seed = 12345; generate = y1y6(2); categorical = y1y6; REPSAVE = ALL; SAVE = M1rep*.dat; ANALYSIS: ESTIMATOR = WLS; Model population: [y1$1*0.5 y2$1*0.5 y3$1*0.5 y4$1*1 y5$1*1 y6$1*1] ; [y1$2*2 y2$2*2 y3$2*2 y4$2*0.5 y5$2*0.5 y6$2*0.5] ; f1 by y1y3* .4; f2 by y4y6* .4; y1y6* 1; f1f2@1; OUTPUT: TECH9; 

Emil Coman posted on Thursday, October 13, 2011  7:03 am



Wjcao, Just a thought: is it because the 2nd thresholds for y4y6 are smaller than the 1st thresholds? If you reverse them e.g., it runs fine. Thanks for posting syntax, Emil 


The TECH9 output should tell you what the problem is. 


I would like to run an external simulation study of a LCA with training data where I save out both the results AND the asymptotic covariance matrix (tech3) from each replication for subsequent analysis. Is it possible to do this using external montecarlo options, or do I need to do this using a batch file of some sort? 


TECH3 cannot be saved with the MONTECARLO command or external Monte Carlo. If you wanted to do this, you would need to run each generated data set separately and save TECH3. On the website, see Using Mplus via R. This may help you. You cannot generate data using the TRAINING option but would have to do it as illustrated in Example 7.24. Example 7.23 is exactly the same but it uses the TRAINING option. 

Emil Coman posted on Friday, October 21, 2011  11:55 am



I noticed that in the examples accompanying the guide http://www.statmodel.com/ugexcerpts.shtml in chapter 7, only this ex7.23.dat data file is missing, I wanted to see how the training variables were specified... is there another place where I can get it? Thanks, Emil 


There is no data for that example because we cannot generate data with training data. The data for the examples come from Monte Carlo studies of the example model. See Slide 48 from the Topic 5 course handout on the website. This shows what the training data look like. 


Hello, I need help with my syntax. I always get an error message indicating that the population matrix is not positive. I am using Monte Carlo analysis to estimate the sample size and misspecification on my model. The indicators are categorical; the response scale of the questionnaire is 3 points. Here is the syntax that I used : Montecarlo: Names are cm1ps6; categorical are all; Nobservations = 100; NREPS = 10000; SEED = 0; Generate = cm1cm6(2) gm1gm6(2) fm1fm6(2) cg1cg6(2) ps1ps6(2); Analysis: Estimator = WLSMV; Model Population: [cm1$1*1.71 cm1$2*1 ...... ps6$2*.5]; F1 by cm1@1 cm2cm6*.82; F2 by gm1@1 gm2gm6*.81; F3 by fm1@1 fm2fm6*.80; F4 by cg1@1 cg2cg6*.70; F5 by ps1@1 ps2ps6*.70; F1F5@1; F1 with F2*.50; F1 with F3*.50; F1 with F4*.50; F1 with F5*.50; F2 with F3*.50; F2 with F4*.50; F2 with F5*.50; F3 with F4*.50; F3 with F5*.50; F4 with F5*.50; Model: same as the information of population model Output: Tech9; 


You need to give residual variances for the factor indicators. See the Monte Carlo setups that correspond to each example in the UG. 


I would like to generate a set of theta scores along with their respective standard errors for an item parameter drift simulation study I'm working on. How would I go about doing that?? I created my own set of parameter estimates for the analysis, so I'm not really interested in generating those in MPlus. I'd really appreciate some help! 


It sounds like you want to fix all parameter estimates to the values you have and only estimate what we would call factor scores plus their SEs. 

Heike B. posted on Thursday, November 10, 2011  12:49 am



Dear Dres. Muthen, as per my previous emails I am working on a manifest path model with categorical, nonnormal data. I estimated the model using WLSMV, now I would like to assess the test power of my individual effects. 1. Is it possible to use the Monte Carlo Method you described in a paper from 2002 ("How to use a Monte Carlo method to decide on sample size and determine power")? 2. As I have to provide population paramters and it is an aposteriori analysis  can I use the effect parameters from the model estimation? 3. Is it necessary also to assess test power on the model level? And what would be the strategy? Many thanks in advance & many thanks for all the helpful answers to my previous postings. Heike 


1. This approach can be applied to any model. 2. This is probably the best you can do although there are issues. See the following paper: O'Keefe, J. Post Hoc Power, Observed Power,A Priori Power, Retrospective Power, Prospective Power, Achieved Power: Sorting Out Appropriate Uses of Statistical Power Analyses. COMMUNICATION METHODS AND MEASURES, 1(4), 291299 3. The approach we write about is for one parameter in the model not the entire model. 

Heike B. posted on Tuesday, November 15, 2011  1:27 pm



Hello Linda, when assessing test power of a path model through the Monte Carlo approach  is it sufficient to provide the population parameters for regression coefficients and explicitely defined covariances between dependent variables? Or do I also have to provide the population parameters for means / thresholds / intercepts as well? Many thanks for your help. Heike 


You must provide population parameter values for all model parameters or zero is used. 

