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SEEP 

Special Educati on Expenditure Project 

Center fo r Special 

CSEF 

Education Finance 



Educating Students with Disabilities: 
Comparing Methods for Explaining 
Expenditure Variation 



Report 7 
May 2004 

Prepared by: Jay G. Chambers, SEEP Director 

Marfa Perez 
Miguel Socfas 
Jamie Shkolnik 
Phil Esra 

Scott Campbell Brown, Project Manager OSEP 



Submitted to: United States Department of Education, 

Office of Special Education Programs 



This study was funded by the U.S. Department of Education under Contract 
Number ED99C00091 . The contents of this report do not necessarily reflect the 
view or policies of the Department of Education. 



IDEAs^ Office of Special 
*“Work Education Programs 




American Institutes for Research 




Explaining Variations in Expenditures for Students with Disabilities 



Acknowledgements 

Primary support for this research comes from the U.S. Department of Education, Office of Special 
Education Programs (OSEP). The authors wish to express their appreciation for the guidance and 
suggestions of Louis Danielson in his capacity as Director, Research to Practice Division, Office of 
Special Education Programs, and Scott Brown in his capacity as Project Officer for the Special Education 
Expenditure Project (SEEP). 

The authors would like to express their gratitude to Priyanka Anand who played an important role in the 
completion of this report. Her research support, comments, and feedback contributed significantly to the 
quality of this final report. Thanks also go to Joel Knudson and Trevor Chambers for their significant 
support and excellent work. 

The following is a comprehensive list of all the individuals who have contributed to the SEEP during the 
course of the past four years and their various capacities with the project. 

Project Design Team: Jay Chambers (Project Director), Tom Parrish (Director, Center for Special Education 
Finance), and Roger Levine (Task leader for Sample Design). 

Senior Consultants: Margaret McLaughlin, Institute for the Study of Exceptional Children and Youth, University 
of Maryland; Margaret Goertz, University of Pennsylvania, Philadelphia, Pennsylvania. 

Technical Work Group: Stephen Chaikind, Gallaudet University; Doug Gill, Office of Superintendent of Public 
Instruction, Washington State; Diane Gillespie, Virginia Tech, Blacksburg, Virginia; Bill Hartman, Pennsylvania 
State University, University Park, Pennsylvania; John Hemer, Division of Special Education, Ohio Department of 
Education; Donald Kates, Georgetown University, Child Development Center; Brian McNulty, Adams County 
School District 14, Commerce City, Colorado; Jim Viola, New York State Education Department. 

State Directors of Special Education in the nine extended sample states: Alabama , Mabrey Whetstone, State 
Director, and Barry Blackwell, liaison; Delaware , Martha Brooks, State Director and Debbie Stover, liaison; 
Indiana , Robert Marra, State Director, and Hank Binder, liaison; Kansas , Bruce Passman, State Director, and Carol 
Dermyer, liaison; Missouri , Melodie Friedebach, State Director, and Bill Daly, liaison; New Jersey , Barbara 
Gantwerk, State Director, and Mari Molenaar, liaison; New York , Larry Gloeckler, State Director, and Inni Barone, 
liaison; Ohio , Ed Kapel, State Director; Rhode Island , Tom DiPaola, State Director, and Paul Sherlock, member, 
Rhode Island legislature. 

Managers of data collection and processing: James Van Campen, Rafi Youatt, Marie Dalldorf, and Kristi Andes 
Peterson. 

Data collectors and support teams include the following: 

Team leaders: Peg Hoppe, Michael “Chad” Rodi, Jennifer Brown, Andy Davis, Leslie Brock, Jeanette Wheeler, 
and Jean Wolman. Team members: Mary Leopold, Claudia Lawrence, Patrice Flach, Bette Kindman-Koffler, 
Brenda Stovall, Danielle Masursky, Ann Dellaira, Eden Springer, Jack Azud, Nancy Spangler, Melania Page- 
Gaither, Raman Hansi, Chris White, Lori Hodge, Freya Makris, Megan Rice, Amynah Dhanani, Melinda Johnson, 
Carmella Schaecher, Iby Heller, Hemmie Jee, and Irene Lam. 

Data collection support team: Emily Campbell, Ann Win, Sandra Smith and Diana Doyal. 

Data analysis team: Maria Perez, Gur Hoshen, Jamie Shkolnik, Amynah Dhanani, Irene Lam, Bob Morris, and 
John DuBois. 

Report production team: Phil Esra, Jenifer Harr, Jamie Shkolnik, Jean Wolman, and Michelle Bullwinkle. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



SEEP Reports 

This document is a part of a series of reports based on descriptive information derived from the 
Special Education Expenditure Project (SEEP), a national study conducted by the American 
Institutes for Research (AIR) for the U.S. Department of Education, Office of Special Education 
Programs (OSEP). SEEP is being conducted by AIR under the auspices of the Center for Special 
Education Finance (CSEF). It is the fourth project sponsored by the U.S. Department of Education 
and its predecessor, the Department of Health, Education and Welfare, in the past 40 years to 
examine the nation’s spending on special education and related services. See Kakalik, Furry, and 
Carney (1981), Moore, Strang, Schwartz, and Braddock (1988), and Rossmiller, Hale, and Frohreich 
(1970). 

The SEEP reports are based on analyses of extensive data for the 1999-2000 school year. The SEEP 
includes 23 different surveys to collect data at the state, district, and school levels. Survey 
respondents included state directors of special education, district directors of special education, 
district directors of transportation services, school principals, special education teachers and related 
service providers, regular education teachers, and special education aides. Survey responses were 
combined with other requested documents and data sets from states, schools, and districts to create 
databases that represented a sample of approximately 10,000 students with disabilities, more than 
5,000 special education teachers and related service providers, approximately 5,000 regular 
education teachers, more than 1,000 schools, and well over 300 local education agencies. 

