Control variables in SEM PreviousNext
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 Pankaj P posted on Monday, June 05, 2006 - 5:13 pm
How do I model control variables in SEM? I am trying to do it in the spirit of Hierarchical regression. If I treat my control variable as a mediator variable, would it work?
 Bengt O. Muthen posted on Monday, June 05, 2006 - 5:41 pm
Remind me about the definition of a "control variable".
 Pankaj P posted on Monday, June 05, 2006 - 7:13 pm
Controlling for a variable means explaining relationship between independent and dependent variables AFTER we extract the impact of control variable on the DV. In other words, we run regresion on the residuals of regression between control variable and the DV.
I read in a paper somewhere, that I could do the same in SEM i.e. run SEM on residual covariance matrix. However, would considering indirect effects take care of controlling (besides its mediating effect)?
 Linda K. Muthen posted on Tuesday, June 06, 2006 - 10:19 am
Yes, you can use the residual covariance matrix as data in Mplus. I don't think that indirect effects achieve what you want.
 Bonnie Zhang posted on Sunday, June 18, 2006 - 11:45 am
For a simple mediation model, if I control for var1 and var2 for path a), is that required to control for the same variables for path c) or b)? In other words, do I need to control for the same variables for each path in the model? Thanks a lot!
 Bengt O. Muthen posted on Sunday, June 18, 2006 - 6:22 pm
I think that is a good idea.
 tommy lake posted on Thursday, June 14, 2007 - 9:24 pm
Dr. Muthen,

I'd like to follow up this discussion. Is it required to control for the same variables for each path in an SEM model, even when theory does not require so?

 Linda K. Muthen posted on Friday, June 15, 2007 - 7:53 am
No. I can see why you might have thought this from Bengt's answer but this is not required.
 Chris Chen posted on Wednesday, December 22, 2010 - 7:38 pm
Dear Dr. Muthen,
When putting control variables in SEM models, could one simply use the following command in addition to the other pathes of the model:

dependent variable ON control variable

if there is a mediation relationship, X->M->Y, should the control variable be inputted as

M ON Control variable
Y ON control variable

Should "X ON control variable" be specified also, or are the control only required on the outcome variables?

Thank you!
 Linda K. Muthen posted on Thursday, December 23, 2010 - 10:26 am
You would regress the dependent variables and the mediators on the control variables. You would not regress the covariates on the control variables.
 Chris Chen posted on Sunday, December 26, 2010 - 4:59 pm
Dear Dr. Muthen,

Thanks for the reply.

For the statement "you would not regress the covariates on the control variables" in your message, do you mean would not regress the "independent variables" on the control variables? It seems to me "covariates" and "control variables" are the same term. could you please clarify?

Thank you!
 Linda K. Muthen posted on Monday, December 27, 2010 - 5:58 am
Control variables are covariates as as the x variables you mention. I would not regress x on the control variables.
 Heather Knous-Westfall posted on Tuesday, September 27, 2011 - 1:21 pm
i'm having some issues with adding control variables into my sem model. i have two latents and am looking at both of them as potential mediators. when i run the model i have proposed, the model fit is excellent and most of the hypothesized paths are significant. however, when i add in a control variable, the model fit is terrible and i'm not understanding why that may be.

for example:

x2 is my control/confound variable

F1 BY y3 y7; F2 BY y9 y10 y12-y14;
F1 on x6; F2 on x6; y15 on F1 F2 x6;
F1 with F2;
F2 on x2;
y15 IND x6

when i run the above, model fit is horrible, but when i run this one, it's fine.

F1 BY y3 y7; F2 BY y9 y10 y12-y14;
F1 on x6; F2 on x6; y15 on F1 F2 x6;
F1 with F2;
y15 IND x6

any ideas on what that may mean? thanks!
 Linda K. Muthen posted on Tuesday, September 27, 2011 - 2:20 pm
When you add x2, you are not just adding the path from x2 to f2. You are adding other paths also but are fixing them at zero. This can cause model misfit.
 caroline masquillier posted on Wednesday, October 24, 2012 - 1:13 pm
Dear Prof. Muthen,

When I add manifest variables in my structural model (with four latent factors), they have an influence on some of the factor loadings of these latent constructs. Some of the factor loadings diminish far below the 0.40 boundary line in comparison with the measurement model (with all the latent factors together where all factor loadings were above 0.40). Can this be possible? Is there a solution for this ?

thank you very much in advance.
 Linda K. Muthen posted on Wednesday, October 24, 2012 - 3:07 pm
This may suggest the need for direct effects from the covariates to the factor indicators. Ask for MODINDICES (ALL) in the OUTPUT command to see if this is the case.
 saeid posted on Wednesday, March 20, 2013 - 8:21 am
Dear Dr. Muthen,
I have a question regarding using control variable:

How is possible to use binary variables (such as gender) in SEM (as control variables)?

