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)? Thanks!
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!
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?
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.
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; MODEL INDIRECT: 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; MODEL INDIRECT: y15 IND x6
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 ?
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?
Control variables are treated as any other covariate.
dvl posted on Thursday, December 12, 2013 - 8:09 am
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!
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!
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.?