Printer Friendly

Categorical and programming changes in special education services.

The yearly cross-sectional estimates provided in the Annual Reports to the Congress (e.g., U.S. Department of Education, 1989) show increases in the U.S. special education population. These estimates, however, do not indicate whether more children are being identified or fewer are being terminated from services. Moreover, an increase in one disability (e.g., learning disability) might stem from the identification of new pupils or from reclassification of youngsters from other disability categories (e.g., mild mental retardation). Cross-sectional data make it difficult to assess the nature of classification changes within special education programs.

Only two major studies have addressed the issue of what proportion of school-age students remain in these programs. First, Walker et al. (1988) studied special education students in elementary school in three urban districts during a 2-year period. Investigating students with both mild and severe disabilities, they found that categorical changes were highly related to the initial primary category of disability. Second, Wolman, Thurlow, and Bruininks (1989) investigated the rate of categorical change with 10th- to ]2th-grade suburban students with mild disabilities who had received at least 2 years of special education services. Consistent with the findings of Walker and her colleagues at Harvard University, these researchers found that an initial classification of speech and language impairment was closely associated with categorical change.

The present study extended the works of Walker and Wolman in the following ways:

* It focused on rural students in special education

programs. * It increased the grade levels examined from

preschool through secondary school. * It included the amount or frequency of services. * It assessed the relationship between changes

in the multidisciplinary educational team

(MET) and eligibility/programming. * It administered individual measures of intelligence

and achievement, rather than group




A total of 654 students from 10 school districts composing a tricounty or intermediate school district (ISD) in a north central state were identified as having disabilities by state guidelines, which closely paralleled federal guidelines. The sample consisted of students in six special education categories. These included speech and language impairments (SLI), learning disabilities (LD), emotional disturbance (ED), mild mental retardation (MMR), and severe mental retardation (SMR). The sixth category, a combination of visual impairments, hearing impairments, and physical and other health-related impairments, was called sensory-motor (SM).

In 1987-88, we identified all students who were receiving special education services during the 1984-85 school year and followed them forward for at least a 3-year period. Identified students who transferred into the ISD were included when there were pertinent data on file for the time period of the study. The students were overwhelmingly white. Except for Native Americans, who constituted 2.3% of the sample, no other minority groups were represented. Of the 654 students with disabilities, 90 were at the preschool level, 435 at the elementary school level, and 129 at the secondary school level at the time of initial placement. Two-thirds of the students were male. Based on federal income guidelines, 36% of the students qualified for a free lunch or a reduced-price lunch.


We reviewed school records, especially the students' individualized education programs (IEPs) and the MET reports. These records included both individual student files and records that had been stored on microfiche in the central records at the ISD. Achievement and IQ data were generally available in the central files on students whose suspected disabilities required this information. Even then, however, data were not always forwarded to the central ISD files. For instance, IQ scores, and achievement data on reading, arithmetic, and written language were available in the central files on 275, 282, 288, and 274 students, respectively. Reexamination of 25 randomly selected records indicated no discrepancies in the recording of the data.

We gathered specific information regarding classification (diagnosis), type of service, and frequency of service during the 1984-85 school year. Specifically, the data gathered from the files included gender, initial grade level, local school district, initial primary category of disability, number of concurrent classifications, whether therapy was received and the type of therapy, whether consultative or classroom services were received and the type of service received, and the total number of minutes in therapy and classroom services each week. We reviewed subsequent educational plans through June 1988 and gathered the same information. We compared the data to determine any changes that had occurred in the classification (terminated, reclassified, same) or programming (frequency and type) areas since the time of the 1984-85 school year. Other information collected from the central files included the IQ scores from the Wechsler Intelligence Scale for Children-revised (WISC-R) (1974) (initial and follow-up results) and achievement test scores from the Woodcock-Johnson Psycho-Educational Battery (1978) (follow-up results), which had been individually administered.

We assessed parental satisfaction with the child's "overall special education program" by questionnaire for both 1984-85 and 1987-88 school years, using a 4-point scale ranging from "very dissatisfied" to "very satisfied." Parents of 223 students returned the questionnaire. We randomly contacted an additional 10.3% of nonresponders by telephone. No significant differences were found on the dependent variables between those who responded by mail or by telephone.

