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Modeling Depression in Adolescents: A Multi-Wave Analysis

This study presents a comprehensive multivariate analysis to understand depression among adolescent females using four indicators: Positive Affect (PA), Depressive Affect (DA), Somatic Symptoms (SO), and Interpersonal Issues (IN). Data from 433 participants, collected across four waves spaced six months apart, are examined through various statistical models, including Trait-State-Occasion (TSO) models, Latent Growth Curve models, and autoregressive models. Results demonstrate fit indices supporting the hypotheses about the interplay of traits and states in adolescent depression.

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Modeling Depression in Adolescents: A Multi-Wave Analysis

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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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