The Perceived Dimensions of Jobs:

A Multidimensional Scaling Approach °

bv

Kathryn A. Welch

Dissertation submitted to the Faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in

Psychology

August, 1989

Blacksburg, Virginia

The Perceived Dimensions of Jobs:

A Multidimensional Scaling Approach

by

Kathryn A. Welch

Roseanne J. Foti, Chairperson

Psychology

(ABSTRACT)

Recent research has revealed ambiguous evidence for the validity

of the cognitive complexity (CC) construct. Some authors (Bieri, Atkins,

Briar, Leaman, Miller, & Tripodi, 1966; Scott, Osgood, & Peterson, 1979)

have suggested that a potentially useful method for examining CC is

multidimensional scaling. The present study examined such an operational

definition. The present study also examined the perceptual dimensions

that underlie individuals' perceptions of jobs.

Three hundred and five subjects rated the similarity of pairs of

job titles, completed the Role Construct Repertory Test (REP), and later

rated videotaped vignettes in a performance appraisal simulation.

Multidimensional scaling extracted the subjects‘ dimensionality. Due to

an unstable solution, the study°s first three hypotheses (that

dimensionality would predict rater accuracy, that dimensionality would

predict rater accuracy better than the traditional Role Construct

Repertory test, and that dimensionality and the REP would be correlated)

were untestable. Multidimensional scaling was not a useful approach in

this context.

The fourth hypothesis stated that the present data would replicate

the three-dimensional job characteristics model of Stone and Gueutal

(1985). Results indicated that the Stone and Gueutal configuration was

not supported. Thus, job design efforts predicated on their model appear

premature.

ACKNOHLEDGEMENTS

The author wishes to thank her advisor, Dr. Roseanne J. Foti, for

her expertise and guidance in the preparation of this dissertation. Her

enthusiasm for research and dedication to her teaching are contagious.

Moreover, her patience in the face of “inheriting" new advisees made a

difficult situation better.

In addition, Doctors Robert J. Harvey, Neil Hauenstein, Robert

Madigan, Christopher Peterson, and Albert Prestrude have all provided

considerable guidance and support. Their efforts are sincerely

appreciated.

iv

V

TABLE OF CONTENTS

A Page

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

TABLE OF CONTENTS..................... vi

LIST OF TABLES ......................viii

LIST OF FIGURES ..................... x

INTRODUCTION ....................... 1Overview ........................ 1Defining Cognitive Complexity.............. 2Operationalizing Cognitive Complexity....,..... 8Construct Validity Issues................ 14Importance of Cognitive Complexity in Work Settings. . . 18Summary and Hypotheses ................. 33

Performance Ratings.................. 34' Frequency Ratings................... 35

Job Characteristics.................. 36

METHOD .......................... 37Overview ........................ 37Pilot Study....................... 38

Pilot Study Sample .................. 38Pilot Study Measures ..............,.. 38Pilot Study Procedure................. 40

Main Data Collection Phase ............... 41Main Data Collection Sample.............. 41Main Data Collection Phase Measures.......... 42Main Data Collection Phase Procedures......... 44

RESULTS.......................... 47Reliability Estimates.................. 47Deriving Dimensionality................. 47Summary of Results ................... 72

DISCUSSION ...................,.... 100

REFERENCES ........................ 110

APPENDIX A: FAMILIARITY QUESTIONNAIRE .......... 118

APPENDIX B: BIOGRAPHICAL QUESTIONNAIRE.......... 121

APPENDIX C: JOB SIMILARITY QUESTIONNAIRE......... 124

vi

APPENDIX D: REP GRID MEASURE............... 141

APPENDIX E: BIOGRAPHICAL QUESTIONNAIRE.......... 146

APPENDIX F: PERFORMANCE RATING QUESTIONNAIRE....... 148

APPENDIX G: PLOTS OF GROUP STIMULUS SPACE ........ 152

APPENDIX H: PLOTS OF GROUP STIMULUS SPACE FORFOUR SUBSETS ................ 160

APPENDIX I: SUBJECT WEIGHTS MEASURING THE IMPORTANCE OFEACH DIMENSION ............... 189

APPENDIX J: PLOTS OF INDIVIDUAL SUBJECT WEIGHTS . . .. . 196

APPENDIX K: CORRELATIONS FOR DEMOGRAPHIC VARIABLES.... 203

APPENDIX L: COMPUTER PROGRAM FOR DETERMININGACCURACY SCORES ............... 205

VITA ........................... 219

vii

LIST OF TABLES

Table Page

1 Stress and R2 for Dimensionalities Two to Six(INDSCAL)..................... 50

2 Stimulus Coordinates for Four-Dimensional Solution . 52

3 Stimulus Coordinates for Four-Dimensional Solution:Subset A (INDSCAL) ................ 57

4 Stimulus Coordinates for Four-Dimensional Solution:Subset B (INDSCAL) ................ 58

5 Stimulus Coordinates for Four-Dimensional Solution:Subset C (INDSCAL)..... . .......... S9

6 Stimulus Coordinates for Four-Dimensional Solution:Subset D (INDSCAL)........ . ....... 60

7 Stress and R2 by Subset (INDSCAL) ......... 61

8 Coefficients of Congruence: Two-DimensionalSolution (INDSCAL) ................ 64

9 Coefficients of Congruence: Three—Dimensiona1Solution (INDSCAL) ................ 65

10 Coefficients of Congruence: Four-DimensionalSolution (INDSCAL) ................ 66

ll Coefficients of Congruence: Five-DimensionalSolution (INDSCAL) ................ 67

12 Coefficients of Congruence: Six-DimensionalSolution (INDSCAL) ................ 69

13 Stress and R2 for Dimensionality One to Six(ALSCAL) ..................... 73

14 Stimulus Coordinates: Two Dimensional Solution(ALSCAL) ..................... 75

viii

15 Stress and R2 for Dimensionality by Subset(ALSCAL) ..................... 76

16 Stimulus Coordinates for Two-Dimensional Solution:Subset A (ALSCAL) ................ 79

17 Stimulus Coordinates for Two-Dimensional Solution:Subset B (ALSCAL)................. 80

18 Stimulus Coordinates for Two—Dimensional Solution:Subset C (ALSCAL)................. 81

19 Stimulus Coordinates for Two-Dimensional Solution:Subset D (ALSCAL)................. 82

20 Coefficients of Congruence: One-Dimensional Solution(ALSCAL)...................... 83

21 Coefficients of Congruence: Two-Dimensional Solution(ALSCAL)...................... 84

22 Coefficients of Congruence: Three-Dimensional Solution(ALSCAL)...................... 85

23 Coefficients of Congruence: Four-Dimensional Solution(ALSCAL)...................... 86

24 Coefficients of Congruence: Five-Dimensional Solution(ALSCAL)...................... 87

25 Coefficients of Congruence: Six-Dimensional Solution(ALSCAL)...................... 89

26 Coefficients of Congruence for Stone and Gueutal°sFinal Solution and Six ALSCAL Solutions ...... 91

27 Stimulus Coordinates: Stone and Gueutal (1985).... 92

28 Descriptive Statistics................ 96

29 Correlations for Hypothesized Relationships ..... 98

30 Analysis of Variance on the Effect of Work Experience

on Differential Elevation ............. 99

ix

LIST OF FIGURES

Figure: Page

1 Stress and Proportion of Variance Accounted for as aFunction of the Number of Dimensions (INDSCAL) . . . 51

2 Proportion of Variance Accounted for as aFunction of the Number of Dimensions: Subsets A & B. 62

3 Proportion of Variance Accounted for as aFunction of the Number of Dimensions: Subsets C & D. 63

4 R2 and Stress as a Function of Dimensionality(ALSCAL)....................... 74

5 Stress as a Function of Dimensionalityz SubsetsA & B (ALSCAL).................... 77

6 Stress as a Function of Dimensionality: SubsetsC & D (ALSCAL).................... 78

x

INTRODUCTION

OYERYIEK

During the 1960°s and l970°s, cognitive complexity (CC) was

considered a variable of importance in the study of stimulus perception

and social relations. More recently, the construct has fallen victim to

benign neglect at best or to disrepute at worst. In short, it seems that

Foa and Turner°s (1970) prophecy of an increased focus on cognitive

structures by the year 2000 may not be accurate in the case of CC. The

literature reveals both an explanation for this turn of events and yet a

possible justification for the reexamination of the cognitive complexity

construct.

A reexamination of CC is potentially important for research in work

settings. For example, if raters' cognitive capacities constrain the

manner in which they perceive stimuli, the potential importance of CC in

work settings cannot be overstated. Such workplace endeavors as job

design, job analysis, job evaluation, and performance appraisal may all

be susceptible to the effects of individuals° cognitive complexity.

Accordingly, another look at cognitive complexity is undertaken here.

First, this dissertation defines the CC construct. Second, several

methods of operationalizing CC are described and evaluated. Third, the

present paper suggests a number of contexts in which a new look at CC may

make a contribution in work settings. Fourth, one potentially useful

method for operationalizing CC is proposed. This methodology permits the

measurement of not only cognitive complexity but also its convergent and

1

predictive validity. Fifth, the present study attempts to replicate the

underlying job dimensions of Stone and Gueutal (1985). Finally, the

implications and limitations of the present study are discussed.

DEF[N1§G COG§ITI!§ COMPLEXLLX

Human information processing involves two distinct classes of

information: content and structure (Schroder, Driver, & Streufert, 1967).

Content variables provide information concerning the nature, magnitude,

and duration of to—be-remembered stimuli, whereas structural variables

concern the manner in which people process stimuli. The existence of

structural variables has numerous implications for the study of

individual differences in stimulus perception. Cognitive complexity

(Bieri, 1955; Kelly, 1955, 1970) has been proposed as one such structuring

variable.

Derived from Lewin°s (1936) notion of differentiation and from

Kel1y°s (1955) personal construct theory, cognitive complexity was

originally conceived in terms of an individua1's capacity to

differentiate stimuli along various dimensions. Kelly viewed people as

scientists who use their own personal systems of constructs or mental

representations to impose meaning on the world. According to this view,

people use constructs to understand, predict, and control their

environments. These constructs are bipolar dimensions (such as shy versus

outgoing, energetic versus lazy, etc.). A more cognitively complex person

would perceive greater dimensionality (i.e., use more constructs) in

determining the attributes of stimuli than would a less cognitively

complex person. According to Bieri (1955, 1971) and Bieri, Atkins, Briar,

2

Leaman, Miller, and Tripodi (1966), the term gggnitiye comglexity refers

to the manner in which individuals structure their social worlds.

Specifically, Bieri et al. defined CC as the capacity to "construe social

behavior in a multidimensional way" (p. 185). The present paper extends

Bieri et al.'s definition to encompass the perceiving of any stimulus in

a multidimensional way.

Bieri et al.'s definition suggests that CC is a unitary construct.

But, there is considerable evidence that CC is a multidimensional instead

of a unitary construct. For example, Vannoy (1965) discovered evidence

that CC is multidimensional. In a factor—analytic study of a battery of

CC measures, he extracted eight factors, none of which included all of

the measures. Vannoy's investigation revealed evidence for several

important dimensions of cognitive complexity: (a) the degree of

sensitivity to judgment variables, (b) the degree of discriminability on

judgment variables, and (c) the degree to which the world is viewed in a

narrow and ordered way.

Seaman and Koenig (1974) also provided experimental evidence for

the multidimensionality of CC. In a study of 146 undergraduates, they

employed seven measures of CC derived from the REP test. Results

indicated that three factors accounted for 78% of the total variance in

ratings. These factors were "positive affect cognitive

complexity-simplicity," "negative affect cognitive

complexity-simplicity," and assumed dissimilarity (p.387).

In subsequent research, Jones and Butler (1980) suggested that CC

involves not only dimensionality ge; se but also the fineness of the

distinctions made along the various dimensions. They employed subgroup

3

analysis to examine the factor structures of environmental

characteristics ratings for high, medium, and low-complexity raters. The

operational definition of CC involved a technique adapted by Jones and

Butler (1980) from Bem and Allen (1974) and Solomon (1977) which assesses

the variability of responses to a work-environments perception

questionnaire. A standard deviation was computed for each individual°s

responses to each questionnaire composite. Finally, the authors summed

these deviation scores to obtain an overall complexity score. Although

high-complexity raters provided six factors (vs. four for low-complexity

raters), some dimensions were consistent across subgroups. These stable

dimensions included leadership patterns, work-group interactions, and

role characteristics. However, there were differences between subgroups

"in the degree to which organizational level characteristics were

distinguished (a) from task and other domains and (b) among themselves"

(p. 8). For example, the authors found that low-complexity judges

produced a component which reflected a more global description of the

organization (such as role conflict), while high-complexity judges

produced components which made finer the distinction between role

conflict and organizationally-based conflict.

Another proposed aspect of CC is integration. Crockett (1965),

Schroder et al. (1967), and Scott, Osgood, and Peterson (1979) argued that

abstract thought involves both dimensionality and integration. Indeed,

Schroder et al. asserted that there is no necessary correspondence between

the number of dimensions one perceives and the level of one°s information

processing. What is relevant are both the manner in which an individual

combines attributes (dimensions) and the distinctions made along the

4

various dimensions. Streufert and Streufert (1978) have revealed that

cognitive integration is related to moderate levels of differentiation

along various dimensions. Unfortunately, the usual methods for measuring

integration (Schroder et al., 1967; Zajonc, 1960) have been more

problematic than have efforts to tap dimensionality. Scott et al. (1979)

have summarized these shortcomings by suggesting that there is no

consensus, at either the conceptual or operational levels, for measuring

integration.

Because cognitive complexity is multidimensional, Scott et al.

asserted that it would be more accurate to use the term dimensionality

when speaking of differentiative functions. The term gomglexity, they

argued, is more accurately applied to both differentiative and

integrative functions. Thus, the argument for examining integration in

any study of CC is compelling. But, any examination of a multidimensional

construct must begin somewhere. It makes sense that a reasonable first

attempt at reoperationalizing CC should focus on dimensional capacity.

The present study, therefore, takes this focus. More importantly,

however, the present methodology lends itself to an extrapolation not only

of subjects° cognitive dimensionality but also their integration

(Schroder et al. 1967). Thus, the present study also lays the groundwork

for an examination of integrative complexity. This issue will be

explained more fully in a later section. (It should be noted here that

henceforth, except when otherwise indicated, reference to the term

comglexity means dimensionality.)

Another important issue pertaining to CC is that of domain

specificity. Although Bieri and Blacker (1956) provided evidence for the

5

generalizability of CC across stimulus domains (people and inkblots),

Sechrest and Jackson (1961) did not confirm such generalizability.

Similarly, Crockett (1965) contended that an individual°s dimensionality

is specific to a particular stimulus domain. To test further the domain

specificity hypothesis, Durand and Lambert (1976) administered three

versions of the Bieri Role Construct Repertory test (described in a later

section): one for automobiles, one for toothpaste, and another for

interpersonal relations. The results revealed that although CC was

significantly correlated for automobiles and toothpaste (r=.33, p <.05)

and for automobiles and interpersonal relations (r=.22, p <.O5), the

correlations tended to be low enough to be of doubtful practical

importance. Thus, as a practical matter there was little generalizability

across domains.

