Post on 26-Jun-2018
Meta-Analysis 1
Quantitative Research Synthesis:
Meta-Analysis of Research on Meeting Special Educational Needs
Kenneth A. Kavale Regent University
HANDBOOK OF SPECIAL EDUCATION
Lani Florian (Ed.) Sage Publications
Meta-Analysis 2
Introduction
Since the passage of landmark federal law (now IDEA) in 1975, special education has
witnessed significant change but not necessarily real progress. The consequences are found in
attitudes that oscillate between optimism and pessimism about the prospects for special
education (see Zigler & Hodapp, 1986). For example, there is optimism about the law’s success
in providing access to special education but pessimism about whether or not the appropriate
education provision is achieving the desired outcomes (Finn, Rotherham & Hakanson, 2001).
Such pessimism is not new; the innovative program developed by Jean-Marc-Gaspard Itard for
Victor, the “wild boy of Aveyron” (Itard, 1806/1962) was perceived as a “failure” (e.g., Kirk &
Johnson, 1951) because of Victor’s modest attainments. In reality, the gains were meaningful
and demonstrated the potential of special education (Gaynor, 1973).
Questions about the efficacy of special education are thus long-standing (e.g., Milofsky,
1974) and typically take the form of asking: Is special education special? Answers may become
confounded because the special in special education possesses two meanings: a) teaching special
students, and b) using special instruction. Too often, however, the desire to enhance education
experiences for special students means that teaching may be based on uncritical decisions about
the efficacy of techniques used for special instruction. The fact that the most effective teaching is
predicated on scientific ground may be ignored when a teacher is faced with the challenge of
teaching special students (see Gage, 1978). Without a scientific foundation for practice, special
education is likely to become a variable enterprise that may be effective or not effective.
To determine whether methods for special instruction are effective (i.e., Do they work?),
special education how long-endorsed the scientific method where decisions about efficacy are
based on empirical evidence (Kauffman, 1987). The empirical evidence is available but too often
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remains isolated when individual study findings do not agree. Which findings are to be believed?
Differences among studies are not a problem if individual findings are combined to produce
“usable knowledge” (Lindblom & Cohen, 1979) that may be used to judge the efficacy of special
instruction.
Traditional methods for combining individual study findings (e.g., narrative review) are
too subjective and may produce biased findings (see Jackson, 1980). In an effort to reduce the
subjectivity associated with traditional methods of reviewing research findings (see Cooper &
Rosenthal, 1980), quantitative methods, usually termed “meta-analysis” (Glass, 1976), have
become an accepted means of combining empirical findings. Meta-analysis is the application of
statistical procedures to collections of empirical findings from individual studies for the purpose
of integrating, synthesizing, and making sense of them (Glass, McGaw, & Smith, 1981).
As a research methodology, meta-analysis uses rigorous and systematic procedures that
parallel primary research activities including: 1) problem formulation (Is intervention X
effective?), 2) sampling (a comprehensive and representative set of studies from the domain
under investigation), 3) study classification (organizing and coding study information), 4) data
analysis (calculation of the “effect size” (ES) statistic that permits quantification and
standardization of individual study findings), and 5) ES interpretation (Kavale, 2001b).
An ES is most often interpreted as a z-score indicating level of improvement on an
outcome assessment for students initially at the 50th percentile. To gain greater insight, two
additional ES interpretations are provided. The “common language effect size” (CLES)
(McGraw & Wong, 1992) converts ES into a probability that a score sampled from one
distribution will be greater than a score sampled from another. For example, in a sample of
studies investigating the use of intervention Y with a CLES of .83, 83 out of 100 would show
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that a subject using intervention Y would improve when compared to subjects not using
intervention Y. The “binomial effect size display” (BESD) (Rosenthal & Rubin, 1982) addresses
the question: What is the percentage increase in the number of successful responses when using a
new instructional practice? Based on converting an ES to r, the BESD for the use of intervention
Z (ES = 1.16, for example) would show an increase in success rate from 25% to 75%. The 50-
percentage-point spread between treatment (75%) and comparison (25%) success rate shows that
the use of intervention Z possesses, not only statistical significance, but also practical
significance. Finally, Cohen (1988), based on notions of statistical power, offered “rules of
thumb” for classifying ES as small (.20), medium (.50), or large (.80). Thus, the ES metric
imparts a clarity and explicitness to empirical findings that make synthesized evidence more
objective and verifiable (Kavale, 1984).
