I built a causal model for investigating help-seeking. I have collected data to test my model through SEM. I have collected data three times from the same population over three months (using the same questionnaire).
I was able to obtain a good fit for each collected data set. However, for example, one of the path (relationship) between concept A and B is not significant for the first data set, but this relationship is significant for the other two data sets. However, many of the relationships are consistently significant for three data sets.
I can exclude the non-significant (NS) paths and re-run SEM and obtain a better model fit. However, I guess I should keep these NS paths and discuss why they are not significant, and highlight the ones that are consistently significant.
What do you think about this method? If you were able to test a causal model with three different data sets, would you discard the NS paths? How would you organize your results, and interpret them?
Here is my Mplus code:
VARIABLE: NAMES = X1-X41;!X59; USEVARIABLES ARE X1-X4 X5 X7 X10 X12-X41; !x1=id x2=g x3=t GROUPING IS X3 (1 = 1 2 = 2 3 = 3); cluster is X1; ANALYSIS: TYPE IS complex; ESTIMATOR=ML; model: REL BY X4 X5 X7 X10; TS BY X12-X15; INT BY X16-X18; COSTS BY X19-X22; BENFS BY X23-X25; INST BY X26-X28; EXEC BY X29-X31; MAST BY X32-X35; PERM BY X36-X41; model 1: BENFS ON TS REL MAST (a1); COSTS ON TS REL PERM (a2); EXEC ON PERM COSTS(a3); INST ON MAST BENFS(a4); INT ON BENFS COSTS MAST REL(a5); model 2: BENFS ON TS REL MAST (b1); COSTS ON TS REL PERM (b2); EXEC ON PERM COSTS(b3); INST ON MAST BENFS(b4); INT ON BENFS COSTS MAST REL(b5); model 3: BENFS ON TS REL MAST (c1); COSTS ON TS REL PERM (c2); EXEC ON PERM COSTS(c3); INST ON MAST BENFS(c4); INT ON BENFS COSTS MAST REL(c5); model constraint: a1 = b1; a1 = c1; a2 = b2; a2 = c2; a3 = b3; a3 = c3; a4 = b4; a4 = c4; a5 = b5; a5 = c5;