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Example Models for Multi-wave Data

Example Models for Multi-wave Data. David A. Kenny. Example Data. Dumenci, L., & Windle , M .  (1996 ). Multivariate Behavioral Research, 31 , 313-330.

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Example Models for Multi-wave Data

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  1. Example Models for Multi-wave Data David A. Kenny

  2. Example Data Dumenci, L., & Windle, M.  (1996). Multivariate Behavioral Research, 31, 313-330. Depression with four indicators (CESD)               PA: Positive Affect (lack thereof)              DA: Depressive Affect              SO: Somatic Symptoms              IN: Interpersonal Issues Four times separated by 6 months 433 adolescent females Age 16.2 at wave 1  

  3. Models • Models • Trait • Autoregressive • STARTS • Trait-State-Occasion (TSO) • Latent Growth Curve • Types • Univariate (except TSO) -- DA • Latent Variable

  4. Latent Variable Measurement Models • Unconstrained • c2(74) = 107.71, p = .006 • RMSEA = 0.032; TLI = .986 • Equal Loadings • c2(83) = 123.66, p = .003 • RMSEA = 0.034; TLI = .986 • The equal loading model has reasonable fit. • All latent variable models (except growth curve) are compared to this model.

  5. Trait Model: Univariate • Test of Equal Loadings: No • Model Fit: RMSEA = 0.071; TLI = .974

  6. Trait Model: Latent Variables • Model with just the trait factor does not fit as well as the saturated model: c2(74) = 1xx.81 • More Trait than State Variance • Trait Variance: 12.64 • State Variance 10.39

  7. Autoregressive Model: Univariate • Fixed error variances equal. • Good fitting model: c2(2) = 4.98, p = .083 Reliabilities Stabilities 1: .657 1  2: .802 2: .650 2  3: .847 3: .597 3  4: .738 4: .568

  8. Autoregressive Model: Latent Variables • Not a very good fitting model compared to the CFA • c2(3) = 60.08, p< .001 • Overall Fit: c2(xx) = 1.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx • Stabilities 1  2: .xxx 2  3: .xxx 3  4: .xxx

  9. Growth Curve Model: Univariate • Unlike other models it fits the means. • Fit: c2(74) = 1xx.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx Intercept Slope Mean Variance

  10. Growth Curve Model: Latent Variables Fit: c2(74) = 1xx.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx Intercept Slope Mean Variance

  11. Trait State Occasion Model • Standard TSO does not have correlated errors, but they are added. • Fit: c2(74) = 1xx.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx • Variances • Trait • State

  12. STARTS Univariate • Difficulty in finding trait factor. None of the models converged. • Trait factor as Seasonality: Loadings in the Fall are 1 and in the Spring are -1 • Models converged.

  13. Univariate STARTS Results • Fit: c2(74) = 1xx.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx • Variances • Seasonality • ART • State • AR coefficient:

  14. Latent Variable STARTS • Fit: c2(74) = 1xx.81, p < .0xx, RMSEA = 0.0xx; TLI = .9xx • Variances • Seasonality • ART • State • AR coefficient:

  15. Summary of Fit: Univariate • Trait • Autoregressive • Growth Curve • STARTS

  16. Summary of Fit: Latent Variables RMSEA TLI No Model 0.034 .986 Trait Autoregressive Growth Curve TSO STARTS

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