Title Page
 Front Matter
 Table of Contents
 Review of the literature
 Analysis and results
 Discussion, conclusions, implications,...
 Reference notes
 Biographical sketch

Group Title: influence of selective attention on the performance of learning disabled students /
Title: The Influence of selective attention on the performance of learning disabled students /
Full Citation
Permanent Link: http://ufdc.ufl.edu/UF00099078/00001
 Material Information
Title: The Influence of selective attention on the performance of learning disabled students /
Physical Description: viii, 199 leaves : ill. ; 28 cm.
Language: English
Creator: Becton, Daniel Walker, 1947-
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 1982
Copyright Date: 1982
Subject: Learning disabilities   ( lcsh )
Attention in children   ( lcsh )
Reading (Elementary)   ( lcsh )
Foundations of Education thesis Ph. D   ( lcsh )
Dissertations, Academic -- Foundations of Education -- UF   ( lcsh )
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
Thesis: Thesis (Ph. D.)--University of Florida, 1982.
Bibliography: Bibliography: leaves 188-197.
General Note: Typescript.
General Note: Vita.
Statement of Responsibility: by Daniel Walker Becton.
 Record Information
Bibliographic ID: UF00099078
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000352336
oclc - 09654449
notis - ABZ0302


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Table of Contents
    Title Page
        Page i
        Page ii
    Front Matter
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        Page iv
    Table of Contents
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    Review of the literature
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    Analysis and results
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    Discussion, conclusions, implications, and recommendations
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    Reference notes
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    Biographical sketch
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Full Text






Copyright 1982


Daniel Walker Becton

Attention has been compared to the beam of a searchlight moving
about in the dark. It may focus on the world without or on inner
thoughts and fancies. But the act of paying attention is much more
than the focusing of a searchlight. It selects and draws into the
foreground, while initiating neuronal action on the stage of conscious-
ness. Other action may go on off stage. Off-stage action is not
arrested, but it is ignored only what happens on stage is recorded
and remembered.

Wilber-Penfield, 1969, p. 164-5


I wish to express my sincere appreciation to my committee members,

Dr. Robert Jester, Dr. Don Avila, and Dr. William Wolking, for their

support, encouragement, and constructive suggestions over these years.

Special thanks to Dr. Jester for detailed help in many meetings at

various times and places, including his home during his vacation.

I would like to acknowledge the extensive help I received from

Dr. Bill Edenfield in making possible the collection of the data. I

also appreciate the cooperation of Dr. Ron Bobay, Dr. Lee Rowell, Pat

Gardner, Peggy Williams, Lori Hadley, and Sarah Becton, my mother, in

acquiring the data.

I would like to extend my appreciation to Mrs. Carolyn West, Director

of Special Services, for her encouragement. I would also like to thank

the administration of Central Florida Community College for their support.

A very special thank you goes out to Mrs. Bonnie Dingler. As

secretary for Special Services, my friend, and the typist throughout

many drafts, she has been extremely loyal in her tolerance and encourage-

ment of my work.

To my parents, who undoubtedly are breathing a sigh of relief,

I thank them for their sacrifices, support, and encouragement through-

out my educational career. Finally, I thank my fiancee, Patricia, and

the boys, Mike and David, for their interest and patience during this




ACKNOWLEDGEMENTS ................................ ....... iv

ABSTRACT............... .................................. vii

I INTRODUCTION.............................................. 1

Rationale for the Study .............................. 1
Specification of Terms................................ 3
Statement of the Problem .............................. 4
Significance of the Study ............................. 5
Research Questions................................... 5
-Limitations of the Study.............................. 7

II REVIEW OF THE LITERATURE ... .............................. 9

Selective Attention................................... 9
Precision Teaching................................... 29
Modality Processing................................... 37
Multisensory Teaching ................................ 46

III METHODOLOGY............................................... 48

Hypotheses ........................................... 48
Research Design.................................. ..... 53
Subjects ......................... .................... 61
Setting............................................. 62
Instrumentation....................................... 63

IV ANALYSIS AND RESULTS ...................................... 78

Section One. ......................................... 78
Section Two ....................................... 94
Section Three ...................................... 112
Summary .............................................. 125

IMPLICATIONS & RECOMMENDATIONS.......................... 127

Discussion.. ........................................ 127
Conclusions ........................................ 135
Theoretical Implications............................. 139
Recommendations for Further Research................. 141


MARION COUNTY............................................. 142



D META-ATTENTION TASK .................................... 156

E DATA COLLECTION FORM................................... 164

F TEACHER RATING FORM FOR SIMS PROGRAM...................... 166

G CHARACTERISTICS OF THE LD STUDENTS ....................... 169


REFERENCE NOTES. ............................................. 187

REFERENCES .................................. ................... 188

BIOGRAPHICAL SKETCH............................................ 198

Abstract of Dissertation Presented to the Graduate Council
of the University of Florida in Partial Fulfillment of
the Requirements for the Degree of Doctor of Philosophy



Daniel Walker Becton

December 1982

Chairperson: Dr. Robert E. Jester
Major Department: Foundations of Education

The purpose of this study was to investigate the importance of

visual selective attention to learning disabled (LD) students' reading

performance. Another objective was to examine how this was influenced

by modality preference. The final goal was to investigate the reli-

ability and validity of the measurement instrument, Hagen's Central

Incidental Attention Task.

Section One compared a sample of LD students in the Systematic

Instructional Management Strategies (SIMS) precision taught reading

program (N = 19) to LD students in a multisensory program (N = 20).

Selective attention was significantly related to the dependent vari-

able, reading recognition, but the reading group and the interaction

were also significantly related to reading. Consequently, this result

was possibly due to differences existing between the subjects in the

reading groups prior to the study.

Section Two used a different, larger sample of LD students from

the SIMS program group (N = 67). No significant relationship between

selective attention and reading was found. Modality preference was

significantly related to reading achievement only when analyzed with

selective attention defined as group levels. Modality preference was

not significantly related to selective attention scores. Students

with low performance in visual modality subtests had decreasing selec-

tive attention scores paired with increasing intelligence (IQ) scores,

suggesting that modality preference was learned. However, the inter-

action was not significant.

Section Three used LD students (N = 16) from the multisensory

group to study the selective attention task. The results showed low

correlations with other measures of selective attention. A meta-

attention task showed correlations between selective attention and

the rating of "interest" as an important condition for attention.

Age and achievement performance correlated positively with rating

"reward" and negatively with rating "quiet" as important conditions.

The instrument did correlate significantly with teacher ratings of

some classroom attending behaviors.

In summary, the relationship found between selective attention

performance and reading achievement was not significant. Combining

central and incidental scores (C + I) for selective attention did pro-

duce the closest relationships. Modality preference seemed to be a

learned adaptation, not significantly related to selective attention.



The purpose of this study was to investigate the importance of

visual selective attention to learning disabled students' reading

performance. This study also examined how this relationship was

influenced by the students' modality preferences.

Rationale for the Study

Behaviors related to deficits in attention have often been cited

as a common characteristic of children with learning disabilities (LD).

Furthermore, many research studies and theories have asserted the impor-

tance of studying attention for a better understanding of learning dis-


Within the medical literature on learning disabilities, Silver

(Note 1) describes minimal brain dysfunction (MBD) children as dis-

tractible with short attention spans. He even refers to a subgroup

of "attentional deficit" children. Within psychological practice, the

Diagnostic and Statistical Manual of Mental Disorders (American Psychi-

atric Association, 1980) details the Attention Deficit Disorder which

is sometimes used in diagnosing children who have learning disabilities.

In special education, Mercer's (1979) basic textbook on learning dis-

abilities presents the cognitive process interpretation, which focuses

on attention and memory, as one of three major contexts for interpret-

ing the psychological processes involved in learning disabilities.

Researchers studying learning disabled children have documented

LD students' attentional difficulties. Conners, Kramer, and Guerra

(1969), Grassi (1970), Lasky and Tobin (1973), and Swanson (1980) have

investigated the auditory attentional difficulties. Anderson, Halcomb,

and Doyle (1973), Atkinson and Seunath (1973), Hallahan, Kauffman and

Ball (1974), Mercer (1975), and Swanson (1980) have documented the

attentional deficits in the visual domain. There is evidence that this

extends to inattentiveness in the classroom (Bryan & Wheeler, 1972)

and influences visual selective attention strategies in reading (Schworm,

1982). Torgesen (1977) has studied how these problems in selectively

attending lead to general deficiencies in problem solving strategies.

Samuels and Edwall (1981) asserted that "attention emerges as one likely

candidate for intensive investigation in the search for the etiology

of learning disability" (p. 353). Ross (1976) recommended that the

definition of a learning disability should include the inability to

sustain selective attention.

In the literature, no studies were found which examined the re-

lationship of selective attention to the reading performance of LD

students while in a precision teaching program. Hallahan (Note 2)

also reported that this relationship has not been investigated. In-

vestigation of the influence of selective attention on reading is

designed to increase understanding of the implications of the selective

attention literature for applications in classes for learning disabled


Specification of Terms

Learning disabled children. These were children identified as

such by a public school system. The selective criteria included an

intelligence test score not less than two standard deviations below

the mean, academic achievement below "expectancy age" guidelines, and

evidence of a psychological process disorder (see Appendix A).

Attention or selective attention. Selective attention refers

to the ability to attend to relevant features of a stimulus array that

also contains irrelevant proximal distractors. This was operationally

defined as scores obtained on the Hagen Central Incidental Attention

task (Mercer, 1975) as follows (see Appendix B):

1. The central task (C) was the one that the LD students

received instructions about initially.

2. The incidental task (1) was the later, unexpected

matching task.

3. The selective attention efficiency index (%C %I) was

the percent of the central task correctly completed

minus the percent of the incidental task correctly

completed (Tarver, Hallahan, Kauffman, & Ball, 1976).

4. The combined selective attention measure (C + I) was

simply the sum of both measures, reflecting all of

the interaction, distraction, and capacity.

5. The selective attention levels were defined by com-

bining overall capacity (C + I) with efficiency

(%C %I) or incidental task scores (I).

Reading achievement. This was defined by reading test scores on

either the Wide Range Achievement Test (WRAT) (Jastak & Jastak, 1978),

or the Peabody Individual Achievement Test (PIAT) (Dunn & Markwardt,


Modality preference. This was defined by performance on the Detroit

Tests of Learning Aptitude (Baker & Leland, 1967). Three groups were

identified. They were LD students with only low visual subtests, LD

students with only low auditory subtests, and LD students with a mixed

preference, i.e. any other combination.

Attending behavior. This was defined by a series of questions

about classroom behaviors that reflect attending (see Appendix C).

Meta-attention. The child's awareness or understanding of his/

her own attentional processes, strategies, and capacity. This was

tested using the instrument used by Loper, Hallahan, and lanna (1982)

(see Appendix D).

Statement of the Problem

The purpose of this study was to investigate the relation of visual

selective attention to reading performance of learning disabled children.

The question developed from the extensive literature asserting that

learning disabled children are frequently characterized as exhibiting

attention deficits and because of the literature showing precision

teaching to be a technique effective for teaching reading to learning

disabled children. The major question was: Do the selective attention

processes of learning disabled children underlie or determine their

reading performance in this program?

Significance of the Study

Because of the widespread and expanding use of precision teaching,

defining the relationship between academic performance when using this

technique and selective attention processes became important. The

literature suggested selective attention as the critical characteristic

that produced learning disabilities. This became an important applied

research question because it applied directly to so many LD classrooms

and training programs. This study focused on relating selective atten-

tion research more directly to current strategies, endorsed by many LD

teachers, that were actually being used in LD classrooms. Continuing

this concern for relevance or generalizability, this study used achieve-

ment and process measures currently widely used in LD programs.

Furthermore, a proliferation of research on Cognitive Behavior

Modification (CBM) has cited the selective attention research for support.

This study has some implications for the use of CBM to compensate for

selective attention deficits because it provides achievement perfor-

mance in a precision teaching intervention for comparison, identifying

selective attention characteristics of the population. Thus new

knowledge of how selective attention influences reading performance

coupled with the other research on how it related to CBM should help

provide for effective integration of the techniques in LD classrooms.

Research Questions

This study was designed to answer the following questions regarding

the influence of selective attention on reading performance.

1. Did using the Systematic Instructional Management Strategies

(SIMS) program favorably alter attending behavior so that LD

students experiencing reading problems attributable to selec-

tive attention would be differentially helped compared to

another method for teaching reading? Would the reading

performance in this other reading method and in non-learning

disabled students have been influenced by selective attention?

2. Was this relationship best characterized in LD students as

due to a limited filtering or limited capacity process

(%C %I), where central performance limits incidental

performance? Or was it best characterized as due to the

total processing capacity with distraction (C + I)?

3. Without comparisons to other teaching methods, would grouping

of LD students by level of selective attention performance

show differential reading improvement?

4. Would the effect of selective attention on reading perfor-

mance, after allowing for age differences, have been best

explained by incidental performance limiting central perfor-

mance as in the efficiency measure (%C %I), or by the

total capacity with distraction measure (C + I)?

5. Did low visual selective attention scores correspond to low

visual modality aptitude? Could these results have been due

to visual modality aptitude or a process underlying it?

6. Was selective attention in the LD group more closely related

to underlying modality aptitudes or to general intellectual


7. Were the effects ascribed to selective attention possibly

due to teacher differences?

8. Was this extensively used research instrument, Hagen's

Central Incidental Attention Task, measuring the same

underlying construct, selective attention, as other

instruments and techniques? Did the incidental score

really appear to increase at the expense of the central


The research study was organized around these questions in three

sections. The first section used three small samples of subjects. They

were students using the SIMS program, students using a traditional LD

reading program, the Visual-Auditory-Kinesthetic-Tactile program (VAKT),

and a sample of students referred but not admitted to the LD program

using the regular basal reading program. This section investigated

questions one and two. The second section used a sample of LD students

in the SIMS program larger than many of the selective attention

research samples and investigated questions three through seven. The

final section used students from the first section but compared their

performance on several other tests to their initial performance on

the selective attention task in investigating question 8.

