The Course of Reading Disability in First Grade: Latent Class Trajectories and Early Predictors

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The Course of Reading Disability in First Grade
Latent Class Trajectories and Early Predictors

• Don Compton, Lynn Fuchs, and Doug Fuchs

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Criticisms of Current Learning Disabilities
Definition

• Too many children are inappropriately identified
• Many children are classified as LD without
  participating in effective reading instruction in
  the regular classroom
• Too costly

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Criticisms of IQ-Achievement Discrepancy

• IQ tests do not necessarily measure intelligence
• IQ and academic achievement are not independent
  of each other
• In the case of word reading skill deficits,
  IQ-achievement discrepant poor readers are more
  alike than different from IQ-achievement
  consistent poor readers
• Children must fail before they can be identified
  with a learning disability

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What is Meant by an RTI Model?

• RTI refers to an individual, comprehensive
  student-centered assessment model. RtI is
  sometimes referred to as a problem-solving model.
  RtI models focus on applying a problem solving
  framework to identify and address the students
  difficulties using effective, efficient
  instruction and leading to improved achievement.

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Typical RTI Procedure

• All children in a class, school, district are
  tested once in the fall to identify student at
  risk for long-term difficulties.
• The response of at-risk students to GE (Tier1) is
  monitored to determine whose needs are not met
  and therefore require more intensive tutoring
  (Tier 2).
• For at-risk students, research-validated Tier 2
  tutoring is implemented. Student progress is
  monitored throughout intervention. Students are
  re-tested following intervention.
• Those who do not respond to the validated
  tutoring are identified
• As LD
• For multi-disciplinary team evaluation for
  possible disability certification and special
  education placement.

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Advantages of RTI Approach

• Provides assistance to needy children in timely
  fashion. It is NOT a wait-to-fail model.
• Helps ensure that the students poor academic
  performance is not due to poor instruction.
• Assessment data are collected to inform the
  teacher and improve instruction. Assessments and
  interventions are closely linked.

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Within RTI Identification

• Tier 2 tutoring is viewed as the test to which
  at-risk students respond to determine disability.
• That response needs to be measured and
  categorized as responsive (not LD) or
  unresponsive (LD) using an appropriate tool for
  such measurement.

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RTI Three Tiers

• Tier 1
• General education
• Research-based program
• Faithfully implemented
• Works for vast majority of students
• Screening for at-risk pupils, with weekly
  monitoring of at-risk response to general
  education
• Tier 2
• Small-group preventative tutoring
• Weekly monitoring of at-risk response to tier 2
  intervention
• Tier 3
• Special education

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Tertiary Prevention Specialized
Individualized Systems for Students with
Intensive Needs
CONTINUUM OF SCHOOL-WIDE SUPPORT
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Secondary Prevention Specialized Group Systems
for Students with At-Risk Behavior
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Primary Prevention School-/Classroom- Wide
Systems for All Students, Staff, Settings
80 of Students
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RTI Tier 2 Standardized Research-Based
Preventative Treatment

• Tutoring
• Small groups (2-4)
• 3-4 sessions per week (30-45 min per session)
• Conducted by trained and supervised personnel
  (not the classroom teacher)
• In or out of classroom
• 10-20 weeks

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What does Tier 2 look like?Hypothetical Case
Studies
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Purpose of the Study

• To explore
• Effects of multiple Tier 1 (classroom) and Tier 2
  (pullout) instructional approaches on at-risk
  childrens reading growth in a 9-wk treatment
  period in fall of 1st grade.
• How responsiveness to the instructional
  approaches can be used to identify children as LD
  at the end of 1st grade.
• Effects of alternative methods of LD
  classification on prevalence and severity.
• Can characteristic growth patterns of children
  who are either LD and not LD be identified for
  Tier 1 and Tier 2 instruction?

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Reading Study Sample

• 42 1st-grade classes in 16 schools (8 Title)
• Six lowest readers from each class on WIF and
  RLN, with teacher corroboration (252
  low-study-entry children)
• Beginning 1st grade, 6 children from each class
  rank ordered and, within class, split into 2
  strata
• Within each stratum within each class, randomly
  assigned to 3 groups (n 84 per condition)
• No tutoring (n55 65.5 complete data at end
  grade 3)
• Fall 1st-grade tutoring (n61 72.6 complete
  data at end grade 3)
• Spring 1st-grade tutoring, but only with
  inadequate slope/final intercept for fall 1st
  grade (n64 76.2 complete data at end grade 3)
• Three groups comparable demographically and on
  RLN, WIF, IQ, WRMT WID/WA, TOWRE SW/PD
• 18 weekly Word Identification Fluency
  measurements
• End of 3rd grade, disability lt85 on latent
  variable of word reading, nonsense word reading,
  comprehension

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Evidence-Based Tutoring

• Tutoring
• Letter-Sound Recognition
• Phonological awareness and decoding
• Sight Words
• Fluency
• Four Groups
• Fall Tutoring (n61)
• Spring Tutoring for Nonresponsive Children (n32)
• Spring No Tutoring for Responsive Children (n32)
• Controls (No Tutoring, n55)
• Sessions
• Conducted by research assistants
• 2-4 students per group
• 4 sessions/week
• 45 minutes/session
• For a total of 36 sessions of tutoring

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Questions

• Identify 1st-grade growth trajectories
  characteristic of later disability versus ND
• Examine effects of 1st-grade tutoring on
  trajectories
• Explore cognitive profiles associated with each
  latent class

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General Model for Identifying Trajectory Classes
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Analysis Plan

• Conventional growth modeling to evaluate
  appropriateness of the hypothesized quadratic
  model
• Multiple group growth mixture modeling with a
  distal latent factor (F, at end 3rd grade in
  reading end 2nd grade in math) and beginning
  1st-grade covariates to identify disability and
  nondisability populations within each known
  group.
• Distal latent factor was regressed on the
  categorical latent variable (C), representing
  subpopulation CBM growth characteristics in 1st
  grade.
• Subpopulation variable (C) was regressed on the
  known class variable (CG).
• Growth parameters (I, S, Q) and C were regressed
  onto the time-invariant covariates.

