Cognitive performance of children referredfor clinical and educational diagnoses.
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Authors Axinn, Dede Susan.
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Cognitive performance of children referred for clinical and educational diagnoses
Axinn, Dede Susan, Ph.D.
The University of Arizona, 1994
V·M·I 300 N. Zccb Rd. Ann Arbor, MI 48106
COGNITIVE PERFORMANCE OF CHILDREN REFERRED
FOR CLINICAL AND EDUCATIONAL DIAGNOSES
by
Dede Susan Axinn
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF EDUCATIONAL PSYCHOLOGY
In Partial Fulfillment of the Requirements For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
1 9 9 4
1
THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE
2
As members of the Final Examination Committee, we certify that we have
Dede Susan Axinn read the dissertation prepared by -------------------------------------entitled Cognitive Performance of Children Referred for
Clinical and Educational Diagnoses
and recommend that it be accepted as fulfilling the dissertation
Date
Df. Harl,Y Christia~sen
Q.h&~~M~
1/-1- q« Date
/)- 9- 9/ Date
Date
Date
Final approval and acceptance of this dissertation is contingent upon the candidate's submission of the final copy of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requir'e'ment.
~~ ~) \vtS~ )l-1-1~ Digsertation Director ~D-a-te----~--------Dr. Shitala P. Mishra
3
STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under the rules of the Library.
Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation form or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.
11 J SIGNED: /./Ctf( / "f-e=::;;:""
4
ACKNOWLEDGMENTS
The author wishes to express her sincere appreciation to her committee members and to those who have provided encouragement throughout her doctoral program and during the preparation of this dissertation.
Dr. Shitala P. Mishra, my dissertation chairman, has my enduring respect for inspiring the study and indebtedness for his efforts in proofreading and suggesting meaningful improvements. Dr. Mishra's student focus is evident by his consideration for and popularity among graduate students as a dissertation chair and committee member in our department. Dr. Janiece Lord-Maes has my gratitude for her judicious editorial insights and generous time availability. I am very thankful to Dr. Harley D. Christiansen who is a most compassionate professor and responsible for providing an opportunity for me to assist him in teaching, to excel in psychometrics, and to develop the skills needed to complete the study.
Appreciation is also expressed to Jessie Fryer, Jo Ann Hurley, Rick Haan, and especially, Debbie.
Thanks to a very supportive family, partner, and Perseverance, my goal to earn a doctorate has been achieved. I thank all of you for hanging in there with me.
5
TABLE OF CONTENTS
Page
LIST OF TABLES............................... 7
ABSTRACT. • . • • • • • • • • • • • • • • • • • • • • • • • . • • • • • • • • • • 8
1. INTRODUCTION •••.••••••••••••••••••••••••••••• 9
2. REVIEW OF THE LITERATURE •••••••••••••••••.••• 15
History and Development of Wechsler Scales. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Structure of Wechsler Scales............ 16 WISC-III Content........................ 17 Psychometric Properties................. 20 WISC-III Changes........................ 22 Factor Analysis......................... 23 Individual comparison................... 27 Clinical and Educational Uses of
the wIse-III....................... 28 Group Differences....................... 31 Related Research........................ 33 WISC-III Reviews........................ 34 Summary of Literature................... 36
3. METHOD •••••••••••••••••••••••••••.••••••••••• 38
Sample. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Instrument. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Procedure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Hypotheses. . • . . . . • . . . . . . . . • . . . . . . . . . • . . . 42 statistical Analysis of Data............ 42
4. RESULTS ..•••••••••.••••••••••••.••••••••••••• 44
Between Group Differences............... 44 Results Related to Hypothesis 1.... 44 Results Related to Hypothesis 2.... 46 Results Related to Hypothesis 3.... 47
Comparison of Underlying Factor Structure................... 49 Results Related to Hypothesis 4.... 49 Results Related to Hypothesis 5.... 53
5. DISCUSSION ...••...•..••....••...•.••......... 61
6
TABLE OF CONTENTS--continued
Page
APPENDIX A: Subtest Comparison Between Groups. . . . . . . . . . . . . . . . . . . . . . . . . . 65
APPENDIX B: IQ Comparison Between Groups.... 67
APPENDIX C: Factor comparison Between Groups. . . . . . . . . . . . . . . . . . . . . . . . . . 69
APPENDIX D: Permission to Reproduce......... 71
REFERENCES. • . • • . . . . . . • . • . . . . . . . • • • • • . . • • • . . . • 73
7
LIST OF TABLES
Table Page
1 WISC-III Factors............................. 24
2 Bannatyne's Recategorizations................ 28
3 Study Participants by Age and Gender......... 39
4 Subtest Means, Standard Comparison and t-ratios for Two Samples ••••.•••••••••••.••• 45
5 Mean IQs, Standard Deviations, t-Ratios for Two Samples.............................. 46
6 Factor Means, Standard Deviations, and t-Ratios for Two Samples ••.••..•.• ~...... 48
7 Comparative Factor Loadings For Sample Groups. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
8 WISC-III Factor Structure: Comparison of Study and Normative Samples............... 55
9 Comparison of Factor Loading Patterns for Clinical, Educational, and Norming Samples...................................... 56
10 Coefficients of Congruence Between Z,latching Factors for Two Sample Groups................ 58
11 Coefficients of Congruence Between Matching Factors in Total Sample and Educationally Referred Groups................ 58
12 Coefficients of Congruence Between Matching Factors in Total Sample and Clinically Referred Groups................... 59
13 Coefficients of Congruence Between Matching Factors in Total Sample and Norming Samples.......................... 59
8
ABSTRACT
The Wechsler Intelligence Scale for Children, third
edition (WISC-III) (Wechsler, 1991) was used to compare
cognitive performance of children who were referred for
diagnosis in an educational setting with others referred for
diagnosis in a clinical setting. The WISC-III was
administered to 60 school-aged children in the present
study. An exploratory factor analysis was used to examine
the four-factor structure for both groups of children as
well and compared to the WISC-III norming sample. Factor
analytic results indicated some similarities as well as
differences in the factorial composition of the two groups
of subjects. Factor 1 (Verbal Comprehension) and Factor 3
(Freedom from Distractibility) were found to be similar for
the two groups. No significant differences in the average
performance of the two groups of children were found in
terms of subtest scores, IQs, or factor index scores. The
implications for the use of the WISC-III based on these
results are discussed.
CHAPTER 1
INTRODUCTION
9
Research studies dealing with individual differences in
cognitive abilities among children clearly point to
variations in processing information, solving problems,
comprehending verbal information, perceiving spatial
relationships, and demonstrating social competence
(Bjorklund, 1989). Cognitive style describes the way in
which a person uses her/his capacity and competence to
process information. Once a cognitive style has been
established, educational or clinical intervention can be
tailored to the primary modality of the individual child.
Psychometric assessment has been used to identify
individual and group differences according to various
traits, characteristics, and constructs. Information
obtained from psychological assessments is used in various
settings and for many types of diagnoses. Psychological
evaluations are intended to provide and organize patient
information, provide a common language among professionals,
anticipate treatment outcomes, select intervention methods,
and enhance theoretical understanding of problematic human
behaviors and pathologies (Mezzich & Mezzich, 1987).
Psychological tests are used for many purposes in
educational, clinical, and occupational contexts (Anastasi,
10
1976). Psychometric tools may be administered individually
or to groups of people at a time. Individually administered
tests are preferred by many psychologists over group
administered tests because they provide an opportunity to
observe behaviors during the testing session. Results of
psychological tests have been used to provide guidance in
terms of employment selection, job training, development of
treatment programs, educational placement, and taxonomy
(Anastasi, 1976). Intelligence or cognitive potential is
the most common construct measured. Federal requirements
for special education students (e.g., P.L. 94-142 and P.L.
101-476) make individual intellectual assessment the
foundation of school psychology. The interpretation of
intellectual assessments may qualify or disqualify a student
for special services or funding (Deitz & Repp, 1989).
The most popular individual intellectual assessments
used in schools and clinical settings are the Wechsler
Intelligence Scales (Klausmeier, Mishra, & Maker, 1987).
The Wechsler Intelligence Scale for Children, revised
(WISC-R) (Wechsler, 1974b) was the most frequently used
intelligence test among school psychologists surveyed (Goh,
Teslow, & Fuller, 1981; Hutton, Dubes, & Muir, 1992). The
prominence of the Wechsler tests, especially the WISC-III,
is based on a significant history of improvements,
erudition, psychometric quality, and ease of administration
11
and scoring (Detterman, 1985). Three contrary reviews of
the WISC-III (Carroll, 1993; Shaw, Swerdlik, & Laurent,
1993; Sternberg, 1993) concluded by suggesting the
elimination of the use of the WISC-III.