Heike B. posted on Wednesday, November 23, 2011  5:30 am



I try to run a Monte Carlo simulation to assess test power for a path model containing one continous dependend variable and 6 categorical dependend variables. I use WLSMV, and MPLUS aborts with: *** FATAL ERROR THE POPULATION COVARIANCE MATRIX THAT YOU GAVE AS INPUT IS NOT POSITIVE DEFINITE AS IT SHOULD BE. 1. Which MPLUS system matrix does this message refer to? 2. Through which output can I see this matrix? (MPLUS aborts before a RESIDUAL output is printed) 3. What could I try? (I have provided means and variances for the independend variables and residual variances for the dependend variables). Thanks a lot in advance. Heike 


1. The population covariance matrix. 2. It is not in the output. It is the numbers you give in MODEL POPULATION. 3. Any parameter not given a population parameter value is given the value zero. You have probably not given all variances population parameter values. 

Heike B. posted on Thursday, November 24, 2011  10:32 am



Thank you, Linda. That helped. Heike 


1. Is it possible to use the Monte Carlo functionality in Mplus to get a distribution of eigenvalues out (as are output when one runs a Type = EFA). This would be nice as it would then provide a way (within Mplus) to code up a parallel analysis test of the number of factors and use the fact that MPlus uses polychoric correlations. 2. Is it possible to incorporate complex sampling weights, strata, and cluster into the data generation process within the Monte Carlo functionality? We could generate externally, but it would be nice if it could be done internally. 


1. This is not currently possible. Version 7 will contain parallel analysis. 2. The only complex survey data feature available with the MONTECARLO command is clustering. The two relevant options are CSIZES and NSIZES. 


Hi I am running a montecarlo simulation with categorical variables, i am testing the effect of generating the indicators as ordinal and analyzing then as continuous MONTECARLO: NAMES ARE V1 V2 V3 V4 V5 V6; GENERATE = V1V6(2); NREPS = 200; SEED = 3141593; NOBSERVATIONS = 50; RESULTS = results_50_0.7_.txt REPSAVE = 1; SAVE = TEST*.DAT but using this code the variables are generate and analyzed as continous, the save data sete shows the as continous, only adding the CATEGORICAL = V1V6; command the indicators are generate as categorical, but also analyzed as continous How can i generate the data as categorial and analyze it as continous? Thank you 


The GENERATE option controls the generation of the data. The CATEGORICAL option controls the analysis. I'm not sure why you think this is not happening. In your input, you are generating categorical. You have no CATEGORICAL option so the variables will be analyzed as continuous. 


Hi when i use the following code MONTECARLO: NAMES ARE V1 V2 V3 V4 V5 V6; GENERATE = V1V6(2); NREPS = 200; SEED = 3141593; NOBSERVATIONS = 50; RESULTS = results_50_0.7_.txt REPSAVE = 1; SAVE = TEST*.DAT; the dat set file TEST1.DAT shows conntinous variables like this 0.345664 0.103435 2.310518 I check this after seing the results When i used the CATEGORICAL option does create categorical variables 


Please send the full output and your license number to support@statmodel.com. 

Jacky Luo posted on Tuesday, May 01, 2012  1:06 pm



Hi I am trying to run a Monte Carlo simulation of multigroup cfa to test for measurement invariance. My indicators are all categorical, and I use WLSMV as the estimator. If I understand correctly, difftest has to be saved for the chisquare test. Would it be possible to run such a Monte Carlo study in Mplus without using any other software? Suppose I've generated data externally. Thanks very much 


You can first save the Monte Carlo data sets. Then you would need to run each data set separately using DIFFTEST first in the SAVEDATA command and then in the ANALYSIS command. See if the Monte Carlo Utility under How To on the website can help. 

Jan Stochl posted on Monday, June 25, 2012  4:21 am



Dear Linda, I have finished simulation of data from multilevel CFA model (30 items, categorical data, 5 correlated factors). I am now writing a paper and one of the reviewers asked to provide between and within population covariance matrices that have been used to generate the simulated datasets. Is there any way how to get population correlation/covariance matrices for within and between level in Mplus? I cannot see them in the output... MODEL POPULATION: %WITHIN% f1 by y1@0.7 y2y7*0.7; f2 by y8@0.7 y9y16*0.7; f3 by y17@0.7 y18y21*0.7; f4 by y22@0.7 y23y25*0.7; f5 by y26@0.7 y27y30*0.7; f1@1; f2@1; f3@1; f4@1; f5@1; f1f5 WITH f1f5@0.4; %BETWEEN% y1y30@0.4; [y1$1*0]; [y1$2*0.5]; [y1$3*1]; [y1$4*1.5]; [y1$5*2]; [y1$6*2.5]; ....I have specified other thresholds in a similar fashion, but do not copy them here 


We don't use the between and within population covariance matrices to generate the data. We use population parameter values. I can't think of a way to get these matrices other than to create them using the population parameter values. This seems unnecessary because you know the population parameter values which is more detailed information that is given in the covariance matrices. 

