It has been well over 25 years since Hallahan and Kauffman (1977)
published their seminal piece in The Journal of Special Education
entitled, "Labels, Categories, Behaviors: ED, LD, and EMR
Reconsidered." In that article the authors concluded that little
evidence existed to justify differential special education treatment in
the three categories of high-incidence disability--learning disability
(LD), mild intellectual disability (MID), and emotional/behavioral
disability (E/BD). The authors stated, "It is nearly impossible to
separate children meaningfully into these three areas of special
education" (p. 144). As a result of opinions typical of those found
in Hallahan and Kauffman and others in support of noncategorical special
education service delivery (see Algozzine & Ysseldyke, 1983;
Marston, 1987; Reschly, Tilly, & Grimes, 1999; Reynolds & Birch,
1977; Ysseldyke & Marston, 1999), and because of public school
administrative convenience and cost, many students with the three types
of high-incidence disabilities are now educated side-by-side in the same
Although Hallahan and Kauffman's (1977) conclusions were an
early catalyst for cross-categorical special education service delivery
in the schools, more recent opinions on this issue extend far beyond
those first offered in the late 1970s. The issue of cross-categorical or
noncategorical special education appears far from dead and now more
complex than when first brought to light. Ysseldyke and Marston (1999),
for example, concluded that categorical special education in the schools
is problematic, that it can no longer be justified, and that
"diagnostic efforts to differentiate groups should be
diminished" (p. 6). Reschly and Tilly (1999) called for elimination
of the present categories in special education, especially those related
to students with high-incidence disabilities, and use of a
noncategorical system with different functional criteria used for
eligibility. Tilly, Reschly, and Grimes (1999) recommended a
"problem-solving assessment" model for noncategorical special
education identification and eligibility, as well as program planning.
It is clear that the noncategorical service delivery model has also
gained considerable Support. Careful analysis of studies comparing two,
or all three, groups should lead to better understanding of all
individuals identified as having high-incidence disabilities.
Perhaps the most influential work that may determine whether
educators think students with high-incidence disabilities are truly
similar or dissimilar for instructional purposes does not originate in
research. The President's Commission on Excellence in Special
Education (PCESE; 2002), in an effort to streamline identification and
eligibility for special education purposes, suggested that the term
"developmental disabilities" should include "specific
learning disabilities (SLD), speech and language impairments, emotional
disturbance, mild mental retardation and developmental delay" (p.
21). The experts on the PCESE did not see the need for continuing the 13
categories of disabilities found in earlier versions of the Individuals
with Disabilities Education Act (IDEA). The Consortium for Citizens With
Disabilities (CCD; 2002), a coalition of nearly 100 disability
organizations, challenged the conclusions of the PCESE and found it
inappropriate to group the various disability categories together. The
CCD promised to "fiercely resist" the alteration of the 13
disability categories under the IDEA and stated that the essence of the
issue should be whether the current system correctly identifies all who
are eligible. Time will tell whether the recommendations of the PCESE
are the best way to categorize those with high-incidence disabilities
for schooling. One necessity to solve the categorization issue is a
comprehensive analysis of research that examined similarities and
differences that exist among students in the categories of
Since 1977 there have been more than 150 published articles,
studies, monographs, and book chapters that discussed similarities and
differences across students with the three types of high-incidence
disabilities. A synthesis of the high-incidence disabilities
cross-categorical research, however, does not exist. This synthesis
could help defend or refute the arguments concerning the need for
categorical, cross-categorical, or noncategorical special education in
the schools. Because of the plethora of studies that are now available
that have examined similarities and differences across the three types
of high-incidence disabilities, and because of the support that
cross-categorical and noncategorical special education service delivery
possess, it is now time to reexamine the accuracy of previous
conclusions regarding the similarity of students across the three
categories of high-incidence disabilities.
When Hallahan and Kauffman (1977) compared and contrasted the
cognitive and behavioral characteristics of students with LD, MID, and
E/BD, they did not have access to meta-analysis or the ability to
cluster and synthesize quantitative results of research. Meta-analysis
uses study means, standard deviations, and sample size (and other data)
in quantitative research to explain the magnitude of difference between
two groups on the same dependent variable. An effect size (ES) is
derived in meta-analysis that is an estimate of the difference, or lack
thereof, between two samples using their respective means, standard
deviations, and sample size. A major advantage of the ES statistic is
that it is a common metric that permits the combining of results from
many studies on the same construct. The ES aggregation in a
meta-analysis allows for an indication of an overall pattern or strength
of difference between two groups. A meta-analysis not only takes into
consideration study means and standard deviations forming ESs but also
is dependent on sample sizes. Studies with larger samples attain more
weight and contribute more robustly to the overall results of a
meta-analysis than do investigations with smaller samples. In other
words, a study yielding an ES of .30 with a sample of 500 participants
will Contribute more to the overall meta-analysis results than the same
.30 ES with a sample of 50. Meta-analysis shows that the larger the
ES--in either a positive or negative direction--the better the
distinction between two groups.
