Reconceptualizing Definitions,
Assessments, & Interventions for
Learning Delayed Students
Annual Convention
National Association of School Psychologists
Adams Mark Hotel, Dallas Texas
Thursday April 1, 2004
James McDougal, Psy.D.
Michael LeBlanc, Ph.D.
SUNY Oswego
Sheila Clonan, Ph.D.
Syracuse University
LD Assessment:
Past and Future
 Discrepancy based
procedures
 Problems with these models
 A new approach
LD Assessment: Past & Future
The Ghost of LD PAST
– Current Definitional
Concerns
– What is LD?
– What isn’t LD?
– Discrepancy based
models
– Wait to fail
 LD Future
– A New Era
– Validated Models
– Response to Intervention
– Services First,
Assessment Later
– Monitoring techniques
– What’s my role?
NY Learning Disability Definition
A student with a disorder in one or more of the basic
psychological processes involved in understanding or in using
language, spoken or written, which manifests itself in an
imperfect ability to listen, think, speak, read, write, spell, or
to do mathematical calculations. The term includes such
conditions as perceptual handicaps, brain injury, neurological
impairment, minimal brain dysfunction, dyslexia and
developmental aphasia. The term does not include students
who have learning problems which are primarily the result of
visual, hearing or motor handicaps, of mental retardation, of
emotional disturbance, or of environmental, cultural or
economic disadvantage. A student who exhibits a discrepancy
of 50 percent or more between expected achievement and
actual achievement determined on an individual basis shall be
deemed to have a learning disability
IDEA's Definition of Learning Disability
". . . a disorder in one or more of the basic psychological
processes involved in understanding or in using language,
spoken or written, that may manifest itself in an imperfect
ability to listen, think, speak, read, write, spell, or do
mathematical calculations, including conditions such as
perceptual disabilities, brain injury, minimal brain
dysfunction, dyslexia, and developmental aphasia."
However, learning disabilities do not include, "…learning
problems that are primarily the result of visual, hearing, or
motor disabilities, of mental retardation, of emotional
disturbance, or of environmental, cultural, or economic
disadvantage."
Problems with these Definitions?
 Heterogeneity hypothesis:
– A catch-all definition
– what is LD & how do we intervene?
 Exclusionary hypothesis:
– What LD is not vs. what it is
– Difficult to justify
– Routinely overlooked in practice
 Discrepancy Hypothesis:
– Too many to list! Beginning with…….
Example of State Requirements for LD Diagnosis
Achievement Intelligence
Discrepancy
Severe Discrepancy Determination by Formula
Kate obtains an IQ score of 90 and an achievement score of 74. Is
this 16-point difference large enough to be considered a ‘significant
difference’ between ability and achievement?
Data:
Ability Score ………………………………………………... 90
Reliability of Ability Score ……………………………. … 0.91
Achievement Score ……………………………………….. 74
Achievement Reliability ………………………………….. 0.91
Correlation Between Ability and Achievement Scores .. 0.47
Methods for Determining Severe Discrepancy
 Deviation from Grade Level
 Standard Deviation from the Mean
 Standard Score Comparison
 Regression Formula
Deviation from Grade Level
 difference between grade level functioning and
placement
 “Is a student’s measure of grade level functioning
significantly different than his or her grade
placement?”
