This dissertation has been microfilmed exactly as received 70-6920

WOOD, Nancy Carolyn, 1940- DEVELOPMENT OF COGNITIVE COMPLEXITY IN ADOLESCENTS.

The Ohio State University, Ph.D., 1969 Psychology, general

University Microfilms, Inc., Ann Arbor, Michigan DEVELOPMENT OF COGNITIVE COMPLEXITY IN ADOLESCENTS

DISSERTATION

Presented in Partial Fulfillment of the Requirements for Line Degree Doctor of Philosophy in the Graduate School of The Ohio State University

by

Nancy Carolyn Wood, B.A., M.A.

*********

The Ohio State University 1969

Approved by

d

The author’s formal appreciation is extended to the

Educational Testing Service for permission to copy the Hidden

Figures Test and to the Northeast Local School District in

Springfield, Ohio, for cooperation in providing subjects for

the research. VITA

October 17, 1940 .... Born - Moundsville, West Virginia

1962 ...... B.A. with major in psychology and minors in philosophy and iDolitical science.

1962-1963 ...... Graduate Assistant, Psychology Depart­ ment, The Ohio State University.

1963-1966 ...... Research Assistant for project on read­ ing disability and vision, Psychology Department, The Ohio State University.

1965-1966 ...... Assistant Instructor (half-time), Psychology Department, The Ohio State University.

1966-1967 ...... Clinical Psychology Intern, Psychology Depai-'tment, University of Colorado Medical Center, Denver, Colorado.

1967-1968 ...... Teaching Associate, Psychology Department, The Ohio State University.

Summer, 1968 ...... Visiting Assistant Professor, Psychology Department, The Ohio State University.

1968-1969 ...... Consulting Psychologist (part-time), Department of Psychology and Child Development, Children’s Hospital, Columbus, Ohio.

FIELDS OF STUDY

Major Field: Developmental Psychology

General examination minor: Experimental Psychology

Clinical Psychology Internship

iii TABLE OF CONTENTS

Page ACKNOWLEDGMENTS...... ii

VITA ...... iii

LIST OF TABLES...... vi

LIST OF FIGURES...... ' . . viii

Chapter

I. INTRODUCTION ...... 1

The Differentiation Process Cognitive Differentiation Measures of Complexity and Differentiation Cognitive Complexity in Adolescents Statement of the Problem

II. METHODOLOGY...... 24

Sample Measures Procedures

III. RESULTS ...... 38

Cognitive Complexity Number of Different Words Number of External Constructs Field Independence Development of Cognitive Complexity Summary of Results

IV. DISCUSSION 62

Limitations Issues

V. SUMMARY AND CONCLUSIONS 70

iv APPENDIXES Page A. The Modified Role Construct Repertory Test . 74

B. Reptest Modification Used in this Study . . . 77

C. Hidden Figures Test Instructions .... 81

D. Criteria for the External Construct Score . 82

E. Negative Constructs ...... 84

F. Cell and Marginal Mean Scores for Comparison Groups...... 88

G. Intercorrelation Ma t r i x ...... 90

BIBLIOGRAPHY ...... 91

v LIST OF TABLES

Table Page

1. Analysis of Variance of Intelligence Test S c o r e s ...... 36

2. Test-Subtest Intercorrelations of Reptest Cognitive Complexity (CCa) Scores .... 40

3. Test-Subtest Intercor:relations of Reptest Cognitive Complexity (OCb) Scores .... 40

4. Analysis of Variance of Reptest Cognitive Complexity (CCa) Scores ...... 42

5. Analysis of Variance of Reptest Cognitive Complexity (CCb) Scores ...... 46

6. Analysis of Variance of Number of Different Words Usedas Constructs...... 48

7. Analysis of Variance of Number of External Constructs ...... 52

8. Analysis of Variance of Hidden Figures Test Scores ...... 54

9. Analysis of Variance of Number of Negative Constructs...... 85

10. Analysis of Variance of Number of Neutral Constructs...... 86

11. Analysis of Variance of Number of Positive Constructs...... 87

12. Cognitive Complexity Mean Scores (CCa) . . . 88

13. Cognitive Complexity Mean Sco'res (CCb) . . . 88

14. Mean Number of Different Words Used . . . 88

15. Mean Number of Neutral Constructs .... 89

vi Table Page

16. Mean Number of Positive Constructs . . . 89

17. Mean Number of Negative Constructs . . . 89

vii LIST OF FIGURES

Figure Page

1. Reptest Cognitive Complexity (CCa) Mean Scores...... 44

2. Reptest Cognitive Complexity (CCb) Mean Scores ...... 45

3. Mean Number of Different Words Used as Constructs...... 50

4. Mean Number of External Constructs (ECS) . . 51

5. Hidden Figures Test Mean Scores .... 56

viii CHAPTER X. INTRODUCTION

This study explores the course of development of cogni­ tive complexity in adolescents. Originally derived from

Kelly’s (1955) personal construct theory, "cognitive complex­ ity" has received a number of different definitions most noticeably different at the operational level. However, all who use the construct explicate its definition as a process of cognitive differentiation. A cognitive system is con­ sidered relatively complex in structure when it demonstrates a high degree of cognitive differentiation. Definitions also typically include some implication of relatively extensive interrelationship among cognitive elements, although the latter is generally not operationalized.

As a differentiation process, cognitive complexity is theoretically relevant to most general theories of human be­ havior, and to the psychology of cognition. The literature on the construct of differentiation lends a necessary perspec­ tive for viewing cognitive complexity in adolescents. The following discussion includes (a) a selective review of the literature on differentiation, (b) a survey of cognitive dif­ ferentiation measures, and (c) implications for adolescents* development. The concluding section of this chapter discusses the organization and hypotheses of the present research.

1 The Differentiation Process

Holistic theories of development, both biological and psychological, generally assume a process of change from primitive and unformed states to more differentiated and specific functioning. Most of the major theories of psychologi­ cal development postulate that as an individual develops he first differentiates himself from the outside world. He then distinguishes himself from others, people and things from each other, and eventually develops a complex system of discriminat­ ing and responding to the complicated events in his physical and social environment.

Theories that include such a differentiation process differ with respect to how explicit and how central that process is considered to be. They also differ in the extent to which they take a developmental perspective. This section briefly reviews several broad theories of human behavior involving a differentiation process. Theories treating differentiation more explicitly are discussed at greater length.

Freud describes development from unorganized primitive energy to highly organized structures that compromise between biological drives and limits set by the external world.

Others, like Erikson, accepting Freud’s general model, place even more emphasis on complex Ego mechanisms that develop from simpler structures. Piaget’s (1950) concepts of schema, structure, and operation provide an underlying continuity to his

stage theory, and explain how cognition develops through inter­ action with the environment. Operations occur when schemata and structures become interrelated, and operations, more complex than schemata or structures, begin to approximate a logical model. Experience with Concrete Operations provides the basis for a child’s capacity to solve problems more formally. The adolescent’s and adult’s Formal Operations are highly differentia­ ted cognitive processes that allow for logic and abstract problem solving.

Several other general psychological theories focus more centrally and explicitly on a differentiation process. Werner’s

(1948) orthogenetic principle states that ’’whenever development occurs, it proceeds from a state of relative globality and lack of differentiation to a state of increasing differentiation, articulation, and hierarchic integration” (1957, p. 126). Struc­ turally, the organism is said to develop from the syncretic and the diffuse to the discrete and articulated. Functionally, development proceeds from rigid and labile to the flexible and stable.

Werner’s organismic view, with its careful analysis and numerous examples of the differentiation process may well be impossible to test empirically. It has, however, generated more than a respectable amount of research and theoretical re- finement, and its breadth may well have provided a comfortable home for differentiation constructs developed independently of the theory. In fact, a recent review of cognitive com­ plexity (Crockett, 1965) defines that construct in essential­ ly Werner’s terms of differentiation and hierarchic integra­ tion.

Lewin (1951), like Werner, considered differentiation as a central process in development, but for him it had a more restricted meaning. In his topological representation of the person, regions of the person increase in number as the indivi­ dual grows, region boundaries become more rigid, behavior be­ comes more organized, and the life space expands. Differentia­ tion is conceptually defined as an increase in the number of regions in the person (Barker, Dembo, & Lewin, 1941). Despite this restricted conceptual relationship to theoretical variable

Lewin gave extensive treatment to the differentiation concept

(1951). according to Lewin, the degree of differentiation (or complexity) and its hierarchical organization are the two major aspects of a person-system. The relative decree of differentia tion is directly related to the variety of emotional, motor, intellectual, and social behavior the person shows. As the system becomes more differentiated, "spreading" from one part of the system to another decreases with the result that the individual is better able to respond uniquely and differential­ ly as his environment changes. KellyTs (1955) personal construct theory differs from

other theories discussed so far in that it is not a develop­

mental theory. Disregarding maturational factors and denying

the need for motivation constructs, Kelly dealt with interper­

sonal perception and prediction in cognitive terms. He postu­

lated that a person’s psychological processes are determined

by the way he anticipates events and that events are anticipated

by construing replications. Individuals thus invoke constructs,

defined by a trait and its opposite, in predicting behavior of

others. Constructs can be inferred from a person's verbal be­ havior in response to persons he knows personally. The theory with its major postulate and 11 corrollaries, focuses upon the nature of construct systems, and the individuality of personal

constructs. Constructs may be connected to each other in a variety of ways; they may be more or less differentiated, more

or less organized, and more or less related to "core constructs.”

Personal construct theory and its cognitive approach provides an introduction to a review of more recent literature on cognitive differentiation.

Cognitive Differentiation and Cognitive Complexity

American psychology in "rediscovering its mind" (Martin,

1959) has taken a decided turn toward the study of the thinking process. By definition, cognitive theories involve constructs that explain how people think, postulating that an individual's experience of the world around him is mediated by the operation of cognitive variables. Although some learning psychologists would describe this mediation simply as an association of events, the thinking process is more often explained in terms of structures or operations. Variously labeled schemata, controls, styles, etc., such structural variables are called upon to explain how individuals select and organize complex stimuli, respond variably, and show some semblance of rational behavior. Cognitive theories including structural variables often explain the development of such structures as a process of increasing differentiation.

Critical questions posed by all theorist-researchers of cognition, include the operations of intelligence, language, and learning. Typically, cognitive variables are considered different from if not exclusive of, intelligence. Whether language is a necessary or sufficient condition for thinking, is a question not adequately explained by all students of cognition. How thoughts, ideas, or concepts are attained is another important issue for cognitive theorists.

There are equal3.y important differences within the psychology of cognition. Theories differ with regard to the particular constructs employed to explain thinking; general­ ity of those constructs; and relationship to more general psychological theories. They also differ in how explicitly 7 the construct of differentiation is used to explain the develop­ ment of and individual differences in, cognitive functioning.

Relevant to the present study are theoretical frameworks that

(a) include constructs fairly clearly defined as differentiation constructs, and (b) that postulate that those constructs repre­ sent some aspect of a more generalized cognitive differentiation process. The selected review that follows, focuses upon the work of Witkin (et a l .,1954; et a l ., 1962) and Bieri (1956, 1961,

1965, 1966).

Witkin (et al., 1954), in the Werner tradition, treats differentiation as central to psychological development. Witkin*s concept of field independence, essentially a differentiation process, was originally developed from studies on perception of the upright. Field independence has since been shown to be related to the ability to analyze one stimulus from an array of extraneous stimuli, the ability to verbally articulate one's external world, analytical ability in intellectual functioning, a child's sense of identity, and a variety of other variables.

The central thesis of Witkin's work is that psychological dif­ ferentiation is an important personality variable with manifesta­ tion in many areas of behavior. Psychological differentiation increases with age, but there are individual differences among children of the same age.

