Path analysis with a binary outcome v... PreviousNext
Mplus Discussion > Categorical Data Modeling >
 Manon Burgat posted on Thursday, June 13, 2013 - 7:25 pm
Hi there,

I am using MPlus 6.12 to conduct a path analysis with 1 binary outcome variable (DV), a continuous mediating variable, and 5 continuous IVs. I have a couple of questions regarding this:

(1) I expect from past research that my 5 IVs will be correlated. Should I therefore specify this in the input syntax? If so, should I use WITH (i.e. v3 WITH v2), or something else? I read that Mplus assumes that exogenous variables are correlated but I have noticed that I am given different model fit results depending on whether or not I specify these correlations.

(2) At the moment I am using WLS estimator for this model, but I have noticed from previous posts that others have used WLSMV. Can you tell me which is best in my particular case?

(3) Lastly, I have a broad question regarding improper solutions for SEM and/or path analysis. Is it incorrect to have standardized estimates that are negative (less than 0)? Or does this just indicate a negative relationship between the variables?

Thanks in advance.
 Linda K. Muthen posted on Friday, June 14, 2013 - 9:00 am
1. You should not bring the covariates into the model. The model is estimated conditioned on the covariates. Their means, variances, and covariances are not model parameters. If you want to see their correlations, get their descriptive statistics. With WLSMV doing this changes the model.


3. Factor loadings can be positive or negative. They are regression coefficients.
 Manon Burgat posted on Friday, June 14, 2013 - 6:53 pm
Thank you Linda. I have a follow-up question. I'm wondering what the advantage of using an ESTIMATOR=ML approach with my model may be? I do not have any missing data, so if I did employ ML would I need to use numerical integration? Lastly, can I obtain estimates of the indirect effects if I use ML? I know that MODEL INDIRECT cannot be used with ML but I'm wondering whether these can be obtained another way?
 Linda K. Muthen posted on Sunday, June 16, 2013 - 3:43 pm
You would need numerical integration for maximum likelihood and categorical outcomes. MODEL INDIRECT is not available in this case. You would need to use MODEL CONSTRAINT to define the indirect effects.
 lopisok posted on Wednesday, March 11, 2015 - 4:43 am
Dear forum,

I'm doing a path analysis with a binary outcome, 2 continuous mediators and 6 continuous IV's.

I followed the instructions from EXAMPLE 3.17: PATH ANALYSIS WITH A CATEGORICAL
Using MLR and montecarlo integration.

Everything works fine but I was wondering why I do not get any fit statistics like TLI, CFI, RMSEA, SRMR?
Can I only compare models through the BIC AIC?

When I ask MODINDICES I get the message " MODINDICES option is not available for ALGORITHM=INTEGRATION. Request for MODINDICES is ignored."

I read in other posts that when using listwise deletion the montecarlo integration command can be dropped. I did this but I still get the same message. Is it just impossible or senseless to ask for cfi, tli for some reason I don't understand at the moment? :-)

Kind regards,
 Linda K. Muthen posted on Wednesday, March 11, 2015 - 12:25 pm
That example uses maximum likelihood estimation. ML and categorical outcomes requires numerical integration. In this situation chi-square and related fit statistics are not available. You can use the default WLSMV instead. This will give probit rather than logistic regression.
 lopisok posted on Friday, March 13, 2015 - 4:04 am
Dear Linda,

Thank you very much for your answer. Is ML recommended or WLSMV? Is there an advantage in using one estimator over the other? Does the advantage mainly lie in WLSMV providing fit statistics?

Does it remain correct if I use ML and report the standardized coefficients?
 Linda K. Muthen posted on Friday, March 13, 2015 - 6:45 am
You can use either estimator. WLSMV is better with more factors because ML with categorical requires one dimension of integration for each factor with categorical indicators. ML has better missing data handling. And if fit statistics are important to you, you can get those with WLSMV.

It is correct to use ML and the CATEGORICAL option.
 MSP posted on Tuesday, December 08, 2015 - 12:00 pm
Hi. I am trying to do a path analysis with model:

Y1 on X1-X10;
Y2 on X1-X10;
Y3 on X1-X10;
Z on Y1-Y3;
!X and Y are continuous (Ys are factor scores of Xs)
!Z is dichotomous outcome

When I don't use an estimator (default WLSMV), it outputs this:

When I put estimator = MLR, it outputs normally and the fit indices are just the Loglikehood (H0) and the Information Criteria (AIC/BIC).

My questions would be:
1. Is MLR estimator an acceptable choice for this path model?
2. Are two fit indices acceptable in this case? No chi-square available?

I appreciate any input on this. Thanks.
 Bengt O. Muthen posted on Tuesday, December 08, 2015 - 6:15 pm
It seems strange that you have both factor scores Ys and their indicators Xs in the model.

It is also strange that WLSMV would have problems when ML doesn't. Perhaps you Xs have very large variances.

We need to see the two outputs to say more - send to Support along with your license number.
 MSP posted on Thursday, December 10, 2015 - 10:51 am
Thanks for the response. It is strange.

I was advised to compare a model where the factors are fitted (model with formative factors), and a model where the factor parts are 'constrained' (i.e. factor scores). I was trying to do the second model with this query.

I will send my outputs to the support email. I am using a school computer lab desktop though and not sure how to get license number. Please advise.
 Linda K. Muthen posted on Thursday, December 10, 2015 - 4:52 pm
Ask you IT people.
 Tanya posted on Thursday, June 02, 2016 - 5:22 am
Dear professors,
I run path model with binary outcome (dependent variable and mediator are continuous). My data was collected through a two-stage stratified cluster sampling procedure and I also took into account sample weights.
The model looks like X-M-Y. I use command Model indirect to estimate total and indirect effect.
I have a couple of questions regarding this model.

1. What measure is used to estimate
relationship between X and M?
2. It seems like total, direct and indirect effect are given as a probit coefficient, but coefficient that shows effect from X to M is given in other units of measurement.
I multiplied coefficients ( coefficient from X to M )* (coefficient from M to Y ) by myself to get total effect and I
got the same total as mplus gave me. So does mplus multiply coefficients inspite of their different units of measure?
3. Is it possible to apply KHB method in mplus?

Thank you!
 Linda K. Muthen posted on Thursday, June 02, 2016 - 1:40 pm
1. I think you meant above independent and mediator variables are continuous so this is a linear regression.
2. Direct effect is a probit coefficient with WLSMV which I assume you are using. Indirect effect is a product.
3. What is KHB?
 GaŽlle Cyr posted on Wednesday, July 05, 2017 - 1:06 pm
Dear Professors,

I am testing a path analysis with all observed variables. My X and M are continuous, whereas my Y is categorial (see syntax example bellow). Considering the mix of linear and non-linear analysis in the model, I am wondering whether the value and bootstrap CI of the indirect effect provided by Mplus will be adequate, or if I need to calculate them myself. If so, where can I find the formula ? If not, how do I interprete them ?








Thank you so much !

GaŽlle Cyr
 Bengt O. Muthen posted on Wednesday, July 05, 2017 - 6:12 pm
How many categories does Y have? I assume it is ordinal.
 GaŽlle Cyr posted on Wednesday, July 05, 2017 - 8:12 pm
It has two : perpetrator or non-perpetrator of sexual coercion.
 Bengt O. Muthen posted on Thursday, July 06, 2017 - 7:02 pm
Then use ML and look for "counterfactually-defined" effects.

See also our web page:

which explains when and why you need counterfactually- defined effects and what they mean in the binary Y case.
 GaŽlle Cyr posted on Thursday, July 06, 2017 - 9:11 pm
This is very helpful, thank you very much !
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