Jan Stochl posted on Tuesday, June 26, 2012  3:36 am



thanks a lot Linda for useful info 


I want to simulate a CFA with categorical indicators and have 2 questions. First, how do I control what the thresholds are? I'd like half the items with low thresholds and the other half with high thresholds. 2nd, I want to analyze the data both continuously and categorically and can't get the categorical analyses to run. How do I get the categorical analyses? The syntax I have tried is as follows: Montecarlo: Names are y1y8; Generate = y1y8(1); Nobservations = 200; NREPS = 100; Repsave = All; Save = GerardMontePilot1*.dat Seed = 0000001; Generate = y1y8 (1); CATEGORICAL ARE y1y8; Model Population: f By y1y8*1; y1y8*1; f@1; Model: f By y1*1 (L1) y2*1 (L2) y3*1 (L3) y4*1 (L4) y5*1 (L5) y6*1 (L6) y7*1 (L7) y8*1 (L8); y1y8*1 (R1R8); f@1; Analysis: parameterization = theta; But the output says that whereas I requested 100 replications, 0 were completed. Thanks! 


See the Monte Carlo counterpart of Example 5.17 as a starting point. 


thanks for the speedy reply Linda but the only CFA Monte Carlo examples I see are for continuous indicators. 


See the Monte Carlo counterpart for Example 5.17. It is called mcex5.17.inp. It is for categorical variables and the Theta parametrization. It is for two groups but you can adjust for that. 


thanks Linda  where does one find the Monte Carlo counterpart examples? I don't see them in the User's Guide or in the examples one can access from the webstie 


please ignore my last post  I just found the example. Thanks Linda! 


Hello, We are trying to generate data using Monte Carlo that specifies a negative slope over time, but never generates outcomes that go below zero. Currently, we are running into a lot of negative numbers in the outputs, which lacks practical application for what we are wanting to test. Any suggestions? 


Please send the output and your license number to support@statmodel.com. 


Hello, i have a question regarding the threshold concept in mplus. I want to generate data with 5 categories, so I need 4 thresholds. Now I want that the resulting dataset resembles a normal distribution as close as possible (skewness and kurtosis ~ 0). My problem is, that I have to choose the thresholds on my own, so I can't be sure if the arbitrary chosen thresholds could be optimized. Is it somehow possible to let mplus choose the thresholds on its own? Or maybe another way to automatically generate "normally distributed" categorical data? Thank you very much Walid 


No automatic way. And I don't know of a precise way unless you look into the literature on numerical integration with 5 quadrature points. Otherwise, take a look at the paper on my UCLA website that you get to via "About Us" on this website: 12) Muthn, B., & Kaplan D. (1985). A comparison of some methodologies for the factor analysis of nonnormal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171189. [Available as PDF] 

Alvin posted on Tuesday, September 02, 2014  5:23 pm



Hi Dr Muthen, I am attempting to simulate a monte carlo path model (with continous variables)  my understanding is that I should specify population values for the path coefficients, variances, and residuals. I notice you use the following population values (0.8 for factor loadings, 0.36 for residua variances, 0.25 for factor correlations) in your 2002 paper. How do I determine the best population values in this case? Many thanks 


It's up to you. Whatever values are motivated by your realdata situation. 

Alvin posted on Thursday, September 04, 2014  8:51 pm



Hi Dr Muthens, I ran into this error while running a MC path model: *** FATAL ERROR THE POPULATION COVARIANCE MATRIX THAT YOU GAVE AS INPUT IS NOT POSITIVE DEFINITE AS IT SHOULD BE. Based on the comments above, this is because I have not provided population values for all parameters in my model? I have tried (including all parameters, path coefficients, variances, covariances, residual variances, mean) but still having the same problem.. Do I need to include intercepts as well (if so, how) ? is this the correct way of specifying values for mean [var1*.8]? 

Alvin posted on Thursday, September 04, 2014  10:45 pm



I've included the relevant syntax here  MODEL MONTECARLO: !%OVERALL% epdstot2 on ptsdavg*.8 comstress*.7 dvcat*.7 fireshelter*.3; ptsdavg on hrtraum*.8 witness*.8 dvcat*.7 comstress*.7; witness with hrtraum*.8; fireshelter with hrtraum*.5 witness*.3; comstress with hrtraum*.8 witness*.2 fireshelter*.8; dvcat with hrtraum*.1 witness*.1 fireshelter*.1 comstress*.3; hrtraum@1; !variance witness@1; fireshelter@1; comstress@1; dvcat@1; ptsdavg*.8; !residual variance epdstot2*.8; [hrtraum*.8]; !mean [witness*.7]; [fireshelter*.1]; [comstress*.2]; [dvcat*.6]; 


Please send the full output and your license number to support@statmodel.com. 

