Jian Wang posted on Tuesday, December 06, 2011 - 12:17 pm
Dear Drs. Muthen&Muthen,
I am working on a mediator model with a binary outcome Y, two continuous mediators M1 and M2 and a continuous initial variable X. I am trying to use the logistic regression. The input file is as following:
TITLE: Two-mediator example DATA: FILE IS data1.txt; VARIABLE: NAMES ARE x m1-m2 y; CATEGORICAL IS y; ANALYSIS: ESTIMATOR = ML; MODEL: m1 ON x(a1); m2 ON x(a2); y ON m1(b1); y ON m2(b2); y on x; m1 WITH m2; MODEL INDIRECT: y IND m1 x; y IND m2 x; OUTPUT: CINTERVAL;TECH3; SAVEDATA: RESULTS ARE results_data1.txt; TECH3 IS tech3_data1.txt;
However, when I run it, I got an error message like:
*** ERROR MODEL INDIRECT is not available for analysis with ALGORITHM=INTEGRATION.
I am not quite sure what the error message means. Thank you a lot for your help.
You would need to use MODEL CONSTRAINT to create the product term of the indirect effect. Note that this is the indirect effect of the latent response variable underlying y. If you are interested in the indirect effect of the observed variable y, see on the website:
Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Submitted for publication.
Jian Wang posted on Tuesday, December 06, 2011 - 3:57 pm
Thank you very much for your quick response.
1. Now I have tried to use the Model constraint command, and it seems work. But when I tried to use bootstrap to get the confidence interval, it gave me the error message again:
*** ERROR in ANALYSIS command BOOTSTRAP is not allowed with ALGORITHM=INTEGRATION.
Does it mean I can not use bootstrapping for logistic regression?
2. I remember that when the outcome is binary, I need to rescale the coefficients to make the coefficients comparable across equations. (http://nrherr.bol.ucla.edu/Mediation/logmed.html) I wonder if the mplus will rescale the coefficients? If yes, what option I should use?
1. Yes, bootstrap is disallowed with integration due to the worry about computational time. If you are concerned about the indirect effect having a non-normal distribution, you can switch to Estimator=Bayes.
2. That rescaling is not necessary - the approach your refer to is about two generations of papers behind now. The first generation change is described in
MacKinnon, D.P., Lockwood, C.M., Brown, C.H., Wang, W., & Hoffman, J.M. (2007). The intermediate endpoint effect in logistic and probit regression. Clinical Trials, 4, 499-513.
The second generation change is the Muthen (2011) paper Linda referred to (it comes with Mplus scripts).
Jian Wang posted on Wednesday, December 07, 2011 - 7:11 am
With categorical dependent variables and maximum likelihood estimation, chi-square and related fit statistics are not available because means, variances, and covariances are not sufficient statistics for model estimation.
We have a path analysis with one categorical dependent variable (a two-class solution of a latent profile analysis of health behaviors) and two sets of predictor variables: 3 proximal predictor variables and four more distal predictor variables (e.g. sociodemographics). Predictor variables are either Likert, ordinal or binary.
Can I ask two follow-up questions to make sure that I understand how to proceed: (1) In this model with a categorical dependent variable, are there any usable fit indices?
(2) If yes, which should we use and which values would indicate acceptable fit?
Many thanks for your reply in advance, all the best, Mario
There are no absolute fit statistics. Nested models can be compared using -2 times the loglikelihood difference which is distributed as chi-square. BIC can be used to compare models with the same set of observed variables.
I have a follow-up question: As recommended by you, I have used the MODEL CONSTRAINT option to obtain Odds Ratios for both the direct and the indirect effects (via a continuous mediator). How can I interpret these total effect-ORs, especially when it is summarized from two paths with opposite directions? Do you know any reference I could refer to?