 For example:
– Kate is in grade 6 and is achieving at a 3rd grade level
– the 50% discrepancy would be considered a severe
discrepancy
Deviation from Grade Level (continued)
 Problems:
– grade equivalent scores are not based on equal units
– learning is not linear
– example: a third grader two years behind is not
comparable to an 11th grader two years behind
– least psychometrically sound method
Standard Deviation from the Mean
 Difference between obtained achievement and
normed averages
 Compares an individual to a group
 “Is a student’s score on an achievement test
discrepant from the test mean by a standard value”
 To calculate:
– change achievement score to z-score
– compare the z-score to some predefined discrepancy
(e.g., 1.5sd or 1.75 sd)
Standard Deviation from the Mean
(continued)
 Example of Kate
– if a severe discrepancy is defined as 1.5 sd
– Kate’s achievement score of 74 would transform to a z-score of
(74-100)/15=-1.73
– Kate’s discrepancy would be considered a severe discrepancy
 Problems:
– conceptually different from measures of intra-personal
discrepancies & would qualify all low performing individuals
– would not identify many students who would be expected to
perform better than the average
– does not consider measurement error
Standard Score Comparison
 Difference between standard scores from ability
and achievement tests
 Compares an individual to himself or herself
 To calculate:
– obtain measures of achievement and ability
– change scores to z-scores
– subtract achievement z-score from ability z-score and
divide by standard error of the difference
– compare to predefined severe discrepancy score
Standard Score Comparison (continued)
 Example of Kate
– if a severe discrepancy is defined as 1.5 sd
– Kate’s achievement score of 74 would transform to a z-score of
(74-100)/15=-1.73
– Kate’s ability score of 90 would transform to a z-score of
(90-100)/15=-0.66
– use formula (Zach-Zability)/((1-rxx) + (1-ryy))1/2
= (-1.73+.66)/.42
= -2.5
– compare -2.5 to 1.5 (note the severe discrepancy cutoff point is
expressed as a positive value but think of it as a discrepancy
between achievement and ability that would be a negative value
when used to define ld)
– because Kate’s discrepancy is larger than the predefined severe
discrepancy
– Kate’s discrepancy would be considered a severe discrepancy
Standard Score Comparison (continued)
 Problems:
– assumes that measures of ability perfectly correlate with measures
of achievement
– e.g., assumes that Kate’s measured IQ of 90 would mean that we
expect her achievement score to be 90
– does not consider measurement error
Regression Formula
 Difference between standard scores from ability
and achievement tests using regression formulas
– use regression to predict an individual’s achievement
score from his or her ability score
– includes corrections for measurement error and
regression to the mean
Regression Formula (continued)
 Example regression formula:
y’ = rxy(Sy/Sx)(IQ - `x) + `y
where:
y’ = predicted achievement score
rxy = correlation between IQ and achievement test
Sy = standard deviation of achievement test
Sx = standard deviation of IQ test
`x = mean of IQ test
`y = mean of achievement test
Effects of Test Reliability or Error of Measurement
0.03
0
40
55
70
85
74
100
98
115
130
145
160
130
145
160
Score
Tests with high reliability
0.03
0
40
55
70
85
74
100
98
115
Score
Tests with low reliability
Regression Formula (continued)
 After predicting achievement based on IQ
– discrepancy is formed by calculating difference
between actual and predicted achievement
– the calculated discrepancy is tested for
significance
– is the discrepancy so large that we would
consider it not likely due to chance?
 Determination is made
Regression Formula (continued)
 Calculation discrepancy using a severe
discrepancy calculator:
 Kate’s Ability Score
 Achievement Score
90
74
 Reliability of Ability Score .91
 Achievement Reliability
.91
 Correlation Between Ability and Achievement
Scores
.47
Regression Formula (continued)
 Predicted Achievement Score
95
– note: based on IQ score of 90, Kate’s predicted achievement score
is “pulled towards the mean”
 Difference between Predicted and Actual Achievement
21
 Magnitude of Difference required at .05 level
22
 Kate’s discrepancy would not be considered a severe
discrepancy
Regression Formula (continued)
 Problems:
– complex calculations
– excludes many students in lower ability range
who would be included using simple
discrepancy method
 Benefit:
– most psychometrically sound method
Summary
 Determination of LD Diagnosis is based in
part on:
– State determinations of severe discrepancy
– method of calculating severe discrepancy
 Different methods of calculating a
discrepancy will result in different students
being classified
Validity
 Learning disability is result of unexpected
low achievement.
 Also implies that children with unexpected
low achievement (LD) are distinct from
expected low achievement (i.e., low
achievement and low intelligence).
Validity
 Validity of construct relies on its uniqueness
and utility
 Validity of a discrepancy based model
assumes that ability-achievement
discrepant children are qualitatively
distinct from regular “low achievers.
 Also assumes that identifying LD will
lead to useful interventions specific to
that group.
Assessing Validity of LD
 Fletcher et al. (2001) describe means of
validating LD diagnosis
– Prognosis
– Response to intervention
– Distinct cognitive profiles
Cognitive Domains
 Meta-Analysis
 Hoskyn & Swanson (2000)
 Stuebing et al. (2002)
Stuebing et al.
