growth model considerations in early literacy research
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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 - PowerPoint PPT PresentationTRANSCRIPT
Growth Model Considerations in Early
Literacy Research
Yaacov PetscherFlorida 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• Causes of individual differences in
individual change
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?
Univariate Longitudinal Factor Analysis
Multivariate Longitudinal Factor Analysis
Univariate Simplex Modeling
Cross-Lagged Latent Regression
Latent Growth
Parallel Process Latent Growth
Latent Growth SEM
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?
Latent Change Scores
Bivariate Latent Change Scores
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?
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
Analyses
• Univariate LCS– Evaluate patterns
• Multivariate LCS– Evaluate contributors to LCS
• Multilevel LCS– Evaluate differences in estimated
effects by classes and students
LNFCFI = .95TLI = .95RMSEA = .11SRMR = .08
PSFCFI = .94TLI = .95RMSEA = .09SRMR = .09
NWFCFI = .90TLI = .90RMSEA = .12SRMR = .09
ORF CFI = .94TLI = .94RMSEA = .12SRMR = .06
Just…no…
Model χ² df RMSEA CFI TLI BICContrained MLCS 784652 523 0.14 0.68 0.69 18221136Freed MLCS 468059 462 0.11 0.91 0.90 17905348
LNF ΔLNF1 ΔLNF2 ΔLNF3 ΔLNF4LNF1 0.80LNF2 -0.16LNF3 0.12LNF4 -0.04PSF1 0.10PSF2 0.03PSF3 -0.01NWF1 0.36NWF2 0.08NWF3 0.10
g0 18.01g1 0.50
∆ 𝐿𝑁𝐹=𝛼 𝑙𝑛𝑓 +𝛽𝑙𝑛𝑓 𝐿𝑁𝐹 [𝑡 −1 ]+𝛾𝑙𝑛𝑓 ,𝑝𝑠𝑓 𝑃𝑆𝐹 [𝑡 −1 ]+¿𝛾 𝑙𝑛𝑓 ,𝑛𝑤𝑓 𝑁𝑊 𝐹 [𝑡− 1]
.50 – (.16*LNF[t-1]) + (.10*PSF[t-1]) + (.36*NWF[t-1])
Range Differences
NWFΔNWF1 ΔNWF2 ΔNWF3 ΔNWF4 ΔNWF5 ΔNWF6 ΔNWF7 ΔNWF8 ΔNWF9
NWF1 0.65NWF2 0.93NWF3 0.7NWF4 0.58NWF5 0.4NWF6 0.11NWF7 0.3NWF8 0.22NWF9 0.2PSF1 0.15PSF2 -0.3PSF3 -0.05PSF4 0.24PSF5 -0.44PSF6 0.04 0.04LNF1 0.35LNF2 -0.4LNF3 0.01LNF4 -0.36LNF5 0.52ORF1 0.18ORF2 -0.21ORF3 0.21ORF4 -0.06ORF5 0.12ORF6 0.11
NWFΔNWF1 ΔNWF2 ΔNWF3 ΔNWF4 ΔNWF5 ΔNWF6 ΔNWF7 ΔNWF8 ΔNWF9
NWF1 0.65NWF2 0.93NWF3 0.7NWF4 0.58NWF5 0.4NWF6 0.11NWF7 0.3NWF8 0.22NWF9 0.2PSF1 0.15PSF2 -0.3PSF3 -0.05PSF4 0.24PSF5 -0.44PSF6 0.04 0.04LNF1 0.35LNF2 -0.4LNF3 0.01LNF4 -0.36LNF5 0.52ORF1 0.18ORF2 -0.21ORF3 0.21ORF4 -0.06ORF5 0.12ORF6 0.11
How to use the scores
• Create vector plots• Determinant importance– Comparing graphs– Relative importance– Screening applications
Multilevel LCS
• Model Comparisons– Parallel Process– Constant Change• Fixed Proportional at Levels
– Dual Change-Constrained Lag
Model X2 df AIC BIC RMSEA
Parallel Process Growth 23146 54 2401799 2401939 0.105
Constant - Fixed BW 18664 56 2397313 2397443 0.093
Constant Change - Fixed Between 18466 54 2397119 2397260 0.094
Constant Change - Fixed Within 17572 54 2396226 2396366 0.092
Dual Change 17403 52 2396061 2396212 0.092
Δχ² (2) = 169, p < .001
Conclusions
• LCS can help inform change and causation
• May be useful for informing multivariate screening
• Better target interventions• They are a pain to run