I am fitting a cross-lagged autoregressive model with two constructs measured over four waves. I had been unable to get an acceptable SRMR (i.e., ~.11) although the other fit indices (i.e., CFI, TLI, and RMSEA) indicated acceptable fit. I came across an article (Anderson & Williams, 1992) that suggested it may be important to include autocorrelated disturbances within a construct over time (i.e., disturbance of construct 1 at T2 correlated with disturbance of construct 1 at T3, etc.). When I included these autocorrelated disturbances, the SRMR was reduced substantially, but now I have a negative R-squared value for construct 1 only at T4. I'm confused as to why this occurred and wondering whether there are any additional models I could try that may reveal adequate fit without the within-construct correlated disturbances. Thanks very much in advance.
I have a date-set with five time-points and I used cross-lagged autoregressive analysis to capture direction of effects. For my cross-lagged autoregressive model, I used SEM analysis with Mplus. And I used MODINDICES in the syntax. I found that variable X at times 1 and 2 did predict variable Y at times 2 and 3 respectively. The sign (positive) of the causal link and the direction are in line with theory. However, when we add “with” to improve the model, the direction did not change but the sign is reversed (negative). Also, some of the “with” values are negative and some are positive. Please note that without the “with” the fit indices are poor. 1) How much we interpret these findings 2) And is it possible to have negative “with” values Thank you
Thank you very much for your response, as suggested, I tried running the model with only 2 time points (T1 and T2), everything was fine. Then, I tried T2 and T3 and that resulted in a negative sign value but it was not significant (XT3 on YT2). Then I tried T1 T2 T3 without any “with”, and I also got the negative sign value but again it was not significant (XT3 on YT2). The modification indices suggest adding “XT3 WITH XT2”, “XT2 with YT2” and “YT2 with YT3”: when I run this a significant negative sign appears on the value “XT3 on YT2”. I notice that adding “XT3 WITH XT2”, significantly increases the fit of the model and causes the significant negative sign on the value “XT3 on YT2”. What do you think is happening?