This would be a bit of a bother to do. You would need to use MODEL CONSTRAINT to estimate the indirect effect and its standard error for each imputed data set. Then you would have to calculate the standard error of the indirect effect using the formula in Schafer.
Yes. I have had to do something similar with HLM estimates with the imputed data sets. Thank you for your help Dr. Muthen!
Alex Buff posted on Wednesday, September 23, 2009 - 9:03 am
Dear Dr. Muthen
I also wanted to calculate indirect effects, but realized that MODEL INDIRECT is not supported with imputed data.
As far as I understand the discussion above, I have to run five analyses with my five datasets and then combine the five results - raw (unstandardized) regression coefficients and their standard errors - according the formula of Rubin/Schaffer (for example in NORM). The resulting combined regression coefficient and its standard error is then to be used the usual way. Correct?
What I don’t understand is that you wrote: “ … need to use MODEL CONSTRAINT to …” What exactly do you mean by that? I have just a simple path model with observed variables (but imputed data).
Following from the messages above, I am not sure either why one would need to use MODEL CONSTRAINT. Could not one use MODEL INDIRECT with each of the imputed dataset and then combine the results using Rubin/Schafer formula?
Just a precision: If we analyze each imputed dataset separately, is it mathematically sound to treat these datasets like 'normal' datasets, and hence, not specifying IMPUTATION and use MODEL INDIRECT on each of them, for, later, combining the results following Schafer formula?
Hello, I have 5 imputed data and use Type=Imputation to do the analysis. In the first step, I want to create one latent variable based on several observed variables. I tried to use "savedata" command to save the factor scores, but it did not work. Can you tell me how I can save factor scores in the multiple imputed data? Below are the commands I used:
savedata: file is F:\Mplus\imputfactor.dat; save=fscores;
I guess this will give me the same factor scores for these 5 imputed data. Are these factor scores just the average of 5 sets of factor scores based on these 5 imputed data? Another related question is if I want to do other analyses by using other software, should I do these analyses based on the same factor scores for these 5 imputed data, or should I do CFA for each imputed data seperately and get 5 different sets of factor scores.
I am still fairly new to Mplus. Currently, I am using version 6.1. Does Mplus provide a way the pool fit indices such as RMSEA, CFI, and TLI? If not, do you have any suggestions or references I may look into for further investigation?
fritz posted on Wednesday, March 02, 2011 - 7:29 am
Hello. Following your answer from February 27, 2010:
Would it be alright to save factor scores for each imputed data set seperatly and use newly created data files including factor scores for further analyses with "TYPE=IMPUTATION" (meaning that analyses based on these factor scores will be combined)?
I'd think so, but just want to make it sure. Thanks in Advance!
I am planning to use plausible values for successive analyses. Referring to the Skrondal (2001). Regression among factor scores - article my understanding is, that unbiased structural parameters can only be obtained, if the factor scores for exogeneous variables are generated using the regression method, factor scores for endogeneous variables using the Bartlett-method. Can you tell me whether using the mean of plausible values for exogeneous as well as endogeneous variables generates unbiased results? Are there alternative ways, given that the convential approach of using FScores does not work due to the number of missing values?
You can use the Mplus Bayes estimator to generate plausible values for both exogenous and endogenous factors and then do path analysis on those data sets with the factors treated as observed variables. See the UG ex 11.6 for how to get plausible values. See Section 4.2 of
Asparouhov, T. & Muthén, B. (2010). Plausible values for latent variables using Mplus. Technical Report.
for a simulation showing the superiority of plausible values over factor scores. This paper also gives references that discuss the properties of the plausible values.
Dear Dr. Muthen, I have a further question regarding the plausible values I could not find an answer for in the MPlus user guide: When I generate a summary file using the "PLAUSIBLE"-command one part of the output file consists of the within-level and between-level plausible values for factors specified in the MODEL part. Additionally for all variables in the model, the same variable names followed by an asterisk (*) or prefaced by "B_" are included in the output (variable* and B_variable). Can you please clarify for me, what the meaning of those variables is? Thank you once again very much for your help.