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Latent State-Trait Analysis |
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Hello, I am using latent state-trait approach and longitudinal design with 3 measurement points. The idea is to test whether the trait component will increase the predictive ability beyond that provided by the score of the construct measured at T3. Model 1 contains regression: SWL_T3 ON VI_T3 (VI_T3 is VI measured at T3) – result: VI_T3 is a sig predictor Model 2 contains measurement invariance restriction (invariant factor loadings, intercepts, items’ unique variances), LST-modelling and the regression: SWL_T3 ON VI (VI = trait component of VI measured at T1, T2, and T3) – result: VI is a sig predictor Model 3: Model 1 + Model 2 - two regressions: SWL_T3 ON VI and SWL_T3 ON VI_T3; If I use MLR I get the following error: MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -8523.947 THE STANDARD ERRORS FOR H1 ESTIMATED SAMPLE STATISTICS COULD NOT BE COMPUTED. THIS MAY BE DUE TO LOW COVARIANCE COVERAGE. THE ROBUST CHI-SQUARE COULD NOT BE COMPUTED. THE STANDARD ERRORS FOR THE STANDARDIZED COEFFICIENT COULD NOT BE COMPUTED. If I use ML I get the following error: MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -8523.947 THE STANDARD ERRORS FOR THE STANDARDIZED COEFFICIENT COULD NOT BE COMPUTED. I conducted the same analysis using samples with and without missing data. That makes no difference. Thanks, Vanessa |
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It sounds like you have low covariance coverage, that is, some pairs of your variables are observed in less than 10% of the sample. You should check why that is so low. |
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