The series of SEEP reports will provide descriptive information on the following issues: 

• What are we spending on special education services for students with disabilities in the U.S.? 

• How does special education spending vary across types of public school districts? 

• What are we spending on due process for students with disabilities? 

• What are we spending on transportation services for students with disabilities? 

• How does education spending vary for students by disability and what factors explain 
differences in spending by disability? 

• What role do functional abilities play in explaining spending variations for students with 
disabilities? 

• What are we spending on preschool programs for students with disabilities? 

• Who are the teachers and related service providers who serve students with disabilities? 

• How are special education teaching assistants used to serve students with disabilities? 

• What are we spending on special education services in different types of schools? 

• How does special education spending vary across states classified by funding formula, 
student poverty, special education enrollment levels, and income levels? 

One of the SEEP reports will also be devoted to describing the purpose and design of the study. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



Table of Contents 

I. Introduction 5 

II. The Role of Disability Categories 7 

Variation In Expenditures due to Disability Category 8 

III. The ABILITIES Index: A Measure of Students’ Needs 11 

IV. The Contribution of the ABILITIES Index 16 

V. The ABILITIES Index with and without IDEA Disability Categories 17 

VI. Conclusions 20 

Bibliography 22 

Exhibits 

Exhibit 1 . Percentage of Students within Each Disability Category Who have None, 

One, Two or Three, and Four or More Secondary Disabilities 8 

Exhibit 2. Percentage of the Variation in Total Expenditures Explained by 

Characteristics of Student With Disabilities, 1998-1999 9 

Exhibit 3. Relationship Between Total Expenditures and Characteristics of Students 

with Disabilities, 1998-1999 10 

Exhibit 4. ABILITIES Index Measurement Tool 1 3 

Exhibit 5. Profile for Two Students in Different Disability Categories 14 

Exhibit 6. Profile for Two Students in the Same Disability Category 15 

Exhibit 7. Relationship Between Total Expenditures and Overall Health, Intentional 

Communication, Behavior and Social Skills, and Limbs 16 

Exhibit 8. Relationship Between Total Expenditures and Behavior and Social Skills ... 17 
Exhibit 9. Relationship Between Total Expenditures and Measures of Overall Health. 18 
Exhibit 10. Relationship Between Total Expenditures and Measures of Limbs 19 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



Highlights 

• Disability categories alone explain only a small amount of the variance in special 
education expenditures. A model that includes only the primary disability category explains 
only 10 percent of the variance in special education expenditures. Adding control variables 
such as students' background information and community/regional characteristics increases 
this percentage to 23 percent. Including secondary disability measures results in 27 percent of 
the variance being explained by the model. 

• The ABILITIES Index measure is designed to combat diagnostic ambiguity and the 
overlap of categories found in the traditional disability classification system. The 

traditional classification system is useful in identifying a child’s limitations so that resources 
and supports can be easily identified and applied based on a child’s diagnosed disability. 
However, the disability categories are specific to one area of ability (e.g., vision) and may 
not address other areas in which the student may have needs. The ABILITIES Index allows 
students' characteristics to vary within each disability category and allows students to be 
assessed in several domains as opposed to a primary category. 

• Adding the ABILITIES Index measures to the model increases the explanatory power. 

By providing a more accurate representation of students’ characteristics, the ABILITIES 
Index also improves our ability to explain expenditure variations among special education 
students. A model that includes a continuous measure of the ABILITIES Index among 
students’ background information and community/regional characteristics, explains an 
additional 15 percent of the variation in expenditures. 

• One strength of the ABILITIES Index is that it provides a picture of the severity of 
students’ abilities in nine functional domains. When disability categories are not included 
in the model, the percentage of the variance explained by the ABILITIES Index is 40 
percent. When disability categories are added into the model they explain an additional 2 
percent of the variance. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



I. Introduction 



In 1999-2000, schools in the U.S. were spending an average of $6,556 to educate a student 
without disabilities. At the same time, schools were spending an average of $12,639 on each 
student eligible for special education. 1 This spending varied quite a bit by disability category, 
from a low of $10,058 for students with specific learning disabilities to a high of $20,095 for 
students with multiple disabilities, and there was considerable variation even within the disability 
categories. 2 

Identifying students with disabilities and determining the most appropriate array of services that 
these children should receive is a difficult task (Bailey et al. 1993). Professionals in the field of 
education, psychology, medicine, and health have addressed this challenge in a variety of ways. 
One common approach is to classify children according to the etiology of their impairment. 
Another approach is that mandated by IDEA, which establishes primary disability categories to 
determine whether or not a student is eligible for services under the Act. Each child is identified 
by one category and the severity of the disability within each category is not identified. 

Although these disability categories are intended to be employed for diagnostic purposes to 
determine eligibility, the categories should not be driving the array of services that students 
received. The main determinant of services provided to students should be the evaluation 
provided through the Individualized Education Program (20 USC 1414, Section 614). The IEP is 
intended to determine individual needs and, hence, derive the configuration of services. 

However, a recent report published by the Center for Special Education Finance (CSEF) 
documents that no fewer than sixteen States having funding systems that are pupil weighted 
based on disability, and seven additional States have resource based systems that are nothing 
more than a pupil weighted system disguised as a resource based system in which staff are tied to 
students with certain disabilities (Parrish, Harr, Anthony, Merickel, and Esra, May 2003). 