The present study addressed two general questions:

1. How stable are special education classifications

over time? 2. What factors are related to changes in special

education classification and programming?

Statistical Analyses

We used two approaches, descriptive and inferential statistics, to synthesize and summarize the data. We used descriptive statistics (means, frequencies, and cross tabulations) to aggregate the information across cases and to present the percentages of children who experienced different outcomes, according to their family, school, and student characteristics. We used the chi square test statistic to examine whether the obtained proportions of change differed significantly between groups. In addition, an analysis of variance (ANOVA) was used to investigate whether there were significant differences among the means on several independent factors.


Question 1. How stable are special education classification and services?

Students Leaving the District. Only 6.4% of the students with disabilities moved out of the ISD area during the 3-year period. Although the rates of moving out of the district varied among initial classifications, the percentage did not differ significantly, [x.sup.2](5, N = 654) = 7.98, p = .158. The two most mobile categories were students with SLI (9.5%) and MMR (9.7%). Students with LD (5.7%), ED (4.8%), and SM (5.1%) showed moderate mobility. Students with SMR (1.3%) were the least likely to leave the ISD. An additional 2.6% dropped out of school or died during the interval. The overall percentage of special education students leaving this ISD (9%) is comparable to the overall percentage of 7% reported by Walker et al. (1988).

Students Terminated from Special Education. Table I shows that total percentage of change by termination across all classifications was 21.9%. In contrast to leaving the school system, termination from special education services was strongly related to the child's initial diagnosis. The most frequently terminated students were those with SLI (55%), followed by SM (11%), LD (10%), and ED (5%). No students with MMR or SMR were terminated from services during the 3-year period of this study.


Walker et al. (1988) found a similar termination rate of 17.2% over a 2-year period, and Raber and Frechtling (1985) found a 13% termination rate for preschoolers over a 3-9-year period. Although the termination rate within the SLI group was larger in this study (55%) than in the Harvard study (33.1%) (Walker et al., 1988), both studies found SLI to be the most fluid special education category. Informal conversations with MET members suggest that SLI is a relatively innocuous label that is less stigmatizing than other labels (e.g., ED). Future research that addresses types of SLI problems, severity, comorbidity, and motivations for using this label should provide greater insights into the reasons for the high termination rates found for this special education category. Students with LD and ED in this study were far less likely to be terminated (10% and 5%, respectively) than was true in the Harvard study (14.9% and 9.1%, respectively). Fassbender's (1986) study of 122 students with ED found a 10% termination rate over a 10-year period. Wolman et al. (1989) did not address classification changes by termination.

Students Reclassified in Special Education. Overall, students were less frequently reclassified (16.3%) than terminated (21.9%). The most frequently reclassified special education students were those with MMR (39%) and SLI (23%). Moderate rates of reclassification were noted for students with ED (16.7%), SM (14.3%), and LD (11.4%). Few students with SMR were reclassified (5.1%), rendering them the least fluid special education group. Differences in rate of reclassification are strongly related to initial classification, [x.sup.2] (5, N = 613) = 27.56, p [is less than or equal to] .0001.

The overall reclassification rate (16.3%) fell between that found by Walker et al. (1988) (12.3%) and that reported by Wolman et al. (1989) (24%). Reclassification rates for specific disabilities range widely across studies (Fassbender, 1986; Raber & Frechtling, 1985; Walker et al., 1988; Wolman et al., 1989). For students with SLI, the rates ranged from 20% to 66%; for students with LD, from 6.6% to 18%; for students with MMR, from 7.7% to 39.3%; and for students with ED, from 4% to 38.8%. The conflicting findings most likely stem from a multitude of factors, such as the unreliability of special education diagnoses (e.g., Singer, Palfrey, Butler, & Walker, 1989); settings studied (urban, suburban, rural); length of follow-up; severity of disability; lack of common definitions; socioeconomic differences in samples; accessibility to compensatory educational programs; availability of fewer services/programs after elementary school; and the later, more valid special education diagnoses as the child's condition becomes more evident.