 Substantial overlap between IQ-discrepant
& IQ-consistent poor readers
 Differences between groups on several
cognitive domains were negligible or small
 Research indicates little need for using IQ
tests in assessing LDs
Prognosis
 Do LD students and ordinary low-achievers
differ in development of reading ability?
 O’Mally et al. (2002) found little evidence
of differences between groups.
 Several longitudinal studies found little or
no differences in reading development
between groups.
Response to Intervention
 Research generally finds that discrepancy
based LD vs. low-achievers do not respond
differently to interventions.
 Vellutino, Scanlon, Lyon (2000) reported
that IQ-achievement discrepancy did not
predict differences between groups on
responses to intervention or which group
would be more easily remediated.
Assessing Validity of LD:
Summary
 Research indicates little or no differences
between discrepancy based LD students and
ordinary low achievers based on:
– Cognitive Profiles
– Prognosis
– Response to intervention
Validity
 Current definitions and diagnosis of LD
students lacks uniqueness (distinct group of
learners) and utility (clear differences in
treatment and prognosis).
A New Era: Revitalizing Special
Education for Children and their
Families
President’s Commission on
Excellence in Special Education
July 1, 2002
Introduction to a New Era
 Students with disabilities drop out of high
school at twice the rate of their peers
 Most public school educators do not feel
well prepared to work with children with
disabilities
Introduction to a New Era
 Almost half of the children in special
education are identified as having a specific
learning disability- a 300% increase since
1976
 80% of of those with SLD (40% of Sp Ed
students) are there because they haven’t
learned how to read
7 Sections- Assessment & Id changed most
 Federal Reg’s &
 Post secondary results,
Monitoring,
paperwork reduction,
increased flexibility
 *Assessment &
Identification
 Sp Ed finance
 Accountability,
flexibility, parental
empowerment
effective transition
services
 Teacher/administrator
preparation, training,
retention
 Sp Ed research and
dissemination
Assessment & Identification
 1. Identify and Intervene Early
“Services first, assessment later,”
• Commissioner Steve Bartlett
Assessment & Identification
 2. Simplify the Identification Process.
– And clarify the criteria used to determine the
existence of a disability, particularly high
incidence disabilities.
 I would like to encourage the commission to
drive a stake through the heart of this over
reliance on the discrepancy model for
determining the kinds of children that need
services. It doesn’t make any sense to me.
I’ve wondered for 25 years why it is that we
continue to use it and over rely on it as a
way of determining what children are
eligible for services in special education.
• Commissioner Wade Horn
Assessment & Identification
 Incorporate Response to Intervention.
– Implement models during the identification and
assessment process that are based on response
to intervention and progress monitoring. Use
data from these practices to assess progress in
children who receive special education services
 The real tragedy is that conceptualizations
of LD have not changed over 30 years
despite the completion of significant
research in the past 15 years. What we
know form research now needs to be
implemented.
• Lyon, Fletcher, et al.
Assessment & Identification
 Incorporate Universal Design in
Accountability Tools.
– Ensure all tools used to assess students for
accountability and the assessment of progress
are designed to include any accommodations
and modifications for students with disabilities
Assessment & IdentificationSummary
…We are still in need of data indicating that
the cognitive processing of dyslexic and
garden variety poor readers reading at the
same level is reliably different, data
indicating that these 2 groups have a
differential educational prognosis, and data
indicating that they respond differently to
certain educational treatments. These data
of course should have been presented in the
first place. Stanovich, 1991
New Assessment Models:
NASP Recommendations
Identification and Eligibility
Determination for Students with
Specific Learning Disabilities
April 25, 2003
NASP Recommendations
 Maintain current LD definition but change
eligibility criteria
 Eliminate ability-achievement discrepancy
 Introduce multi-tier model with dual
criteria- significantly low
underachievement, insufficient response to
intervention
Significantly Low Achievement
 States or School Districts may set criteria
for “significantly low achievement”
 As in current law exclusionary criteria
would still apply- not primarily the result of
visual, hearing…..