Children are often referred for intellectual testing as
part of a diagnostic evaluation in clinical and educational
settings. The source of referral may be parents, teachers,
or others. Children may be referred as a result of
perceived intellectual or emotional difficulties or a
combination of problems.
Referral of children by school personnel is often
motivated by academic difficulties. Formal assessment using
intelligence tests helps to discover the child's strengths
and weaknesses. Suggestions for remediation usually focus
on weak areas, while reinforcing strengths. Educational
referrals typically focus on the child's inability to
function in the classroom and/or to keep pace with peers.
The etiology of many referred classroom problems may be
physical, chemical, information processing, behavioral, or
situational. Once a child's strengths and weaknesses have
been identified, an individualized educational program can
enhance academic achievement and classroom performance.
The same procedure for referral, data review,
assessment, interpretation, and intervention are used in all
assessment settings (Cooper, 1982). For children in
12
clinical settings, evaluation using individual intelligence
tests should be part of a diagnostic formulation (Rapoport &
Ismond, 1990). The etiology of problems for children
evaluated in clinical settings may stem from a number of
sources. Each possible source of the child's symptoms must
be examined and eliminated through formal assessment.
Children who are referred for diagnostic evaluation in
clinical settings frequently manifest social and/or academic
problems in the classroom. The intellectual quotient (IQ)
is an important factor in their diagnostic formulations as
well as a good predictor of clinical treatment outcomes
(Harris, 1987).
Overall IQ scores are the starting point for test
interpretation, followed by discrepancies in score
combinations. Meaningful variations are identified and
reviewed from normative and ipsative perspectives. The
pattern of individual intellectual test performance is
evaluated for statistical differences within individual
protocols. Pattern analysis can identify intraindividual
personality and neurological variations and abnormalities,
yet this approach is widely debated (McDermott, Fantuzzo, &
Glutting, 1990). The ipsative differences infer cognitive
strengths and weaknesses. The WISC-III is able to reduce
pairwise comparison from seven comparisons to four with
factor index scores evaluated against the child's mean scale
13
score (Nag1ieri, 1993). Unusual differences are clustered
to discern whether specific groups of children (e.g.,
learning disabled) score in a similar pattern, while
diverging from other specific groups of children.
Hypotheses are generated based on subtest combinations using
factor analytic studies to identify discrete abilities or
influences (Piotrowski & Siegel, 1984). The ability to
differentiate among groups of children based on distinctive
cognitive results may lead to early identification and
preventive intervention. By utilizing the ipsative approach
to test interpretation, normative comparisons are minimized,
thus allowing for a focus on the child's cognitive assets
and liabilities (Zachary, 1990).
Numerous studies have examined pattern analyses of the
Wechsler Intelligence Scales for various groups (Dean, 1978;
Greenblatt, Mattis, & Trad, 1991; Hale & Saxe, 1983;
McDermott et al., 1990; Silverstein, 1984). The WIse-III
successfully differentiated between specific groups of at
risk children in a study by Hishinuma and Yamakawa (1993);
however, further research on the 1991 WISe-III (Wechsler,
1991) is needed.
The purpose of this study was to examine similarities
or differences in cognitive performance of school-aged
children referred for diagnostic evaluations. The
comparison of performance for two groups of individuals was
14
carried out in a different but highly related fashion.
First, an attempt was made to obtain a comparative picture
of the performance of an educationally referred group and a
clinically referred group of children on various subtest
performances. In addition to examination of subtest
performances, three types of IQ scores and factor index
scores obtained from administration of the WISC-III were
compared. Consistent with the overall purpose of the study,
intellectual performance of the sample children also
included a comparison of the factor structure underlying
performance on the WISC-III.
15
CHAPTER 2
REVIEW OF THE LITERATURE
This chapter examines the literature related to the
history, developmental structure, and content of the
WISC-III and reviews the literature as it relates to the
overall purpose of the current investigation. In addition,
the psychometric properties, factor analysis, and clinical
and educational uses of the WISC-III are discussed.
Wechsler intelligence tests are the most researched
children's cognitive assessments (Sattler, 1982), and
empirical investigations have rendered widely divergent
findings. Indeed, the WISC may be both the most endorsed
and most condemned assessment tool (Kamphaus, 1993).
Support for both views is examined in this chapter.
History and Development of Wechsler Scales
The measurement of intelligence has a long history and
continues to be an evolving science. In the 1880s Sir
Francis Galton was the first to measure individual
differences in children. In 1904 Alfred Binet and Theophile
Simon developed the predecessors of current intellectual
assessments (Reynolds, Gutkin, Elliot, & Witt, 1984).
Wechsler's first intelligence test, Wechsler-Bellevue I, was
developed in 1939 to measure the cognitive abilities of
adults. A plethora of research over the past several
16
decades led to the development and downward extensions for
intellectual evaluation of younger people. The Wechsler
intelligence test was revised and expanded to three
versions, Wechsler Preschool and Primary Scale of
Intelligence, Revised (WPPSI-R) (Wechsler, 1989); Wechsler
Adult Intelligence Scale, Revised (WAIS-R) (Wechsler, 1981);
and Wechsler Intelligence Scale for Children, third edition
(WISC-III) (Wechsler, 1991). The Wechsler intelligence
tests are theoretically based on Spearman's (1927) "g"
factor and define intelligence as "the aggregate or global
capacity to act purposefully, to think rationally, and to
deal effectively with his environment" (Wechsler, 1944, p.
3) •
Structure of Wechsler Scales
Wechsler Scales use a variety of subtests to measure
and assess a wide range of abilities, illustrating many
aspects of intelligence (Wechsler, 1991). All Wechsler
intelligence tests divide subtests into two scales, Verbal
and Performance. Each scale produces an IQ, and the
combined average of the Verbal and Performance Scale scores
formulate the Full Scale IQ. Test profiles suggest a
child's strengths and weaknesses although nonintellectual
components also influence test results (Wechsler, 1991).
Wechsler Scales consider intelligence as a global capacity,
and the tests are divided into communication skills and
17
practical abilities. Many diverse subtest areas provide
additional opportunities to evaluate a person's global
cognitive capacity (Wechsler, 1974b). The g factor accounts
for most of the variance in the test and remains an
unrotated factor in factor analysis (Kamphaus, 1993). The
Verbal and Performance Scales do not represent separate
abilities; they are distinct styles applied collectively to
measure intelligence (Wechsler, 1974b).
WISC-III content
The Verbal Scale contains five standard subtests and
one that is optional. The Performance Scale has five
standard and two optional subtests. Raw scores are
converted to scale scores, and IQ and factor indices are
derived from composite scale scores.
Four factor-based index scores are optional provisions
of the WISC-III. The Verbal Comprehension Factor score is
constructed from the scale scores on Information,
Similarities, Vocabulary, and Comprehension subtests. The
Perceptual Organization Factor score is constituted from
scale scores on the Picture Completion, Picture Arrangement,
Block Design, and Object Assembly subtests. The Freedom
from Distractibility Factor score is composed of scale
scores on the Arithmetic and Digit Span subtests. The
Processing Speed Factor score consists of scale scores on
the coding and Symbol Search subtests.
18
The standard subtests on the Verbal Scale are
Information, similarities, Arithmetic, Vocabulary, and
comprehension. Digit Span is an optional Verbal subtest,
and it can be substituted for another subtest as
appropriate. Digit Span is used to measure the child's
immediate auditory recall for number sequences. In the
first section, the child must repeat an increasing number of
digits in the same order as the examiner. In the second
section, the child must recite the digits in reverse order
from the examiner's presentation.
The Information subtest taps the child's long-term
recall for facts and cultural exposure using oral questions
about people, events, places, and objects. The Similarities
subtest requires the child to evaluate the relationship
between things and ideas and to categorize them into logical
groups. Pairs of words are presented for the child to
identify the commonality between them in the Similarities
subtest. The Arithmetic subtest is timed, and the examiner
reads numerical word problems for the child to solve without
the use of pencil and paper. The final six Arithmetic items
are read by the Child, and bonus points are awarded for
correct responses given within ten seconds. The Vocabulary
subtest evaluates the child's ability to define and express
the meaning of words. This subtest is considered the best
indicator of Verbal ability (Sattler, 1988). The
19
Comprehension subtest assesses the child's ability to solve
practical problems using social judgment and common sense
reasoning. On many Verbal subtests, only specific responses
may be queried.
