Anonymous posted on Sunday, June 26, 2005 - 5:39 pm
I am trying to develop a monte carlo study to show some of the robust qualities of CFA with nonnormal data. However, I am having trouble developing datasets that have controlled skewness and kurtosis. Would you provide me with a few syntax lines that will accomplish skewness and separately kurtosis? Also, are there any examples of how to develop skewness and kurtosis into my simulation models? Thank you in advance for addressing my issue.
BMuthen posted on Tuesday, June 28, 2005 - 8:11 am
Mplus does not have a setup for generating specific skewnes and kurtosis. In the Muthen and Muthen Monte Carlo paper in SEM, we used a mixture approach to get non-normal data. You can see that paper. You can use such an approach and get the skewness and kurtosis you want by trial and error.
Hello, I am generating data for a simulation study in MPLUS by using another program (SAS) to generate the replication datasets. The first line of the datasets contains the variable names (e.g., x1 x2...x15) and data begin on the second line.
Is there a way to tell MPLUS to 'skip' the first line when it reads in the datasets? Thanks for your advice
Deleting the variable names from each replication isn't an option -there are too many replications.
I should be able to move to the MPLUS framework: I'm interested in simulating data that is underlying normally distributed but categorized and follows different distributional characteristics (e.g., uniform, normal, etc.). However, in the examples, I don't see an example to generate categorical data.
How can the thresholds be specified to achieve the different observed distributional characteristics? In LISREL, Z-values are used to create the thresholds - is it the same in MPLUS?
If deleting the names is not an option, perhaps SAS has an option so that they do not put the names on the first record. Mplus will stop if it finds character data as the first record.
Examples 11.3, 11.8, and 11.9 generate categorical data. Also, any example that analyzes categorical data has a Monte Carlo counterpart where you can see how the data were generated. The Monte Carlo counterpart of Example 5.10 shows how categorical factor indicators are generated when thresholds are included in the model.
finnigan posted on Monday, September 03, 2007 - 2:28 pm
I'm trying to use a montecarlo simulation to generate ordinal data to asertain sample size for a CFA. The survey I'm using contains 50 items with a 5 point likert scale which results in a five factor model. As far as I understand population values are required for each indicator. Do these values refer to the factor loadings. Example Esteem By x1*.62 x3*.55.
Prior research using this survey assumed that the data was continuous and not ordinal. Do you know what approaches are taken if one does not know the poulation parametre values for ordinal data?
Yes, those would be the population values used for the factor loadings.
If you have data, you can analyze it and use the parameter estimates as population values. This is shown in Example 11.7.
finnigan posted on Tuesday, September 04, 2007 - 3:09 am
I do not have data yet. I will be using factor loadings from a previous study to generate the MPLUS output. If previous studies found no covariance between the factors then do I use zero in the with statement between factors. eg f2 with f1*0 or just insert the insignificant value taken from published research?
I am using a longitudinal approach. Does this need to be considered in the MPLUS code? Thanks
Yes. See the Monte Carlo counterpart inputs that come with Mplus. Growth models are found in Chapter 6.
finnigan posted on Wednesday, September 05, 2007 - 4:13 pm
Are there any concerns that arise from using loadings from PCA in a CFA montecarlo? I noticed from published research that some of the x's load on different factors. For the purposes of the montecarlo analysis is it appropriate to select the highest factor loading? In the case of say x1=.62 on one factor but x1= -.64 on another factor. In this case which factor loading is selected for the value of x in the montecarlo model.
A CFA Monte Carlo should not generate data using parameter values from a PCA. PCA assumes a model where the residual variances of the factor indicators are zero. CFA estiamtes residual variances of the factor indicators. PCA can be seen as an estimator for CFA but it is a biased estimator.
Eric Teman posted on Friday, May 25, 2012 - 1:36 pm
Is there a way for non-positive definite results to be flagged in the .dat results files in Monte Carlo studies?
Eric Teman posted on Friday, May 25, 2012 - 2:34 pm
If there's not a way to eliminate non-positive definite results from Monte Carlo replications, how should these be dealt with? It is unfeasible to open each .out file to investigate cases of non-positive definiteness prior to analyzing results from a Monte Carlo study.