MODEL: precoll BY V1-V4; V2 WITH V1; instemp BY V7-V10; V9 WITH V7; peers BY V11-V13; fit BY V14-V17; V17 WITH V15; attract BY V18-V21; V19 WITH V18; intent BY V22-V24; V5 V6 ON precoll; fit ON V5 V6 instemp peers; attract ON fit; intent ON precoll attract;
1. What is the best method to estimate my model given that I have missing data and both categorical and continuous variables?
2. When I ran the model, it did not produce model fit statistics and it said that it couldn't produce MODINDICIES because of the binary variable. Is there a way to gather this information?
3. I would like to estimate indirect, direct, and total effects between all of the variables. What is the best way to do that? What would I have to include beside IND or VIA statements?
Also, when I ran the model using ML without the model indirect commands, all of the Betas were 0.000. Is there a way to get path coefficients when there are continuous and categorical variables in the model?
I am conducting a moderation-mediation path analysis and building a model with a binary exposure, a binary outcome, 2 binary mediators, and 1 binary moderator. I am also adjusting for 4 confounders. I am using longitudinal data and my sample has 466 individual clusters and a total of ~ 2000 observations over time.
One of the confounders I am adjusting for is also hypothesized to be on causal path however, from a theoretical perspective it is not a mediator of interest to my research question since it is at the individual-level and my question is focused on social-structural mediators. When I remove this confounder/mediator from my model and do not adjust for it, the hypothesized indirect mediated pathways are strongly significant. When I adjust for it, pathways are only marginally significant at best.
Fit indices for model where I do not adjust the coufounder are below. RMSEA looks ok but TLI is negative (see more below).
Chi-Square Test of Model Fit Value 43.533* Degrees of Freedom 5 P-Value 0.0000