The MMR and SLI groups warrant further comment. Although only 31 of the 654 students were initially classified as MMR (less than 5%), these students experienced the highest reclassification rate with 24.1% being reclassified as SMR, 10.3% as LD, 3.4% as ED, and 1.5% as SM. Given the high percentage of children with MMR being reclassified as SMR, it appears that MET members might be inclined to place the less stigmatizing label of MMR on young children who have a borderline classification between mild and severe mental retardation. Or, as abstract language and conceptual knowledge become more of a focus of evaluation, the severe nature of the mental disability might well become more apparent. Whatever the explanation, it is intriguing that we found the highest rate of reclassification among the lowest prevalence category in this study.

When students with SLI are reclassified, these students tend to require more intense services than students who were not originally classified with SLI. This finding suggests the need for early and intensive intervention. It seems that students with SLI can be sorted into three groups that differ in severity. By far the largest of the three groups consists of students with mild SLI who are terminated from services (55%) within a 3-year period. There is also a group with severe SLI or other disabilities who are eventually reclassified (23%) and receive more intensive programming. Last, there is a third group (22%) with SLI, with no change. The present study does not tell us whether we can reliably differentiate among these three groups. In any event, almost 78% of students with SLI experience a change in their category of disability over a 3-year period. The next most fluid special education group consists of pupils with MMR, about 40% of whom experience change in their status over a comparable time period.

In sum, one finding about classification changes (both termination and reclassification) is clear. Substantial change occurs with special education classifications over time. Changes are most pronounced among students with SLI, occurring in almost 4 out of 5 cases. Changes also happen with other categories of mild disabilities. Almost 2 out of 5 students with MMR undergo reclassification within a 3-year period. More than I out of 5 students with LD and ED experience a change in classification. Even change within what is considered the more severe, low-incidence SM category is not uncommon, affecting I of every 4 students. Classification change is, however, very infrequent only among SMR students, affecting only 1 in 20 students. Regardless of setting (urban, suburban, rural), change in the classification of special education students appears to be a far more common occurrence than many special educators and school psychologists believe.

Programming Changes. Changes in both the type of programming [x.sup.2] (1, N = 461) = 58.64, p [is less than or equal to] .0001, and in frequency or amount of service, [x.sup.2] (1, N = 462) = 22.49, p [is less than or equal to] .0001, were significantly related to classification. The type of service (consultant, resource room, categorical room placements) changed for 32.8% of the students and frequency of service changes occurred for 71.2% of the students after an interval of 3 years in special education. The degree of restrictiveness of program remained unchanged for two thirds of the students; of these, the SMR students showed no change as they were consistently found in full-time categorical programs. Of the approximately one third who experienced a change in the type of program, there was a greater percentage moving into a more restrictive program (19.2%) than moving into a less restrictive program (13.9%). When there was a change in type of program, more students with MMR, SLI, and SM received more restrictive programming, while more students with LD and ED received less restrictive programming over time. There were significant differences across initial classifications in the percentage of students whose program restrictiveness remained the same, [X.sup.2] (5, N = 468) = 52.87, p [is less than or equal to] .0001, or increased, [x.sup.2] (5, N = 468) = 48.84, p [is less than or equal to] .0001. One of the few studies (Edgar, Heggelund, & Fischer, 1988) to investigate the issue of restrictiveness confirmed the present study's finding that students with SLI and MMR moved to more restrictive programs but did not confirm the present findings regarding type of change experienced by students with LD, ED, SMR, and SM. The discrepant findings between the two studies are difficult to interpret for the students in the Edgar et al. study began in regular education placements, whereas the restrictiveness of placements varied for pupils in the present study.

Differences in the amount of time spent in special education programs were also significantly related to the initial classification of students (see Table 2). Of the special education students who were not terminated, only 28.8% received the same amount of service over time. Those with changes were fairly evenly divided between those receiving more (39%) and less (32.2%). For the ED and LD groups, nearly as many received less service as received more service over time. On the other hand, students with MMR, SLI, and SM received more time in special education programs over time rather than less. Following kindergarten-identified students for 3-4 years, Raber and Frechtling (1985) found similar overall rates of change with respect to restrictiveness. Other studies do not consistently confirm the present findings regarding changes in restrictiveness vis-a-vis specific special education categories (Travis, Thomas, & Fuller, 1985).


Question 2. What factors are related to changes in special education classification and programming?