Given these practices in close to half of the States, one would then expect that a student's 
disability category should clearly delineate student need for particular services and, hence, 
expenditures. In recent years, however, scholarship in the disability field has indicated that a 
move away from disability categorical approaches and towards functional approaches may be in 
order. For instance, over twenty years ago, the World Health Organization (WHO 1980, p.143) 
defined disability as follows: “In the context of health experience, a disability is any restriction 
or lack (resulting from an impairment) of ability to perform an activity in the manner or within 
the range considered normal for a human being.” WHO then operationalized this definition in a 
classification system. More recently, WHO has abandoned defining disability in its newly 
developed International Classification of Functioning, Disability and Health (ICF) and has 
abandoned classification through negative terms. In its conceptual framework, WHO (2001, p. 
212), states that. . . "Functioning is an umbrella term.. .It denotes the positive aspects of an 
individual (with a health condition) and that individual's contextual factors (environmental and 



1 See SEEP Report 1 (Chambers, Parrish, & Harr; 2002). This includes all spending to provide regular education, 
special education, English language learner programs (ELL), and compensatory education services, as well as the 
corresponding administrative, support, and transportation services necessary for special education students. 

2 See SEEP Report 5 (Chambers, Parrish, & Perez; 2002). 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



personal factors)." In such an approach, all aspects of function can be considered and assessed 
according to severity. 

Because of the limitations of the traditional approaches to classifying a child’s characteristics, a 
number of authors have proposed to describe children on the basis of a defined set of functional 
skills or abilities rather than by etiological or deficit category (Bailey et al. (1993); Holt (1957); 
Linden (1963)). Building on this earlier work, Simeonsson and Bailey (1988) developed the 
ABILITIES Index (AI), an index designed to assess functional characteristics rather than skills. 
Although the ABILITIES Index was not originally designed to link student characteristics to the 
patterns of expenditure variations, the size of the SEEP student sample offers a unique 
opportunity to apply the AI classification scheme to explore how such a tool might be used to 
explain the patterns of variations in expenditures on student with disabilities. 3 The AI is not 
necessarily a holistic diagnostic tool for need for an array of services, as envisioned by the ICF. 
It does, however, differ from the disability categories in three important ways: 1) each student’s 
functional attributes are assessed in more than one domain, 2) the severity of the domains is 
taken into account and 3) the actual domains of the Index are somewhat different from the IDEA 
disability categories. 

The study behind this report, the Special Education Expenditure Project (SEEP), provides a 
unique database with which to explore these patterns. The centerpiece for the analysis is the 
individual student database, containing information gathered in SEEP surveys sent to schools 
across the country. The database includes a nationally representative, stratified, random sample 
of over 9,000 individual students with disabilities. 

The purpose of this paper is to compare the variance in expenditures explained by the IDEA 
disability categories with the dimensions delineated in the ABILITIES Index. This report does 
not suggest that the AI should replace the traditional system of disability categories or be used to 
develop a special education identification process or funding system. Rather, the report 
demonstrates that the AI and IDEA disability categories are useful both independently and 
together in explaining the variation in expenditures on students with disabilities, while 
controlling for other student background and community/regional variables. 

The analysis that follows includes three stages. The first stage determines the percentage of the 
variation in spending explained by the student’s primary disability (from among the 13 IDEA 
disability categories). The second phase explores whether a particular child has a second, third, 
or more disabilities, and explores the extent to which these disabilities contribute to expenditure 
variations. The third phase of the analysis examines an alternative approach to classifying 
students. It the ABILITIES Index to characterize special education students. 



3 Another attractive feature of the AI for the SEEP data collection and analysis is that the AI requires a fairly 
minimal investment of time for a teacher to complete on behalf of a student. Moreover, further research of Bailey, 
Simeonsson, and Buysse (1993), and Buysse, Smith, and Bailey (1993) suggests that the AI is fairly reliable across 
different raters including parents, clinicians, and educational professionals 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



II. The Role of Disability Categories 

Professionals in the field of education, psychology, medicine, and health have addressed the 
challenge of describing and categorizing a child with disabilities in a variety of ways. One of the 
approaches used has been to categorize children according to their disability category. 
Classifying children on this basis enables one to determine their eligibility for services (Bailey et 
al. (1993)). This report initially uses the student’s primary and secondary disability categories to 
try to infer the level of spending. The assumption behind this approach is that students who fall 
into similar disability categories may also have similar needs, and therefore require similar 
educational services. 

This section analyzes the variation in expenditures by the IDEA disability categories. The 1997 
reauthorization of the Individuals with Disabilities Education Act (IDEA97) states (20 USC 
1401, Section 602 Definitions 3A), “IN GENERAL, the term ‘child with a disability’ means a 
child (i) with mental retardation, hearing impairments (including deafness), speech or language 
impairments, visual impairments (including blindness), serious emotional disturbance 
(hereinafter referred to as ‘emotional disturbance’), orthopedic impairments, autism, traumatic 
brain injury, other health impairments or specific learning disabilities; and (ii) who, by reason 
thereof, needs special education services.” Developmental delay, the most recently added 
disability category, is applicable only to children ages 3 through 9, and its use for students ages 6 
through 9 is optional for States and Local Educational Agencies (LEAs). 4 

More than half of the students served under IDEA during the 1999-2000 academic year had a 
primary disability category of specific learning disability. The top four categories — specific 
learning disability, speech or language impairment, mental retardation, and emotional 
disturbance — account for about 90 percent. 

In addition to each student’s primary disability, the SEEP survey asked whether the student 
could be classified according to any secondary disabilities. Exhibit 1 shows the percentage of 
students in each primary disability category that have secondary disabilities. 