This chapter reviews meta-analyses investigating the effectiveness of special education in
order to make decisions about “what works” (see Kavale, 2001a).
The Nature of Special Education
The definition of special education as “specially designed instruction…to meet the
unique needs of a child with a disability (U.S. Department of Education, 1999, p. 12425)
emphasizes individualized instruction but does not stipulate the nature of the special instruction
to be delivered. Special education, in an effort to differentiate itself from general education, has
historically opted for developing unique and exclusive methods. Although “special” methods
provided a distinct identity, they also introduced a separateness from general education that
produced a skepticism about their benefits on the part of general education. Because of its higher
costs, special education was being held increasingly accountable: Could special education
substantiate its benefits?
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Special education has historically assumed a goal of correcting or reversing the altered
learning functions of students. Beginning with Itard, special education has focused on enhancing
cognitive processes so students in special education may then be able to learn in the same way as
general education students. Consequently, process training has long been a primary form of
special education (see Mann, 1979). Although intuitively appealing, does research support the
theoretical assumption that training processes enhances learning ability?
A large body of empirical research has investigated the efficacy of process training but
difficulties arise in deciding “what the research says” as was illustrated in the case of
psycholinguistic training, a prominent form of process training during the 1960s and 1970s.
Psycholinguistic training was developed by Samuel A. Kirk and embodied in the Illinois Test of
Psycholinguistic Abilities (ITPA). The model was based on the assumption that psycholinguistic
ability is comprised of discrete components and that these components can be improved with
training. By the mid 1970s, empirical research summaries revealed very different interpretations
about the efficacy of psycholinguistic training.
A review of 39 studies by Hammill and Larsen (1974) concluded that, “the idea that
psycholinguistic constructs, as measured by the ITPA, can, in fact, be trained by existing
techniques remains nonvalidated” (p. 11). In response, Minskoff (1975) offered a more positive
evaluation and concluded that psycholinguistic deficits can be remediated. The Minskoff review
was immediately challenged by Newcomer, Larsen, and Hammill (1975) who concluded that,
“the reported literature raises doubts regarding the efficacy of presently available Kirk-Osgood
psycholinguistic training programs” (p. 147). The divergent interpretations made it increasingly
difficult to determine “what the research says” about the efficacy of psycholinguistic training.
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Several years later, Lund, Foster, and McCall-Perez (1978) re-evaluated the original 39
studies, and concluded that, “It is, therefore, not logical to conclude either that all studies in
psycholinguistic training are effective or that all studies in psycholinguistic training are not
effective” (p. 319). Hammill and Larsen (1978) contested the Lund et al. analysis and concluded
that, “the cumulative results…failed to demonstrate that psycholinguistic training has value” (p.
413). Although polemics abounded, a primary question remained unanswered: What is really
known about the efficacy of psycholinguistic training?
Meta-analysis and the Efficacy of Special Education
Psycholinguistic Training
The traditional methods of research integration used to evaluate psycholinguistic training
failed to accumulate knowledge in an objective manner. To provide verifiable and replicable
conclusions, Kavale (1981) conducted a meta-analysis on 34 studies that yielded an average ES
of .39. In a statistical sense, an ES shows outcomes in standard deviation (SD) units that can be
interpreted in terms overlapping distributions (treatment vs. control). The ES of .39 indicates that
the average treated subject would gain 15 percentile ranks on the ITPA and would be better off
than 65% of control (no treatment) subjects. Using Cohen’s (1988) rules of thumb, an ES of .39
approaches a “medium” level but does not represent an unequivocal endorsement of
psycholinguistic training.
To gain insight, ES data were aggregated by ITPA subtest and five of nine ITPA subtests
revealed “small,” albeit positive, effects. Such a modest response suggests that training would
not be warranted in these five cases. For four subtests (Auditory and Visual Association, Verbal
and Manual Expression), however, training improves performance from 15 to 24 percentile ranks
Meta-Analysis 7
and makes the average trained subject better off than approximately 63% to 74% of untrained
subjects.