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Estimated Parameters of Interest

• Average latent class probabilities likelihood
  each individual belongs to each class
• Class-specific profiles likelihood each
  individual in the class scores above/below
  criterion for disability on distal latent class
  indicator
• Means/variances on
• Growth parameters (I,S,Q)
• Beginning 1st-grade performance
• Cognitive predictors
• End-study performance as function of known class
  and disability/nondisability trajectory class
• Class-specific probabilities for categorical
  latent variable as function of the covariates

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Data Analysis

• Growth model analyses with Mplus 4.0
• Model estimation used maximum likelihood
  estimator with robust standard errors
• CBM data centered on initial assessment
• Mplus missing data module (maximum likelihood
  missing at random estimation procedures)
• Estimated starting values derived from multiple
  group analysis of growth using only the CBM data
• Covariates centered on grand means

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Results Conventional Growth Modeling

• Word identification fluency (WIF)
• 18 weekly across fall and spring
• Quadratic model improved overall fit of model
  over linear model
• I 14.20 words (SE0.719 z 19.74)
• S 1.80 words per week (SE0.138 z 13.09)
• Q -0.015 words2 per week (SE0.006 z -2.31)

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Results Fall Tutoring
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Results Spring Tutoring Necessary
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Results Spring Tutoring Unnecessary
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Results Control
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Results Growth Mixture Modeling

• For each trajectory class, intercept and slope
  was significantly greater than zero and necessary
  for describing growth.
• Quadratic term significantly different from zero
  only for
• Fall tutoring (z -2.574)
• Spring tutoring-necessary (z 4.346)

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Average Probability of Latent Class Assignment
and Class-Specific Profiles on the Distal
Reading Latent Class Indicators
Class-Specific Probabilities on Latent Class Indicatorsb Class-Specific Probabilities on Latent Class Indicatorsb Class-Specific Probabilities on Latent Class Indicatorsb
Latent Class Latent Class Probabilitya WRMT-R WID WRMT-R WA WRMT-R PC
Fall Tutoring RD .964 .022 .501 .005
Fall Tutoring NRD .995 .954 .999 .833
Spring Tutoring Necessary RD .942 .242 .934 .071
Spring Tutoring Necessary NRD .993 .985 1.000 .941
Spring Tutoring Unnecessary RD .943 .826 .995 .534
Spring Tutoring Unnecessary NRD .927 1.000 1.000 1.000
Control RD .912 .571 .983 .242
Control NRD .952 .984 1.000 .937
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Results Growth Mixture Modeling(across entire
sample)

• Average latent class probability Probability
  child is assigned to correct disability
  trajectory class within the known class .912 to
  .995 (precise)
• Class-specific profiles on 3rd-grade latent class
  indicators of disability (WID, WA, PC)
  Probability child in that class would score gt 85
• WA Across disability groups, poor precision.
• WID and PC More consistently distinguished RD
  from ND.
• For spring tutoring-unnecessary RD group,
  class-specific probabilities indicate this class
  does not have a characteristically RD profile.
• For control RD group, high class probability of
  scoring normal on WID, but low class probability
  of scoring normal on PC. So, poor reading
  comprehension is the defining characteristic of
  untreated at-risk students.

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Estimated Multinomial Regression of Latent Class
Variable on Covariates
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Estimated Multinomial Regression of Latent Class
Variable on Covariates
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Plots represent estimated class-specific
probability of class membership as function of
one covariate, while keep other covariates
constant

• Sound matching and vocabulary distinguished
  latent class membership, but only in control
  group.
• Control students with lower sound matching scores
  have greater probability of being assigned to
  control RD class.
• Control students with higher vocabulary scores
  have greater probability of being assigned to
  control ND class.

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Conclusions

• First-grade trajectory classes associated with
  3rd-grade disability status can be identified
  with high precision using WIF. So, WIF can be
  used for 1st-grade progress monitoring within
  RTI, as an indicator of long-term RD status.
• In control (untreated) group, RD and ND
  trajectory classes had same intercept, but vastly
  different slopes. So, slope can be used to index
  responsiveness.
• Only 2 classes had significant quadratic term.
• For fall tutoring, growth decelerated across
  year.
• For spring tutoring-necessary, growth accelerated
  across year.

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Conclusions

• 3rd-grade WID and PC measures distinguished RD
  from ND WA did not.
• Spring tutoring-unnecessary NRD was a relatively
  pure group of NRD students. So, using WIF in fall
  semester of 1st grade to select children at-risk
  students may be efficient.

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Conclusions

• For control RD students, reading comprehension
  skill was defining characteristic. Interesting
  because 1st-grade trajectory classes formed
  exclusively with WIF. Also, no way to distinguish
  control RD and NRD using intercept.
• 1st-grade cognitive predictors most useful for
  untreated students. For control students, low
  sound matching associated with RD high
  vocabulary associated with NRD.
• Within treated students, RTI (trajectory class)
  was what distinguished RD from NRD, effectively
  overriding initial individual differences on
  sound matching and vocabulary.

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Thank You