Limitations of the Study

Hagen's Central Incidental Attention Task has been restricted to

research use and, because of the nature of incidental measures, its


use was only theoretically justified and supported. Validity and reli-

ability information were not available and there was some debate about

whether it clearly measured selective attention (Douglas & Peters, 1979).

This use of learning disabled students also placed restrictions

on the generality of these findings. The generality was also bound

by the limits of the instruments used for diagnosis and the public school

LD criteria (see Appendix A).

The variability of LD students also limited generality. These

research questions were based on other research using selective atten-

tion as a general characteristic of the group of students labeled

learning disabled.



An extensive literature asserting that learning disabled children

frequently exhibit attention deficits revealed the importance of this

research study. In addition to those studies already mentioned in

Chapter I which relate attention deficits to learning disabilities, this

chapter focused on research with Hagen's Central Incidental Attention

Task and similar instruments. Secondly, the importance of reading

achievement in the Systematic Instructional Management Strategies (SIMS)

program was established in this chapter by reporting the results of

achievement data on major precision teaching projects. Finally, current

research on the traditional learning disability concepts, modality

preference and multi-sensory teaching, was presented. The result has

been a clear rationale for the importance of investigating the interac-

tion between a concept that was called the "origin" of a learning dis-

ability (LD), selective attention, and one of the most promising tech-

niques for teaching LD students.

Selective Attention


Selective attention refers to any method of information processing

that allows the student to respond on the basis of a limited portion

of the available stimulus information. Two types of models explained

selective attention studies. One type, the filter theories, was developed


by Broadbent's (1958) research. These theories led to the development

of Hagen's Central Incidental Attention Task. They postulated mechanisms

that screen certain kinds of input at various stages of information process-

ing. Thus attention capacity was limited and the filtering mechanism

shut out irrelevant stimuli. The other type, the capacity theories,

postulated that the system, based on goals, selected certain inputs

and ignored others because of insufficient resources within the process-

ing system. The greatest success of capacity theories was their ability

to explain contradictory divided attention studies by ascribing different

capacity.requirements to the tasks being combined.

Samuels and Edwall (1981) did a comprehensive review of research

on attention focusing on aspects such as arousal, alertness, capacity,

and selectivity. In several studies it seemed poor readers and LD students

had difficulty focusing attention on a narrow band demanded by the task,

i.e., selectivity. The authors decided that vigilance did not tap the

sustained aspect of attention corresponding to school tasks. Further-

more, they reported that such tasks did not differentiate LD students

from normal students. Instead it differentiated hyperactive students

from other students.

Keogh and Margolis (1976) also reviewed a large number of attention

studies. However, they focused their review on the following three

aspects of attention that they felt related to remediation: (1) coming

to attention, (2) decision making, and (3) maintaining attention.

They felt that such a differentiated approach to any deficit had more

direct implications for remediation.

Harris (1976) reviewed the attention research, also focusing on

educational attention control. But he felt that attention as studied

related too closely to classroom behaving and that "the mechanisms

of attention have no correlation with academic performance" (p. 55).

He reported how techniques of drug therapy, reduced environmental stimula-

tion, and operant conditioning of drug therapy, reduced environmental

stimulation, and operant conditioning effectively produced behaviors

operationally called attention. He also suggested verbal self-direction

may be a future technique for improving attention behaviors. But generally

he felt "attention training should not be expected to improve academic

skills" (p. 108).

Koppell (1979) criticized the attentional deficit explanation

of learning disabilities saying that research must examine the strength

of the association between the extent of the learning disability and

performance in the experimental paradigm. However, if the measure

of the extent of a learning disability is defined by the academic per-

formance, then this is the major goal of the research questions from

Chapter I. Thus, unlike Harris' opinion, the relation between the

extent of LD students' academic deficiencies and the students' deficits

in selective attention seemed to be the critical measure of the impor-

tance or the usefulness of the hypothesis of selective attention deficits.

Several researchers have dealt with other deficits, such as main-

taining attention, as characteristics of LD students. Anderson, Halcomb,

and Doyle (1973) used a visual vigilance task successfully in signifi-

cantly differentiating between LD students and normal students. A

higher crror rate and longer response latency -reflected vigilance atten-

tion deficits. In a further study, Doyle, Anderson, and Halcomb (1976)

used this task with a visual distractor. While the LD subjects showed

evidence of an attentional deficit, hyperactive LD students were more

severe and much of the difference can be attributed to them. Swanson

(1980) also found older nomoactive (CA = 12) LD children made signi-

ficantly fewer correct detections and more false responses than did

normal children on a vigilance task.

Ross (1976), in an extensive review of the literature, reported

how studies of reading or learning disabled students led to his con-

clusion that "learning disabilities may thus be viewed as the result

of delayed development in the capacity to employ and sustain selective

attention" (p. Gl). He showed that studies using measures like motor

responses, heart-rate changes, changes in brain electrical potential,

and auditory messages repeatedly show that selective attention capacity

improved with age. He offered a theoretical model for selective atten-

tion development. The first stage was overexclusive (also overselec-

tive) attention. Aspects of a stimulus capture attention to the ex-

clusion of other aspects in this sage which normally extended from

infancy to preschool. The next stage was overinclusive attention.

This corresponded to a period of maximum incidental learning, cover-

ing preschool and elementary schooling. Finally, the student developed

selective attention, which influenced the decline in incidental learning

seen around age 12.

Ross' (1976) explanation for learning disabilities suggested some

possible problems in investigating the research questions from Chapter

I. The LD selective attention problem was not permanent, but a delay

causing the student to be in earlier, inappropriate stages during his

reading education. Thus the academic deficit developed, continued,

and hindered future learning, even after the selective attention capacity

matured. Thus maturation over time weakened the relationships in research

questions one to four. An analysis by age groups (hypothesis three)

clarified this.

Ross (1976) also suggested that some results reporting a preference

of auditory digits over visual digits may be a result of negative reactions

to the visual stimuli from repeated reading failure. He thus suggested

that such a modality based reaction may show a consequence rather than

a cause of the students' reactions. He also implied that the modality

dimension of stimuli may become less dominating of attention with

maturation. This suggested that a narrowing or broadening of attention

exaggerates, then reduces, LD students' modality preferences. This

will be elaborated on later in this chapter.

Dykman, Ackerman, Clements, and Peters (1971) also offered a com-

prehensive review of research as evidence of a specific learning dis-

ability syndrome with the cardinal symptom of defective attention. They

suggested that this was due to the interaction of the excitation from

the brain-stem reticular formation and the forebrain inhibitory system,

centering on the diencephalon. They used designations similar to Ross',

but only hyperactive LD children were considered to be overattentive

and hypoactive LD students were underattentive.

Schworm (1979, 1982) also reviewed the literature on LD attentional

deficits, but with an emphasis on reading. In one study (Schworm, 1979)

the treatment consisted of cues to get the student to direct attention

to distinctive features and invariant word properties. He suggested

that inadequate readers failed to develop the grapheme-phoneme corre-

spondences because they did not attend selectively to important stimulus


Schworm (1982) recently reported an impressive investigation very

similar to the goals of this research study. He administered a test

of selective attention, the Select-A-Figure-From-Many (SAFFM) test.

He used the Spelling Pattern Tests which he had developed (Schworm,

1979). Finally, he used the Wide Range Achievement Test (Jastak & Jastak,

1978), which was also used in this study, as outlined in Chapter III.

Comparisons between the LD readers and the low, average, and high

achieving readers from regular classrooms showed the LD readers required

significantly more trials on both parts of the selective attention task.

The letter pattern test results of the regular classroom readers corre-

lated r = .72 with their SAFFM results. Significant correlations showed

that both better achieving and older students completed both tests better

than other students.

There were also significant differences between the LD students

and other students on the two letter pattern and three letter pattern

of the Spelling Pattern Test (Schworm, 1982). Finally, the debriefing

of the selective attention task was analyzed for number of cues reported,

types of cues reported, and degree of interaction recorded (help from

experimenter), using multiple regression analysis. They were good pre-

dictors of reading achievement (R2 = .69), two letter spelling pattern
identification (R = .79), and three letter spelling pattern identifi-
cation (R = .69), as well as performance on both parts of the selective

attention test Al (R2 = .70) and B1 (R2 = .62). The present research

study also investigated the relation of selective attention to reading,

but used Hagen's Central Incidental Attention Task.

The Central Incidental Task

Tarver and Hallahan (1974) performed a literature review of 21

experimental studies. They noted how the hypoactive and hyperactive

LD students differed in some studies. They also noted several ways

that subjects with learning disabilities were different from other subjects.

LD students were deficient in their ability to maintain attention for

prolonged periods (vigilance). LD subjects were more impulsive, or

less reflective. LD students' hyperactivity could be situational-specific.

Finally, LD subjects exhibited more distractibility on tasks with embedded

contexts, or tests of incidental versus central learning. Their own

work with Hagen's Central Incidental Attention Task was a major part

of that research.

Maccoby and Hagen (1965) used Broadbent's ideas (1958) about

"focusing of attention" in studying individual responses to information

in the stimulus complex that exceeds their processing limit. Theoret-

ically the young child was handicapped in focusing attention selectively,

lacking previously established discrimination between task-relevant

and task-irrelevant aspects of the stimulus complex. They used a task

arrangement with background color matching as the central task and

picture matching as the incidental task. They also had a "distractor"

or subsidiary task. The subjects had to tap whenever a single bass note

played on a tape of a piano melody of high notes. The results showed

that central recall increases regularly with age. Distraction reduced

efficiency on the central task as errors rose by 26%. However, in-

cidental recall declined with age, and errors under distraction in-

creased by 15%.

Hagen and Kail (1975) reviewed a series of studies involving

central and incidental stimuli in the task, both auditory and visual.

The major pattern was replicated, with central recall increasing with

age while incidental recall did not change significantly until it

declined in older subjects. Though different modifications were tried,

no differences in central task performance occurred due to pairing

conditions. Apparently, just the mere presence of incidental pictures

was distracting, regardless of how they were modified. They suggested

that older students used verbal labeling and other task-relevant

strategies which younger students lacked. Several studies using differ-

ent tasks supported this trend with incidental learning declining

between ages 12 and 14 years. They also cited studies with retardates

that showed that selective attention improved as the MA label increased.

In Section Two, Chapter IV, of this dissertation, selective attention

was compared to increasing IQ scores in LD students.

Using a questionnaire with this attention task, Druker and Hagen

(1969) found that with increasing age there was a progressive rise in

the tendency to rehearse by saying only the task-relevant items. More

of the older subjects indicated they tried to look at only the relevant

item on the card. Another finding of this study with 80 normal subjects

was that developmental changes monitored by the task did not involve

improved visual discrimination.

In another study, Hagen (1967) used a vigilance distractor with

the Central Incidental Attention Task. Distraction significantly affect-

ed task-relevant performance and did not affect incidental performance

except at the oldest grade. He also had a control group using cards

with only the central stimuli. The results indicated that the presence

of a second picture had a deleterious effect at all age levels.

In a similar study, Hagen, Meacham, and Mesibov (1970) found that

with college students (N = 40) there was no deleterious effect shown

between one versus two stimuli. However, a verbal rehearsal condition,

repeating the name of the animal, had a detrimental effect on the college

students, primarily because of a primacy decrement. The verbal rehearsal

condition had a favorable recency effect. This verbal labeling did

not affect children (N = 96) ages 9 to 14. Hagen also reported an earlier

study which found that verbal rehearsal improved performance of children

from ages 6 to 8. A follow-up study (Hagen, Hargrave, & Ross, 1973)

used children of ages 5 to 8 to induce the verbal rehearsing found with

older children. However, in comparing a rehearsal alone to a rehearsal

with prompting, recall only improved at the younger ages, and only with


Wagner (1974) reported a study in Yucatan, Mexico, using Hagen's

Central Incidental Attention Task. They found the same developmental

patterns as other studies in an urban sample, but a different pattern

in a rural sample. The rural sample did not show a marked primacy effect.

An additional study found that unschooled urban adults were like the

rural sample and significantly different from schooled urban adults.

The results suggested that formal education is a major factor in the

ability to use verbal rehearsal or other attention and memory strategies

as measured in this attention task.

In his review of studies of faulty attention in children, Hallahan

(1975) dealt specifically with selective attention, attention span,

hyperactivity, impulsivity, and with distractibility in general. The

same central incidental task that Hagen developed was used in most of

these studies. The ability of normal children to attend to central

information and ignore incidental material increased with age until a

major drop in incidental recall at twelve to thirteen years of age.

With the groups equated by mental age, educable children performed below

normals while those not institutionalized performed similarly to normals.

Another study with educable retarded, spastic, cerebral palsied and

normal children showed no difference between groups on this task when

equated on mental age. Hallahan (1975) also cited a study where insti-

tutionalized retarded children did not differ from a normal group of

children on mental age.

Further studies (Hallahan, 1975) using Hagen's Central Incidental

Attention Task showed that low-achieving sixth grade boys performed

poorly compared to high-achieving boys. Also poor performance corres-

ponded with impulsive performance on Kagan's Matching Familiar Figures

Task. Hallahan (1975) reported that in several studies learning dis-

abled (LD) children had deficient selective attention performance, using

the central incidental task. Other studies using LD children or under-

achieving children and proximal distractors, such as the Stroop Color-

Word Test, the Portable Rod and Frame Test or other embedded designs,

showed less successful attending than normal children when facing dis-

tracting information. Hallahan (1975) suggested that distal distraction

conditions had not found the same negative effect as proximal distraction.

