11

The Course of Reading Disability in The Course of Reading Disability in First Grade: Latent Class First Grade: Latent Class

Trajectories and Early PredictorsTrajectories and Early Predictors

Don Compton, Lynn Fuchs, and Doug FuchsDon Compton, Lynn Fuchs, and Doug Fuchs

22

Criticisms of Current Learning Criticisms of Current Learning Disabilities DefinitionDisabilities Definition

Criticisms of Current Learning Criticisms of Current Learning Disabilities DefinitionDisabilities 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

33

Criticisms of IQ-Achievement Criticisms of IQ-Achievement DiscrepancyDiscrepancy

Criticisms of IQ-Achievement Criticisms of IQ-Achievement DiscrepancyDiscrepancy

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

44

What is Meant by an RTI What is Meant by an RTI Model?Model?

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

55

Typical RTI ProcedureTypical RTI ProcedureTypical RTI ProcedureTypical 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.

66

Advantages of RTI ApproachAdvantages of RTI ApproachAdvantages of RTI ApproachAdvantages of RTI Approach

Provides assistance to needy children in timely fashion. It is NOT a wait-to-fail model.

Helps ensure that the student’s 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.

77

Within RTI IdentificationWithin RTI Identification

Tier 2 tutoring is viewed as the “test” to which Tier 2 tutoring is viewed as the “test” to which at-risk students respond to determine at-risk students respond to determine disability.disability.

That response needs to be measured and That response needs to be measured and categorized as “responsive” (not LD) or categorized as “responsive” (not LD) or “unresponsive” (LD) using an appropriate tool “unresponsive” (LD) using an appropriate tool for such measurement.for such measurement.

88

RTI: Three TiersRTI: Three Tiers Tier 1Tier 1

− General education General education Research-based program Research-based program Faithfully implementedFaithfully implemented Works for vast majority of studentsWorks for vast majority of students Screening for at-risk pupils, with weekly monitoring of at-risk Screening for at-risk pupils, with weekly monitoring of at-risk

response to general educationresponse to general education Tier 2Tier 2

− Small-group preventative tutoringSmall-group preventative tutoring− Weekly monitoring of at-risk response to tier 2 interventionWeekly monitoring of at-risk response to tier 2 intervention

Tier 3Tier 3− Special educationSpecial education

99

Primary Prevention:School-/Classroom-Wide Systems for

All Students,Staff, & Settings

Secondary Prevention:Specialized Group

Systems for Students with At-Risk Behavior

Tertiary Prevention:Specialized

IndividualizedSystems for Students with

Intensive Needs

~80% of Students

~15%

~5%

CONTINUUM OFSCHOOL-WIDE

SUPPORT

1010

RTI Tier 2:RTI Tier 2: Standardized Research-Based Standardized Research-Based

Preventative TreatmentPreventative Treatment

TutoringTutoring Small groups (2-4)Small groups (2-4) 3-4 sessions per week (30-45 min per session)3-4 sessions per week (30-45 min per session) Conducted by trained and supervised personnel (not Conducted by trained and supervised personnel (not

the classroom teacher)the classroom teacher) In or out of classroomIn or out of classroom 10-20 weeks10-20 weeks

1111

What does Tier 2 look like?What does Tier 2 look like?

Hypothetical Case StudiesHypothetical Case Studies

What does Tier 2 look like?What does Tier 2 look like?

Hypothetical Case StudiesHypothetical Case Studies

1212

1313

1414

1515

Purpose of the StudyPurpose of the Study To explore:To explore:

− Effects of multiple Tier 1 (classroom) and Tier 2 (pullout) Effects of multiple Tier 1 (classroom) and Tier 2 (pullout) instructional approaches on at-risk children’s reading instructional approaches on at-risk children’s reading growth in a 9-wk treatment period in fall of 1growth in a 9-wk treatment period in fall of 1stst grade. grade.

− How responsiveness to the instructional approaches can How responsiveness to the instructional approaches can be used to identify children as LD at the end of 1be used to identify children as LD at the end of 1stst grade. grade.

− Effects of alternative methods of LD classification on Effects of alternative methods of LD classification on prevalence and severity.prevalence and severity.

− Can characteristic growth patterns of children who are Can characteristic growth patterns of children who are either LD and not LD be identified for Tier 1 and Tier 2 either LD and not LD be identified for Tier 1 and Tier 2 instruction?instruction?

