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

Meta-Analysis 3

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

Meta-Analysis 4

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?

Meta-Analysis 5

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

Meta-Analysis 19

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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