The standard subtests on the Performance Scale are
Picture Completion, Coding, Picture Arrangement, Block
Design, and Object Assembly. The Picture Completion subtest
taps a child's visual memory, attention to visual detail,
and understanding of content quality. Illustrations are
presented with a relevant detail omitted for the child to
point out. The Coding subtest requires the ability to learn
nonverbal material, as well as visual memory, visual motor
speed, and coordination. The child must copy symbols that
have been paired with geometric shapes or numbers, and the
score is determined by the amount completed within a time
limit. The Picture Arrangement is a timed subtest that
employs sequential planning ability, distinguishing relevant
details, and anticipation of social outcomes. Each item in
Picture Arrangement presents a series of pictures for the
child to rearrange in the appropriate sequence. The Block
Design is a timed subtest which measures a child's
perceptual organization of abstract figures constructed from
parts and spatial processing ability. object Assembly is
also a timed subtest, which assesses the ability to
recognize and assemble parts of a whole picture. Success on
20
this subtest requires adequate spatial relations,
visual-motor coordination, and persistence. Children of all
ages are given every item on the Object Assembly subtest.
Symbol Search and Mazes are both optional subtests
which require the psychometrician to time the child's speed
in task completion. The Mazes subtest evaluates problem
solving and the ability to follow a visual pattern within a
time limit. In a series of mazes, the child must trace a
line from the center of the maze to the exit without going
through lines or entering blocked pathways. In the Symbol
Search subtest, the child must visually scan two sets of
symbols and determine whether the symbol in the target group
appears again in that row of symbols.
Psychometric Properties
Across all age groups, the average reliability
coefficients of WISC-III subtests, IQ, and factor scores in
the WISC-III Manual range from .76 on Picture Arrangement to
.96 on the Full Scale IQ. However, for individual subtests,
age-specific reliability coefficients plummet to .60 on
Object Assembly for 14-year-olds. No individual reliability
coefficient on subtests exceeds .92 (Block Design for age
15). IQ reliability coefficients range from .89
(Performance IQ, age 14), to .97 (Full Scale IQ, age 14).
Factor index coefficients are lower, ranging from .80
(processing Speed, age 7), to .95 (Verbal Comprehension,
21
ages 12,14 and 15). (See WISC-III Manual for additional
information.)
WISC-R subtest composites have good reliability;
however, age-related differences exist. Verbal subtests
tend to have.higher reliability than Performance subtests.
Greater reliability is established as factors include more
subtests (Piotrowski & Siegel, 1984). Kaufman (1993) found
that subtest stability and reliability decreased from the
WISC-R to the WISC-III; however, Bracken, McCullum, and
Crain (1993) reported that the revised edition had higher
subtest composite reliabilities.
Construct validity is demonstrated with factor analytic
evidence, correlation with other cognitive assessments, and
scholastic performance. WISC-III construct and
criterion-related validity were evident with at-risk
students (Hishinuma & Yamakawa, 1993) as well as with
hearing- impaired children (Maller & Branden, 1993). There
was significant predictive and discriminant validity between
the WISC-III and Woodcock-Johnson Psycho-Educational
Battery-Revised (W-J-R) (Woodcock & Mather, 1989) for
seriously emotionally disturbed youth (Teeter & Smith,
1993). The significant correlation between the WISC-III and
the WISC-R supports the validity of the current version,
through a history of extensive empirical studies. Between
WISC-R and WISC-III administrations, concurrent validity
22
studies provide evidence of .89 correlation for the Full
Scale IQ scores (Wechsler, 1991). The WISC-III Manual
confirmed the criterion-related validity of the factor
structure with small clinical groups (Wechsler, 1991).
(Refer to WISC-III Manual for more information.)
Prewett and Matavich (1994) compared test results of
the WISC-III and the Stanford-Binet Intelligence Scale,
Fourth Edition (S-B IV) (Thorndike, Hagen, & Sattler, 1986b)
and found lower scores on the WISC-III, thus suggesting
different diagnoses.
WISC-III Changes
Notable improvements between the WISC-R and the revised
WISC-III included more extensive standardization and the
provision of four racial/ethnic classifications instead of
two. The WISC-III standardization was stratified according
to the 1988 United states Census according to age, gender,
ethnicity, geographic region, and parental education level.
The proportions of race, gender, region, and parent
education were maintained in each age group. The 2,200
children assessed formed 11 age groups and were divided into
four national regions. Each age group was represented by
200 children. pilot studies were conducted to ensure
standard scoring criteria. Examiners were trained, and
interrater reliability was controlled using
computer-generated feedback to identify discrepancies.
23
The WISC-III altered items and added color yet kept the
dual-scale format of its 1949 and 1974 prototypes. The
WISC-III retained approximately 70% of the items from the
WISC-R. changes included extended basal and ceiling items,
altered scoring criteria, sequence of subtest
administration, and a new subtest, Symbol Search. The
Symbol Search subtest was developed to augment the Freedom
from Distractibility Factor; however, factor analysis
methods proved that it created a separate factor (Wechsler,
1991). The WISC-III increased the number of items on 10
subtests to extend the basal and ceiling levels. As a
result the scoring criteria were more objective, and the
placement of the scoring criteria in the administration
section was more convenient. The color and increased size
of some visual stimuli were attractive and had better racial
and gender balance (Kaufman, 1993).
Factor Analysis
Factor- analysis is used to distinguish constructs that
characterize the association among sets of interconnected
variables (Shavelson, 1988). The amount of variance between
two sets of IQ scores is measured by factor loadings
(correlations) (Kline, 1991). Factors cluster in various
combinations to identify a specific construct, but
equivalent samples can cluster differently. When a limited
number of factors are considered, this statistical analysis
24
simplifies complex data. The size of the shared factors is
measured by eigenvalues (Kline, 1991). Eigenvalues range
from zero to V (V = number of variables in the factor
analysis). The conventional eigenvalue criterion in social
science analyses is set at 1.0 (Sattler, 1982); however,
this level may not be stringent and appropriate (Roid,
Prifitera, & Weiss, 1993).
Intelligence tests, particularly the WISC-III, provide
more information than the intelligence quotient score.
Kaufman (1975) identified three interpretive factors on the
WISC-R: Verbal Comprehension, Perceptual Organization, and
Freedom from Distractibility. In addition, Processing
Speed, derived from one new subtest, Symbol Search, and a
retained subtest, Coding, were added to the WISC-III
(Wechsler, 1991) (see Table 1).
Table 1
WISC-III Factors
Factor 1 Factor 2 Factor 3 Factor 4
(Verbal (Perceptual (Freedom from (Processing Comprehension) Organization) Distractibility) Speed)
Information Picture Completion Arithmeti~ Coding
Similarities Picture Arrangement Digit Span Symbol Search
Vocabulary Block Design
Comprehension Object Assembly
25
Despite variations in theoretical perspectives and
statistical methodology, Kaufman's (1975) three main factors
emerge in Wechsler's Scales (silverstein, 1986). A strong
Verbal Factor, a lesser Performance Factor, and a weak
Memory/Freedom from Distractibility Factor are established
(Matarazzo, 1972). The third factor, Freedom from
Distractibility, may help in the interpretation of
discrepancies between Verbal and Performance scores
(Kaufman, 1979).
The Verbal Comprehension Factor is intended to
represent verbal capacity and mental processing
comprehension (Sattler, 1988). The Perceptual Organization
Factor theoretically evaluates a child's discrimination of
visual stimuli and processing arrangement skills (Sattler,
1988). The intent of the Freedom from Distractibility
Factor is to measure concentration and sustained attention
(Sattler, 1988). The Symbol Search subtest was developed to
enhance the Freedom from Distractibility Factor. It is
considered to measure visual processing speed but has a
higher factor loading on Perceptual Organization than the
Coding subtest (Kamphaus, 1993). The Processing Speed
Factor hypothetically evaluates the amount of time required
to evaluate and solve problems visually.
26
The interpretation of these Verbal to Performance
discrepancies is the most common factor analysis of Wechsler
Intelligence Scales (Kaufman, 1976). Discrepancies on 11
points are common in the general population and correspond
with familial education and vocation (Sattler, 1982).
