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20/20 Analysis: taking a close look at the margins.

Prevailing practices in establishing eligibility of students for special education and related services involve a two-step process. First, it must be determined that the student has a disability. Students are classified according to an array of categories, such as learning disabilities, mental retardation, and emotional disturbance. Special education instructional programs and teacher credentialing systems usually are organized according to the same categories and bear the same labels as the students. Second, it must be established that the student needs special education.

School districts become eligible to draw special education funds as soon as a student is declared eligible and placed in a special education program. Thus, the financial incentives are oriented to inputs, not to results or outcomes achieved. This fact may be related to the poor attention given to evaluation of special education programs.

Besides special education, most school districts conduct other categorical programs, such as Chapter 1 for students who are economically disadvantaged, migrant education for children of migrant farm workers, immigrant assistance programs for recent immigrant children, and limited-English-proficiency programs for children whose primary language is other than English. Dozens of still other kinds of categorical programs are offered in various school districts across the United States. It is through such categorical programs, sometimes described as a "second system" of education (Wang, Reynolds, & Walberg, 1989), that schools have attempted to accommodate marginal students in an increasingly diverse student population.

Historically, the number of categories used in organizing school programs has grown gradually over time, often based on the observation that students in a given category were showing poor progress in school learning or behaving unacceptably in the classroom. Political action has fol1owed to add a category. The accretion in categories continues even today as pressures mount, for example, to create special programs for students with "attention deficit disorders" and a broad but ill-defined group described as students from "at-risk" circumstances.

Often the categories are based on presumed causes of school problems; but, in fact, the presumed causes usually bear little relation to instructional approaches found useful in serving the students. In a summary paper concerning research on disadvantaged students, with frequent references to special education, Brophy (1986) concluded that research has provided very little evidence indicating a need for qualitatively different forms of instruction for exceptional students; but he made it clear that some students need more instruction than other students. A similar point has been made by Bereiter (1985):

For any sort of learning, from swimming to reading, some children learn with almost no help and other children need a great deal of help. Children whom we have labeled educationally disadvantaged are typically children who need more than ordinary amounts of help with academic learning. Why they need help is open to all sorts of explanations. But suppose that, instead of reopening that issue, we simply accept the fact that youngsters vary greatly in how much help they need and why. (p. 541 )

The burgeoning categorical programs present enormous problems of management, coordination, and efficiency. Each category tends to have its own "special" teachers, eligibility requirements, timeline, reporting and accountability system, and bureaucratic managers. Problems and issues include the following:

* Flawed but expensive classification and placement systems (Epps & Tindal, 1987; Heller, Holtzman, & Messick, 1982; Hobbs, 1980; Reynolds, 1991).

* Disincentives for program improvement.

* Excessive regulatory requirements.

* Fragmentation and lack of coordination of programs (Allington & McGill-Franzen, 1989; Haynes & Jenkins, 1986).

* Loss of program control by local school administrators.

* Large numbers of students who "fall through the cracks" among categorical programs and who are unserved in any special way.

* Serious shortages of appropriately prepared teachers to serve pupils with multiple needs (e.g., students with limited English proficiency and disabilities).

* Consumption of much of the working time of school psychologists in simple psychometrics just to help allocate students to various categorical programs.

These problems have been disturbing to some special educators, school psychologists, and others and caused efforts for reforms. For example, the National Association of School Psychologists (NASP) and the National Coalition of Advocates for Students (NCAS) have called for changes in these terms: "Necessary support services should be provided within general education, eliminating the need to classify children as handicapped 1n order to receive these services" (NASP, 1985, p. 2). Deep structural reforms rather than minor incremental changes are required. However, we propose an experimental approach with thorough evaluation rather than massive policy alterations at this time,

The goal of 20/20 Analysis is to advance procedures that might be useful in particular schools in identifying students most in need of special help. By including the top 20% as part of the analysis in each school, educators might combine their energies and concerns for high-achieving students (including some who may be variously categorized as gifted and/or talented) and for students with disabilities and low-achieving students. Students who are marginal in both directions require adaptations in their instructional programs. In most schools, the adaptations made for the top 20% students are less rich and diverse than for the low 20% students, except for students showing high abilities in athletics and music. We propose that the further development of adapted programs for high achievers is an urgent matter and believe that broad strategies such as 20/20 Analysis may be a way of accelerating needed developments.