Emil Coman posted on Wednesday, April 22, 2015  8:42 am



Hi, I can't seem to find the description of the data format in the 'SAVEDATA: ESTIMATES ARE" option to save data. I found the 12.7 example, with its ex12.7estimates.dat data, and I looked at it but cannot guess which is which; found also the excellent http://www.ats.ucla.edu/stat/mplus/seminars/introMplus_part2/saving.htm , but didn't clarify the estimates format itself. The Step 1 output ex12.7step1.html that generated these ESTIMATES only says: Save file \ex12.7estimates.dat \ Save format Free. Thanks! Emil 


The file contains the parameters estimates from the analysis in free format delimited by a space. 


Hi is there other method to generate nonnormal data in MPlus apart from the Muthen and Muthen Monte Carlo paper in SEM? My question in this paper: 1. a mixture approach was used to get nonnormal data, but is possible to use the generated data for type=random under analysis command? 2. is it Ok if we generate normal data using type=random instead of type=mixture and then to get nonnormal type=mixture ? Thanks 


1. Yes. You generate in "internal Monte Carlo" and analyze any way you want in "external Monte Carlo" (DATA Type=MonteCarlo). 2. If you generate normal data you will not get more than one mixture class. You can generate nonnormal using Distribution = Skewt. 


Thank you Bengt To make it clear, all my variables are continuous (factors + indicators). I am using Mplus version 7.4. I have done montecarlo simulation assuming that the factors are normally distributed as: ANALYSIS: ESTIMATOR = MLR;TYPE = RANDOM; ALGORITHM = INTEGRATION; Its because the model has latent interaction. Next I want to generate nonnormal data with specified skew and kurtosis for example skew=3 and kurtosis=20 with same model under: ANALYSIS: ESTIMATOR = MLR;TYPE = RANDOM; ALGORITHM = INTEGRATION; I am trying to see your paper "How To Use A Monte Carlo Study To Decide On Sample Size..." under Appendix 1 if it can help me to generate nonnormal data for my model. But i think i can't. For example, with similar syntax in your paper and type=mixture, i got this message: "*** ERROR in MODEL command To declare interaction variables, TYPE = RANDOM must be specified in the ANALYSIS command". My idea was to follow your paper and use mixture approach to generate nonnormal data and then use type =random for my model as you replied (1) for me on July 30,2016. Now the problem is type of my model (having latent interaction) and can't use mixture approach. I also asked you if there is alternative method and found that distribution=skewt "not available with type=random" as error message in mlpus. Thank you and looking forward for your advice 


Use TYPE=MIXTURE RANDOM; 


Thank you prof Linda, What about if we want to specify skewness and kurtosis directly? 


This cannot be done. 

Jan Ivanouw posted on Wednesday, August 17, 2016  1:54 am



Hi I am wondering about the note in mcex5.2: "! Note that the u* variances should be 1 in order for the Delta parametrization to give estimates in the correct metric" How do I fix u* variances to 1 in a Monte Carlo study? Thanks 

Jan Ivanouw posted on Wednesday, August 17, 2016  5:40 am



Hi again I have a second question: When working with the MC version of mcex5.2 example I have changed the categorical dependent variables from 2 unto 3 categories (2 thresholds). I specify the list of u1$1, u1$2, u2$1 etc in the MODEL MONTECARLO: section, which works fine. However, when I add the same specifications to the MODEL: section, the program stops and I get the error message: "The following MODEL statements are ignored: * Statements in the GENERAL group: [u1$1] [u1$2] [u2$1] etc" What am I doing wrong? Thanks 


To get V(u*)=1 with Delta you choose the right combination of values for the factor loading, factor variance, and residual variance for the indicator. To get 2 thresholds (3 categories) you need to change the 1 to 2: Generate = u(2); 

Jan Ivanouw posted on Wednesday, August 17, 2016  11:30 pm



Hi Bengt Thank you for your answers. The problem of which combination of factor loading, factor variance and residual variance to obtain V(u*)=1  can you give me a hint about which conditions to satisfy in the combination of these parameters? The problem of the program stopping: In fact, I did use generate = u(2), and its works to generate the data. The program also works fine when I do not specify the thresholds in the MODEL: section  however misspecified since the analysis of the generated data then is performed as if there were only two categories (and the default threshold of 0). The problem is that the program will not allow me to specify the thresholds in the MODEL: section. So, apparantly I have done something wrong, but I don't know what. Thanks 


Say that the factor variance is 1 and the loading is 0.7. Then the u* variance is 0.7*0.7*1 + V(e) so in Model Population you should choose V(e) = 0.51 to make the u* variance = 1. To see why the program complains, please send output and license number to Support. 

Jan Ivanouw posted on Thursday, August 18, 2016  1:36 pm



Thank you for your answer. While preparing my data analysis for sending, I found the stupid error I have committed. 

