Testing equality of coefficients in multigroup model

[Felix B.]

Dear all,

my main model is a RI-CLPM which I estimated using lavaan, which works fine. Now I want to extend the model and compare 2 groups (boys vs girls). This also works fine. Next I would like to compare some regression coefficients between the two groups. Say, the coef of interest for boys is 1.4 and for girls 1.7. I would like to conduct something like a Wald-Test that tells me whether these two coefficients are significantly different between the two groups. I researched this a lot but all I found are either global model fit comparisons or coefficient tests between regressors (but not between groups). Help is much appreciated!

[Daniel Morillo Cuadrado]

If I understand you well, I think what you need is a likelihood-ratio test comparing two models: One with your coefficient of interest constrained to be equal across groups, and another one without that constraint.

Although this is a global model fit comparison between two models, you are only performing a contrast on that precise parameter as it is the only difference between the two; you will notice that the statistic is distributed as chi-squared with precisely one degree of freedom.

Please LMK if this answers your question.

--

Daniel Morillo, Ph.D.

GitHub | ORCID

El jue, 7 mar 2024 a las 9:21, 'Felix B.' via lavaan (<lav...@googlegroups.com>) escribió:

Dear all,

my main model is a RI-CLPM which I estimated using lavaan, which works fine. Now I want to extend the model and compare 2 groups (boys vs girls). This also works fine. Next I would like to compare some regression coefficients between the two groups. Say, the coef of interest for boys is 1.4 and for girls 1.7. I would like to conduct something like a Wald-Test that tells me whether these two coefficients are significantly different between the two groups. I researched this a lot but all I found are either global model fit comparisons or coefficient tests between regressors (but not between groups). Help is much appreciated!

--
You received this message because you are subscribed to the Google Groups "lavaan" group.
To unsubscribe from this group and stop receiving emails from it, send an email to lavaan+un...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/lavaan/2ea61218-d8fe-4761-a962-e0747014cf28n%40googlegroups.com.

[Felix B.]

Thanks for the reply. If you think this is the best way to achieve my goal, sure, I am open to this approach. How would this work? In the documentation I see that I can restrict all "regressions" in total, see https://lavaan.ugent.be/tutorial/groups.html#constraining-groups-of-parameters-to-be-equal-across-groups ("regressions: all regression coefficients in the model"). But how would this look like for a single regression parameter, say the effect of x1 on y2?

[Daniel Morillo Cuadrado]

In the right above section, you have: https://lavaan.ugent.be/tutorial/groups.html#constraining-a-single-parameter-to-be-equal-across-groups

I think this is what you are looking for.

--

Daniel Morillo, Ph.D.

GitHub | ORCID

To view this discussion on the web visit https://groups.google.com/d/msgid/lavaan/b73259fc-07c7-499d-899a-5b080cada7b5n%40googlegroups.com.

[Felix B.]

Thanks, I will test this.

[Christian Arnold]

https://groups.google.com/g/lavaan/c/7XF4plbROOU/m/hh_Fju-WAAAJ

[Felix B.]

Thanks a lot. Finally it clicked in my head and how this notation works. I tested both approaches and they gave sensible results.