1616

Reading Study SampleReading Study Sample 42 142 1stst-grade classes in 16 schools (8 Title)-grade classes in 16 schools (8 Title) Six lowest readers from each class on WIF and RLN, with teacher Six lowest readers from each class on WIF and RLN, with teacher

corroboration (252 low-study-entry children) corroboration (252 low-study-entry children) Beginning 1Beginning 1stst grade, 6 children from each class rank ordered and, within grade, 6 children from each class rank ordered and, within

class, split into 2 strataclass, split into 2 strata Within each stratum within each class, randomly assigned to 3 groups (Within each stratum within each class, randomly assigned to 3 groups (nn = =

84 per condition)84 per condition)− No tutoring (No tutoring (nn=55 [65.5%] complete data at end grade 3)=55 [65.5%] complete data at end grade 3)− Fall 1Fall 1stst-grade tutoring (-grade tutoring (nn=61 [72.6%] complete data at end grade 3)=61 [72.6%] complete data at end grade 3)− Spring 1Spring 1stst-grade tutoring, but only with inadequate slope/final intercept -grade tutoring, but only with inadequate slope/final intercept

for fall 1for fall 1stst grade ( grade (nn=64 [76.2%] complete data at end grade 3)=64 [76.2%] complete data at end grade 3) Three groups comparable demographically and on RLN, WIF, IQ, WRMT Three groups comparable demographically and on RLN, WIF, IQ, WRMT

WID/WA, TOWRE SW/PDWID/WA, TOWRE SW/PD 18 weekly Word Identification Fluency measurements18 weekly Word Identification Fluency measurements End of 3End of 3rdrd grade, disability: <85 on latent variable of word reading, grade, disability: <85 on latent variable of word reading,

nonsense word reading, comprehensionnonsense word reading, comprehension

1717

Evidence-Based TutoringEvidence-Based Tutoring Tutoring Tutoring

− Letter-Sound RecognitionLetter-Sound Recognition− Phonological awareness and decodingPhonological awareness and decoding− Sight WordsSight Words− FluencyFluency

Four GroupsFour Groups− Fall Tutoring (n=61)Fall Tutoring (n=61)− Spring Tutoring for Nonresponsive Children (n=32)Spring Tutoring for Nonresponsive Children (n=32)− Spring No Tutoring for Responsive Children (n=32)Spring No Tutoring for Responsive Children (n=32)− Controls (No Tutoring, n=55)Controls (No Tutoring, n=55)

SessionsSessions− Conducted by research assistantsConducted by research assistants− 2-4 students per group2-4 students per group− 4 sessions/week4 sessions/week− 45 minutes/session45 minutes/session− For a total of 36 sessions of tutoringFor a total of 36 sessions of tutoring

1818

QuestionsQuestions

Identify 1Identify 1stst-grade growth trajectories -grade growth trajectories characteristic of later disability versus NDcharacteristic of later disability versus ND

Examine effects of 1Examine effects of 1stst-grade tutoring on -grade tutoring on trajectoriestrajectories

Explore cognitive profiles associated with Explore cognitive profiles associated with each latent classeach latent class

1919

General Model for General Model for Identifying Trajectory Classes Identifying Trajectory Classes

CBM1 CBM2 CBM3 CBMi. . .

SI

CCG

U1 U2 Ui

F

X1 X2 XiKnown Classes Reading - Fall tutoring - Spring tutoring necessary - Spring tutoring not necessary - Control Math - Average - Control - Tutoring

Covariates - Reading

SM, VOC, RDN - Math

LANG, SM, MR, CO, WM, INATT

Unobserved Subpopulations Reading: RD & NRD Math: MD & NMD

SlopeIntercept

First Grade Follow-up Testing - Reading: Third Grade - Math: Second Grade

Q Quadratic

Curriculum Based Measure- Reading: WIF- Math: COMP

Categorical Outcomes - Reading: WID, WA, PC - Math: CALC, SP

2020

Analysis PlanAnalysis Plan Conventional growth modeling to evaluate appropriateness of Conventional growth modeling to evaluate appropriateness of

the hypothesized quadratic modelthe hypothesized quadratic model Multiple group growth mixture modeling with a distal latent Multiple group growth mixture modeling with a distal latent

factor (F, at end 3factor (F, at end 3rdrd grade in reading; end 2 grade in reading; end 2ndnd grade in math) grade in math) and beginning 1and beginning 1stst-grade covariates to identify disability and -grade covariates to identify disability and nondisability populations within each known group.nondisability populations within each known group.