Tao Yang posted on Tuesday, January 24, 2017  7:47 am



Hello, I'd like to run Monte Carlo power analysis of the interactions of x (binary predictor) with z and w (continuous variables). I generated data sets for x, z, w, and y to be used for external Monte Carlo in Mplus (so that I can create interaction terms using DEFINE). Syntax below. Without "MODEL: y ON x", I got an error message that "Only xvariables may have cutpoints in this type of analysis..." So I included MODEL command line only to tell the program to treat x as an exogenous variable. Does the MODEL command influence data generation? In other words, are data sets generated based on specifications in MODEL POPULATION or MODEL? MONTECARLO: NAMES = x z w y; NOBSERVATIONS = 500; CUTPOINTS = x(0); NREPS = 100;REPSAVE = ALL; SAVE = rep*.dat; ANALYSIS: TYPE = RANDOM; MODEL POPULATION: zb BY z@1; zb@.50; z@.01; wb BY w@1; wb@.30; w@.01; xzb  zb XWITH x; xwb  wb XWITH x; y ON x*.20 zb*.10 wb*.30 xzb*.21 xwb*.35; [x@.50 zb@0 wb@0 z*.35 w*.12]; x@.25; [y*.12]; y*.02; MODEL: y ON x; 


Only Model Population influence the data generation. 


Dear Drs. Muthen, In a Monte Carlo LCA simulation I am looking to see if class enumeration is complicated when variables, correlated within class, are misspecified. I used an external monte carlo to create data at different levels of correlation. Then I want to run the model with and without a correlation specified within classes. I was able to do this at a low/moderate correlations, .25 AND .35. But my .55 model won't run, neither will the .75 correlation model. The output for the .55 correlation model is an exact copy of the input. The output for the .75 model show the results for 0 data sets. I am not sure this has to do with the correlation strength, but everything else is the same in the input. The data files created in the first step of the external monte carlo looks good and is complete. Do you have any suggestions? Thanks, Amanda Thank you for your help. 


That's not enough information to go on. I don't know how you create the withinclass correlation or how you model it. So we probably need to see the inputs for those 2 problematic runs  send to Support along with your license number. 


Hello, I'm running into the same problem as Janke C. ten Holt (July 18, 2008  5:27 am) and Mauricio GarnierVillarreal (February 14, 2012  9:40 pm). Their posts are both on this thread. I want to misspecify a LCA model by generating continuous data and then dichotomizing it and treating it as binary in the analysis. I am using similar code to their code and running into the same errors they found. Linda asked them to submit their syntax privately. Has the issue been resolved? Is there a different code to use? Thanks! Melissa 


You would do that in 2 steps, first generating the continuous data and then in an external Monte Carlo run you dichotomize it in Define. See UG ex 12.6 for an example of a 2step MC. I don't recall what input errors the previous posters made. 


I am running a Monte Carlo simulation to estimate sample size for a proposed study in which we will measure parenting in couples. I plan to do analysis with TYPE = COMPLEX, so I am following example 12.6 generating the date as twolevel and run analysis as COMPLEX. Do I have to have both between and within variables? Do all variables need to be declared as either between or within? My model is latent growth model of an outcome variable (y) and mediator(m) and I will also have a timevarying independent variable x (not sure how to code). Syntax: MONTECARLO: NAMES ARE y11y13 m11m13; NOBSERVATIONS = 600; NREPS = 10000; SEED = 53487; NCSIZES = 1; CSIZES = 300(2); REPSAVE(ALL); SAVE = ex12.6rep*.dat; ANALYSIS: MODEL population: %WITHIN% x@1; iout sout  y11@0 y12@1 y13@2; [y11y13@0]; y11y13*.5; [iout*.5 sout*1]; iout*1; sout*.2; iout with sout*.1; !similar model here for the mediator; sout on smed *.3;! this is the parameter I am interested for power; imed with iout*.1; ANALYSIS: TYPE = TWOLEVEL; MODEL: %WITHIN% !repeat of above model; 


Because you have only 2 cluster members, husband and wife, why don't you instead use a singlelevel, wide approach? So if you measure the outcome at 3 time points, you will have 6 columns of outcomes in the data. We have examples in our short course handouts of this; see Topics 3 or 4. 


I am worried that we will not be able to afford a large enough sample size to test the mediation, especially in the LGM context. Currently, the budget is for 300 couples. If we do the wide approach, I think we have a sample size of 300. If we do a long approach with TYPE = COMPLEX, do, we have a larger effective sample size, which depends on how closely related our outcomes measures are within the the couple? 


The power is the same for the two approaches because the models are identical. 


I have looked for examples of this in Topics 3 and 4 (and 7 and 8), but I can't find it. I don't understand how to set up the model . I understand using a wide format for longitudinal data, for instance for an LGM, but not for couples data. Any chance you could be more specific about where to find an example of this? Thanks so much! 


See Topic 8, slides 5256 and the Khoo reference on slide 54. 

Dallas posted on Thursday, January 18, 2018  12:18 pm



Hi. I'm trying to simulate data to reflect some real data. Suppose I have a real dataset with 200 observations. U1 is a binary variable with mean= .245 and VAR=.186. X1 is also a binary variable with mean= .545 and variance =.249. The logistic regression of U1 on X1 results in an intercept = 1.47 and a beta of .593. I think the relevant Montecarlo code to simulate data to match this data would be: ... generate = u1(1); cutpoints = x1(.11303854); !invnormal(.545)=.113 ... analysis: estimator=ml; link=logit; model population: [x1@0]; x1@1; /// u1 ON x1@.5927822 ; [u1$1@1.470852]; ... However, when I run that code and read in the data. I find that X1's mean is .425 (VAR=.246) and the logistic results are intercept = 1.498772 and a beta of .734. What I am missing in the generation bit so that the simulated data matches the observed data? 