The generality of field independence has been well demon­ strated, although Witkin is careful in defining each occurrence 0

of the phenomenon with respect to the area of behavior under

study. A good example of his caution is illustrated in his

more recent (Witkin, 1967) definition of field independence

as a "perceptual subcomponent of a global-articulated cogni­

tive style dimension."

Differentiation is also regarded as a core construct

in the work of Bieri (1955, 1961, 1965, 1966) and others.

Bieri's cognitive complexity variable was originally developed

from Kelly's personal construct views but now seems relatively

autonomous of the theory which fathered it. However, Bieri's

work with cognitive complexity has maintained Kelly's focus

on interpersonal or social perceptions and generally involves

some variation of Kelly's Reptest measure. Bieri first (3955)

described a cognitively complex structure as allowing for a

higher level of differentiation than would be possible with a

simple structure. A more recent definition is "the tendency

to construe social behavior in a multidimensional way such

that a more cognitively comp3.ex individual has available a

more versatile system for perceiving the behavior of others than does a less cognitively complex person" (Bieri, 1961).

The current "dimensional" focus appears to allow more oppor­ tunity to relate differentiation of cognitive structures to dimensional characteristics of social stimuli (Bieri, 1966). Cognitive complexity has been studied in relation to a number

of personality and cognitive variables (Bonarius, 1965;

Crockett, 1965) and most recently the emphasis in cognitive

complexity literature has been on impression formation and

prediction of social behavior.

Harvey, Hunt, and Shroder (1961) also employ differen­

tiation as a central construct in their ’'conceptual systems" theory. This viewpoint considers cognitive and dynamic aspects of behavior as interdependent structural and functional

characteristics (respectively) of the same conceptual system.

Thus, broader than a theory of cognition, it has inspired research directed simultaneously toward social, intellectual, and personality changes (Hunt, 1962; 1965). Its major premise involves "organismic" development through four major conceptual

systems or stages. Each stage is a prerequisite for later stages, and each stage is characterized by particular "work."

"Work"involves learning about oneself, about others, about relations between self and others, and structural organization.

As conceptual levels change the individual increases in con­ ceptual complexity and interpersonal maturity. Later stages involve a more highly differentiated interpersonal orientation.

There has been little direct evidence with which to evaluate whether conceptual systems accurately describe development, although development from concrete to abstract thinking among 10 adolescents has been demonstrated (Hunt, 1962) and its catego­ ries appear to have some utility for homogeneous classroom grouping (Hunt, 1965).

Several other research-theorists of cognition use dif­ ferentiation constructs more implicitly. Kagan, Moss, and

Sigal (1960, 1963) for example, describe cognitive development in terms of three conceptual categories: analytic-descriptive; relational; and inferential-categorical. Their analytic- nonanalytic dimension is regarded as a preference for analyz­ ing stimuli (pictures of persons, visual forms) into differ­ entiated parts as opposed to a nondifferentiated global ac­ ceptance of the entire stimulus; and analytic responses in­ crease with age while relational responses decrease. Barron

(1953) discusses his complexity-simplicity dimension as the degree of preference for complex asymmetrical line drawings versus simple symmetrical drawings. Bruner (et al., 1956; et al., 1366), like his "friend and mentor"Piaget, does not emphasize a differentiation construct, but in his framework of categorizing styles category width is an inverse measure of degree of articulation-differentiation.

Measures of Cognitive Complexity and Cognitive Differentiation

Measures of cognitive complexity and cognitive differ­ entiation that derive from different frameworks are more directly connected to theoretical orientations than to each other. The interplay between theoretical constructs and in­ struments is not always clear (which derived from which), or maximally logical, but a variety of measures have been de­ veloped to generate ideas and understanding of cognitive dif­ ferentiation. For this reason, the variable under study re­ quires an examination of some of the instruments used as indicators.

The Role Construct Repertory Test or Reptest was de­ veloped by Kelly from his personal construct theory (1955).

The original form begins with a "role title list" containing

20 or more different roles, e.g., self, spouse, mother, father, person you dislike. The S is asked to write the names of people he knows personally who fit each role. The names are called "figures." The S is then presented with 3 of the figures and asked to describe some important ways in which 2 of those figures are alike and different from the thirti. This

"construct" is written down with its opposite, the "contrast."

The construct and contrast together are considered a "construeL dimension." This procedure is repeated several times (20 to

30) with different triads of figures, until a number of con­ structs are elicited. The verbal content, or the constructs and contrasts used by S, is called the listform of the test.

For the gridform the S is asked to list figures on one side of a grid, and to list construct dimensions on the side per­ pendicular to the figures. He then puts a check mark under each of the figures to whom each construct can be applied. 12

When a contrast applies to a figure the S leaves a void. Kelly

used a "nonparametric factor analysis" of checks and voids to

reduce an S Ts data to a few basic dimensions. Constructs that

are functionally similar (those for which patterns of checks

and voids are alike) were considered a factor.

The Reptest has seen many modifications, and perhaps

has had more attention than the theory from which it developed.

Bonarius (1965) points out that the instrument's attraction,

to a large extent, lies in its potential for variation and

modification, but cautions that each modification requires its

own reliability and validity data.

Variations of the test have frequently been used as a

measure of cognitive complexity. In Bonarius’ list of 10 dif­

ferent measures of complexity (1965, p. 15), 8 are variations

of the Reptest. Jones (1954), the first to use the term cog­

nitive complexity, used the number of factors extracted from

the gridform of checks and voids. Of the several Kelly stu­

dents who continued to work with this construct, Bieri has

been most original and prolific in developing both the con­

struct and its measurement.

One of the most refined Reptest measures of cognitive

complexity is Tripodi and Bieri's 1963 modification, which

reduces the gridform to a 10-figure by 10-construct grid. In

this particular form, Ss judge each figure on a 6-point Likert

IW 8 8 W J 8 W W 13

Scale (+3 through -3) for each of 10 construct dimensions. For figures, Ss use people they know personally, but construct di­ mensions are a standard list of 10 characteristics. The authors report that this provided-construct form is comparable to the

S-provided construct form.

A less frequently used measure of cognitive complexity is the number of verbally different constructs when S produces as many constructs as possible on each of several triad compari­ sons (Mayo, 1960 and Ashcraft, 1963, as cited by Bonarius, 1965).

A related measure is the variability in overlapping verbal labels of constructs (Bieri & Blacker, 1956). A more unusual

Reptest measure of complexity is the External Construct Score

(ECS). It was originally developed as a measure of "the tendency to utilize the immediate external qualities of material as op­ posed to a tendency to use more internal or abstract stimulus qualities" (Bieri, Bradburn, & Galinsky, 1958). This study involved an exploration of sex differences in field independence.

Operationally, ECS was the number of external constructs S used in describing differences among 7 people in 25 triad compari­ sons. Hunt (1962) used the ECS as a measure of differentiation among junior high and senior high school students.

Criteria for external constructs placed greater emphasis on more apparent superficial behaviors — not scored as ex­ ternal were constructs that emphasize motivation, emotional ex­ pression, or qualitative aspects. Bieri, et al., found that 14 the use of external constructs was significantly (and nega­ tively) related to a modified Embedded Figures measure of field independence for women, but not for men. The less ’'external" female was likely to be more field independent. For both male and female Ss, the ECS discriminated between high and low field independence. The authors considered ECS results as supporting Witkins’ contention that individuals who are field independent in perceiving others (low ECS) are field independ­ ent in solving a perceptual task. Cutting across verbal and nonverbal behavior, this finding further supports the general­ ity of field independence. It aJ.so suggests a potential link between the differentiation constructs of field independence and cognitive complexity.

Field independence, another explicit differentiation construct discussed above, also has a variety of measures at­ tached to it . The initial measure was the Rod and Frame test

(Witkin, 1954) which evaluates an individual’s perception of the upright. A rod, frame, or S ’s chair are tilted in various combinations, and S adjusts the rod, frame, or both, to what he considers a vertical position. The more field independent

S makes fewer errors; that is, he is more likely to perceive the upright independent of "field” clues. Other perceptual measures of field independence used by Witkin and his collea­ gues are described in his report on psychological differen­ tiation (Witkin, et al., 1962). They include the tilting-room tilting-chair tests, the Embedded Figures Test (EFT), and the

Body Adjustment Test. Chief among field independence measures used by other researchers is the EFT. This test also requires

S to separate an item from a field in which it is incorporated, but it does not involve body orientation. S is to find a simple visual form within a larger complex figure. The complex figure includes both lines and colors and S ’s score is the total amount of time he takes to find 24 items. The higher the score, the more influenced is the S by contextual clues, and the more field dependent he appears.

The usefulness of the EFT has stimulated the development of a comparative group measure of field independence, the Hidden

Figures Test. The latter requires Ss to find one of five simple forms in each of 32 achromatic complex figures.

This review does not exhaust the gamut of measures that have been used as indicators of cognitive complexity. For example, Barron (1952) as mentioned above, uses preference for complex line drawings over simple drawings. Hunt (1962) and others have developed a series of "differentiation'’ instruments to evaluate the conceptual systems model. An overview is pro­ vided, however, and serves as an introduction to the measures used in the present research. It also illustrates the varied methods used to tap a generalized psychological differentia­ tion process. 16

The overlap of construct meaning along with the use of various indicators is (perhaps necessarily) often found in psychological literature. Any two series of studies, each arriving at some conclusion about the development of cognitive differentiation, do not necessarily employ the same differentia­ tion construct, similar measures, or sample the same realmis of behavior. Despite these differences, however, both theory and evidence point to the conclusions that (a) individuals differ from each other in the complexity level with which they cogni­ tively differentiate the environment, and that (b) children are different from adults along the same dimension. The next section discusses the literature on developmental changes from childhood to adulthood.

Cognitive Complexity in Adolescents

Developmentalists who deal with the thinking process vary in the degree to which adolescence is considered a separate de­ velopmental period. This is true in part because the limits of

"maturity" are usually not clear. Piaget (1950)is perhaps the most explicit regarding adolescence, in postulating this de­ velopmental period as the stage of Formal Operations. As describ­ ed in his research (Inhelder & Piaget, 1958), Formal Operations are attained around age 11 to 14. and culminate in late adoles­ cence (ages 16 or 17). Formal thought or propositional opera­ tions approximate the formal system of modern logic, and the .17 adolescent becomes increasingly capable of reasoning abstractly.

Piaget's work then, suggests that more and mox'e experience with formal operations would lead to gradual increments in the degree to which an adolescent differentiates his environment.

Werner (1948) mentions adolescence only as an example of the slow transformation from childhood to adulthood that occurs in Western cultures. The slowness presumably allows for more time to learn and organize finer and finer discriminations. A prediction of increasing complexity level during adolescence could be derived from Werner's thinking, but the course of that development is not clear.

A limited amount of research is available on developmental differences in level of cognitive complexity or cognitive differ­ entiation during adolescence. Witkin (1962) provides most of the data available. He reports both longitudinal and cross- sectional data indicating that differentiation as measured by both perception of the upright and other perceptual field in­ dependence tasks gradually increases at least from age 10 to

17. Thereafter, he finds a tendency toward field dependence, especially with women, although the increase is slight compared to the previous decline. A longitudinal study of 30 girls and

30 boys from ages 14 to 17 indicated stable individual differ­ ences over time for several field independence measures.

Stability coefficients were higher for girls than for boys 18

except for performance on the EFT where males and females were

equally stable. Witkin concludes that the pace of development

or individual starting points, determine individual differences

among children in field independence.

Bieri’s published work with adolescents is minimal, al­ though in a 1966 publication he emphasizes the importance of

exploring how individuals develop in the complexity with which they regard other people. Hunt reports an orderly increase in

conceptual level (including complexity and differentiation) with age, but his data indicate a slight decline in differen­ tiation at ages 16 and 17 (Hunt, 1962).

In summary, most of the available evidence suggests that complexity or differentiation of cognitive structures gradually increase during adolescence.