4 Source: U.S. Department of Education, Office of Special Education Programs (1999, March). In 1999-2000, 22 
States used the developmental delay category for children ages 6 through 9. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



Exhibit 1. Percentage of Students within Each Disability Category Who have 
None, One, Two or Three, and Four or More Secondary Disabilities 5 



Primary Disability 


No secondary 
disabilities 


One 

secondary 

disability 


Two or three 
secondary 
disabilities 


Four or more 
secondary 
disabilities 


Autism 


38% 


28% 


29% 


5% 


Deaf-blindness 


50% 


9% 


32% 


10% 


Developmental delay 


63% 


30% 


3% 


5% 


Emotional disturbance 


45% 


37% 


17% 


1% 


Hearing impairment/deafness 


44% 


36% 


15% 


5% 


Mental retardation 


46% 


33% 


18% 


4% 


Multiple disabilities 


22% 


21% 


37% 


21% 


Orthopedic impairment 


43% 


36% 


19% 


2% 


Other health impairment 


33% 


45% 


20% 


3% 


Specific learning disability 


63% 


28% 


10% 


0% 


Speech or language impairment 


72% 


21% 


6% 


0% 


Traumatic brain injury 


51% 


31% 


11% 


6% 


Visual impairment/blindness 


54% 


28% 


17% 


1% 



The exhibit shows that students with the more common disabilities, such as specific learning 
disability, appear to have fewer secondary disabilities. For example, only 10 percent of students 
with specific learning disability and 6 percent of students with speech or language impairment 
have two or three secondary disabilities. Almost none of these students have more than three 
secondary disabilities. 

Variation In Expenditures due to Disability Category 

To analyze how accurately the educational expenditures on a student can be predicted by that 
student’s disability category, multivariate regression analyses were conducted, using data on 
total expenditures for students with disabilities. The expenditure figures presented in this report 
are based on total spending to educate a special education student for the school year 1999- 
2000. These figures include all school resources, including both the special and regular 
education services used to provide a complete educational program. This is important because 
special education students spend a substantial amount of time in the regular education program 
and benefit from the same administrative and support services as other students without 
disabilities. 6 

Exhibit 2 shows the percentage of the variation in total expenditures explained by information 
about student’s disability, student background, and community/regional characteristics. The 
results displayed in Exhibit 2 suggest that primary disability categories explain about 10 percent 
of the variation in total expenditures on special education students. The number of secondary 
disabilities explains about 8 percent of the variation in total expenditures when taken by itself. 



5 The sum of each row should equal 100 percent except for rounding errors. 

6 For a more detailed explanation about total spending used to educate special education students, refer to Chambers, 
Parrish, & Harr (2002). 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



Student background information, such as age, gender, and ethnicity, accounts for only 2 percent 
of the variation in expenditures. This suggests that student background characteristics alone do 
not provide much information about student needs nor influence the process by which the 
patterns of services and expenditures are determined. On the other hand, community/regional 
characteristics, measured by district size, the cost-of-education index, and state indicators, 
explain 13 percent of the variation. When combined, measures of student needs, background 
information, and community/regional characteristics are able to explain 27 percent of the 
variation in total expenditures for students in special education. 7 

Exhibit 2. Percentage of the Variation in Total Expenditures Explained by 
Characteristics of Student With Disabilities, 1998-1999 



Method of Categorizing Percentage of the 

Students (Sample Size: 8,390 students) Variation Explained 

1 . Students’ disability: 

Primary disability category 1 0% 

Number of secondary disabilities 8% 

2. Students’ background information (age, gender, & ethnicity) 2% 

3. Community/regional characteristics (district size, cost-of-education index, & state indicators 13% 

All Combined (1 , 2, and 3) 27% 



The following exhibit presents results for two different models used for analyses in this report. 
Model 1 includes information about the primary disability of the student, plus student 
background information and community or regional characteristics as control variables. Model 2 
adds information about the number of secondary disabilities. The “percent effect” is how much 
more is spent on a student in a given disability category than is spent on a student in the control 
group of specific learning disability. For example, 66 percent more is spent on a student with 
autism than on a student with a specific learning disability. 



7 The combined variation explained is not simply the sum of the percentage of variation explained by each 
component because these components move together to some degree — when taken one at a time, each reflects some 
component of the variation in the other explanatory variables. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



Exhibit 3. Relationship Between Total Expenditures and Characteristics of 
Students with Disabilities, 1998-1999 



Model 1 

Primary Disability Model 


Model 2 

Primary Disability & Number of 
Secondary Disabilities Model 


Percent Effect 


Primary Student Disability 






Specific learning disability 


Control group 


Control group 


Autism 


66 


54 


Deaf-blindness 


(sample too small) 


(sample too small) 


Developmental delay 


(sample too small) 


(sample too small) 


Emotional Disturbance 


24 


18 


Hearing Impairment/Deafness 


45 


38 


Mental Retardation 


45 


36 


Multiple Disabilities 


75 


48 


Orthopedic Impairment 


44 


37 


Other Health Impairment 


20 


11 


Speech or Language Impairment 


1 


3 


Traumatic Brain Injury 


36 


30 


Visual Impairment/Blindness 


51 


46 


Number of Disabilities 






Primary disability only 


Control group 


Control group 


One secondary disability 




17 


Two secondary disabilities 




31 


Three secondary disabilities 




40 


More than three secondary disabilities 




56 


Adjusted R-Square 


0.23 


0.27 



In Model 1, the speech or language impairment category is the only disability that does not 
exhibit statistically significant expenditure differences when compared with the specific learning 
disability category. All other disability categories show positive and statistically significantly 
higher levels of spending. These expenditure differences from specific learning disabilities range 
from a low of 20 percent more for other health impairment to a high of 75 percent more for 
multiple disabilities. 