Kaufman's (1975) Verbal Comprehension and Perceptual
organization Factors loosely parallel Wechsler's Verbal and
Performance Scales. The third factor, Freedom from
Distractibility, contributes the least amount of variance in
WISC-R factor analysis (Reschly, 1978).
There are many problems associated with factor
analysis; providing impractical statistical abstractions
rather than augmenting clinical intervention is particularly
troublesome (Heim, 1975). Disparate findings result from
various methodological flaws (Cattell, 1978). Differences
in results using the same data set may be caused by the
formula used in factor analysis, number of factors
considered, and the infinite number of possible rotations.
In addition, the nomenclature of factors may distort
understanding of the variables (Bracken et al., 1993).
Stedman, Lawlis, Cortner, and Achterberg (1978)
reported that the WISC-R test factors were not discrete, and
nonverbal factors contributed to the Perceptual Organization
Factor. Extreme variability can distort large differences
among subtest scores, scale, or factor scores because they
27
are established by a mean. Many educationally placed
children score three or more points lower on Freedom from
Distractibility than on Verbal Comprehension or Perceptual
organization Factors (Kaufman, 1979).
Individual Comparison
A five-point hierarchical analysis of test results is
recommended (Sattler, 1982), where the broadest factors are
evaluated to interpret a child's score before examining
lower-order factors (Truch, 1993). The suggested order of
interpretive analysis is IQ scores, factor indices, high and
low subtest scores, subtest pattern, and response styles.
One purpose of pattern analysis is to identify
personality and neurological abnormalities and to
differentiate among separate homogeneous groups. The
diagnostic utility of pattern analysis is disputed among
psychologists (Sattler, 1974). Pattern analysis uses
simultaneous comparisons to provide clusters of statistical
inference, yet the problems involved with multiple
comparisons must be avoided (Silverstein, 1982). Subtest
scatter is another type of pattern analysis used for
intellectual assessments. The average subtest scatter on
the WISC-R was 9.7 scale score points for children in the
norming sample (Kaufman, 1976). Bannatyne's (1974)
clustering of subtests, provides a common pattern analysis
(see Table 2). Bannatyne's (1974) cluster analysis groups
Table 2
Bannatyne's Recategorizations
Conceptual
Similarities
Vocabulary
Comprehension
Spatial
Picture Completion
Block Design
Object Assembly
Sequencing
Arithmetic
Digit Span
Coding
28
Acquired Knowledge
Information
Arithmetic
Vocabulary
WISC subtests into four factors: conceptual, Spatial,
Sequencing, and Acquired Knowledge. Bannatyne's
recategorization is also applied to the WISC-III (Truch,
1993).
Intra-subject test comparison loses 60% of WISC-R
reliability variance (McDermott et al., 1990). Significant
statistical differences on WISC-R profiles did not enhance
identification of groups of children (Miller, 1980).
Profiles with broad subtest scatter should be interpreted
with caution (Blaha & Wallbrown, 1984). WISC-R profile
analysis was unable to predict children's achievement in
reading and arithmetic (Hale & Saxe, 1983).
Clinical and Educational Uses of the WISC-III
Some theorists dispute the diagnostic utility of
profile differences on the WISC-R (Sattler, 1974). In 1939,
Wechsler identified emotional factors from performance on
29
individual subtests. Intellectual assessments are designed
to measure cognitive ability, although personal response
styles reveal an individual's strengths, weaknesses, and
emotional characteristics (Wechsler, 1941).
Despite a very high correlation between the WISC-III
and the WISC-R, scores tend to be lower on the more recent
versions (sternberg, 1993). Larrabee and Holroyd (1976)
found lower scores on the WISC-R compared to the original
WISC (Wechsler, 1949). Lower scores impact placement
decisions and result in more mentally handicapped children
being placed in special education classes (Dumont & Faro,
1993a). Such placements can manifest in social
stigmatization and negatively affect school district
budgets. In contrast fewer children are likely to qualify
as learning disabled using the WISC-III because the
discrepancy between achievement and ability scores is
reduced (Post & Mitchell, 1993). A triennial comparison of
educationally referred students tested using the WISC-R and
the WISC-III found a smaller score differential between the
two over the period, and lower scores were more frequent on
the Performance IQ for both versions (Graf & Hinton, 1994).
Dyslexic children's Verbal IQ was five points lower upon
WISC-R to WISC-III retest; however, there was no significant
change on Performance IQ scores (Newby, Recht, Caldwell, &
Schaefer, 1993). Average score differences between the
30
WISC-III and the WISC-R were significantly greater for black
than white children (Graf & Hinton, 1994).
Although the WISC-III attempted to remove biased items,
(Dumont & Faro, 1993b), it remains culturally entrenched and
verbally laden (Kaufman, 1993). The elimination of
emotionally provoking items on the WISC-III also diminished
Wechsler's goal of augmenting clinical interpretation
(Kaufman, 1993). Lower Performance scores may be attributed
to visual distraction resulting from adding color to the
Picture Completion and Picture Arrangement subtests (Graf &
Hinton, 1994).
Barona (1989) reported that the pattern of WISC-R
factors affecting special education qualification decisions
was similar for learning disabled, mentally retarded, and
ineligible students from three major ethnic groups. The
WISC-R factor structure was analogous among Anglos, blacks,
Chicanos, and Papagos (Reschley, 1978). Weiss, Prifitera,
and Roid (1993) demonstrated WISC-III predictive validity
across gender and ethnic groups. Different processing modes
were evidenced between gifted and learning disabled Navajos
on the WISC-R (Mishra, Lord, & Sabers, 1989). However, no
distinct profiles emerged for learning disabled
Mexican-American (Mishra, 1984a) or Papago Native American
(Mishra, 1984b) children. The'WISC-III Verbal IQ and Full
Scale IQ scores were approximately one standard deviation
31
below the mean for Tohono O'odham children, thus making few
eligible to meet state criteria as learning disabled
(Tanner-Halverson, Burden, & Sabers, 1993).
Group Differences
David Wechsler (1974a) hypothesized that significant
discrepancies between Verbal and Performance scores
indicated psychopathology. Higher Verbal scores were
expected among psychiatric patients diagnosed with neurotic,
psychotic, or organic conditions. Juvenile delinquents and
persons with mental retardation were expected to score
higher on the Performance subtests. wechsler compiled test
profiles to distinguish characteristics for five clinical
groups. He found that Block Design subtest scores were
lower for subjects with organic conditions, while
Similarities subtest scores were depressed among mentally
retarded persons (Wechsler, 1941). Unique psychometric
patterns were discernible for schizophrenic children on the
WISC, identifying 40% of schizophrenic children with a 2%
error rate (Wechsler & Jaros, 1965). More than half of that
sample had significant scale score divergence from the mean
on three subtests (Wechsler & Jaros, 1965). Specific
WISC-III subtest patterns were more frequent in profiles of
children diagnosed with learning disabilities and attention
deficit hyperactivity disorders than in profiles of children
in the norming sample (Prifitera & Dersh, 1993). Moreover,
32
students diagnosed with attention deficit hyperactivity
disorder had a significantly lower Processing Speed Factor,
Freedom from Distractibility Factor, and Verbal IQ scores
than the standardization group but higher Perceptual
Organization Factor scores (Schwean, Saklofske, Yackulic, &
Quinn, 1993).
The factor structure of the WISC-R was similar in a
comparison of psychiatric and normal children (Culbert,
Hamer, & Klinge, 1989; Hodges, 1982). Equivalent factor
structure was confirmed in a study with psychiatric and
normal adults on the WAIS-R (Beck, Horwitz, Seidenber,
Parker, & Frank, 1985; Atkinson & Cyr, 1984). Perceptual
integration was lower among learning disabled children,
while emotionally disturbed children scored lower on verbal
sections of the WISC-R (Dean, 1978). A lower ACID profile
(Arithmetic, Coding, Information, and Digit Span subtests)
was common for children with learning problems (Truch,
1993). There was no specific ACID pattern found in an
investigation of a child psychiatric population (Greenblatt
et al., 1991). Th.e ACID profile may be useful for
generatin9 hypotheses, although it cannot diagnose
educational problems (Truch, 1993). The results of the
Digit Span, Coding, and Mazes subtests are often disparate
from the trend established by other subtests (Kamphaus,
1993).
