Hello, I am trying to run a multiple group analysis and test for differences between the groups (men & women). The model is comprised of all observed, continuous variables. I understand how to use the grouping function and constrain the paths (see syntax below). However I'm having trouble understanding the output. The approach I learned was to go step by step: First constrain path coefficients to be equal and test for differences. Then add on constraint for intercepts and test for differences. Then add on constrain for disturbances and test for differences. This way, you know where the difference lies. I'm confused what MPlus is doing with the syntax below and how to go about examining these differences. Any help would be much appreciated!
MODEL: ib ON smsr (1) icsrle (2); cisst ON smsr (3); pswq rrs ON smsr (4) icsrle (5) ib (6) cisst (7) sxc (8); rrs WITH pswq (9);
ANALYSIS: TYPE IS GENERAL; ESTIMATOR IS MLM; ITERATIONS = 1000; CONVERGENCE = 0.00005;
See TECH1 or the results to see the effect of the equality constraints that you have placed. I think you may not mean some of them, for example,
pswq rrs ON smsr (4)
holds the regression coefficients of pswq on smsr and rrs on smsr equal to each other and equal across groups.
To test the equality across groups, you can use chi-square difference testing of the model with constraints versus the model without constraints. You can also use the MODEL TEST command to obtain the Wald chi-square test.
Thanks for your help. I am now trying to calculate the Chi-Square difference test using the Satorra-Bentler Scale Chi-Square. I have followed steps 1-4 using the following data and come up with a TRd = 21.68. What should I use for degrees of freedom to get the significance value?
Hello, I am doing a CFA multigroup analysis for testing invariance (scalar). Mplus by default fixes the mean of the latent variable to 0 in the first group. The intercepts and loadings are equal across groups. I would like to use something different from the Mplus default. I am interested in the means of the latent variable. So, I fixed an intercept for one observed variable (tau)to 0 and I want to have all latent variable means free. I would like to know how to get a latent variable mean also in the first group? I hope my question is clear.
An example is below: .... analysis: type= meanstructure;
model: MEDIA by TV* RD* NWSP@1; TV* RD* NWSP* ; MEDIA* ; [TV* RD* NWSP@0] ; [MEDIA*];
You have to free the mean in the first group. From what you show, I can't tell which is the first group. If you can't figure this out, please send the input, data, output, and your license number to email@example.com.
To confirm - is this via multiple group analysis, where I constrain the predictor path (and/or covariate paths)? And then compare the non-constrained model and constrained model by analyzing the x2 and df?
How, in this situation, would multiple group analysis differ from adding an interaction term? Would I arrive at the same conclusion of moderation or no moderation?
This is an alternative way of testing for an interaction where you use the DEFINE command to create the interaction variable.
Anjali Gupta posted on Wednesday, September 02, 2009 - 11:15 am
Thank you for the response. To clarify - could using multiple groups analysis or interactions be 'interchanged'. That is, are there advantages/disadvantages to using either technique?
Or times one should be used over the other?
Also - to replicate running a regression on subgroups, say in SAS, would one, using Multiple Group Analysis, ideally constrain all paths (predictors, covariates, autoregressive paths)? Or focus on the predictor to Dep. Var path?
It's my guess that to replicate regression models with subgroups - one would compare unconstrained versus all paths constrained - including the covariates.
I am trying to do a two-group CFA analysis with categorical indicators. Hence, I have to perform the difftest:
ANALYSIS: type = mgroup; estimator = wlsmv; difftest is deriv.dat;
I did this test by firstly estimating the less restrictive model (letting all loadings be free under 'MODEL group:' command with 'SAVEDATA: DIFFTEST IS deriv.dat;') then I estimated the more restrictive model (restricting equality of parameters under the 'MODEL:' command and 'ANALYSIS: difftest is deriv.dat;').
Hence, I got the following result indicating that constraining the parameters of the nested model significantly worsens the fit of the model:
Chi-Square Test for Difference Testing Value 42.291 Degrees of Freedom 14** P-Value 0.0001
However, I am afraid this result may still be driven by the large sample size of my data (group 1 N=2000 and group 2 N=1600). Are there other test I can use to show that the theorized factor structure fits the data well in both groups? Or, rather are there other tests to conclude that the loadings in the two groups are sufficiently similar to proceed with a full group analysis?
Is it possible to free certain loadings across say, race, in a MIMIC model while regressing the latent factors on race? I would want to do this if the mentioned loadings actually fit statically significantly differently across race in a multi-group analysis.
My second question: what does the direct regression effect from a factor indicator to race mean in a MIMIC model that also simultaneously regresses the latent factors on race?
Hello, Much like the first post in this thread, I am interested in running an MGA in a path model with all observed indicators. This a moderation model whereby the dichotomous groups are thought to moderate the direct effect of a predictor on 5 outcomes. I understand that individual or summary data can be used. Is it okay to use a correlation matrix if one chooses to use summary data? I am unclear if this is an appropriate strategy in that all variables except the moderator (in the model) are continuous.
Thank you. Two further questions. If individual level data in our analyses are mean composite variables does this render the data as mean structure? Secondly, in testing the models at various levels of the moderator, we are interested only in a certain subset of paths that might differ as a function of moderator level. Thus, the overall chi-square test of model fit (which would test all paths in the model) would seem inappropriate. Is there a specific way to compare only a subset of paths in the model at a given level of the moderator? In both models (i.e., at both levels of the moderator) we will constrain these paths to equality for theoretical reasons.