Consistent with the domain specificity hypothesis, Schroder et al.

(1967), Streufert (1982), and Streufert and Streufert (1978) suggested

an interactive view of complexity, positing that (a) the level of

information processing varies within and between subjects and (b) there

is some optimal level of dimensionality/integration. Streufert (1982)

asserted that complexity involves a U-shaped function in which behavioral

complexity is plotted as a function of environmental complexity.

Different curves occur for cognitively complex versus cognitively simple

individuals. Along these lines, Schneier (1977) suggested a cognitive

compatibility theory that posits an ideal match between task/environment

characteristics and cognitive complexity of the rater in a performance

appraisal task. But, subsequent research has not supported this view

(Bernardin, Cardy, & Carlyle, 1982; Lahey & Saal, 1981). In response to

6

such null results, Streufert (1982) argued that a behaviorally anchored

rating task, such as the one used by Bernardin et al., does not

necessitate a high degree of differentiation and integration.

Peterson and Scott (1975) have taken a compromise position in this

controversy. They asserted that cognitive style is “to some degree topic

specific and to some degree generally manifested" over several stimulus

domains (p. 372). Peterson and Scott concluded that cognitive style

depends not only upon the stimulus domain but also upon the interaction

of the person and the domain.

The issue of interactive complexity is important for several

reasons. If complexity is both person and situation specific (i.e.,

interactive), then generalizability across persons and situations may be

difficult. From the discussion above, it is evident that stimulus domains

such as job titles must be examined individually. In addition, the

interactive nature of cognitive complexity must be taken into account in

any measurement strategy. This matter will be discussed further in the

methods section.

The present section considered some definitional issues pertaining

to the cognitive complexity construct. In particular, evidence was

presented which suggested that CC involves both differentiative and

integrative capacities. In addition, it was suggested that CC involves

the fineness of distinctions along the various dimensions. As it is

conceived here, CC is not a trait which is stable across various content

domains, but rather a process variable which may interact with features

of the stimulus. Finally, because research has suggested substantial

stimulus domain specificity, the importance of examining

7

CC-dimensionality within a particular stimulus domain, such as jobs, was

discussed. These definitional considerations have important implications

for the operationalizing of CC. A discussion of operational issues is

presented below.

OPERATIO§ALIZl§G COGEITIYE COMPLEXITY

As Bieri et al. (1966) and Scott (1963) indicated, the measurement

of cognitive structures, including cognitive complexity, has proven

problematic. A variety of operations for measuring the construct have

been employed. Indeed, Bonarius (1965) has enumerated at least ten

different methods for operationalizing CC. Because of the plethora of

measures, the comparability of CC across studies is difficult. Some of

the approaches to operationalizing CC include the Role Construct

Repertory Test (REP), Scott et al.°s (1979) Listing and Comparing Measure,

and factor analyzed semantic differential scales. Scott et al. (1979)

have also proposed a geometric solution (to be described later in this

section). Additionally, Crockett (1965) developed a Role Construct

Questionnaire (RCQ). Finally, there is the multidimensional scaling

approach suggested by Bieri et al. (1966) and Scott et al. (1979) and

adapted for the present study.

Among the measures of dimensionality, the most commonly used is the

REP test. Actually, the REP test is somewhat of a misnomer (Neimeyer &

Neimeyer, 1981). These authors asserted that "the REP is not a single

test, or a questionnaire with fixed content, but a broad variety of

techniques sharing certain methodological features" (p. 194). One

version of this instrument consists of a grid with columns (for persons

l 8

to be compared) and rows (for the constructs underlying the rated

similarity of the comparison persons). Comparison persons hold such role

titles as parents, friends, teachers, etc. Persons are compared three at

a time. Subjects identify a construct along which two of the persons are

alike yet different from the third person. Then, using a six-point rating

scale, subjects rate all persons on all characteristics. An index of

complexity is calculated by comparing the grid ratings in each row to

every other row. It is important to note that in this approach subjects

generate their own constructs.

There are several reasons for questioning the usefulness of the REP.

First, it lacks the requisite isomorphism between the construct and the

measure (Scott, 1963). As Scott (1963) noted, "the procedure for

analyzing data should be structurally appropriate to the property being

assessed" (p. 269). What the REP seems to provide is an indication of

the constructs that are used and whether or not they are used in the same

way across the ratees. Thus, the REP is not a direct measure of the

dimensionality of the rater. Second, the REP assesses cognitive

complexity less directly than other methods such as sorting tasks and MDS

(Scott, 1963). Third, support for the REP°s predictive validity,

particularly with respect to the prediction of rating accuracy, has been

weak (Bernardin et al. 1982; Sechrest & Jackson, 1961). (This issue is

discussed more fully in a later section.) Fourth, the use of the REP is

constrained by potential bias in subjects' sampling of constructs

(Bonarius, 1965). Fifth, the traditional REP task is very tedious for

subjects. Finally, the traditional person-roles REP may be confounded

by familiarity. That is, subjects' ratings may be differentially affected

9

by knowledge of the ratee. One benefit of the REP, however, is that

(except in its modified version) it does not assume the constructs a

priori.

Bieri et al.‘s (1966) modification of the REP test provides subjects

with constructs that they use to perform the rating task. This approach

offers the advantage of saving time and reducing subject tedium. Although

Bieri et al. (1966) and Tripodi and Bieri (1963) assert that provided

constructs and subject—generated constructs produce similar results, the

evidence on this issue is equivocal (Crocket, 1965). Thus, caution is

required concerning the use of experimenter-provided constructs.

Another measure of dimensionality, Scott et al.°s Listing and

Comparing Measure (LCM), requires that subjects

generate a set of objects as well as successive subsetsthat are alike for some characteristic.Objects that clearly differ on that attribute arealso clearly noted, but a classification of everyobject is not required (p.104).

LCM dimensionality is computed from the measure of dispersion, "H"

(Attneave, 1959), which is an index of the number of dimensions in the

subject°s grouping system. Logarithms are required for the calculation

of this index. By their own admission, Scott et al.°s LCM is constrained

by the representativeness of the domain. Because the domain of objects

has been generated separately by each subject, it may not be a

representative sample of objects.

Another method for estimating the dimensional capacity of

individuals is the application of Scott et al.°s geometric model to

ratings or checklists. This approach requires that subjects rate a

(standard) set of objects according to a (standard) set of attributes.

10

It is then possible to estimate what Scott et al. term "redundancy"

between a pair of attributes by computing the squared correlations for

the set of objects. Then dimensionality (D) may be computed according

to the following formula:

D=—Ä‘*j‘ [llK+-2Er

"where k is the number of attributes employed by the respondent and Erz

is the sum of all the squared intercorrelations among the k attributes“

(p. 108). This geometric approach has not been widely adopted in studies

employing CC-dimensionality as a predictor variable.

The semantic differential scale (Osgood, 1962) is another means of

estimating dimensionality. This approach imposes constructs by

requiring subjects to rate persons or objects according to pairs of

bipolar adjectives such as: (1) ineffective-effective, (2) shy-outgoing,

(3) weak·strong, etc. Ratings are then factor analyzed. Foremost among

the criticisms of this approach to recovering subjects° dimensionality

is that semantic differential scales do not in fact recover subjects°

dimensionality at all, but rather the dimensionality of the scale

developer (Goldstein & Blackman, 1978; Pervin, 1973).

As a means of extracting dimensionality, Crockett (1965) developed

the Role Construct Questionniare (RCQ). The measure consists of short

descriptions written by subjects within a three-minute time limit.

Vignettes describe eight individuals who fit particular roles. These

vignettes are scored according to the number of interpersonal constructs

ll

employed by each subject. One major limitation of this procedure is that

subjects° performance may be constrained by verbal fluency.

Although multidimensional scaling (Kruskal, 1964; Kruskal & Wish,

1978) has been employed most frequently to describe results for groups

of subjects, Tucker and Messick (1963) paved the way for the use of MDS

to tap individual differences in dimensionality. Indeed, Schroder et al.

(1967) and Scott et al. (1979) suggested that such an

individual-differences approach would be useful in the study of cognitive

dimensionality. Another important development was Carroll and Chang°s

(1970) algorithm (INDSCAL), which facilitates the retrieval of individual

differences in dimensionality. More recently, Peterson and Scott (1983)

have employed MDS to reveal the number of dimensions that individuals

employ when perceiving lists of nations. In so doing, these authors have

obtained considerable convergent validity between MDS-dimensionality and

other measures (checklist description and rating scale approaches) of

dimensionality.

Because of its usefulness, multidimensional scaling warrants

further discussion here. MDS assumes that the similarities (or

dissimilarities) between stimuli may be portrayed in a multidimensional

space. The associated spatial representation is derived by having

subjects rate the similarity of pairs of objects. In doing so, raters

impose an implicit set of attributes on their ratings. It is the task

of MDS to recover the implicit dimensions by estimating the number of

independent dimensions defining such a space. To do this, the INDSCAL

algorithm produces a geometric representation of the psychological

distance between pairs of stimuli. It is assumed that for each subject

12

there is a unique configuration of stimulus coordinates. From this

configuration, the procedure extracts the best n-dimensional space for

the group. Dimensionality is defined as the scaling solution (number of

retained dimensions) beyond which additional dimensions do not

significantly increase the proportion of variance accounted for (Kruskal

& Wish, 1978). Unlike traditional MDS techniques, INDSCAL accommodates

individual Variation while the traditional approaches treat such

Variation as error.

Though beyond the scope of the present paper, integration may also

be inferred from MDS analyses. Schroder et al. (1967, pp. 180-1)

summarized MDS°s usefulness in this regard, indicating that: (1) The

presence of multiple dimensions provides suggestive evidence for a

schema. MDS dimensions, they argue, are by definition integrated. A

dimension will not be extracted unless it contributes to each judgment

of similarity. (2) The number of dimensions uncovered by MDS analysis

reflects the number of sources of information. One may also determine

how important each source of information is to the rating of similarities.

This information is found in the individual subjects° weights. (3) "Often

the meaning of MDS-dimensions may be revealed by the arrangement of

stimuli on the dimension" (p. 180). The upshot of this summary of the

usefulness of MDS is that MDS may provide more information than merely

the number of dimensions which contribute significantly to average

similarity judgments.

This section discussed the relative advantages and disadvantages

of several methods of operationalizing CC-dimensionality. It was argued

that, although a variety of operations have been applied to the

p 13

measurement of CC-dimensionality, one potentially useful approach (MS)

has received insufficient attention. This operational definition may be

a useful means for examining CC-dimensionality in work settings.

COMSTRUCT YALIDITY ISSUES

Construct validity (the extent to which a measure assesses what it

purports to measure) is assessed not by a quick yes-or—no assessment but

by a painstaking accumulation of evidence. This evidence includes: (1)

the reliability (e.g., test-retest) of the measure; (2) the internal

consistency reliability of the instrument (does the measure assess one

construct or a number of constructs?); (3) the content validity of the

measure (i.e., is the adequacy of the sampling of the content domain);

(4) prediction (does the construct "behave" as it "should" -- that is,

does it correlate with the constructs the theory predicts it should and

remain uncorrelated with other constructs?). Por this latter line of

investigation, questions of convergent and discriminant validity

(Campbell & Piske, 1959) must be assessed. In other words, the measure

should correlate highly with other measures of the same construct

(convergent validity) and correlate little with similar-method measures

of different constructs (discriminant validity).

A fundamental consideration is the reliability of any measure

because reliability sets the limits for validity. Schneier (1979) reports

a test—retest reliability of .82 (p <.00l) for the REP. However,

Goldstein and Blackman (1978) have reviewed the reliabilities of various

modifications of the REP. These authors found test-retest reliabilities

ranging from .46 to .86. Their conclusion is that although the

14

test-retest reliabilities are statistically significant, they are

"somewhat below the level that is generally expected" (p.11l). In their

literature review, O°Keefe and Sypher (1981) found test-retest

reliabilities below .70 for most measures of complexity. Furthermore,

0°Keefe and Sypher (1981) report that the REP is unusually sensitive to

Variations in its administration and procedures.

Regarding internal consistency, as indicated in this introduction,

research has shown that cognitive complexity is a multidimensional

construct. As O°Keefe and Sypher have concluded, the Various measures

of cognitive complexity may not all be assessing the same thing. This

fact would argue against overall cognitive complexity being internally

consistent. However, such a multidimensional feature does not preclude

the possibility that any one component of CC, such as dimensionality, may

be important. Indeed, in other areas of industrial-organizational

psychology (job satisfaction, for example), an examination of the facets

of a multidimensional construct has been somewhat more fruitful than

relying on the global measure.

The internal consistency issue is relevant to content Validation

in the sense that any effort to tap only one aspect or dimension of CC

does not sample from the entire content domain. It would appear that

content validity in such a case would be necessarily low. Again, however,

this does not preclude the possibility that one component of CC may be

worth examining empirically.

As indicated earlier in this introduction, the convergent validity

of CC-dimensionality has been explored by Peterson and Scott (1983). In

their attempt to arrive at fundamental measurement, these authors

15

examined the intercorrelations between several methods of measuring

cognitive dimensionality: (1) pair-wise comparison, (2) rating scale,

and (3) checklist description. The intercorrelations ranged from .53 to

.70 and were all significant (g<.O0l). In another study (Schneier,l979),

convergent validity was demonstrated between Scott°s (1962) measure of

complexity and the Bieri (1966) grid form of the REP (; = -.19, g<.05).

However, the correlation, while statistically significant, was

unimpressive. Despite the apparent relatedness of various CC-measures

revealed by Peterson and Scott (1983), other studies show that the

convergent validity issue is less certain. For example, O°Keefe and

Sypher (1981) report intercorrelations between Bieri's REP and Crockett°s

(1965) measure, ranging from -.19 to +.14.

Evidence related to the discriminant validity of CC suggests that

CC does not appear to be significantly related to such variables as

intelligence and verbal ability. (Bieri & Blacker, 1956; Crocket, 1965;

Leventhal, 1957; Scott, Osgood, & Peterson, 1979; Wicker, 1969).

Streufert and Streufert (1978) have reviewed the literature concerning

this issue and found that any potential relationship between cognitive

complexity and intelligence appears to be at the lower end of

intelligence. That is, persons of very low intelligence are not likely

to be multidimensional. More recently, O‘Keefe and Sypher (1981) reviewed

the literature and again found that correlations between between

cognitive complexity and intelligence tend to be low and nonsignificant

(-.20 to .20 for Bieri°s REP measure).

Cognitive complexity also appears to differ from field

dependence/independence (FD/I). Field dependence/independence (Witkin,

16

Dyk, Faterson, Goodenough, & Karp, 1962) reflects the tendency of people

to rely either on the field or themselves as referents. In other words,

field-dependent individuals rely on some aspects of the environmental

field when they view stimuli. Field-independent individuals rely on some

internal orientation or standard. This is a different notion from

differentiation along the various dimensions or constructs used in one°s

conceptualization of the world (Witkin & Goodenough, 1977).