When the number of secondary disabilities is included in the model (Model 2), the percent 
effects of the primary disability categories decline. For example, the percent effect of autism 
decreases from 66 percent to 54 percent. This suggests that the disability category itself is 
correlated with the number of secondary disabilities reported by individual children, and that the 
disability category in Model 1 is picking up some of the variation that should have been 
attributed to the existence of these secondary disability conditions. The adjusted R-square, a 
statistical measure of ability to predict relative expenditure levels, increases from .23 to .27 for 
Model 2. In other words, the information in Model 2 is able to explain 27 percent of the variation 
in spending on special education students, compared with 23 percent for Model 1. 

The percent effects for number of disabilities in Model 2 indicate that students who have more 
than one disability exhibit higher total expenditures than students with only one disability. The 
effects range from about 17 percent more for one secondary disability to 56 percent more for 
more than three secondary disabilities. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



III. The ABILITIES Index: A Measure of Students’ 
Needs 

The ABILITIES Index was developed by Rune J. Simeonsson and Donald B. Bailey in response 
to concerns about the existing way students with disabilities were classified: 

A number of limitations have contributed to long-standing problems of inappropriate 
categorization of children and inadequately defined populations in special education 
practice and research. Diagnostic ambiguity and overlap of categories have served to 
complicate communication about individual children and the design of appropriate 
interventions. There is a need for functional approaches to document child 
characteristics, which build on conceptual models of disability and yield consistent 
terminology. (Simeonsson, Bailey, Smith, & Buysse, 1995, p. 267) 

In response to these concerns, Simeonsson and his colleagues developed the ABILITIES index 
as a functional measure that encompasses nine major domains of ability/disability in order to 
provide an accurate child profile. Ultimately, the ABILITIES Index seeks to evaluate a child 
holistically so the child can receive appropriate intervention. 

The ABILITIES Index builds upon earlier efforts to assess function. The first work presented in 
this direction was PULHESTIB scale (Holt, 1957). This PULHESTIB scale consisted of ordinal 
ratings from 1 (normal) to 4 (abnormal) assigned to major domains of Physique, Upper limbs. 
Locomotion, Hearing, Eyes, Speech, Toilet, Intelligence, and Behavior. Subsequently, Lindon 
(1963) proposed the PULTIBEC scale, where nine areas of abilities are rated from 1 (normal) to 
6 (abnormal). These areas include Physical capacity, Upper limbs. Locomotion, Toilet, 
Intelligence, Behavior, Eyes, and Communication. 

The ABILITIES Index is a potential system designed to combat diagnostic ambiguity and the 
overlap of categories found in the traditional IDEA disability classification system. According to 
Simeonsson et al., the traditional categorical system for classifying students has three specific 
problems. First, terms like disability, dysfunction, and deficit are incorrectly used 
interchangeably, often leading to complicated communication about a subject’s profile. Second, 
because the disability categories are specific to one area of ability (e.g., vision), they do not give 
a complete picture of the student’s needs. Third, the disability classification system is often 
subjective and arbitrary, and children are often evaluated differently based on a primary 
observation. 8 



8 For a more complete discussion and the rationale for the development of the ABILITIES Index, the reader should 
refer to Simeonsson, Bailey, Smith, & Buysse (1995). Also, for a discussion of the psyhometric properties of the 
Abilities Index, see appendix F in this report. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



The central feature of the ABILITIES Index is an assessment of characteristics along a range of 
ability/disability. ABILITIES is an acronym that represents these nine domains: 





Domain 


Sub-domain 


A 


Audition 


Left Ear, Right Ear 


B 


Behavior & Social Skills 


Social Skills, Inappropriate Behavior 


1 


Intellectual Functioning 


— 


L 


Limbs 


Left Hand, Left Arm, Left Leg, Right Hand, Right Arm, Right Leg 


1 


Intentional Communication 


Understanding Others, Communicating with Others 


T 


Tonicity 


Degree of Tightness, Degree of Looseness 


1 


Integrity of Physical Health 


— 


E 


Eyes 


Left Eye, Right Eye 


s 


Structural Status 





The resulting profile provides a more comprehensive picture of the child’s abilities and 
disabilities than a disability category alone. It not only identifies differences between individuals, 
but also tries to detect intra-individual variability. In each domain, the teacher rates the student, 
where normal is assigned a score of 0 and profound disability is assigned a score of 5. The 
maximum score that a student can get is 95; meaning completely disabled in all domains; and the 
minimum score is 0, meaning that the student is rated normal in all of the measured domains. 

It is useful to explain the terms we use to describe special education students in relation to the 
ABILITIES Index. As mentioned, the ABILITIES Index score ranges from 0 to 95. The higher 
the score, the higher the level of dysfunction, or disability. Students with higher scores may have 
higher levels of need, and may require more resources and more time in special education 
programs. 

The International Classification of Functioning (ICF), in its effort to standardize the language 
used in describing disability, has taken the approach that classification should be applicable to all 
people irrespective of health condition. Furthermore, the ICF suggests that domain names be 
worded neutrally, so that classification can express positive or negative aspects. The ABILITIES 
Index takes a similar approach, rating individuals on an axis of functional ability across several 
domains, rather than using one term to summarize a student’s disabilities. The following exhibit 
shows the ABILITIES Index chart that was included in the SEEP student questionnaire. 



