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?
You can't give several labels on the same row as you have done - you need to separate them by semi colons.
DavidBoyda posted on Wednesday, February 12, 2014 - 12:45 pm
Thank you so much Bengt, it worked wonderfully.
However, I have a follow up question. If the indirect effects are the product of OLS regression coefficient and probit coefficient, how on earth do would you interpret the indirect effects estimates since they are the product of two different scales?
These issues are discussed in my paper on our website:
Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus. Click here to view the Technical appendix that goes with this paper and click here for the Mplus input appendix. Click here to view Mplus inputs, data, and outputs used in this paper.
DavidBoyda posted on Wednesday, February 12, 2014 - 2:57 pm
I am conducting a similar model to Mario, with 6 continuous IVs, 1 continuous mediator and a binary DV. Based on readings on this forum, I have opted for the mlr estimator versus wlsmv.
I have 2 questions: 1.Is the following syntax appropriate for a sensible interpretation of indirect effects - or is another step was required given the combination of OLS and logit coefficients? 2. to compute total effects do I simply add the indirect and direct effects? or again should I be weary of the combination of OLS and logit coefficients?
ANALYSIS: estimator is mlr; type is missing; integration = monte;
MODEL: abexp on sesgez_low gender; abexp on sch1 sch2 sup con consci socanx;
peer on sch1 (p9); peer on sch2 (p10); peer on sup (p11); peer on con (p12); peer on consci (p13) ; peer on socanx (p14);
abexp on peer(m1);
MODEL CONSTRAINT: new (ind_sch1); new (ind_sch2); new (ind_consci); new (ind_con); new (ind_sup); new (ind_anx);
I have completed a mediation model using Bayes since the results of MLR gave me product estimates that were non significant even though I have significant AB paths.
However I am a little bit confused over one of the results of the Bayes estimation. I have significant P values but the credibility intervals contain zero. So is the mediated effect significant or not?
m1 on x1 is significant (0.313, p =0.008, 95%CI= 0.063 - 0.555)
y1 on m1 is significant??? (0.098, p 0.035, 95%CI= -0.009 - 0.207)
and the mediated effect is:
Y1_M1_X1 = (0.028, p = 0.043, 95%CI= -0.003 - 0.085).
With MLR and a binary distal outcome and a continuous mediator, you can use the product specification for an indirect effect involving the latent variable underlying the binary distal outcome. You can also use this with WLSMV. For further information about indirect effect specification, see
Muthén, B. (2011). Applications of causally defined direct and indirect effects in mediation analysis using SEM in Mplus.
Thank you for your prompt response. I have one follow up question - if I use WLSMV can I also use MODEL INDIRECT to calculate indirect effects and make use of bootstrapping? Or given that I have a binary outcome with continuous mediators and predictors is the MODEL CONSTRAINT method preferred?
namer posted on Wednesday, April 30, 2014 - 6:14 am
Yet another follow up question. If I wanted to add two binary covariates to the model, would WLSMV with MODEL INDIRECT still be a valid method? It seems to me the combination of continuous and categorical predictor variables are fine with WLSMV. These are control variables, which are only regressed on binary DV, not continuous M.
I am trying to stick with WLSMV instead of switching back to MLR as long as valid, due to the benefit of the fit statistics produced.
Furthermore, I normally interpret my indirect effects as: for one unit in change in x, y changes by the value of the indirect effect. However, it is not clear to me how to translate this combined probit/OLS indirect effects I have in this model. I guess I cannot change these coefficients into probabilities as with the strictly probit coefficients? Nor can I interpret as I would with OLS coefficients? So how do you advise to interpret them?