In view of the number of orientations that involve cog­ nitive complexity or cognitive differentiation constructs, it seems important to clarify the particular definitions used in this study. An attempt is made to be consistent with the literature on cognitive complexity, most of which involves differentiation of social or interpersonal stimuli. Cognitive complexity in this study refers to the degree to which an individual cognitively differentiates or articulates differ­ ences among individuals that he knows personally. Cognitive d ifferentiation is considered a more general construct referring to the degree to which an individual differentiates

or articulates differences in his environment, independent of whether stimuli are social or nonsocial.

Purpose of the Study

The major purpose of this study was to investigate de­

velopmental differences in the complexity with which adoles­

cents think. Cognitive complexity is defined here as the degree to which an individual articulates differences and makes differentiations among people he knows personally. It is considered to be an indicator of a more general construct of cognitive differentiation.

Exploring the complexity with which adolescents think involves some consideration of variables such as sex and in­ telligence; indicators of developmental level; and generality of the construct under study. A brief discussion of these issues may clarify the direction of this research and specify how the questions are operationalized. Chapter II includes a more detailed description of methodology.

The ubiquitous appearance of sex differences in de­ velopmental data, and especially in studies of cognition, makes consideration of that variable almost necessary in developmental studies of thinking. Both males and females are included in the present research. Intelligence is another important variable to acknowledge in cognitive research. As mentioned above, cognitive differentia­ tion is considered to be different from the construct of intelli­ gence, both theoretically and empirically. The two are not mutually exclusive, however, and individual differences in cog­ nitive variables are sometimes explained by differences in in­ telligence. One example is a study reported by Bieri (1965) that indicated rather substantial differences in mean and individual score reliabilities among high school students of high, medium, and low intelligence ranges. Subject groups for the present study were matched on distribution and variability in intelligence test scores. Scores used were those made on a group intelligence test routinely administered by the school system.

Age in months or age in years are perhaps the most fre­ quent indicators of developmental level found in the literature although for school children, grade-in-school is also often used. Both are rather rough gauges of an individual’s level of general development. School grade as an indicator is vul­ nerable to the criticism that by virtue of school failures and dropouts lower grades are likely to include children with more variability in age and intellectual level than do higher grades.

Age in years is equally vulnerable to variability in intelli­ gence and educational experience. School-grade is used as the 2.1 indicator of developmental level in the present study and is refined to some extent by controlling for range and variability of intelligence. Grades 7 through 12 of a rural-suburban school district are included.

As discussed above, a variety of measures have been used as indicators of cognitive complexity. Chief among those mea­ sures are variations of Kelly's Reptest and the most refined is

Tripodi and Bierirs (1963) 10x10 Reptest modification. This measure with S-provided constructs, is the measure of cognitive complexity used in this study. In the discussion which follows,

Reptest is used to refer to this particular measure un3.ess stated otherwise. CC is used to refer to the number of matched ratings, or the Reptest cognitive complexity score.

The above review demonstrates that the literature on cog­ nitive complexity in adolescents contains few studies and few clearcut predictions of developmental change. The literature on other differentiation constructs suggests a gradual increase with development. Thus, the extent to which results of this study can be generalized to other differentiation constructs, even to other cognitive comp3.exity measures, is an important consideration.

Providing some guide for generalizing results took two forms; first, field independence, as measured by the Hidden

Figures Test was included. Second, two less frequently used Reptest measures were included: (a) the number of different words used in S-provided construct dimension, and (b) the Ex­ ternal Construct Score. These measures were regarded as dif­ ferent ways of tapping a generalized cognitive differentiation process.

In summary, the nature of cognitive complexity among adolescents was investigated by examining Reptest cognitive complexity (cc) scores among a group of adolescent males and females, grades 7 through 12. Grade-sex groups were matched on the basis of distribution and variability of intelligence test scores. Differences in cognitive complexity level were examined in terms of grade and sex. The major question is whether de­ velopmental differences occur' during adolescence, and if so, what the course of that development may be. The question was further explored by comparing complexity level (CC)with field independence (Hidden Figures Test performance), and with the less frequently used measures of the External Construct Score

(ECS) and number of different words used by S in providing constructs.

The major and secondary questions of this study can be summarized as follows.

Major Question: Do levels of cognitive complexity show de­ velopmental differences during adolescence, and if so, what is 23 the course of that development? Operationally, the question is the relationship among CC scores, school grade, and sex in a group of adolescents. Developmental theory and available re­ search would suggest a gradual increase in complexity with de­ velopmental level.

Secondary Question: To what extent does the development of complexity in adolescents represent a generalized cognitive differentiation process? Operationally, are CC scores related to performance on (a) the Hidden Figures Test, (b) the number of different words used in Reptest constructs, or (c) the ex­ ternal construct score? Theoretical and research implications suggest a moderately high relationship qualified to some extent by differences in behavior realms sampled. CHAPTER II. METHODOLOGY

Measures

The SRA Tests of Educational Ability (1962 Edition) is not labeled as a test of intelligence although it is gener­ ally catalogued as a group intelligence test. It is described as "striking a middle ground" between a differential analysis of intelligence and the "single score global approach" (Hor- rocks, 1965). It was standardised by grade (4 through 12) on a sample of 64 schools, stratified by region and school size.

Language, reasoning, and quantitative scores are combined and converted to an intelligence quotient. Correlations between the SRATEA total score and other frequently used intelligence tests are quite high (Ahmann, 1965). Validity in terms of correlations between total score and SRA Achievement Survey scores is .84 at grades 6 to 9 (Hoirrocks, 1965). Ahmann

(1965) cites reliability coefficients as exceeding .90, and the standard error of measurement as not exceeding 4 IQ points.

The school system, from which the sample in the present study was obtained, routinely administers the SRATEA to all students in grade 7. Thus, each S ’s intelligence quotient represents performance on the SRATEA when that student was in grade 7.

24 Tripodi and Bieri's (1963) form of the Reptest, as described in Chapter I, is a 10 figure by 10 construct grid.

Ss rate each of 10 persons they know personally on each of .10 constructs, and CC represents the total number of matched rat­ ings. The Tripodi and Bieri form was used in this study with one exception; rather than rating figures on provided constructs,

Ss provided their own constructs as in Kelly's original form.

The 10 role titles or stimulus figures were those suggested by

Bieri* for high school students. Ss were required to (a) identify figures, (b) produce constructs arid conti’asts by triad comparisons, and (c) rate each figure on a 6-point scale

(+3 through -3) for each construct dimension. Cognitive com­ plexity (CC) was scored by the number of matched ratings per figure as described by Tripodi and Bieri. Instructions, Rep­ test form, and scoring sheet used in this research are in­ cluded in Appendix B.

In developing the Reptest CC measure of cognitive com­ plexity, Tripodi and Bieri supported the use of provided con­ structs by comparing those ratings with ratings of personal constructs. The reliabilities of CC scores were .86 and .76 respectively and score distributions did not differ. The rank order correlation of matched ratings for the two forms was .50.

* Personal communication from James Bieri, March 4, 1968. With high school age Ss, Bieri* found test-retest reliabilities between .50 and .80 for different classes of students. Another reliability study of the Reptest CC (Bieri, 1965) indicated that reliabilities differ for high school Ss of different in­ telligence levels. Reliabilities were highest for the middle intelligence group (r = .80), lowest for the high group (r - .46) and moderately high for the low intelligence group (r = .58).

Except for the low intelligence group, differences in mean scores between the two administrations were not significant.

Thus, the reliability coefficients indicate more individual score differences among Ss of high and medium intelligence.

The use of personal (S-provided) constructs versus in­ vestigator-provided constructs deserves additional attention.

Tripodi and Bieri reported higher reliabilities for CC scores with provided constructs although the difference was not great

(.76 to .86). Mitsos (1961) had Ss select 9 most personally meaningful constructs from 3 lists of 7 semantic differential dimensions, after which Ss described all adjectives according to the semantic differential procedure. He found personally meaningful adjectives to be significantly better differentiated

(£^.01) than all 21 adjectives. In reviewing this study and others on personal versus provided constructs, Bonarius (1965) concludes that "the research shows convincingly that the

* Personal communication from James Bieri, March 4, 1960. 27

individual prefers to express himself and to describe others joy

using his own personal constructs rather than provided dimen­

sions.” Individual preference, however, does not necessarily

lead to high reliability or validity. While providing more

information about how Ss think about people, personal constructs

may not lead to CC scores equivalent to the Reptest CC score

with provided constructs.

During the course of scoring CC using the Tripodi and

Bieri system, scorei's observed that number of matched ratings

could be easily influenced by the positive or negative connota­

tion of the constructs Ss provided. Constructs provided for Ss

in Bieri and Tripodi’s form (see Appendix A) are all stated in

positive or neutral terms, and contrast terms are neutra.l or

negative. When an S uses his own constructs, however, the con­

struct row can include conventionally negative terms as well

as positive or neutral terms. For example, an S may use nice-

mean for his first construct dimension and mean-nice for

another triad comparison. The number of matches in this case would be fewer than if S used nice-mean, nice-mean; while the

complexity of thinking would be very similar. These score

differences would obviously not reflect accurate differences

in cognitive complexity. The same would hold true for any

reversal of a construct dimension. For example, boy-girl, girl-

boy would be likely to show much lower complexity scores than 2H boy-girl, boy-girl. A similar but less obvious example is the case of an S using nice-mean, and friendly-snobbish to mean the same thing. If the construct order is nice-mean, snobblish-friendly, the number of matched ratings will be far fewer than if the same subject writes nice-mean, friendly- snobbish; and the lower CC score would inaccurately reflect a higher level of cognitive complexity.

This nonrandom source of error may well explain why

Bieri chooses to provide the construct dimensions rather than using personal constructs. It was regarded sufficiently im­ portant to this research to warrant an attempt at correction. < This investigator could not derive a system of correcting for all of the possible sources of error in CC scores from the

Reptest when S ’s own constructs are used. One of the most obvious error sources described was somewhat controlled, how­ ever, by the following method. Two adults judged each construct for each S as positive, negative, or neuti'al, by "conventional” judgments. Where there was any disagreement between judges about whether a construct term was conventionally considered negative, the term was categorized as neutral. Only constructs were rated; contrast terms were used only as clues to construct meaning. Appendix E includes examples of constructs assigned to each category. For each construct considered negative, the sign for each rating (for each figure) on that dimension was changed. 29

For example, if figure 7 received a rating of +2 and the construct was a negative term (e.g., obnoxious), the 4-2 was changed to a

-2. This change of signs in ratings would logically seem to allow for a degree of impi'wed accuracy in CC scores despite the informal and error-vulnerable qualitative judgment of con­ structs. Analyses of variance of positive, negative, and neutral constructs, showed that in no case did sex, grade, or sex-grade interaction, contribute a significant amount of the variance

(see Appendix E).

From this point, CCa is used to refer to the total number of Reptest matched ratings, and CCb refers to the corrected score, i.e., to total number of matched ratings when signs are changed for ratings on negative constructs. Comparison of the two scores is discussed in the results.

The number of different words used as constructs and the number of external constructs were scored from the same Reptest protocols as CC. The number of different words or verbal labels used to describe differences among people, has been used as a logical, if crude, measure of complexity. The May (1960,as cited in Bonarius, 1965) and Ashcraft (1963,as cited in Bonarius,

1965) counts, however, allowed S to generate more constructs than 10, and included more than 10 triad comparisons. Neither study reported reliability estimates. Fjeld and Lanfield

(1961), however, report a high level of agreement (r = .79) 30 between S-provided constructs for the same figures over a 2- week period. A similar measure is the number of interpersonal constructs S uses in writing 3-minute descriptions of indivi­ duals, each of whom fits a particular role in his life

(Crockett, 196 5).

