I specified a cross-lagged model using MLR with two waves and 5 variables (1 = manifest, continuous + 4 = latent factors assuming strong measurement invariance). This model and moderations by age and gender (via multi-group) worked fine.
I was asked to rerun this model including only manifest, binary variables to assess the clinical meaningfulness. I'm wondering if it's adequate to specify such a model? If yes, is this specification done properly using WLSMV:
X1 ON SES; X2-4 ON age gender SES;
Y1 ON SES; Y2-4 ON age gender SES;
X1 WITH X2 X3 X4 X5; X2 WITH X3 X4 X5; X3 WITH X4 X5; X4 WITH X5;
I specified a cross-lagged panel model between three variables (X, Y, Z) and I have 5 time points. The participants come from two datasets and I would like to adjust the model for 2 confounding variables, age and the dataset. I am confused where I add these two confounders in my model. Do I add them to all the paths (Y2 on X1 Z1 age dataset; Y3 on X2 Z2 age dataset; etc)? Or only the association between these confounders and X Y Z at the first measurement (X1 on age dataset; Y1 age dataset; Z1 age dataset; Y2 on X1 Z1; Y3 on X2 Z2; etc.)? Or something completely else?
Thank you for your suggestion. Two of the three main variables in my model are dichotomous. Is the RI-CLPM also possible with dichotomous variables? And if so, do I have to change something to the syntax (other than stating these variables as categorical).
Thank you, I have added the confounding variables to all the paths. This is my syntax:
VARIABLE: NAMES = id cohort Y1 Y2 Y3 Y4 Y5 X1 X2 X3 X4 X5 Z1 Z2 Z3 Z4 Z5 age sex ; MISSING IS ALL (-99); IDVARIABLE = id; USEVARIABLES ARE cohort age X1 X2 X3 X4 X5 Z1 Z2 Z3 Z4 Z5 Y1 Y2 Y3 Y4 Y5; CATEGORICAL = Y2 Y3 Y4 Y5 X2 X3 X4 X5; ANALYSIS: type=general; parameterization=THETA; MODEL: ! Estimate the covariance between the observed variables at the first wave Z1 with X1 Y1; Y1 with X1; ! Estimate the covariances between the residuals of the observed variables Z2 with Y2 X2; Y2 with X2; Z3 with Y3 X3; Y3 with X3; Z4 with Y4 X4; Y4 with X4; Z5 with Y5 X5; Y5 with X5; ! Estimate the lagged effects between the observed variables Z2 ON Z1 Y1 X1 cohort age; Z3 ON Z2 Y2 X2 cohort age; Z4 ON Z3 Y3 X3 cohort age; Z5 ON Z4 Y4 X4 cohort age; X2 on Z1 Y1 X1 cohort age; X3 ON Z2 Y2 X2 cohort age; X4 ON X3 Z3 Y3 cohort age; X5 ON X4 Z4 Y4 cohort age; Y2 ON Y1 Z1 X1 cohort age; Y3 ON Y2 Z2 X2 cohort age; Y4 ON Y3 Z3 X3 cohort age; Y5 ON Y4 Z4 X4 cohort age;
I checked with MOD INDICES if there would be interesting suggestions to improve the model. These included ON statements between the 3 DV's at the first measurement and the confounders, such as:
X1 ON AGE; Z1 ON COHORT; Z1 ON AGE; etc.
I find it difficult to determine if this makes sense in a CLPM. What would you suggest?
I am estimating a cross-lagged model with 4 time points. The model fit is poor. The modindices command suggest that I add additional on statements between x4, x3, x2 and x1, and y4, y3, y2 and y1, respectively:
x4 on x2 x1; x3 on x1; y4 on y2 y1; y3 on y1;
does it make sense to add these paths? Can I still interpret the cross-lagged effects similarly with these paths included in the model?