From the MODEL command, f1 WITH f2 is a residual covariance. From your MODEL command, f13 WITH f7 is also a residual covariance. Note that you have two factor loadings fixed to one in your BY statements.
Alex Zammit posted on Tuesday, August 05, 2008 - 5:22 am
When I run a MIMIC model with the following statement:
FACTR1 ON AGE GENDER SEVERITY ACCDAYS LITIGATE EDUCN; the model results for GENDER are:
Estimate S.E Est/S.E. -0.452 0.184 -2.453
I believe the Est/s.e. of -2.453 means the regression of FACTR1 on GENDER is significant.
However, if I change the above statement to FACTR1 ON Gender; the model results for GENDER are:
Estimate S.E Est/S.E. 0.092 0.146 0.631
Now it seems the regression is no longer significant. Why does the significance change when the additional covariates are removed?
In the first case, the coefficient for gender is a partial regression coefficient controlling for age, severity, accdays, litigate, and educn. In the second case, it is not a partial regression coefficient. Another issue is sample size. It may have changed between the two analyses.
Alex Z posted on Tuesday, August 05, 2008 - 4:08 pm
Thanks for your response.
It would then seem that using GENDER as a partial regression coefficient, controlling for age, severity, etc., would be the correct way to demonstrate the significance of the effect of GENDER on FACTR1? is this because all other effects have been eliminated and GENDER is being considered in isolation?
1) I'm having difficulty understand "why" a significant effect of "u1 ON covariate" indicates differential item functioning. It is my understanding that would simply indicate that one group endorses that item at a higher rate. It's possible that higher rates of endorsement do not indicate DIF.
2) Also, my second concern is how to interpret the DIF result. For example, if I had a 1-factor model (f1) where u1 ON covariate (male = 0, female =1) was significant (estimate = .433). How would that be interpreted as affecting the difficulty parameter by sex?
1) The "u1 on covariate" effect is only half of it. It is not a matter of a marginal difference in u1 ("higher rates of endorsement") at certain covariate values, but instead the conditional difference in u1 at certain covariate values given a certain factor value. In other words, for two people with the same factor value, the u1 probability differ depending on their covariate value.
2) See our handout for the Topic 2 course on our web site where we go through the ASB example and gender differences in shoplifting. This course is also available for free viewing as a video on the web - see our home page.
I have a very basic question about CFA with covariates (MIMIC) to assess DIF. I am trying to use a MIMIC model in a paper for the first time using Mplus. In my case, I have a single factor, a single covariate, and binary items. I am using the default WLSMV estimator. In the short course handout, the first step involves establishing the model without covariates. In the ASB example, all indicator errors are uncorrelated. Would anything change in the following steps (i.e., add covariates, add direct effects suggested by modification indices, and interpret the model) if a model includes factor indicators with one or more correlated errors?
I am attempting to run a MIMIC model in a MC simulation. I would like to have the mean/variance of the latent variable differ between groups, while simultaneously being able to regress the latent variable/indicators onto the grouping variable (hence MIMIC model). Is it possible to do this in MPlus? I would appreciate any help on this matter. Thanks!
I have a quick question about specifying covariates (Xs) in a mimic model. If I have some Xs with missingness while the others don't, do I need to mention the variance of all X or just the Xs with missingness in the syntax?
Because if I only mention covariates with missingness, the results shows the covariates without missingness are not correlated with covariates with missingness. I am not sure if I understand how to interpret this and your help is appreciated.
For MIMIC/CFA with Covariates, is it typically necessary to examine the multicollinearity/high correlations of the covariates, like in regression studies? Particularly when covariates are nominal in nature.
I have read classic MIMIC studies (Jöreskog & Goldberger, 1975; Muthén, 1979, 1981, 1982) and haven't found any researchers addressing this topic.
Any reference will be greatly appreciated! Thank you for your advice!
For instance, in "Age Differences in the Symptoms of Depression: A Latent Trait Analysis (Gallo, Anthony, Muthén, 1994), a total of 7 covariates were included in the MIMIC model: Age, Sex, Minority, Education, NMSE scores, Work, and Spouse.
Is there any research on how many covariates could be included in a MIMIC model?
I am running a MIMIC model comparing a constrained vs. an unconstrained model for certain indicators.
When at the very end in the constrained model I run:
ele7-ele42 on grade1@0; ELE7 ELE8 ELE15 ELE19 ELE24 ELE36 ELE38 ELE39 ELE41 ELE42 on grade1;
I get this message:
*** ERROR in MODEL command Variances for categorical outcomes can only be specified using PARAMETERIZATION=THETA with estimators WLS, WLSM, or WLSMV. Variance given for: ELE7 And the error messages continue for all the involved items.
But if I run the model like this:
ele7-ele74 on grade1@0; ELE7 on grade1; ELE8 on grade1; ELE15 on grade1; ELE19 on grade1; ELE24 on grade1; ELE36 on grade1; ELE38 on grade1;
Then the model runs without issues. Could you please tell what is the reason for this?
All the variables in this example are categorical. grade 1 is a dummy variable.
We need to see your full outputs - send them to Support along with your license number.
David posted on Thursday, March 07, 2019 - 11:57 am
I am conducting a DIF analysis within the MIMIC framework across 5 groups (dummy coded) with binary observed items. My understanding is that the STD standardization values are the most interpretable because the covariates and observed variables are both binary. Is that correct?
However, when I try to calculate the MH-D-DIF effect size, the results do not seem reasonable. The MH-D-DIF is -2.35 X natural log of the conditional odds ratio of reference group compared to the focal group. Is there a standardization beta value in mplus that corresponds to the ln of this conditional odds ratio?
Please send your output to Support along with your license number.
Olev Must posted on Sunday, May 12, 2019 - 11:13 pm
I am conducting a MIMIC modeling. I regressed the latent factor on the binary group variable. This effect is described by the regression coefficient. In 2-group case the regression coefficient should show the difference between (latent) means. I would like to present this difference in another system, I would like to present the difference in the system with SD =15 (in IQ units). Is this operation/convertion possible? (I have binary outcome variables, the invariance of loadings and thresholds are estimated separately according to solutions of Dimitrov).
You can fix the factor variance to set the metric instead of fixing the first loading. So fix V(f) at 225.
Olev Must posted on Wednesday, May 15, 2019 - 3:09 am
Thank you very much.
Kelsey Bush posted on Monday, September 16, 2019 - 9:35 am
Hello Drs Muthen,
I am considering doing MIMIC analyses to look at differential item functioning in a rating form that has recently been revised. I would like to use a sequential free baseline approach as to not pick a high-DIF item as an anchor. I believe that MIMIC modeling traditionally uses a backwards approach (or fully constrained model) as default. Would it be possible to use a sequential free baseline approach for MIMIC analyses in MPlus? And if so, how would I represent this in my input?
I believe so, but analysis strategies for carrying it out is a good question for SEMNET.
Kelsey Bush posted on Friday, September 20, 2019 - 2:11 pm
Benget - thank you for your reply!
I have a follow up question. I have run a MIMIC CFA model with five factors associated with a total of 64 categorical observed variables and one covariate. I would like to address the effects of race (binary dummy variable) on factor 1 (i.e., f1 ON race;).
My output gives me the following error: WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE F5.
Do I need to add additional specifications to my model in order to fix this error?