Factors Related to Change in Classification or


* School Factors

Initial grade level was significantly related to changes in classification, [x.sup.2] (2, N = 598) = 21.14, p = .0003; type of service, [x.sup.2] (2, N=469) = 32.63,p [is less than or equal to] .0001; and frequency of service, [x.sup.2] (2, N = 469) = 48, p [is less than or equal to] .0001. Reclassification occurred at the lowest rate at the secondary level (11%) at an average rate (16.7%) in elementary school, most often at the preschool levels (24.7%).

In terms of type of program changes, there were significant differences related to grade level [x.sup.2] (2, N = 468) = 7.2, p = .027. Overall, there was a steady increase with grade level in the percentage of students receiving the same type of programming. The elementary school level was very close to the average for all grades; most students (66.2% of elementary students) maintained the same level of programming over time. For those elementary students who experienced a change in type of program, changes toward more restrictive services (19.5% of elementary students) were more likely than less restrictive (14.2% of elementary students) services. For those identified in the preschool years, the changes were almost uniformly toward a more restrictive type of programming. In the secondary school, however, although change was less frequent, when it did occur there was a moderate shift toward less restrictive types of programming. The differences between grade levels in students changing to more or less restrictive settings was highly significant, [x.sup.2] (2, N = 155) = 25.5, p [is less than or equal to] .001.

Change was far more common with respect to frequency of service (71.2%) than with type of program (32.8%). Again, initial grade level was significantly related to the changes, [x.sup.2] (2, N = 468) = 32.65, p [is less than or equal to] .001. The pattern of change mirrored that for type of program except that fewer students remained the same over time. Most students initially in the preschool age group moved toward more rather than less time in special education services. At the elementary school level, this tendency continued, but in a less dramatic way. At the secondary school level, the nature of change reverses, and over twice as many students who have a change in amount of service receive less service. The differences between grade levels and change to more or less time in special education was highly significant, [x.sup.2] (2, N = 334) = 19.0, p [is less than or equal to] .001.

Personnel changes in the makeup of the MET between 3-year reevaluations were associated with changes in programming with respect to type of service, [x.sup.2] (1, N = 467) = 15.62, p [is less than or equal to] .001, and frequency of service, [x.sup.2] (1, N = 468) = 8.14, p [is less than or equal to] .004, but not associated with rates of change in classification, [x.sup.2] (1, N = 595) = 2.76, p = .097.

* Child Factors

The number of concurrent classifications (comorbidity) was found to be significantly related to change in classification, [x.sup.2] (1, N = 596) = 23.25,p [is less than or equal to] .001; type of program, [x.sup.2] (1, N = 468) = 21.41, p [is less than or equal to] .001; and frequency of service, [x.sup.2] (1, N = 469) = 43.66, p [is less than or equal to] .001. The rate of reclassification decreased from 44.3% for students with a single classification to 21.8% for students with two or more classifications.

The nature of change (reclassification or termination) was also significantly related to comorbidity, [x.sup.2] (1, N = 234) = 34.317, p [is less than or equal to] .001. Students with a single classification were more likely to be terminated (64.4%) rather than reclassified (35.6%), whereas students with two or more classifications were much more likely to be reclassified (93.1%) rather than terminated (6.9%). Regarding type of program change, students with a single classification changed more often than students with two or more classifications (39% versus 17.6%). As for the amount of service, the mean number of minutes of service at the time of follow-up was 677; 1,032; and 1,541, for students with one, two, and three or more classifications, respectively, F (2,331) = 32.7, p [is less than or equal to] .001. The increased frequency of time in special education for students with more than one classification is consistent with the view that comorbidity is an empirically based indicant of severity (Clarizio, 1990).

Among the other child factors (gender; IQ; and current achievement in reading, written language, and mathematics), the student's IQ on the WISC-R was significantly related to changes in classification, F(3,275) = 8.25, p [is less than or equal to] .001 and type of service, [x.sup.2] (4, N = 329) = 28.9, p [is less than or equal to] .001. As Table 3 shows, reclassification occurs more frequently among those with lower IQs, and termination is associated with higher IQs. Lower IQs were related to receiving more restrictive programming (see Table 4) and more time in special education, while higher IQs were related to receiving less restrictive programming and less time in special education. Full Scale IQs below 80 or above 90 appear to be signals that changes in classification and type of service are likely in the future of children in special education programs.