The purpose of the meta-analysis found herein was to determine
whether it is now possible to meaningfully separate students into the
categories of LD, MID, and E/BD on the basis of research. Our
meta-analysis was limited to three dependent variables that are
important and necessary for special education eligibility and that make
a difference when trying to educate students with disabilities. We
combed the literature to find studies to be grouped into the following
domains: IQ, academic achievement, and behavior. The meta-analysis was
limited to these dependent variables for they define in large part how
students perform in school. Statements concerning the three variables of
interest are found in the definitions of each category of high-incidence
disability, and performance on the three dimensions accounts for a
majority of students identified as disabled and eligible to receive
special education (Reschly & Tilly, 1999). Without consideration of
IQ, academic achievement, and behavior it is nearly impossible to
describe objectively any student with a LD, MID, or E/BD.
The three domains of interest chosen herein also accurately
describe the who in special education (Bateman, 1994). The who, along
with the how and where, are core concepts and perpetual questions of
great importance in the field of special education (Kauffman &
Landrum, 2006). Our modest, but worthy goal herein was to provide
greater illumination of the who in the categories of LD, MID, and E/BD.
We attempted to answer the following global research question: Do
meaningful, educationally related and quantitative differences exist
across school-identified students in the three categories of
Studies that compared two or three of the high-incidence disability
categories were located in the Educational Resources Information Center
(ERIC), EdInfo, Education Abstracts, and PsycInfo databases between the
years 1977 and 2003. Different keywords were used for the categories of
E/BD (e.g., emotional disturbance, behavior disorder, etc.) and MID
(e.g., educable mental retardation, mild cognitive disability,
developmental disability, etc.) because of the variety of titles used in
different states and studies. In addition, a hand search, or
"footnote chasing" was performed on the reference lists of the
articles published during the 1980s, 1990s, and early 2000s so that
additional studies could be found and examined for possible inclusion.
This initial literature search yielded a total of 152 reports that
included chapters, discussion pieces, articles in monographs, position
papers, dissertation abstracts, and empirical studies that could be used
for our preliminary research inspection for the meta-analysis.
STUDY SELECTION CRITERIA AND CHARACTERISTICS
We examined each research report to refine its suitability for
further analysis and devised specific a priori criteria for inclusion in
the present meta-analysis. The inclusion criteria were selected after
discussion among the authors and to partially control for study quality
following the suggestions of Lipsey and Wilson (2001). Our study
inclusion rationale included the following:
1. Samples of two, or all three types of high-incidence
disabilities had to be represented. Participants had to have been
previously recognized (i.e., before the research was conducted) as
having a school-identified high-incidence disability, and not because of
the actual assessment performed in the research examined for the
2. Participants had to be identified as having a high-incidence
disability under IDEA and corresponding state law active at the time the
study was published, and only in the United States.
3. Each study had to use norm-referenced, standardized tests of
achievement, ability, and behavior and report the results in a
traditional manner. Each test used in the studies had to have known
reliability and validity statistics. Because assessment for special
education eligibility depends heavily on norm-referenced test
performance, and because such measures are also used to judge
students' progress in school over time, we wanted to use
traditional data that are typically found where students with
high-incidence disabilities are educated.
4. The investigation had to report sufficient statistics (i.e.,
means, standard deviations, sample sizes, t-test and F-ratio results,
etc.) for the groups of interest on at least one of the three dependent
5. The samples had to be of proper age covered under IDEA. In light
of our specific interest in students eligible for special education in
the United States under IDEA, we limited the research samples in studies
to only those between the of ages of 3 to 21, or that included data on
students when they were between those ages.
6. Studies found in the behavior domain had to have either parent
or teacher (or both) ratings of participants' conduct or behavioral
traits. We did not allow for participant self-ratings of behavior or
7. Each study had to be a group design investigation. In keeping
with the recommendations of Lipsey and Wilson (2001), we did not mix
single-subject designs with the group design studies sought for the
8. Each study had to be published in a peer-reviewed journal or
similar outlet (e.g., peer-reviewed monograph).
This selection system reduced the original total of 152 related
literature items to 58 service able research reports from 26 different
peer-reviewed journals. Our selection criteria reduced the final number
of published literature to just more than one third of that originally
found; most of the peer-reviewed studies excluded from the present
meta-analysis were used in an additional descriptive review of the
literature (see Sabornie, Evans, & Cullinan, 2006).