Indeed, Streufert and Streufert (1978) have outlined the nature of

the distinction between these two different usages of the term

"differentiation." They cite Witkin, Goodenough, and Karp (1967), who

viewed psychological differentiation as a kind of differentiation

relevant to perceptual-motor tasks. In contrast, according to Streufert

and Streufert, "differentiation" for research in complexity theory

concerns "cognitions involving verbal °concepts,° 'dimensions,° etc." (p.

14). Bieri (1971) suggests that cognitive complexity is a more specific

notion than FD/I. CC, he asserts, pertains to differences in the way

people process information about their social worlds.

Regarding spatial ability, Thompson, Mann, and Harris (1981)

conclude that cognitive complexity is related to spatial task performance

for males but not for females. On the other hand, FD/I correlates highly

with a number of spatial ability tests (Vernon, 1972).

Research reported in this section illustrates some of the

construct-validity issues which have proven problematic for cognitive

complexity researchers. It was shown that (1) reliability, which

circumscribes the limits for validity, was somewhat weak; (2) the content

validity for measures of cognitive complexity is generally low; (3)

17

internal consistency is low; and (4) there is mixed evidence for the

convergent and discriminant validity of the construct.

POTEELIAL IMPORTAECE OF COGEITIYE COMPLEXITY IE EORK SETTINGS

This section describes the potential importance of

reoperationalized cognitive complexity. If MDS-dimensionality can

actually be demonstrated in this context, the implications for behavior

in work settings are enormous. As Jones and Butler (1980) pointed out,

it is likely that cognitive style influences the manner in which

behavioral outcomes and causal inferences about those outcomes are

derived. This may be so because raters who are constrained by their

ability to dimensionalize stimuli may err in the judgments that underlie

performance appraisal systems and wage structures. Thus, job analysis,

job evaluation, and performance appraisal could be particularly

vulnerable to the effects of CC. Furthermore, job design efforts, which

are predicated on rating judgments, would be highly susceptible to the

influence of such cognitive structuring variables. A brief discussion

of the literature in the areas of job design and performance appraisal

follows.

As indicated above, the job—design literature is relevant for two

reasons: (1) job design is based on the perceptual dimensions underlying

jobs and (2) job design efforts, which are based on rating judgments, may

be susceptible to rating errors which in turn may be influenced by

cognitive dimensionality. The perceptual dimensions underlying jobs have

both theoretical and practical importance in the area of job design.

q 18

From a theoretical perspective, one may have an interest in the

manner in which people perceive their jobs. As a practical matter,

practitioners cannot redesign jobs unless they know the dimensions along

which jobs should be redesigned. The task attributes approach (Hackman

& Lawler, 1971; Hackman & Oldham, 1975, 1976; Stone & Gueutal, 1985;

Turner & Lawrence, 1965) emerged to provide such dimensional information.

As noted by Stone and Gueutal (1985), most investigations of the

attributes underlying jobs have employed measures such as the Job

Diagnostic Survey (JDS) of Hackman and Oldham (1975), the Yale Job

Inventory (YJI) of Hackman and Lawler (1971), the Requisite Task Attribute

(RTA) of Turner and Lawrence (1965), and other similar questionnaires.

Stone and Gueutal (1985) pointed out that all of the existing measures

of job characteristics are rooted in the work of Turner and Lawrence

(1965). However, since by their own admission, Turner and Lawrence

identified their underlying dimensions a prrgri, it is unclear how well

the RTA and other similar questionniares parallel employees° perceptual

dimensions.

In a similar manner, Hackman and Lawler (1971) and later Hackman

and Oldham (1975, 1976) refined and supplemented the concepts explored

earlier by Turner and Lawrence. The result was the Hackman and Oldham

(1975) Job Characteristics Model. Its five core dimensions are described

as fol lows:

1. Skill Variety (SV): reflects the degree to which the jobrequires incumbents to perform a variety of activities.

2. Task Identity (TI): concerns the extent to which the jobinvolves the completion of a whole unit of work.

19

3. Task Significance (TS): is the extent to which the jobmay significantly affect others.

4. Autonomy (A): is the extent to which the incumbentsare free to schedule their work activities andto determine procedures.

5. Feedback (F): is the extent to which the incumbentsreceive clear information about the effectivenessof their performance.

According to their model, the five core dimensions (task identity,

skill variety, task significance, autonomy, and feedback) relate to the

critical psychological states (experienced meaningfulness of work,

experienced responsibility, and knowledge of results). These critical

psychological states lead to personal and organizational outcomes such

as enhanced performance, increased satisfaction, reduced turnover, etc.

It should be noted that Growth Need Strength or GNS (an index of the

extent to which a person desires an enriched job) acts as a moderator

variable for both the relationship between core dimensions and critical

psychological states and the relationship between critical psychological

states and personal/organizational outcomes. In addition to describing

these five critical psychological states, Hackman and Oldham (1975)

arrived at a combinatory rule for the establishment of a unidimensional

index of job scope:

Motivating Potential Score = (SV + TI + TS)/3 x A x F

At first, the above research appeared to offer a framework for

future job-design applications and job—design investigations. Such

research also held the promise of recognizing the basis of individual

20

reactions to jobs. However, this early research was also vulnerable to

a number of criticisms.

For example, Dunham (1976), Harvey, Billings, and Nilan (1985), and

Roberts and Glick (1981) have pointed out that there is a lack of

independence among the dimensions. This finding contradicts Hackman and

Oldham (1975), who argue that the core dimensions are independent.

Roberts and Glick (1981) argue further that a five-factor model, such as

Hackman and 0ldhams°s, is not consistently demonstrated in the

literature. Indeed, studies by Aldag, Barr, and Brief (1981), Dunham

(1976, 1977), Dunham, Aldag, and Brief (1977), Lee and Klein (1982),

Pierce and Dunham (1978), and Pokorney, Gilmore, and Beehr (1980) have

failed to capture the factor structure of the traditional Hackman and

Oldham (1975) job characteristics model. Roberts and Glick (1981) also

criticized such traditional job-characteristics research for, among other

things, inadvertently capitalizing on common method variance and ignoring

the question of perceptual vs. objective task assessments. The

method-variance problem in the use of the JDS has also been documented

by Harvey et al. (1985). The issue of perceptions and their role in task

assessment is one of the major criticisms by Stone and Gueutal (1985) and

will be discussed more fully later in this section.

Other work casts further doubt on the earlier Job Characteristics

Model of Hackman and Oldham (1975). Having meta—analyzed a number of

factor-analytic studies, Fried and Ferris (1987) revealed that while

there appears to be more than one perceptual dimension underlying jobs

there may not be the five posited by Hackman and Oldham (1975). In a

recent attempt to shed additional light on the factor structure of jobs,

21

Kulik, Oldham, and Langer (1988) contrasted the factor structure of the

Job Diagnostic Survey (JDS) with a revised version by Idaszak and Drasgow

(1987). They found that the revised version conformed more closely to a

five-factor structure than the original JDS. Idaszak, Bottom, and Drasgow

(1988) reported further that -- with the exception of a new, somewhat

weak sixth factor -- the revised JDS has an invariant factor structure

across the five job characteristics of Hackman and Oldham (1975).

However, this finding does not negate the criticism that the Turner and

Lawrence (1965), the Hackman and Oldham (1975), and the Idaszak et al.

(1988) dimensions were not empirically derived.

In another study, Stone and Gueutal (1985) attempted to empirically

derive job dimensions. They reported that three dimensions underly jobs.

Those dimensions are: (1) the complexity of the job, (2) the extent to

which the employee works with the public, and (3) the physical demands

of the job. According to Stone and Gueutal, multidimensional scaling

stimulus coordinates for the first dimension correlate highly with levels

of "skill variety," "complexity of job," "job requires a great deal of

reasoning," etc. (pp.387-388). The second dimension indicates the extent

to which incumbents interact with people on the job and provide a service.

Finally, the last dimension is a reflection of the extent to which the

job involves working in an environment that is clean and safe as opposed

to dirty and unsafe. The 1985 Stone and Gueutal study provides the

advantage of determining subjects° own perceptual dimensions of jobs

rather than deriving them from a priori assumptions as did Turner and

Lawrence and, by extension, Hackman and Lawler (1971) and Hackman and

Oldham (1975, 1976). Stone and Ruddy (1987) have found evidence for the

22

generalizability of the Stone and Gueutal (1985) results to samples

outside the laboratory. More recently, Zaccaro and Stone (1988) have

reaffirmed the Stone and Gueutal model. These authors claim that the

dimensions of jobs are probably best protrayed by a model different in

kind from the traditional five-dimensional job characteristics model. In

addition, they assert that such early approaches unnecessarily "restrict

the capacity to account for variance in such criteria as job satisfaction

and intent to leave an organization" (p. 249). They report greater

success at accounting for variance in job satisfaction with the use of

such empirically derived job dimensions as the Stone and Gueutal

dimensions. For example, their results suggest that two of three Stone

and Gueutal dimensions (those approximating complexity and physical

demands/danger) significantly predict overall satisfaction over and above

motivating potential score (MPS).

It would seem that the direction of current research may be shifting

toward an embracing of the Stone and Gueutal dimensionality. This is

despite the fact that research rooted in the job-analysis tradition

appears to bear a somewhat different message concerning the factor

structure of jobs (Harvey, Friedman, Hakel, 8 Cornelius, 1988). Using

the Position Analysis Questionnaire (PAQ) and the Job Element Inventory

(JEI), Harvey et al. (1988) found that the five-factor structures of the

PAQ and JEI are similar. The factors revealed by Harvey et al. are: (1)

decision-making/communication/general responsibility, (2) skilled job

activities, (3)information-processing activities, (4) physical

activities/related environmental conditions, and (5) using equipment/

providing service (p. 641). Although apparent similarities between the

l 23

Harvey et al. and Stone and Gueutal dimensions exist (complexity vs.

skilled job activities; physical activities/environmental conditions vs.

physical demands; using equipment/providing a service vs. works with the

public), the number of dimensions retrieved by Harvey et al. differs from

the number captured by Stone and Gueutal. Moreover, the use of the job

analysis technology may be preferable to requiring subjects to report on

their perceived similarity of job titles.

In view of the brewing controversy, additional work documenting the

replicability of Stone and Gueutal's (1985) three-dimensional solution

seems warranted. The present study, with its large sample size, provides

an excellent opportunity for such an investigation.

Job—design research (Cherrington & England, 1980; Stone, 1976;

Stone, Mowday, & Porter, 1977) has suggested also that a number of

individual—difference variables (for example, desire for an enriched job,

need for achievement, and work values) may interact with job

characteristics to influence incumbents‘job perceptions. These

perceptions may in turn influence job outcome measures such as

performance, general satisfaction, and satisfaction with the job itself.

A logical extension of such individual—difference studies is the

hypothesis that dimensionality also may moderate the relationship between

job characteristics and job characteristics ratings. According to this

line of reasoning, individual differences in ability to conceptualize

to—be-rated stimuli ought to affect ratings of the stimuli. But when

Stone and Gueutal (1985) used multidimensional scaling to examine job

similarity ratings for trichotomized cognitive complexity subgroups, no

effects for REP-CC were noted. Specifically, Stone and Gueutal found that

24

multidimensional scaling solutions did not differ significantly for high,

medium, or low cognitive-complexity subgroups. This null result

underscores the controversy surrounding the cognitive complexity

(dimensionality) construct. A clarification of cognitive

dimensionality°s role is one important aspect of the present research.

Another area which may be influenced by cognitive dimensionality

is performance appraisal accuracy. Most prior research concerning the

REP test and rater accuracy pertains to the accuracy of predicting

behavior. In other words, researchers have typically asked the question,

"How well does cognitive complexity (operationalized by the REP)

correlate with peoples° accuracy of predicting others° social behaviors?"

Bieri (1955), for example, found that subjects who were high in REP—CC

were better predictors of others° behaviors than were their low-CC

counterparts. This finding has been supported by subsequent research

(Addams—Webber, 1969). However, it should be noted that in the Bieri

study, the author examined the relationship between REP scores and the

efficacy of predicting a classmate°s responses on a social-situations

test. As Goldstein and Blackman (1978) point out, the results were

significant only when a one-tailed test for significance was used. In a

variation of this line of investigation, Leitner, Landfield, and Barr

(1974) summarized the literature by concluding that cognitive complexity

does correlate with accuracy of predicting differences but not

similarities in 0thers° behavior. Their conclusions have been refuted

in a recent study by Lal and Singh (1984), who found that one°s level of

cognitive complexity did not predict one°s judgment accuracy. It would

seem that CC°s ability to predict interpersonal behavior is uncertain.

25

Addams-Webber (1969) explored the hypothesis that one°s level of

cognitive complexity can predict the accuracy with which one predicts

another°s constructs. The results indicated that high-CC subjects were

better at identifying the constructs of their partners in the study than

were low-CC subjects. On the other hand, Sechrest and Jackson (1961)

investigated whether or not high-CC raters would better predict the

construct systems of others. Results indicated an insignificant

relationship between complexity and accuracy. However, the ambiguous

evidence regarding CC-REP°s ability to predict both interpersonal

behavior and others‘ constructs does not address the issue of REP°s

potential ability to predict performance appraisal accuracy. It should

be noted that neither the Addams—Webber nor the Sechrest and Jackson

studies investigated performance appraisal accuracy. Unfortunately,

early research in the area of CC has given little attention to performance

appraisal accuracy.

To discuss properly the issue of performance appraisal (rater)

accuracy, it is first necessary to identify pertinent definitional

issues. Separate from the matter of cognitive complexity

(dimensionality), rater accuracy in performance appraisal situations has

received considerable attention in the literature (Becker & Cardy, 1986;

Borman, 1977; Cronbach, 1955; Murphy, Garcia, Kerkar, Martin, and Balzer

(1982). Cronbach°s landmark (1955) paper established that rater accuracy

may be broken down into four components: elevation (E), differential

elevation (DE), stereotypic accuracy (SA), and differential accuracy

(DA).

26

Thus, ratings will depend on the overall level of a rater's ratings

(E), the average rating for the dimension upon which a ratee is judged

(SA), the average rating for each ratee across dimensions (DE), and the

residual or (DA). According to Cronbach, DA indicates the rater°s

ability to differentiate between ratees on any particular item.

Differential accuracy, as it is defined, is really more properly

rater INACCURACY. This is so because accuracy is conceived as the overall

difference between ratings and true scores, hence inaccuracy. Similarly,

the components of accuracy (of which differential accuracy is one) reflect

aspects of the overall difference between ratings and true scores. In

other words, a score of zero indicates perfect accuracy for that

component.

A few additional comments concerning DA are relevant. Differential

accuracy (or rather inaccuracy) is described by Murphy et al. (1982) and

Cardy and Dobbins (1986) as the "finest grain measure of accuracy" (p.

674). This is because differential accuracy is a measure of the

difference between ratings and true scores (when controlling for overall

rating level as well as rating levels for both dimensions and particular

ratees). In other words, Cardy and Dobbins suggest that DA is a measure

of non-systematic noise. Cronbach's equation for differential accuracy

is as follows:

[2]Il ij

27

Note that rij and tij are rating and true score for ratee i on dimension

j; ?i_ and Ei_ are mean rating and true score for ratee i; F_j and F_j

are mean rating and true score for dimension j; and F__ and F__ are mean

rating and true score over all ratees and dimensions (Becker 8 Cardy,

1986). (This equation will be used in the analyses described later.)