The findings regarding the Associative and Expressive constructs appear to belie the
conclusion of Hammill and Larsen (1974) that, “neither the ITPA subtests nor their theoretical
constructs are particularly ameliorative” (p. 12). The meta-analytic findings should not, however,
be interpreted as approval for psycholinguistic training. In the case of Auditory Association, for
example, there are difficulties in defining the skill: What is Auditory Association? Additionally,
it is important to determine whether improvement in Auditory Association provides enhanced
functioning in other than that discrete ability. In contrast, the case for Expressive constructs
particularly Verbal Expression presents a different scenario because it represents the tangible
process of productive language behavior whose improvement is critical for school success. In
fact, the Verbal Expression ES (.63) exceeds what would be expected from six months of general
education language instruction (ES=.50). Thus, the Kavale (1981) meta-analysis showed where
psycholinguistic training might be effective and might be initiated when deemed an appropriate
part of an intervention program.
Process Training
Mann (1979) suggested that, “process training is, in fact, one of the oldest forms of
education and that, despite periodic discontinuities in its practice, it has continued unabated into
our own day” (p. 537). Table 1 reveals that popular forms of process training demonstrate
limited efficacy. (The reported ES were obtained from the meta-analyses listed in Appendix A
and represent either the ES reported in a single meta-analysis investigating a particular
intervention or a weighted mean ES from meta-analyses investigating the same intervention.) For
example, perceptual-motor training, the embodiment of 1960s special education had practically
Meta-Analysis 8
no effect on improving educational performance; famous programs such as those developed by
Kephart (ES=.06) and Frostig (ES=.10) revealed very modest effectiveness. The limited efficacy
of process training may be related to difficulties in attempting to ameliorate unobservable
(hypothetical) constructs. The outcomes of training (products) are the only observable
component while the means by which those products were achieved (process) are not observable.
Although these difficulties are evident for constructs like perception, the same problems can be
identified for, as an example, social skills training where the actual skills represent products that
are presumed related to the hypothetical construct of social competence.
Although attacks on process training have been vigorous (e.g., Mann, 1971), its
historical, clinical, and philosophical foundation creates a resistance to accepting negative
evidence (e.g., Hallahan & Cruickskank, 1973) because, “the tension between belief and reality
provides a continuing sense of justification for process training” (Kavale & Forness, 1999, p.
35). The failure to change beliefs about efficacy was found for modality-matched instruction
(ES=.14) which has received a number of previous negative evaluations (e.g., Arter & Jenkins,
1979; Larrivee, 1981; Tarver & Dawson, 1978). The negative evidence is resisted because
teachers have maintained a strong belief that students learn best when instruction is modified to
match individual modality patterns (Kavale & Reese, 1991). The beliefs, however, must be
modulated by the empirical evidence indicating that interventions developed to define the
uniqueness of special education (e.g., process training methods not likely to be used in general
education) are not effective. Such evidence needs to be heeded because, “schools must view the
time, money, and other resources devoted to [process training] as wasteful [and] as an
obstruction to provision of appropriate services (Council for Learning Disabilities, 1986, p. 247).
Creating Effective Special Education
Meta-Analysis 9
The long dominant tradition of process training in special education reflected a pathology
model; academic problems were regarded as a “disease” and interventions were aimed at
“curing” the disease (i.e., removing the pathology) (Kauffman & Hallahan, 1974). By about
1975, the realization that process training was not producing desired outcomes shifted attention
to an “instructional imbalance” model where school failure was viewed as the result of a
mismatch between instructional methods and student developmental level (Hagin, 1973). The
“effective schools” research (see Bickel & Bickel, 1986) was a major influence that stressed, for
example, the importance of teachers believing that all students can achieve, that basic skill
instruction should be emphasized, and that clear instructional objectives should be used to
monitor student performance.
At the same time, a “learning process” model emerged that viewed teaching within a
“process-product” paradigm where variables depicting what occurs during teaching are
correlated with products (i.e., student outcomes) (Needels & Gage, 1991). Research revealed the
importance of a number of principles like, for example, encouraging student’s active engagement
in learning, exploring innovative approaches to grouping and organizing classroom instruction,
and making learning meaningful by keeping it enjoyable, interesting, student-centered, and goal-
oriented (see Brophy & Good, 1986). These principles became “best practice” and were
interpreted for special education (e.g., Christenson, Ysseldyke, & Thurlow, 1989; Reith &
Evertson, 1988; Reynolds, Wang, & Walberg, 1992).