Two of the co-authors completed the literature search, and there
was 100% agreement on matters of study inclusion, data to be used for
computer analysis, variables, and samples of interest. Coding of study
data was also performed by the same two co-authors. A reliability check
of the coders' study data was performed on 20 (34%) randomly
selected studies used in the meta-analysis. This intercoder agreement
check was used to verify that statistics used in the effect size
computations (using computer programs; see "Effect Sizes" in
the following) were exactly as they appeared in the original published
study involving sample sizes, means, standard deviations, t-test, and
F-ratio data. This check involved a total of 288 separate comparisons.
The intercoder agreement percentage was calculated by summing the
agreements and then dividing the sum by the total number of agreements
plus disagreements, times 100. Two minor errors (decimal place values)
were found during the intercoder agreement calculations for an overall
concurrence rate of 99.3%.
The reporting of detailed participant demographics varied widely
across the available studies used in the present analysis. The
participants in the 58 studies used for the present meta-analysis
totaled 14,528 individuals with high-incidence disabilities. Those with
LD numbered 7,876 (54%), there were 3,057 (21%) persons with MID, and
3,595 (25%) participants were identified as having E/BD. Twenty-three
(40%) of the selected studies reported only on preschool (n = 1) or
elementary level participants in the categories of high-incidence
disabilities, 14 (24%) of the studies examined a combination of
elementary and middle school level students, 6 (10%) studies used only
middle school age pupils, 2 (3%) studies used a combination of middle
school and high school age participants, 6 (10%) studies inspected only
high school aged students, and 7 (12%) studies used participants from
the elementary to high school levels (percentages were rounded).
Only 28 (48%) studies examined herein provided separate gender data
related to all the participants. In these studies there were 4,500 male
participants (71%) and 1,876 female participants (29%). This represents
44% of all the participants examined in the present analysis. Among the
studies reporting gender statistics of the samples, of the pupils with
LD 71% were boys, of students with MID 56% were boys, and in the group
with E/BD 78% were boys. Very few studies reported IQ, academic
achievement, or behavior scores separately by gender in
Twenty-two (38%) of the 58 studies in the analysis reported racial
demographics that could be separated into disability designation. The 22
studies with useable racial data included 28% of the total participants
in this report. Sixty-five percent of the reported participants were
Caucasian, 31% were African American, 2% were Latino, and 2.5%
"other." In some cases the other category included all
participants who were non-Caucasian. Again, few studies reported
participants' performance separated by race. It would be unwise to
draw any conclusions from the gender or racial data found in this
analysis because of the low number of studies that reported
participants' characteristics in these areas.
Using the computer statistical packages JMP (SAS Institute, 2001)
and ES (Shadish, Robinson, & Lu, 1999), study data were coded into
the three domains of interest, groups examined, and study and sample
characteristics. Data were also coded into outcome measures and
statistics necessary for calculating effect sizes and related
information including pooled standard deviation (SD) per study, standard
error (SE) of the ES, inverse variance weight of the ES (IVW), IVW x ES,
weighted mean ES ([ES.sub.w]), SE of the [ES.sub.w], 95% confidence
intervals around the [ES.sub.w], and Q statistic. The formulas for all
of the above ES and related statistics originate with Lipsey and Wilson
(2001) and are found in the Appendix. For studies with means, SDs,
t-test results, and F ratios reported, ESs were calculated for each
two-group comparison (i.e., LD vs. MID, MID vs. E/BD, and LD vs. E/BD)
on each dependent variable measure using Cohen's d statistic, or
the standardized mean difference (SAID) methodology. The SMD calculation
with pooled SDs used in the formula denominator (see Appendix) was
chosen because in each study the participants with LD, MID, and E/BD
were already assigned group membership and were nonexperimentally
defined (i.e., there were no randomly assigned or selected treatment vs.
comparison group investigations). Using a two group, pooled SD in ES
calculations that do not include traditional experimental and control
groups has been used previously and is the recommended procedure (Lipsey
& Wilson; Rosenthal, 1991).
Many studies provided the opportunity to obtain more than one d
statistic per investigation. In such cases we followed the
recommendation of Lipsey and Wilson (2001) and calculated separate
Cohen's d statistics and separate pooled SDs, one for each
comparison of interest. We then averaged the d statistics and pooled SDs
to calculate one single ES and pooled SD for each study. This was
performed in the IQ (e.g., averaging ESs for verbal and performance
IQs), academic achievement (e.g., averaging ESs for academic achievement
subtest scores), and behavior (e.g., averaging ESs for behavior subtest
scores) domains. As a check for this data aggregation method, in each of
the two group comparisons across high-incidence disabilities (e.g., LD
vs. MID, LD vs. E/BD, MID vs. E/BD) and three dependent variable
domains, the Pearson product-moment correlations between the number of d
statistics produced within a study and average ES per study were not
significant (at the p < .05 level). In other words, averaging the ES
data within an investigation to produce one ES per study did not
overestimate group differences. Homogeneity analyses (see the following)
also verified that our chosen method of study data and ES aggregation
A data coding consideration in the behavior domain was the
direction of the ES based on the characteristics of the instrument used
to rate participants' traits. Studies that used the Behavior
Problem Checklist, on which lower scores equal more appropriate
behavior, had to be coded differently from studies using other tests
(e.g., SSRS--social skills subtest) where higher scores translate into
positive outcomes (K. A. Kavale, personal communication, June 21, 2000).