In the present study, all different words in S ’s list of

10 constructs were counted unless they were exactly the same words. Similar words such as man and male, child and kid, were regarded as the same; other synonyms were counted as different.

The external construct score (ECS) is described in Chapter

I as a measure of the tendency to differentiate among people on the bases of more apparent, surface qualities as opposed to more internal qualitative dimensions. The ECS then, has more to do with the content of constructs used by S than it does with structural or dimensional characteristics of S ’s thinking. It has been used, however, as a measure of cognitive differentiation

(Hunt, 1962) and discriminates well between high and low field independence Ss (Bieri, et al., 1958). The potential for pro­ viding some conceptual relationship between cognitive complexity and other differentiation constructs, and its accessability from S-provided constructs, made ECS a logical choice Cor com­ parative data.

ECS in this research included the total number of "ex­ ternal" descriptive terms used by S in his construct-contrast dimensions. Both construct and contrast terms wei*e scored, making a range of 0-20 points possible. The criteria used for classifying constructs were the same as those described by

Bieri and colleagues (1958) as "(a) physical characteristics

(dark hair); (b) relationships (married); (c) interests (in­ terested in ballet); (d) similarities (similar tastes); (e) activities (drive a car); and (f) liking (like science).'' Not all constructs used by Ss in this research fell neatly into thes categories, and for those cases, some elaboration of criteria was necessary. A description of how external categories were interpreted in this study is included in Appendix D.

The Hidden Figures Test was developed from research by

Jackson, Messick, Myers (19S4) as a group test comparable to the individually administered Embedded Figures Test (Witkin,

1950). Witkin1s EFT, a major source of data in research on field independence is variously called a measure of field in­ dependence, psychological differentiation, and field articula­ tion. The HFT consists of 32 achromatic complex patterns, each containing 1 of 5 simple figures which appear at the top of each page. The figures to be located in the complex pat­ tern are the same size and orientation as those at the top of the page. The test is divided into 2 parts, 16 items each, and for adults there is a 10 minute time limit for each part. The score is the number correct in twenty minutes. HFT instructions as they appear on the cover page of the test, are included in

Appendix C. Part I of the HFT is nearly the same as Form III in the

Jackson, Messick, and Myers (1S64) study, and Part II is the

same as Form IV in that study with color removed. These author

report correlation coefficients of .62 and .68 respectively

with individual forms of the Embedded Figures Test. The author

concluded that the substantial magnitude of the correlations

of these embedded figure forms with other group forms and with

WitkinTs original individually administered version, justifies their use as a measure of field independence. Data fi'om an un­

published study by Witkin, Messick, and Dermena reveal that the

group imbedded figures test correlates moderately well with

both the Rod and Frame (r = .50) and the EFT (r = .57). The

same data show a split-half reliability coefficient of .50

for 89 males, and .79 for 129 females on the HFT.

With high school students, grades 7 to 12, Kropp and

Stoker (1966) report alternate form reliability (Part I-Part

II) of .78 and a reliability estimate by the Spearman Brown

prophecy formula of .88. An unpublished study by Messick and

Derman b of 628 high school students in grades 11 and 12 indi­ cated relatively high reliability (r = .70) by Part I versus

a‘ Personal communication from Diran Dermen of Educa­ tional Testing Service, February 16, 1968. ’ b Personal communication from Diran Dermen of Educa­ tional Testing Service, March 4, 1968. 33

Part II comparisons. The same data suggest that the HFT is

relatively difficult for adolescents; that is, mean scores for

both jun.ioi's and seniors were 13.55 and 16.59 respectively from

a possible range of 32 points.

In this research, an HFT was used as published in the

ETS Kit of Reference Tests for Cognitive Factors (French, et al.,

1963), except for increasing the time limit to 15 minutes per

section. The latter change was recommended by Dermen* as more

appropriate for high school students.

Subjects

The subjects were public school students from grades 7

through 12 in a suburban-rural district in south-west Ohio.

The school district serves a population of about 8,500 in an

area that is changing from farmland to space and agricultural

industry. The student population is rarely sampled for research

purposes, and is relatively unsophisticated where psychological

testing is concerned.

Students were available as potential Ss as they had at

least one study hall period during a week’s school schedule, and

if that study hall occurred when a room was available for test­

ing. Of these students, those having an intelligence quotient

of below 90 on the school-administered group intelligence test

(SRATEA) wei’e not considered as potential Ss.

* Personal communication from Diran Dermen of Educational Testing Service, March 4, 1968. An initial attempt to match distributions of intelligence quotients by sex and grade group occurred at this point. For example, of 59 sophomore girls available for the research, 25 were selected whose intelligence test scores approximated the distribution of test scores of 25 sophomore boys, as well as

25 junior girls, etc. Twenty to 25 students per group were asked to participate in the study.

Procedures

A total of 274 students were selected as potential Ss, and scheduled to take the HFT and the Reptest during 2 different class periods one week apart. The Reptest was administered at the first session. Those who did not complete the Reptest were requested to finish it at the second session after the SO minute administration of the HFT.

The research was explained to students as a study of how teenagers think about people (the Reptest), and how teenagers think about more abstract things (HFT). Considerable efforts were made to assure students of the confidentiality of the respon­ ses. Code numbers and color coding were used for identification on test forms, and names of people were not required on the

Reptest.

HFT instructions were presented as printed on the test form with the exception of allowing 15 minutes per section rather than 10 minutes. Reptest instructions were read aloud

one step at a time. Questions occurring during the test ad­

ministration were answered by rephrasing the instructions when

possible. The most frequently asked question was, "What if

there's no one I dislike?" in response to the Reptest role

title "Person you dislike." The question was answered, "Think

of someone you like less well than others." When questions

were asked about how to rate on a dichotomous dimension (e.g.,

boy-girl, adult-child), Ss were asked to rate as if "ish" or

"ly" were added to words they had written as constructs.

The assurance of confidentiality may have to some ex­

tent interfered with the students’ motivation to take their

test responses seriously. Occasionally (more often at the junior

high level), some students were obvious about their disinterest

in cooperating with the research. Those cases (no more than 15)

were eliminated as subjects. There may well have been others

whose lack of motivation was more discreet.

From the 249 Ss with complete and scorable data, 180 Ss were selected for final data analysis. This selection was made

in order to (a) minimize the differences among groups in dis­ tribution and variability of intelligence test scores, and (b) make comparison groups of equal size. Where more than one

elimination was possible, Ss were eliminated randomly. There were 15 Ss in each of the 12 grade-sex comparison groups. The smallest mean IQ score for any group was 107.33 and

the largest mean score was 107.80. A t test of significance

between smallest and largest group mean scores showed no

significant difference between the groups (t-.00602). An F

test of significance between groups with the largest and small­

est standard deviations showed no significant differences in

group variance (Fzl.2081). Intelligence comparability among

comparison groups is further supported by an analysis of

variance (Table 1) which shows that grade-sex, and grade-sex

interactions, contributed a negligible amount of the total

score variance.

TABLE 1

ANALYSIS OF VARIANCE OF INTELLIGENCE TEST SCORES

Sum of Mean Source df Squares Squares F

Between Grade 5 0.51 0.10 .001

Between Sex 1 0.80 0.80 .0.1.1

Grade x Sex 5 1.40 0.28 . 004

Within 168 12368.26 73.62

Total 179 12370.97

Measures were scox'ed as described above, and the data were analyzed by a simple analysis of variance design by grade,

sex, and grade-sex interaction. The relationships among cogni­ 37 tive complexity (with each measure), field independence, grade, sex, and intelligence were further examined by correlation coef­ ficients. CHAPTER III. RESULTS

Results are described in the context of the development of cognitive complexity during adolescence, with consideration given to the generality of that construct. The results of Rep­ test CC meastires are followed by comparisons of those results with data from other measures of cognitive differentiation.

More specifically, performance on the Reptest cognitive com­ plexity measures of CC, number of different words used as constructs, and number of constructs descriptive of external characteristics are compared with each other, and with the related differentiation construct of field independence as measured by the Hidden Figures Test. The data are reported in terms of group means; variance contributed by grade, sex, and grade-sex interaction; and Pearson r correlation coeffi­ cients. An intercorrelation matrix of all measures, group mean scores, and the raw data are included in Appendix F.

Cognitive Complexity as Measured by Reptest CC

The Reptest and the two CC scores are described in pre­ vious chapters. Briefly, Ss rated 10 persons that they know personally on a 6-point scale (-3 to +3) for each of 10 con­ structs. Each S fs constructs were self-provided by comparing

38 10 different triads of persons drawn from the role title list.

The resulting grid contained 10 ratings for each of the 10 persons. CCa refers to the total number of matched ratings per role title, or per stimulus figure. CCb refers to the total number of matched ratings per stimulus figure when rat­ ings on constructs that are conventionally considered negative judgments (e.g., obnoxious) are reversed. The range of possible scores is 40 to 450. It is important to remember that for both

CCa and CCb, higher scores reflect lower levels of complexity and low scores indicate a high degree of complexity.

CCa and CCb scores were closely related (r = .85). Ss in general provided many more constructs in positive or neutral terms (by conventional standards) than negative; CCb then, does not represent a completely different score. The second score was devised to correct for overestimates of complexity level, so higher scores (lower complexity) for some subjects were ex­ pected. There were, in fact, higher mean scores for each com­ parison group on CCb, with largest differences in grade nine. Tables 2 and 3 show intercorrelations of matched ratings per figure and CC (total number of matched ratings) scores.

Very few intercorfelations were not significant at the .01 level of confidence, and all of those were significant at a lower confidence limit (p< .05). The number of matched ratings per figure, or "subtest" scores were more closely related TABLE 2

TEST-SUBTEST CORRELATIONS OF REPTEST COGNITIVE COMPLEXITY (CCa)

Reptest Figures 1 2 3 4 5 6 7 8 9 10

1 1.00 2 .28 1.00 or\ .46 .33 1.00 4 .19* .20 .31 1.00 5 .36 .48 .37 .26 1.00 6 .40 .26 .44 .21 .21 1.00 7 .42 .32 .41 .31 .46 .34 1 .00 3 .24 .30 .29 . 15* .26 .41 .35 1.00 9 .23 .37 .30 .27 .41 .39 .44 .30 1.00 10 .20 .37 .27 .22 .40 .31 .36 .30 .41 1.00 TOTAL .62 .64 .68 .47 .70 .62 .69 .57 .65 .62

* .05>p >.01

TABLE 3

TEST-SUBTEST INTERCORRE- LATIONS OF REPTEST COGNITIVE COMPLEXITY (CCb)

Reptest Figures 1 2 3 4 5 6 7 8 9 10 1 1.00 2 .29 1.00 G .41 .32 1.00 4 . 17* . 15* .28 1.00 5 .36 .49 .33 .18* 1.00 6 .37 .33 .35 .28 .25 1.00 7 .42 .29 .27 .25 .42 .25 1 .00 8 .26 .40 .30 .22 .25 .48 .32 1.00 9 .33 .25 .23 .23 .37 .34 .37 .31 1.00 10 .22 .28 .21 .20 .32 .36 .27 .44 .40 1.00 TOTAL .63 .64 .61 .44 .67 .65 .61 .66 .61 .62

* .05>p >.01

40 to total scores, than to each other. Correlation coefficients of CCa scores with subtest scores ranged from .57 to .70 witii the exception of Figure 4; CCb's subtest-total score coeffi­ cients ranged from .61 to .67 with the same exception. Figure

4, "Person you feel sorry for," elicited fewer matched ratings overall, than did any of the other figures. Subtest intercorre­ lations and test-subtest correlation coefficients support a high degree of internal consistency for both CCa and CCb.