Additionally, other components of inaccuracy (elevation,

differential elevation, and stereotypic) may be calculated according to

the following formulaez

a2=(?__ —E__)2 [3]

DEZ = 1/n;[(?i_ - ?_ _) — (EL - E_ _)]2 [4+}1 .

SA2=1,’kX[(?_j—F__)—(Z-j_ —E__)]2‘ [5]1

Cronbach (1955) also identified subcomponents of differential accuracy,

differential elevation, and sterotypic accuracy -- DACOR, DECOR, and

SACOR respectively. The equation for these are:

DAr = r ' '‘ [6]

r,t

DACOR

where r indicates ratings, t indicates true scores and (°) indicates that

these values are deviation scores.

DEr [7]1. 1.

DECOR

28

where ?i_ is the mean rating for each ratee across all dimensions and

Ei_ is the mean true score for each ratee across all dimensions.

SAr = r;·j E~j [8]

SACOR

where F_j is the mean rating for each dimension across all ratees and

?_j is the mean true score for each dimension across all ratees.

Cronbach (1955) and later Becker and Cardy (1986) have pointed out

that the above subcomponents may be important aspects of ratings, which

should be examined. Cronbach (1955) and Becker and Cardy (1986) also

have contended that one‘s choice of an accuracy measure ought to be guided

by the purpose of the research.

In the area of performance appraisal, research has provided some

evidence that individual difference variables such as cognitive

complexity may affect raters° perceptions. For example, Leventhal (1957)

determined that cognitive complexity (CC) influences one°s perceptions.

Campbell (1960) revealed that people scoring low on CC were more likely

to categorize ratees as either good or bad than were their high-CC

counterparts. Some investigators of performance appraisal have suggested

that such cognitive individual-difference variables may influence rating

behavior (Bieri et al., 1966; Feldman, 1981; Schneier, 1977). Feldman

(1981) adds that "since cognitive complexity is based on the number of

categories and relationships among the categories used by an individual,

it is not surprising that those with a more differentiated category system

would be able to make the relevant judgments with less error" (p. 141).

29

This, of course, assumes that highly differentiated raters do not, gg

ayegage, attend to irrelevant dimensions.

Schneier (1977) examined the effects of REP—cognitive complexity

(high versus low) and rating format (Behavioral Expectation Scales versus

graphic rating scales) on response-set errors in a rating task. Results

indicated that cognitively complex raters gave lower average ratings

(i.e., were less lenient) when using the BES than when using the alternate

format. Conversely, cognitively simple raters gave lower average ratings

when using the alternate format than when using the BES format.

Cognitively complex raters had less halo than did cognitively simple

raters for both the BES and alternate formats. Regarding restriction of

range, cognitively complex raters had less restriction of range than

cognitively simple raters for the BES format. For the alternate format,

restriction of range results were not significant. Schneier°s efforts

were directed at supporting his theory of cognitive compatibility. This

theory suggests that complex raters do better with complex rating formats

such as BES whereas cognitively simple raters do better with simple

formats such as graphic rating scales. In addition, his findings suggest

enhanced accuracy for cognitively complex raters.

Contrary to Schneier's findings, Lahey and Saal (1981) assert that

the combined effects of rater complexity and rating formats do not predict

rater accuracy. These authors examined cognitive complexity, using REP,

factor analytic data from REP, and a sorting task. In each of its

operational definitions, cognitive complexity failed to significantly

predict rater accuracy and failed to interact with rating format as

Schneier°s cognitive compatibility model would suggest.

30

Other evidence poses difficulties for the validity of CC as

previously operationalized. For example, Weiss and Adler (1981) have

questioned the validity of cognitive complexity as an individual

difference variable in rating tasks. They found that subjects differing

in CC did not necessarily differ in their leadership prototype ratings.

Bernardin and Cardy (1981) examined the effect of REP-cognitive

complexity on rating accuracy. No significant effects were revealed by

that study. One other study, Sauser and Pond (1981), involved a subgroup

analysis (high versus low cognitive complexity subjects) to determine

whether CC moderated the "effectiveness with which ratees are evaluated"

(p. 571). The results suggest that REP-CC did not moderate rating

effectiveness.

As indicated earlier, Bernardin et al. (1982) also failed to reveal

effects for REP-CC. In a series of studies, Bernardin et al. concluded

that (1) CC was unrelated to rating accuracy, (2) CC was unrelated to halo

error and rating formats when police sergeants rated patrol officers, and

(3) the construct validity of CC was not supported in a study of student

ratings of managerial performance and instructor effectiveness. These

results tend to confirm Sechrest and Jackson's (1961) finding that the

REP is not significantly correlated with differential accuracy in

ratings. Thus, although a logical case may be made for the existence of

a cognitive structuring variable (such as CC-dimensionality) in rating

tasks, experimental evidence supporting the viability of this construct

is ambiguous at best. For every study purporting to demonstrate the

effects of CC, there are studies arguing the opposite. Unclear at this

point is whether or not the difficulties encountered by investigators

31

of cognitive complexity are the result of: (a) the lack of evidence for

the validity of the construct itself, or (b) the operationalizations of

the construct. Accordingly, additional effort at resolving the

operational issues seems warranted.

The present study makes additional contributions. Although MDS has

been employed in previous studies of the dimensions underlying jobs,

several differences between those studies and the present one are notable.

When Burton (1972) used MDS to reveal the perceptual dimensions underlying

job titles, he did not constrain raters to base their ratings upon the

work activities for the job titles. Although Stone and Gueutal (1985)

prescribed that the ratings concern work activities, these authors

examined group data; they did not assess individual differences

MDS-solutions. Dubin, Porter, Stone, and Champoux (1974) also examined

the dimensions underlying job titles, but were concerned with dimensions

more as a function of organizational role than as a function of cognitive

individual differences variables such as cognitive complexity

(dimensionality). Peterson and Scott (1983) considered the convergent

validity of MDS dimensionality and other measures of dimensionality

within the context of their effort to arrive at dimensiona1ity°s

fundamental measurement. However, they concerned themselves neither with

job activities nor with an external criterion such as rating accuracy.

In contrast, this study presents a methodology for examining

individual differences in reoperationalized CC within a performance

appraisal context. Moreover, the present study requires the raters to

dimensionalize job activities. The convergent and predictive validities

of MDS-dimensionality also are considered. Finally, the present study,

32

with its large sample size and methodology parallel to Stone and Gueutal

(1985}, provides an excellent vehicle for assessing the generalizability

of those authors° three-dimensional model of job characteristics.

SQMMARY AND HYPOTHESES

In sum, it has been suggested that efforts to investigate cognitive

complexity have been constrained by the operationalization of the

construct. Various methods of operationalizing CC (REP, semantic

differential scales, etc.) have been discussed. The approach suggested

by Scott et al. (1979) —- that multidimensional scaling be used-- appears

to be a fruitful method for freeing CC from its operational constraints.

Indeed, it has been argued that MDS dimensionality is closer

conceptually to notions of dimensionality than is REP-cognitive

complexity. Consistent then with the recommendations by Bieri et al.

(1966) and Scott et al. (1979), the present study employs the MDS

operational definition of cognitive complexity (dimensionality) in order

to identify individual dimensionality. In addition, the apparent domain

specificity of cognitive dimensionality appears to require the use of jobs

as a stimulus set.

Accordingly, a methodology detailing the selection of such a

stimulus set is outlined in the following pages. As indicated above, the

present paper offers several extensions of the literature on cognitive

complexity. This paper suggests that job duties are a focus in a

job-related activity and that individual differences in MDS

dimensionality are at issue. The paper also employs an external criterion

33

and assesses both convergent validity and differential correlation (r

REP,DA·vs. r MDS,DA). The following hypotheses are examined:

Performance Ratings. Although past efforts to operationalize

cognitive complexity (dimensionality) have yielded disappointing results,

the literature suggests that an MDS operational definition may be

predictive in performance appraisal tasks. This leads to Hypothesis 1.

Hypothesis l states that MDS dimensionality will besignificantly and negatively related to rater differentialinaccuracy (noise). Thus, raters who have available manydimensions with which to perceive their worlds will be lessinaccurate (will produce ratings with less non-systematicnoise) than will raters who have few dimensions available.

But a test of Hypothesis 1 leads only to the conclusion that

dimensionality and accuracy are related. Although this issue is

important, an issue of greater importance is whether or not MDS

dimensionality better predicts rater accuracy/inaccuracy than does the

REP test. Accordingly, an additional hypothesis must be tested.

Hypothesis 2 states that MDS dimensionality will predict raterinaccuracy in performance ratings better than will theREP test. In other words, MDS dimensionality will bemore highly related to rater inaccuracy than will REPcomplexity.

Another matter of importance is the issue of convergent validity (Campbell

& Fiske, 1959). Any effort to tap a construct ought to be correlated with

other measures of that same construct. But, as explained earlier, the

REP measure of CC is not isomorphic to the concept being measured. For

example, although Scott et al. (1979) have found moderate

intercorrelations between measures of dimensionality, Goldstein and

Blackman (1978) have argued that -- unless derived from a common

instrument format -- measures of dimensionality are not intercorrelated.

34

Despite this apparent controversy, one would expect that the two

operational definitions of CC would be related but not isomorphic.

Indeed, evidence for convergent validity is expected. But because REP

is a less direct measure of CC than is MDS, convergence is expected to

be moderate. Hypothesis 3 follows from this point.

Hypothesis 3 states that MDS dimensionality and REP-CC will besignificantly related, but not isomorphic.

Frequency Ratings. In addition to these hypotheses, some

additional questions arise. For example, is dimensionality related to

the accuracy of frequency judgments? As indicated earlier, one criticism

of the Bernardin et al. (1982) behavioral observation task was that it

did not require complex judgments and that therefore MDS dimensionality

and/or the REP may not be related to such ratings. However, others

(Murphy et al., 1982) present a differing view. These authors have noted

that, even though behavioral observation and performance evaluation are

not totally independent of each other, they do require different

processes. Behavioral observation involves such judgments as the

frequency of a behavior°s occurrence. Performance ratings, on the other

hand, require evaluative judgment concerning the quality of performance.

Indeed, Murphy et al. suggest that accurate behavioral observation is a

requirement of accurate performance rating. It is therefore of interest

to examine the question of whether or not the present data reveal any

relationship between dimensionality and frequency ratings.

l) Does MDS dimensionality predict frequency ratings?

2) lf MDS dimensionality does predict frequency ratings,is it a better predictor than the REP?

35

Joh Qhgracteristics. In light of the continuing controversy

concerning the Hackman and Oldham (1975; 1976) Job Characteristics Model

and the more recent Stone and Gueutal (1985) three-dimensional model of

job characteristics, the present study will also examine the issue of the

replicability of Stone and Gueutal°s (1985) findings. The present data

will permit an examination of the overall (group) MDS solution for its

agreement with Stone and Gueutal°s three-dimensional solution.

Hypothesis 4 states that there will be congruence between thecaptured dimensions of Stone and Gueutal (1985) and the retrieveddimensions of the present study.

The remainder of this paper outlines the methodology and results

of the study. A discussion of its implications and limitations is then

presented. Finally, possible directions for future research are

discussed.

36

MEIQQD

OYERYIEK

A pilot study was conducted to develop materials for the main data

collection phase. The purpose of this preliminary investigation was to

assure adequate familiarity of subjects with the stimulus materials that

were used later. Additional pre-testing assured that subjects understood

the directions for the measures.

In the main study individuals° cognitive complexity was to be

estimated, using the number of independent dimensions along which

subjects perceived stimuli to vary. This estimation involved tapping the

underlying dimensionality of subjects° perceptions of job titles. Then,

dimensionality was to be correlated with rating accuracy to determine the

predictive validity of cognitive dimensionality. Also in the main data

collection phase, subjects completed a version of the REP, modified to

include job titles. This REP was included so that a determination could

be made whether or not dimensionality via MDS better predicted rating

accuracy than did the REP. In addition, the hypothesized convergent

validity between a traditional measure of cognitive complexity

(dimensionality) and MDS dimensionality could be established. Because

the job-titles REP was used (i.e., the content domain of the two

predictors was held constant), significant differences in predictiveness

between the REP (jobs) and dimensionality would suggest that the procedure

rather than the content of the measure is important.

37

The dependent measure was differential accuracy (DA) in performance

appraisal ratings. DA was determined as follows: Subjects were asked

to rate the teaching effectiveness of four instructors presented on a

videotape. The videotape consisted of four vignettes employed by Murphy

et al. (1982). (The videotaped vignettes are described more fully in the

procedures section.) Differential accuracy was assessed according to a

procedure developed by Cronbach (1955) and later employed by Murphy et

al. (1982). Differential accuracy scores for each subject were used in

subsequent analyses.

Finally, correlations between dimensionality and the REP, between

dimensionality and DA, and between the REP and DA were standardized,

using Fisher°s r to Z transformation. A determination whether or not

dimensionality was more predictive than the REP was then made.

PILOT STUD!

Pilot Study Sample

The pilot study sample consisted of 39 (20 male and 19 female)

undergraduates in psychology at a large southeastern university. A

laboratory sample was employed since findings by Stone and Ruddy (1987)

indicated that the dimensionality which underlies college student ratings

is similar to the dimensionality of subjects drawn from neighboring

communities. Extra course credit was offered to these participants.

Pilot Study Measures

The materials for the pilot study consisted of: (1) a set of 20

job titles (to be described below); (2) the job titles familiarity

38

questionnaire; and (3) biographical questionnaires which ask respondents

for demographic information concerning themselves.

Based upon prior research (Schroder et al., 1967; Scott et al.,

1975; Sechrest & Jackson, 1961; Streufert & Streufert, 1978; Streufert,

1982), cognitive dimensionality is best examined within a particular

domain. Job titles and/or performance dimensions would appear to offer

job-relevant content domains. However, the use of performance dimensions

would impose dimensionality upon subjects. Thus, the use of job titles

seems preferable.

Earlier, it was noted that Stone and Gueutal (1985) found no

significant effects for trichotomized CC subgroups on job-dimensionality.

One matter of interest, then, is whether the operationalization of CC

accounted for Stone and Gueutal°s null results for CC. To facilitate an

extension of the Stone and Gueutal (1985) study, the present project will

retain job titles as a stimulus set.

Job titles (n=20) that were used for this study were those selected

by Stone and Gueutal (1985) in a procedure described as follows: Titles

from a larger stimulus set (n=52) were originally drawn from the

Dictionary of Occupational Titles (1965). To reduce their original set

of titles, these researchers first determined the subjects° (88

undergraduate management students°) familiarity with the target jobs.

The familiarity ratings ranged from 1 ("not at all familiar") to 5

("totally familiar"). Job titles eliciting a rating of greater than or

equal to 3.0 were retained in the stimulus set. A final reduction in the

number of stimulus job titles was accomplished by selecting those jobs

which were "(a) both heterogeneous with respect to such criteria as job

39

incumbent°s "collar color," employment sector, job status, and other

variables, and (b) generally representative of civilian jobs" (p. 381).