The 20/20 Analysis procedure begins with data analysis and then focuses on adaptations of the instructional program to meet needs of exceptional or marginal students. This brief report presents only the data-analysis part of the process.

Data from two elementary schools where 20/20 Analysis was implemented are presented here. The schools are located in the second largest school district in the nation. Each school offers an array of categorical programs, including special education, bilingual education, Chapter 1, gifted education, migrant education, and immigrant education. We selected these schools because districtwide test data showed that many pupils enrolled in them were achieving very poorly.

School A had an enrollment of 1,085 students: 96.5% Latino, 1.8% Asian American, 1.6% African American, and. 1% Anglo. The majority of the school population resided in city housing projects, and many families spoke no English or limited English. The annual mobility rate of students was approximately 30%; 97% of the school population received free or reduced-cost lunch. School B had an enrollment of 816 students: 83.5% Latino, 14% Asian American, 1.6% African American, and .8% Anglo. The surrounding community was characterized by single-family homes and apartment dwellings in which multiple and extended family members resided in a single unit. The annual mobility rate of students was approximately 20%; 87% of the school population received free or reduced-cost lunch. Table 1 shows basic demographic information for each school.


The steps in performing 20/20 Analysis were as follows:

1. Select a dimension of learning to be used.

2. Identify the grade and schoolwide percentile distributions compared to national norms.

3. Identify the percentile cutoffs of the high 20% and the low 20% for each grade level.

We selected reading as the dimension of school learning for analysis, mainly because it was expressed as a chief concern by the staff of Schools A and B. It happens that poor reading ability is frequently a cause of referral to special education or to Chapter 1 programs in many schools.

We began by examining the reading scores of all students in each school on the Comprehensive Tests of Basic Skills (CTBS). The CTBS is a standardized achievement test administered to students, grades 1-6, throughout the state in May of each year. For 20/20 Analysis, it was essential that data be obtained for every student in the building; this made it necessary to use some other tests and assessment procedures, besides the CTBS, for some students.

The second step involved identification of percentile distributions for reading achievement at each school, based on national norms. For the present analysis, CTBS percentile ranks obtained in May 1990, were used for studying students in the 1990-91 school year. Accordingly, our analysis included only students in Grades 2-6. Students in Grade 2 took the CTBS at the end of the previous school year, when they were in Grade 1. Spanish versions of the CTBS were used for students designated as limited English proficient (LEP) (the LEP designation was made uniformly for all students by performance on district-selected measures); all others were administered English versions. For some students enrolled in special education programs, a variety of individually administered achievement tests had been used, including the Brigance, Woodcock-Johnson, Basic Achievement Skills Individual Screener (BASIS), and Wide Range Achievement Test (WRAT). Percentile ranks for these special education students, using general grade norms, were assigned based on reading scores from testing completed at the time nearest to the May 1990 date. Because test results were not available for 10 special education students, teachers were asked to describe the grade-level reading abilities of those pupils. In all 10 instances, teachers described these pupils as "non-readers" or at "preprimer" levels, thus making it clear that they would fall far down in the lowest scoring group at the school (i.e., well within the bottom 20% of the school).

The third step involved identification of percentile cutoffs representing the low 20% and high 20% scorers at each grade level and for the school as a whole. For School A, 20% of students scored below the 6th percentile in reading on national norms;20% scored above the 5 8th percentile. For School B, 20% of the students scored below the 10th percentile; 20% scored above the 62nd percentile. We then listed all students in 20/20 groups by names.

As a secondary check on test results, we gave all teachers a list of students in the low 20% and high 20% groups and asked them to indicate whether they agreed or disagreed with the accuracy of the 20/20 lists. In a high percentage of cases (84% for School A; 85% for School B), the teachers confirmed the test-derived 20/20 groupings. On this basis, we proceeded in the analysis using 20/20 groups thus formed.

After developing the name lists, we identified all special designations and categorical program placements for students in the 20/20 groups. Table 2 shows distributions for the low 20% group at each school, as well as rates of absence and of transfer out of the school between May 1990 (the time of testing) and December 1, 1990 (the date used for the present analysis). Table 3 shows comparable data for the top 20% groups.


The following patterns were identified from analysis of program designations for 20/20 groups (see Tables 1-3). Most students enrolled in special education programs were in low 20% groups: 42 of 59 in School A and 33 of 36 in School B.