A test very similar to Hagen's Central Incidental Attention Task was

the Fruit-Distraction Test (Santostefano, 1964). It also investigated

proximal distraction and was used to study LD students. The test was

used for studying cognitive style; however, it related to selective

attention research when it dealt with the inability to effectively and

actively select, organize, assimilate and process information in the

context of distracting stimuli or information competing for attention.

Santostefano (1964) used this test for studying constricted-flexible

cognitive style. He found a "brain-damaged" group from a residential-

educational center did more poorly than a regular school group; however,

their IQ score means were 72 and 110 respectively. Santostefano and

Paley (1964) also found non-significant evidence of a developmental

trend from constricted to flexible, i.e., less difficulty in selectively

devoting attention to the central stimulus with age.

Santostefano, Rutledge, and Randall (1965) used 24 boys with a

reading disability and 23 matched control subjects. They found that

poor readers recalled significantly more background figures in the Fruit-

Distraction Test II and took significantly longer to read the distraction

card. Cotugno (1981) replicated these findings with 17 subjects having

reading disabilities and 17 control subjects. He concluded that there

were significant differences between disabled and non-disabled readers

on field articulation tasks requiring attention to relevant information

in the presence of irrelevant or peripheral and contradictory information.

These results are consistent with other studies of proximal distractors,

particularly Hagen's Central Incidental Attention Task.

Hallahan (1975) cited evidence for an adverse effect on LD student

performance from a linguistic distractor condition. With a simultaneous

presentation of auditory and visual material, older LD students seemed

to be deficient relative to normal in recalling the stimuli in pairs

while younger LD students did more poorly than normal students in re-

calling the stimuli in one modality and then the other. Hallahan (1975)

suggested that auditory stimuli are more distracting than visual ones.

Using other measures of attention, Hallahan (1975) cited studies

suggesting a negative relationship between attention skills and hyper-

activity. His own research related Hagen's Central Incidental Attention

Task to behavioral observation measures of attention and hyperactivity

in institutionalized learning disabled children. He stated the following:

There was no relationship between the laboratory
measure of selective attention--Hagen's C-I
task--and the behavioral measures of attention
and hyperactivity. Although at first glance it
perhaps seems appropriate to question the
utility of Hagen's task in predicting or explain-
ing relevant behaviors of learning disabled
children, the task has differentiated learning
disabled from normal children. (Hallahan, 1975,
p. 213)

This was an important consideration for evaluating proposed applications

of research built upon this central incidental task.

Tarver, Hallahan, Kauffman, and Ball (1976) studied selective atten-

tion, using Hagen's Central Incidental Attention Task, in younger and

older learning disabled boys. The study supported the idea that learning

disabled students have a deficit in selective attention which improves

with age. This suggested a developmental lag. They used 18 learning

disabled (LD) students approximately 8 years old. They found that the

central recall was greater for normal students, primarily due to the

lack of a primacy (positions 1 and 2) effect. This was also cited as

evidence for a verbal rehearsal deficit. It has been shown that verbal

rehearsal strategies underlay primacy recall.

Tarver et al. (1976) did a second study which had two matched groups

of older LD students. The average age was about 13 years for two groups

and about 10 years for the other two. This study dealt more directly

with the verbal rehearsal assumptions. While one matched group received

the normal Central Incidental Attention Task, the other group received

directions to verbally label, chunk, and rehearse the items of the task.

In these older students a primacy and recency effect occurred in all

groups, thus negating the influence of the special instructions of this

theoretical measure of verbal rehearsal. However, a light trend to

increased central measures and decreased incidental measures combined

in the efficiency calculation (%C %I) and showed rehearsal to have

a significant favorable effect.

The results supported the idea of constant developmental increases

in central recall with increasing age in LD students. This idea was

later developed fully into a developmental lag hypothesis. Reference

to the model of selective attention development in normals was used to

suggest that a developmental lag of 2 years was evident. The authors

further suggested that the absence of a significant decline in inciden-

tal learning at any of the age levels investigated suggested that develop-

ing verbal rehearsal strategies caused increases in central recall with

age rather than improvements in selective attention. Yet the theoretical

distinction between verbal encoding and selective attention was not

well defined. Furthermore, the relation of the verbal rehearsal treat-

ment to the efficiency measure (%C %I) was not clearly defined and the

results supporting this relation seemed tenuous in theory, magnitude,

and the number of subjects involved.

Swanson (1979) used 15 LD students and 15 normal students on Hagen's

Central Incidental Attention Task. He reported that when matched on

mental age (MA), there were no significant differences between groups

in selective attention scores.

Vrana and Pihl (1980) studied selective attention using both proximal

and distal placements of incidental stimuli. However, there were two

obvious, major faults in the study. The control group was significantly

different from the learning disabled group on intelligence. Also, after

making an incidental measure on the fifth card, on the sixth card an

effort was made to make another incidental measure. After students

were questioned once about an incidental stimulus, they probably did

not respond to the same stimulus as if it was an incidental part of

the task.

Vrana and Pihl's (1980) experimental procedure was very similar

to Hagen's task (the proximal condition). It provided insight on using

the technique. They used solid black circles as the central recall

item on large white cards with solid black squares as the incidental

stimuli. The stimuli were either close to each other in the center

of the card or apart from each other at each end of the card. On the

second, third, fourth and sixth cards questions about the circles were

used to calculate central recall. On the fifth and sixth cards, questions

about the squares were used for the incidental recall, along with a

question about the rows on card six.

Besides the problems already mentioned, the results were comparable

with other research, with the advantage in this study that proximal and

distal aspects were compared while minimizing other stimuli differences.

Thus the results, that learning disabled children had significantly

lower recall of central stimuli than normal children when both stimuli

were presented together proximall) but not in distal or incidental con-

ditions, clearly agreed with the suggestions of Hallahan (1975) about

proximal vs. distal distractors.

Tarver, Hallahan, Cohen, and Kauffman (1977) analyzed the previous

research on verbal rehearsal and selective attention and developed a

model for the developmental lag in learning disabled students based

on studies of normal students and 8, 10, and 13-year-old learning disabled

students. They then extended this research with a study of 15-year-

old learning disabled boys (N = 14) to demonstrate the overall pattern.

Their reliance on previous research such as Hagen's studies led

them to omit any control group. Thus the assumption of a direct com-

parison between the trends reported for normal children and this par-

ticular experimental group seemed to run some risk of an unrecognized

systematic difference being the real causal element. Use of the research

literature, primarily their own studies with small sample sizes, did

not seem justified, particularly with a task having limited psychometric

study. Consequently, in terms of experimental design, sample character-

istics and test administration in this study (Tarver, Hallahan, Cohen,

& Kauffman, 1977) the absence of a control group seemed unjustified.

The results of this study (Tarver, Halldhan, Cohen, & Kauffman,

1977) were compatible with the developmental lag hypothesis. There

was clear evidence of increasing selective attention at the older ages.

Central recall did not differ from the 15 and 13-year-olds. But a

steeper primacy effect for the 15-year-olds was used as evidence for

continuing development of verbal rehearsal strategies. Reciprocity

between central and incidental measures again was refuted.

A study by Hallahan, Gajar, Cohen, and Tarver (1978) attempted

to explore aspects underlying selective attention differences. Twenty-

eight LD junior high students completed two locus of control measures

and a group selective attention measure. This measure was a modification

of Hagen's Central Incidental Attention Task. The results showed that

the LD students scored significantly lower than normals on selective

attention central recall. They differed significantly from normals

on both locus of control measures, showing a greater degree of external

control. One surprising finding was that the two locus of control

measures did not correlate highly, leading the authors to speculate

that the LD student's external locus of control is extremely broad.

The general conclusion supported the idea of a quantitative developmental

lag by showing that the LD student is inefficient in using problem-

solving strategies.

Hallahan, Tarver, Kauffman, and Graybeal (1978) began the effort

to relate the research on selective attention and the developmental

lag more directly to the teaching techniques mentioned in several pre-

vious articles for making learning disabled children more actively in-

volved in using problem solving strategies. First they developed a

new version of Hagen's task that would allow repeated tests. Three

different classes of stimuli were used, i.e., animals, household objects,

and geometric figures.

Using this instrument they investigated the effects of reinforcement

and response cost. In the reinforcement condition (R) the subject

received 3 for every correct first card and 1% for the second card.

A response cost condition (RC) caused a loss of 3t for every incorrect

response from 60t initially given. The control condition (C) allowed

2t for responses to both cards.

The reinforcement condition resulted in both a recency and a primacy

effect, while the others showed only a recency effect. A measure of

the child's efficiency in focusing on high payoff (first probe) versus

low payoff (second probe) was presented as analogous to selective atten-

tion. Thus the significantly better performance under reinforcement

for recall of first-probed stimuli (7.56) than second-probed stimuli

(3.81) was explained as showing the child giving up attention to the

low payoff stimuli. However this theoretical relationship needed to

be examined more closely.

The same relation between a primacy effect and verbal rehearsal

suggested that LD students in the reinforcement condition were using

a verbal strategy to attend better. This suggested to the authors that

the LD child is able to use an appropriate strategy, and usually has

difficulty because, even knowing mediators, he is unable to produce

them at the appropriate time. The study by Hallahan, Tarver, Kauffman,

and Graybeal (1978) clearly agreed with the earlier research findings

and their implications for programs. The following by Tarver, Hallahan,

Kauffman, and Ball (1976) was still supported:

A gradual, though slower rate of progression through
the normal developmental sequence is indicative of a
quantitative learning deficit, and suggests that those
procedures which have been found to increase rate of
learning in all populations, e.g., repetition and
reinforcement, should be emphasized in the education
of the learning disabled. In contrast, confirmation
of a more basic, qualitatively different mode of
cognitive functioning would imply that teaching
methods should be qualitatively different for the
learning disabled and normal children. (p. 383)

Torgesen (1981) summarized much of his research on attention and

memory by saying that LD students have a less active, organized approach

to memory tasks. These were defined as control process inefficiencies.

This was compatible with the idea of a developmental lag producing a

quantitative problem. Torgesen even suggested intervention strategies

for learning to compensate for control process inefficiencies. He men-

tioned the use of incentives or reinforcement programs, the use of direct

instruction in processing strategies, and the use of orienting tasks,

such as those that require the repetitive manipulation of material.

There have also been studies using relaxation training or desensitiza-

tion training to improve attending, such as the study by Omizo and Michael

(1982) with hyperactive boys.

Argulewicz (1982) also used a direct intervention. Subjects were

trained in attending behaviors. Compared to a group only exposed to

modeling, this group did significantly better on the indexes of selective

attention used in the study. The indexes chosen were the digit span

subtest of the Wechsier Intelligence Scale for Children--Revised (WISC-R) and

the Memory for Sentences II (MSII) subtest of the Stanford-Binet In-

telligence Scale. In Section Three of Chapter IV these measures were

compared to Hagen's Central Incidental Attention Task.

Educational procedures falling under the rubric of cognitive

behavior modification have been suggested for the strategy-deficient

inactivity of learning disabled students. In one example, an LD student

was taught to monitor his on and off-task behavior, using a cue from

a tape recorder to pace his monitoring. On-task behavior in math and

handwriting was dramatically increased. Eventually use of the tape

recorder, and then self recording were gradually phased out (Hallahan,

Lloyd, Kosiewicz, Kauffman, & Graves, 1979).

In another study Hallahan, Marshall, and Lloyd (1981) used self-

recording to increase on-task behavior of three severely LD students

in one reading group. Observer ratings of the on-task behavior showed

a doubling due to self-recording. They also gradually withdrew wrist

counters and then the tape recorder. Lloyd, Hallahan, Kosiewicz, and

Kneedler (1982) did a comparison of self-assessment and self-recording.

Self-assessment consisted of the student asking himself whether he was

paying attention when he heard the tape-recorded cue. Using three sub-

jects in the second part of the study, self-recording was found to be

superior to self-assessment, but neither increased productivity.

A final approach to investigating selective attention dealt with

the student's awareness of his attending, i.e., meta-attention. Loper,

Hallahan, and lanna (1982) developed an instrument to measure meta-

attention in LD students. Performance on the meta-attention task corre-

sponded to academic achievement for normal subjects, but not for the

LD students. They also noted a developmental trend from younger children

being more impressed with rewards to older children placing more value

on interest when asked the circumstances leading to better attention.

Tarver (1981) attempted to integrate this research using the term

underselective to refer to LD attention behaviors. Her article described

the development of explanations based on the results of studies of under-

selective attention. Early explanations of distractibility were refined

to differentiate proximal distractors for measuring selective attention.

Next analysis of the patterns of central recall scores by serial

position led to identification of a primacy effect deficit. This was

interpreted as a verbal rehearsal deficit. Evidence of a failure to

use effective verbal rehearsal strategies, even when known by the subject,

also suggested problems with metacognition. Other research has studied

how linguistic subskills of attention and memory contribute to the LD

information-processing deficit. She also outlined the creative ability

hypothesis. Thus the LD child's underfocused, underselective attention

might have been indicative of high creativity,

Investigations of selective attention have led to a wide range

of related investigations. The most important concern was the distinc-

tive academic deficit that LD students experience. The merit of these

other constructs will ultimately be the degree to which they explained

and helped to remediate the academic problems. The effectiveness of

precision teaching in interacting with the selective attention deficits

to produce reading achievement was an example of these critical basic


Precision Teaching

Attending Behavior

The behavioral approach to attention described it as an unobservable,

unmeasurable phenomena, and attending or its actual verification through

observable responses is the relevant concern (Haring, 1968). Thus the

question of attention centered on the teacher's efforts in guiding attend-

ing and responding to relevant dimensions of stimuli. Attending was

a learning behavior. Haring (1968) further asserted that "faulty attend-

ing and inaccurate, slow responding can be corrected through more precision

in the manipulation of instiuctional conditions available to the class-

room" (p. 45). Precision teaching used most of the features of applied

behavior analysis recommended by Haring. Thus it should be particularly

helpful in guiding attending and responding to relevant dimensions

of stimuli.