With evidence for internal consistency for both CCa and

CCb, and a relatively high relationship between the two, there was no clear cut empirical advantage of one CC measure over the other. The rationale of taking account of the positive or negative quality of constructs when differences in construct x’atings indicate level of complexity, gives CCb some advantage in face validity. In addition, CCb would logically seem to more closely approximate the Reptest CC measure with investiga­ tor-provided, or standard construct dimensions (Tripodi f,

Bieri, 1963). While both Reptest scores are included in che comparisons below, CCb is considered to be the more refined measure of cognitive complexity with S-provided constructs.

Neither CC score was related to intelligence; correlation coef­ ficients with group intelligence test scores were less than .06 in both cases. The only significant correlation coefficient of either complexity measure with grade or sex, was CCb with 42 sex (r = .15*, p < .05). Males generally had higher CCb scores than females although the low correlation does not account for much of the variance.

An analysis of variance of CCa scores by sex and grade

(Table 4) indicated that grade accounts for a statistically significant amount of the score variance (p<\05). The F statistic for grade is barely significant, however, and this developmental indicator explains less than half of the total

TABLE 4

ANALYSIS OF VARIANCE OF REPTEST COGNITIVE COMPLEXITY SCORES (CCa)

Source df Sums of Squares Mean Squares F

Between Sex 1 2960.56 2960.56 1.81

Between Grade 5 18106.44 3621.29 2 2 ?**

Sex x Grade 5 14431.41 2386.28 1.77

Within 163 274427.36 1633.50

Total 179 309925.77

**£<•05 (significance criterion is 2. 21)

* For data analysis, male Ss were coded 1, and female Ss were coded 0. Thus, a positive correlation coefficient indicates that male Ss made higher scores thaii females. Negative coeffi­ cients with sex, indicate higher scores for* females. variance. Furthermore, an examination of group mean scores

(Figure 1) illustrates that variation among grade levels does

not reflect an increase in complexity with higher grade levels,

but a more sporadic fluctuation of complexity scores with 9th

and 11th grade Ss appearing more complex. Males and females

in both 7th and 12th grades have similarly high CCa scores; 3s

in grade 10 approximate the same performance. Figure 1 also

demonstrates wide sex differences at grades 8 and 9. Ninth

grade females evidenced a higher level of complexity than any

other group, and both males and females in grade 12 had higher

scores (lower complexity) than younger schoolmates.

The CCb score shows a somewhat different picture in

Figure 2. Sex differences are still greatest at 8th and 9th

grade levels, but sex differences across grades conti'ibute

over half of the total variance (Table 5). Equally important,

is that grade is not a statistically significant source of

variance in CCb scores despite the apparent orderly increases

in level of complexity from grade 7 to grade 9.

Why the change in ratings on negative constructs (CCb

over CCa) should make for such wide differences in source of

variance is not clear from the data. Mean score comparisons

(see Appendix F) did not appear large enough to explain the differences. An analysis of variance (included in Appendix E) of the number of negative constructs used by Ss, provides no 155 150 145 c, 1 4 0 o 135 . o co 130 § 125 * 120 o 115

110. 105 100 )( Females m Males 95 O Total H ------1------1 - (---- 8 9 10 11 12 School Grade

Figure 1. Mean Reptest Cognitive Complexity Scores (CCa)

44 CCb Kean Score 115 120 125 130 135 140 145 150.4 155 160 165 170 0 ...... _ .. . , I Figure 2. Mean Reptest Cognitive Complexity Scores Complexity Cognitive Reptest Mean 2.Figure (CCb) by sex and grade groups. gradesexand (CCb) by School GradeSchool 45 10 H H females Total © ® Males ai

i T li- " TABLE 5

ANALYSIS OF VARIANCE OF REPTEST COGNITIVE COMPLEXITY SCORES (CCb)

Source df Sums of Squares Mean Squares F

Between Sex 1 8862.05 8862.05 4.52*

Between Grade 5 18904.43 3700.89 1.93

Sex x Grade 5 9237.25 1857.45 .95

Within 168 329394.15 1960.68

Total 179 366447.88 i r *£<.05 evidence that sex, grade, nor sex-grade interaction accounts for the distribution of number of negative constructs. The correla­ tion coefficient between grade level and number of positive con­ structs (r ~ .16, £<.05) provides a weak source for hypotheses.

It is possible, however, that data from older Ss, 'who used more positive constructs, had fewer individual S changes than did data from younger Ss. Mean scores of CCa and CCb would not sensitively reflect individual score changes. The analysis of variance statistic may be more sensitive to individual score differences between CCa and CCb. Another possible hypothesis is that CCb is a more accurate indicator of cognitive complex­ ity and thus more sensitive to ’’real" differences. 47

In summary, minimal but statistically significant evi­ dence was found for an increase in cognitive complexity during adolescence as measured by the Reptest CC with S-pz'ovided con­ structs. A scoring system refined to reduce overestimates of complexity level as measured by this same Reptest modification, showed a more consistent picture of increasing complexity level with school grade, up to grade 10 for females and grade 11 for males, after which each sex shows decreasing levels of complex­ ity. Older adolescents (grade 12) showed less difference between sex groups on both measures; and grade 12 Ss appeared to think less complexly than some younger S groups. Grade differences were not statistically significant, however. Sex differences on the more refined measure were statistically significant, with females appearing generally more complex than males.

These differences among S groups show a pattern of wide sex differences during grades 8 and 9 with both males and females evidencing increases in complexity level during that period.

CC scores made by males during this adolescent period are lower than for females, although both older and younger Ss showed fewer sex differences. Both males and females in grade .12 evidenced less complex thinking than did 11th grade Ss.

Cognitive Complexity and Number of Different Words Used in Constructs______

The number of constructs score, as described above, is the number of different words used by each S in Hie 10 constructs 48 he provided to describe differences among people he knows person­ ally. The only semantically similar words not included in this score were synonomous dichotomies of sex (man-woman, boy-girl, male-female) and of age (adult-child, grownup-kid). Other synonomous terms were counted as different. The possible score range was 1 to 10.

Correlation coefficients of number of different words with grade (r = .23) and with sex (r =-.28) were both signifi­ cant at the .01 level of confidence. The variance contributed by sex is highly significant (p< .01, see Table 6), and grade and sex-grade interaction are significant at the .05 level of confidence.

TABLE 6

ANALYSIS OF VARIANCE OF NUMBER OF DIFFERENT WORDS USED AS CONSTRUCTS

Source df Sum of Squares Mean Squares F

Between Sex 1 47.02 47.02 11.44*

Between Grade 5 35.24 7.05 2.47*

Sex x Grade 5 35.04 7.01 2.45*

Within 168 480.80 2.86

Total 179 598.11

*£<.05

* * £<.01 Mean score comparisons (Figure 3) indicate a gradual increase in number of words used from grades 7 through 12.

Males in grade 8 used the least number of words, and grade 11 females used the most. Females used more different words than males through grade 11 with the largest sex differences occurring at grade 8. Males and females were more similar to each other at grades 7 and 12 than at other developmental levels.

This number of different words score appears to have no relationship to intelligence (r = .04), and is negatively re­ lated to CC scores (r =-.2o with CCb, and r =-.25 with CCa).

The latter correlational relationships suggest that adolescents whose ratings of people they know personally are more highly differentiated, were likely to have used more different words in comparing those persons with one another. An obvious predic­ tion perhaps, but the two cognitive complexity measures show different developmental patterns. Grade and sex differences were more pronounced on the different words measure, as well as statistically significant. As described above, data regarding cognitive complexity measured by CC scorns less clearly discrimina­ ted developmental increases. The simpler measure of different words used to describe differences among people, may be as ade­ quate, if not more so, as an indicator of the development of cognitive complexity. Other possible interpretations are dis­ cussed in the following chapter. 50

(0 'O 8 s ■pd 0) d 0) m <+H •H di m O d 0) d

School Gi'ade

Figure 3. Mean number of: different words used as constructs by grade and sex groups.

Cognitive Complexity and Number of External Constructs

The External Construct Score (ECS) is the number of con­

structs used by S that are descriptive of external factual

characteristics rather than internal or interpretive characteris­

tics. The ECS is more fully described in Chapters I and II and

a more complete explanation of scoring criteria used in this

study is included in Appendix D. Both construct and contrast 51

terms were scored by ECS criteria allowing for a possible score :f G so ZC.

Figure 4 illusti'&tes grade-sex group comparisons ,r; 1 V :

performance. Grade 7 Ss had lower score than grade' 3 Ss, but

after grade 8, the number of external constructs used decreased

with increasing grade level. Again, males and females are con­

siderably different, although differences are greatest at grade

12.

© Males 11. XFemale ©Total 10.

9.

isCD o o w co

(0G (D 2

a n 8 9 10 11 12 School Grade

Figure 4. Mean Number of external constructs (ECS) by sex and grade groups. 52

10 rather than at grades 8 and 9 as was true for other complex­ ity measures. ECS correlation coefficients with grade and sex were -.29 (£<.01) and .16 (£< .05)respectively. The analysis of variance reported in Table 7 shows statistical significance for both grade andsex as sources of ECS score variance. Females consistently used fewer external constructs than males, but for grades 11 and 12, differences between sexes were negligible.

The latter finding is consistent with the above reported re­ sponse to cognitive complexity measures, each of which showed fewer sex differences in late adolescence than at other develop­ mental levels.

TABLE 7

ANALYSIS OF VARIANCE OF NUMBER OF EXTERNAL CONSTRUCTS (ECS)

Source df Sums of Squares Mean Squares F

Between Sex 1 164.36 164.36 5.15*

Between Grade 5 669.27 133.85 /] . 19**

Sex x Grade 5 115.31 23.06 .72

Within 168 5365.06 31.93

Total 179 6314.00

* p<.025 **£<.01 The use of external constructs appears to be independent of intelligence (r - .02), and was also not related to SS per­ formances on CCa (r = .06) or CCb (r = .07). This latter find­ ing raises some question of research that describes the ECS essentially as a measure of cognitive differentiation, if CC

is assumed to represent the same process. The relationship of

ECS to the number of different words score is negative and significant (r =-.27, p<.01). That is, the more diffei'ently worded constructs an S used, the less likely it was that those constructs would be descriptive of "external” characteristics.

Cognitive Complexity and Field Independence

To evaluate the generality of cognitive complexity as a differentiation construct, performance on a standard group measure of field independence was compared with cognitive com­ plexity measures. The Hidden Figures Test is described in

Chapter I and II, and instructions are included in Appendix C.

The HFT requires Ss to differentiate a single simple visual form from a more complex pattern. The score used in this study is the sum of items correctly identified in both 20-minute parts of the test. The range of possible scores is 0 to 32.

It is important to keep in mind that the HFT has been reported as more difficult for adolescents than for adults.

The range of scores in this research sample was 2 to 32 54

although the range of group mean scores (7.47) to 15.4G) and the mean score for all 180 Ss (M = 11.73, S.D.-5.63) suggests

a somewhat skewed score distribution relative to the number

of items included in the measure.

Despite the difficulty level, however, this indicator

of field independence discriminated well among S groups. 3s

differed significantly with respect to both grade level (p<.01)

and sex (p<.05). Table 8 describes the analysis of variance,

and Figure 7 gives a more complete view of the highly signifi­

cant developmental differences. The latter might be described

as an overall increase in performance with grade up to grade .11, a plateau between grades 9 and 10, and a slight decline in per­

formance between grades 11 and 12.