An earlier study of three elementary schools in a rural Minnesota community reported that 100% of the special education students "could be" identified simply by using a 20th percentile cutoff point on a curriculum-based measure of academic progress (Peterson, Heistad, Peterson, & Reynolds, 1985). In a study of four elementary schools in Utah, 91% of special education students were identified by using a 20th percentile cutoff on reading test scores (Stone, Cundick, & Swanson, 1988). Considering both Schools A and B in the present study, 79% of special education students were identified as below the 20th percentile (in their own school) in reading achievement. These several findings suggest that a very simple procedure may serve quite well to identify most special education students who are now identified by rather complicated, expensive, and somewhat unreliable procedures.

It appears to us that many of the children in low 20% groups in Schools A and B who were not enrolled in special education programs could easily be qualified for such placements. In School A, 68% (N=89) of the students scoring below the 6th percentile (national) in reading were enrolled in no program offering intensive forms of instruction; in School B, the comparable figure was 62% (N=54) in the low 20% group but receiving no special help. It is true that almost all low 20% students (129 of 131 in School A and 80 of 87 in School B) were "Chapter 1 eligible" (see Table 2). It happens, however, that in these schools the Chapter 1 programs do not involve intensive, individualized help for eligible pupils. We think it would be reasonable to treat the total low 20% groups in Schools A and B as "exceptional" and as in need of very intensive help in basic subjects. That approach could produce about twice as many exceptional students as are now served in special education programs in an "average" school for the nation as a whole. But if one considers the total numbers of children served in Chapter 1 and other categorical programs, as well as in special education, the 20% figure is realistic.

In each school, some current special education students were not identified in low 20% groups (17 in School A and 3 in School B). We do not propose that 20/20 procedures should be the only approach used in a school to identify exceptional students. Referrals for possible special education placement should always be permitted by parents and teachers; and it should be expected, we believe, that some students, perhaps especially those with distinct sensory and physical disabilities, will require special education even though they show achievements above the 20th percentile in their schools.

It is significant to note that in these two schools the percentage of students designated LEP was lower in the low 20% than in high 20% groups. Many students designated LEP received no bilingual instruction; this was especially true for low-achieving students, probably reflecting a specific shortage of teachers who are prepared both for special education and bilingual instruction.

Both schools operate programs for "gifted" students; but only a small percentage of high 20% pupils (15% in School A and 17% in School B) were enrolled in such programs. In School A the absence rate for school attendance was almost twice as high for low 20% as for high 20% pupils.


The 20/20 plan is proposed as a promising approach for overcoming the excessive disjointedness of current narrowly framed, input-oriented, categorical approaches to educating marginal students. By drawing together procedures across categories and with emphasis on both low 20% and high 20% pupils, 20/20 Analysis encourages a broad, systematic, outcomes-oriented approach to school improvement. It is expected that over time and with experience in many schools, 20/20 Analyses will be helpful in revealing policy problems and in forming ideas for solutions.

We propose one major policy idea on the basis of 20/20 Analyses performed to this time. It is that a school offer a guarantee to parents that whenever a child fails into a low 20% or high 20% group in rate of progress on basic school subjects (such as reading) they will be informed and asked to join with the school staff to plan for adaptations in the school and broader life experiences of the child that may strengthen the child's learning progress. Something like an individualized education program would be created cooperatively by teachers and parents. It would be essential to permit parents and teachers to nominate other children for individualized help also, so that children with limited vision or hearing, for example, could also receive intensive, specialized help when needed. But in none of this would students be labeled and categorized in traditional ways; and resources now distributed across many categorical programs would be redistributed and coordinated to help improve instruction as necessary. In the short range, perhaps the changes in policy and funding can be accomplished through waivers of rules and regulations under conditions of careful evaluation. In the longer range, we believe funding systems will need to be changed to support essential programs and to make programs the "triggering" unit for issuing categorical funds. This would entail a turning away from processes of categorizing and labeling children and counting labeled children to allocate the amounts of "special" money to school districts.

The field of special education has shown a remarkable ability to remake itself over the years to accommodate emerging needs and priorities. Now, we believe, there is need for special educators to help in leading a very large transformation, one that attends to outcome-oriented data in shaping educational programs. There is significant movement now in the remaking of regular and categorical programs, with leadership coming from "regular" educators (Felix, Hertlein, McKenna, & Rayborn, 1987). The study sponsored by the National Association of State Boards of Education (NASBE) suggesting that special education should be reformed in tandem with general education may be especially significant as a sign of emerging policies and practices (NASB E, 1991). We believe that special educators should help lead the way in these changes.

