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  • Stata's sem builder, gsem option, control variables

    Hi, everyone,

    I'm new at SEM and I wonder if it is advisable to include several control variables (such as sex, age, education, size of the organisation, activity sector and so on) as can be done in logit or ologit regressions, where you can easily fit fairly large numbers of both main independent and control variables, as long as you have a good and large sample?

    As it is now, my model already contains 30 observed and 8 latent variables - 5 of which are mediators, 2 are moderating/intermediate dependent variables, and one is the final dependent variable - and 39 paths, so it looks quite complex already. Besides, when I estimate different parts of the model separately, it takes quite a long time to produce the outcomes (several hours in some cases). Therefore, I wonder whether I should exclusively focus on the paths in my model or if I may as well consider the effects of control variables simultaneosuly.

    If so, could anyone recommend a good and reliable source where I can find the details on how to approach the inclusion of the control variables using the sembuilder on its gsem mode to draw path diagrams?

    Thank you in advance.

  • #2
    Stata's SEM documentation -help sem- and GSEM -help gsem- is a good point to start and Alan C. Acock's "Discovering Structural Equation Modelling using Stata" is a great resource. You should read them first before diving into such complex models. I wonder can you narrow down your research questions. It may look blurry when it will come to interpreting the results with so many variables, mediators and moderators. Probably a more scientific approach will be to build your model step by step, with significant predictors and mediators rather throwing them all in the model altogether. This approach will also reduce the estimation time.
    Roman

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    • #3
      Thank you, Roman.

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