

Longitudinal CFA with multicollinearity 

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I am trying to fit a longitudinal CFA with 3 indicators at each of 4 time points, with a 1 year time lag. The model runs fine when I have 3 time points, but the model fails when I add the fourth time point. It appears that the model may fail because of multicollinearity among the latent factors (the correlation between the latent factors at T3 and T4 = .996). I have already specified the withinindicator residual covariances across time, but it does not solve the problem. Here are the correlations of the 4 latent vars: lag T=1: .988, .990, .996 lag T=2: .976, .967 lag T=3: .937 Any ideas for how to specify the longitudinal CFA given the high correlation among the latent variables across time? The 3 indicators represent questionnaires by three raters: mothers, teachers, and fathers. Thanks in advance! 


I assume that you have one factor at each time point. I can't think of a way to reduce the factor correlation over time beyond what you have done. If the factors are so highly correlated over time, is there really any change in their means to motivate a longitudinal study? 


I am trying to fit a longitudinal CFA with 3 indicators at each of 4 time points, with a 1 year time lag. The model runs fine when I have 3 time points, but the model fails when I add the fourth time point. It appears that the model may fail because of multicollinearity among the latent factors (the correlation between the latent factors at T3 and T4 = .996). I have already specified the withinindicator residual covariances across time, but it does not solve the problem. Here are the correlations of the 4 latent vars: lag T=1: .988, .990, .996 lag T=2: .976, .967 lag T=3: .937 Any ideas for how to specify the longitudinal CFA given the high correlation among the latent variables across time? The 3 indicators represent questionnaires by three raters: mothers, teachers, and fathers. Thanks in advance! 


Sorry about the repost. Not sure why it did that.. At the indicator level, we find a good amount of change in the slope over time (meanlevel decreases). I'm surprised the latent variable correlations are so high. The withintime correlations of the indicators range from .23 to .67, and the crosstime correlations of the indicators range from .52 to .69. 


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