If a history of ineffective patterns of attending was also reflected

in a minute sample of behavior, Hagen's test, then it could be used to

test whether academic performance corresponds to student's attending

patterns even when teaching with a program guiding attending. Precision

teaching should be particularly effective with those students with poor

selective attention performance. One assumption, which is not supported

by a behavioral view, was that poor selective attention performance

corresponded to a history of academically ineffective patterns of attending.

General Features

Mercer and Mercer (1981) described applied behavior analysis (ABA)

as one of the newer approaches in special education. They outlined

its features as including (a) measurement systems, (b) precision

teaching, (c) instructional aims, and (d) learning principles.

Thus, precision teaching is characterized by a direct,
continuous, and precise measurement system, and it
provides an accurate measurement of student progress.
The teacher can obtain a record of past performances,
a plan for where the student is going, and an estimate
of when the student will get there. (Mercer & Mercer,
1981, p. 6)

Precision teaching is "one way to plan, use, and analyze any teaching

style, technique, method, or theoretical position--old or new" (Kunzelman,

Cohen, Hutten, Martin, & Mingo, 1980, p. 12).

Bradfield (1971) outlined four components to precision teaching.

The first was the system of recording and charting data, using a chart

such as the six-cycle logarithmic chart developed by Lindsley. The

second component was the need for precise selection and definition of

the target behavior, called "pinpointing." Bradfield (1971) emphasized

two of Lindsley's basic characteristics for "pinpointed behaviors" or

"movements": (1) They must be a complete movement cycle, having a

definable beginning and end; (2) They must pass the "dead man's test,"

i.e., if a dead man can do the behavior, it can not be counted. The

third component Bradfield (1971) used is Lindsley's IS-DOES formula.

This referred to the five basic parts of the learning environment.

In the IS formula, the five parts were parts that only have the potential

to change behavior. They are as follows:

1. Program, environmental setting such as location, time

of day, etc.

2. Antecedent event, factors such as instructions, demon-

strations, materials which might result in the behavior.

3. Movement cycle, the behavior being measured.

4. Arrangement, the numerical ratio between the movement

and the subsequent event.

5. Subsequent events, events which may result from the

movement cycle such as praise, grades, withdrawal of

privileges, etc.

This DOES part of the formula is reserved for when these have demon-

strated a behavioral function and become the following: (a) disposition

components, (b) stimuli, (c) responses, (d) contingencies, and

(e) consequences. The final, fourth component outlined by Bradfield

is the Behavior Bank. It used a computer to store cases as examples

of procedures for particular behavior problems.

Mercer (1979) presented a very concise description of the teacher's

activities in precision teaching. They include the following:

(a) selecting a pinpoint or target behavior; (b) developing a task

sheet or probe for evaluating pupil progress in daily timings;

(c) graphing these data daily, setting instructional aims, and par-

ticularly teaching; and (d) analyzing the data and making instructional


The critical elements were the measurement, the rate of the behavior,

and the charting of this measurement. This usually meant that frequency,

the count of behaviors divided by the number of minutes, was recorded

on the Standard Behavior Chart. The chart's sensitivity ranged from

1 movement per 1440 minutes (24 hours) to 1000 movements per minute

(Koorland and Martin, 1975). The widespread use of this chart promised

to improve communication between researchers and instructors.

Related Terms

The following were some terms used in precision teaching:

Accuracy Measure--The proportion of correct behaviors to inappropriate


Frequency--How often something happens in a certain unit of time.

Celeration--The measure for summarizing the trend and direction of

day-to-day frequencies on a chart.

Acceleration--A daily increase in the celebration line.

Deceleration --A daily frequency value decrease causing the celebration

line to move down the chart.

Aims --The specification of precise pupil response, the conditions

surrounding it, and the criteria for acceptable performance.

Proficiency --The level of student performance that is automatic enough

to show a desired frequency (rate) representing the performance

of successful performers, reflecting mastery.

Validation Studies

The Precision Teaching Project of Great Falls, Montana, was one

of the most successful projects in the country (Beck, Note 3). Six

schools were used in the study, two with 5% of their families' incomes

under $5,000, two with 12%, and two with 20%. One member of each pair

was randomly assigned to the experimental group. The study involved

students in kindergarten and grades one to three who had "learning

deficits." The degree to which this category overlapped with learning

disabilities is not known. The screening for this group involved per-

forming at one-half or less of the median celebration of classroom peers.

Though screened in exactly the same manner, learning deficit students

in the control schools were not identified until the project was com-

pleted. In the experimental schools the remediation consisted of only

20 to 30 minutes per day, utilizing minute drill sheets focusing on

basic tool skills.

The results included the three grades in the three types of schools

with three behaviors: write numbers, hear-write numbers, and see-say

letters. Nineteen of 27 pretest comparisons showed no significant difference

or a significant difference favoring the control groups. Of these 19

comparisons, the posttest results for 14 showed the experimental groups

to be significantly superior. Three comparisons showed no differences.

Only one, the see-write numbers task in grade two of the second type

schools (12%), had no significant difference on the pretest and sig-

nificant difference favoring the experimental group on the posttest.

Analysis of eight cases, five being see-say letters tasks, was not yet

available because the experimental groups were superior on the pretest.

Finally, in rating the educational importance of these studies, the

report noted the following facts (Beck, Note 3):

1. The magnitude of the effects of precision teaching were


2. The procedure was most effective in grade 1.

3. The procedure was not sensitive to economic status.

4. Its continued use was inexpensive.

Next a precision teaching approach to regular education was developed

(Beck, Note 4). Precision teaching was implemented at Sacajawea Elemen-

tary School. This implementation actually focused on the following

five components:

1. Screening and Identification

2. Direct and Daily Measurement

3. Charting

4. Data Decisions and Instructional Intervention

5. Support (Materials Bank)

Another school was designated initially as having equivalent or relevant

variables of socio-economic status and achievement test patterns. Over

a previous four year period, there was no difference in the schools.

The Sacajawea first grade precision teaching pupils had significantly

higher reading scores (an eight month gain) on the Wide Range Achievement

Test (WRAT) reading subtest. There developed a large percentile difference

between the schools, favoring the precision teaching subjects. The

Iowa Tests of Basic Skills was used to compare fourth grade students

for four years. During the year with no treatment and the first year

of precision teaching there was little difference between Sacajawea

and the district. During the third and fourth years (1976-77) there

was a dramatic improvement over the district of +24 percentile points

in reading and +32 percentile points in math.

The SIMS Program

The Systematic Instructional Management Strategies (SIMS) Program

was originally designed to provide a comprehensive self-contained edu-

cational program for severely learning disabled students. The Armatage

SIMS Center was established in 1972. Based on its success, the program

was extended to include resource rooms which serve the mild to moderately

learning disabled students (Harvey, Note 5; Wiseman, Note 6).

There were two aspects to this program, the SIMS materials and

the SIMS procedures. The SIMS materials included a highly structured

phonics sequence which encompassed the basic coding skills of reading

and spelling. Coding skills were divided into 53 categories. Each

category had two category word lists, sentence lists, and four stories.

The procedures advocated that in each category a 90-100% performance

criterion be used for accuracy. Proficiency was monitored daily on

word lists with a criteria of 40-60 words correct per minute with two

or less errors for mastery. Daily rates for the stories suggested 80-

100 words correct per minute with two or fewer errors. In this way

the SIMS program combined task analysis, criterion referenced teaching

and testing, contingency management, and precision teaching (Harvey,

note 5; Wiseman, Note 6).

SIMS Evaluation

Though the SIMS program used precision teaching and individual

charts, group comparisons were used to assess the program's effective-

ness in the 1976-77 year. Students made statistically significant

gains. The SIMS group was also compared to the students in the Minne-

apolis Special Education Division school based resource program. The

SIMS students were originally chosen from these same resource programs

based upon their failure to make "acceptable gains." The results

showed a 1.05 average gain, with 0.77 being the gain bythe elementary

students in the year prior to being in the SIMS program, and with .84

being the gain of the comparison group. The report also presented

charts of the average student's time in each category and the weekly

celebration rate for each category (Harvey, Note 5).

When the validation report was submitted to HEW's Joint Dissemina-

tion and Review Board (JDRP), WRAT reading standard scores were compared

between the SIMS group (N = 118) and the control group (N = 147) yielding

a significant difference (p<.001). In the evaluation for the 1977-78

year, t-tests between SIMS students' pre-posttest gains were greater

than might be expected by chance (p <.001). A second comparison, prac-

ticed by the United States Office of Education, compared the average

gain to one-third the normed standard deviation of the test, "suggesting

the gain is educationally significant" (Wiseman, Note 6, p. 27).

Thus there was evidence supporting the SIMS as a successful pre-

cision teaching program, though some of the analysis seems weak or in-

appropriate. The use of gains scores has well-known inherent weaknesses.

The extension of this into a predicted gain score using the following

formula seemed unjustified:
1 Score
Years x Time

Score = years in school at time of entry

Time = time elapsed between pre and posttests

Furthermore, there were reasons to expect that all LD students would

make some improvements in reading in this program. But student per-

formance while in this program could be used to represent reading

achievement in studying the relationship of student's visual selective

attention levels to reading performance.

Modality Processing


Selective attention referred to a method of information processing

while modality processing also referred to the way information was pro-

cessed. Thus several research questions asked how selective attention

scores corresponded to modality aptitude scores. They were attempting

to clarify whether one process underlay the other, whether they were

separate processes, or whether they were both expressions or symptoms

of other underlying processes.

This question was very important because the concern for psycho-

linguistic or modality processes was one of the earliest and most dis-

tinctive characteristics of research, identification, and remediation

of learning disabilities. Most definitions of learning disabilities,

except for the behavioral definitions like those used with precision

teaching, still defined it as due to underlying psychological processes.

Psychological testing focusing on cognitive, perceptual, and expressive

abilities of these students had been used to identify, diagnose, and

to suggest special instructional strategies and materials. Modality

preference was one of the more global aspects of these psychological


The modality model referred to LD students as primarily auditory

or visual learners according to relative strengths and weaknesses in

their auditory and visual channels. Matching the modality preference

to particular teaching strategies had been studied as possible aptitude -

treatment interactions (ATI).

Arter and Jenkins (1977) found that with LD teachers in Illinois,

97% were familiar with the modality model, 95% believed research supported

it, 99% felt modality should be a major consideration in educational

prescriptions, and 78% reported using it frequently. Of 700 questionnaires

sent out, this analysis was based on 340 (48.57%). Olson, Mercer, and

Paulson (1981) suggested that testing for a process disorder is con-

tinuing, even when such a determination was not required in the criteria

for identifying a learning disability.


Two testing instruments that were widely used to measure such

abilities in LD children were the Detroit Test of Learning Aptitude

(DTLA) and the Illinois Test of Psycholinguistic Ability (ITPA). Numerous

research studies, often using the ITPA, have failed to establish the

utility of modality preference for instructional strategies. Very little

research has investigated if the DTLA measures psychological processes

related to academic performance.

The study by Sandstedt (1964) used the DTLA with 45 LD students.

She found students were more successful with the visual test of unrelated

objects than with the auditory test of unrelated words. This was a

significant difference. Olson, Mercer, and Paulson (1981) used the

DTLA with 65 adolescents. In correlations with the WRAT and DTLA, the

highest were oral directions correlating .35 with math, verbal opposites

correlating .33 with reading, and auditory attention span for objects

correlating .27 with reading. It was also interesting that visual atten-

tion span for objects correlated -.03 with reading. They concluded

that the DTLA measures are not related to academic achievement.

Braggio, Braggio, Varner, Smathers, and Lanier (1980) studied the

relation of optimal response modes in 34 LD students to methods of task

presentation. The students' optimal response mode was determined by

administering 10 subtests of the DTLA. Items missed from five selected

subtests were then randomly assigned to several methods of task presen-

tation. One possible criticism was that the five subtests were all

heavily visual; however this may have been necessary for several options

in task presentation. In the first condition, the cover response (CR)

condition, the student could make no visual or auditory response. In

the vocal response (VR) condition the student told the examiner what

was required for a correct response. On the last, the visual-manual

response condition (V-MR), the student demonstrated the response by

moving a finger. Then a DTLA posttest allowed a determination of which

method produced the greatest decrease in errors and was thus the optimum

response mode. Then a paired associate task was administered using

a method of task presentation that resembled the optimal response mode.

In the unmatched condition, the presentation was in the least effective

mode. Using a one-way ANOVA, a significant main effect for CR, VR,

and V-MR treatments was found. On the paired associate task, signif-

icantly more paired-associate items were recalled under the matched

condition. They explained their results by saying that "LD children

may have difficulty filtering the demands of the task from irrelevant

cues . and then selecting the optimal response mode" (p. 94).

Lilly and Kelleher (1973) found a significant interaction using

educationally handicapped subjects and simply one modality memory tasks.

The tasks were very similar to the auditory DTLA attention span for

unrelated words and the visual attention span for objects except that

in the visual test words were used instead of pictures. Stories from

Level 2 of the Durrell Analysis of Reading Difficulty were presented

on tape or print. Students scoring highest on the visual test performed

best on the print while students scoring highest on the auditory test

performed best on the tape.

Bursuk (1971) classified 90 readers by their sensory modality learning

preference. She used aural-visual teaching and only visual approaches.

There was not a simple interaction since all subjects in the aural-

visual approach performed significantly better than the other group.