Effective Special Educational Practice
Research investigating the teaching-learning process has identified a number of effective
instructional practices. Table 2 shows a sample of effective instructional practices and reveals
Meta-Analysis 10
that substantial positive influence on learning are possible by modifying the way instruction is
delivered.
The use of effective instructional practices moves special education toward the general
education teaching-learning model and away from a reliance on “special” interventions (e.g.,
process training). For example, mnemonic instruction (MI) is a strategy that transforms difficult-
to-remember facts into a more memorable form through recoding, relating, and retrieving
information (Mastropieri & Scruggs, 1991). A student receiving MI would be better off than
95% of students not receiving MI and would show a 45 percentile rank gain on an outcome
assessment. In a sample of studies investigating MI, 87 out of 100 would show that students
receiving MI would demonstrate improvement when compared to students in the control
condition (CLES=.87). The BESD shows a 64% increase in success rate which indicates
substantial practical significance. Compare the success rate of MI to, for example, perceptual-
motor training (ES=.08) where the modest 4% increase in success rate indicates a negligible
statistical effect and almost no practical significance.
Effective Special Education Instruction
The ultimate purpose of implementing effective instruction is to enhance academic
performance. Achievement outcomes are shown in Table 3 and indicate the potential for
substantial gains across subject areas. All achievement domains show “large” ES with gains
ranging from 29 to 41 percentile ranks on academic achievement measures. On average, almost
eight out of ten investigations of effective special education instruction will likely show
improvement (CLES=.77). The success rate increases from 27% to 73% indicating an average
46% improvement for students showing a positive response to instruction.
Meta-Analysis 11
The example of reading comprehension demonstrates how meta-analysis can be useful
for judging the magnitude of “real” effects. Two meta-analyses contributed almost all ES
measurements and produced ESs of 1.13 and .98, a modest three percentile rank difference in
outcomes (87 vs. 84). When specific methods for improving reading comprehension are
compared, the two meta-analyses revealed a similar pattern of findings. The largest effects
(ES=1.60 and 1.33) were found for metacognitive techniques (e.g., self-questioning, self-
monitoring). Text enhancement procedures (e.g., advanced organizers, mnemonics) produced ES
of 1.09 and .92. The least powerful (but nevertheless effective) techniques involved skill training
procedures (e.g., vocabulary, repeated reading) with ES of .79 and .62. The consistency of
findings across these two meta-analyses provides confidence in concluding that it is possible to
enhance reading comprehension.
The meta-analytic evidence suggests that, on average, the “real” effect of reading
comprehension instruction is 1.05, a level comparable to one year’s worth of reading
comprehension instruction in general education (ES=1.00). Thus, methods adapted for the
purposes of special education produced the same effect as 1 year of general education instruction
but did so in approximately 20 hours. Clearly, special education students can significantly
improve their ability to better understand what they read.
Effective Special Education Related Services
A hallmark of special education is the provision for related services to be provided when
deemed appropriate in augmenting the instruction program. Table 4 shows a sample of adjunct
activities and most demonstrate, at least, “medium” ES. On average (ES=.65), related services
produced a 24 percentile rank gain on an outcome assessment. In 68 out of 100 cases, a positive
Meta-Analysis 12
response to the related service was achieved (CLES=.68). Thus, related services appear to be
useful supplements to the instructional program.
Placement has often been viewed as having a positive influence on student performance
(see Kavale & Forness, 2000). The ES magnitude (.12) negates such a view and indicates that the
success rate associated with placement increases only 6% from 47% to 53% (BESD). The
“small” ES suggests that “what” (i.e., nature of the instruction) is a more important influence on
student outcomes than “where” (i.e., placement). In contrast, prereferral activities revealed
significant positive effects. The CLES (.78) indicates that in 78 of 100 cases prereferral activities
produce positive outcomes. Prereferral “works” because it is predicated in modification of
instructional activities, and its 48% success rate means that almost half of students given
preferential activities will not need to enter special education.