TABLE 3

ANALYSIS OF VARIANCE OF HIDDEN FIGURES TEST SCORES

Source df Sum of Squares Mean Squares F Between Sex 1 121.69 121.69 4.40*

Between Grade 5 778.13 155.63 5.63**

Sex x Grade 5 155.91 31.18 .1 .13

Within 168 4643.46 27.64

Total 179 5699.20

* £ < .05 **£<•01 Sex differences are found fairly consistently in re­

search on field independence; males generally show more field

independence than do fema3.es. Differences found in this

research sample were also in the direction of more differentia­ tion among males, although sex differences were not consistent across grades. In grades 9 and 12, in fact, male and female

group mean scores are nearly the same. The decrease in fie.Id

independence of late adolescent females reported by Witkiri (et al., 1962) was not true for Ss in this research. More surpris­ ing is that finding that mean scores for older ma3.es showed the decline from grades 11 to 12 predicted of females by Witkiri’s

report. The HFT is the only differentiation measure used in this study that is significantly related to intelligence. The cor­ relation coefficient (r = .23), while only moderate, is significant above the .01 level of confidence. An even higher correlation might be expected of an S population representative of a broader intelligence range.

Performance on the HFT measure of fie3.d independence had limited similarity to performance on cognitive complexity mea­ sures. Correlation coefficients with CCa and CCb were neg3.i- gible (r =-.02 and -.03 respectively). Although the correla­ tional relationship between number of external constructs and

HFT scores approached significance (r =-.13), there was no evidence that ECS or number of words (r = .07) represent — or Mean HFT Score Figure 5. Mean Hidden Figures Test scores by sexandbyscores FiguresHidden Test Mean 5.Figure gradegroups. School Grade aMales' ©Total jCFcmales 56 57

are even related to — the differentiation construct of field

independence. Those similarities were limited to the follow­

ing: for both differentiation constructs, there is evidence

of developmental differences, sex differences, and of less

apparent sex differences in older adolescents (grade 12).

Development of Cognitive Complexity

The data do indicate developmental differences in the

degree of complexity with which adolescents think. They do not

consistently show, however, orderly gradual increases with de­

velopmental level. Developmental patterns differ widely between

males and females and the several differentiation measures are

not replicas of the same trends. This section summarizes de­

velopmental findings across measures and concludes with a

summary discussion of evidence for generalization. CCb is used

as the primary CC measure, and at this point, it seems important to reiterate that, for both CC and ECS, a low score represents a high level of complexity.

An expected finding is that older adolescents demonstrate higher complexity levels than do younger adolescents. Orderly

increases with grade level occurred at times but sex groups

varied and patterns varied for different measures of complexity.

The most consistent increases in complexity level were shown by males (grades 7 to 11 on HFT and CCb, and 0 to 12 on number of words and ECS). Female groups were more inconsistent in per- formance across grades except for the number of different con­

structs used.

Gex differences were consistently significant. Females

appeared more complex on Reptest complexity measures and males

showed higher levels of differentiation on the field independence

measure. Males and females differed least at grade 12 and sex

differences at grade 7 were small. The differences that ac­

counted for statistical significance occurred most frequently

at grades 8, 9, and 10. Differences were greatest at grades 9

and 10 on the CC measures and for the number of different words.

Widest differences occurred at grade 10 ori the number of externa]

constructs and in field independence. Sex differences in per­

formance on the latter measure wei'e also wide at grades 8 and

11, and almcs t absent at grade 9. Thus, for all complexity indi­

cators, sex differences for "middle” adolescents were signifi­

cant, with the magnitude of difference varying among grades 8 to

11. Grade-sex interaction was significant only in the number

of words used as Reptest constructs. In this case, female groups* performance between 8 and 11 was nearly a plateau and males showed increases from grades 8 to 12.

Cognitive complexity then appears to develop differently for males and females. For neither sex was there evidence for a developmental course of gradual increases with developmental level. In each case, there was some point of irregularity, or a decreasing with increasing grade level. On each differentia­ tion measure, one or both sex groups demonstrated this irre­ gular decline in late adolescence. It occurred at grade 11 for both sexes on Reptest CC and for females on the number of external constructs; at grade 12, males decline in performance on the HFT and females decline in the number of words used as constructs. Notably, except for CCb, sex groups that consis­ tently show superior performance on a particular measure, indi­ cate sizeable drops in performance at later grade levels.

Consequently, the minimal sex differences at grade 12 resu3.ted from performance declines in level of complexity as well as performance increases for those that showed earlier low com­ plexity levels.

Summary

In summary, there is evidence that level of cognitive complexity increases during adolescence, although sex differ­ ences and grade comparisons do not indicate gradual or orderly change. Males and females show different developmental patterns and sex differences were greatest at middle grade levels.

Males show more consistent increases with grade level in cogni­ tive complexity and in field independence. Sex differences at grade 7 were small and at grade 12 were even smaller. Each sex group, however, at some point on all measures shows a decline in complexity level from that of an earlier grade. The above summary is applicable only to findings across

measures of cognitive differentiation. The measure c].ose.ly

tied in the literature to the construct under study is the

Reptest CC score. In this study, CC showed more fluctuation

over grade than any other differentiation measure. It was the

only complexity measure not to show statistically significant

increases with developmental level. Correcting for S-provided

constructs (CCb) led to a smoother grade by grade comparison,

although developmental changes still differed from performance

on other measures. Performance by the corrected score increased

in the direction of more complexity from grades 7 to 9 for the

total group, and from grades 7 to 11 for males. Statistically

significant sex differences were accounted for by wide differ­

ences at grades 8 and 9. Sex differences at other grade levels

were small.

Results from Reptest CC with S-provided constructs ap­

peared to have limited generality to other complexity and dif­

ferentiation measures. Correlation coefficients for CCb were highest with the noncorrected CCa score. Moderately high and

significant were CCb correlations and number of different words

(r =-.28). The number of different words an S uses is likely to

influence the ratings he makes, so the latter relationship could

be expected. Correlation coefficients were not significant with

other differentiation measures. The number of different words and the number of external constructs (r = -.34) were also significantly related. Perfor­ mance on the HFT was not significantly related to any other dif­ ferentiation measures. The few and small correlations among these measures provide minimal evidence that cognitive complex­ ity can be generalized from one measure to another. Neither is there evidence for inferring from any one complexity measure to a more general process of psychological differentiation. Each measure discriminated developmental and sex differences and showed some similar patterns. Apparently, however, the similari­ ties were not due to similarity of individual performance, and consequently do not represent the same psychological process. CHAPTER IV. DISCUSSION

The results of this study can be considered from the over­ lapping perspectives of psychological processes and research de­ sign. This chapter reviews both by discussing limitations of the research and issues related to the development of cognitive complexity.

Limitations The sample included a total of 180 Ss with 15 Ss in each grade-sex comparison group. The comparability of distribution and variability of intelligence among groups was demonstrated; it can be assumed that the results are not confounded by differ­ ences in intelligence. The range of intelligence represented is somewhat restricted however, (IQ range of 90 to 125) thus Ss in this study are not representative of the normal population. Nor is the sample representative of a wide range of socioeconomic, racial, or cultural differences. Nearly all Ss were white, from the Midwest, and only rural, working class, and suburban backgrounds were presented. Results should lie considered in the context of these sampling limitations.

Design limitations consist primarily of lack of fo.13.ov/-up, or reliability data on the same sample. Reliability information

62 might help to clarify the low correlations of performance on differentiation measures. Conceivably, one or more of the measures does not provide reliable data from adolescent popu­ lations. In addition, a cross-section design has built-in limitations upon the kind of inferences that can bo made about developmental changes within individuals.

As is true of most psychological research, measurement limitations are the most critical. Using personal (S-provldcd) constructs rather than providing standard constructs on the

Reptest, is consistent with personal construct theory and generates more data. It did, however, reduce the comparability of these results with other studies of cognitive complexity.

The attempt to correct for error in CC scores when personal con­ structs are used suffers from the relative unreliability of judging whether a construct is positive or negative in a parti­ cular S Ts thinking. Further, the use of personal constructs may confound the CC score in that it requires more differentia­ tion among people than does the provided construct form. That is, ratings are distinctions made among people as well as dis­ tinctions (dimensional judgments) made within particular per­ sons ; for the S-provided form the constructs themselves are additional and perhaps basic distinctions made among people.

It is generally assumed that the role title list of the

Reptest adequately samples the persons that an S knows per­ 64 sonally. In this study, "Person you feel sorry for" (Figure 4) was seen more complexly than the other 9 figures. Other research has indicated that "negative" figures are seen more complexly than familiar or liked figures (Irwin, Tripodi, Bieri, 1967) and that constructs elicited by KellyTs "representative" role titles are more reliable than constructs elicited by a set of

"19 friends" (Mitsos, 1358). This evidence raises questions about the importance of the particular figures included in the role title list, such as the nature of differences among figures, and whether developmental changes occur in the way different figures are regarded. One might postulate that figures eliciting empathy, for example, such as "person you feel sorry for,: are less easily classified as positive or negative than are other figures included on the standard Reptest form.

The Reptest CC may incorporate two important, if not in­ dependent aspects, of developing cognition. Crockett (1965) describes the measure as essentially a measure of differentia­ tion, i.e., number of discriminations an individual makes about people. He defined cognitive complexity, however, as incorporat­ ing both differentiation and hierarchic organization of constructs.

If it is the case that adolescents are simultaneously making finer discriminations, and developing hierarchies (Werner, 1948); or using more logical operations (Piaget, 1950); or developing superordinate constructs (Kelly, 1955), the differentiation and distinctions they make are being simplified by some ordering process. One's degree of cognitive complexity then, would in­ volve both differentiating and ordering processes, and Reptest

CC would be influenced by both. Constructs and dimensional judgments would be subject to both number of distinctions made, as well as how distinctions are ordered. How the hierarchical organization and differentiation processes interact is not clear from theory or research findings. However, the number of words a person uses as constructs, and his ability to dif­ ferentiate a simple visual form from a more complex figure, would seem to be less influenced by such hierarchical ordering.

The number of different words used as constructs, then, may more accurately reflect ’differentiation" of one’s interpersonal world. The field independence measure of distinguishing simple visual forms from complex figures is operationally a nonverbal perceptual task. That it correlates with a wide range of be­ haviors, perceptual and conceptual, verbal and nonverbal, establishes considerable generality for the psychological pro­ cess that it measures. That performance on the HFT was not correlated with cognitive complexity variables in this study, suggests that performance on the verbal-social instruments are not indicative of the same differentiation process. The important questions left unanswered is whether they indicate separate process(es) or nothing at all. The number of external constructs an S uses in describ­

ing people is a less theoretically apparent indicator of com­ plexity level. Judging the content of constructs would not

seem to tap either cognitive structure or structural complex­

ity. ECS1 correlate in the Hunt (1962) study was that of

abstract thinking, and in the original research (Bieri, et a .I.,

1958) was field independence in females. That the internal-

external dimension in social judgment has been related to dif­ ferentiation constructs, raises some questions about the dis­ tinctions made in research on cognitive complexity between cognitive structure and value judgments. This issue will be discussed below.

All measures used in this research were group forms, and group tests are generally considered to be less accurate than psychological instruments that can be administered individually.

The error variance is counterbalanced to some extent by sample size, although the results should be interpreted in the context of group measurement limitations.

Issues

"The smoothness of the growth curve is very much a func­ tion of the kind of behavior one is looking at, and in some instances, how carefully one looks," (Bruner, et al., 1966). 67

Research design might be considered as the "carefulness of look­ ing," and psychological processes as "the behavior one is looking at." This section discusses issues related to the kind of be­ havior observed and reported in this study.

The most significant observations were increases in com­ plexity -differentiation levels with grade; wide sex differences in performance on all measures for some grade groups; small or no sex differences in younger and older adolescents; and the lower complexity level of several older adolescent groups. The latter finding is consistent with Witkinrs repeat of a small decline in field independence of older adolescents. The in­ crease in differentiation with grade was consistent with predic­ tions although differences did not appear as gradually as ex­ pected from developmental theory. Except for the number of different words score, middle adolescent groups (grades 8, 9, and 10) appear to be more different from each other -- grade to grade, and between sexes — than either younger or older groups.

One possible explanatory correlate is the wide individual dif­ ferences consistently noted, in physical and physiological growth during this developmental period.