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Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



Exhibit 4. ABILITIES Index Measurement Tool 





A 


B 


1 


L 


1 


T 


1 


E 


s 




AucStion 

(Hearing) 

Rate Both 


Behavior & Social Skills 

Rate Both 


Intellectual 

Functioning 


Limbs 

(Use of hands, arms, and legs) 

Rate All 


Intentional Cormrurii cation 

Rate Both 


Tonicity 
(Muscle Tone) 

Rate Both 


Integity of 
Physical health 


Eyes 

(Vision) 

Rate Both 


Structural Status 




Left 

Ear 


Right 

Ear 


Sodal I Inapprop. 

Skills ! Behavior 


Thinking & 
Reasoning 


Left 

Hand 


Left 

Arm 


Left 

Leg 


Right 

Hand 


Right 

Arm 


Right 

Leg 


Under- 
stand ng 
others 


CorrmLni eating 
v\/ith others 


Degee cf 
tightness 


Degee cf 
looseness 


Overall Health 


Left 

Eye 


Right 

Eye 


Shape, Body 
Form & Structure 


0 


Nc 


xrral 


All behaviors typical & 
appropriate for age 


Normal for 
age 






Com 

norm 


plete 
al use 






Normal 


Normal 


Normal 


Normal 


General good 
health 


Non 


mal 


Normal 


1 


Sus 

hear 


pected 
ing less 


dsability 


Suspected 

inapprop. 

Behaviors 


cisability 






Susp 

ciffic 


acted 

:Uty 






dsability 


dsability 


dsability 


Suspected 

dsability 


Suspected 

health 

problems 


Suspe 

vision 


Bded 
i loss 


Suspected 

dfferenceor 

interference 


2 


Mild 

1 


hearing 

css 


Mild 

cisability 


Mildy 

inapprop. 

Behaviors 


Mild 

cisability 






i 

i 

Mildd 


1 

1 

fficUty 






Mild 

dsability 


Mild 

dsability 


Mild 

dsability 


Mild 

dsability 


Minor ongoing 
health 
problems 


Mild visi< 


on loss 


Mild dfference or 
interference 


3 


Mo 

hear 


derate 
ing less 


Moderate 

cisability 


Moderately 

inapprop. 

Behaviors 


Moderate 

cisability 






Mock 

ciffic 


arate 

:Uty 






Moderate 

dsability 


Moderate 

dsability 


Moderate 

dsability 


Moderate 

dsability 


Ongdng but 
rredcally- 
coritrolled 
health 
problems 


Moderate 

Ioe 


e vision 
;s 


Moderate 
difference or 
interference 


4 


Sc 

hear 


svere 
ing less 


Severe 

cisability 


Severely 

inapprop. 

Behaviors 


Severe 

cisability 






Se\ 

ciffic 


ere 

:Uty 






Severe 

dsability 


Severe 

dsability 


Severe 

dsability 


Severe 

dsability 


Ongoing 

poorly- 

controlled 

health 

problems 


Severe 

Ice 


vision 

;s 


Severe 

dfferenceor 

interference 


5 


Prc 

hear 


jfound 
ing less 


Extreme 

cisability 


Extremely 

inapprop. 

Behaviors 


Profound 

cisability 






Profc 

cJffi 


aund 

cdty 

i 






Profound 

disability 


Profound 

dsability 


Profound 

dsability 


Profound 

dsability 


Extreme health 
problems, near 
total restriction 
of activities 


Profounc 

lOE 


d vision 

;s 


Extreme 

dfferenceor 

interference 



American Institutes for Research, Page 13 



Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



Exhibit 5 shows the ABILITIES Index profile for two special education students who have 
different disability classifications. One of the students has speech or language impairment, and 
the other has emotional disturbance. As shown, the two students, despite different classifications 
in the traditional system, have a very similar ABILITIES Index profile. 

Exhibit 5. Profile for Two Students in Different Disability Categories 





A 


3 


1 


L 


t 


T 


1 


E 


s 




Audition 
(Hearing 
Rate Both 


Behavior & Sooai SWIfe 
Rate Both 


intellectual 

Functioning 


Limbs 

\ Use of hands , arms, and fogs) 
Rata All 


intentional Communication 

Rate Both 


Tonicity 
(Musde Tone) 
Rato Both 


Integrity ol 
Physical heaiih 


Eyes 

(Vision) 

Rale Both 


Structural Status 




Left 

Ear 


Right 

far 


Social ' 
Skills ; 


Inapprop 

Behavior 


Thinking & 
Reasoning 


Left 

Hand 


Left 

Arm 


Left 

Lag 


Right 

Hand 


Right 

Pm 


Right 

ug 


Under- 

standing 

others 


Communicating 
with others 


Degree of 
tightness 


Degree of 
loose.- ess 


Overall Health 


Utft 

Ey* 


Right 

Eye 


Shape. Body 
Form & Structure 


G 






All hehaviws tvoical & 


Normal tor 
age 






Complete 








Normal 


N'orntdl 




General good 












__flj»mariale ter age 




normal use 












kaith 








I 


Sus; 

fwarii 


>ec1ed 
ng logs 


Suspected 

disability 


1 Suspected 
Vnapprop. 
Vehaviors 


Suspected 

^lisability 






Suspected 

difficulty 






Suspedeli 

disability Y 


Suspected 
L disability 


Suspects# 

dieabiij# 


Suspected 

disability 


Suspreted 

hiitiii 

problems 


vision 


fcted 

loss 


Suspected 
difference or 
interference 


2 


Mild ? 
Ic 


tearing 


Mild 

disability 


l ma^rop 


Mild 

disability 






Middi 


fficulty 






Mild 

disability 


\ disability 


dfeafiPty 


Mid 

disability 


Minor ongtxkj 
health V 
problems \ 


Milc/'isii 


on loss 


Mild difference or 
interference' 


3 


Moc 

hearii 


lerate 
ng loss 


Moderate 

disability 


Moderate!'], 1 

inapprop, 

behaviors 


Moderate 

disability 






Modi 

cffflic 


;rate 

wlty 






Moderate 

disability 


Moderate 

disability 


Moderate 

disability 


Moderate 

disability 


Ongoing but 
medically- 
contr oiled 
health 
problems 


VjcK'i!' 

