Dear Statalisters,

My dataset is the following:
  • 1 IV (independent variable), which ranges from 1 to 10 and I treat as continuous
  • 1 M (mediating variable), which is dichotomous
  • 1 DV (dependent variable), which is dichotomous as well
  • 10 control variables, which are all categoric. Hence, when split up in dummies, I have around 50 dummy variables. 36 of them describe the country of origin.
  • 3100 different firms. Important: these firms have responded to the survey in consecutive waves. I have pooled their responses. As some firms have participated in plenty of consecutive waves, I have 3500 of such pooled observations. I cluster the standard deviations around the firm id to account for potential heterogeneity caused by this.
I thus do not have any latent variables. I use SEM given the dichotomous nature of my mediating variable, which renders Hayes' PROCESS macro unusable.
My code is the following

Code:
sem (IV -> M, ) (IV-> DV, ) (controls-> M, ) (controls-> DV, ), vce(cluster firm_id) nocapslatent
The results show there indeed is a mediating effect.

However: I am now interested in goodness-of-fit statistics.
  • Code:
    estat gof
    yields the following outcome:
    Code:
     model was fit with vce(cluster); only stats(residuals) valid.
    Moreover, it states that R-squared is 0.41 while the SRMR (standardized root mean squared residual) is 0.000; which would point to a perfect fit
This perfect fit worries me. Hence, my questions:
  1. Am I right to assume that this perfect fit is, probably, caused by the many dummy variables in the model?
  2. If so, what would you suggest I do?
  3. More importantly: I would like to have a chi-squared gof and the other gof-statistics, which are now not generated. Any idea how I can calculate these given the above mentioned constraint that the model was fit with vce(cluster)?
  4. I am yet to find an empirical paper which follows this methodology, that is mediation with SEM when there are no latent variables. If you know one, please share!
Thank you very much for your insights. Any hints are welcome!