Drug treatment is often an integral part of the treatment regimen for some students in
special education. Stimulant medication (usually Ritalin) is the most popular and produces
significance positive changes in behavior. In 2 out of 3 cases (CLES=.67), positive outcomes
were found in gains averaging 23 percentile ranks on behavior ratings and checklists. The ES
(.62) was obtained primarily from a meta-analysis done in 1982 (ES=.58) and a replication
completed in 1997 (ES=.64). The consistency of the obtained ES (i.e., .58 and .64) provides
confirmation for the positive influence of stimulant medication. Special education, however, has
long criticized the use of stimulant medication and has sought more natural and unobtrusive
treatments. One such alternative, popularized during the 1970s, was the Feingold diet designed to
eliminate all foods containing artificial additives from the diet. The ES (.12) obtained for the
Feingold diet (see Table 1) clearly indicates that it has limited influence on modifying behavior.
A comparison of the two treatments shows stimulant medication to be better than five times
Meta-Analysis 13
more effective than the Feingold diet; the debate about the efficacy of stimulant medication
appears unequivocal.
Evaluating Special Education
Special education has demonstrated increased efficacy that may be attributable to a
change in instructional emphasis. Until about 25 years ago, special education emphasized its
“special” nature by developing singular and different methods not found in general education.
The goal was to enhance hypothetical constructs (e.g., “processes”) that were presumed to be the
cause of learning deficits. Basic skill instruction was a secondary consideration until processes
were remediated and learning became more efficient. When intervention activities emphasize,
for example, process training and basic skill instruction is subordinate, the nature of special
education can be conceptualized as SPECIAL education, with a focus on unique and exclusive
“special” interventions. The limited efficacy of SPECIAL education (see Table 1) suggest that
process deficits are difficult to “fix” and such an intervention focus produces little benefit.
The recognition that “special” interventions did not produce desired outcomes moved
special education to emphasize “education” in an effort to enhance academic outcomes. When
intervention activities emphasize alternative instructional techniques, the nature of special
education can be conceptualized as special EDUCATION. Such instructional techniques usually
originate in general education and are adapted to assist students with disabilities in acquiring and
assimilating new knowledge; special EDUCATION demonstrates significant success (see Table
2) and produces improved achievement outcomes (see Table 3).
The difference between the two forms of special education are seen in the mega ES
(mean of means) for “special” (.15) versus “education” (.89) techniques. The comparison reveals
special EDUCATION to be six times more effective than SPECIAL education; it produces
Meta-Analysis 14
achievement outcomes (mega ES=1.04) that exceed one year’s worth of general education
instruction (ES=1.00). On average, SPECIAL education provides only a 6% advantage meaning
that the students in special education receiving primarily “special” interventions exceeds only
about 56% of the group not receiving such interventions; this level of improvement is only
slightly above chance (50%). Additionally, across meta-analyses investigating SPECIAL
education, about 25% of the calculated ES were negative indicating that in one out of four cases
the student not receiving the “special” intervention performed better. Clearly, there is little
reason to include SPECIAL education in most intervention programs.
In contrast, the methods associated with special EDUCATION provide an efficacious
foundation for designing an academic instructional program. The use of effective techniques is
likely to move the average student in special education from the 50th to the 81st percentile. The
31-percentile-rank gain is better than 5 times the gain found with the use of “special”
interventions, and indicates students are better off than 81% of those not receiving the preferred
special EDUCATION. For example, Direct Instruction (DI), a behaviorally oriented teaching
procedure based on an explicit step-by-step strategy (ES=.93) is 6 ½ times more effective than
the intuitively appealing modality-matched instruction that attempts to enhance learning by
capitalizing on learning style differences (ES=.14). Students in special education taught with DI
would be better off than 87% of students not receiving DI and would gain over 11 months credit
on an achievement measure compared to about one month for modality-matched instruction.
With its grounding in effective instructional methodology, special EDUCATION can sometimes
be up to 20 times more effective than SPECIAL education.