2 issues: Note that mean (X1) = Prob (x1=1, not 0). So the cutpoint needs to be negative for a Prob > 0.5. You probably have the sign reversed. Note also that if the intercept is 1.47, the threshold that you specify with [$] is the negative of that. 

Dallas posted on Thursday, January 18, 2018  5:05 pm



Hi. Thanks for your quick response. I apologize. I meant to write "threshold" and not intercept. The threshold is 1.470852. I'm not clear about your cutpoint comment. The probability that X1=1 is 0.545. That is a zscore of ~.113. What happens is that when I run the Monte Carlo code as written: generate = u1(1); cutpoints = x1(.11303854); ... analysis: estimator=ml; link=logit; model population: [x1@0]; x1@1; /// u1 ON x1@.5927822 ; [u1$1@1.470852]; I am expecting to find that the probability of X1=.545 in the simulated data. But, it doesn't. It equals 0.425. Not surprisingly, if I run a logistic regression in Mplus on the simulated data, the parameters differ from their fixed values in the Monte Carlo part. It seems that a zscore cutpoint of .113 should result in an X1 with a probability of .545 but I'm not getting that. Sorry I wasn't clear and correct in my language earlier. Thanks again! 


A N(0,1) variable has 0.5 probability of being above 0. So if you want a 0.545 probability, the cutpoint has to be less than 0. The zscore of 0.113 that you used makes the probability of being below that value 0.545, but the probability of being above it (which is what you are looking for) is 10.545 = 0.455. See also UG ex 12.1 which uses Cutpoint. 

Dallas posted on Friday, January 19, 2018  3:03 am



Oh. I see. I misunderstood (obviously!). If I change the value to .113 and leave the rest, x1's resulting mean is .515 not .545 and a logistic regression does not recover the expected parameters. Let me ask the question differently. Suppose I wanted to generate data with the following properties: N=200 A binary outcome with mean= .245 and variance=.186. A binary predictor X1 with mean= .545 and variance =.249. And a logistic regression of U1 on X1 results in U1 ON X1=0.593 U1$1=1.471 What Monte Carlo code would I use so that the generated data had those EXACT properties? Thanks! 


In Monte Carlo studies, the results won't be exact unless you use a sample size of infinity. You get closer and closer with increasing N. 

Dallas posted on Friday, January 19, 2018  12:45 pm



Ah. I thought that was true when one used the "*" symbol but that the "@" symbol would fix the values regardless of the sample size. I specified a very large sample and the parameters were as expected. Thanks. Given your comment, what is the best way to accurately describe what fixing a value does within the Monte Carlo context? Thanks! 


In Model Population it doesn't matter if you use * or @  the randomly generated data are based on the the values given. But data are randomly generated (like drawing a random sample) so no single data set (no single draw) is expected to have exactly the features of the population model. 

Dallas posted on Friday, January 19, 2018  5:24 pm



I see. This was/is very helpful. Thank you so much for your prompt and informative replies. Have a great weekend. 


See also the V8 UG page 465 and on. Plus chapter 3 of our book Regression and Mediation using Mplus. 

Ti Zhang posted on Saturday, March 10, 2018  8:38 pm



Hi, Dr. Muthen, I am trying to conduct a Monte Carlo study to examine the consequences of ignoring both noninvariant loadings and noninvariant thresholds in orderedcategorical data under a twogroup CFA framework. I am trying to generate data in Mplus. I specified noninvariant loadings, but I'm struggling with how best to specify noninvariant thresholds. I attempted to use the idea of asymmetrical thresholds, but the results were problematic (specifically, the average latent factor mean difference across all replications was a negative value, which does not make sense because data were generated with a latent mean differences of +0.2). The data generating model and the estimated model does not match in terms of the parameters are equal and unequal. Any ideas? Thank you. 


We need to see the full output to be able to tell  send to Support along with your license number. 

Yue Yin posted on Friday, May 18, 2018  8:16 am



Hi, when I ran the Monte Carlo simulation, the code was as follows: MONTECARLO: NAMES ARE y1y6 Age; GENERATE = y1y6(1 p); CATEGORICAL = y1y6; NOBSERVATIONS = 500; NREPS = 50; !SEED = 4533; ! I'm not sure the benefits of specifying this manually. ; !CUTPOINTS = Age(0); MISSING = y1y6; MODEL POPULATION: Age@1; [Age@0]; Fac by y1y6*.8; Fac@1; [y1$1*1.25 y2$1*.75 y3$1*.25 y4$1*.25 y5$1*.75 y6$1*1.25]; Age with Fac@.1; MODEL MISSING: [y1y6@1.1]; ! 50% missing on all variables; y1 ON 0.69*Age; y2 ON 0.69*Age; y3 ON 0*Age; y4 ON 0*Age; y5 ON 0.69*Age; y6 ON 0.69*Age; MODEL: Fac by y1*1.5 y2y6*1.5; Fac@1; [y1$1*1.25 y2$1*.75 y3$1*.25 y4$1*.25 y5$1*.75 y6$1*1.25]; ANALYSIS: ESTIMATOR=WLSMV; PROCESSORS=16; OUTPUT: TECH3 TECH9; The result showed error: THE POPULATION COVARIANCE MATRIX THAT YOU GAVE AS INPUT IS NOT POSITIVE DEFINITE AS IT SHOULD BE. But if I changed the estimator in MLR with same code, there is no problem at all, I am wondering why? I was intended to compare the two estimator methods. 