33
Research using Digit Span indicates that the subtest
measures more than short-term memory (Mishra, Ferguson, &
King, 1985). Neurological dysfunction may be apparent from
different abilities required in Digit Span Forward and Digit
Span Backward~ Developmentally delayed children with right
cerebral hemisphere deficits produce significantly higher
scores on Digit Span Forward than Digit Span Backward (Rudel
& Denckla, 1974). Digit Span has a .85 factor loading for
Freedom from Distractibility, but no intercorrelation is
listed for the Full Scale IQ (Wechsler, 1991). The Digit
Span subtest has the highest specificity value and evaluates
a unique ability (Kamphaus & Platt, 1992).
Related Research
Both support and opposition to using IQ tests,
specifically of the WISe-III, are justified. Objections to
intellectual assessments include arbitrary school placements
based on test results, following a medical model of illness,
racial and/or ethnic biases, and inaccurate prediction of
future behavior (Branden & Reschly, 1993). Wechsler scores
are not ratio scales; the absence of a zero point allows for
only simple comparison to determine an individual's
cognitive performance (Truch, 1993). Responding to his
opponents' arguments, Wechsler (1974a) stated that IQ is a
numerical ratio, identifying relative abilities by comparing
an individual score to the average score for his/her age
34
cohorts. Moreover, the use of a deviation IQ in Wechsler
Scales reduces some IQ psychometric problems (Zimmerman &
Woo-Sam, 1985). The use of Wechsler's IQ tests is further
justified by demonstrating significant correlations between
IQ and academic achievement and between Wechsler
Intelligence Scales and other IQ tests.
WISC-III Reviews
The revised WISC-III has received both support and
criticism from psychologists. According to Carroll (1993),
the WISC-III produced insufficient improvements, especially
in factor indices, to justify its popularity. The allure of
Wechsler intelligence tests may be attributed to its simple
administration procedures, yet its greatest weakness is that
results are explained in terms of only one index (Detterman,
1985).
Verbal Comprehension and Perceptual Organization are
significant factors on the WISC-III; however, Freedom from
Distractibility and Processing Speed have not been proven to
be discrete (Carroll, 1993). Freedom from Distractibility
and Processing Speed Factors have small factor loadings.
Hence, further investigation is warranted, and caution is
recommended in interpretation (Kamphaus, 1993). Kaufman
(1993) predicted that the Processing Speed Factor will soon
be obsolete.
35
WIse-III factor stability confirmed the factor
structure in the standardization sample in a study by Roid
et ale (1993). Processing Speed was more robust than
Freedom from Distractibility, and Sattler (1992) found that
the third and fourth factors were not significant for 4 of
the 11 age groups. Sattler's (1992) findings were
challenged by Roid et ale (1993), who claimed that setting
the eigenvalue criterion at 1.0 invalidated pertinent
statistical methodology. The third and fourth factors were
easily misinterpreted because they were derived from only
two subtests and contributed a minimal amount of total test
variance (Dumont & Faro, 1993b). Mean factor scores on
Freedom from Distractibility and Processing Speed were low
for children referred for evaluation in clinical settings
(Wechsler, 1991).
There is no concomitant intellectual theory to support
the new WISe-III factors, as required for adequate test
construction (Kamphaus, Benson, Hutchinson, & Platt, 1994).
Factor index scores aid test interpretation but should not
be reported. The WISe-III cannot diagnose clinical or
emotional illness or dyslexia (Truch, 1993).
Prewett and Matavich (1994) reported dissimilar
classifications based on a comparison of WISe-III and S-B IV
(Thorndike, Hagen, & Sattler, 1986a), with WIse-III scores
averaging 9.4 points lower. Emotionally disturbed children
36
earned WISC-III factor scores similar to those of the
Woodcock-Johnson Psychoeducational Battery, Revised edition,
reasoning scores (Teeter & smith, 1993).
Summary of Literature
Wechsler Intelligence Scales share a rich history and
wide popularity in the measurement of cognitive abilities.
The Psychological Corporation (1991) sUbstantiated adequate
reliability and validity of the WISC-III. This finding
contrasts with lower IQ scores reported on the WISC-III than
the WISC-R (Sternberg, 1993) and lower scores than the S-B
IV produces (Prewett & Matavich, 1994). Considerable effort
is apparent in the WISC-III standardization, and the current
version retains 70% of WISC-R items.
Factor analysis is a statistical procedure used to
identify related constructs. Kaufman (1975) identified
three main factors on the WISC-R; the WISC-III identifies
four factors. No theoretical foundation preceded the
development of the four factors. The new subtest, Symbol
Search, was developed to load on the Freedom from
Distractibility Factor; however, factor analysis results
find it to be unrelated. The investigation of factor
structure may provide numerical abstraction rather than
augmenting clinical diagnosis and intervention.
The literature on the ability to distinguish among
specific groups based on Wechsler Intelligence test scores
37
has been inconsistent. More comparative studies are needed
to evaluate the WISC-III fully. The literature has been
generally supportive, although it remains divided on the
merits of the WISC-III.
CHAPTER 3
METHOD
38
This chapter provides a detailed description of the
methodology used for the substantiation of major hypotheses
of the study. In doing so, various components of
methodology such as sampling procedure and sample, data
collection procedure, statement of major hypotheses,
instrumentation, and statistical analyses are described.
Sample
The 60 participants selected for the study were
referred for psychological assessments from a clinical and
an educational setting. All subjects between the ages of 6
years 3 months and 16 years 10 months who were referred for
diagnostic evaluations were sampled from a small school
district and a Rocky Mountain child psychiatric facility.
Children referred for diagnostic evaluation in two locations
were matched by age, and use of this process resulted in a
sample of 60 subjects. From the pool of children referred
for evaluation, 30 came from an educational setting, and the
re~aining 30 were referred in a clinical environment. It
should be noted that although no systematic attempt was made
to obtain gender balance in this sample, the sampling
process used in this study resulted in a very close gender
match. The referred participants included 23 males from the
39
educational setting, 20 males from the clinical setting, 7
females from educational environment, and 10 females from
the clinical environment. Characteristics of sample
subjects are summarized in Table 3.
Table 3
study Participants by Age and Gender en = 60)
Education (n = 30) Clinical (n = 30)
Age Groups Male Female Male Female
6 - 6.11 1
7 - 7.11 4 1 5
8 - 8.11 3 3
9 - 9.11 3 1 3 1
10 - 10.11 1 1
11 -11.11 2 1 2
12 - 12.11 1 1
13 -13.11 4 1 4 1
14 - 14.11 2 2
15 - 15.11 4 2 2
16 - 16.11 1 1
23 7 20 10
40
All children in both settings were evaluated by the
same certified professional who was trained and supervised
in the administration of psychological tests. The
assessments were administered according to the standard
procedures outlined in the WISe-III Manual (Wechsler, 1991).
In the educational setting, referrals were either
federally mandated re-evaluations for special education
eligibility or parent or teacher referred based on
scholastic and/or behavioral concerns. The special
education secretary provided the psychometrist with a list
of students to be assessed as scheduling allowed. Most
evaluations took approximately two hours, and all were
performed individually in a quiet office setting.
The clinically referred children, with psychological
diagnoses such as major depression and conduct disorders,
were in-patients at a child psychiatric facility. Generally
these children were referred as a result of their parents
concerns, although some were referred from outpatient
therapists, and others were admitted after a suicide
attempt. All children admitted were given complete
diagnostic formulations by a team of professionals in
various specialty areas. Within a day or two after
admission, subjects were psychologically evaluated in a
quiet office setting for approximately three hours.
41
Instrument
The WISe-III is an individually administered test of
children's cognitive abilities developed for children
ranging in age from 6-0 to 16-11. Cognitive abilities are
measured by subtest raw scores and converted into scale
scores. Subtest scale scores range from 1 to 19 with a mean
of 10, and a standard deviation of three. The sum of
subtest scale scores comprise three composites, the Verbal
IQ, Performance IQ, and Full Scale IQ. The Full Scale IQ
score is obtained by averaging the Verbal and Performance
IQs. Factor index and IQ scores have a mean of 100 and a
standard deviation of 15. IQ scores are intended to
estimate a child's intellectual aptitude.
Test administration alternates Performance and Verbal
subtests. The starting point for many subtests differs
depending on the child's chronological age. Items become
progressively more difficult in each subtest.
Procedure
The subjects participating in this study were assessed
by a state certified school psychologist using the third
edition of the Wechsler Intelligence Scale for Children.
The Wechsler Scale was administered as part of a battery of
tests in an appropriate environment in accordance ~'ith the
standardized procedures outlined in the WISC-III Manual.
The children assessed were referred for full diagnostic
42
evaluation. The examiner was supervised and trained in the
administration and interpretation of the WISC-III and was
employed to perform psychological evaluations. Rapport was
developed with participants prior to test administration.