Retained job titles included cashier, janitor, fire fighter, telephone

operator, bank teller, mail carrier, taxi driver, file clerk, accountant,

short—order cook, actor, assembly—line foreman, machine operator,

elementary school teacher, flight attendant, waiter/waitress,

construction worker, tailor, lawyer, and retail store manager. The job

titles were randomly ordered on the pilot study questionnaire.

Directions for the pilot study required each subject to rate his/her

familiarity with the characteristics of the work activities of

incumbents. The purpose of this phase of the project was to assure that

subjects were sufficiently familiar with all of the items used in the

questionnaires. This measure is included in Appendix A. The ratings

scales ranged from l ("not at all familiar") to 5 ("totally familiar").

A demographic questionnaire (Appendix B) also was administered to

subjects. This biographical questionnaire permitted the examination of

the potential effects on familiarity ratings of work history and other

demographic variables.

Pilot Study Procedure

The research was conducted in accordance with the principles for

the treatment of human subjects prescribed by the American Psychological

Association. Informed consent forms were completed by all participants.

Subjects were reminded that their participation was voluntary and that

they were free to withdraw from the study at any time. Names of

participants wishing further information about the project were recorded.

40

Subjects completed a biographical questionnaire and the familiarity

rating questionnaire. Subjects for the pilot study were debriefed and

thanked for their participation.

Job title familiarity ratings were analyzed to estimate whether or

not the stimulus words for the similarity measure would be sufficiently

familiar to subjects. As in Stone and Gueutal (1985), all job titles met

the criteria of a 3.0 mean familiarity rating and were retained in the

stimulus set. descriptive statistics for this measure are contained in

Appendix A.

MAI§ DATA COLLECTIO§ PHASE

Main Data Collection Sample

Three-hundred-and-five undergraduates who were enrolled in

introductory psychology completed the main data collection phase.

Ninety-five percent of the subjects have held paying jobs. Fifty—three

different job titles were represented by subjects' occupations. Subjects

were on average 18.893 years of age (range=17 to 31). Most participants

had worked at paying jobs (M=2.164 years, range= 0 to 14 years). Of the

original subject pool (n=344), 25 were dropped from the study because they

did not return for the second session. Thirteen were dropped because they

failed to complete one of the hypothesized predictor measures. One

subject was dropped from the study because that subject°s data prompted

a fortran warning during multidimensional scaling. Extra course credit

was provided to participants.

41

Main Data Collection Phase Measures

—The predictor measures for this phase of the study included the Job

Similarity Measure and the Modified Job REP. These appear in Appendices

C and D and are described more fully below. A biographical questionnaire

(Appendix E) also was included to permit the assessment of effects of

demographic variables such as work history, sex, etc. The criterion

measures were performance measures identical to those used by Murphy et

al. (1982). These questionnaires appear in Appendix F and are also

described more fully below.

The Job Similarity Measure was identical to that employed by Stone

and Gueutal (1985). Instructions for the questionnaire directed subjects

to rate the similarity of all possible pairs of listed job titles.

Specifically, when making similarity ratings, subjects were asked to

consider the job duties for each job title. Subjects were not given a

dimension on which to rate the comparability of each pair of words.

Instead, they imposed their own underlying dimensions. Each pair of words

was accompanied by a 6-point similarity scale. Anchors ranged from 1

("not at all similar") to 6 ("almost completely similar"). Word pairs

were randomly ordered within the similarity questionnaire. Although the

200 pairs that made up the list included only 190 unique combinations,

all 200 word-pairs were used in each set. The inclusion of duplicate

pairs allowed for the assessment of the reliability of the measures. This

strategy is identical to that employed by Stone and Gueutal (1985).

To determine the relative contribution of the dimensionality

approach, a REP-type guestionnaige was developed for use with job titles.

For this measure, subjects generated constructs that underlie their

42

perceptions of jobs. They were presented with lists of triads drawn

randomly from the list of twenty job titles in the pilot study (used for

the similarity measure). Subjects decided what made any two of the words

in a particular triad alike and yet different from the third word.

Subjects indicated this similarity by selecting a descriptive word.

Subjects also indicated the polar opposite of each descriptive word. For

example, one subject may have perceived the jobs, telephone operator/bank

teller/construction worker, using the pair of words "automated" versus

"manual". Such a bipolar pair could then be used in the second part of

the grid task, which involved assigning a numerical value to each job

title for each pair of bipolar opposites. Thus, for example, the

above-mentioned hypothetical subject may have rated the job, CASHIER, on

a scale of -3 to +3 for a total of ten bipolar opposites that were unique

to each subject.

Grids were scored according to the procedure outlined by Bieri

(1966) and described below. The rating in each row was compared to the

rating directly below it (for the same job) in the other rows on the

matrix. A score of one was given each time there was agreement. This

matching procedure was carried out for all unique row comparisons (900

comparisons in a 20 x 10 matrix). A high REP score indicates low

complexity. In other words, a high score indicates that the subject used

the same numerical rating frequently. Such a rater would employ construct

dimensions in an identical manner.

Regarding the performance measures (Appendix F), the first set of

performance-related questions asked subjects to rate the frequency with

which each of twelve behaviors occurred on videotaped performances of

43

instructors. (For example, "Lecturer spoke fast," "Lecturer gave clear

answers to questions," "Lecturer spoke in a monotone for a sustained

period.") Responses were anchored on a seven-point Likert-type scale

(l="never," 2="almost never," 3="a few times," 4="about half the time,"

5="often," 6="most of the time," 7="all of the time"). The second set

of questions asked that subjects rate the lecturer whom they had just

observed for such dimensions as thoroughness of preparation, grasp of

material, organization and clarity, speaking ability, overall rating,

etc. Responses to these questions were anchored on a five-point Likert

scale (l="very bad," 2="poor," 3="average," 4="good," 5="very good").

Main Data Collection Phase Progedures

This phase consisted of two sessions, the first lasting about an

hour to an hour and one-half and the second lasting about 30-40 minutes.

At the first session, each subject completed an informed consent form,

the Job Similarity Measure, the Job REP questionnaire, and the

biographical questionnaire. All subjects answered the questionnaires in

the same order because the presentation of the REP Grid task first could

have primed the subjects for the similarity task. To minimize possible

fatigue effects, the experimenter told subjects that they could take a

quiet break from the task if they felt their attention diminishing.

Approximately two weeks later, subjects returned to view four

videotaped lectures. The lectures were selected from the eight vignettes

developed by Murphy et al. (1982). Scripts for the videotapes (one

concerning crowding and stress and the other self-fulfilling prophecy)

were developed by holding constant the content across the two same-topic

44

lectures. However, the thoroughness and organization of the lectures and

the clarity of answers to questions varied both between and within the

same-content videotapes. For example, one script concerning crowding and

stress contained a good, well·organized lecture, but poor answers to

questions, while the other contained a poorly organized lecture but good

answers (Murphy et al., l982). Lecturers were drama students from a

Houston-area university. Low, low-moderate, moderate, and moderate-high

levels of performance were represented. True scores were established for

the videotapes by Murphy et al. (1982), modifying a procedure developed

by Borman (1977) and described more fully by Murphy et al. In essence,

Murphy asked "expert raters" to generate true scores by having them rate

the videotaped performances. These true scores (mean performance rating

across all the experts) facilitated the calculation of accuracy scores

that were used in the analyses.

Rating instructions informed subjects that they were participating

in a teacher evaluation study and that their ratings would be used for

research and feedback purposes only. Subjects were told that their

ratings would not affect the employment status and salary of the ratees

and that individual responses would be confidential. Subjects were told

also that shortly they would view four instructors on the videotape, each

one at a time. After subjects viewed each instructor, the experimenter

would stop the videotape and the subjects would rate the person they had

just observed. This aspect of the procedure was intended to minimize

potential memory effects. Subjects were asked to read the cover page of

the rating booklet and to familiarize themselves with the rating

questionnaire.

45

Following the rating task, subjects were fully debriefed.

Instructions for the rating task had suggested to subjects that ratings

would be for research and feedback purposes only and would not affect

ratees° employment status or salaries. At the debriefing, subjects were

told that, although the instructions suggested the ratees were teachers,

the persons they had observed were actually actors. Subjects were asked

not to discuss the nature of the experiment with anyone. Names of those

subjects wishing further information about the research were recorded.

Subjects completed extra credit point sheets and were thanked for their

participation.

46

RESULTS

RELIABILITY ESTIMATES

Scorer reliability for the REP Grid measure was assessed by randomly

selecting ten grids for rescoring by an independent rater. The interrater

reliability estimate was .96.

A reliability estimate for judgments indexed by the Job Similarity

measure was derived by correlating ratings on the ten pairs of duplicate

items. These correlations were calculated separately for each subject.

Individual subjects' correlations were transformed by Fisher's r to Z

transformation. The average Z transformed value was .829 (;.d.=.52).

This index of subject consistency compares favorably with prior

consistency estimates for this measure (average Z=.76) by Stone and

Gueutal (1985).

DERlyI§G DIME§SIO§ALITY

In order to examine dimensionality°s predictive validity, it was

necessary to derive subjects° perceived dimensions of job titles.

Following is a description of the results of this preliminary analysis.

Dimensionality was assessed by submitting pair·wise similarity

ratings for the 190 unique similarity items to non-metric,

individual•differences multidimensional scaling (INDSCAL algorithm).

Program option specifications were as follows: level = ordinal, similar,

shape = symmetrical, condition = matrix, model = INDSCAL, maxdim = 6,

mindim = 2, iterations=10, converge = .001, untie. It should be noted

47

that maxdim and mindim are set at the maximum and minimum (respectively)

allowed by the INDSCAL program. In addition, the iterations required to

attain convergence at .001 were always less than ten. First, the overall

(group) MDS solution will be reported.

As indicated earlier, INDSCAL employs a least-squares criterion for

fitting the data to the model. In other words, the iterative process

attempts to keep S-STRESS (overall "badness of fit") at a minimum. The

program iterates until some specified level of S-STRESS reduction is

achieved. Program output indicates R2 as well as Kruskal°s STRESS1, which

is another "badness of fit" measure averaged across subjects° matrices.

According to Davison (1983), the major difference between S-STRESS and

STRESS1 is that S-STRESS uses squared distances and squared disparities

while STRESSl does not square the distances and disparities. The results

indicated that a two-dimensional model accounts for .64 of the variance

(STRESS1=.245), a three—dimensional model for .67 of the variance

(STRESS1=.184), a four-dimensional model for .70 of the variance

(STRESSl=.137), a five-dimensional model for .73 of the variance

(STRESS1=.113), and a six-dimensional model for .74 (STRESS1=.096).

These data are presented in tabular format in Table 1 and in graphic

format in Figure 1. The data reflect values (variance accounted for)

which compare favorably with those Young and Lewyckyj (1979) termed4

"moderately good" (p. 103), but not excellent. INDSCAL also identifies

the importance of each dimension averaged across subjects. These data

as well as the stimulus coordinates for the four-dimensional solution are

contained in Table 2.

48

One convention (Kruskal & Wish, 1978) which guides the selection

of the appropriate dimensionality per number of stimuli is that I (the

number of stimulus objects) should be greater than AR (where R is the

number of dimensions). In other words, a six—dimensional solution may

require approximately 24 stimulus objects, while a four-dimensional

solution may require approximately 16 stimulus objects. The present data

(with 20 stimulus objects) satisfies this criterion for the

four—dimensiona1 solution.

More importantly, a determination of the dimensionality reflected

by the data depends also on the satisfaction of a number of other

criteria. The first is whether or not the addition of a particular

dimension contributes to a substantial increment in R2. Note that r for

the group solution is calculated by the following equation:

2 1/2 [9]K?·(1,j,s)*iö1js · 6. . .>“Z(1,j,s)<61js · Ö. . JJ

where 6* is the grand mean of the actual scalar products and 6* is the

grand mean of the estimated scalar products, ij refers to the stimulus

pair, and s refers to the subject (Davison, 1983). In other words, öij

refers to the proximity (distance) measurement of stimulus pair (i,j).

A proximity measure is an index of the degree of similarity of pairs of

objects. But INDSCAL provides more than a group stimulus space. As

indicated in the introduction of this dissertation, INDSCAL also provides

a direct means of assessing the importance of various recovered dimensions

for individual subjects. These importance weights are computed by the

49

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51

TABLE 2

Stimulus Coordinates: Overall Solution (INDSCAL)

Dim. 1 Dim. 2 Dim. 3 Dim. 4

Cashier -0.7107 -1.4787 -0.9338 0.1874Retail Mgr. 0.9179 -1.2090 -0.6739 -0.9854Bank Teller -0.3921 -1.1328 -1.4195 0.5982Waiter -0.4785 -1.0359 1.0723 0.9322Cook -1.4438 -0.9054 0.7681 -0.5960Tailor 0.2856 -0.9645 0.8882 -1.5965Actor 1.4734 -0.2234 1.6309 0.9317File Clerk 0.2103 0.5466 -1.5393 0.4589Lawyer 2.0704 0.0571 -0.5335 0.7375Teacher 1.6552 0.3144 0.7455 -0.1756Tel. Op. -0.4855 0.5923 -1.0409 0.9007Taxi Driv. -1.6196 0.3028 0.3778 0.7758Firefighter 0.3490 2.2694 0.5504 0.0331Const. Work -0.4173 1.4743 0.2744 -1.5891Mail Carrier -0.6023 1.3408 -0.3423 1.3453Machine Op. -1.2478 0.5309 -0.7065 -1.2603Accountant 0.9779 -0.9189 -1.4132 0.3367Assembly Frmn. 0.4091 0.2979 -0.4236 -1.9071Flight Attend. -0.1503 -0.6380 1.0704 1.3692Janitor -0.8009 0.7802 1.6484 -0.4967

Variance 0.2051 0.1812 0.1742 0.1431

52

following equation:

{[2 · ·«6*- -6* Fm; ·(3*--€* 2 1/2 *10]

(1,j)~1js ..s (i,j) 1JS ..s)]}

where 6* and 6* are the mean actual and estimated scalar products for the

particular subject, s, (Davison, 1983). lt should be noted that r, the

correlation between each subject°s actual and estimated scalar products,

indicates the strength of the relationship between the dimension and the

subject°s matrix.

For the group space, R2 is plotted against the number of dimensions.

An "elbow" in the ascent of the plotted line indicates the dimensionality

beyond which additional dimensions are not of much importance in

interpreting the group space. The plot for the present study is presented

in Figure 1. This determination is, by necessity, a matter of judgment

since there are no tables for INDSCAL (Davison, 1983; Kruskal & Wish,

1978; Schiffman, Reynolds, & Young, 1981). Although Monte Carlo tables

have been developed for statistical hypothesis testing of more

traditional approaches to MDS, there are no such tables for INDSCAL. Nor

is there a convention such as in factor analysis. An additional

limitation is that techniques such as policy capturing via stepwise

regression requires an external criterion. Thus, while policy capturing

may provide additional information about the relative importance of

particular dimensions in the prediction of rater accuracy, the technique

is not appropriate for the "internal" statistical hypothesis testing of

similarity dimensions (Davison, 1983). The plot of Rz against the number

53

of dimensions appears to favor a four dimensional solution. However, it

should be noted that at either three or four dimensions R2 and Stress are

somewhat disappointing.