Sex differences are in the frequently reported direction of females appearing more sophisticated in social-interpersonal realms than with visual perceptual stimuli; and vice versa for males. The minimal sex differences among older and younger adolescents is an interesting source for speculation. One

possible hypothesis is that the social versus visual analytic

differences are sex role preferences, with which preadolescent

and older adolescents take less extreme positions.

The distinction between social judgment and cognitive

structure is not clear in much of the research on cognitive

complexity. In many cases, the construct describes a generali

differentiation process independent of particular realms of ex

perience. Even when the definition is limited to articulation

of differences among people, the construct still implies a

cognitive structure independent of affect or value orientation

The use of external constructs as a measure of ''abstractness"

or differentiation (Hunt, 1962) is an example of the overlap

of content and structure implications. The structuring of

one’s interpersonal and social experiences, however, do in­

volve affective and value judgments. CC ratings elaborate dimensions of both. The number of values a person has, their nature (absolute or relative), and how they are interrelated would affect both personal constructs and dimensional ratings.

It may not be possible to exclude social value judgments in the study of how individuals structure interpersonal exper­

ience, but the issue is important enough to warrant acknowl­

edgement. It is interesting to note that such distinctions

between psychological processes can be omitted in research on

psychological differentiation. Cognitive complexity is often regarded as the percep­ tion judgment of differences among people. The dimensional nature of CC (matched ratings per figure) may be thought of as perception/judgment of differences within persons, or how the same individual appears to S on different dimensions. Possibly, during adolescence, individuals become more complex in differen­ tiating among people, but change less in the degree to which they discriminate differences within the same individual.

Assuming that the number of different words used as constructs measures the first, and CCb measures the latter, the data sup­ port that hypothesis. Results may well be confounded by a variety of other vari­ ables such as social desirability, motivation to achieve, language development, or social sophistication. Any one or a combination of these variables could possibly clarify the de­ velopmental differences that were found, and the lack of evidence that these ’’differentiation” measures are in fact measuring the same process. Data from this study suggest some of the "whats" of the development of cognitive complexity in adolescents. The next important steps would be to expore how and why adolescents do develop in the complexity with which they conceptualize interpersonal differences. CHAPTER V. SUMMARY AND CONCLUSIONS

The major purpose of this study was to explore the de­ velopment of cognitive complexity in adolescents. Cognitive complexity was defined in accordance with much of the litera­ ture as the degree to which an individual articulates differ­ ences and makes differentiations about people he knows per­ sonally . The sample included 180 Ss, males and females, grades 7 through 12, from a Midwestern rural-suburban public school district. Grade-sex comparison groups of 15 Ss each were se­ lected to minimize differences in range and variability of intelligence among groups. Group intelligence test scores

(SRATEA) routinely administered to 7th grade students were used for selecting comparison groups.

Tripodi and BieriTs (1963) Reptest modification with per­ sonal constructs was the measure of cognitive complexity. This form is a 10x10 grid which requires Ss to list 10 "figures" or people he knows personally to fit 10 provided role titles

(e.g., mother, self, friend of opposite sex, etc.) 3 then makes 10 different triad comparisons of those figures, listing as constructs how 2 figures are different from the thi'rd. Con­ struct opposites are listed as contrast terms. The S then 71 rates each of the 10 figures on a 6-point scale (i-3 to -3) for each construct-contrast dimension. The number of matched ratings per figure were counted and summed for the CC score.

An attempt was made to correct for overestimates of com­ plexity level from ratings on S-provided dimensions, by revers­ ing rating signs on constructs judged as conventiona?J-y nega­ tive. The total number of matched ratings, after this correction is called CCb and the uncorrected score is called CCa.

CCa and CCb as measures of cognitive complexity v;ere com­ pared with performance on other cognitive differentiation mea­ sures. The number of different words used as constructs, and the number of constructs that reflected ''external” characteris­ tics were counted from S ’s listed constructs. The Hidden

Figures Test, a group form of the Embedded Figures Tost, was also administered for comparison of cognitive -complexity with field independence. The ilFT requires Ss to differentiate simple visual forms from more complex figures, and is considered a measure of Witkin’s (et a l ., 1954; et a l .,1962) differentia­ tion construct of field independence.

The data were analyzed by a simple analysis of variance design by grade, sex, and grade-sex interaction. The relation­ ship of Ss performance on the various differentiation measures were further examined by Pearson r correlations. Major questions of the research were summarised as: (a)

Do levels of cognitive complexity show developmental differ­

ences among adolescents, and if so what is the course of that

development? Theory and research would suggest a gradual in­

crease in cognitive complexity with developmental level, (b)

To what extent does the development of cognitive complexity

represent a generalized cognitive differentiation process?

The literature suggest a moderate to high relationship quali­

fied to some extent by differences in behavior realm sampled.

Findings provided evidence that level of cognitive differentiation increases with developmental level although

sex differences and grade comparisons do not indicate gradual or orderly change. Cognitive complexity as measured by the

Reptest CCa dees show significant differences among grade level

barely beyond the significance criterion. CCb scores increased with grade through grade 9, but grade 9 to 12 differences were not significant. Sex and grade differences were significant on other Reptest differentiation measures and on the field inde­ pendence measure. Males and females show different developmental patterns and sex differences were greatest at grades 0, 9, and

10. Ma3.es show more consistent increases with grade .level in cognitive differentiation. Performance on all measures showed small sex differences at grade 7, and at grade 12, even smal3.er.

Sex differences were in the direction of females showing more 73 differentiation with respect to social-interpersonal stimuli, and males more differentiation on the field independence measure.

Results from Reptest CC with S-provided constructs had limited generality to other complexity and differentiation measures. The few and small correlations of performance among measures provided little evidence that cognitive complexity and cognitive differentiation as measured here reflect the same developmental differentiation process. APPENDIX A.

The Modified Role Construct Repertory Test*

Name:

INSTRUCTIONS

The persons described below represent specific individuals that you know personally. In each of the numbered spaces at the Cop of the grid on the following page, write the first name or initials of the person who is correspondingly numbered below on this page. For example, in space 1 at the top of the grid, write your, name or initials; in space £ write your mother's name or initials, etc. Do not repeat any names. If a person is already listed, select a second choice.

You will notice on the page containing the grid there are often pairs of traits along the right side of the grid. Starting with the first pair (outgoing, shy), you are to decide for each person you have listed which half of the pair, "outgoing" or "shy," best describes him. If a person is better describ­ ed by the trait "outgoing," write in the box under his name the degree to which that person is "outgoing" on a three point scale from mildly outgoing ( 1) to extremely outgoing ( 3). However, if a person is better described by the trait "shy," write in the box under his name the degree to which that per­ son is shy on a three point scale from mildly shy (-1) to extremely shy (-3). After you have rated all ten indivi­ duals on the first pair of traits, repeat the process for the next pair (adjusted - maladjusted) and so on until you have rated every person on all the pairs of traits. Be sure to rate all persons listed on each pair of traits BeTore proceed­ ing to the next one. When you are finished, there should be a rating m every box. Do not leave any boxes blank.

* Personal communication from James Bieri, March 24, 1968.

74 <15 , 03 o O 05 05 3 3 G P rG X O o g 4k 05 3 4k 3 3 3s >. o O rC o O XO <1) m X 0) G G ll) G G ,X G G 03 0) G CD 03 00 . 0 G to o G G £ 3 P G ,C 10 (—1 to G X CD to 3 0) (D CJ iH 3 P G to G G P •H rj G D> •H p 05 3 3 o O 0) >H O H

+3 +2 +1 -1 -2 -3 outgoing shy

well-adjusted poorly adjusted

decisive indecisive

calm excitable

generous selfish

cheerful grouchy

responsible irresponsible

considerate inconsiderate

self-reliant dependent

interesting dull

V APPENDIX A. (continued)

Instructions for Scoring Cognitive Complexity

If the subject records his responses directly on a F.ep Test grid or matrix, the following instructions apply. If the subject uses the Tfindex-card" procedure, the scorer must first transfer the responses from the index-card answer sheet to the appropriate grid, such that there are n columns (role titles) and n rows (construct dimensions). It is not necessary, of course, that the number of persons judged be the same as the number of constructs provided, but that is the procedure we have followed in recent research.

Note that whether responses are made directly onto the grid or transferred to the grid, the scoring schei. below assumes that a Likert-type rating response was used, i.e., responses that may range from 3 to -3 on each construct dimen­ sion. The following example (using for illustrative purposes a 4 x 4 matrix or grid), indicates how the scoring is done:

Persons 1 2 3 4 2 3 -2 -1 a

3 3 -1 2 b Constructs 2 -1 -1 -2 c

3 On 2 2 d

1 2 0 0 Number of 1 1 1 1 Matches 0 0 0 0 2 3 1 1 Total matches 7 CC score

The above example indicates that scoring is done by count­ ing within each column the number of times each response matches all other responses below it in that column. Thus, in column .1, the first response, 2, is matched once by a subsequent 2 response; the next response in that column, 3, is matched once by a response below it, and the third response, 2, has no matches below it. It is evident that in the example above, if all responses in a column were identical, then the number of matches for that column would be 6. If the grid were 3x8, the maximum number of matches in any column would be 23.

Note that this measure reflects an inverse relationship between cognitive complexity and the score "oh'tained.

76 APPENDIX B.

Reptest Modification Used in this Study

INSTRUCTIONS (read aloud, step by step)

Put your code number and your initials in the blanks at the bottom right hand side of the page. Be sure that you use the correct code #.

The persons described in lines 1 through 10 represent specific individuals that you know personally. In each of the numbered spaces at the top of the grid, write the first name or initials of the person who is described. For example, in space 1 at the top of the grid, write your name or initials; in space 2, write your mother’s name or initials, and so on. Do not repeat any names. If a person is already listed, select a second choice.

You will notice on the grid, there are ten double spaces along the right side of the grid. Now look at the first row of squares. Note that the three squares at the right (squares 8, 9, and 10) have circles in them. This means that you are first to consider the three people whose names appear in columns 8, 9, and 10. Think about these 3 people. Are two of them alike in some important way that makes them different from the third person? Keep thinking about them until you remember the important way in which two of them are alike and which sets them apart from the third person.

When you have decided which two it is, and the important way in 'which they are alike, put a diagonal line in the two circles corresponding to the two who are alike. Do not put any mark in the third circle.

Nov; write in the blank under ’’Construct” the word or short phrase that tells how these two are alike. Next write in the blank under ''Contrast” what you consider to be the opposite of this characteristic.

Now goto the second row. Think about persons Number 1, 2, and 3, the three who have circles under their names. In what im­ portant way are two of these distinguished from the third? Put diagonal lines in the circles to show which two are alike. Now write in the blank under ’’Construct” the word or short phrase that tells how these two are alike. Next write in the blank

77 under "Contrast” what you consider to be the opposite of this characteristic, even if it does not describe the third person. Do you understand? Do you have any question? . . . DO THIS FOR EACH ROW.

Now go back to the first row of squares. Starting with the first pair of words you have listed, you are to decide for each person you have listed, which word best describes hirn. For e.g., if you used the words "outgoing-shy” as your first pair of words -- take person number 1 — if person number 1 is better described by the word "outgoing," write in the box under number 1, the de­ gree to which that person is "outgoing” on a three point scale from mildly outgoing ( 1) to extremely outgoing ( 3). However, if person number 1 is better described by the "Contrast" word, "shy,” write in the box under his name the degree to which he is shy on a three point scale from mildly shy (-1) to extremely shy (-3). After you have rated all ten individuals on the first parr of words, repeat the process for the next pair of words, and so on until you have rated every person on all pairs of words. BE SURE TO RATE ALL PERSONS LISTED ON EACH PAIR OF WORDS BEFORE PROCEEDING TO THE NEXT ROW. EVEN THE BOXES WITH DIAGONAL LINES IN THEM SHOULD HAVE A RATING NUMBER IN THEM. When you are finished, there should be a rating in every box. Do not leave any boxes blank.