T los 


e vision 

3 


Moderate 
difference or 
interference 


4 


Severe 

ic 


i hearing 
as 


Severe 

disability 


Severely 

Inapprop. 

behaviors 


Severe 

disability 






Seti 

ditfir 


ere 

5u% 






Severe 

disability 


Severe 

disability 


Severe 

disability 


Severe 

tSsability 


Ongoing 

pearly- 

controlled 

health 

problems 


Severe 

fos 


vision 

|S 


Severe 
difference or 
interference 


5 


Prol 

hearii 


found 
ng loss 


Extreme 

disability 


Exlrsmaly 

inapprgp. 

behaviors 


Profound: 

disability 






Prcff 

dim 


jund 

putty 






Profound 

disability 


Profound 

disability 


Profound 

disability 


Profound 

disability 


Extreme health 
problems, near 
total restriction 
of activities 


Ptfofouni 

los 


i vision 

3 


Extreme 
different* or 
interference 



A. Speech Language Impairment 



B. Emotional Disturbance 



American Institutes for Research, Page 14 



Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



On the other hand, Exhibit 6 shows how two students with the same disability categories can 
have very different ABILITIES Index profiles, confirming that the traditional system can lead to 
variability within a category. Ultimately, combining disability categories with the ABILITIES 
Index methodology allows a more accurate profile of a child’s needs, facilitating personalized 
and effective interventions. 

Exhibit 6. Profile for Two Students in the Same Disability Category 




A. Multiple Disabilities B. Multiple Disabilities 



While the ABILITES Index was not designed for analyzing patterns of expenditure variations, it 
provides information about student’s characteristics that helps explain patterns of services and 
expenditure. However, in order to incorporate the ABILITIES Index into a regression analysis in 
which total expenditure was the dependent variable, it was necessary to restructure the data. 
Eight final domains were included in the regression analysis: audition, behavior and social skills, 
intellectual function, intentional communication, overall health, vision, limbs, and tonicity and 
structural status. Note that the estimated effects of each of the factors are relative to a control 
group. For instance, the expenditure effect of having one functional domain with a mild 
disability is relative to the effect of the control group, in this case, to normal functionality in that 
domain. 



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Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



IV. The Contribution of the ABILITIES Index 

As shown in Section II, disability categories are able to explain only 27 percent of the variation 
in total educational expenditures on students with disabilities (see Exhibit 2). This is because 
students' characteristics vary considerably within disability categories. In order to obtain a richer 
picture of students' needs, and therefore of the expenditures on these students, Model 3 adds the 
ABILITIES Index to the analysis, which permits each domain to have different effects on 
expenditures depending on the degree of dysfunctionality. 

The adjusted R-square (a statistical measure of the model’s ability to predict relative expenditure 
levels) increases from 0.27 (Model 2, Exhibit 3) to 0.42 in Model 3. This suggests that 
incorporating the ABILITIES Index may explain an additional 15 percent of the variation in the 
total educational expenditure for special education students. This is a significant increase in the 
explanatory power of the model. (On a technical note, an F-test shows that this increase in the R- 
square is statistically significant at 1 percent.) 

Exhibit 7 presents results obtained from Model 3 (results are included for all the domains that are 
statistically significant at the 5 percent level). Expenditures appear to increase at a rising rate for 
students rated with suspected or mild dysfunctionality in overall health, but increase at a 
decreasing rate after that point. In the domain of intentional communication, expenditures 
increase at a rising rate as the level of dysfunctionality rises. Spending appears to rise with the 
level of dysfunctionality in behavioral and social skills, but flattens out beyond a certain point. 
With respect to limbs, expenditures appear to increase to a point and then decrease thereafter, a 
somewhat counterintuitive result. 

Exhibit 7. Relationship Between Total Expenditures and Overall Health, 

Intentional Communication, Behavior and Social Skills, and Limbs 




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Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



V. The ABILITIES Index with and without IDEA 
Disability Categories 

The previous sections have shown that the ABILITIES Index improves our ability to explain 
variation in expenditures for special education students by identifying the level of dysfunction 
within each disability category and allowing students to be assessed in more than one category. It 
is important to realize, however, that the ABILITIES Index can be used in conjunction with 
analysis of traditional disability categories. Using the ABILITIES Index as a measure of 
students’ characteristics without the traditional disability categories generates an adjusted R- 
square of 40, indicating that this model explains 40 percent of the variation in expenditures. 
Model 3, which includes disability category information, has an adjusted R-square of 42, 
explaining 42 percent of the variation, a slight improvement. 

Exhibits 8 through 10 show the relationship between different ABILITIES Index domains for 
Model 3 (with traditional disability categories) and Model 4 (without disability categories). 

As shown in Exhibit 8, using the ABILITIES Index for behavior and social skills without the 
disability categories creates an upward bias in the relationship between this domain and total 
expenditure. In other words, the coefficients associated with behavior and social skills in Model 
4 are capturing part of the effects that are actually due to the disability categories that are not 
accounted for in Model 4. Once the disability categories are included (Model 3), those effects are 
isolated and the effect of this domain does not appear as strong; in other words, the accuracy of 
the demonstrated relationship between total expenditures and students’ behavior and social skills 
increases when the disability categories are added to the model. 