The ES (Shadish et al., 1999) program allows for such data entry
nuances, and we used it exclusively in such situations.
Cohen's d statistic, reflecting the magnitude of group
difference, was derived for each available dependent variable in a study
and for each available two-group comparison. We were unable to compile d
statistics using sample means and standard deviations in four studies.
In Scruggs and Mastropieri (1986), for example, two ESs using t-tests
for second- and third-grade students with LD versus pupils with E/BD (in
academic achievement) were provided in the study. In this case we
averaged the two ESs provided into one study ES for Scruggs and
Mastropieri. In two additional studies (Harris, King, Reifler, &
Rosenberg, 1984; Sprafkin & Gadow, 1987), the available data allowed
for using the ES program (Shadish et al., 1999) to calculate
Cohen's d with sample size and t-test results. Gajar (1979)
provided sufficient data to use the ES program to compute Cohen's d
using sample sizes with F ratios found in the report.
Homogeneity Testing. If a study had dependent variables in both IQ
and academic achievement (e.g., Kortering & Braziel, 1999), the IQ
scores were placed in the IQ ES domain, and the academic achievement
scores were used in an ES calculation for that domain. If a study
investigated dependent variable performance for all three groups (e.g.,
Cullinan & Epstein, 1985), then that study produced three study
effect sizes. Thus, a total of 130 ES measurements were derived from the
58 studies for an average of 2.2 ESs per study.
An important aggregation test used in meta-analyses is that of
homogeneity, which attempts to determine whether studies can be
described as sharing a common effect size or population ES (Hedges &
Olkin, 1985; Lipsey & Wilson, 2001). Because a major apprehension in
any meta-analysis is the extent to which studies are commingled to find
a common ES for two groups on a dependent variable, testing for
homogeneity is a characteristic of any good meta-analysis (Davies &
Crombie, 2003). The homogeneity test is based on the Q statistic. We
performed a test of homogeneity to yield a Q statistic in each dependent
variable domain and for each two group comparison using the formulas
found in the Appendix. The Q statistic, when summed across all studies
in a domain, is distributed as the [chi square] statistic with n - 1
degrees of freedom. A non-significant [chi square] statistic indicates a
homogeneous distribution. "In a homogeneous distribution, the
dispersion of the ESs around their mean is no greater than that expected
from sampling error alone (the sampling error associated with the
subject samples upon which the individual effects sizes are based). In
other words, in a homogeneous distribution an individual effect size
differs from the population mean only by sampling error" (Lipsey
& Wilson, p. 115). A nonsignificant test for homogeneity (or Q
statistic), therefore, produces statistical support for the ES
aggregation method used in a meta-analysis. The pooling of ES method
used herein (i.e., one study, contributing one domain ES, based on
averaging all within-study ESs on the same construct) was shown to be
proper for all group comparisons across all three domains. Each
two-group comparison in each domain was shown to be homogeneous, and the
representative Q statistics are presented in Table 1. Given the
favorable Q statistic results, therefore, and using best practice in
meta-analysis (i.e., according to Cooper & Hedges, 1994), no further
analysis of our aggregation method was necessary.
The outcome variables examined in the IQ domain included
full-scale, verbal, and performance IQ scores on norm-referenced
intelligence tests such as the Wechsler scales (all versions except for
the WPPSI; i.e., WISC, WISC-R, WISC-III, WAIS), Kaufman Assessment
Battery for Children (K-ABC), Stanford-Binet (all versions), and
McCarthy Scale General Cognitive Index. Several studies reported
full-scale IQ measures and separate results for verbal and performance
IQ scores. In such cases we used only full-scale IQ scores. We also
included IQ data from studies where participants were examined in
domains other than IQ (e.g., Sabornie, Kauffman, & Cullinan, 1990),
but presented group means and SDs on IQ in matching of participants on
various demographic variables. For the IQ dependent variable, 11 studies
examined all three groups with high-incidence disabilities, 14 studies
compared IQ across only the groups with LD and MID, and 15 separate
studies investigated IQ differences with only those having LD versus
participants with E/BD.