However the aural-visual approach appeared to be more effective with

the auditory learners while the visual approach was more effective with

visual learners. However, this was not clear evidence for a modality-

treatment interaction since the aural-visual treatment involved both

modalities and possibly required more involvement or attention.

Using normal students, Robinson (1972) found only auditory dis-

crimination contributed to reading achievement for both sight and phonics

programs over three years. Ringler and Smith (1973) used modality specific

treatments, though all subjects received pictoral materials and oral

discussion, resulting in no significant differences among groups.

Vandever and Neville (1974) found no differences between groups taught

to modality strength or to weakness. Smith (1971) used culturally dis-

advantaged students and found no significant differences. Foster, Reese,

Schmidt, and Ohrtman (1976) also found significant differences favoring

audible learners when taught by auditory methods, but there was no

significant disordinal interaction. Newcomer and Goodman (1975) failed

to find an ATI interaction with 167 fourth grade subjects. They did

find that the modality variable was most important for low auditory

learners. The visual treatment mode was superior for both modality

preferences. In an attempt to relate auditory or visual psycholinguistic

skills to phonic decoding (more auditory) or sight-word reading,

Richardson, DiBenedetto, Christ, and Press (1980) concluded that subjects

can not be "successfully sorted into auditory and visual learner categories"

(p. 77)..

Several people have reviewed large numbers of studies looking for

a modality and treatment interaction. Arter and Jenkins (1977) found

only one supporting study out of 15. Haring and Bateman (1977) listed

the results of the first review and mentioned three additional studies

that related reading instruction to traits other than relative modality

patterns. They reported one significant modality interaction in addition

to the Bursuk (1971) study. Silverston and Deichmann (1975) discussed

many of the same studies. They pointed out the lack of investigation

into relationships of tactile and kinesthetic modalities to reading as

well as the need for more investigations of perceptual shifting and

reading. Ysseldyke (1973) reviewed many of these same studies and

pointed out some of the problems in looking for ATI relations. He also

said many problems were due to the lack of ITPA subtest reliability.

Tarver and Dawson (1978) reviewed 15 studies dealing with the inter-

action of perceptional modality preference and method of teaching reading,

including 11 studies covered in the other reviews. The results showed no

interactions between modality preference and the method of teaching reading.

Larrivee (1981) also reviewed studies for the effect of modality

preference on commercial reading programs in schools, specific reading

skills using reading related treatment tasks, and comparisons of auditory

and visual modes as mediational channels. She emphasized that in most

studies it was difficult to identify preferred modality. Many studies

used different measurement instruments. Often classification was seem-

ingly arbitrary and only a small percentage of the subjects showed

a marked preference. Most measurement devices lacked the necessary

reliability. She concluded that differentiating instruction by modality

preferences did not facilitate learning to read.

Hammill and Larsen (1974) provided one of the most comprehensive

reviews, covering 39 studies using the Illinois Test of Psycholinguistic

Ability (ITPA). Of the 280 experimental-control comparison, only 106

supported training of psycholinguistic skills. One interesting point

in their table was that 11 articles dealt with educably mentally re-

tarded subjects, five with trainably mentally retarded subjects, 19

with disadvantaged subjects, and four with "other" subjects. The

category of LD was not presented in the table codes. The conclusion

was that "the efficacy of training psycholinguistic functioning has

not been conclusively demonstrated" (p. 12). Minskoff (1975) provided

a criticism of many aspects of Hammill and Larsen's (1974) review.

Three major problems involved the incompatibility of the nature of

the subjects, the nature of the treatments, and the experimental designs.

She emphasized that many of the subjects did not have learning disabilities.

Lund, Foster, and McCall-Perez (1978) reanalyzed 24 of the same 38

studies. They found that six studies showed positive results. Of

10 studies reported as showing negative results, they reported four

actually showed positive results and were reported inaccurately. They

reported that two others contained insufficient data, and two compared

treatment groups instead of trained versus untrained groups. An addi-

tional three studies were positive but limited in scope. Finally,

the last four studies had miscellaneous problems in the way they were


Hammill and Larsen (1978) replied by focusing on the 10 articles

showing negative results. They agreed that one study reported as nega-

tive was actually positive, but the other three were correctly reported.

They also replied persuasively to the other criticisms.

Larsen and Hammill (1975) also did a review of 60 studies of visual

perceptual abilities and school learning. They found the results to

be mainly contradictory. The major contaminating influence was the

fact that most studies lacked control for age and intelligence.

Kavale and Glass (1981) explained the need for meta-analysis in

exceptional education using the effect size statistic. They cited

the example of the psycholinguistic debate as an example of the need

for this more systematic technique for generalization across studies.

Kavale then did a meta-analysis of visual perceptual skills and read-

ing (Kavale, 1982). Kavale (1981) also performed a meta-analysis on

the literature assessing the efficacy of psycholinguistic training.

He examined the original 38 studies and six additional studies since

1974. Six were not obtained and two were eliminated, so he based the

analysis on 36 studies. He concluded that the literature had demon-

strated the effectiveness of psycholinguistic training.

Larsen, Parker, and Hammill (1982) replied with a series of crit-

icisms of Kavale's study, primarily focusing on the articles chosen

since Kavale (1981) did not use all of the same studies. They also

performed their own meta-analysis including the additional studies.

In no case did the change due to training exceed the standard error

of measurement (SEM). Again the results failed to demonstrate that

psycholinguistic training had value.

Torgesen (1979) pointed out that in addition to the controversy

over training psycholinguistic processes, generalization to academic

skills still lacked solid evidence. He suggested that task-centered

process assessment be substituted for the child-centered process assess-

ment. This would be a type of task analysis to measure the processes

required for the performance of specific academic tasks in specific


A unique approach to the problem was the single subject design

study by Koorland and Wolking (1982). Using behavior analysis, they

investigated the consistency of modality preferences across tasks when

reinforcement contingencies were either consistent or inconsistent

with the preferences. Experimental control was demonstrated rapidly.

When reinforcement contingencies favored visual responding, visual

responses more than doubled the baseline, and exceeded the auditory

responses (two out of three). The surprising result was that, using

reinforcement, performance in the nonpreferred modality approached

or even exceeded performance in the preferred modality.

These results suggested that modality preference is heavily in-

fluenced by the reinforcement the subject receives. Thus it would

seem that modality preference was a measure of learning heavily in-

fluenced by whatever reinforcers the subject had been receiving. This

explanation was compatible with the ambiguity of the research in the


Multisensory Teaching

Multisensory approaches to teaching reading and spelling have

been used with reading disabled students since the 1920s. The modality

processing theories about learning disabilities reinforced the use

of these reading approaches. The theories explained program effective-

ness as due to overcoming the limits of weak modality processing by

using several modalities, some stronger, to reinforce each other. Thus

the literature on modality preference, particularly on aptitude-treat-

ment interaction, also applied to multisensory teaching.

One of the most widely known techniques is the Visual-Auditory-

Tactile-Kinesthetic (VAKT) Technique (Fernald, 1943). This technique

was used with the comparison group, in place of the SIMS program, for

the Section One comparisons in this dissertation.

Thorpe, Lampe, Nash, and Chiang (1981) did a single subject design

study comparing the VAKT technique to a visual-auditory technique.


The results showed the VAKT to be the superior procedure. A review

of the literature by Myers (1978) reported 16 studies where the method

was useful or successful, and several studies where the results were

negligible. His conclusion was that there was little research finding

"differences of gain in reading" when using the multisensory approach.

The VAKT technique does represent a widely used method representative

of traditional LD reading remediation.



The purpose of this research was to investigate the importance

of visual selective attention to learning disabled student's reading

performance. Eleven hypotheses were used to investigate the important

aspects of this study. They were designed to especially compensate for

two major limitations in the selective attention literature review, i.e.,

the size'of the learning disabled (LD) student samples studied, and the

relevance of the cognitive concept, selective attention, to the reading

performance of students in precision taught reading programs.

Section One

As stated earlier the research questions were investigated in

three sections. The first section used three small samples of subjects.

These subjects were either in the SIMS reading program, in a traditional

LD reading program (the Visual-Auditory-Tactile-Kinesthetic or VAKT

approach) or, for the small sample of students referred but not admitted

to the LD program, in a regular classroom basal reading program. This

section investigated hypotheses 1 and 2.

Hypothesis 1. Precision teaching improves attending and thus

alleviates selective attention's effects on reading performance among

the three reading groups. Therefore, a VAKT taught group's reading

scores will be most influenced by selective attention, a precision

(SIMS) taught LD group's reading scores will be less influenced, and

a non-LD group's scores will be least influenced by the student's selec-

tive attention level.

This first hypothesis investigated the research question which

asked if precision teaching (SIMS) favorably affected attending be-

havior enough that LD students experiencing reading problems attri-

butable to selective attention would be differentially helped compared

to other reading teaching methods. It also revealed how reading per-

formance in all groups would be influenced by selective attention.

Hypothesis 2. Selective attention will relate to reading perfor-

mance, i.e., lower visual selective attention performance is related

to lower reading achievement performance. This will be shown by the

total task performance measure, combined selective attention (C + I),

more strongly than it will be shown when measured by the percentage

selective attention measure (%C %I). This last measure has often

been used as an indication of selective attention efficiency (Tarver,

Hallahan, Kauffman, & Ball, 1976).

This hypothesis investigated whether the relationship between

selective attention and reading was best characterized as due to the

limited filtering or limited capacity process defined in the literature

(%C %I), or to the student's total processing capacity under distraction

(C + I). It used both ways of defining selective attention.

Section Two

The second section used a sample of LD students in the SIMS program

larger than most of the selective attention research groups, It inves-

tigated hypotheses three though seven.

Hypothesis 3. Among three groups of LD students differing in

selective attention, the most effective selective attention group will

have the highest reading achievement score. The less effective selec-

tive attention group with good ability will have lower reading achieve-

ment scores. The less effective selective attention group with low

memory/attention ability will have the lowest reading achievement scores.

This hypothesis investigated the research question asking whether

grouping LD students by selective attention performance would show

differential reading improvement among the groups. The three groups

of students were identified according to their scores on the Hagen's

Central Incidental Attention Task. Group one had the poor selectors,

i.e., students showing poor selective attention but better memory or

attention. Group two had the poor performers, i.e., students with

both low selective attention and low memory/attention performance.

Group three had the subjects who showed little distraction with good

selective attention performance.

Hypothesis 4. Selective attention will relate to reading perfor-

mance on the Wide Range Achievement Test (WRAT). Low selective atten-

tion performance will be more closely related to low reading achieve-

ment when measured by combined selective attention (C + I), than when

measured by the composite, selective attention efficiency measure (%C -


This hypothesis investigated the question which asked if selective

attention's effect on reading performance would be explained better

by the incidental performance limiting central performance efficiency

measure (%C %I) or by the total capacity measure (C + I). This was

similar to hypothesis 2 in comparing the different reading methods

except that it used the large LD student sample.

Hypothesis 5. The visual selective attention task will relate

to modality test performance so that students low on visual modality

aptitude will score low on visual selective attention, resulting in

a high correlation. This will also help clarify any interaction be-

tween different but related underlying cognitive structures.

Hypothesis 6. The selective attention task theoretically measures

an underlying LD student characteristic. Therefore more of this variance

will be explained by student modality preference (Detroit Test of Learn-

ing Aptitude) than by student IQ scores.

These hypotheses examined how visual modality aptitude, one of

the first "process" constructs used for explaining learning disabilities,

related to selective attention. Low visual selective attention was

expected to relate to low visual modality aptitude, and these would

be related more to the processes underlying the specific learning dis-

ability than to general cognitive processes. Investigating these re-

lations provided information on how the two psychological constructs

related to each other.

Hypothesis 7. Differences between the students' LD teachers will

influence their reading achievement scores more than the influence

of the students' selective attention performance. This hypothesis

was intended to make sure that any effects ascribed to selective atten-

tion were not due to other influences, such as teacher differences.

Section Three

The third section used students from the first section. It com-

pared their performance on several other tests to their initial per-

formance on the selective attention task.

Hypothesis 8. Scores obtained from Hagen's Central Incidental

Attention Task will correlate significantly with other measures of

selective attention. This used the indices recommended by Argulewicz


Hypothesis 9. Hagen's Central Incidental Attention Task scores

will correlate significantly with meta-attention scores, revealing

a developmental trend from reward orientation to interest orientation.

These two hypotheses were intended to help determine when Hagen's Central

Incidental Attention Task was measuring the same underlying selective

attention construct as other instruments and techniques used to study


Hypothesis 10. Practice effects in a test-retest administration

of Hagen's Central Incidental Attention Task will produce an improve-

ment in incidental task scores at the expense of central task scores.

These data were intended to clarify the filtering or limited capacity

ideas by readministering the task.

Hypothesis 11. Teacher ratings of classroom attending behavior

will correlate significantly with LD student performance on Hagen's

Central Incidental Attention Task.

This hypothesis represented an attempt to clarify the relation

between attending behavior in the classroom and the experimental instru-

ment. The attending behavior in the classroom should have been ade-

quately represented by ratings given by the student's regular classroom


Research Design

This study was designed to systematically proceed through the

investigation of these 11 hypotheses in three sections. Each hypothesis

will be the subject of a separate analysis. Each section represented

a different group of subjects.

In Section One, the first analysis used three reading groups from

Marion and Brevard Counties, the SIMS program LD group, the VAKT (Visual-

Auditory-Tactile-Kinesthetic) program LD group, and the classroom program

non-LD group. Later, because of the small size of the third group,

only the SIMS and VAKT program groups were used. In Section Two a

different, larger group of LD students in the SIMS program in Marion

County was used. In Section Three a small sample of students from

Section One was used again. These group labels, i.e., SIMS, VAKT,

and Regular, are used in Table 1 to clarify the variable involved in

the testing of each hypothesis. Specific information on the subjects

will be given later.