Hypotheses
The following null hypotheses were investigated.
1. There will be no significant difference in the
WISC-III subtest performance of subjects referred
for clinical diagnosis from those referred for
educational diagnosis.
2. IQ scores of the educational and clinical
sample groups will not be significantly different.
3. Similarity of factor index scores will exist among
children referred in clinical and educational
settings.
4. There will be similarity in underlying factor
structure of WISC-III performance between the
educational and clinical sample groups.
5. The underlying factor structure in the current
sample will not be meaningfully different from
the factor structure listed in the WIse-III
Manual.
statistical Analysis of Data
consistent with the major hypotheses, three kinds of
statistical analyses were performed. SAS computer software
43
was used for data analysis. One-way ANOVA and t-tests for
independent samples were used to examine Hypotheses 1, 2,
and 3. These methods quantify significant statistical
differences between mean scores. The factor structure for
both groups was evaluated using the principal components and
varimax rotated methods of factor analysis to investigate
Hypotheses 4 and 5. The principal component factor method
was used to estimate the correlation matrix by finding the
eigenvalues and vectors in the matrix (Kline, 1994). The
varimax factor method is an orthogonal rotation that
maximizes the sum of variances of squared loadings in the
columns of the factor matrix (Kline, 1994). The number of
factors was predetermined at four to examine the WISC-III
factor structure. The Mazes subtest was not included in the
factor analysis because the WISC-III Manual did not suggest
that it contributed to a factor score.
The varimax factor method of orthogonal rotation was
used to adjust and interpret the differences among factors.
The varimax method verifies factors that are completely
independent of each other and polarizes factor loadings to
minimize and maximize differences. The first factor loads
the highest, the second factor accounts for variance that
was not in the first factor, and the later factors account
for those omitted in the earlier factors.
CHAPTER 4
RESULTS
44
This chapter summarizes the findings obtained by
presenting the results that emerged from the statistical
analyses. Each hypothesis is restated and evaluated against
the results.
Between Group Differences
Results Related to Hypothesis 1
The first hypothesis postulated that no similarities
would exist between children referred for clinical and
educational diagnoses in terms of their average performance
on WISC-III subtest scores. Table 4 summarizes findings
involving subtest performances of subjects in two samples.
Independent t-test comparisons confirmed that no
statistically significant differences in subtest performance
exist for subjects referred for these two types of
diagnoses. In general, the average subtest scores of
subjects in both groups were found to be below the normative
mean. The subtest means of subjects in the group referred
f~r educational diagnosis ranged from a low of 6.63 (SD =
2.94) on the Arithmetic subtest to a high of 8.5 (SD = 3.14)
on the Picture completion subtest. The average subtest
performance for the group referred for clinical diagnosis,
in contrast, ranged from a low 7.06 (SD = 3.47) on the
45
Table 4
Subtest Means, Standard Deviations, and t-Ratios for Two
Samples
Educational Clinical
(n=30) (n=30) (df=58)
M SO M SO t-ratio *p values
Information 7.56 2.83 8.93 2.58 1.95 0.06
Similari ties 7.10 2.99 8.26 3.54 1.37 0.17
Arithmetic 6.63 2.94 7.80 3.14 1.48 0.14
Vocabulary 7.16 2.67 7.96 3.31 1.02 0.30
Comprehension 7.20 3.56 8.20 3.36 1 .11 0.26
Digit Span 8.00 2.61 7.73 2.44 0.40 0.68
Picture 8.50 3.14 7.93 2.50 0.77 0.44
Completion
Coding 7.23 2.68 7.60 3.65 0.44 0.65
Picture 7.53 3.13 7.06 3.47 0.54 0.58
Arrangement
Block Design 7.76 3.58 7.80 3.73 0.03 0.97
Object 7.93 3.55 8.53 4.02 0.61 0.54
Assembly
Symbol Search 8.13 2.87 7.90 2.99 0.30 0.75
46
Picture Arrangement subtest to a high of 8.93 (SD = 2.58) on
the Information subtest. The obtained t-values (ranging
from 0.31 to 1.96) for all 12 subtest performances led to
the acceptance of Hypothesis 1, suggesting no significant
differences in WIse-III subtest performance between the two
groups of children. Equivalent statistical findings were
also obtained by analyzing data using a one-way ANOVA (see
Appendix A). F-ratios indicated no differences in
performance of subjects on verbal or non-verbal tests for
children in either group.
Results Related to Hypothesis 2
The second hypothesis stated that no differences in the
obtained IQs (Full Scale, Verbal, and Performance IQ) would
be found for children in the two groups. The results
related to such comparisons are presented in Table 5.
Table 5
Mean lOs, Standard Deviations, and t-Ratios for Two Samples
Educational Clinical (n=30) (n=30) (df=58)
M SD M SD t-ratio *p values
Intelligence Quotients
Full Scale IQ 84.10 13.98 87.43 15.73 0.86 0.38
Verbal IQ 83.83 14.09 90.40 14.88 1.75 0.08
Performance IQ 86.60 14.08 86.13 16.00 0.11 0.90
47
It is evident from the information contained in Table 5
that no significant differences were found between the two
sample groups in terms of the mean IQ scores. The results
obtained from one-way ANOVA (see Appendix B) provided
additional support for substantiating Hypothesis 2. The
discrepancy between the two samples on average Verbal IQ
scores appeared to be the largest, though not significantly
so. The mean Verbal IQ was 83.83 (SO = 14.09) for children
referred for educational diagnosis, while the average Verbal
IQ for the group referred for clinical diagnosis was 90.40
(SO = 14.88). The Full Scale and Performance IQ scores were
highly similar between the two groups. The Performance IQ
mean was 86.6 (SO = 14.08) for the sample tested in an
educational setting, while the average for the children
assessed in a Clinical environment was 86.13 (SO = 16.00).
The type of environment from which children were referred
evidenced no differences in average IQ scores. The
similarity between average IQ scores between the two sample
groups indicated that Hypothesis 2 cannot be rejected.
Results Related to Hypothesis 3
The third hypothesis stated that average factor index
scores between the two sample groups assessed at different
types of facilities would be similar. The comparison of
factor indices is depicted in Table 6.
Table 6
Factor Means, Standard Deviations. and t-Ratios for Two
Samples
Educational Clinical
(n=30) (n=30) (df=58)
48
M SO M SO t-ratio *p values
Verbal Comprehension
85.46 13.71 91.86 15.25 1.70 0.09
Perceptual Organization
88.40 15.11 87.13 15.86 0.31 0.75
Freedom from Distractibility
85.73 12.49 87.83 13.86 0.61 0.54
Processing Speed
88.20 13.57 89.30 17 .12 0.27 0.78
As indicated in Table 6, independent t-test results
concluded that there were no significant differences in
average factor index scores between the two sample groups.
commensurate findings were also noted using one-way ANOVA
(see Appendix C). The factor scores corresponding to the
subtest scores were below the normative mean for both sample
groups. Verbal Comprehension was the highest average factor
index score for the clinically referred children 91.86 (SD =
15.25), yet it was the lowest mean factor score for the
49
group of children referred for educational diagnoses 85.46
(SD = 13.71). The average index score on Factors 2, 3, and
4 was remarkably homogeneous between the two groups of
sample sUbjects. Since no significant differences were
identified in factor index scores between the two groups,
the null hypothesis is accepted.
The first three hypotheses investigated revealed that
there were no statistically significant differences between
the groups on any WISC-III variable at the .05 probability
level. Subtests, factors, and IQs, which have higher "g"
loadings, were closer to the .05 significance level.
Components that contributed less to the overall test
variance were more similar.
Comparison of Underlying Factor Structure
Results Related to Hypothesis 4
The fourth hypothesis suggested that groups of children
referred for diagnosis in separate environments would have
meaningfully different factor structures (see Tables 7 & 8).
As stated earlier, the performance of two groups of
subjects was factor analyzed by using a principal component
method with varimax rotation (SAS software). The
performance for each group of subjects was forced into four
factors based on the factor structure of subjects included
in the norming sample.
