I am running a parallel process growth model for BMI and depression. A reviewer suggested that autocorrelation might be a problem, particularly for BMI. Can you tell me how to test for autocorrelation? Based on your reply to a previous post, I tried the following, but I'm not sure if it's correct in this case.
MODEL:ibmi sbmi | bmi05@0bmi10@1 bmi15* bmi20*; idep sdep | depsum05@0depsum10@1 depsum15* depsum20*; ibmi on ex3_age female nocoll05; sbmi on ex3_age female nocoll05; idep on ex3_age female nocoll05; sdep on ex3_age female nocoll05; sbmi on idep; sdep on ibmi; idep with ibmi; idep with sdep; ibmi with sbmi; sdep with sbmi; bmi05 with bmi10; bmi10 with bmi15; bmi15 with bmi20; depsum05 with depsum10; depsum10 with depsum15; depsum15 with depsum20;
If the correlations are significant, does that indicate autocorrelation? If one or more of the correlations is not significant, should I remove the non-significant ones from the model? Thank you for your help.