Gender was also related to programming changes [x.sup.2] (1, N = 155) = 5.22, p = .02) in that males were more likely to move toward less restrictive settings and reduced time in special education. Although more males were placed in special education, it may be that the females placed are those with more severe disabilities, and thereby given more time in special education programs.

School achievement, as measured on the Woodcock-Johnson Psycho-Educational Battery (W-JPB) (1978), showed significant differences between student classification changes and reading, F(2,282) = 5.52, p = .005; mathematics, F(2,288) = 14.55, p [is less than or equal to] .001; and written language, F(2,274) = 5.76, p =.004 (see Table 5). This finding on school achievement is consistent with the Wolman et al. (1989) study, which found reading achievement predictive of categorical change by reclassification.


Factors Unrelated to Change in Classification

and programming.

* Family Factors

Socioeconomic status (SES), as measured by family income (eligibility for free or reduced lunch), was not significantly related to changes in classification, [x.sup.2] (1, N = 247) = .22, p = .64, or programming type, [x.sup.2] (1, N = 203) = .68, p = .41, with two exceptions - reclassification and frequency of service. Although children from all SES levels were equally likely to retain the same classification, when change did occur, significant differences, [x.sup.2] (1, N = 76) = 12.45, p [is less than or equal to] .001, were apparent in the nature of change. The low-SES students were more likely to be reclassified (69.0% to 27.7%), whereas other students were more likely to be terminated (72.3% to 31.0%) from special education. Moreover, when the amount of time spent in special education was examined, significant differences between income groups were found in the pattem of change in frequency of service, F (1,104)=4.88,p=.029. Over the 3-year period, low-SES students received 112 min less per week than initially (reducing their total per week to 849 min), whereas other students received an additional I 0 1 min per week, for a total of 750 min. Both income groups moved in toward the mean. Overall, it is reassuring to note the absence of a socioeconomic bias with respect to classification and programming changes.

Parental satisfaction, as measured on a global basis, was the second family variable found to be unrelated to change in either classification, [x.sup.2] (1, N = 245) = .69, p = .41; programming type of service, [x.sup.2] (1, N = 199) = .01, p = .91; or frequency of service, [x.sup.2] (1, N = 200) = 1.52, p = .22. For students experiencing a change in programming, parental satisfaction was not related to restrictiveness of the setting, [x.sup.2] ( 1, N = 66) = .46, p = .50. Regarding frequency of service, however, parental dissatisfaction was found to be related to sizable increases in special education programming, [x.sup.2] (1, N = 140) = 4.94, p =.026. When parents were initially dissatisfied (1984-85), students were likely to receive more (73%) rather than less (20.7%) time in special education. When parents were satisfied, changes in time were more evenly distributed between receiving more (56.8%) and less time (43.2%) in special education programs. The Harvard study (Walker et al., 1988) in contrast to this study, found satisfaction to be a significant factor related to classification. It is possible that a focus on global change rather than specific assessment of changes in classification and programming obscured relationships between parental satisfaction and special education services. Also, finding a relationship between parental satisfaction and educational programming in urban areas may be enhanced by the availability of more educational options than typically exist in rural areas.

School Size

Schools were classified as large, medium, and small, in accordance with the state High School Athletic Association criteria. School size was found to be unrelated to classification changes, [x.sup.2] (2, N = 518) = 1.02, p = .60, or type of service changes, [x.sup.2] (2, N = 391) .02, p = .89.


To address the general question of the stability of special education services, 654 students in special education programs from preschool to graduation were followed over a 3-year period. More than 1 in 5 students were terminated from services, and about 1 in 6 were reclassified within a 3-year period, yielding a total rate change of 38.2% regarding classification changes. Highly significant differences between classifications were found in the rate of change. Consistent with previous studies, students with speech and language impairments were most likely to change, whereas students with severe mental retardation were least likely to change. Of particular interest was that more than 1 in 5 students with mild disabilities (emotional disturbance, mild mental retardation, and learning disabilities) and those with sensory-motor impairments had a change in classification. Changes in programming were more common; 71.2% had a change in the amount of time in special education. The rate of change, again, significantly varied among classifications.