50
Table 7
Comparative Factor Loadings for Sample Groups
E C E C E C E C
Factor 1 Factor 2 Factor 3 Factor 4
Information 78* 85* 1 - 7 36 18 11 19
Similarities 70* 35 16 21 54* 55* -10 60*
Arithmetic 70* 73* 28 48* 12 14 21 27
Vocabulary 87* 70* 22 26 16 43 - 2 18
Comprehension 76* 63* 36 36 - 11 31 20 22
Digit Span 15 22 12 13 8 12 95* 90*
Picture 12 42 43* 40 61* 62* 18 2
Completion
Coding 7 9 85* 92* - 1 10 - 2 7
Picture 14 24 - 3 54* 84* 27 6 51*
Arrangement
Block Design 30 33 71* 20 24 75* 27 20
Object Assembly 34 12 52* 11 52* 89* - 12 16
Symbol Search 32 20 82* 88* 13 29 13 20
Educationally Referred Sample
Eigenvalues 3.29 2.64 1.89 1.14
Percent of 27.4% 22% 15.8% 9.6%
Clinically Referred Sample
Eigenvalues 2.73 2.62 2.55 1. 72
Percent of 22.8% 21.9% 21.3% 14.4%
Variance
51
Note. E = Educationally referred group, n = 30. C =
Clinically referred group, n = 30. Printed values are
multiplied by 100 and rounded to the nearest integer.
Values greater than 0.43 have been marked by an 11*." The
Mazes subtest was omitted.
This analysis related to Hypothesis 4, which compared
the similarity or difference in the factorial composition of
the WISC-III for two sample groups of sUbjects. An
examination of WISC-III factorial composition for subjects
referred for educational diagnoses demonstrated four factors
as evidenced by factor loadings. The first factor was
defined by high loadings on the Information, Similarities,
and Comprehension subtests (range of loadings = .70 to .78).
Because these loadings were produced by these three
subtests, Factor 1 can appropriately be called Verbal
Comprehension. The subtests that characterized the Verbal
Comprehension Factor produced significantly high loadings;
however, for this group, an unanticipated but equally high
factor loading was contributed by the Arithmetic subtest.
Arithmetic's significant loading on Factor 1 may be
explained by the fact that word problems, which require
adequate verbal skills, are used to solve the numerical
problems on the WISC-III.
Factor 2 is identified as Perceptual Organization and
was characterized for the educationally referred youth by
52
such marker subtests as Picture Completion, Picture
Arrangement, Block Design, and Object Assembly. Factor
loadings on these four subtests ranged from a low of .3,
produced by Picture Arrangement, to a high of .71,
contributed by Block Design. Two other subtests, Coding and
Symbol Search, also demonstrated high loadings on Factor 2.
These subtests seemed to tap different cognitive functions
as evidenced by the face value of skills required for
success on Coding and Symbol Search.
The cluster of subtests contributing to Factor 3 for
the educationally referred sample included Coding, Symbol
Search, Block Design, Object Assembly, and Picture
Completion. This factor theoretically represents Freedom
from Distractibility and significant loadings ranged from a
low of .43 on Picture Completion to a high of .85 on the
coding subtest.
The new fourth factor, Processing Speed, was derived
from an independent and very high (.95) factor loading on
the Digit Span subtest.
The sample group obtained through clinical referrals
loaded different subtests contributing to the four confirmed
factors. The subtests which comprised Factor I nearly
mirrored each other between the groups. The exception was
that the Similarities subtest did not load on the Verbal
Comprehension Factor for the clinically referred sample.
53
The subtests which created the Perceptual Organization
Factor, Factor 2, were counter-intuitive to the
nomenclature. Significant contributing subtests to Factor 2
for the clinically referred youth included Arithmetic,
Coding, Picture Arrangement, and Symbol Search. These
factor loadings ranged from a low of .48 on the Arithmetic
subtest to a high of .92 on the Coding subtest.
Freedom from Distractibility is the designated label
that the WISC-III Manual uses for Factor 3. The subtests
that substantially contributed to this factor for the
clinically referred group included Similarities (.55),
Picture Completion (.62), Block Design (.75), and Object
Assembly (.89).
An odd cluster of subtests create Factor 4 for the
clinically referred sample: Similarities, Digit Span, and
Picture Arrangement. The lowest significant factor loading
was .51 on the Picture Arrangement subtest, and the highest
was .90 on the Digit Span subtest. Factor 4 is designed to
measure Processing Speed, which logically would include
subtests requiring time limits, unlike the Similarities, and
Digit Span subtests.
Results Related to Hypothesis 5
The fifth hypothesis postulated that the factor
structure underlying WISC-III would be similar to the factor
structure of the entire study sample. A comparison of the
54
emerging factor structure was made using technical data
reported in the WISC-III Manual for the national norming
sample. Table 8 presents a summary of comparative factor
composition of the WISC-III for these two samples.
The composition of Factor 1 was parallel between the
total sample and norming sample except that the Arithmetic
subtest was a significant variable contributing to the
Verbal Comprehension Factor for the total sample.
The Block Design subtest loads equally in this
comparison on Factor 2: however, the other matching
significant subtests, Picture Completion and Object
Assembly, have discrepant loading values. Factors 3 and 4,
Freedom from Distractibility and Processing Speed, have no
significant variables in common between the norming group
and the total sample in the present study. Note. T = Total
sample. N = Normative Sample. The Mazes subtest was
omitted. Data for the norming sample was obtained with
permission from Wechsler Intelligence Scale for Children -
Third Edition Manual (p. 192-193) by D. Wechsler, 1991, San
Antonio: The Psychological Corporation. Copyright 1991 by
The Psychological Corporation (see Appendix D). Eigenvalues
and percentage of variance are from the total study (**),
and this information was not available for the WISC-III
norming group.
55
Table 8
WISC-III Factor structure: ComI;!arison of study and
Normative SamI;!les
T N T N T N T N
Factor Factor 2 Factor 3 Factor 4
Information 84* 72* 21 29 - 6 25 10 9
Similarities 55* 72* 49* 29 6 23 44* 9
Arithmetic 74* 42 11 27 38 73* 28 15
Vocabulary 78* 79* 33 22 18 28 17 16
Comprehension 71* 65* 23 19 27 17 17 19
Digit Span 27 26 - 1 19 14 34* 71* 18
Picture 15 38 67* 53* 21 10 33 8
Completion
Coding 11 11 14 13 94* 9 11
79*
Picture 11 33 33 37* 15 8 77* 25
Arrangement
Block Design 35 29 70* 70* 31 24 8 17
Object Assembly 27 26 86* 69* 11 11 4 14
Symbol Search 25 20 34 35 81* 19 23
56*
Eigenvalues ** 3.04 2.38 1.98 1.62
Percent of variance**
24.3% 19.9% 16.5% 13.5%
The pattern of factor composition for each sample
group, as obtained from the analysis of data, and the
WISC-III normative sample is summarized in Table 9.
Table 9
Comparison of Factor Loading Patterns for Clinical.
Educational. and Norming Samples
Factor 1 Factor 2 Factor 3 Factor 4
Information N C E
Similarities N E C E C
Vocabulary N C E
Comprehension N C E
Picture Completion N E C E
Picture Arrangement N C E C
Block Design N E C
Object Assembly N E C E
Arithmetic C E C
. Digit Span N C E
Coding C E
Symbol Search C E
N
N
N
56
57
Note. N = WISC-III norming sample, E = Educationally
referred sample, C = Clinically referred sample. The Mazes
subtest was omitted.
As illustrated in Table 9, both sample groups and the
WISC-III norming sample identified the Information,
Vocabulary, and Comprehension subtests as significant
variables which contributed to Factor 1. No other factors
were identical to the WISC-III norm group among the two
groups studied. Factors 3 and 4 were most discrepant; no
sample group clustered in a similar pattern to the WISC-III
standardization subjects. The factor loadings for the youth
in the present study did not cluster in a similar fashion to
the groupings listed in the WISC-III Technical Manual.
However, the children referred for educational diagnoses had
a closer relationship on subtests that constituted factors
than the group referred from a clinical facility.
Similarity of factor structure for the two groups of
children was empirically determined by using the statistical
method called coefficient of congruence (Harmon, 1967).
These coefficients are presented in Tables 10 -13.