− Distal latent factor was regressed on the categorical latent Distal latent factor was regressed on the categorical latent variable (C), representing subpopulation CBM growth variable (C), representing subpopulation CBM growth characteristics in 1characteristics in 1stst grade. grade.

− Subpopulation variable (C) was regressed on the known Subpopulation variable (C) was regressed on the known class variable (CG).class variable (CG).

− Growth parameters (I, S, Q) and C were regressed onto Growth parameters (I, S, Q) and C were regressed onto the time-invariant covariates.the time-invariant covariates.

2121

Estimated Parameters of InterestEstimated Parameters of Interest

Average latent class probabilities: likelihood each individual Average latent class probabilities: likelihood each individual belongs to each classbelongs to each class

Class-specific profiles: likelihood each individual in the class Class-specific profiles: likelihood each individual in the class scores above/below criterion for disability on distal latent class scores above/below criterion for disability on distal latent class indicatorindicator

Means/variances onMeans/variances on− Growth parameters (I,S,Q) Growth parameters (I,S,Q) − Beginning 1Beginning 1stst-grade performance-grade performance− Cognitive predictorsCognitive predictors− End-study performance as function of known class and End-study performance as function of known class and

disability/nondisability trajectory classdisability/nondisability trajectory class− Class-specific probabilities for categorical latent variable as Class-specific probabilities for categorical latent variable as

function of the covariatesfunction of the covariates

2222

Data AnalysisData Analysis

Growth model analyses with Mplus 4.0Growth model analyses with Mplus 4.0 Model estimation used maximum likelihood Model estimation used maximum likelihood

estimator with robust standard errorsestimator with robust standard errors CBM data centered on initial assessmentCBM data centered on initial assessment Mplus missing data module (maximum likelihood Mplus missing data module (maximum likelihood

missing at random estimation procedures)missing at random estimation procedures) Estimated starting values derived from multiple Estimated starting values derived from multiple

group analysis of growth using only the CBM datagroup analysis of growth using only the CBM data Covariates centered on grand meansCovariates centered on grand means

2323

Results: Conventional Growth ModelingResults: Conventional Growth Modeling

Word identification fluency (WIF)Word identification fluency (WIF) 18 weekly across fall and spring18 weekly across fall and spring Quadratic model improved overall fit of Quadratic model improved overall fit of

model over linear modelmodel over linear model I: 14.20 words (SE=0.719; I: 14.20 words (SE=0.719; zz = 19.74) = 19.74) S: 1.80 words per week (SE=0.138; S: 1.80 words per week (SE=0.138; zz = 13.09) = 13.09) Q: -0.015 wordsQ: -0.015 words2 2 per week (SE=0.006; per week (SE=0.006; zz = - = -

2.31)2.31)

2424

Results: Fall TutoringResults: Fall Tutoring

2525

Results: Spring Tutoring NecessaryResults: Spring Tutoring Necessary

2626

Results: Spring Tutoring UnnecessaryResults: Spring Tutoring Unnecessary

2727

Results: ControlResults: Control

2828

Results: Growth Mixture ModelingResults: Growth Mixture Modeling

For each trajectory class, intercept and slope For each trajectory class, intercept and slope was significantly greater than zero and was significantly greater than zero and necessary for describing growth.necessary for describing growth.

Quadratic term significantly different from Quadratic term significantly different from zero only forzero only for

− Fall tutoring (Fall tutoring (zz = -2.574) = -2.574)− Spring tutoring-necessary (Spring tutoring-necessary (zz = 4.346) = 4.346)

2929

Average Probability of Latent Class Assignment and Class-Specific Profiles on the Distal Reading Latent Class Indicators

Class-Specific Probabilitieson Latent Class Indicatorsb

Latent Class Latent Class Probabilitya

WRMT-RWID

WRMT-RWA

WRMT-RPC

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

3030

Results: Growth Mixture ModelingResults: Growth Mixture Modeling(across entire sample)(across entire sample)

Average latent class probability: Probability child is assigned Average latent class probability: Probability child is assigned to correct disability trajectory class within the known to correct disability trajectory class within the known class: .912 to .995 (precise)class: .912 to .995 (precise)

