I have conducted mediation analysis with INDIRECT using bootstrapping and I am interested in whether the significant mediation holds in the sub-groups. Can I use INDIRECT in multiple group analysis? I have done the multiple group testing up to the structural level per the UG recommendations. Thanks, Sue
We are testing mediation models and we would like to see if there are sex differences on the IND pathway (multi-group analyses).
When searching on the Mplus forum discussion, we found this solution:
'If you have the estimate of the mediated effect and its SE for each of the 2 groups, you can simply use those numbers to create the approximately normal test variable:
(e1 - e2)/(se(e1-e2)),
where the denominator is sqrt(var(e1-e2)), where var(e1-e2) is var(e1) + var (e2), where var(e) is the square of the SE(e).'
Our questions are: (1) Is there another way to conclude about differences between groups on the mediated effect (i.e., Mplus command)? (2) If the formula above is the only way to conclude about differences between groups, is there a reference we could use?
You can use MODEL TEST to compute a Wald test or you can do difference testing of the model where the parameter of interest in free across groups compared to a model where the parameter is held equal across groups. Difference testing is discussed on pages 434-435 of the Version 6 Mplus User's Guide.
I have a question regarding an error I get when i try to run the following model: INPUT INSTRUCTIONS
data: File is P.csv; variable: Names are P I M; Usevariables are P I M; Categorical are I M; Analysis: Type=general; bootstrap=5000; Model: I ON M P; M ON P; Model indirect: I IND P;
*** ERROR The length of the data field exceeds the 40-character limit for free-formatted data. Error at record #: 1, field #: 1 *** ERROR The number of observations is 0. Check your data and format statement. Data file: P.csv
Dear Linda, thanks a lot for your fast response, I figured it out and Mplus is now working like a dream ;-)
Please, can you advice me how to report the results below (A mediation-analysis with one dichotomous variable) And I need to report confidence intervals for the bootstrap analysis. I am a bit in a doubt how to report it. I haves search APA, but without luck. Can you help me???
CONFIDENCE INTERVALS OF TOTAL, TOTAL INDIRECT, SPECIFIC INDIRECT, AND DIRECT EFFECTS
Thanks a lot, so to get it clear (sorry, but I am new in the realm of mediation-analysis ;-)
My mediator is the MAS-variable (dichotomous), so I report the point estimate -0.082 and 95% CI [-0.151, -0.024], right (see above). And then the S.E. Est./S.E. as well for the MAS variable? and that will be it?
I am wondering whether it is possible to do mediation analysis in Mplus with a categorical variable consisting of 4 subcategories? I have done it with a dichotomous variable, but would be very excited if it is possible with a 4-category mediator variable as well????
Dear Linda, Thanks a lot, it sure looks very interesting and promising for future research possibilities...
I have looked into the article and the appendix as well, and I can see that there is syntax for a 3-categorical mediator, but can you in anyway help me with a syntax, if my mediator consist of 4 categories? I would be very grateful since I have some very interesting psychological data where it would be superb with this opportunity.
I'm using all observed variables in a path analysis with multiple groups (2 groups). I have the following in the model: m1 m2 on x; y1 y2 on m1 m2 x;
model indirect: y1 ind x; y2 ind x;
model lowses: y1 on x (a1); m1 on x (b1); y1 on m1 (c1); y1 on x (a3); m2 on x (b3); y1 on m2 (c3);
model highses: y1 on x (a2); m1 on x (b2); y1 on m1 (c2); y1 on x (a4); m2 on x (b4); y1 on m2 (c4);
model constraint: new (f g); f=(b1*c1)+(b3*c3); g=(b2*c2)+(b4*c4); model test: 0=f-g;
However, y1 and m1 are based on similar measures (across time) and y2 and m2 are based on similar measures (across time). Should I run 2 models (one for y1 and m1 and one for y2 and m2) or keep the analysis as one (as above)? If I run it as above, I need to run one model constraint for predicting the indirect effects for y1 across the 2 groups and another for predicting the indirect effects for y2, right? Thanks!
Thanks so much for your response! These boards as well as the manual help so much.
I was trying to re-run my model using Bootstrapping and CINTERVAL in the output. I know you said that MODEL TEST cannot be used and so 0 = f - g needs to be brought into the MODEL CONSTRAINT field. However, when I do this, I get an error of convergence: NO CONVERGENCE. SERIOUS PROBLEMS IN ITERATIONS. ESTIMATED COVARIANCE MATRIX NON-INVERTIBLE. CHECK YOUR STARTING VALUES. Do you have any advice?
Hi, I was wondering if it is computionally right to test with MODEL INDIRECT a model in which a latent dimension has an effect on a DV via a second latent dimension of which the DV is one of the observed variable. From a tehoretical perspective it make sense, but I am not sure if I cant translate it into SEM, that is:
f1 BY x1 x2 x3; f2 BY z1 z2 dv1;
f2 on f1 (7); dv1 on f1 (8);
MODEL INDIRECT: dv1 on f2 f1;
In addition I wanted to test with a multi-group approche whether the mediation model holds in different group. Thus I constrained the relation between variables to be equal across groups.
Greetings - I'm curious as to whether a solution was discovered for the problem reported here Friday, January 20, 2012 - 8:55 am. I have a model of multiple group mediation, and when the testing statement is brought into the model constraint statement, I get the NO CONVERGENCE. SERIOUS PROBLEMS IN ITERATIONS. ESTIMATED COVARIANCE MATRIX NON-INVERTIBLE.CHECK YOUR STARTING VALUES. error messages.
Dear Dr. Muthen, Thank you for your help. I tried to run the model as you suggested but I am still getting the same error. I tried this alternative model and it did run: MODEL CONSTRAINT: NEW(a1a2 b1b2 c1c2 diff diff1 diff2); a1a2=a1*a2; b1b2=b1*b2; c1c2=c1*c2; diff=a1a2-b1b2; diff1=b1b2-c1c2; diff2=c1c2-a1a2;
The output gave me these new parameters: New/Additional Parameters A1A2 -0.023 0.011 -2.079 0.038 B1B2 -0.008 0.006 -1.300 0.193 C1C2 -0.032 0.021 -1.549 0.121 DIFF -0.014 0.012 -1.162 0.245 DIFF1 0.024 0.021 1.105 0.269 DIFF2 -0.009 0.023 -0.405 0.685
Is there any other way of testing whether these are significantly different from each other?
One follow up question, i ran two models one with the direct paths constrained and one with them unconstrained. There does not seem to be significant change in model fit. If the direct paths are the same across group, couldn't I just assume that the indirect paths are the same as well?