The outcome variables used in the academic achievement domain were
scores from norm-referenced academic achievement tests such as the
Woodcock-Johnson Psychoeducational Battery (all versions), Wide Range
Achievement Test (all versions), the achievement scale of the K-ABC,
Kaufman Test of Educational Achievement, Peabody Individual Achievement
Test, Clinical Evaluation of Language Functions, and Gilmore Oral
Reading Test. With the exception of three studies that reported grade
level performance, the majority of studies used in the academic
achievement domain reported standard scores of participants'
performance. In the academic achievement domain, six studies compared
all three groups with high-incidence disabilities, three contrasted
performance of only those with LD versus participants with MID, and only
nine studies examined differences with students identified as either LD
The outcome measures examined in the behavior domain included
scores on norm-referenced instruments that assessed various behavioral
traits of the participants. Tests included the Behavioral and Emotional
Rating Scale, Behavior Problem Checklist, Social Skills Rating System,
Children's Adaptive Behavior Scale, Social Behavior
Assessment-Teacher Version, Conners Teacher Rating Scale (and
abbreviated version), Homework Problem Checklist, Walker-McConnell Scale
of Social Competence and School Adjustment, School Social Behavior
Scales, Child Behavior Checklist, Attention Deficit Disorders Evaluation
Scale, and Teacher Ratings of Social Skills. Behavioral ratings of the
participants were provided by both teachers and parents and included
behavioral and emotional strengths, general overt behavior and problem
behaviors, social competence and other socially oriented behavior,
depression-like symptoms, perceived academic behavior, and hyperactivity
problems. Last, in the behavior domain, nine studies compared behavioral
traits across all three groups of students with high-incidence
disabilities, two research reports uncovered differences in participants
only with LD or MID, and nine investigations examined students with LD
and E/BD only.
We performed Pearson product-moment analyses between the year of
study publication and the ES for each study across all dependent
variable domains and two group comparisons. The only significant
correlation (at the p < .05 level) found was between year of
publication of the study and its corresponding within-study ES for
groups with MID and E/BD in IQ (r = -.63, p < .05). As year of
publication increased, the IQ ES between these two groups decreased, or
vice versa. The mean year of publication of the 58 studies was 1990
(median = 1989).
Correlational analyses were also performed on the age of
participants in each study with calculated within-study ESs across
domains and two group comparisons. The only significant correlation in
these analyses concerned the comparison between students with LD versus
those with MID in the behavior domain. The age of participants in these
two groups was significantly negatively correlated (r = -.71, p <
.05) with the within-study ES. As the age of these two types of
participants increased, the within-study ES decreased, or vice versa.
A weighted mean ES ([[bar.ES].sub.w]) was calculated for each of
the two group comparisons in each of the three domains of interest. As
in all meta-analysis calculations, a positive [[bar.ES].sub.w] favors
the first group in the comparison and a negative [[bar.ES].sub.w] favors
the second group. Table 1 presents the major findings for the
meta-analysis performed herein.
IQ Domain. Little IQ difference can be found in the studies
comparing performance across the groups identified as LD and E/BD. In
the remaining two IQ comparisons involving participants with MID,
however, statistically significant weighted mean ESs separate such
students with those having either LD or E/BD. Because [[bar.ES].sub.w]
estimates can be translated similar to z scores (i.e., an ES of 1.00 = 1
SD), the [[bar.ES].sub.w] of [+ or -] 2.17 or greater means that more
than 98% of students with either LD or E/BD outscored the average
student with MID.
Academic Achievement Domain. The [[bar.ES].sub.w] estimates across
studies in the academic achievement domain span the range from weak to
strong differences across the three comparison groups, but each one was
statistically significant. The standardized test performance levels
between students with MID versus those with E/BD showed the greatest
disparity, and the comparisons between participants with LD and pupils
with E/BD showed the least. The moderate levels of academic achievement
difference between students with LD and MID indicate that at least two
thirds of the group with LD outperformed the average student with MID.
Approximately 76% of students with E/BD scored higher than the typical
pupil with MID.
Behavior Domain. The [[bar.ES].sub.w] estimates in the last domain
of interest provide somewhat surprising differences across the
comparison groups. What is particularly striking is the lack of
difference in many types of school-related behavior between students
with LD and those with MID. It is not surprising, however, that students
with LD and pupils with MID both significantly outperform participants
with E/BD in teacher- and parent-rated behavior. Approximately 76% of
students with LD were rated better than the average pupil with E/BD, and
about 77% of youth with MID surpassed the average person with E/BD. The
finding concerning the statistically significant difference between
youth with MID and those with E/BD is surprising given the fact that
students in the former group have their own set of adaptive behavior
problems. Figure 1 presents a schematic of the various weighted mean
effect sizes across groups and domains.
[FIGURE 1 OMITTED]
This study compared and contrasted some of the school-related
characteristics of students with LD, MID, and E/BD. The results show
that students with high-incidence disabilities can be separated
categorically on measures of IQ, academic achievement, and behavior.