Class-specific profiles on 3Class-specific profiles on 3rdrd-grade latent class indicators of -grade latent class indicators of disability (WID, WA, PC): Probability child in that class disability (WID, WA, PC): Probability child in that class would score > 85would score > 85

− WA: Across disability groups, poor precision.WA: Across disability groups, poor precision.− WID and PC: More consistently distinguished RD from ND.WID and PC: More consistently distinguished RD from ND.− For spring tutoring-unnecessary RD group, class-specific probabilities For spring tutoring-unnecessary RD group, class-specific probabilities

indicate this class does not have a characteristically RD profile.indicate this class does not have a characteristically RD profile.− For control RD group, high class probability of scoring normal on For control RD group, high class probability of scoring normal on

WID, but low class probability of scoring normal on PC. So, poor WID, but low class probability of scoring normal on PC. So, poor reading comprehension is the defining characteristic of untreated at-reading comprehension is the defining characteristic of untreated at-risk students.risk students.

3131

Estimated Multinomial Regression Estimated Multinomial Regression of Latent Class Variable on of Latent Class Variable on

CovariatesCovariates

3232

Estimated Multinomial Regression Estimated Multinomial Regression of Latent Class Variable on of Latent Class Variable on

CovariatesCovariates

3333

Plots represent estimated class-specific Plots represent estimated class-specific probability of class membership as function probability of class membership as function

of one covariate, of one covariate, while keep other covariates constantwhile keep other covariates constant

Sound matching and vocabulary distinguished Sound matching and vocabulary distinguished latent class membership, but only in control latent class membership, but only in control group.group.

Control students with lower sound matching Control students with lower sound matching scores have greater probability of being scores have greater probability of being assigned to control RD class.assigned to control RD class.

Control students with higher vocabulary Control students with higher vocabulary scores have greater probability of being scores have greater probability of being assigned to control ND class.assigned to control ND class.

3434

ConclusionsConclusions First-grade trajectory classes associated with 3First-grade trajectory classes associated with 3rdrd-grade -grade

disability status can be identified with high precision using disability status can be identified with high precision using WIF. So, WIF can be used for 1WIF. So, WIF can be used for 1stst-grade progress monitoring -grade progress monitoring within RTI, as an indicator of long-term RD status.within RTI, as an indicator of long-term RD status.

In control (untreated) group, RD and ND trajectory classes In control (untreated) group, RD and ND trajectory classes had same intercept, but vastly different slopes. So, slope can had same intercept, but vastly different slopes. So, slope can be used to index responsiveness.be used to index responsiveness.

Only 2 classes had significant quadratic term.Only 2 classes had significant quadratic term.− For fall tutoring, growth decelerated across year. For fall tutoring, growth decelerated across year. − For spring tutoring-necessary, growth accelerated across For spring tutoring-necessary, growth accelerated across

year. year.

3535

ConclusionsConclusions

33rdrd-grade WID and PC measures distinguished -grade WID and PC measures distinguished RD from ND; WA did not.RD from ND; WA did not.

Spring tutoring-unnecessary NRD was a Spring tutoring-unnecessary NRD was a relatively pure group of NRD students. So, relatively pure group of NRD students. So, using WIF in fall semester of 1using WIF in fall semester of 1stst grade to grade to select children at-risk students may be select children at-risk students may be efficient.efficient.

3636

ConclusionsConclusions For control RD students, reading comprehension skill was For control RD students, reading comprehension skill was

defining characteristic. Interesting because 1defining characteristic. Interesting because 1stst-grade trajectory -grade trajectory classes formed exclusively with WIF. Also, no way to classes formed exclusively with WIF. Also, no way to distinguish control RD and NRD using intercept.distinguish control RD and NRD using intercept.

11stst-grade cognitive predictors most useful for untreated -grade cognitive predictors most useful for untreated students. For control students, low sound matching associated students. For control students, low sound matching associated with RD; high vocabulary associated with NRD.with RD; high vocabulary associated with NRD.

Within treated students, RTI (trajectory class) was what Within treated students, RTI (trajectory class) was what distinguished RD from NRD, effectively overriding initial distinguished RD from NRD, effectively overriding initial individual differences on sound matching and vocabulary.individual differences on sound matching and vocabulary.

3737

Thank YouThank You