Table 1

Variables Examined for Each Hypothesis


SIMS/Marion LD
VAKT/Brevard LD

SIMS/Marion LD
VAKT/Brevard LD

SIMS/Marion LD



SIMS/Marion LD

SIMS/Marion LD


thesis #



VAKT/Brevard LD (C) WISC-R
Digit Span

(C) Stanford-
Binet Subtests



2. 19

3. 67

6. 67

7. 67

8. 16

S. Attention


S. Attention
Groups (2)

Rdg. Programs

S. Attention

R. Programs (2)

S. Attention

Age Group (4)

S. Attention

S. Attention

Mod. Pref.

Mod. Pref.

IQ Score

S. Attention

LD Teacher


PIAT Reading

PIAT Reading

WRAT Reading

WRAT Reading

WRAT Reading

S. Attention
(C + I)

WRAT Reading

Table 1 continued

Hypo- Subjects Independent Dependent
thesis # Number Group Variables Variables

9. 16 VAKT/Brevard LD (C) Meta- S. Attention
Attention Task Subscores

10. 16 VAKT/Brevard LD (C) Selective S. Attention
Attention Task Task


11. 16 VAKT/Brevard LD Attending S. Attention
11 Non-LD Behavior Task

C = Continuous variables

Independent Variables

As shown in Table 1, the independent variables were measures of

student characteristics. They included selective attention perfor-

mance, modality preference group, intelligence (IQ) scores, and

chronological age.

Selective attention. Selective attention performance was ob-

tained from the results of testing with Hagen's Central Incidental

Attention Task (Mercer, 1975). These results, including the subtests,

were reported the following five different ways:

1. Central Scores (C)

2. Incidental Scores (I)

3. Selective Attention Efficiency Index (%C %I)

4. Selective Attention Combined Capacity (C + I)

5. Selective Attention Level

The central task score (C) was expected to reflect primarily atten-

tion and memory processes, like a recall test. The incidental task

score (I) was much more sensitive to attention or selective attention

and was expected to vary more within the subject.

The selective attention efficiency index (%C %I) was used in

several research studies (Tarver, Hallahan, Kauffman, & Ball, 1976).

It produced an overall score that attempted to reflect the idea that

performance onthe incidental measure (I) reduced the capacity for

performance on the central measure (C).

The.combined selective attention measure reflected the total

capacity for attention under the interaction, distraction, and capacity

limitations of the two measures (C + I). Because there are 12 central

items and six incidental items, the score was weighted toward the as-

signed task. Consequently, poor selection emphasizing the incidental

stimuli would lower the central score more than it could have potentially

raised the incidental score.

Two types of assignments to levels based on selective attention

performance were used. In Section One, only high and low selective

attention performance levels were identified:

1. High: performance with a central score greater or equal

to 6, and a selective attention efficiency index greater

than 8%.

2. Low: performance on C or on the efficiency index less

than the high level's criteria.

In Section Two, three performance levels were identified. These

were first differentiated by observation, looking for people who did

well on the central task while showing some attention to the incidental

task. The three levels found were as follows:

1. Level one poor selectors with a central score less

than or equal to 8 and a total score (C + I) greater

or equal to 9.

2. Level two subjects with poor overall performance

having central scores less than or equal to 8 and

a total score (C + I) under 9.

3. Level three subjects with good selective attention

performance having central scores equal to or greater

than 8 and incidental scores under 5.

Groups one and two differed in the overall level of performance.

Group one only showed poor selective attention while still attending

and remembering a lot of information. Group two had a more general

problem, either in attention or memory, and showed both poor selective

attention and also overall poor performance.

Modality preference. This was determined by ratings of LD student's

performance on the Detroit Tests of Learning Aptitude (DTLA) (Baker

& Leland, 1967). The ratings were performed in a global manner, using

low age-equivalent scores previously reported by teachers as evidence

of the LD disorder (Appendix A). These scores were identified by the

teacher as 80% or 70% or less of the expectancy age (EA) calculated as

2 x MA + CA EA

But rather than emphasize this quantitative approach, using very in-

adequate norms, these ratings consisted of recognizing overall patterns

in the low scores reproted as follows:

1. Group 1: low scores in visually oriented subtests only

2. Group 2: low scores in auditorily oriented subtests only

3. Group 3: low scores in both areas or no low scores.

Intelligence scores. These are total IQ scores reported from

individually administered intelligence tests. Most of the scores were

from the Slossen or the Wechsler Intelligence Test for Children Revised


Chronological age. Ages were reported in years and decimal fractions

of years. This was the age at the first testing with Hagen's Central

Incidental Attention Task.

Data Collection

The following types of data were collected:

1. The description of the student diagnosed as learning disabled

(LD) included the student's chronological age, intelligence

test score or mental age, a number indicating different

teachers' classes, and the teacher's judgment as to whether

hyperactive or emotional behaviorvas also exhibited by the

student in addition to his LD learning problems.

2. The achievement test performance included scores on the

instruments at the end of the 1979-80 school year, and

at the end of the 1980-81 school year. The analysis

focused on the oral reading scores (decoding) and com-

prehension scores from the Peabody Individual Achievement

Test (PIAT) for Section One and the Wide Range Achieve-

ment Test (WRAT) for Section Two. Spelling and mathe-

matics test performance was recorded but not analyzed

(Appendix E).

3. Modality performance was obtained by rating teachers'

reports of low subtest scores on the Detroit Test of

Learning Aptitude as explained above.

4. The scores on Hagen's Central Incidental Attention Task

were recorded as a central score, an incidental score,

an efficiency index (%C %I), and combined capacity

(C + I). It was administered at the end of the 1980-81

school year.

The rest of the data was obtained only on certain subgroups.

5. All the elementary level LD teachers in Marion County

(N = 15), including those involved in this study, were

asked to complete a questionnaire giving their opinion

of the SIMS program at the end of the 1980-81 school

year (Appendix F). Seven of this group completed the

same questionnaire at the end of the 1981-82 school


6. Additionally, teacher ratings were obtained on the 31

Brevard County students at the end of the 1980-81

school year. This included the 20 LD students in

the VAKT reading program and the 11 non-LD students

in the regular classroom program. These were ratings of

the LD student's attentional behavior in the classroom

(see Appendix C). The ratings were again collected on 16

subjects remaining from the original group at the end of

the 1981-82 school year.

7. As shown in Table 1, 16 subjects from the Brevard VAKT

group received additional testing at the end of the

1981-82 school year. These results provided infor-

mation for developing correlations with the initial

administration of Hagen's Central Incidental Attention

Task. First the indices of selective attention used

by Argulewicz (1982) were obtained by using the

Wechsler Intelligence Scale for Children Revised

(Wechsler, 1974) and the Stanford-Binet Intelligence

Scale (Termin & Merrill, 1973).

8. Next a meta-attention task devised by Loper, Hallahan

and lanna (1982) was administered to the 16 subjects.

9. Hagen's Central Incidental Attention Task was also

readministered to these 16 subjects, providing a one

year interval between testing.

Data analysis. The general relationship and distribution of the

variables were investigated using procedures from the Statistical Analysis

System (SAS) program package (Barr, Goodnight, Blair, & Chilko, 1979). Bar

graphs and correlations helped clarify the relationships among the

variables (see Appendix G). The univariate procedure was used to

determine if the characteristics of the samples in Section One and

Section Two were normally distributed. Again, a major limitation

was the use of small and unbalanced samples.

As indicated in Table 1, for the first seven hypotheses a re-

gression analysis of the data was done usinq the General Linear

Models (GLM) procedure in the Statistical Analysis System (SAS) program

package. In most cases this allowed for the use of the pretest reading

score as-a covariate. In addition to beinq the best predictor of

posttest scores, the pretest was the ideal measure for including the

influence of history, training, and native ability in one measure.

Though it would be influenced by the learning disability and selective

attention, usinq the pretest as a covariate with the posttest as a

dependent variable was chosen as the best way of representing academic

reading performance.

For hypotheses 8, 9, and 10, Pearson product-moment correlations

were chosen for evaluating the comparisons. The scores on the instru-

ments were continuous, ratio type measures. Because of the rating

scale (Appendix C), hypothesis 11 was analyzed using Spearman rank-

order correlations.


The selective attention testing was done with volunteer teachers,

so there was a definite teacher bias. However, testing was done with

all their LD students, so the student sample was not biased. Testing

of the SIMS students was completed in the spring term of 1981. All

the students in the SIMS program were eligible for, and participating

in, the Learning Disability Program in the Marion County, Florida, public

schools. All these students met the LD criteria of Marion County

(Appendix A). Modality information and recent SIMS reading performance

were obtained on the entire population of the LD program in Marion

County, Florida. Only data from those classes undergoing the selective

attention testing were actually used.

The VAKT program LD subjects were an entire LD class in Brevard

County, Florida. The LD criteria were the same (Appendix A). The non-

learning disabled subjects were referrals to that LD class from the

total school. They were tested and not staffed into the LD program.

Therefore they constituted an excellent comparison group since teachers

considered their behavior similar to that of LD students but they were

found not to have met the eligibility criteria.


The Marion County Public School System is located in the central

part of Florida. It is a rapidly growing area of approximately 100,000

people. A large proportion of the population is in rural areas. The

socioeconomic level of this sample ranges from low to upper middle

class. The Brevard school is in a middle to upper class residential

beach area.



This study attempted to maximize its implications for LD students

by using three instruments widely used in LD programs. Thurlow and

Ysseldyke (1979) studied the Child Service Demonstration Center LD

models. They found that only five assessment devices were used by

over half of the centers. This study used two of these widely used

measures, the Wide Range Achievement Test (WRAT) used by 59.0% (Jastak

& Jastak, 1978), and the Peabody Individual Achievement Test (PIAT)

used by 53.8% (Dunn & Markwardt, 1970).

The Wide Range Achievement Test

Description. The Wide Range Achievement Test (WRAT) (Jastak

& Jastak, 1978) had norms from age 5-0 to 11-11 for Level I. It

featured three subtests and took between 20 and 30 minutes to administer.

The reading test was administered individually. It required supplying

the subject with the list of words and asking that they be pronounced

aloud. In Level I, a pre-reading task, a vestibule test, was administered

when there were failures in the first line of the reading test. The

student then named 13 letters presented, matched 10 letters, and named

the first two letters of his name.

The WRAT manual (Jastak & Jastak, 1978) described the normative

population. All ethnic groups were included in the norms as they occurred

in the population at large. In Level I the number of subjects in each

population, i.e., 14 age groups at half year intervals from 5-0 to

11-6, ranged from 400 to 600. No attempt was made to obtain a representative

national sampling. Thurlow and Ysseldyke (1968) did a comparison of

the 1976 and 1978 normative data. They found that standard scores

on the two editions of the WRAT were closely comparable. Merwin (1972)

criticized the ambiguity in the identity and nature of the groups.

Validity and reliability. Validity and reliability information

in the manual (Jastak & Jastak, 1978) involved several editions because

all editions of the WRAT were based on identical test items. Standard

scores and percentiles were comparable for all editions, though the

1978 edition featured changes in the scaling techniques for the WRAT

norms. Previous editions used a scaling of arbitrarily assigned grade

ratings. In the 1978 edition, the raw scores were scaled.

Split-half correlation coefficients were r = .98 for Level I.

Correlation coefficients between the two forms (Level I and II) yielded

correlations between .94 and .88. The authors reported from "clinical

experience" that the coefficients varied from .90 and .95. In studying

unique variance of the WRAT subtests with the WAIS and WISC, the authors

decided unique variance represented unreliability and thus developed

reliability coefficients for reading from .92 to .97.

Thurlow and Ysseldyke (1979) rated reliability as technically

adequate. Merwin (1972) referred to these as "questionably high reli-

ability coefficients" (p. 64). Thorndike (1972) referred to the "start-

ling" split-half reliability and suggested this was influenced by the

test being timed and by the reading test being stopped after a specified

number of failures.

The manual's estimates of validity cited a study by Wagner where

WRAT levels and mid-term grades correlated +.88 (Jastak & Jastak, 1978).

They also supported the observation that raw score performance re-

flected growth factors since at no time did an older school group obtain

a smaller raw score than an earlier group.

Thurlow and Ysseldyke (1979) rated the validity of the WRAT as

technically inadequate, based on the manual's report. Merwin (1972)

questioned the degree to which these test tasks measured "achievement."

Thorndike (1972) complained that content validity was not even considered

and questioned whether the vestibule tests were measuring the same attri-

bute as the body of the test. He characterized the validity information

as containing "bizarre conceptions, and...somewhat exotic procedures"

(p. 68). Thorndike (1972) and Merwin (1972) both ended suggesting

the WRAT as primarily a clinical or research tool for a quick estimate

of general level of ability and educational background.

The Peabody Individual Achievement Test

Description. The Peabody Individual Achievement Test (PIAT) (Dunn

& Markwardt, 1970) was an individually administered achievement measure.

The norms were based on 13 levels (K-12). It featured five subtests:

mathematics, reading recognition, reading comprehension, spelling,

and general information. It was an untimed power test (30 to 40 minutes).

The reading recognition subtest had 84 items. Items 1 to 9 involved

matching letters. Items 10 to 18 presented letters to be named. Items

19 to 84 were words which the subject read aloud.

The PIAT manual (Dunn & Markwardt, 1970) reported that the national

test standardization sample used the same distribution as the population

for geography and type of community (urban, suburban, rural, etc.).

It had approximately 200 subjects in each of the 13 grade levels. Ages

ranged from 5 to 12. Thurlow and Ysseldyke (1979) performed a factor

analysis on the data from the standardization sample. For younger

grades both of the reading subtests and the spelling subtest formed

one factor, a verbal comprehension and reasoning factor. For higher

grades, this factor included the mathematics, general information,

and reading comprehension subtests, while reading recognition and spell-

ing formed a separate factor.