58
Table 10
Coefficients of Congruence Between Matching Factors for Two
Sample Groups
Educational Setting
Factors I II III IV
I Verbal Comprehension .82
Clinical II Perceptual Organization .48
Setting III Freedom f. Distractability .90
IV Processing Speed .43
Table 11
Coefficients of Congruence Between Matching Factors in Total
Sample and Educationally Referred Groups
Educational Setting
Factors I II III IV
I Verbal Comprehension .98
Total II Perceptual Organization .44
Sample III Freedom f. Distractability .92
IV Processing Speed .54
59
Table 12
Coefficients of Congruence Between Matching Factors in Total
SamQle and Clinically Referred GrouQs
Clinical Setting
Factors I II III IV
I Verbal Comprehension .83
Total II Perceptual Organization .99
Sample III Freedom f. Distractability .99
IV Processing Speed .65
Table 13
Coefficients of Congruence Between Matching Factors in Total
SamQle and Norming GrouQs
Norming Sample
Factors I II III IV
I Verbal Comprehension .96
Total II Perceptual Organization .61
Sample III Freedom f. Distractability .57
IV Processing Speed .58
60
The sample groups were compared in shared factors by fou~
coefficients of congruence, and results suggested that
equivalence was obtained for Factor 1 (Verbal Comprehension)
and Factor 3 (Freedom from Distractibility). Perceptual
organization and Processing Speed factors exhibited a
differential pattern of loadings between the groups. The
minimal correlation between the sample groups on Factor 2
and 4 imply dissimilarity in factor composition.
A similar pattern of congruence is apparent in Table
11, with greater correspondence between the total sample and
the educationally referred group on Factors 1 and 3. The
relationship between factors for the group assessed in a
clinical setting as compared to the total sample resulted in
substantially higher consonance on Factors 2 and 3 and lower
but apparent correlation on Factor 1. Table 13 displays
that only Factor 1, Verbal Comprehension, was significantly
correlated between the total sample and norming group.
The congruence between WISC-III factors among the
sample groups studied was greater than the match of factors
between the total sample and the norming sample. Verbal
Comprehension is a perennial factor emerging on analyses of
the WISC-III, indicating that linguistic processing ability
is the underlying core to this assessment instrument.
CHAPTER 5
DISCUSSION
61
This study was designed to investigate the factor
structure of the 1991 version of the Wechsler Intelligence
Scale for Children, WISC-III, and to determine whether a
group of clinically referred children could be distinguished
in cognitive performance from a sample of educationally
referred children.
Contrary to the assumption that cognitive performance
would be different between groups of children based on
referral setting, the two sample groups were found to be
remarkably similar. There were small mean differences
between the groups in subtest scores, such as Arithmetic;
however, large standard deviations reduced the statistical
power. The clinical group of children had higher average
scores than the educational group on all required Verbal
subtests, reflected in higher Verbal IQ and Verbal
Comprehension Factor Index scores. This difference was
manifested in disparate diagnostic categories for the Verbal
IQ and Verbal Comprehension Factor score.
The smaller mean differences on the Performance Scale
may be explained by the lower reliability coefficients and
lower correlations of Performance subtests to the g factor
that theoretically underlies the concept of intelligence.
62
The Digit Span subtest was slightly higher for the groups of
children assessed in an educational environment. These
results implied that memory, concentration, and processing
speed may be negatively influenced for the children referred
for clinical diagnoses. Psychotropic medication or
psychomotor retardation as a symptom of depression may
contribute to diminishing the test score on this cognitive
ability (Kaufman, Grossman, & Kaufman, 1994).
Both sample groups and the WISC-III norming sample
coincided with Information, Vocabulary, and Comprehension
subtests as significant variables which contributed to
Factor 1. No other factors were matched to the WISC-III
norm group among the two groups studied. Factors 3 and 4
were most discrepant; no sample group clustered in a similar
pattern to the WISC-III standardization subjects. The
factor loadings for the youth in the present study did not
cluster in a similar fashion to the groupings listed in the
WISC-III Manual. However, the educational sample had a
closer relationship on subtests that constituted factors
than the group from a clinical setting.
Factor 1, Verbal Comprehension, for the total sample
consisted of similar but not identical subtests as reported
for this factor in the WISC-III Manual. However, the
evaluation of factor structure for each group,
independently, did not include the same variables for any
63
factor. Verbal Comprehension was the highest factor score
for the clinical group and the lowest factor score for the
educational group.
This investigation lends support to the validity of the
.WISC-III. Children who were matched on age had similar
overall cognitive performance. This study was unable to
infer specific diagnoses based on distinctive cognitive
performance among groups. The WISC-III is most beneficial
in predicting academic potential.
The conclusions of the present study should be
interpreted in light of some limitations which might have
impact on the generalizability of the obtained findings.
The primary confinement was the use of small sample groups.
A larger N in this study may have had enough power to
distinguish differences between the groups. Moreover,
without a forced four-factor pattern, differences between
the groups may be more likely to be statistically
significant. The range of children's abilities was narrowed
in the present study by evaluating only referred children.
The average Full Scale IQ in the present study was
approximately 15 points lower than the normative mean.
Similar IQ scores between groups limit the range of
variability on test profiles (Wechsler & Jaros, 1965), thus
minimizing statistical differences. Diagnostic utility is
minimal when there is homogeneity in test score patterns.
64
Psychologists must be aware that the WISC-III is likely to
produce lower IQ scores than the WISC-R and S-B IV. Lower
scores will impact special education eligibility decisions,
and caution is recommended in test comparison and
interpretation. Practitioners who depend on subtest
profiles for classifications are likely to miSdiagnose
children. The responsible psychodiagnostic ian will
demonstrate sophisticated interpretation and decision making
with WISC-III results based on further empirical
SUbstantiation.
In conclusion, it should be noted that the intellectual
performance of two groups of referred children as evidenced
by their performance on the WISC-III was found to be
remarkably similar. The similarity was also noted to a
great extent for the functions underlying test performance
for children of both groups. The obtained factor structure
was found to be similar for both study samples and for the
normative sample. However, differences in the pattern of
loadings did seem to emerge for the study as well as
normative samples. These findings should provide some
useful information for practitioners in the field.
65
APPENDIX A
SUBTEST COMPARISONS BETWEEN GROUPS
66
SUBTEST COMPARISONS BETWEEN GROUPS
df MS F *p values
Information 1 28.01 3.80 0.06
Similarities 1 20.41 1.90 0.17
Arithmetic 1 20.41 2.20 0.14
Vocabulary 1 9.60 1.06 0.30
Comprehension 1 15.0 1.25 0.26
Digit Span 1 1.06 0.17 0.68
Picture Completion 4.81 0.60 0.44
Coding 1 2.01 0.20 0.65
Picture Arrangement 1 3.26 0.30 0.58
Block Design 1 0.01 0.00 0.97
Object Assembly 1 5.40 0.37 0.54
Symbol Search 0.81 0.09 0.75
67
APPENDIX B
IQ COMPARISONS BETWEEN GROUPS
68
IQ COMPARISONS BETWEEN GROUPS
df MS F *p values
Intelligence Quotients
Full Scale IQ 1 166.66 0.75 0.38
Verbal IQ 1 646.81 3.08 0.08
Performance 10 1 3.26 0.01 0.90
69
APPENDIX C
FACTOR INDEX COMPARISONS BETWEEN GROUPS
70
FACTOR INDEX COMPARISONS BETWEEN GROUPS
df MS F *p values
Factor Indices
Verbal Comprehension 1 614.40 2.92 0.09
Perceptual Organization 1 24.06 0.10 0.75
Freedom from Distractibility 1 66.15 0.38 0.54
Processing Speed 18.15 0.08 0.78
71
APPENDIX D
PERMISSION TO REPRODUCE
o THE PSYCHOLOGICAL CORPORATION®
November 9, 1994
Ms. Dede S. Axinn Graduate Student Department of Educational Psychology The University of Arizona Tucson, Arizona 85721
Dear Ms. Axinn:
The Psychological Corporation 555 Academic Court San Antonio, Texas 78204-249H Tel 210-299-1061 Telex 5106015629 TPCSAT Fax 210-270-0327
72
Thank you for your patience while we reviewed your request to reproduce certain WISCIII material for use in your doctoral dissertation.
This letter will serve as formal authorization for the reproduction of the total sample values from Table 6.2 on page 189 of the WISC-III manual, and for reproduction of Tables 6.3, 6.4, 6.5 and 6,6 on pages 192 and 193 of the manual. Please be sure the full copyright notice from the manual appears with each table.
If you have other questions or needs, please contact us.
Sincerely,
'/ 1/ -., ; , ; /, I !., / . (, /. i /. I , t....-... < . ;. J. '. t.! ..... L. / ( __ ' • I. •. t." _ L '.j _ '_ '"
Christine Doebbler Supervisor Rights & Permissions
.·1 Sl/hsiditlry u(l/tI/"C()/lr/ Urllc(' ,-. C'UIIIIUIU\'
73
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