In addition to the criterion of proportion of variance accounted

for, other criteria are important. For example, as Shepard (1972) has

maintained, parsimony, stability, and visualizability are important.

Generally, a trade·off arises because when dimensions are added fit

improves at the expense of parsimony. The parsimony criterion would

suggest that, since little additional variance is accounted for by the

addition of the fifth and sixth dimensions, the group data is best

described by a four-dimensional solution. Indeed, Shepard contends that

except when using INDSCAL (as the present study does) dimensionality

should rarely exceed two or three dimensions. The stimulus coordinates

and variance accounted for by each factor are indicated in Table 2.

Because they maximize fit, data reduction techniques such as MDS

and factor analysis are susceptible to shrinkage. Accordingly, an

assessment of the solution°s stability, the second of Shepard°s (1972)

criteria, is important. This stability may be assessed by randomly

dividing the data into four subsets and comparing the results of the four

analyses. Therefore, four subsets, each containing one—fourth of the

subjects° matrices, were extracted from the data set. These subsets were

then analyzed separately by INDSCAL, using the same program options that

were used for the full data set. Stimulus coordinates for these data are

reported in Tables 3-6. The results appear to suggest that the original

solution is stable. A four-dimensional solution again appears to describe

the data (R2 =.717, STRESSl=.134; R2 =.653, STRESSl=.148; R2 =.737,

54

STRESSl=.132; and R2 =.720, STRESS1=.l35 for the four subsets

respectively). These results are presented in Table 7. Figures 2 and 3

illustrate R2 plotted against dimensionality for this subset analysis.

However, when coefficients of congruence are examined for the two subsets,

stability seems less certain. The Job Title Stimulus Coordinates were

used to determine coefficients of congruence between subsets A, B, C, and

D for each dimension. These data are presented in Tables 8-12.

Coefficients of congruence (Burt, 19&8; Wrigley 8 Neuhaus, 1955)

work directly from the scores rather than from the deviations (Cattell,

1978). Cattell calls the procedure "joint mean and deviation factoring,"

which retains information concerning the "variation about the means and

the absolute means themselves" (p.468). ‘The equation for this statistic

is found in Gorsuch (1983):

C12 = XPV1PV2.._ ._. [ll]

@*31 @*31

"where C12 is the coefficient of congruence between factor one and factor

two, Pvl are the factor loadings for the first factor and Pv2 are the

factor loadings for the second factor" (p. 285). The formula may also

be applied to stimulus coordinates in MDS.

Coefficients of congruence index the extent to which a factor or

dimension is stable (i.e., is reproduced) by another. Consider the

dimensional solution illustrated in Table 8.

55

There would be evidence of stability for dimension one if the

coefficients in column 1A were high (in the high .90 range) for one of

the dimensions in each of subsets B, C, and D, but low for the other

dimension within each subset. In Table 8, for example, column 1A should

indicate numbers in the high .90 range rather than -.87, -.83, and .94.

Conversely, coefficients of congruence for 1A with 2B, 2C and 1D should

all be much lower. In the present study, changing the dimensionality did

not substantially improve stability.

The third of Shepard°s criteria is visualizability

(interpretability). A visual inspection of the three and four dimensional

solutions is not particularly enlightening. And, as indicated earlier,

since external criteria were unavailable, statistical relationships

between the captured dimensions and particular external constructs could

not be empirically demonstrated.

In light of the aforementioned instability of the

individual-differences scaling solution, the data were unsuitable for the

tests of Hypotheses 1, 2, and 3. Therefore, the analyses did not proceed

to the tests of these hypotheses. Instead, the data were submitted to

group multidimensional scaling (ALSCAL or Alternating Least Squares

Scaling). Program options were level=ordinal, similar,

shape=symmetrical, condition=matrix, model=Euclid,maxdim=6, mindim=1,

iterations=10, converge=.OO1, untie.

Results revealed that a one—dimensional solution accounted for .579

of the variance (STRESSl=.373), a two-dimensional solution for .607 of

the variance (STRESSl=.249), a three-dimensional solution

56

TABLE 3

Stimulus Coordinates: Subset A (INDSCAL)

Dim. 1 Dim. 2 Dim. 3 Dim. 4

Cashier -0.6245 1.2302 -1.2014 0.5032Retail Mgr. -0.2179 -0.6645 -1.8402 -0.0607Bank Teller -1.1455 0.7552 -0.8372 1.1625Waiter -0.8364 0.9189 0.2477 -1.1448Cook 0.4267 1.5233 -0.4688 -1.0516Tailor 0.6493 -0.1298 -1.4118 -1.3439Actor -0.9841 -1.2069 0.5639 -1.6701File Clerk -0.2365 -0.4549 -0.0526 1.7253Lawyer -1.3280 -1.9705 -0.0520 0.2995Teacher -0.1718 -1.6497 0.2263 -0.7973Tel. Op. -0.0471 0.3123 0.4319 1.3956Taxi Driv. 0.1077 1.6425 1.0568 0.1011Firefighter 0.8102 -1.0343 1.8279 0.2197Const. Work. 2.1461 -0.3064 0.3794 -0.2798Mail Carrier 0.0147 0.3513 1.6669 1.1372Machine Op. 1.6059 0.8934 -0.2243 0.7314Accountant -1.3479 -0.4436 -1.1454 0.9814Assembly Frmn. 1.2847 -0.7055 -1.0450 0.3441Flight Attend. -1.2365 0.5373 0.8046 -0.8555Janitor 1.1307 0.4017 1.0731 -1.3971

57

TABLE 4

Stimulus Coordinates: Subset B (INDSCAL)

Dim. 1 Dim. 2 Dim. 3 Dim. 4

Cashier 0.3401 1.3399 0.6699 1.2448Retail Mgr. -1.1773 0.9337 -0.1445 1.0986Bank Teller 0.3054 1.6295 1.0394 0.2887Waiter 0.4152 -0.7986 1.0605 1.2019Cook 1.4921 -0.2337 -0.3312 1.3557Tailor -0.5230 -0.3084 -1.1178 1.6715Actor -1.3451 -1.7526 0.8217 0.0876File Clerk -0.0671 1.0849 0.7011 -1.2201Lawyer -1.9896 0.2041 0.4678 -0.9782Teacher -1.5084 -1.0689 -0.1278 -0.2319Tel. Op. 0.7941 0.5806 0.8990 -1.0068Taxi Driv. 1.5627 -0.7359 0.4760 -0.0816Firefighter 0.0066 -0.9835 -0.8053 -1.9818Const. Work. 0.4275 -0.1225 -1.9900 -0.7668Mail Carrier 1.0529 -0.1683 0.6934 -1.5154Machine Op. 1.0470 0.9659 -1.3613 -0.1931Accountant -1.2240 1.3922 0.5493 -0.0878Assembly Frmn. -0.4560 0.5823 -1.7798 0.2475Flight Attend. 0.1403 -1.0157 1.3814 0.7956Janitor 0.7065 -1.5251 -1.1007 0.0716

58

TABLE 5

Stimulus Coordinates: Subset C (INDSCAL)

Dim. 1 Dim. 2 Dim. 3 Dim. 4

Cashier 0.5131 1.6877 0.5497 0.1336Retail Mgr. 0.9807 0.5581 -0.6234 -1.5093Bank Teller 1.0851 1.3815 0.6202 0.2593Waiter -1.0951 1.2502 -0.4441 0.6526Cook -1.2412 1.1559 0.8089 -0.5761Tailor -0.8180 0.6324 -0.4036 -1.8103Actor -0.5468 -0.2756 -2.3876 0.6111File Clerk 1.3246 -0.0064 0.9559 0.3631Lawyer 1.6105 -0.1857 -1.5142 0.2670Teacher 0.0957 -0.7601 -1.6498 -0.4945Tel.0p. 0.9010 -0.5724 0.5997 0.9874Taxi Driv. -0.8931 0.0998 0.6307 1.5057Firefighter -0.5093 -2.2716 -0.2778 0.4578Const. Work. -0.7921 -1.5376 0.8181 -1.1141Mail Carrier 0.1803 -0.7879 0.6939 1.8077Machine Op. -0.1493 -0.4390 1.6575 -0.7178Accountant 1.6743 0.8743 -0.2449 -0.2149Assembly Frmn. 0.4135 -0.9377 -0.0029 -1.7396Flight Attend. -0.9994 0.6321 -0.7493 1.1100Janitor -1.7344 -0.5520 0.9629 0.0216

59

TABLE 6

Stimulus Coordinates: Subset D (INDSCAL)

Dim. 1 Dim. 2 Dim. 3 Dim. 4

Cashier 0.5140 -1.4909 -0.5167 -0.8782Retail Mgr. -0.7631 -0.7341 0.3722 -1.6712Bank Teller 0.1284 -1.5394 -0.9508 -0.5289Waiter 0.7025 0.7147 -1.3615 -0.5609Cook 1.4970 0.3097 0.1858 -1.1682Tailor -0.9615 0.3475 1.5558 0.0818Actor -0.9964 1.8103 -1.1437 -0.3810File Clerk -0.7520 -1.1046 -0.0733 1.1492Lawyer -2.1488 0.0708 -0.7694 0.3149Teacher -1.2778 1.1441 0.0246 -0.8875Tel.0p. 0.2485 -1.0539 -0.2868 1.0704Taxi Driv. 1.6510 -0.0153 -0.4546 0.6725Firefighter -0.1030 1.3711

l0.6160 1.9339

Const. Work 0.5640 0.4568 1.9065 0.8115Mail Carrier 0.4062 -0.1243 -0.5380 1.9754Machine Op. 1.0295 -0.9062 1.4775 0.3462Accountant -1.2828 -1.2289 -0.7672 -0.3958Assembly Frmn. -0.1443 -0.2555 1.5654 -1.3306Flight Attend. 0.5003 0.7707 -1.4949 -0.3735Janitor 1.1883 1.4575 0.6533 -0.1801

60

— TABLE 7

Stress and R2 for Dimensionality by Subset

(INDSCAL)

Subset Dimension Stress R2

A 2 .236 .6633 .178 .6854 .134 .7175 .111 .7336 .094 .754

B 2 .265 .5823 .199 .6094 .148 .6535 .121 .6816 .104 .695

C 2 .238 .6523 .175 .6974 .132 .7375 .107 .7606 .091 .778

D 2 .244 .6533 .178 .6804 .135 .7205 .109 .7536 .092 .761

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for .623 of the variance (STRESS1=.l91), a four-dimensional solution for

.679 of the variance (STRESS1=.140), a five-dimensional solution for .694

of the variance (STRESS1=.1l2), and a six-dimensional solution for .710

(STRESS=.100). These results are presented in tabular format in Table

13 and in graphic format in Figure 4. Stimulus coordinates for the

two-dimensional solution appear in Table 14. ALSCAL subset analyses are

reported in Tables 15-19 and Figures 5 and 6.

Regarding dimensionality, only the two-dimensional solution

indicates an "elbow" in the plots of stress along the number of

dimensions. (For ALSCAL, stress rather than Rz should be used to

determine dimensionality, according to Davidson (1983) and Young and

Lewyckyj (1979)). A two-dimensional solution seems reasonable since

authors such as Shepard (1972) recognize that, except for INDSCAL,

dimensionality should rarely exceed two or three dimensions. An

additional criterion requires that stress be at a minimum. This would

appear to pose a problem for the two-dimensional solution, however, since

that dimensionality permits stress (or "badness of fit") to be .249.

However parsimonious a solution, this is a disappointing result. The

stability of the solution looked somewhat more promising. When

coefficients of congruence were computed for four subsets of the data,

the two-dimensional solution did appear relatively stable. Relatively

high congruence was indicated within dimension, but across the subsets.

And relatively low congruence was indicated across dimensions. These data

are provided in Tables 20-25.

Regarding Shepards°s (1972) visualizability criterion, at first

glance, Dimension II looks as if it approximates Stone and Gueutal°s first

71

dimension, which they named complexity. However, closer examination

reveals a number of inconsistencies in the ordering of job titles for the

present Dimension II. Dimension I in the present data does not

approximate either of Stone and Gueutal°s remaining dimensions. This is

evident from both visual inspection and the coefficients of congruence

presented in Table 26. Stone and Gueutal's job title stimulus coordinates

are reported in Table 27. The present data suggest that Stone and

Gueutal's (1985) three—dimensional representation of job dimensions was

not supported.

SUMMARY OF RESULTS

As indicated in the previous section, Hypotheses 1, 2, and 3 were

untestable because of the unstable INDSCAL solution. Hypothesis 1 stated

that the number of dimensions subjects had available to them would predict

their differential accuracy in performance appraisal ratings. Hypothesis

2 stated that the number of dimensions subjects had available to them

would better predict rater accuracy than would the more traditional REP

questionnaire. Again, no conclusions regarding this hypothesis were

possible. Hypothesis 3 stated that MDS dimensionality and REP-CC would

be significantly related, but not isomorphic. The outcome of such a

comparison must likewise await further study.

Hypothesis 4 stated that the present data were expected to replicate

Stone and Gueutal°s three—dimensional job characteristics model. Several

lines of evidence suggest that this hypothesis was not supported. First,

a two-dimensional solution best describes the present data, while a

three-dimensional solution was reported by Stone and Gueutal (1985).