78 1 .“four self

_ 2 .Mother 3.Person you — dislike ' 4.Person you feel — sorry for _5.Father 6.Friend of your — own sex 7.Person hard to — figure out 8.Friend of oppo- — site sex __9. Teacher who made you work, hard _10.Adult you admire

Code Number_ Your Initials Columns (Number ratings per column/number of matches) Conversion key n each rating matches 123^56789 10 1 0 -3 J / _____/ _____/_ _J _____/ _____/ _____/ ______/_ 2 1 3 3 - 2 J ____/ ______/ _____/ _____/ _____/ _____/ ______/ ____/ ______/__ 4 6 5 10 -1 / A / ___/ ____/ ____/ ____/ ____/ ____/„ J 6 15 7 21 +1____/____/ „ J _____/ _____/ _____/ _____/ ______/ ____/ _ _ / _ 8 28 9 36 + 2 J ____/ ______/ _____/ _____/ _____/ ____/. _____/ ____/ ______/_ 10 45

+3 / / _ / ____ /____ / ____ /____ / _/___ / /

Complexity Score

Subject Code Number

Number of Constructs HIDDEN FIGURES TEST — Cf-1

This is a test of your ability to te ll which one of five simple figures can he found in a more complex pattern. At the top of each page in this test are five simple figures lettered A, B, C, D, and E. Beneath each row of figures is a page of patterns. Each pattern has a row of letters beneath it. Indicate your answer by putting an X through the letter of the figure which you find in the pattern. NOTE; There is only one Of these figures in each pattern, and this figure w ill always be right side up and exactly the same size as one of the five lettered figures. Now try these 2 exam ples.

I

ABODE mABODE The figures below show how the figures are included in the problem s. Figure A is in the first problem and figure D in the second.

x S

X B C D E A B C X E

Your score on th is test w ill be the number marked correctly minus a fraction of the number marked incorrectly. Therefore, it w ill not be to your advantage to guess unless you are able to elim inate one or more of the answer choices as wrong. You w ill have 10 minutes for eac o.' the two parts of this test. Each part has 2 pages. When you have finished Part 1, STOP. Please do not go on to Part u2 n t i l y o u a r e a B k e d t o d o s o .

DO NOT TURN THIS PAGE UNTIL ASKED TO DO SO. Permission to copy for research purposes granted 2/68 by ETS Copyright (c) 962 I by Educational Testing Service. A ll rights reserved. Developed under NIMH Contract M-U186 81 APPENDIX D.

Criteria for the External Construct Score

The ECS was described by Bieri, Bradburn, and (1958)in a study of sex difference correlates of field indepen­ dence. Not all responses fell neatly into the descriptive cate­ gories described in that report. Below is a description of how categories were interpreted for scoring in this research.

A. PHYSICAL CHARACTERISTICS included age (adults, teenagers, older, younger); roles (mothers, husbands, teachers, farmers); factual descriptions (high grades, in service, dropout, have children); organizations (in 4-H, National Honor Society, track team); and judgments of physical characteristic (pretty, ugly, cute). Not included were facts that contained judgmental or predictive components (good homes, broken homes, mature, hard workers, college bound).

B. RELATIONSHIPS included only relationships that could be described as factual (neighbors, relative, live together, engaged, going steady, live near each other). Excluded were relationship constructs that involved feeling or qualitative judgments (can't figure each other out, friends, believe in each other, don't like each other, good time together).

C. SIMILARITIES were scored regardless of the dimension of similarity or difference if terms such as "same," "similar," "different," "uncommon," or "alike," were used. This category included constructs such as same sex, same age, same person­ ality, same problems, enjoy same things, very different.

D. LIKING AND INTERESTS were often difficult to distinguish. Since the ECS is the sum of all external constructs, not dependent on which external categories are used, "liking" and "interest" constructs were considered as only one category. The criteria .was that the object of liking be factual rather than judgmental. Scored were constructs such as: interested in religion, biology lovers, likes liquor, sports minded, sports fans. Not scored were constructs such as: I like, likes every- thing-picky, likes challenge and excitement, likes people, likes to have own. way.

E. ACTIVITIES overlapped to some extent with liking and in­ terest categories. The criteria again involved physical or observable activities. Included were: goes to school, took

82 83 secretarial courses, color our hair, smokes, teaches, drinks, sews. Not scored were: worries a lot, rules with an iron hand, do our own things, do bad things.

Constructs that did not fit clearly into (or not into) external categories, were called questionable, but not scored. Examples of these are: good education, blushes, wealthy, bad breath, good at algebra, strong. APPENDIX E.

Negative Constructs

As described in the text, CCb was devised as a method of correcting for errors in Reptest Cognitive Complexity scores when subjects provide their own constructs. CC generally refers to Reptest measures of cognitive complexity. It often refers more specifically to the number of matched ratings on a Reptest grid when S rates each figure on a 6-point Likert scale for each construct. The latter measure of cognitive complexity, developed by Tripodi and Bieri (1963) provides a standard list of 10 construct dimensions, allowing 43 to -3 ratings. The Tripodi and Bieri form is included in Appendix A. The standard construct terms for this form are stated in what might be con­ ventionally considered positive or neutral terms.

S-provided constructs allow for more error in CC scores, in that the total number of matched ratings is easily influenced by the nature of S's constructs. An S's identically worded construct dimensions, when reversed (e.g., boy-girl; girl-boy), would yield lower CC scores than if not reversed (boy-girl; boy-girl) when the level of complexity is similar if not the same. The CC score would also be overestimated any time a subject used both a negative and positive or neutral constructs in the same construct list. That is, ratings on "stuck-up- friendly " and "favorite people-too far out" in that order, would likely yield fewer matches (lower scores) than if the second dimension were reversed, regardless of how well differentiated the ratings.

Not all potential sources of error in CC with S-provided constructs were accounted for in this research data. The attempt was made, however, to minimize the potential for over­ estimates of complexity level (higher scores) due to variation in the positive-negative quality of S Ts constructs.

Two adults judged each construct for each S as positive, negative or neutral by "conventional" judgments. Because "conventions" can vary considerably, any construct about which either lMter raised a question, was considered neutral. For each construct categorized as negative, the signs used by S in rating on that dimension were changed. For example, if 3 provided a construct such as "ugly-pretty," that S rs negative (-) ratings were changed to positive (+), and positive ratings

84 05 to negative for that construct dimension. No other changes were made; positive and neutral categories were used only to examine the distribution among comparison groups. An analysis of variance for number of constructs on each category, indicated no significance across grade, sex or sex-grade interaction.

The number of matched ratings when rating signs on nega­ tive constructs have been changed, is referred to as CCb iri the text. This appendix includes several examples of constructs judged as positive, negative and neutral and tables of the

analyses of variance mentioned above. Mean number of construct''u.

in each category per comparison group are reported in Appendix TJ

Negative: thinks they know everything; no fun; skinny; bullheaded; act stupid and silly; conceited; stuckup, insecure; cruel; downgrade people; mean; do things the hard way; warped sense of humor; unfriendly.

Neutral: same ideas; never know what they are thinking; related”;' like to talk; in 4-I-I; have short hair; mothers; tall; same political views; older; outgoing; athletic minded; good education; boys; musician; adolescent.

Positive: my friend(s); easy to talk to; nice and easy going; sensitive; lcindhearted; good sense of humor; I like them; neat; made me think; great potention; cheerful.

TABLE 9 ANALYSIS OF VARIANCE OF NUMBER OF NEGATIVE CONSTRUCTS

Source df Suin of Squares Mean Squares F

Between Sex 1 1.80 1.80 . 95NS

Between Grade 5 8.73 1.75 . 93NG

Sex x Grade 5 11.47 2.29 .1 .2INS

Within 168 316.80 1.89 TOTAL 179 338.80 86

TABLE 10

ANALYSIS OF VARIANCE OF NUMBER OF NEUTRAL CONSTRUCTS

Source df Sum of Squares Mean Squares

Between Sex 1 0.08 .09 .01 N

Between Grade 5 9.13 1.83 .30 N

Sex X Grade 5 63.91 3 2.73 .09 N

Within 163 1013.07 6.07

TOTAL 179 1092.20 87

TABLE 11

ANALYSIS OF VARIANCE OF NUMBER OF POSITIVE CONSTRUCTS

df Sums of Squares Mean Squares

Between Sex 1 6.05 1.56 NS

Between Grade 5 27.03 .1.39 NS

Sex s Grade 5 20.32 4.06 1.04 NS

Within 168 656.13 3.91

TOTAL, 179 709.53 APPENDIX F.

Cell and Marginal Mean Scores for Comparison Groups

TABLE 12

COGNITIVE COMPLEXITY MEAN SCORES (CCa)

Grade 7 8 9 10 11 12 Total

Males 140.27 153.93 132.87 136.07 114.80 144.27 13 7.03

Females 145.93 121.53 101.20 140.27 127.60 137.00 128.92

Total 143.10 137.73 117.03 138.17 121.20 140.63 132.90

TABLE 13

COGNITIVE COMPLEXITY MEAN SCORES (CCb)

Grade 7 8 9 10 11 12 Total

Males 167.20 162.00 148.73 143.20 125.33 150.20 149.44

Females 154.13 127.20 119.00 134.67 132.60 144.87 135.4.1

Total 160.67 144.60 133.87 138.93 128.97 147.53 142.43

TOTAL 14

MEAN NUMBER OF DIFFERENT WORDS USED

Grade 7 8 9 10 11 12 Total

Males 7.53 6.60 7.20 8.13 8.07 9.07 7.77

Females 8.07 9.00 8.67 9.00 9.40 8.60 8.79

Total 7.80 7.80 7.93 8.57 8.73 8.83 0.28 APPENDIX F.(continued)

TABLE 15

MEAN NUMBER OF NEUTRAL CONSTRUCTS

Grade 7 8 9 10 11 12 Total

Males 5.80 6.27 4.80 4.53 4.27 4.00 5.0 7

Females 4.53 4.60 5.47 4.87 6.13 5.13 5.12

Total 5.17 5.43 5.13 4.70 5.20 4.97 5.10

TABLE 16 NUMBER OF POSITIVE CONSTRUCTS

Grade 7 8 9 10 11 12 Total

Males 3.13 3.00 3.73 2.80 3.73 4.87 3 .54

Females 2.80 3.00 3.40 3.27 3.47 3.13 3.18

Total 2.97 3.00 3.57 3.03 3.60 4.00 3.36

TABLE 17

NUMBER OF NEGATIVE CONSTRUCTS

Grade 7 8 9 10 11 12 Total

Males 1.87 1.47 1.67 1.53 2.20 1.07 1.63 Females 1.93 1.40 1.47 0 .93 1.20 1.67 1.43

Total 1.90 1.43 1.57 1.23 1.70 1.37 1.53

89 90 APPENDIX G.

Intercorrelation Matrix

No .of Age Sex Grade SRATEA HFT CCb CCa Words ECS in Mos 1 2 3 4 5 6 7 3 9

1 1.00 2 0.00 1.00 3 0.00 0.00 1.00 4 .15 .34 .23 1.00 5 .16 -.12 -.03 -.03 1.00 6 .10 -.05 -.02 .85 .85 1.00 7 -.28 .23 .04 .07 -.28 -.25 1.00 8 .16 -.29 .02 -.13 -.07 .06 -.34 1.00 9 .08 .78 -.03 .20 -.06 -.02 .13 -.23 l.l BIBLIOGRAPHY Ahmann, J. 8. Review of SRA tests of educational ability. In 0. K. Buros (Ed.), The sixth mental measurements yearbook. Highland Park, N. J.: Gryphon Press",' "1965.

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