One group that stands out remarkably from the other two in the
meta-analysis is the cluster with MID. The group with MID differs
significantly from groups with other high-incidence disabilities,
especially in the IQ and academic achievement domains. Given the
parameters of the federal definition of intellectual disability, the
cognitive domain differences demonstrated here for persons with MID are
not surprising. Conversely, it is possible that students with MID
performed more poorly in the cognitive domain than the other groups
because of (a) low expectations; (b) inadequately trained teachers; (c)
full inclusion practices that ignored academic instructional needs; or
(d) a combination of those and other, unknown, confounding variables.
The large effect size differences between those with MID and
school-age students in the other two high-incidence disability
categories can also be explained by changes over time in the population
identified as having MID. More than 20 years ago, Polloway and Smith
(1983) reported that individuals with MID were now "patently more
disabled" than those so labeled previously. This means that
students with MID are now (i.e., post-Polloway & Smith's
comments) in need of greater functional and life skills, personal care
skills, communication ability, social skills, and transition-oriented
emphases in their education. At least compared with students with LD or
pupils with E/BD, the present meta-analysis results verify the
phenomenon that Polloway and Smith first reported in the early 1980s.
Perhaps an array of placement options (i.e., the full continuum of
placement alternatives) may be necessary to meet those needs.
In an interesting discussion related to labeling in special
education, MacMillan, Siperstein, and Gresham (1996) suggested changing
the term mild mental retardation to generalized learning disability when
referring to students with MID. The authors recommended this change
because they believed that students with MID were more similar to those
with LD than they were to other students with more severe levels of
intellectual disability. Except in the behavior domain, the data
reported here do not support the contention that students with LD and
MID are similar. Individuals with MID have starkly different cognitive
characteristics from those with specific LD, and the studies synthesized
here confirm this disparity.
The present results confirm that students with LD, when compared
with those in other categories of high-incidence disability, demonstrate
the highest levels of functioning in the three domains of our analysis.
Students with LD were found to be more successful than those with MID
and E/BD in two thirds of the group comparisons. Although statistically
significant differences exist between those with LD and the other two
groups in IQ, academic achievement, and behavior, one must question the
practical significance of these findings. That is, conquering academic
and behavior problems must continue to be paramount in educational
settings for students with LD. The learning and behavior needs of
students with LD will be appropriately served only when they receive
explicit, intensive instruction, using appropriately designed curricula,
for a sufficient duration.
The present academic achievement findings of students with E/BD
relative to those with LD and pupils with MID are also generally in
accord with other research (see Trout, Nordness, Pierce, & Epstein,
2003). Trout et al. also noted that the amount of research on academic
achievement of students with E/BD has (a) declined in recent years, (b)
predominantly examined students in restrictive educational environments,
and (c) rarely measured potentially important modifier variables such as
socioeconomic status. It is therefore essential to increase research
attention concerning the educational achievement of Students with E/BD.
Present findings on behavior and emotional problems were based on
rating scales completed by teachers or parents. Our results show that
the average problem behavior score of students with E/BD was worse than
a significant number (approximately 76%) of students with LD and youth
with MID. These findings, however, could be specific to the measurement
system used in the studies included in our analysis. It follows that
research is needed to investigate emotional and behavioral problems of
students with high-incidence disabilities as measured by a variety of
methods including technically adequate self-reports, objective
personality tests, sociometric methods, target behavior recording, as
well as rating scales completed by adults. Another consideration about
rating scales is that they have a restricted measurement range and may
be prone to ceiling effects. If so, other measurement methods (e.g.,
target behavior recording in terms of frequency or duration) might
reveal even greater differences in behavior problems between students
with E/BD and those in the two other categories.
A limitation of the present analysis that should lead to caution
when interpreting these findings concerns the "file drawer
problem" found in meta-analysis reports or any other review of
research literature. This dilemma concerns the extreme view that
journals are filled with the 5% of studies that show significant
differences in the results, whereas the file drawers of researchers are
filled with the other 95% of studies that show nonsignificant findings.
Although a statistical technique to overcome this weakness does not
exist, calculations are available to indicate the minimum number of
studies (or "fail-safe N") necessary for a meta-analysis to
avoid the file drawer problem (see Orwin, 1983). Extrapolating
Orwin's calculations to the present data, one would need 93 studies
with a [[bar.ES].sub.w] of zero to reduce our [[bar.ES].sub.w] to .5
when contrasting students with LD versus those with MID on IQ, and 37
studies when comparing pupils with MID to participants with E/BD on IQ
with similar statistical parameters. As can be seen in the present
study, for the largest obtained [[bar.ES].sub.w] estimates, we had fewer
than the necessary number of investigations, so the file drawer problem
is plausible. Moreover, we may have inflated the file drawer problem by
excluding unpublished research (e.g., dissertations) examining the same
three factors across the three groups. Our analysis also did not
categorize the published research into "high" or
"low" quality because of the difficulty in professional
agreement over what is quality empiricism (see McGuire et al., 1985;
Wortman, 1994). The study inclusion criteria did not allow for studies
conducted outside the United States. Similar studies do exist (in
Israel; see Margalit, 1989, 1995; Margalit & Roth, 1989), and it is
likely that including these additional international studies would have
affected the ESs shown here.
