Hi everyone,

I'm new to structural equation modeling but my advisor suggested I try it based on my data. I used to SEM builder to get this output, but I need some help with interpretation. Previous to using the SEM builder I ran a few regressions that led me to build the model in this way.

My main issue was that identity (different variables) were not statistically significant predictors for sense of belonging (r_belong) but theoretically it didn't make sense. I found that gender (wom3) and being a person of color (stu_color) were significant predictors of leadership (leaderdi) when I ran a logit.

Now, I see that my p-values look fine but I'm not sure if I need to run additional tests or if this model "works" or what to do next. To explain this, would I explain similar to using regression equations?

Thanks for any help!


. gsem (diversity -> engagement, ) (diversity -> r_belong, ) (covidimpact -> engagement, ) (covidimpact -> r_belong
> , ) (time -> r_belong, ) (stu_color -> leaderdi, family(binomial) link(logit)) (wom3 -> engagement, ) (wom3 -> le
> aderdi, family(binomial) link(logit)) (international -> engagement, ) (leaderdi -> r_belong, ), nocapslatent

Iteration 0: log likelihood = -576.40004
Iteration 1: log likelihood = -576.35145
Iteration 2: log likelihood = -576.35144

Generalized structural equation model Number of obs = 150

Response : engagement Number of obs = 133
Family : Gaussian
Link : identity

Response : r_belong Number of obs = 134
Family : Gaussian
Link : identity

Response : leaderdi Number of obs = 150
Family : Bernoulli
Link : logit

Log likelihood = -576.35144

-----------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
------------------+----------------------------------------------------------------
engagement |
diversity | -2.655908 .6594255 -4.03 0.000 -3.948358 -1.363458
covidimpact | .8127573 .2549232 3.19 0.001 .3131171 1.312398
wom3 | -1.97709 .593799 -3.33 0.001 -3.140915 -.8132658
international | 1.984799 .8251215 2.41 0.016 .3675908 3.602008
_cons | 10.79472 2.21743 4.87 0.000 6.448642 15.14081
------------------+----------------------------------------------------------------
r_belong |
diversity | .5628544 .1406347 4.00 0.000 .2872155 .8384932
covidimpact | .2757642 .0537558 5.13 0.000 .1704047 .3811236
time | -.0449035 .0168292 -2.67 0.008 -.0778881 -.0119189
leaderdi | .3855923 .1277284 3.02 0.003 .1352493 .6359354
_cons | 2.429606 .4749711 5.12 0.000 1.49868 3.360532
------------------+----------------------------------------------------------------
leaderdi |
stu_color | 1.194422 .4191668 2.85 0.004 .3728702 2.015974
wom3 | 1.043215 .3770078 2.77 0.006 .3042937 1.782137
_cons | -2.129559 .4293714 -4.96 0.000 -2.971112 -1.288007
------------------+----------------------------------------------------------------
var(e.engagement)| 11.54901 1.41623 9.081627 14.68675
var(e.r_belong)| .4727939 .057761 .3721183 .600707
----