72

TABLE 13

Stress and R2 for Dimensionality One to Six (ALSCAL)

Dimensionality Stress R2

1 .373 .579

2 .249 .607

3 .191 .623

4 .140 . 679

5 . 117 .694

6 .100 .710

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TABLE 14

Stimulus Coordinates: Two-dimensional Solution

(ALSCAL)

Dim. 1 Dim. 2

Cashier 0.8545 -1.2078Retail Mgr. 1.3557 0.4914Bank Teller 1.1682 -1.0508Waiter -0.3517 -1.0063Cook -0.9727 -1.3249Tailor -0.4444 1.1695Actor 0.1057 1.8595File Clerk 1.0957 -0.5783Lawyer 1.5833 1.1592Teacher 0.6235 1.3674Tel.Op. 0.5063 -0.7793Taxi Driv. -0.8909 -0.9811Firefighter -1.3603 1.0969Const. Work. -1.4888 0.6293Mail Carrier -0.4379 -1.0334Machine Op. -1.0949 -0.3889Accountant 1.5645 -0.1018Assembly Frmn. 0.0023 0.9303Flight Attend. -0.2618 -0.1781Janitor -1.5560 -0.0730

75

TABLE 15

Stress and R2 for Dimensionality by Subset

(ALSCAL)

Subset Dimension Stress R2

A 1 .368 .5812 .246 .6173 .187 .6374 .137 .6885 .113 .7036 .097 .720

B 1 .399 .5222 .267 .5523 .205 .5724 .150 .6295 .124 .6476 .107 .659

C 1 .348 .6242 .246 .6283 .177 .6734 .135 .7125 .111 .7316 .095 .747

D 1 .361 .6002 .245 .6293 .190 .6344 .135 .7015 .112 .7216 .097 .730

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TABLE 16

Stimulus Coordinates: Subset A (ALSCAL)

Dim. 1 Dim. 2

Cashier 0.9478 -1.1827Retail Mgr. 1.2427 0.6264Bank Teller 1.3901 -0.8854Waiter -0.4524 -1.0814Cook -0.8873 -1.3549Tailor -0.0242 1.0777Actor -0.5416 1.7420File Clerk 1.2565 -0.3582Lawyer 1.3482 1.4781Teacher 0.2745 1.3738Tel. Operator 0.5358 -0.6782Taxi Driver -0.7729 -1.1945Firefighter -1.3551 0.9337Const. Work. -1.5702 0.6047Mail Carrier -0.0986 -1.0386Machine Op. -0.9790 -0.3357Accountant 1.6595 -0.0201Assembly Frmn. 0.0381 0.8487Flight Attend. -0.4154 -0.4628Janitor -1.5965 -0.0927

79

TABLE 17

Stimulus Coordinates: Subset B (ALSCAL)

Dim. 1 Dim. 2

Cashier 0.9342 -1.1721Retail Mgr. 1.3436 0.4701Bank Teller 1.1483 -1.0333Waiter -1.1472 -1.1447Cook -1.0594 -1.3325Tailor -0.6788 1.1148Actor -0.6082 1.5944File Clerk 1.0897 -0.5493Lawyer 1.4599 1.2352Teacher 0.5471 1.3682Tel Op. 0.3603 -0.9376Taxi Driv. -0.9418 -0.8911Firefighter -1.2718 1.2067Const. Worker -1.4831 0.6871Mail Carrier -0.5433 -0.9756Machine Op. -1.1630 -0.1986Accouhtant 1.4844 0.1344Assembly Frmn. -0.3212 1.0355Flight Attend. 0.1419 -0.6418Janitor -1.5080 0.0301

80

TABLE 18

Stimulus Coordinates: Subset C (ALSCAL)

Dim. 1 Dim. 2

Cashier 0.8192 -1.0569Retail Mgr. 1.4394 0.6034Bank Teller 1.1422 -0.9000Waiter -0.3472 -1.2096Cook -1.0301 -1.2794Tailor -0.8885 1.1302Actor 0.7093 1.5284File Clerk 0.8788 -0.7673Lawyer 1.7167 0.7825Teacher 0.7516 1.1606Tel. Op. 0.4602 -0.6738Taxi Driv. -0.9488 -0.8625Firefighter -1.3322 1.1954Const. Work -1.3879 0.8963Mail Carrier -0.7704 -0.8205Machine Op. -0.9323 0.3592Accountant 1.4556 -0.2119Assembly Frmn. 0.1096 1.2400Flight Attend. -0.2235 -0.8017Janitor -1.6216 -0.3125

81

TABLE 19

Stimulus Coordinates: Subset D (ALSCAL)

Dim. 1 Dim. 2

Cashier -0.5216 -1.3718Retail Mgr. 1.3940 -0.1073Bank Teller 0.8866 -1.2086Waiter -0.2785 0.6995Cook -1.2015 -0.9674Tailor -0.8179 0.9811Actor 0.5475 2.0159File Clerk 0.8876 -0.8693Lawyer 1.7449 0.9907Teacher 0.8709 1.3151Tel. Op. 0.4107 -0.8136Taxi Driv. -0.9286 -0.7285Firefighter -1.2913 1.2051Const. Work -1.5999 0.0144Mail Carrier -0.5938 -0.8867Machine Op. -0.9602 -0.1111Accountant 1.5658 -0.3347Assembly Frmn. 0.3332 0.2477Flight Attend. -0.0450 0.7697Janitor -1.4459 0.1596

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TABLE 26

Coefficients of Congruence for Stone and Gueutal°s

Final Solution and Six ALSCAL Solutions

Stone/ Stone/ Stone/Gueutal 1 Gueutal 2 Gueutal 3

S/G1 1.00000 0.73636 0.72182

S/G2 0.73636 1.00000 0.77666S/G3 0.72182 0.77666 1.00000

1ALSCAL1 -0.29827 0.10203 0.37681ZALSCAL1 0.26280 -0.13711 -0.40776ZALSCAL2 0.42502 -0.03385 0.214923ALSCAL1 0.24890 -0.16622 ‘ -0.415903ALSCAL2 0.42822 -0.00826 0.217273ALSCAL3 -0.05650 -0.33256 0.170754ALSCAL1 0.24266 -0.16610 -0.423134ALSCAL2 0.41535 -0.04075 0.165634ALSCAL3 -0.07128 -0.40897 0.130824ALSCAL4 -0.08087 0.01956 0.010275ALSCAL1 0.24603 -0.18473 -0.41649SALSCAL2 0.43042 -0.00185 0.21182SALSCAL3 -0.08136 -0.40317 0.14770SALSCAL4 0.07594 -0.01016 -0.01125SALSCAL5 -0.02561 0.06281 -0.064906ALSCALl 0.23349 -0.17851 -0.428376ALSCAL2 0.44024 -0.02192 0.202446ALSCAL3 0.07872 0.40372 -0.14730ÖALSCAL4 0.06092 -0.02050 -0.01655ÖALSCAL5 -0.06775 -0.00370 -0.035976ALSCAL6 -0.01630 0.07603 -0.06864

91

TABLE 27

Stimulus Coordinates: Stone & Gueutal (1985)

Dim. 1 Dim. 2 Dim. 3

Cashier 17.70 30.27 5.28Retail Mgr. 78.89 18.94 34.03Bank Teller 28.25 31.43 1.00Waiter 21.48 82.09 32.93Cook 19.93 61.43 44.09Tailor 53.33 33.42 59.32Actor 88.77 77.42 46.37File Clerk 31.10 22.16 16.80Lawyer 100.00 40.07 29.26Teacher 83.04 55.15 40.27Tel.Op. 19.52 43.88 26.07Taxi Driv. 22.22 70.85 50.21Firefighter 47.95 44.65

A81.09

Const. Work. 30.77 27.09 94.48Mail Carrier 20.70 54.55 41.50Machine Op. 16.17 14.38 73.34Accountant 70.74 9.51 8.41Assembly Frmn. 60.97 20.59 73.24Flight Attend. 36.95 85.38 38.36Janitor 26.94 52.54 79.07

92

Second, a visual inspection suggests that only on one dimension is there

even moderate convergence between any of the present dimensions and the

Stone and Gueutal dimensions. Third, coefficents of congruence computed

for Stone and Gueutal°s job-title stimulus coordinates revealed that

there was substantial overlap between Stone and Gueta1's first and second

dimensions, first and third dimensions, and second and third dimensions

(coefficients of congruence=.736, .722, and .776 respectively, p<.05).

This calls into question the independence of the Stone and Gueutal°s three

dimensions.

Although the study°s hypotheses were not supported, some additional

results are of interest. lnterestingly, although REP did not

significantly predict differential accuracy in performance ratings

(r=-0.027, g>.OS), it did predict differential accuracy in frequency

ratings (r=.148, p<.01). Furthermore, REP also significantly predicted

elevation in performance ratings (r=.143, p<.01). Descriptive statistics

appear in Table 28 and correlation tables are presented in Table 29.

Other results indicated that differential accuracy in performance

ratings (DAP) was significantly correlated with differential accuracy in

frequency ratings (DAF) (r=.20, p <.05). This overlap is not surprising

since the two rating processes were not expected to be totally independent

(Murphy et al., 1982). One unexpected result was that there were a number

of small but statistically significant intercorrelations among some of

the different accuracy components. For example, differential accuracy

in performance (DAP) ratings was significantly correlated with elevation

in performance (EP) ratings, and with stereotypic accuracy in performance

(SAP) ratings. Correlation coefficients are .18 and .17 respectively

93

(Q<.01). Differential accuracy in frequency (DAF) ratings was

significantly related to elevation in frequency (EF) ratings and

differential elevation in frequency (DEF) ratings. Correlations are -.31

and .21 respectively (Q<.0l). These results are despite the fact that

these components are presumed to be additive and orthogonal (Cronbach,

1955). DACOR, DECOR, and SACOR were calculated for frequency ratings.

Again, no significant relationships between these subcomponents and REP

were revealed.

The demographic variables were unrelated to either differential

accuracy in performance ratings or differential accuracy in frequency

ratings. However, some small but significant correlations were noted

between the demographic variables and other components of accuracy. In

particular, it should be noted that the number of years a person had

worked significantly predicted differential elevation in performance

ratings (r=.13, Q <.05). To examine this relationship further, a subset

analysis was performed on the years-worked data. The data were

trichotomized and submitted to a univariate analysis of variance (ANOVA)

procedure. The analysis indicated a significant effect for level of

experience, §(2,283) = 3.23, Q <.05). Means for these groups were .52,

.57 and .59 respectively. Results from this procedure appear in Table

30. Tukey°s studentized range test revealed that there were significant

differences in differential elevation when low and high experience groups

were compared (Q <.05).

Additionally, whether or not a person had been a manager was

slightly but significantly related to both elevation and differential

elevation in performance ratings (r=.13 and r=.12, respectively, Q<.05).

94

Although raters° gender had no significant bearing upon differential

accuracy in the present study, it was correlated with differential

elevation in frequency (DEF) ratings (r=.l3, Q < .05). It deserves noting

that although there are some statistically significant correlates among

the demographic variables, in every case the correlations are small and

are likely a function of high statistical power (large sample size).

Thus, the practical importance of these demographic correlates is

unclear. A discussion of the implications of these findings will follow.

95

Table 28

Descriptive Statistics:

Dimensions, REP, Accuracy Components

Variable n Mean SD Min. Max. Range

REP 305 221.413 56.730 129.000 478.000 349.000

Yrs Worked 286 2.164 1.948 0.000 14.000 14.000

Age 290 18.893 1.406 17.000 31.000 14.000

Diff. Accur.(Perf.) 305 0.487 0.101 0.245 0.809 0.564

Elevation(Perf.) 305 0.492 0.306 0.006 1.556 1.550

Diff. Elevation(Perf.) 305 0.556 0.197 0.094 1.357 1.263

StereotypicAccur. (Perf.) 305 0.308 0.114 0.086 0.972 0.886

Diff. Accur.Corr. (DAr) 305 0.160 0.233 -0.505 0.820 1.330

Diff. ElevationCorr. (DEr) 305 0.810 0.136 0.165 0.999 0.834

StereotypicAccur. Corr. 305 0.489 0.295 -0.619 0.929 1.547

(SAr)

Total Accuracy 305 1.040 0.517 0.244 3.606 3.362

(Perf)

96

Table 28 (Cont°d)

Descriptive Statistics:

Dimensions, REP, Accuracy Components

Variable n Mean SD Min. Max. Range

Diff. Accur.(Freq.) 305 1.817 0.195 1.278 2.476 1.198

Elevation(Freq.) 305 0.498 0.323 0.004 1.588 1.584

Diff. Elevation(Freq.) 305 1.144 0.297 0.356 1.915 1.559

StereotypicAccu:. (Freq.) 305 1.233 0.236 0.714 1.939 1.225

Diff. Accu:.Cor:. (DA: Freq.) 305 -0.198 0.135 -0.564 0.199 0.759

Diff. ElevationCor:. (DE: Freq.) 305 -0.702 0.264 -0.998 0.949 -1.937

StereotypicAccu:. Cor:. 305 -0.125 0.281 -0.736 0.571 1.311(SA: Freq.)

Total Accuracy 305 6.660 1.051 3.958 9.621 5.663(Freq)

97

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98

TABLE 30

Analysis of Variance (ANOVA) for the Effect of

Work Experience on Differential Elevation

Source df Sum of Squares Mean Square F

Experience 2 0.2490 0.1245 3.25*

Error 283 10.9150 0.0386

Total 285 11.1641

99

DISCUSSION

The present study was expected to (a) reoperationalize cognitive

complexity, (b) compare the predictiveness of CC-dimensionality with that

of CC-REP, (c) assess the relationship between CC-dimensionality and

CC-REP, and (d) replicate the three-dimensional job characteristics model

of Stone and Gueutal (1985). The attempt to reoperationalize CC was

consistent with a number of recommendations (Bieri, 1966; Scott, Osgood,

& Peterson, 1979) that MDS is a potentially useful approach to examining

the manner in which subjects dimensionalize stimuli. However, the initial

(INDSCAL) results reveal that this may not be the case.

To extract dimensionality, the study submitted subjects° similarity

ratings of job titles to multidimensional scaling. The

individual-differences scaling results revealed that a four-dimensional

solution appeared to best account for the rated similarity of job titles.

This was determined by the conventional "scree test." In other words,

four perceptual dimensions seemed to underlie subjects° perceptions of

jobs. As indicated in the results section, the proportion of variance

in similarity ratings, which was accounted for by such a four-dimensional

solution, approaches levels considered moderately good (but not

excellent) by authors Young and Lewyckyj (1979), authors of the software

application of the INDSCAL algorithm. However, to determine the adequacy

of the solution, stability was also a requirement.

The stability of the Job Similiarity Measure was first assessed by

computing the average correlations of repeated items for each subject,

100

converting these correlations (using Fisher°s r to Z transformation), and

then arriving at an overall or average Z. The results of this assessment

suggested reasonably high reliability. In light of the moderately high

stability (suggested by the consistency of subjects' ratings of repeated

items), it seems unlikely that the instability of dimensionality is the

result of subjects‘ rating inconsistencies.

However, one particularly problematic aspect of these results was

the relatively low stability of the group multidimensional scaling

solution when a second method was used to determine stability. One method

to assess solution (dimensional) stability is the comparison of Job Title

Stimulus Coordinates (JTSCs) across four subsets of the data. However,

coefficients of congruence for the four random subsets' JTSCs on each

dimension suggest that dimensionality was unstable. This instability

calls into question the usefulness of the group INDSCAL solution in the

present study.

As indicated earlier, one alternative for enhancing stability is

to examine coefficients of congruence for alternate dimensional

solutions. However, the results revealed that changing the number of

dimensions did not result in stable dimensionality.

One usual explanation for solution instability is the use of a

higher convergence option (e.g. converge = .01 to .05). As indicated in

the results section, the MDS (INDSCAL) algorithm iterates until

convergence meets the researcher-specified option (or the default value,

.001). This means that S-STRESS (overall "badness of fit") must decrease

at least to convergence for the program to continue iterating. The

present study employed a convergence option of .001. Although greater

101

stability can be expected at this level, such stability did not result

in the present case. Similarly, the number of iterations that were

requested in the program options (iter=l0) could not have accounted for

the lack of stability because in no case were more than nine iterations

required for convergence at .001. So, although increasing the permissible

iterations may often result in a better solution, this strategy would not

have been helpful in the present case.

A related issue is the inferential testing of particular

n-dimensional MDS solutions. Although Weinberg, Carroll, and Cohen

(1984) have investigated methods to establish confidence regions for

jacknifing and bootstrapping techniques in multidimensional scaling, this

work is in its infancy. Thus, the statistically inelegant strategy of

determining dimensionality by locating an "elbow" in the plots of R2

against the number of dimensions will have to suffice.