Insufficient Response to
Intervention
 Despite at least 2 empirically based
interventions over a period of at least 6
weeks
 Interventions administered in general
education
 Lack of response not due to low effort,
cultural differences, LEP, or nonattendance
Continuum of Effective Support
Tertiary Prevention
Specialized Individual Interventions
Students with
Chronic/Intense
Problems
(1 - 7%)
(Individual Student System)
Secondary Prevention
Students At-Risk
for Problems
Specialized GroupInterventions
(At-Risk System)
(5-15%)
Students
without
Serious
Problem
(80 -90%)
Primary Prevention
Universal Interventions
(School-Wide System Classroom
System)
Characteristics of the Multi-Tier
Model
 Tier 1. High quality instructional and
behavioral supports for all students in
general education
Tier 1. Components include..
 Research based instruction & behavior
supports
 Ongoing CBA of basic skills, instructional
level matched to students skills
 Remedial instruction and group
interventions within general education
Characteristics of the Multi-Tier
Model
 Tier 2. Targeted intensive prevention or
remediation services for struggling
students
Tier 2. Components include..
 Research based/intense services targeted to
the student’s individual needs
 Time limited services
 Linked to a problem solving process
including general & Sp Ed teachers and
support services personnel
 Initiated though formal referral, parental
notification and consent
Tier 2. Problem solving includes.
 Precise charting of progress- general education
interventions
 Formal documentation of progress toward targeted
goals
 A verified level of intervention fidelity
 Comparison to local norms- if available
Characteristics of the Multi-Tier
Model
 Tier 3. Comprehensive evaluation by a
multi-disciplinary team to determine
eligibility for special education and
related services
Tier 3. Components include..
 Low achievement and insufficient response
criteria met
 Referral to a Multidisciplinary Team
 MDT conducts a comprehensive evaluation
Assessment through intervention:
A New IDEA?
 Monitoring and
Evaluation
 Implications for
Intervention
Monitoring Techniques:
Academic
 Curriculum Based Measurement (CBM)
– Reading- fluency, accuracy, some
comprehension
– DIBELS- to assess pre/early-literacy skills
– Math- fluency, accuracy, basic calculations
– Written expression- fluency, accuracy
Monitoring Techniques:
Academic
Other Techniques:
• Permanent Product Monitoring- homework,
class assignments, etc
• Test/ Quiz Grades
• Others..
CBM- Monitoring Interventions
CBM- Testing Interventions
CBM- Testing Interventions
Evaluating Interventions
Evaluating Interventions
BILLY'S DIBELS SCORES
18
16
14
SCORES
12
ISF
10
PSF
LNF
8
NWF
6
4
2
0
1
2
3
4
DAYS
5
6
Implications for Intervention
 Assessment techniques should lend
themselves to intervention
 Assessments that measure important
subskills, are repeatable and directly related
to instruction
Case Example: Using Dynamic
Indicators of Basic Early Literacy Skills
(DIBELS): Background
 Subtest: Letter Naming Fluency (1st Grade)
– Administered K & early 1st grade
– Student presented with upper & lower case
letters in random order and asked to name as
many as they can in one minute
– Students are told that if they don’t know a letter
it will be told to them
– < 25 LNF = at risk; 25-37= Some Risk; > 37
Low Risk
Case Example: Using Dynamic
Indicators of Basic Early Literacy Skills
(DIBELS): Background
 Subtest: Phoneme Segmentation Fluency
(1st Grade)
– Assesses students’ ability to segment 3- and 4phoneme words into individual phonemes
– Words are presented orally
– Student is asked to say each sound they hear
– PSF < 10 Deficit; 10-35= Emerging; >35=
Established
Case Example: Using Dynamic
Indicators of Basic Early Literacy Skills
(DIBELS): Background
 Subtest: Nonsense Word Fluency (1st Grade)
– Assesses student knowledge of alphabetic
principle
– Ability to blend letters into “words”
– Student given page with nonsense words and
asked to read as many as can in 1 minute
– NWF < 13= At Risk; 13-24=Some Risk;
>24=Low Risk
DIBELS Case Example
 1st grader, Fall
 DIBELS:
– Letter Naming Fluency: 30
– Phoneme Segmentation Fluency: 9
– Nonsense Word Fluency: 16
 What to do?
DIBELS Case Example
 Benchmark 1 Fall 1st gr.
 Letter Naming Fluency
– <25 At Risk
– 25-37 Some Risk
– >37 Low Risk
 Phoneme Segmentation Fl
– < 10 Deficit
– 10-35 Emerging
– > 35 Established
 Nonsense Word Fluency
– <13 At Risk
– 13-24 Some Risk
– >24 Low Risk
 “Johnny”
 LNF= 30
 PSF= 9
 NWF= 16
DIBELS Case Example
 What to do?