Thanks Linda. One last question: it indicates in the user's guide that for summary data in the case of MGA that the first group is represented by the first set of summary data found in the summary data set. Thus, if we are opting to use a correlation matrix, would this mean then that the grouping variable (i.e., moderator) should appear as the first variable in a lower correlation matrix?
There is no grouping variable with summary data. See the rest of the writeup where NOBOSERVATIONS and NGROUPS are explained. A correlation matrix is not allowed with multiple group analysis. A multiple group model is not a scale free model.
Thanks. I did read the instructions for summary data and NOBSERVATIONS and NGROUPS after I initially posted. I am having trouble in that when I use my summary data file (a covariance matrix) and lay it out as is indicated with the covariances for g1 on top and g2 on the bottom, I get the warning that there are more NOBSERVATIONS than NGROUPS (NOBSERVATIONS being 138 and 387 respectively). Am I missing something?
Currently I am testing the effect of an intervention on (amongst others) the growth of problem behavior in MPLUS. Therefore I have tested latent growth curve models, and multiple group models. I have noticed that the latent growth curve models sometimes give bad results for the effect of the intervention on problem behavior, whereas multiple group models mostly lead to good results. Can I choose for multiple group models to represent my data, or am I bound to use latent growth modeling?
I can't quite understand what you are doing. Please send the growth and multiple group outputs along with your license number to firstname.lastname@example.org.
Lily Wang posted on Monday, July 02, 2012 - 6:50 pm
Hi Drs. Muthen,
I am working on a multi group analysis and have a question:
I did two sets of analyses, one based on gender and the other on race and I found that a path is significant for both males and females in the gender based multigroup analysis, but not significant for either the white or the minority group in the race based multigroup analysis. This doesn't make intuitive sense, but maybe I miss something important here? Could you provide some guidance as to why this could happen?
Sounds like you need to do a multi-group analysis with gender crossed with race groups.
Lily Wang posted on Tuesday, July 03, 2012 - 6:46 am
Thanks so much for your advice. Could I follow up on that and ask if you could provide any additional information on when to make such a decision--e.g. instead of two sets of analyses respectively based on gender and race, running one analysis with four groups (white female, white male, minority female and minority male)? That is, only after the analyses and I see the weird pattern of that one path, or are there other considerations? Do you know of any references or examples that deal with similar problems? I really appreciate your time!
Hello, I want to measure differences/similarities between men's and women's behavior. My dataset does not allow me to run a group analysis. Instead of have a variable such as gender, it does have different variables for each item, e.g. X= husband hit by mother and Y= wife hit by mother. I ran exploratory and confirmatory analyses, independently, for men and women. The models are exactly the same. I am making my conclusions based on the correlations between four factors as well as their correlations with a few correlates (independent variables). All of the indicators of fit are excellent for both models (CTI, TLI, and RMSEA). Is this a legitimate way to compare these behaviors? Is there any way to run them in the same model?
If the unit of observation is the individual, how can you have variables such as husband hit by mother and wife hit by mother? It seems the unit is either the husband or the wife.
Sara Geven posted on Friday, September 27, 2013 - 12:23 am
I am conducting a multiple group analysis in which I am comparing paths across different groups. Because I am not interested in comparing (latent) means, I checked for metric invariance. In my structural model I now freely estimated the intercepts in each group. This seemed right to me, since I did not test for scalar invariance. Is this the correct approach?
Sara Geven posted on Sunday, September 29, 2013 - 9:57 am
Thanks a lot. It makes me feel more certain about my results. Sara
baozhenzhou posted on Tuesday, February 25, 2014 - 1:27 am
Hello, I am trying to run a multiple group analysis and test for differences between two groups (male & female). The model is comprised of all observed, continuous variables(y is dependent variable,x is independent variable, w is moderator ).
VARIABLE: names are y x w gender; grouping is gender (0 = female, 1 = male); usevariables are x y w gender xw; DEFINE: xw = x*w; ANALYSIS: bootstrap = 1000; MODEL: y on x w xw; MODEL male: y on x w xw; OUTPUT: STDYX CINTERVAL(BCBOOTSTRAP); The output only present the coefficients of the moderation for male and female.My question is how can i decide whether these moderation effects have gender differences? Thank you very much!
You can use MODEL TEST or you can do a chi-square difference test between your model above and the model with regression coefficients held equal:
MODEL: y on x (1) w xw;
baozhenzhou posted on Tuesday, February 25, 2014 - 5:41 pm
Thanks, Linda! Follow your suggestions, I used Model Test. VARIABLE: names are y x w gender; grouping is gender (0 = female, 1 = male); usevariables are x y w gender xw; DEFINE: xw = x*w; ANALYSIS: bootstrap = 1000; MODEL female: y on x (fe_b1) w (fe_b2) xw (fe_b3); MODEL male: y on x (ma_b1) w (ma_b2) xw (ma_b3); MODET TEST: fe_b3=ma_b3; OUTPUT: STDYX CINTERVAL(BCBOOTSTRAP); I am interested in whether the moderation effect has gender difference, so I set the path of moderation to be equal and check the result of Wald test. Is it correct ? Thank you very much!
Thank you Linda. I am a still confused as to why Form B would not be the test I would want to use. For example, Form A would tell me if P2, P3, and P4 were equal to P1, but not if P2 was equal to P3 and P4, or if P3 and P4 were equal.
Is Form B not a more complete test of invariance across all four groups than Form A?