There were a few general criticisms of the test. Bannatyne (1974)

called the PIAT a "high quality" test and restricted his criticisms

to the spelling score. He lamented the lack of a rate of reading measure

and the lack of a frustration reading level measure. Proger (1970)

found a few general criticisms. He requested more directions specific

to special populations, i.e., particularly testing time, decried the

"denigration of professionalism" (p. 464), i.e., the manual stated

that professional experience was not needed for administering the PIAT,

and worried over aspects of the basal-ceiling procedures. French (1972)

also commented negatively on using paraprofessional administrators.

Lyman (1972) complained about the smaller standardization samples and

fewer subtests than group tests. Silverstein (1981) and Reynolds and

Gutkin (1980) complained of the practice suggested in the manual of

comparing raw scores on pairs of subtests. They advocated comparing

the standard score on each subtest to the mean of the standard scores

on all tests, and both articles offered tables of differences required

for significance.

Validity and reliability. The manual reported reliability based

on Pearson product-moment correlations (Dunn & Markwardt, 1970).

Samples of from 50 to 75 subjects at kindergarten, first, third, fifth,

eighth, and twelfth grade levels were tested at one month intervals,

then correlations were calculated on raw scores. Reliability coefficients

varied from .42 for kindergarten subjects in spelling to .94 for third

grade subjects in reading recognition. For the reading recognition

subtest, 60 students in first grade had a reliability coefficient of

.89 and 54 students in third grade had a reliability coefficient of

.94. Finally, 51 students in the fifth grade had a reliability co-

efficient of .89. The standard error of measurement describing the

expected bands of error for these test scores in reading recognition

were 1.74 for grade one, 2.21 for grade three, and 3.90 for grade five.

Lyman (1972) offered one of the few related criticisms when he complained

that the test had lower test-retest reliability coefficients than group

achievement tests.

The PIAT manual focused on two aspects of validity, i.e., item

(content) validity and concurrent validity (Dunn & Markwardt, 1970).

Content validity was developed by reducing 300 items per subtest to

84 items using item discrimination and difficulty indices.

Proger (1970) characterized this content validity as "sound enough"

(p. 466) but decried the lack of evidence for construct validity and

for predictive validity. Lyman (1972) made a similar point in asserting

that more research was needed before establishing the PIAT as a "valid

test" (p. 35).

Concurrent validity was the primary validity evidence in the manual

and the literature. The manual (Dunn & Markwardt, 1970) reported a

correlation with the Peabody Picture Vocabulary Test (PPVT) based on

testing the same subjects used for retesting for reliability data.

The overall median correlation was .57. The authors pointed out that

the PPVT generally correlated with achievement tests in the .50s. Most

concurrent validity studies with achievement tests compared the PIAT

to the other individual achievement test, the WRAT.

Comparison of the WRAT and PIAT

PIAT subtests differed from the WRAT subtests in both content

and approach, except for reading recognition. The PIAT manual (Dunn

& Markwardt, 1970) stated that "correlation with the WRAT cannot be

considered completely appropriate or pertinent evidence on the validity

of the PIAT" (p.51). Yet these were the two individually administered,

multiple subtest achievement tests available.

Several studies comparing the PIAT to the WRAT were summarized

in Table 2. The study by Sitlington (cited in Dunn & Markwardt, 1970)

used educable mentally retarded (EMR) adolescents (see Table 1). The

study by Soethe (1972) used normal students (13), reading disabled

students (12), and mentally retarded (MR) students (15). Wetter and

French (1973) used 23 male and 7 female learning disabled pediatric

outpatients (see Table 2).

Table 2

WRAT and PIAT Correlations


A. Sitling-
ton (Dunn
& Mark-

B. Soethe

C. Wetter &

D. Bray &

E. Baum



40 mixed

30 LD

45 LD

100 LD (by ag
7-8 yr.:
9 yr.:
10 yr.:
11 yr.:

62-66 LD

& Williams

G. Scull & 49 LD

PIAT Rdg.Recog.


















F. Harmer

.76 .74

.83 .83

Bray and Estes (1975) also used a group test, the California Achieve-

ment Test (CAT), and teacher ratings of students' functional academic

levels. The WRAT reading correlated .81 with CAT vocabulary, .72 with

CAT comprehension, and .83 with CAT total reading. The WRAT correlated

.90 with teacher ratings of both reading recognition and comprehension.

The PIAT reading recognition correlated .84 with CAT vocabulary, .65

with CAT comprehension, and .81 with CAT total reading. The PIAT

correlated .85 with teacher ratings of reading recognition and .87

with teacher ratings of reading comprehension. The WRAT and PIAT com-

parisons were summarized in Table 2.

Baum (1975) used 25 subjects from each of 4 age groups of subjects

from self-contained LD classes (see Table 2). Harmer and Williams

(1978) used students from a learning disabilities center (see Table

2). They used 62 subjects for the spelling comparison and 66 subjects

for the word recognition and math subtests. In their discussion, they

presented a few examples of students who had dramatically different

scores on the math subtests. They discussed how the differences in

test content, testing procedures, test format, and method of scoring

accounted for these differences. However, Scull & Brand (1980) pointed

out that these differences only reflected the population's interaction

with the test, and that many subjects in the sample were only experi-

encing mild learning disabilities. In their own study Scull and Brand

(1980) used severely learning disabled students. Both tests were ad-

ministered on admission to a treatment center and again, two years

later. They reported correlations for both original and follow-up

testing (see Table 2).

The Detroit Tests of Learning Aptitude

Descriptions. The process tests for this study are from the Detroit

Tests of Learning Aptitude (DTLA) (Baker & Leland, 1967) which is a

collection of 19 subtests. It has been widely used by LD teachers.

Thurlow and Ysseldyke (1979) reported that 20% of the model centers

used this instrument, and 75% of these used it for placement and instruc-

tional programming, with 62% using it for pupil evaluation and 25%

using it for program evaluation. Olson, Mercer, and Paulson (1981)

reported that in many states teachers and diagnosticians were currently

using the DTLA to evaluate process disorders, even when it was not re-

quired in the criteria for learning disability identification.

There are several limitations to the DTLA. Because of its limited

standardization sample, age of the norms, and lack of adequate reli-

ability and validity information, this research study used the DTLA

only as a research instrument for estimating the relative performance

of the students in auditory versus visual tasks. The tasks, parti-

cularly the auditory attention span (subtest no. 13) for related

syllables and the visual attention span for objects (subtest no. 9),

were similar to visual and auditory techniques used in research for

attention and memory estimation. The DTLA was used in this study to

make a very general distinction among subjects.

The subtests available were as follows: (1) pictorial absur-

dities, (2) verbal absurdities, (3) pictorial opposites, (4) verbal

opposites, (5) motor speed and precision, (6) auditory attention

span for unrelated words, (7) oral commissions, (8) social adjustment

A, (9) visual attention span for objects, (10) orientation, (11) free

association, (12) memory for designs, (13) auditory attention span

for related syllables, (14) number ability, (15) social adjustment

B, (16) visual attention span for letters, (17) disarranged pictures,

(18) oral directions, and (19) likenesses and differences. The authors

suggested that there were eight psychological functions that explained

these tests.

The DTLA manual (Baker & Leland, 1967) reported that the standard-

ization initially involved 50, and later 150 students at every age

level from 3 years to 19 years by 3 month intervals. The initial 50

students ranged in IQ from 90 to 110 as measured by group intelligence

exams that were not specified. The initial standardization data were

developed in 1935. The current norms were developed in 1955 (Buros,

1972). All the norm population were students examined in the Psycho-

logical Clinic of the Detroit Public Schools.

Validity and reliability. Test reliability was reported in the

manual on 48 cases over a five month interval to be .959 (Baker & Leland,

1967). Another group of 792 pupils retested over a two or three year

interval had a correlation of .675. For this group of mentally retarded,

delinquent, and emotionally unstable children, the median IQ (multiplied

by 100) from the DTLA remained almost the same, 70 to 71, and the stan-

dard deviation remained at eight IQ points. The manual also provided

information that the correlations between 16 subtests on 100 children

ranged from .2 to .4 (Baker & Leland, 1967).

The DTLA manual was basically oriented toward computing an IQ

score. This score was inadequate compared to the Wechsler Intelligence

Scale for Children--Revised (WISC-R) and other similar intelligence tests

as well as unrelated to the main usefulness of the DTLA in LD programs.

Silverstein (1978) made the most appropriate criticism by saying that

considering the lack of appropriate reliability and validity after

40 years, one "would think that the situation might have been remedied

long before this" (p. 303). Studies have compared the DTLA to various

intelligence tests (Chiappone, 1968; Sandstedt, 1964) and to achievement

tests (Olson et al., 1981), but the major emphasis here was on whether

the DTLA represented a modality or process pattern with direct impli-

cations for teaching effectiveness.

Chiappone (1968) used the DTLA with educationally mentally retarded

(EMH) students to examine correlations with the Binet Intelligence Test

and the Wechsler Intelligence Scale for Children (WISC). No significant

differences were found between the DTLA and Binet means or the Binet

and WISC means, but the DTLA was significantly different from three

WISC subtests. This showed the DTLA related closely to the verbal

material of both tests, but not as closely to the performance content

of the WISC.

Sandstedt (1964) studied the relationship between a "memory span

test battery" from the DTLA and the WISC. Her subjects were 45 retarded

readers of average general mental ability. The test battery from the

DTLA included 5 subtests: auditory attention span for unrelated words

(No. 6), visual attention span for unrelated objects (No. 9), auditory

attention span for related syllables (No. 13), visual attention span

for letters (No. 16), and oral directions (No. 18).

Sandstedt (1964) found a significant difference for the retarded

readers between their lower performance on the auditory memory span

test of unrelated words and the visual test of unrelated objects. The

correlation between the Detroit total memory span, IQ, and the WISC

full scale IQ was .69. WISC performance scores were more closely

related to total memory span (.66) than were the WISC verbal scores

(.58). The auditory test for unrelated words correlated closer than

the total auditory memory span. This study thus supported the con-

tention that the DTLA really supplies supplementary information on

intellectual aptitude. The relation between the DTLA and academic

performance had been a major research concern as part of the overall

effort to study the relations between modality or process tests and

academic achievement reviewed in Chapter II.

Hagen's Central Incidental Attention Task

Unlike the previous tests, Hagen's Central Incidental Attention

Task (Mercer, 1975) was not commonly used in LD programs. Despite its

limitations, this instrument was chosen because of its frequent use in

the research literature on selective attention in LD students. It

should be pointed out that the Central Incidental Attention Task is

primarily a research instrument. As such, there were no norms for

the test, nor is there validity, or reliability data available. However,

the research literature utilizing this instrument was often used for

comparison and for providing information on construct validity.

The task could not be readministered because once the incidental match-

ing was tested, the student no longer "ignored" these stimuli. Conse-

quently, stability and reliability could not be measured. Another

question concerned the selective attention index of efficiency measure

(%C %I). It was often used and occasionally produced some interest-

ing correlations with other measures. Though it appeared to be most

directly related to the filter theory model where successful selective

attention involves filtering out incidental information, the data had

clearly shown this model to be too simplistic. A detailed explanation

for the continued use of this designation has not been offered. Hallahan

(Note 2) suggested this was a practical measure from the definition

of selective attention. Another measure was the total combined score

of both tasks (C + I), which reflected the influence of both tasks

on each other. This technique was suggested by Mercer (Note 7).

Description. The central task consisted of six 3 x 6 inch stimulus

cards with black line drawings of a household object on the top half

and an animal on the bottom half. Though the drawings were of some-

what poor quality, the same pictures (Mercer, 1975) were used in this

study. Pictures of the deer, dog, and camel were used in the practice

trial so that the subject would be familiar with them. The incidental

task consisted of a 22 x 6 inch piece of cardboard displaying the six

animals and pictures of the six objects. One modification for this

study was that sufficient cards were printed to allow separate packets

of the stimulus cards for each trial. This decreased the time required

for administration.

Development. One of the earlier tasks that led to developing

this instrument was a series of picture cards with distinctively colored

backgrounds (Maccoby & Hagen, 1965). After a brief exposure, a cue

card with the same color as the background of one of the picture cards

was presented to the subject to match to the covered presentation card.

The incidental learning task consisted of matching a set of solid color

cards to a set of the pictures without the background color. Later

the stimuli were changed to cards with objects and animals paired on

each (Hagen & Kail, 1975). Hallahan, Gajar, Cohen, and Tarver (1978)

further modified Hagen's task for use as a group selective attention

measure. Hallahan, Tarver, Kauffman, and Graybeal (1978) made a more

complicated version of Hagen's task to allow repeated tests. Three

classes of stimuli were used, i.e., animals, household objects, and

geometric figures.

Reference groups. There have been several studies outlining the

general performance trends of normal subjects of various ages (Hagen

& Kail, 1975; Maccoby & Hagen, 1965). The effort has begun, with small

samples, to outline the general pattern of performance for various

ages of LD subjects (Tarver, Hallahan, Cohen & Kauffman, 1977).

Procedures. The procedure used for Hagen's Central Incidental

Attention Task began with oral directions to the student: "I will

be showing you some cards. Pay attention to the animal. I will ask

you to point to the correct animal after I show you several cards."

Next the subject was given two practice trials using three stimulus

cards. The examiner showed them to the student, placed them face down,

and then asked the student to point to the camel. This procedure was

then repeated, but the student was asked to point to the dog.

For the central task, the experimenter presented each 3 x 6 stimulus

card face up for two seconds, then placed it face down in front of

the subject in a manner which proceeded from the subject's left to

right. After the last card for each trial, the cue card identical

to one of the trial cards was presented. The subject tried to select

the matching card. On the data sheet, the examiner indicated under

central task response whether the student was correct (+) or not (-).