The issue is the residual variances that need to be given for WLSMV but not for ML. For ML, residual variances are 1. For WLSMV in the default Delta style, the residual variances need to be chosen so that the total variance of each latent response variable y* is 1. Using the WLSMV Theta style, the residual variances should be given as 1 (so like ML but explicitly given). You see examples of this in the Monte Carlo simulation counterparts of the UG examples; all these are on our website at: http://www.statmodel.com/ugexcerpts.shtml Note also that for ML to be comparable to WLSMV, you need to choose Link=Probit. 

Yue Yin posted on Thursday, May 31, 2018  8:42 am



Thank you so much, I adjusted the residual variance, and it works. I have another question, so in this model,I set the missing for each indicators as the syntax above, when I run WLSMV the warning is the covariance coverage approach the limit, the missing algorithm will not be initiated; but when I run MLR, only the the warning which covariance coverage approach the limit appears, there is no additional statement like "the missing data em algorithm will not be initiated". I can adjust the missing to remove the warning, I am just wondering that the same missing patterns in both estimators, why the warning is different? 


Send both outputs to Support along with your license number. 


We are trying to run the following model: MONTECARLO: NAMES ARE HA_A1HA_A3 HA_C1HA_C3 SA_A1SA_A3 SA_C1SA_C3 AN_A1AN_A3 AN_C1AN_C3; NOBSERVATIONS = 210; NREPS = 1000; SAVE = cfa1.txt; MODEL POPULATION: !%OVERALL% fHA by HA_A1HA_A3*.5 HA_C1HA_C3*.5; fSA by SA_A1SA_A3*.5 SA_C1SA_C3*.5; fAN by AN_A1AN_A3*.5 AN_C1AN_C3*.5; fAFF by HA_A1HA_A3*.5 SA_A1SA_A3*.5 AN_A1AN_A3*.5; fCOG by HA_C1HA_C3*.5 SA_C1SA_C3*.5 AN_C1AN_C3*.5; fHA @1; fSA @1; fAN @1; fAFF @1; fCOG @1; HA_A1HA_A3*.50; HA_C1HA_C3*.50; SA_A1SA_A3*.50; SA_C1SA_C3*.50; AN_A1AN_A1*.50; AN_C1AN_C3*.50; fAFF WITH fCOG*0; fAFF WITH fHA*0; fAFF WITH fSA*0; fAFF WITH fAN*0; fCOG WITH fHA*0; fCOG WITH fSA*0; fCOG WITH fAN*0; fHA with fSA*0.6; fHA with fAN*0.6; fSA with fAN*0.6; But we keep getting this error: *** FATAL ERROR THE POPULATION COVARIANCE MATRIX THAT YOU GAVE AS INPUT IS NOT POSITIVE DEFINITE AS IT SHOULD BE. We cant seem to figure out what the problem is. Can you help us? 


Send your output to Support along with your license number. 


Hello, Im very new in using running analyses in Mplus Our model is a multilevel model with two serial mediation. We are trying to run Monte Carlo analysis to calculate the indirect affect, however I couldn't find in the manual or in papers the syntax for this analysis. I was wonder if it is possible to run this type of analysis and if so can you please advice the source for the configuration. 


The key ingredients are: y on m2 x; ! perhaps also y on m1 m2 on m1 x; m1 on x; Assuming that the mediation is on the Within level, the Between level can then model the random intercepts for y, m1, and m2. 


Hi, I am carrying out MC simulation study to find out the appropriate sample size for a CFA. I don't have prior information about two of the factors loadings. What shall I do in this case? Just like Bayesian analysis, is there a way to include no information? Thank you so much. Regards, Anshuman 


Q1: How about having it equal to the average of the others. Q2: No. 


Thank you so much for the information. I will try this option. 


Dear Linda, dear Bengt, I would like to run a posthoc power analysis (Monte Carlo) for a crosslagged model. I have four measurement occasions and three variables at each time point. My N=508. I included: measurement model for latent variables, fixed the factor means to zero, fixed the indicator means of dependent variables to zero, fixed the factor variances of variables to t1 to 1, set the factor residual variances of latent variables, set the residual variances of indicators, and specified my model. Unfortunately, I get an error message that "population covariance matrix that you gave as input is not positive as it should be". Could you please help me to run this model? That would be amazing. Thank you very much in advance! Best Kathi 


We need to see your full output  send to Support along with your license number. 

shonnslc posted on Tuesday, October 01, 2019  12:42 pm



Hi, I saw different answers on if power analysis using Monte Carlo simulation in Mplus allows standardized parameters. Some papers use unstandardized values while other papers use standardized ones. I am wondering which one is correct? Thanks. 


