I am a new Mplus user--thanks for any help or advice!
I'm trying to run a measurement model + multiple paths (SEM). I've pasted my model syntax below. There are 8,037 observations (no missing data). The outcome (o1) is binary; I'm using ESTIMATOR = ML. I have three latent variables (f1, f2, f3), and all of my indicators are binary (a1-a16) or ordinal/categorical (b1-b9, c1-c5). I am testing 4 different measured mediators (m1-m4), and I have 6 measured covariates (x1-x6).
I have two questions:
1. MPlus has been running for almost 2 hours now. I'm wondering if this is usual--perhaps given the complexity, number of dimensions in my model--or if I need to adjust any settings to help the model converge?
2. Are there any issues with combining binary indicators (latent variable f1) and ordinal indicators (latent variables f2 and f3)? E.g., would the interpretation of the coefficients change for these different factors?
MODEL: f1 BY a1-a16; f2 BY b1-b9; f3 BY c1-c5; o1 ON f1 f2 f3 m1-m4 x1-x6; m1 ON f1 f2 f3 x1-x6; m2 ON f1 f2 f3 x1-x6; m3 ON f1 f2 f3 x1-x6; m4 ON f1 f2 f3 x1-x6; f1 ON x1-x6; f2 ON x1-x6; f3 ON x1-x6;
I'm a student & am running the analysis on my own laptop, which has 1 processor (dual core), so perhaps this explains the long running time.
I tried your suggestion with the integration=montecarlo(500) command. This reduced the running time from 4.5 hours to 1 hour.
I did check the TECH8 output. The ABS change was negative after the sixteenth step. Can you tell me how to interpret this? I also noticed that while most of the coefficients are approximately the same, some were fairly different.
I'll give it a try. Thank you again for your help.
mboer posted on Saturday, April 25, 2020 - 4:58 am
Dear Prof. Muthen,
I would like to run a multilevel logistic regression model using 5 datasets with imputed data. I also have 3 dimensions of integration, which takes a lot of computation time (especially since I the estimation runs 5 times).
I have used 'integration = montecarlo(1000)' to speed up the computation time. In 2 out of 5 replications, the ABS change was negative. Are my results unreliable? Do you recommend increasing the integration points?
I am trying to run a logistic regression path model with a continuous X and dichotomous Y, with one moderation (on x->y with a dichotomous V) specified using Define, Model Constraint for 6 indirect effects (where 3 Ms are dichotomous) and their ORs, as well as one moderated mediation (M and V are continuous) and its simple slopes – by combining the code on Chris Stride’s website for Models 4d and 14 (N=1659 - 639 missing Xs = 1020).
When I tried this with Type= General, Estimator=ML, and Bootstrap=10000. I get “FATAL ERROR THIS MODEL CAN BE DONE ONLY WITH MONTECARLO INTEGRATION.” When I added Integration=MonteCarlo, it didn’t stop running for over 12 hours so I had to close the program, I also tried adding "(1000)" but it still continues running (I’m working from home on an old MacBook Air due to Covid-19). Then I tried removing Type, Estimator and Bootstrap and adding "Algorithm=Integration", it runs in one minute, but does not provide confidence intervals for the ORs or bootstrap.
Is there another way to get the CIs for the ORs (or can I interpret based on p-value) and to run ML to get the bootstrap?
There are several issues with what you want to to. It sounds like you have a binary Y and several binary M's. A binary Y calls for "counterfactually-defined" effects as discussed in these papers:
Muthén, B. & Asparouhov T. (2015). Causal effects in mediation modeling: An introduction with applications to latent variables. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 12-23. DOI:10.1080/10705511.2014.935843 Click here to download the paper.
Nguyen, T.Q., Webb-Vargas, Y., Koning, I.K. & Stuart, E.A. (2016). Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention. Structural Equation Modeling: A Multidisciplinary Journal, 23:3, 368-383 DOI: 10.1080/10705511.2015.1062730
This means that it will be hard for you to do the analysis you want because of your 3 binary M's. You can instead assume that the effects are relevant for M* and Y* continuous latent response variables (instead of the observed binary M and Y variables) for which regular linear regression formulas for effects are valid. Then the analysis can be done by WLSMV or Bayes - but it will be probit and not logit analysis, that is, no odds ratio interpretations.
I assume Chris Stride's website concerns M and Y that are continuous where these complications don't arise.
MODEL: m1 ON x1 (a1); m2 ON x1 (a2); m3 ON x1 (a3); m4 ON x1 (a4); m5 ON x1 (a5); m6 ON x1 (a6); y ON m1 (b1); y ON m2 (b2); y ON m3 (b3); y ON m4 (b4); y ON m5 (b5); y ON m6 (b6); y ON MOD1 (b7); y ON MOD2 (b8); y ON x1 (cdash);