James McDougal, Psy.D. Sheila Clonan, Ph.D. Michael LeBlanc, Ph.D. Syracuse University SUNY Oswego

1. Reconceptualizing Definitions, Assessments, & Interventions for Learning Delayed StudentsAnnual ConventionNational Association of School PsychologistsAdams Mark Hotel, Dallas TexasThursday April 1, 2004 James McDougal, Psy.D. Sheila Clonan, Ph.D. Michael LeBlanc, Ph.D. Syracuse University SUNY Oswego

2. LD Assessment:Past and Future • Discrepancy based procedures • Problems with these models • A new approach

3. 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? LD Assessment: Past & Future

4. 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

5. 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."

6. 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…….

7. Example of State Requirements for LD Diagnosis

8. Achievement Intelligence Discrepancy

9. 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

10. Methods for Determining Severe Discrepancy • Deviation from Grade Level • Standard Deviation from the Mean • Standard Score Comparison • Regression Formula

11. 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

12. 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

13. 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)

14. 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

15. 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

16. 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

17. 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

18. 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

19. 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

20. Effects of Test Reliability or Error of Measurement Tests with high reliability Tests with low reliability

21. 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

22. Regression Formula (continued) • Calculation discrepancy using a severe discrepancy calculator: • Kate’s Ability Score 90 • Achievement Score 74 • Reliability of Ability Score .91 • Achievement Reliability .91 • Correlation Between Ability and Achievement Scores .47

23. 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

24. 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

25. 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

26. 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).

27. 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.

28. Assessing Validity of LD • Fletcher et al. (2001) describe means of validating LD diagnosis • Prognosis • Response to intervention • Distinct cognitive profiles

29. Cognitive Domains • Meta-Analysis • Hoskyn & Swanson (2000) • Stuebing et al. (2002)

30. 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

31. 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.

32. 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.

33. 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

34. Validity • Current definitions and diagnosis of LD students lacks uniqueness (distinct group of learners) and utility (clear differences in treatment and prognosis).

35. A New Era: Revitalizing Special Education for Children and their Families President’s Commission on Excellence in Special Education July 1, 2002

37. 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

38. 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

39. Federal Reg’s & Monitoring, paperwork reduction, increased flexibility *Assessment & Identification Sp Ed finance Accountability, flexibility, parental empowerment Post secondary results, effective transition services Teacher/administrator preparation, training, retention Sp Ed research and dissemination 7 Sections- Assessment & Id changed most

40. Assessment & Identification • 1. Identify and Intervene Early

41. “Services first, assessment later,” • Commissioner Steve Bartlett

42. Assessment & Identification • 2. Simplify the Identification Process. • And clarify the criteria used to determine the existence of a disability, particularly high incidence disabilities.

43. 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

44. 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

45. 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.

46. 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

47. Assessment & Identification- Summary …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

48. New Assessment Models:NASP Recommendations Identification and Eligibility Determination for Students with Specific Learning Disabilities April 25, 2003

49. 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

50. 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…..