The number of successful trials was the central task score. There

were 12 trials of 3 to 6 cards in length (see Appendix B).

Hagen's incidental task immediately followed the central task,

trial 12. It only followed the last central task. The student was

presented the 22 x 6 inch piece of cardboard with the six animals

pictured. Next, the student took picture cards (3 x 3 inch) of the

six household objects and matched the household object (incidental

stimulus) with the animal picture (central stimulus). The incidental

score was obtained by recording the number of correct pairings at the

bottom of the data sheet. The combined selective attention measure

was obtained by adding the incidental and central scores (C + I). The

selective attention efficiency measure (%C %I) or index score was

obtained for each subject by subtracting the proportion of correct

incidental scores from the proportion of correct central scores.



The purpose of this reserach was to investigate the importance

of visual selective attention to learning disabled students' reading

performance. The results are divided into three sections corresponding

to those described in Chapter III. The first section reports the

comparison of the influence of selective attention on student perfor-

mance in different reading programs. The second section reports the

performance of students with different levels of selective attention

in the SIMS precision taught reading program. The final section describes

the examination of the instrument, Hagen's Central Incidental Attention

Task (Hagen & Kail, 1975; Mercer, 1975).

Section One

Subject Characteristics

This section involved comparing 19 learning disabled (LD) students

in the Systematic Instructional Management Strategies (SIMS) reading

program in Marion County, 20 learning disabled students in a Visual-

Auditory-Tactile-Kinesthetic (VAKT) type reading program in Brevard

County, and 11 non-learning disabled students referred for testing but

not staffed into the Brevard learning disability program. The difference

in counties confounded the program variable. Additionally, due to their

small numbers and severe attrition, the non-learning disabled students

were later omitted from further analysis.

Examinations of student characteristics were performed using the

univariate procedure in the Statistical Analysis System (SAS) program

package (Barr, Goodnight, & Sail, 1979) to determine the characteristics

of the samples. The mean values for three groups on CA, IQ, and pretest

are presented in Table 3. The distributions of chronological age,

intelligence quotients, and pretest scores for the groups are shown

in the histograms in Appendix G.

Table 3

Means and Standard Deviations of Chronological Age (CA), Intelligence
Quotient (IQ), PIAT Reading Recognition Pretest, PIAT Reading
Comprehension Pretest

Pretest Pretest
CA IQ Reading Rec. Reading Comp.
Group Number Mean SD Mean SD Mean SD Mean SD

SIMS Prog. N = 19 8.75 1.32 96.2 11.52 1.81 .827 1.83 1.25

VAKT Prog. N = 23 8.77 1.39 103.0 13.69 3.50 1.17 3.91 1.30

Non-LD N = 11 9.10 .59 109.0 14.17 *4.41 1.47 *4.80 1.56

*Pretest-Posttest scores were only available for 6 of 11

Table 4 presents the results of the univariate procedure explained

in Chapter III. The study of LD students required that reading achieve-

ment scores would not be normally distributed. These subjects constituted

a sample having scores below a cut-off of the whole population of reading

students. The distribution would therefore be positively skewed, begin-

ning at the point wherever the limits (%EA) for serious deficiencies

were defined, in the most dense part of the distribution. Table 4

reveals that only the SIMS program students showed this pattern.

Table 4

The Shapiro-Wilk W-Statistic and Skewness Statistic
for the Data Values as a Random Sample from a Normal Distribution

W: Normal Statistic
P: Probability
SK: Skewness
PIAT Pretest Reading
Group Number CA IQ Recog. Comp.

SIMS Prog. N = 19 W: .962 .949 .858 .845
P : .61 .42 .01* .01*
SK: -.40 .02 1.43 -.53

VAKT Prog. N = 20 W: .949 .962 .983 .943
P : .39 .57 .95 .42
SK: .40 -.43 -.27 -.39

Non-LD N = 11/6 W: .891 .877 .940 .821
P : .19 .15 .61 .09
SK: -1.37 -.59 .14 1.41

*p < .05

The small sample sizes available imposed limitations on statistics.

The three groups differed on intelligence and reading. The means re-

vealed that the non-LD group had the highest scores and the SIMS program

group had the lowest scores. The VAKT group's scores were higher than

the SIMS program group's scores in IQ and achievement. This made their

comparison difficult, but the use of a covariate technique helped correct

for some of this difference. Likewise, subject characteristics such as

chronological age (CA) and intelligence scores (IQ) had essentially

normal distributions even though the samples were small. However, the

students in the SIMS program had pretest PIAT reading recognition and

pretest PIAT comprehension scores that were not normally distributed.

Hypothesis 1

Hypothesis 1 assumed that because precision teaching improves

attending it thus will alleviate selective attention's effect on

reading performance. Therefore a VAKT taught LD group's reading scores

will be most influenced by selective attention, a precision taught

(SIMS) LD group's reading scores will be less influenced, and a non-

LD group's reading scores will be the least influenced by the students'

selective attention levels. A regression analysis of the data was done

using the General Linear Models procedure (GLM) in the Statistical

Analysis.System (SAS) program package. The three reading treatment

groups (Table 5) were later reduced to two, SIMS and VAKT groups, to

better balance the sample size (Table 6). It was believed that the

following model would best describe the relationships expected:

Y = B0 + B1X1 + B2X2 + B3X3 + B4X2X3 + e

Y = Reading Posttest Achievement Score

X1 = Reading Pretest Achievement Score

X2 = Selective Attention Group

Level 1 = with C score > = 6, and %C %I > 8;

Level 2 = all others

X3 = Membership in treatment group

SIMS program, VAKT program, or regular basal program

(or later just the first two groups)

B4X2X3 = the interaction term for selective attention vs.

membership in treatment group


There was a strong relationship between model 1 and the dependent

variable due to the covariate. There was a significant relationship

between the three groups and PIAT reading recognition (R2 = .87) and
reading comprehension (R = .70), and between the two groups (VAKT
and SIMS) and reading recognition (R = .85) and reading comprehension

(R2 = .64). The results of the regressions for all three reading treat-

ment groups are given in Table 5. The results for just the LD students

in the SIMS program and the VAKT program are presented in Table 6.

Table 5

Results of the Statistical Test for the
Analysis of Hypothesis 1: Three Groups

Reading Recognition as Y and X,

Source df SS F P>F

Pretest X1 1 37.81 112.11 .0001*

SA X2 1 .40 1.18 .28

Rdg. Groups X3 2 .57 .84 .43

Interaction X2X3 2 .29 .43 .65

Error 38 12.81

Total 44 100.48

Reading Comprehension as Y and X1

Source df SS F P>F

Pretest X1 1 43.69 24.55 .0001*

SA X2 1 .17 .09 .76

Rdg. Groups X3 2 2.20 .62 .54

Interaction X2X3 2 7.76 2.18 .13

Error 33 58.73

Total 39 198.85

*p< .05

Using PIAT reading recognition (Y) as the dependent variable,

the F value observed for testing for the interaction of selective

attention, X2, and the three reading methods used, X3, was F = .43.

The probability of obtaining that value when assuming a null hypo-

thesis was .65. Thus, there was not enough evidence to conclude that

such an interaction was present. The F value observed for testing for

the main effect of the selective attention group, high or low, was

1.18, yielding a probability of obtaining that value, assuming a null

hypothesis of .28. Once more, there was not enough evidence to con-

clude that an effect was present.

Using PIAT reading comprehension as the dependent variable, the

results were essentially the same. They lacked any evidence to con-

clude that an effect was present.

Table 6

Results of the Statistical Test for the Analysis
of Hypothesis 1: Two Groups**

Reading Recognition As Y and X1

Source df SS F P>F

Pretest X1 1 30.18 84.99 .0001*

SA X2 1 .05 .14 .71

Rdg. Group X3 1 .62 1.74 .20

Interaction X2X3 1 .03 .08 .78

Error 34 12.07

Total 38 78.87

Reading Comprehension As Y and X,

Source df SS F P>F

Pretest X1 1 24.44 13.49 .001*

SA X2 1 4.12 2.28 .14

Rdg. Group X3 1 2.96 1.64 .21

Interaction X2X3 1 7.23 3.99 .06

Error 29 52.51

Total 33 145.59

*p < .05
** SIMS and VAKT programs

As shown in Table 6, the F value for the interaction was .08,

using Y = PIAT reading recognition score. The probability of obtain-

ing this value, assuming a null hypothesis, was .78. Thus there was

not evidence to conclude that this interaction was present. The F

value observed for testing for the main effect of the selective atten-

tion group (high or low) was .14, yielding a probability of .71.

Once more there was insufficient evidence to conclude that an effect

was present.

Using the PIAT reading comprehension as the dependent variable

(Y), the results were essentially the same. There was a lack of

significance in the tests for the interaction and for the main effect,

selective attention group (X2).

Hypothesis 2

Hypothesis 2 stated that selective attention will relate to read-

ing performance. Low visual selective attention performance will be

related to low reading achievement when measured by the total task

performance measure (C + I) more strongly than when measured by the

percentage selective attention measure (%C %I). This last measure

had often been used as an indication of selective attention efficiency

(Tarver, Hallahan, Kauffman, & Ball, 1976). A regression analysis of the

data to investigate Hypothesis 2 was done using the General Linear

Models (GLM) procedure in the Statistical Analysis System (SAS) program

package. The model best describing this was still model 1 with X =

selective attention changing from the categorical group membership of

hypothesis one to the continuous measures, combined capacity (C + I)


or efficiency (%C %I). Again the model containing the covariate

explained a significant amount of the variance of Y, with R2 = .88

for reading recognition and R2 = .60 for reading comprehension. The

F ratio statistic was used to evaluate hypothesis 2. The results

when defining selective attention as the total capacity under dis-

traction (C + I) are presented in Table 7. The results when using

selective attention as the efficiency index (%C %1) are presented

in Table 8.

Table 7

Results of the Statistical Test for the Analysis of Hypothesis 2:
Selective Attention as Total Capacity (C + I) With Two Groups**

YA = Reading Recognition


Pretest X1

SA Total X2

**Rdg. Group X3

Interaction X2X3




















YB = Reading Comprehension

Source df SS F P>F

Pretest X1 1 30.85 41.05 .0005*

SA Total X2 1 .00 .00 .99

**Rdg. Group X3 1 1.85 .93 .34

Interaction X2X3 1 3.78 1.90 .18

Error 29 57.82

Total 33 145.59
*p < .05
**SIMS and VAKT groups


Using PIAT reading recognition as the dependent variable, the F

value observed for testing for the interaction of selective attention,

defined as total capacity (C + I), with the two LD reading groups was

4.21. This meant that, assuming a null hypothesis, the probability of

observing data that would yield an F value of 4.21 was .05. This led

to the conclusion that there was evidence that a relationship did

exist between Y, PIAT reading recognition, and the interaction, since

it was improbable that this F ratio occurred only by chance. The

data also yielded an F value of 6.50 for the effect of the reading

groups, with a corresponding P value of .02. This was significant at

the .05 level. The F value for the main effect of the continuous

capacity measure of selective attention (C + I) was 9.09. This had

a corresponding P value showing it was significant at the .05 level.

This led to the conclusion that a relationship did exist between

selective attention, measured as total capacity, and reading perfor-

mance, since it was improbable that the observed F ratio occurred

only by chance. However, because the VAKT group's means were higher

in IQ, pretest and posttest measures, and combined selective atten-

tion (C + I), the results may be emphasizing the difference between

the groups.

Using the PIAT reading comprehension scores as the dependent

variable (Y), the F value for the interaction was F = 1.90, with a

corresponding probability of .17. There was not enough evidence to

conclude that this interaction was present. For selective attention

capacity (C + I), the F value for the main effect was .35. The

corresponding probability of obtaining that value of F was .56. Thus,


there was not enough evidence to conclude that an effect was present.

The results using the selective attention efficiency measure (%C %I)

are reported in Table 8. Again model 1 containing the covariate

explained a significant amount of the variance of Y, with R = .85

for reading recognition and R2 = .57 for reading comprehension.

Table 8

Results of the Statistical Test for the Analysis of Hypothesis 2:
Selective Attention as Efficiency Measure (%C %I) With Two Groups**

YA = Reading Recognition

Source df SS F P>F

Pretest X1 1 32.24 95.67 .0001*

SA % X2 1 .48 1.42 .24

**Rdg. Group X3 1 .32 .96 .33

Interaction 1 .27 .83 .37

Error 34 11.45

Total 38 78.87

YB = Reading Comprehension

Source df SS F P>F

Pretest X1 1 39.98 18.53 .0001*

SA % X2 1 .11 .05 .81

**Rdg. Group X3 1 1.00 .47 .50

Interaction 1 .16 .07 .79

Error 29 2.56

Total 33 145.59

*p < .05
**VAKT and SIMS Programs

Using PIAT reading recognition as the dependent variable, the F

value of the interaction yielded a probability of obtaining that value

of .37. Consequently there was not enough evidence to conclude that

an interaction was present. In testing the main effect, selective

attention efficiency, the data yielded an F value of 1.42 which had

a corresponding P value of .24. Again, there was not enough evidence

to conclude that an effect was present.

Using PIAT reading comprehension as the dependent variable, the

F value of .07 for the interaction of selective attention efficiency

(%C %i.) and the reading programs (SIMS or VAKT) yielded a probability

of obtaining that value by chance of .79. Thus there was insufficient

evidence to conclude that such an interaction was present. The F

value observed for testing the main effect, selective attention

efficiency (%C %I), was F = .05, corresponding to a probability of

.81. Thus there was no evidence to conclude that an effect was present.

Table 9 presents the means for these groups on the posttests and

selective attention measure to show the magnitude and direction of these

effects. Gain scores are also included to facilitate comparisons.

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