Another weakness that could have contributed to the present results
concerns the definitions and eligibility criteria used across states to
identify students with LD (see Kauffman, Hallahan, & Lloyd, 1998),
MID (see Denning, Chamberlain, & Polloway, 2000), and E/BD (see
Forness & Knitzer, 1992). It is safe to say that great disparity
exists in definitions and eligibility across states. The student groups
who comprised the research samples in each category of high-incidence
disability were far from homogeneous across studies, and that could have
led to discrepant results in the 58 studies. These difficulties in
design and execution of our analysis, therefore, suggest prudence in
considering the power of the present findings; meaningless statistics
are still a consequence of a meta-analysis of poorly executed studies or
restrictions in study selection.
To some extent the uncertainty over the justification for separate
categories of students with learning, intellectual, emotional, and
behavioral problems involves the following question: Are students with
LD, MID, and E/BD alike or different? Nearly everyone recognizes that
students with E/BD vary considerably among themselves on school-related
characteristics, as do students with LD and youth with MID. Therefore
the question can be restated: Are the average differences across
students with LD, MID, and E/BD greater than the differences among
students within any one of the three categories? The present
meta-analysis results clearly point to a "yes" answer with
respect to intelligence, academic achievement, and behavior problems.
In a classroom serving all three types of students with
high-incidence disabilities, we believe our results translate into
differentiated educational content focus from the teacher. This does not
imply, however, that differentiated teaching behaviors are required
across the three categories because we believe that "effective
instruction is effective instruction" no matter who receives it.
Our analysis is insufficient without additionally knowing the
differential effects of instruction across the three groups. Perhaps the
most significant validation for multiple categories of high-incidence
disabilities would be that knowing a student's category tells the
practitioner how to intervene with that student. Separate special
education categories "A," "B," and "C"
would be useful if educators knew that students in each category
generally respond differentially to a particular intervention. One way
to know this is if investigators could point to research showing that
any student in category A is likely to benefit from a particular
intervention, but any student in categories B or C will not. Such
inquiry in special education appears to be sparse at present. Our
meta-analysis justifies the use of three different groups of
high-incidence disabilities based on research, but interventions and
services must continue to address documented, individual student need
regardless of category.
Formulas (all originate with Lipsey & Wilson, 2001) Used in the
Cohen's d was calculated as: d = [M.sub.gr1] -
[M.sub.gr2]/[SD.sub.pooled] where [M.sub.gr1] = mean score of either the
group with LD or those with MID, on a dependent variable. In order to
standardize all the obtained ESs in the two group comparisons involving
those with LD, participants identified as LD were always considered
Group 1. In comparisons involving only those with MID and E/BD, the
group with MID was classified as Group 1. In other words, those
identified as E/BD were not considered Group 1 in any ES statistic
calculation. [M.sub.gr2] = mean score of either the group with MID or
those with E/BD, on a dependent variable. The group with MID was always
Group 2 when compared with youth with LD. The pooled standard deviation
was calculated as:
[SD.sub.pooled] = [square root of ([n.sub.gr1] -
1)[SD.sup.2.sub.gr1] + (([n.sub.gr2] - 1)[SD.sup.2.sub.gr2]]/
([n.sub.gr1 - 1]) + ([n.sub.gr2] -1])
where [n.sub.gr1] = sample size of Group 1; [n.sub.gr2] = sample
size for Group 2; [SD.sub.gr1] = standard deviation for a dependent
measure score for Group 1; and [SD.sub.gr2] = standard deviation for a
dependent measure score for Group 2.
The standard error (SE) of the ES for each study was calculated as:
SE [SD.sub.pooled] [square roof of 1/[n.sub.gr1] + 1/[n.sub.gr2]]
The inverse variance weight (IVW) of the ES for each study was
IVW = 1/[SE.sup.2] = [n.sub.gr1] [n.sub.gr2]/[SD.sub.pooled]
([n.sub.gr1 + [n.sub.gr2]
The inverse variance weight times effect size (IVW x ES) datum for
each study was calculated simply by multiplying the study INV by the
The weighted mean effect size ([ES.sub.w]) for each study domain
and two group comparison was calculated as: [[bar.ES.]sub.w] =
[summation](IVWxES)/[summation] IVW]. In this equation, each
study's IVW x
ES datum in each domain and two group comparison was summed and
then divided by the sum of all the study IVWs for a domain and two group
comparison. In other words, each domain and two group comparison had
only one [[bar.ES].sub.w]. The [[bar.ES].sub.w] (versus the unweighted
[bar.x] ES) for a domain and two group comparison considers all the
contributing effect sizes with respect to sample size. Studies with
larger sample sizes (versus smaller ns) provide a more robust estimate
of the population parameters and are therefore assigned greater weight.
