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Growth Model Considerations in Early Literacy Research

Growth Model Considerations in Early Literacy Research. Yaacov Petscher Florida Center for Reading Research. What do we want to model?. How students are changing over time Individual differences in change How change in one skill relates to change in another Causes of individual change

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Growth Model Considerations in Early Literacy Research

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  1. Growth Model Considerations in Early Literacy Research Yaacov Petscher Florida Center for Reading Research

  2. What do we want to model? • How students are changing over time • Individual differences in change • How change in one skill relates to change in another • Causes of individual change • Causes of individual differences in individual change

  3. Progress Monitoring • What does growth in syntax ability look like in K? • Do students differ in their growth patterns in syntax? • What is the relationship between growth in syntax and growth in listening comprehension? • What causes growth in syntax? • What causes individual differences in syntax growth?

  4. Univariate Longitudinal Factor Analysis

  5. Multivariate Longitudinal Factor Analysis

  6. Univariate Simplex Modeling

  7. Cross-Lagged Latent Regression

  8. Latent Growth

  9. Parallel Process Latent Growth

  10. Latent Growth SEM

  11. So what? • Each model exists for a specific purpose • Differences contribute to individual practical problems • Minimum N • # of Occasions • # of Variables • Can we combine the growth and causal models to extract similar types of information?

  12. Latent Change Scores

  13. Bivariate Latent Change Scores

  14. Research Questions • What are the growth trajectories of students’ early literacy skills? • Can these be better informed by dynamic developmental relations? • Are there differences in dynamic developmental relations between-students vs. between-classes?

  15. Data and Measures • Sample size = 77,675 students; 4,774 classes • DIBELS Assessments • ISF: Kindergarten • LNF: K-1 • PSF: K-1 • NWF: K-2 • ORF: 1-3 • Something reliability/validity

  16. Analyses • Univariate LCS • Evaluate patterns • Multivariate LCS • Evaluate contributors to LCS • Multilevel LCS • Evaluate differences in estimated effects by classes and students

  17. LNF CFI = .95 TLI = .95 RMSEA = .11 SRMR = .08

  18. PSF CFI = .94 TLI = .95 RMSEA = .09 SRMR = .09

  19. NWF CFI = .90 TLI = .90 RMSEA = .12 SRMR = .09

  20. ORF CFI = .94 TLI = .94 RMSEA = .12 SRMR = .06

  21. Just…no…

  22. LNF .50 – (.16*LNF[t-1]) + (.10*PSF[t-1]) + (.36*NWF[t-1])

  23. Range Differences

  24. NWF

  25. NWF

  26. How to use the scores • Create vector plots • Determinant importance • Comparing graphs • Relative importance • Screening applications

  27. Multilevel LCS • Model Comparisons • Parallel Process • Constant Change • Fixed Proportional at Levels • Dual Change-Constrained Lag Δχ² (2) = 169, p < .001

  28. Conclusions • LCS can help inform change and causation • May be useful for informing multivariate screening • Better target interventions • They are a pain to run

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