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NOT YOUR GRANDPA'S STATISTICS: NEW MODELING APPROACHES TO STUDENT ACHIEVEMENT & RTI . Jill Pentimonti Adrea Truckenmiller Jessica Logan Sara Hart Discussion: Grandpa Schatschneider Presented Feb 6, 2014 Pacific Coast Research Conference, San Diego.

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not your grandpa s statistics new modeling approaches to student achievement rti

NOT YOUR GRANDPA'S STATISTICS: NEW MODELING APPROACHES TO STUDENT ACHIEVEMENT & RTI

Jill Pentimonti

Adrea Truckenmiller

Jessica Logan

Sara Hart

Discussion: Grandpa Schatschneider

Presented Feb 6, 2014

Pacific Coast Research Conference, San Diego

slide2

Individual differences in response to intervention: An application of Integrative Data Analysis in Project KIDS

Sara A. Hart

&

Grandpa

Florida State University

expanding our search for moderators of intervention
Expanding our search for moderators of intervention
  • A little about me
    • Behavioral genetics background
    • PCRC participant
  • Even with modest effect sizes, individual differences in intervention response
  • Bioecological model (Bronfenbrenner & Ceci, 1994)
    • Provides framework for differentiating students based on non-intervention related traits
integrative data analysis ida
Integrative Data Analysis (IDA)
  • Item-level pooled data (Curran & Hussong, 2009)
  • Capitalizes on cumulative knowledge
    • Longer developmental time span
    • Increased statistical power
    • Increased absolute numbers in tails
  • Controls for heterogeneity
    • Sampling, age/grade, cohort, geographical, design, measurement
project kids
Project KIDS
  • Expanded definition of moderators of response to intervention
    • Cognitive, psychosocial, environmental, genetic risk
  • IDA across 9 completed intervention projects
    • Approximately 5600 kids
  • Data entry of item level data common across at least 2 projects
    • ~30 different assessments
  • Questionnaire data collection
proof of concept
Proof of Concept
  • Behavior problems and achievement are associated
  • More behavior problems are typically seen in LD populations
  • Is adequate vs inadequate response status differentiated by behavior problems?
method
Method
  • Participants
    • 2005-2006 ISI intervention project (Connor et al., 2007)
      • RCTish : 22 treatment, 25 contrast teachers, 3 pilot
      • 821 first graders
      • A2i recommendations vs standard practice
    • 2007-2008 ISI intervention through FL LDRC (Al Otaiba et al., 2011)
      • RCT: 23 treatment, 21 contrast teachers
      • 556 kindergarteners
      • A2i recommendations vs enhanced standard practice
method1
Method
  • Measures
    • WJ Tests of Achievement Letter-Word Identification (LWID)
      • Pre- and post-intervention testing periods
    • Social Skills Rating Scale: Behavioral Problems subscale
      • Teacher completed during intervention year
results calibration lwid
Results: Calibration LWID
  • Randomly selected 1 time point/child/project to form “calibration sample” for LWID
  • IRT with decision to include only items > 5% endorsement rate
  • Reduced item sample from 75  36
    • Items 8 to 44
results calibration lwid1
Results: Calibration LWID
  • Generalized linear factor analysis (GLFA)
    • Combines latent factor analysis and 2-PL IRT model
  • Here, equivalent of 2-PL IRT model with DIF
  • No significant DIF was found
results second data sample lwid
Results: Second data sample LWID
  • Using remaining data, GLFA model run again, setting parameters based on calibration sample
  • Separately by project
    • If significant, add DIF estimates to parameters
results ssrs
Results: SSRS
  • IRT to GLFA model with Project DIF on full data
results response
Results: Response
  • Proc mixed: covariance adjusted LWID score
    • 1169 children
results response1
Results: Response
  • 648 treatment children
results response2
Results: Response
  • 648 treatment children

Unresponsive

Cutoff < 20%

N=110!

results response3
Results: Response
  • 648 treatment children

Unresponsive

Cut off

Fall SS = 95

Spring SS= 104

Mean

Fall SS = 86

Spring SS = 96

results response4
Results: Response
  • 648 treatment children

Responsive

Mean

Fall SS = 99

Spring SS = 111

Unresponsive

Cut off

Fall SS = 95

Spring SS= 104

Mean

Fall SS = 86

Spring SS = 96

results1
Results
  • Logistic regression
    • SSRS behavior problems significant predictor of response status (OR = 1.45, CI = 1.12-1.88)
      • average behavior problems = 19% probability of being “unresponsive”
      • greater than average behavior problems(+ 1SD) = 29%probability of being “unresponsive”
      • Less than average behavior problems (-1SD) = 12% probability of being “unresponsive”
conclusions
Conclusions
  • Response status is differentiated by behavior problems
    • Mo’ behavior problems, mo’ (reading) problems!
  • The questionnaire data we will be adding will be real test of bioecological model on response to intervention
overall ida conclusions
Overall IDA conclusions
  • IDA is a “cheap” way to get more power, more n at tails, and show more generalizable effects
  • Given how similar many of our projects are, consider doing item-level data entry
    • Easy potential to combine data
    • Can you do factor analysis and IRT? You can do IDA!
  • These data are more useful together than apart
    • IRT within and between samples?
    • Treatment effectiveness across samples?
    • Characteristics of lowest responders?
acknowledgements
Acknowledgements
  • Stephanie Al Otaiba
  • Carol Connor
  • Chris Schatschneider
  • Great staff & grad students, and a small army of data enterers

NICHD grant HD072286