Effective Special Education
Meta-Analysis 15
The meta-analyses synthesized provide insight into the indications and contra-indications
of special education interventions (Lipsey & Wilson, 2001). The interventions associated with
special EDUCATION may be considered a form of “evidenced-based practice” (EBP) (Odom,
Brantlinger, Gersten, Horner, Thompson, & Harris, 2005) where intervention decisions are based
on empirical findings demonstrating that the actions produce efficacious and beneficial
outcomes. The use of EBP promotes instructional validity where changes can be attributed to the
specific activities and can be used to produce similar results with other students in special
education (generalization).
Although EBP is desirable, the implementation of EBP is often limited by extraneous
factors. For example, tradition (“We have always used it”) and history (“It has worked before”)
are powerful barriers. Additionally, the bandwagon effect, where an intervention suddenly
becomes popular and gains momentum rapidly, may have a significant influence. As pointed out
by Mostert (1999-2000), “Bandwagons are used to champion a cause, engage in sweeping yet
attractive rhetoric, and generally to promise far more than they ever have hope of delivering” (p.
124). Finally, belief, a strong conviction about the truth, although a legitimate consideration in
making intervention decisions, is only appropriate when the belief is grounded in empirical
evidence.
The negative influence of these extraneous factors is one reason why research findings in
special education “are embraced by some, ignored by others, and modified to suit the routines
and preferences of still others” (Gersten, Vaughn, Deshler, & Schiller, 1997, p. 466). Regardless
of how exciting teachers may find new proven techniques, they often resist implementing them
in favor of more comfortable existing practices (Swanson, 1984). Heward (2003) identified ten
faulty notions that may hinder the effective delivery of special education. All told, the obstacles
Meta-Analysis 16
that interfere with making sound instructional decisions are a primary reason why there is a
continuing research-to-practice gap in special education (Greenwood & Abbott, 2001). The
failure to use EBP is a major contributor to the problem of sustainability, the maintained use of
an instructional practice supported by evidence of improved outcomes for students in special
education (Gersten, Vaughn, & Kim, 2004).
Because students in special education, by definition, possess unique learning needs,
instructional decisions are critically important in the design of individualized programs. The
complexities surrounding the instructional decision making introduces a degree of “uncertainty”
(i.e., the program may not work) (Glass, 1979). Besides uncertainty, there is also the possibility
of “risk” (i.e., negative outcomes) that can be described in meta-analysis by the standard
deviation (SD), a measure of dispersion around the mean ES that represents an index of
variability. Taken together, the ES and SD provide a theoretical expectation about intervention
efficacy (i.e., ES ± SD). For example, psycholinguistic training (.39 ± .54) spans a theoretical
range (-.15 to .93) from negative ES to “large” ES; the difficulty is the inability to predict the
outcome (i.e., ES) for a particular student. The mega ES for SPECIAL education (.15) is
associated with a larger mega SD (.48) making “special” interventions actually more variable
than effective (.15 ± .48). The theoretical range for SPECIAL education (-.33 to .63), although
possibly producing “medium” effects, also includes significant risk (i.e., a negative ES indicating
that those not receiving the intervention perform better). In contrast, special EDUCATION (.89 ±
.87) reveals itself to be more effective than variable and, although the theoretical range shows
that it may not “work” in some cases (ES=.02), there also exists the theoretical possibility of
being almost twice as effective (ES=1.76).
Meta-Analysis 17
Although the use of special EDUCATION can reduce risk (i.e., no negative ES), the
special education teaching-learning process remains a capricious enterprise (i.e., variable,
unpredictable, and indeterminate). To create more certainty, instructional decisions should not be
prescriptive (i.e., do A in circumstance X or Y, and do B in circumstance Z) but rather based on
an assortment of effective options (i.e., practices with large ES). This means that teachers are
central characters in the special education decision making process who must replace dogmatic
beliefs with rational choices about “what works.” Instructional decisions thus include elements
of science (theoretical and empirical knowledge) and art (interpretation necessary to initiate
action (see Gage, 1978). The teacher’s goal is to narrow the gap between the state of the art
(what has been demonstrated to be possible) and the state of practice (current ways of providing
instruction). Consequently, the actions of
special education practitioners will need to go beyond the scientific basis of their work …
and must be mediated through the teacher’s own creative rendering of best practice …
because quality education for special education students will always be based on the
artful application of science (Kavale & Forness, 1999, p. 93).