 Linda K. Muthen posted on Wednesday, March 20, 2013 - 10:23 am
You just regress the factors on the binary variable.
 Jetty posted on Friday, November 08, 2013 - 9:34 am
I am testing a path analysis model of X via 4 mediators to Y (ordered categorical). I am using example 3.16 from newest manual, except I specify that the DV is categorical and use X1,X2, and X3 in the second MODEL command (not X2 only as in the text) because I need a fully adjusted model. My question is: Given that X2 and X3 are not included in the MODEL INDIRECT statements, are the indirect effects estimates adjusted for X2 and X3? If not, what is the proper syntax for including them as control variables?
 Linda K. Muthen posted on Friday, November 08, 2013 - 1:32 pm
Control variables are treated as any other covariate.
 dvl posted on Thursday, December 12, 2013 - 8:09 am
Dear professor,

I'd like to ask a question on the control variables included in different regressions in a path model (no latent concepts). As my theory assumes different control variables for each of my endogenous variables in the model, each path has different control variables. As I have read above that is not a problem given that including the same control variables in each equation is statistically not required (to my opinion, it would make path models less interesting if they did). However, the next issues are unclear to me:

(1) How should I interpret indirect and total effects when different control variables are included in each equation? For which variables are the total and indirect effects controlled in this case and how should we report on this?

(2) In a path model all exogenous variables are correlated. For example:

X -> W -> P
C1 = control variable 1
C2 = control variable 2

W on X C1;
P on W C2;

Mplus assumes C1 and C2 to be correlated, even if I do not include C2 as a control in the relationship on W. Is this true? So the regression on W is not controlled for C2, but somehow, the correlation between C2 and C1 should affect the relationship between X and W? You know how I should see this?

I really struggle with this! I hope you can help.
Thanks a lot!
 dvl posted on Friday, December 13, 2013 - 2:45 am

Regarding my question above, I have figured (2) out already but the question that remains open to me is whether it is possible to interpret an indirect effect when the mediator variable and the dependent variable have different control variables? I am really confused about that!

Thanks a lot!
 Bengt O. Muthen posted on Friday, December 13, 2013 - 4:44 pm
I would include all control variables in both equations. Some may not be significant, but that's ok.
 Joris van der Voet posted on Thursday, December 19, 2013 - 1:59 am
Dear dr. Muthen,

in a structural model, I want to control for the effects of a nominal variable (different countries). I am not interested in the effect different countries may have on my dependent variables, I merely want to control for the variance they may explain in the model.

Would I need to construct dummy variables for each of the countries and enter them separately in the model (with one being the reference category), or can I enter a single variable in which country A =1, country B = 2, etc.?

Thank you.
 Bengt O. Muthen posted on Thursday, December 19, 2013 - 10:41 am
You have to construct a set of C-1 dummies, where C is the number of countries.
 dvl posted on Monday, February 03, 2014 - 1:28 am
Dear Professor,

Regarding my question above: I have path model (with all manifest variables, no latent concepts due to data limitations). I want to include the same control variables in each equation in order to know where my indirect effects are controlled for! But in that case, I have a saturated model and I have no fit indices! Can I do something with that kind of models, because in the literature, I see no publications using saturated models? Can you give me some advise on this?
 Bengt O. Muthen posted on Monday, February 03, 2014 - 8:39 am
Although it is true that chi-square model testing can't reject the model, it is not a fatal flaw to consider a saturated model if your theories don't involve excluded paths. There should be many such models in the literature. Regression analysis is another example of a saturated model.
 Imaan posted on Saturday, February 15, 2014 - 2:52 am
I have three mediators in my model. Should i take control variable which have significant relationship with DV and Mediators.But control variables perform differently to DV and mediators. How would i run this analysis. Should i include all control variables to DV and Mediators paths.or should i exclude control variable which has insignificant relationship with Dv or Mediators
 Linda K. Muthen posted on Sunday, February 16, 2014 - 11:19 am
I would use the control variables in every regression. I would not exclude insignificant paths.
 Imaan posted on Sunday, February 16, 2014 - 11:31 am
if we have four control variables, should i show paths with four mediators and dv seperatly
 Bengt O. Muthen posted on Sunday, February 16, 2014 - 12:46 pm
The control variables are covariates, so they don't change the number of mediators (you said you had 3 mediators).
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