Initial grade level and comorbidity were significantly related to classification and programming changes. Twice as many students in special education at the preschool and elementary school levels experienced a classification change compared with those at the high school level. In sharp contrast to those with only one classification, students with more than one classification were far more apt to be reclassified than terminated. In addition, student IQ and gender were significantly related to programming changes, as was the consistency of the membership of the MET. The student's initial classification was significantly predictive of change in classification and programming, whereas IQ and consistency of the MET membership were significant predictors of change in programming.

Although certain limitations characterized the present study (unreliability of MET diagnoses, sample limited to rural white students, and limited operational definitions of socioeconomic status and parental satisfaction), we advance the following conclusions:

1. The high percentage (80%) of students remaining

in special education programs, the research

literature critical of special education

program effectiveness, and the increasing

numbers of students receiving special education

services all underscore the need for an innovative

reconceptualization of special

education (e.g., prereferral practices and the

inclusion movement). 2. Among those classified as having mild disabilities,

students with mild mental retardation

pose the most formidable dilemma. The

data challenge the notion that this condition is

a mild, high-incidence disorder. 3. The relatively high rate of change highlights

the need for thorough reevaluation at least

every 3 years. Maintaining high-quality reevaluation

practices will not prove easy, however,

in an era characterized by fiscal restraint,

continued criticism of testing procedures, and

a belief that automatic reevaluation of every

student lacks merit. 4. The data remind us of the important roles

played by child variables (e.g., measured IQ,

gender, comorbidity) and school factors (e.g.,

grade level and changes in MET composition)

in educational programming for students with



Clarizio, H. (1990). Assessing severity in behavior disorders: Empirically based criteria. Psychology in the Schools, 27, 5-115. Edgar, E., Heggelund, M., & Fischer, M. (1988). A longitudinal study of graduates of special education pre-schoolers: Educational placement after preschool. Topics in Early Childhood Education, 8(3), 61-74. Fassbender, L. (1986). Public school programs for students labeled emotionally disturbed: A tongitudinal study. (Doctoral Dissertation, University of Wisconsin.) Dissertation Abstracts Internationa1, 47(4),1279-A. Raber, S., & Frechtling, J. (1985). Initial special education placement and longitudinal outcomes o!f preschool- and kindergarten-identifted handicapped children. Final Report. Rockville, MD: Montgomery Public Schools. (ERIC Document Reproduction Service No. ED278191) Singer, J., Palfrey, J., Butler, J., & Walker, D. (1989). Variations in special education classifications across school districts: How does where you live affect what you are labeled? American Educational Research Journal, 26, 261-281. Travis, L., Thomas, A., & Fuller, G. (1985). Handicapped students in the least restrictive environment: A longitudinal study. School Psychology Review, 14(4), 521-530. U.S. Department of Education. (1989). Eleventh annual report to the Congress to assure the free and appropriate public education of all children with disabilities. Washington, DC: Author. (ERIC Document Reproduction Service No. 312876) Walker, D., Singer, J., Palfrey, J., Orza, M., Wenger, M., & Butler, J. (1988). Who leaves and who stays in special education: A 2-year follow-up study. Exceptional Children, 54, 393-402. Wechsler, D. (1974). Manual for the Wechsler Intelligence Scale for Children-revised. San Antonio, TX: The Psychological Corporation. Wolman, C., Thurlow, M., & Bruininks, R. (1989). Stability of categorical designations for special education students: A longitudinal study. The Journal of Special Education, 23(2), 213-222. Woodcock, R. (1978). Woodcock-Johnson Psycho-Educational Battery. Hingham, MA: Teaching Resources Corporation.

DOUGLAS W. HALGREN, School Psychologist, Cheboygan-Otsego-Presque Isle Intermediate School District, Gaylord, Michigan. HARVEY F. CLARIZIO, Professor, Department of Counseling, Educational Psychology and Special Education, Michigan State University, East Lansing.
COPYRIGHT 1993 Council for Exceptional Children
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 1993 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Halgren, Douglas W.; Clarizio, Harvey F.
Publication:Exceptional Children
Date:May 1, 1993
Previous Article:Teaching phonological awareness to young children with learning disabilities.
Next Article:A comparison of two approaches for teaching complex, authentic mathematics problems to adolescents in remedial math classes.

Terms of use | Copyright © 2016 Farlex, Inc. | Feedback | For webmasters