We urge people who try 20/20 Analysis to share data with us; we will then supply data we have concerning ongoing experience with 20/20 Analysis, including more information on programmatic developments that follow the data analysis.



Allington, R.L., & McGill-Franzen, A. (1989). School response to reading failure: Instruction for Chapter 1 and special education students in grades two, four and eight. The Elementary School Journal, 89(5), 529-542.

Bereiter, C. (1985). The changing face of educational disadvantagement. Phi Delta Kappan, 66(8), 538554.

Brophy, J. (1986). Research linking teacher behavior to student achievement: Potential implications for instruction of Chapter 1 students. In B.I. Williams, P. A. Richmond, & B.J. Mason (Eds.), Designs for compensatory education: Conference proceedings and papers (pp. 121-179). Washington, DC: Research and Evaluation Associates.

Epps, S., & Tindal, G. (1987). The effectiveness of differential programming in serving students with mild handicaps: Placement options and instructional programming. In M.C. Wang, M.C. Reynolds, & H.J. Walberg (Eds.), Handbook of special education: Research and practice (Vol. 1, pp. 213-248). Oxford: Pergamon Press.

Felix, N., Hertlein, F., McKenna, D., & Rayborn, R. (1987). Combining categorical program services can make a major difference. Phi Delta Kappan, 68(10), 787-788.

Haynes, M.C., & Jenkins, J.R. (1986). Reading instruction in special education resource rooms. American Educational Research Journal, 8, 161-190.

Heller, K.A., Holtzman, W.H., & Messick, S. (1982). Placing children in special education: A strategy for equity. Washington, DC: National Academy Press.

Hobbs, N. (1980). An ecologically oriented service-based system for the classification of handicapped children. In E. Salzinger, J. Antrobus, & J. Glick (Eds.), The ecosystem of the "risk" children (pp. 271-290). Orlando, FL: Academic Press.

National Association of School Psychologists/National Coalition of Advocates for Students. (1985). Advocacy for appropriate educational services for all children. Washington, DC: Author.

National Association of State Boards of Education. (1991 ). Interim report of the Study Group on Special Education. Alexandria, VA: Author.

Peterson, J., Heistad, D., Peterson, D., & Reynolds, M. (1985), Montevideo individualized prescriptive instructional management system. Exceptional Children, 52, 239-243.

Reynolds, M.C. ( 1991 ). Classification and labeling. In J.W. Lloyd, N.N. Singh, & A.C. Repp (Eds.), The regular education initiative: Alternative perspectives on concepts, issues and models (pp. 29-41). Sycamore, IL: Sycamore Publishing.

Stone, B., Cundick, B.P., & Swanson, D,(1988). Special education screening system: Group achievement test. Exceptional Children, 55, 71-75.

Wang, M.C., Reynolds, M.C., & Walberg, H.J. (1989). A rebuttal to Vergason and Anderegg: Who benefits from segregation and murky water? Phi Delta Kappan, 71(1), 64-67.


MAYNARD C. REYNOLDS (CEC #367) is Professor Emeritus of Special Education Programs in the Department of Educational Psychology at the University of Minnesota, St. Paul and Senior Research Associate at the Center for Research in Human Development and Learning, Temple University, Philadelphia. ANDREA G. ZETLIN (CEC CA Federation) is an Associate Professor in the Division of Special Education at California State University, Los Angeles. MARGARET C. WANG is a Professor and Director of the Center for Research in Human Development and Education and National Center for Education in the Inner Cities at Temple University, Philadelphia.

The research reported here was supported in part by funds provided by the Office of Educational Research and Improvement (OERI) of the U.S. Department of Education to the National Center for Education in the Inner Cities and in part by the Temple University Center for Research in Human Development and Education. The opinions expressed do not necessarily reflect the position of the funding agencies, and no official endorsement should be inferred.

Manuscript received October 1991, revision accepted August 1992.
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Author:Reynolds, Maynard C.; Zetlin, Andrea G.; Wang, Margaret C.
Publication:Exceptional Children
Date:Feb 1, 1993
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