The domain and two group comparison Q statistic was calculated as:
Q = [summation] IVW [(ES - [[bar.ES].sub.w]).sup.2]. In this equation,
the single [[bar.ES].sub.w] for each two group comparison in each domain
was subtracted from each study's single (within study) ES, and this
difference was squared. Then, the squared difference was multiplied by
the IVW of each study, then summed across studies, and called the Q
The standard error of the [[bar.[ES.sub.w] was calculated as: SE
[[bar.ES].sub.w] = [square roof of 1/[summation] IVW]
The 95% confidence intervals around the [[bar.ES].sub.w] were
calculated as: [[bar.ES].sub.w] (lower limit) = [[bar.ES].sub.w] -
z(1-.05) (SE [[bar.ES].sub.w]), and [[bar.ES].sub.w] (upper limit) =
[[bar.[ES.sub.w] + z(1-.05) (SE [[bar.ES].sub.w]). "To construct
the confidence intervals, you multiply the SE [[bar.ES].sub.w] by a
critical z-value representing the desired confidence level (in our case
1.96 for [alpha] = .05), and add the product to the [bar.[ES.sub.w] for
the upper limit, and subtract the product from the [[bar.ES].sub.w] for
the lower limit" (Lipsey & Wilson, 2001, p. 114). If the
confidence interval does not include zero, the [[bar.ES].sub.w] (and the
difference between groups) is statistically significant at the chosen
The t-test standardized mean difference ES was calculated as: d = t
[square root of [n.sub.1 + [n.sub.2]/[n.sub.1][n.sub.2]]
The F-ratio standardized mean difference ES was calculated as: d =
[square root of F([n.sub.1 + [n.sub.2]/[n.sub.1] [n.sub.2]]
Manuscript received April 2004; manuscript accepted November 2004.
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EDWARD J. SABORNIE
SUSAN S. OSBORNE
LYNNE B. BROCK
North Carolina State University
EDWARD J. SABORNIE (CEC Chapter #639), Professor; DOUGLAS CULLINAN
(CEC Chapter #617), Professor; SUSAN S. OSBORNE (CEC Chapter #617),
Associate Professor; and
LYNHE B. BROCK (CEC Chapter #639), Doctoral Student, Department of
Curriculum and Instruction, North Carolina State University, Raleigh.
Address correspondence to Edward J. Sabornie, North Carolina State
University, College of Education, Graduate Program in Special Education,
Department of Curriculum and Instruction, Poe Hall, Campus Box 7801,
Raleigh, NC 27695-7801. (e-mail: firstname.lastname@example.org)
We thank David Wilson, George Mason University, for his expert
assistance in the technical and statistical preparation of this
Effect Sizes and Related Meta Analysis Data
Data LD LD MID
vs. vs. vs.
Categories MID E/BD E/BD
[[bar.ES].sub.w] 2.36 * 0.14 -2.17 *
unweighted [bar.X] ES 2.58 0.06 -2.15
95% CI for (low) 1.88 -0.55 -2.96
[[bar.ES].sub.w] (high) 2.83 0.84 -1.38
n of studies 25 26 11
Q statistic (df) 5.34 (24) 1.66 (25) .48 (10)
Domain and Group Comparisons
Data LD LD MID
vs. vs. vs.
Categories MID E/BD E/BD
[[bar.ES].sub.w] .42 * .22 * -.70 *
unweighted [bar.X] ES 1.13 -0.32 -1.07
95% CI for 0.18 0.16 -1.12
[[bar.ES].sub.w] 0.66 0.28 -0.28
n of studies 12 15 6
Q statistic (df) 1.17 (11) 24.91 (14) .99 (5)
Data LD LD MID
vs. vs. vs.
Categories MID E/BD E/BD
[[bar.ES].sub.w] -0.01 .72 * .70 *
unweighted [bar.X] ES 0.13 0.65 0.69
95% CI for -0.26 0.50 0.37
[[bar.ES].sub.w] 0.24 0.94 1.03
n of studies 12 19 10
Q statistic (df) 1.03 (11) 2.08 (18) 1.97 (9)
Note. LD = learning disabilities; MID = mild intellectual
disabilities; E/BD = emotional/behavioral disabilities; ES = effect
size; CI = confidence interval; [[bar.ES].sub.w] = weighted mean
effect size; [bar.X] = mean; df = degrees of freedom.
* p < .05