I am helping examine differential item functioning between men and women on a number of categorical indicators. I had been planning on using the grouping option to specify multiple groups, however, when I do this, the program halts and requests that I use the knownclass option. I have 2 points that I want to clarify:
1) If I change the estimator to WLSMV and run the model under those assumptions, the results of the DIF analyses would be interpretable ftom the difftest command, right?
2) If I use the knownclass command, I would specify:
classes are sex (2) c (2); knownclass is sex (1=male 2=female);
and then run the analysis with an overall model and class specific models to test DIF. Is that correct?
Stata posted on Thursday, October 04, 2012 - 2:13 pm
I am interested in conducting 1) differential item functioning with IRT, logistic regression, and Mantel-Haenszel. I haven't found any examples (syntax) in Mplus website regarding how these DIF methods are used with Mplus. Is it possible to conduct these analysis with Mplus? I hope it is possible.
2)The dataset which will be used for the analysis is a national dataset. In this case, is it necessary to incorporate sampling weight in DIF analysis?
IRT DIF can be investigated in Mplus using the MIMIC (factor analysis with covariates) approach or the multiple-group approach. Please see the Topic 1-2 handouts and videos on our web site. You can use ML, WLSMV, or Bayes estimation.
I am running an IRT model, I would like to assess the Differential Item Functioning on 3 variables: age, gender, and language. age has 3 categories. I used ICC graphs to assess the DIF. However, for age I got only 2 lines instead of 3! I used the following syntax, I am not sure if what I did is right or not!
usevariables are HADS_A1N HADS_A7N HADS_A9N HADS_A13N SF6a3 HADS_A3 SF4Bv gender LANG agen; missing = .;
categorical are HADS_A1N HADS_A7N HADS_A9N HADS_A13N SF6a3 HADS_A3 SF4Bv;
ANALYSIS: ESTIMATOR = ML; PROCESSORS = 8;
MODEL: Anxiety by HADS_A1N* HADS_A7N - SF4Bv; HADS_A1N HADS_A7N HADS_A9N HADS_A13N SF6a3 HADS_A3 SF4Bv on gender; HADS_A1N HADS_A7N HADS_A9N HADS_A13N SF6a3 HADS_A3 SF4Bv on LANG; HADS_A1N HADS_A7N HADS_A9N HADS_A13N SF6a3 HADS_A3 SF4Bv on agen;
You should regress the anxiety factor on all your covariates. You can't identify the direct effects from the covariates to all your factor indicators in addition to the factor regression so you would have to regress one indicator at a time on all covariates.
If you want to treat the 3-category age variable as binary dummy variables, you should use only 2.
Thank you for your response, I just want to make sure that I got the point. So I regressed each indicator on all covariates using the following syntax:
For "age", I dont want to use it as binary, but when I look at age DIF graph, I see only 2 lines instead of 3 (it looks like a binary variable)
MODEL: Anxiety by HADS_A1N* HADS_A7N - SF4Bv; HADS_A1N on gender LANG agen; HADS_A7N on gender LANG agen; HADS_A9N on gender LANG agen; HADS_A13N on gender LANG agen; SF6a3 on gender LANG agen; HADS_A3 on gender LANG agen; SF4Bv on gender LANG agen;