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Table 1
Effectiveness of Process Training ______________________________________________________________________________ Method Mean Effect Size Percentile Rank Power Rating Equivalent ______________________________________________________________________________ Irlen Lenses -.02 49 Negative (34) Perceptual-Motor Training .08 53 Negligible (33) Diet Modification (Feingold) .12 55 Small (31) Modality-Matched Instruction .14 56 Small (32) Social Skills Training .36 64 Small (17, 42, 44) Psycholinguistic Training .39 65 Small-Medium (27, 29) Frostig Visual Perceptual Training .10 54 Negligble-Small (30) ( ) = ES source listed in Appendix A
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Table 2
Effective Instructional Practices
______________________________________________________________________________
Practice Mean Effect Size Common Language Binomial Effect Size Effect Size Display
Success Rate Increase From % To % _____________________________________________________________________________________________ Mnemonic Instruction 1.62 .87 18 82 (39) Self-Monitoring 1.36 .83 22 78 (55, 58, 59) Reinforcement 1.17 .80 25 75 (58, 59, 64) Self-Questioning 1.16 .79 25 75 (55, 57) Drill & Practice .99 .76 28 72 (4, 58, 64) Strategy Instruction .98 .75 28 72 (55, 56, 57, 58) Feedback .97 .75 28 72 (58, 59, 64) Direct Instruction .93 .75 29 71 (1, 2, 65) Visual Displays .90 .74 29 71 (58) Computer-Assisted Instruction .87 .73 30 70 (47, 58) Repeated Reading .76 .71 32 68 (59, 61) Error Correction .72 .70 33 67 (58, 64) Tabled 2 continued on next page
Meta-Analysis 20
Table 2 (continued) Effective Instructional Practices
______________________________________________________________________________
Practice Mean Effect Size Common Language Binomial Effect Size Effect Size Display
Success Rate Increase From % To % _____________________________________________________________________________________________ Early Intervention .71 .70 33 60 (8, 23, 24, 49, 50, 51, 65) Formative Evaluation .70 .69 33 67 (18, 52) Peer Mediation .64 .67 35 65 (58, 59) Diagnostic-Prescriptive Teaching .64 .67 35 65 (58) Peer Tutoring .62 .67 35 65 (10, 41, 59) Positive Class Morale .60 .66 36 64 (64) Grouping .43 .62 40 60 (13, 14, 48) Cooperative Learning .40 .61 40 60 (7, 26, 53) Increased Time .38 .61 41 59 (64) ( ) = ES source listed in Appendix A
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Table 3
Effective Special Education Instruction _____________________________________________________________________________ Subject Area Mean Effect Percentile Rank Common Language Binomial Effect
Size Equivalent Effect Size Size Display Success Rate Increase
From % To % _____________________________________________________________________________ Handwriting 1.32 91 .82 22 78 (21, 56, 58) Oral Reading 1.31 90 .82 22 78 (9, 54, 59) Language 1.27 90 .82 23 77 (36, 43, 50) Reading Comprehension 1.04 85 .77 27 73 (40, 54, 60) Word Recognition .98 84 .75 28 72 (3, 54, 56) Narrative Writing .97 83 .75 28 72 (19, 21, 22) Math .96 83 .75 28 72 (35, 37, 67) Spelling .87 81 .73 30 70 (56, 58) Vocabulary .85 80 .73 30 70 (25, 54, 58) ( ) = ES source listed in Appendix A
Meta-Analysis 22
Table 4 Effective Special Education Related Services and Activities ______________________________________________________________________________ Service Mean Effect Size Common Language Binomial Effect Size
Effect Size Display Success Rate Increase
From % To % _____________________________________________________________________________________________ Memory Training 1.12 .79 25 75 (16, 58) Prereferral 1.10 .78 26 74 (5) Cognitive Behavior .74 .70 32 68 Modification (12, 46) Stimulant Medication .62 .67 35 65 (11, 28, 62) Counseling .60 .66 35 65 (58) Consultation .55 .65 36 64 (45, 58) Rational-Emotive Therapy .50 .64 38 62 (15, 20) Attribution Training .43 .62 39 61 (56, 58) Placement .12 .53 47 53 (6, 57, 63) ( ) = ES source listed in Appendix A
Meta-Analysis 23
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