I never use standardized in Monte Carlo  instead choose the metric you want by how you choose population values for the parameters. if you want a DV with variance 1 you choose parameter values for that. 


Hi all, I am trying to conduct a MonteCarlo simulation to determine power for an intervention study where volunteering hours should be increased from zero in the baseline/control condition to some amount (say 4 hours) in the experimental condition. I am trying to model hours as an overdispersed count variable (with some variant of negative binomial regression) and cannot get my head around the intercept and the dispersion parameters. 1. I assume the intercept is equal to logged mean count if there are no predictors in the model? 2. How do I determine the value of the dispersion, especially if THERE ARE predictors in the model? In the example mcex3.8part2.html, dispersion is double the intercept, but should not this proportion hold for the raw mean and dispersion, not for logged ones? And should not dispersion be actually smaller in the model with predictors (analogously to residual variance)? Sorry for these questions of ignorance and many thanks in advance! Maria 


1. Right 2. The dispersion refers to the raw mean and variance of the counts. Mplus uses the negbin2 parameterization discussed in Hilbe's 2011 book. With a positive dispersion, you get higher probability for low counts than Poisson. We talk about it briefly on page 262 in our RMA book where we show a residual where its exponentiated value has a gamma distribution. So yes one might expect a lower dispersion with more significant predictors. 


Thank you so much, Bengt! I do have followup questions: 1. In the book example 6.4 with affairs1 data, the count variable has a raw mean of 1.456 and a raw variance of 10.864 (if I specify it as continuous and look at sample stats). The NB model without predictors yields alpha = 0.376 (=ln(1.456)) and dispersion (k) of 8.932. If I apply the NB2 formula Var = lambda + k*lambda^2 where lambda = raw mean = exp(alpha) I get Var = 20.287, which is about 2 times larger than raw variance. Am I doing something wrong? 2. The dispersion in the model with predictors is reduced to 6.76. Is there any way to calculate the expected reduction in dispersion (for a simulation study)? As I see, looking at R square doesn't bring anything as it is fixed at 1. Applying the formula for Var doesn't make it clearer as the Var gets even larger because the alpha increased to 0.816. 


1. When I run the affairs example without x's, I get 3 parameters with intercept (mean) = 1.605 which is in the log scale so the mean is exp(1.605)=4.98. The dispersion is 0.872 so the variance is 26.6. So like you, I get a much larger variance than the variance when you consider the variable as continuous. But I think that is the problem  I am not convinced that the continuous variance should match the negbin2 variance. The continuous variance underestimates the variance because of the censoring at zero  it is the wrong model. If you applied a censorednormal model, it would have a larger variance. 2. Not that I know of although it might be possible. 


Thank you Bengt! This makes it clearer. 


Dear Prof. Muthen, I'm conducting a multilevel simulation with Mplus but it keeps collapsing. I have a dichotomous outcome, so WLSMV is chosen as estimator. Simulation data is generated with montecarlo and succeed in running. For some reason, I need to run the data list with another inp (similar to the same inp before, but delete the codes used to generate data), but I repeatedly get the following error message. forrtl: severe (157): Program Exception  access violation Image PC Routine Line Source Mplus.exe 00007FF60BE87816 Unknown Unknown Unknown These errors occur even when the model used to generate data is as simple as follows: MONTECARLO: names are Y; generate = Y(1 p); categorical = Y; ... ANALYSIS: TYPE IS TWOLEVEL; ESTIMATOR = WLSMV; MODEL POPULATION: %WITHIN% %BETWEEN% MODEL: %WITHIN% %BETWEEN% However, when I use ML, these errors dispear. It seems that errors only occur when WLS is used. I wonder what is happening. Is it because Mplus does not support montecarlo when the data is generated outside Mplus and the command of TWOLEVEL and WLSMV is combined? Thank you very much. 


We need to see your full outputs  send to Support along with your license number. 


Hi all, I am conducting a MonteCarlo simulation of a growth model with binary outcomes. The rate for category 1 (vs. 0) starts at zero at T1 and then increases. With the default WLS estimator, zero observations in category 1 at T1 were apparently not allowed. I switched to Bayes estimator and the model ran without problems. However, estimated intercept and slope variances deviated strongly from the population values that I specified. For the following syntax: model population: i s  catt1@0 catt2@0 catt3@1 catt4@1 catt5@1; [catt1$1catt5$1@3]; i@0.5 ; s@0.2 ; [i@0]; [s@1.7] ; group@1 place@1; [group@0 place@0]; s on group@0 place@0; I get estimated I variance of 0.9985 and S variance of 3.1138. Other free parameters also deviate slightly from population values. Is anything wrong with the model? The unconditional model has the same problem. Thank you! 


We need to see your full output  send to Support along with your license number. 

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