Exhibit 8. Relationship Between Total Expenditures and Behavior and Social 
Skills 




American Institutes for Research, Page 17 



Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



Exhibit 9. Relationship Between Total Expenditures and Measures of Overall 

Health 




Exhibit 9 shows the percent effects for the overall health domain. In this case, no significant bias 
is observable when disability categories are excluded from the expenditure equation. That is, 
both models produce a similar relationship between total expenditures and the measure of the 
overall health domain. Only when the rating changes from severe to profound do the coefficients 
associated with overall health in Model 4 (without disability categories) fail to capture the entire 
effect on expenditures. 

Exhibit 10 shows the percent effects on expenditures of changes in the degree of limb 
dysfunction. When disability categories are not included (Model 6), the coefficients associated 
with the ABILITIES Index show a higher percent effect on expenditures. When disability 
categories are included in the model, the percent effects are much lower. Without disability 
categories, a false picture of expenditures is given. 



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Educating Students with Disabilities : Comparing Methods for Explaining Expenditure Variation 



Exhibit 10. Relationship Between Total Expenditures and Measures of Limbs 




As mentioned at the beginning of this chapter, when disability categories are not included in the 
model, the percentage of the variation explained by the ABILITIES Index is 40 percent. When 
disability categories are included, they explain an additional 2 percent of the variation. This 
suggests that disability categories often do not add significant explanatory power to the models. 
Overall, the data above suggest that the effect of combining disability category and AI measures 
on variation in expenditures is inconsistent across functional domains. The exhibits above 
suggest that in some domains, such as overall health (Exhibit 9), the percent effects associated 
with changes in the degree of dysfunctionality on total expenditures do not vary much once 
disability categories are included in the model. In other domains of measure, such as limbs, the 
percent effects do change when disability categories are included. In this case the percent effects 
on total expenditures tend to be much lower once disability categories are incorporated. Opposite 
results are observed when analyzing the behavior and social skills domain, where once disability 
categories are included in the model, the percent effect tend to be higher than when disabilities 
are not included. 

This report does not suggest that the AI should replace the traditional system of disability 
categories. Rather, it shows how the ABILITIES Index and the IDEA disability categories are 
useful both independently and together in explaining the variation in expenditures on students 
with disabilities, while controlling for other student background and community or regional 
characteristics. 



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Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



VI. Conclusions 

This report has analyzed the association between various measures of student’s characteristics 
and total expenditures to educate students with disabilities who are eligible for special education 
services. The analysis shows that the traditional disability categories used by IDEA to classify 
special education students explains a relatively small percentage of the variance (about 10 
percent) in total educational expenditures. The analysis also shows that number of secondary 
disabilities reported for a student when taken alone explains about 8 percent of the variance in 
expenditures. 

An important drawback of the traditional classification approach is that the disability categories 
themselves do not quantify the severity of the student’s disability conditions and not quantify 
more than one of each student’s conditions. Moreover, the traditional categorization tends to 
classify children rather than their functional characteristics. 

The ABILITIES Index, developed by Rune J. Simeonsson and Donald B. Bailey, is a functional 
assessment measure that can be employed for children with disabilities. It provides a detailed 
description of the students’ degree of dysfunction in nine domains: audition, behavior and socials 
skills, intellectual functioning, limbs, intentional communication, tonicity, integrity of health, 
eyes, and structural status. This detailed profile of students adds important information to the 
disability categories of students, enhancing our understanding. 

When either a continuous or discrete measure of the ABILITIES Index is introduced in the 
expenditure regression analysis, our ability to understand the variance in total expenditures 
increases from about 27 percent to about 42 percent. At the same time, taking into account this 
information decreases significantly the effect on expenditures previously assigned to the 
different disability categories. 

When disability categories are not included in the model, the percentage of the variance 
explained by the ABILITIES Index variables is 40 percent. When disability categories are 
included, they explain an additional 2 percent of the variance. The strength of the AI is that it 
considers the severity of each student’s abilities in each of nine functional domains. The fact that 
the traditional disability categories continue to contribute in explaining the variance in 
expenditures suggests that the ABILITIES Index may require some additional dimensions to 
capture fully the needs of students, perhaps in the area of personal functioning. 

Simeonsson, Buysse, Smith, and Bailey (1993) have demonstrated a high degree of reliability in 
the index when different individuals, such as parents and clinicians, rate the same children. More 
research by psychologists and other medical specialists is required to develop and refine tools 
like the ABILITIES Index measures to capture behavior and social dimensions, along with the 
dimensions of intellectual functioning and communication. Additional work is also necessary to 
continue to understand the underlying relations between student’ characteristics, student’s need 
and resource requirements for educational and related services. One goal would be to find more 
objective measures by which one can classify the needs of students in order to be able to deliver 
the necessary the financial resources required to provide appropriate educational services to 
students with disabilities. 



American Institutes for Research, Page 20 




Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



If nothing else, the results of this paper should make one wary of special education funding 
systems that are based on disability categories of children. This is of particular concern given the 
fact that almost half the states rely on funding systems that to some degree use disability 
categories as the basis for determining differential levels of state aid for special education. 

Next steps in this research will also involve exploration of how the ABILITIES Index might help 
us to explain the patterns of assignment of students with disabilities to different kinds of services 
and placements. That is, do these types of measures relate to student needs as reflected by the 
amount of time students spend in the regular classroom or the specific combinations and 
intensities of services received by students with disabilities? 



American Institutes for Research, Page 21 




Educating Students with Disabilities: Comparing Methods for Explaining Expenditure Variation 



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