 What are the areas of concern?
 How might these be problematic?
 How can we address them?
 How can we monitor his progress?
Phonological Awareness and the
Instructional Hierarchy
Instructional Instructional
Stage
Principles
Examples
Acquisition
(accuracy)
Modeling
Prompting
Say it-Move it
Letter Cards
Fluency
Drill & Practice
Reinforcement of
correct responses
Generalization
Practice and more
practice!
“Mad
Minute”/speeded
activities; Word sorts,
Concentration, etc.
Reading controlled
text
Reading;
Variety of PA
activities
2nd Grade Case Example
 Oral Reading Fluency: 24 (At Risk)
 Early reading skills “intact”
 Needs substantial intervention in reading
fluency
 Suggestions?
Oral Reading Fluency Building
Activities
 Practice
 Reinforcement
 Generalization
 Practice
 Reading
 Practice
 Repeated readings
Characteristics of Good
Interventions
 Explicit
 Systematic
 Structured
 Focused and Appropriate
Resources for Assessment and
Instruction
 Free Reading
Resources
 Free Assessment &
Intervention
Resources
 Other Tools
Free Reading Resources
http://www.nationalreadingpanel.org/Publications/summary.html
Report of the National Reading Panel
http://www.ed.gov/index.html
US Department of Education
See publications- search for reading
http://www.ericec.org/osep-sp.html
The ERIC/OSEP Special Project- Offers Research Connections,
Newsbriefs, Topical Briefs, and Awareness campaigns such as
Learning to Read, Reading to Learn.
Free Reading Resources
http://www.reading.org/
International Reading Association
http://www.ldanatl.org/
Learning Disabilities Association
http://www.aft.org/edissues/downloads/remedial.pdf
American Federation of Teachers- Building on what works.
Five promising reading remediation programs
www.ncela.gwu.edu/iasconferences/institutes/diverse/sevenele
ments.ppt
Seven elements of a school wide beginning reading program
Free Reading Resources-Reading First
http://www.fcrr.org/
Florida Center for Reading Research. Provides scientifically
supported reading curriculum guides and lists validated
intervention programs
http://reading.uoregon.edu/curricula/or_rfc_review_2.php
Oregon Reading First Center- Provides empirical reviews of
validated assessment and instructional programs (many reports
that are able to be downloaded
http://www.texasreading.org/utcrla/default.asp
The University of Texas Center for Reading and Language Arts
(UTCRLA)
www.nifl.gov
The National Institute for Literacy-See the “Putting reading
first” report
Free Assessment & Intervention Resources
http://dibels.uoregon.edu/
Good & Kaminski (2002). Dynamic indicators of
basic early literacy skills. Complete assessment and
manual with probes for letter naming fluency, initial
sound fluency, phoneme segmentation fluency,
nonsense word fluency, oral reading fluency and retell
fluency, word use fluency
Free Assessment & Intervention Resources
http://bitwww1.psyc.lsu.edu
LSU Reading Center (Joe Witt). Materials for reading
placement, reading intervention, and CBM
assessments, instructions, and probes.
Free Assessment & Intervention Resources
http://www.interventioncentral.org/
Jim Wright with the Syracuse City School District.
Excellent resource compilation of assessment,
academic and behavioral interventions Favorite
Downloads include Classroom Behavior Report Card
Manual, Curriculum-Based Measurement Manual &
CBM Workshop, Peer Tutor Training, Reading
Interventions Manual, School-Based Intervention Team
(SBIT) materials. Also has a CBM Graphing program
(Chartdog) and excel.
Free Assessment & Intervention Resources
http://alpha.fdu.edu/psychology/
Ron Dumont and John Willis. This website offers an
array of resources for assessment and diagnosis
including test reviews, classification guidelines, etc.
And of course………..
http://www.schoolpsychology.net/
School Psychology Resources online - Sandra
Steingart, Ph.D.
http://www.nasponline.org/index2.html
NASP
 Contact Information
 James McDougal, Psy.D.
mcdougal@oswego.edu
 Michael LeBlanc, Ph.D.
leblanc@oswego.edu
 Sheila Clonan, Ph.D.
smclonan@syr.edu