The disappointing individual-differences scaling results led to an

inability to test Hypotheses 1 and 2. Thus, no test for the relationship

between individual dimensionality and rater accuracy was possible.

Consequently, no comparison could be made between the predictiveness of

individual dimensionality and the predictiveness of REP.

However, regarding Hypothesis 3, REP did predict elevation in

performance ratings. The less the REP-complexity, the greater the

elevation in ratings. Elevation, according to Cronbach (1955), reflects

the rater°s "way of using a response scale" (p.l78). In other words,

elevation, the average rating over all ratees and items upon which the

ratee is judged, indicates the overall level of the ratings. This means

that the higher the REP score (i.e., the greater the tendency to use

102

dimensions in the same way on the REP), the greater the elevation

(in)accuracy. In other words, raters who tend assign the same REP numbers

regardless of the dimension also tend to produce ratings which are

generally discrepant from true scores. However, it should be noted that

statistical power may account for this result and that the practical

importance of this finding is unclear.

REP also significantly predicted differential accuracy in frequency

ratings. This result contradicts a number of previous studies which

revealed no significant relationship between REP and differential

accuracy (Bernardin & Cardy, 1981; Bernardin, Cardy, & Carlyle, 1982;

Lahey & Saal, 1981; Sauser & Pond, 1981; Sechrest & Jackson, 1961).

Though of interest because of its contrast with the above-mentioned

studies, this finding may be less than compelling. The correlation

coefficient, though significant, was small and of doubtful practical

importance. Again, this result may be explained by the statistical power

resulting from a large sample size.

Another possible explanation for the small, but significant,

correlations between REP and some of the accuracy measures may be common

method variance. There may be some construct similarity between the

measures. Perhaps both predictor and criterion measure halo.

The present study revealed that the work experience of raters

contributed to their differential elevation in performance scores.

Specifically, the greater the experience the greater the departure from

true scores were the average ratings for each ratee across dimensions.

Although not breaking new ground, this is an intriguing finding because,

it provides further evidence that performance appraisals for subordinates

103

may be influenced by the number of years a person has worked. It is

possible that experienced workers may be more aware than less experienced

workers of the potential impact of performance appraisal on their

subordinates and therefore be less willing to shoulder the risks of

alienating certain subordinates. The result may be less accurate

appraisals.

In a literature review, Landy and Farr (1980) have documented the

mixed effects of experience on rating quality. Although some studies

(Jurgensen, 1950) reveal that one‘s job experience affects the quality

of ratings, others report that job experience may lead to more lenient

ratings (Cascio & Valenzi, 1977; Mandell, 1956) or to no effects on

ratings (Klores, 1966). The present study does not resolve the confusion

over this issue. Future research may determine the nature of the

relationship between job experience and rating quality.

Another hypothesis concerned the predicted relationship between

individual differences in dimensionality and REP. Again, this hypothesis

was untestable.

The disappointing individual-differences scaling results also

prompted a re-analysis of the present data, using ALSCAL's Euclidian model

option. When the data were re-analyzed, a two-dimensional solution seemed

to best describe the data. This is not unusual, because as indicated

earlier, two to three dimensions are usually sufficient to describe the

data, except in individual-differences models when sometimes more than

three dimensions are necessary (Shepard, 1972). The coefficients of

congruence for the ALSCAL scaling solutions revealed that the Stone and

Gueutal (1985) dimensions were not retieived.

104

This latter result provides probably the most interesting yet

unexpected finding of the present study. Although one objective of the

present research was the replication of Stone and Gueuta1°s

three-dimensional job characteristics model, the present study revealed

that their research may not be as generalizable as once thought (Stone,

1986; Stone and Gueutal, 1985; Stone and Ruddy, 1987). The sample size

for the present study should have provided ample opportunity for such a

test of the generalizability of their model. Moreover, the present

research employed the Stone and Gueutal methodology in the pretest portion

of the study. Still, little support for the Stone and Gueutal model

emerged.

Possible reasons for the failure to replicate Stone and Gueutal

include possible inadequacies of the Job Similarities Questionnaire.

Perhaps asking subjects to rate their perceived similarity of job titles

is an inadequate means of capturing what truly distinguishes job

activities. As Scott (1963) argued, requiring subjects to analyze objects

in a phenomenal space may burden their capacities of introspection. Scott

goes so far as to question whether subjects can easily "distinguish

between such common objects as a rock and a tree" (p. 270). In addition,

he cautions that inordinate demands upon the subject may produce artifacts

of the method. Perhaps the use of job-analysis techniques may be a more

fruitful approach.

Additionally, Stone and Gueutal used correlational evidence to

support their claim that the subset analyses converged. The appropriate

statistic is the coefficient of congruence (Cattell, 1978; Gorsuch,

1983). However, this statistic may provide vastly different results than

105

the correlation coefficient. Thus, the initial reporting of the Stone

and Gueutal findings may have been premature.

Another possible explanation for the lack of convergence between

the Stone and Gueutal dimensions and the present study is potential

subject differences. The present study employed introductory psychology

students (primarily freshmen), whereas Stone and Gueutal employed

undergraduate juniors and seniors. However, any differences between the

two samples° familiarity with jobs should not be large. It is therefore

doubtful that any such differences could account for the lack of

convergence between the dimensions in the two studies.

The failure to replicate the three-dimensional model is of more than

academic interest. Job redesign interventions are necessarily based upon

the dimensions along which jobs are believed to Vary. A failure on the

part of scientific research to delineate stable and generalizable job

dimensions can lead to misguided efforts in the area of job design. How

can the practitioner expect to make sensible changes with respect to "job

enrichment," for example, when the foundation for such changes is built

on an "earthquake fault"? One of Stone and Gueutal's criticisms of prior

job-characteristics research was that such work failed to properly

describe the manner in which people actually perceive jobs. Accordingly,

these authors have directed much energy away from a priori assumptions

about what workers perceive toward an understanding of what workers

actually perceive. Yet, despite this considerable effort, these authors

may not have achieved the breakthrough that was once hoped.

As indicated earlier, cognitive complexity is conceived not as a

trait which is stable across content domains, but rather as a process

106

variable which may interact with features of the stimulus and is somewhat

domain specific. In other words, there are attributes or characteristics

of stimuli that combine with the manner in which we process information

to affect social judgment. The manner in which we process information

is a function of those features, thoughts, etc. which we perceive

concerning stimuli.

The domain specificity issue must be further examined empirically.

For example, research is required to determine not only whether subjects

vary in complexity across broad domains (jobs, roles, etc.), but also

whether complexity varies within domains (i.e., for specific jobs). ln

other words, would one°s level of complexity be different for the job of

teacher relative to other jobs, such as dentist, pipe-fitter, etc.?

Streufert and Streufert (1978) have suggested that such variable

complexity may be a complicating factor.

Future research on cognitive complexity must address the construct

validity issues outlined earlier. Any future examination of CC must first

deal with the issue of an adequate operational definition. Efforts must

focus not only on the number of dimensions or pieces of information people

use to make social judgments but also on the combinatory strategies people

use to integrate such social information. Results gleaned from the social

cognition literature may provide some useful guidance in this regard.

Schema theory has explained social cognition in terms of peop1es'

use of a schema or mental representation of a person, role, object, etc.

(Taylor & Crocker, 1981). For example, people have some mental

representation for the role of teacher, leader, employee, etc. People

also have implicit personality theories or mental representations of how

107

the attributes of people are correlated. Regarding leaders, Rush, Thomas,

and Lord (1977) found that one°s implicit leadership theories may be

reflected in one's ratings of leadership behavior. More recently, Lord,

Foti, and Philips (1982) have suggested that ratings of leader behavior

are the result of a complex information·processing sequence. Individuals

enter the workplace with well-developed prototypes of an effective leader

and an ineffective leader. Raters compare the leader°s behavior to the

characteristics of the prototype and then classify the leader as either

effective or ineffective. Such a categorization process may thus drive

the rater°s overall perception of the leader (Foti, Frazer, & Lord, 1982).

Redirecting CC research by examining more fully both the content and

process of cognitive schemata may provide additional insight concerning

how individuals perceive the attributes of others.

Future research is required also to provide a qualitative

interpretation of the dimensionality data. The present study attempted

but failed to capture the three-dimensional job characteristics model of

Stone and Gueutal (1985). Therefore, efforts were not directed at

exploring potential names for dimensions. So, the number but not enough

of the nature of the dimensions was at issue. Additional research is

needed to examine the nature of the dimensions underlying jobs.

Additional research might also be aimed at determining whether

there is a quantitative representation of cognitive complexity. Should

other exploratory efforts demonstrate correlational evidence of a

dimensionality-to·rating link, controlled laboratory experiments would

likewise be a necessary follow-up.

108

Finally, a major focus of future research should be the development

of more reliable measures. Because reliability sets the limits for

validity, the importance of this prescription cannot be overstated.

In sum, the present study attempted, but failed, to provide a new

operational definition of cognitive dimensionality. Similarly, a

comparison of cognitive dimensionality with the traditional REP-test

could not be made. Moreover, an expected replication of Stone and

Gueutal°s (1985) model of job characteristics was not revealed. The

results, while not supportive of the research hypotheses, nevertheless

provide some answers to persistent questions about the operationalizing

of cognitive complexity and the dimensions underlying jobs. Meanwhile,

it would appear that, as Bernardin, Cardy, and Carlyle (1982) have noted

for cognitive complexity and appraisal effectiveness,it‘s

"back to the

drawing board" (p. 151). It also may be "back to the drawing board" for

the job-characteristics approach to designing jobs.

109

REFERENCES

Addams-Webber, J. R. (1969). Cognitive complexity and sociality. BritishJournal or Social and Clinical Psychology, 8, 211-216.

Aldag, R. J., Barr, S. H., & Brief, A. P. (1981). Measurement ofperceived task characteristics. Psychological Bulletin, QQ,415-431.

Attneave, P. (1959). Applications of information theory to psychology.

NY: Holt-Dryden.

Becker, B. E. & Cardy, R. L. (1986). Influence of halo on appraisaleffectiveness: A conceptual and empirical reconsideration. Journalof Applied Psychology, ll, 662-671.

Bem, D. J. & Allen, A. (1974). On predicting some of the people some ofthe time: The search for cross-situational consistencies inbehavior. Psychological Reyieg, 81, 506-520.

Bernardin, H. J. 8 Cardy, R. L. (1981). Cognitive complexity inperformance appraisal: lt makes no nevermind. Proceedings gf the4lst Annual Meeting of the Academy of Management, lp, 306-310.

Bernardin, H. J., Cardy, R. L., & Carlyle, J. (1982). Cognitivecomplexity and appraisal effectiveness: Back to the drawing board?Journal of Applied Psychology, 61, 151-160.

Bieri, J. (1955). Cognitive complexity-simplicity and predictivebehavior. Journal of Abnormal Psychology, jl, 263-268.

Bieri, J. (1971). Cognitive structures in personality. In H. M.Schroder & Peter Suedfeld, Personality theory and informationprocessing (pp. 178-208). NY: Ronald Press.

Bieri, J., Atkins, A. 1., Briar, S., Leaman, R. L., Miller, H., & Tripodi,T. (1966). Clinical and social judgement: The discrimination of

behayioral information. NY: Wiley.

Bieri, J. & Blacker, E. (1956). The generality of cognitive complexityin the perception of people and inkblots. Journal of AbnormalPsychology, 5;, 112-117.

Bonarius, J. C. (1965). Research in the personal construct theory ofGeorge A. Kelly: Role construct repertory test and basic theory.In R. A. Maher (Ed.), Progress in experimental personalityresearch: Yol Z (PP- 1-46). NY: Academic Press.

110

Borman, W. C. (1977). Consistency of rating accuracy and rating errorsin the judgment of human performance. Organizational Behayior andPuman Performance, gg, 238-252.

Burt, C. L. (1948). The factorial study of temperamental traits. BritishJournal of Psychology, l, 178-203.

Burton, M.(1972). Semantic dimensions of occupational names. In A. K.Romney, R. N. Shepherd, & S. B. Nerlove (Eds.). üultidimensionalscaling: Yol II (PP. 55-71). NY: Seminar Press.

Campbell, V. N. (1960). In W. A. Scott, D. Wayne Osgood, & C. M.Peterson, Cognitiye strncture: Theory and measnrement or indiyidualdifferences. NY: John Wiley & Sons.

Campbell, D. T. & Piske, D. W. (1959). Convergent and discriminantValidation by multitrait-multimethod matrix. PsychologicalBulletin, gg, 81-105.

Cardy, R. L. & Dobbins, G. H. (1986). Affect and appraisal accuracy:Liking as an integral dimension in evaluating performance. Journalof Applied Psychology, gl, 672-678.

Carroll, J. D. & Chang, J. J. (1970). Analysis of individual differencesin multidmensional scaling via an N-way generalization of"Eckart-Young" decomposition. Psychometrika, gg, 283-319.

Cascio, W. P. & Valenzi, E. R. (1977). Behaviorally anchored ratingscales: Effects of education and job experience of raters andratees. Journal of Applied Psychology, gg, 278-282.

Cattell, R. B. (1978). The scientific use of factor analysis in behayioral

and life sciences. NY: Plenum.

Cherrington, D. J. & England, J. L. (1980). The desire for an enrichedjob as a moderator of the enrichment satisfaction relationship.Organizational Behayior and Human Performance, gg, 139-159.

Crockett, W. H. (1965). Cognitive complexity and impression formation.In R. A. Maher (Ed.), Progress in experimental personality

research: Yol. 2 (pp. 47-90). NY: Academic Press.

Cronbach, L. J. (1955). Processes affecting scores on "understanding ofothers" and "assumed similarity." Psychological Bulletin, gg,

177-193.

Davison, Mark L. (1983). Multidimensional scaling. NY: John Wiley.

Dictionary of Occupational Titles, Occupational Classification Vol.2(1965). Washington, DC: U.S. Government Printing Office.

lll

Dubin, R., Porter, L. W. Stone, E. F., & Champoux, J. E. (1974).lmplications of differential job perceptions. Industrialßelatlons, gl, 404-409.

Dunham, R. B. (1976). The measurement and dimensionality of jobcharacteristics. Journal of Annljed Psychology, gl, 404-409.

Dunham, R. B. (1977). Relationships of perceived job designcharacteristics to job ability requirements and job value. Journalof Agnlled Psychology, gg, 760-763.

Dunham, R. B., Aldag, R. J., & Brief, A. P. (1977). Dimensionality of

task design as measured by the Job Diagnostic Survey. Academy og

Management Journal, gg, 209-223.

Durand, R. M. & Lambert, Z. L. (1976). Generalizability of cognitivedifferentiation across product and social domains. PsychologicalRenorts, gg, 665-666.

Feldman, J. M. (1981). Beyond attribution theory: Cognitive processesin performance appraisal. Journal of Annlied Psychology, gg,

127-148.

Foa, U. G. & Turner, J. L. (1970). Psychology in the year 2000: Goingstructural? American Psychologist, gg, 244-247.

Foti, R. J., Frazer, S. L. & Lord, R. G. (1982). Effects of leadership

labels and prototypes on perceptions of political leaders. Journalof Anplied Psychology, gl, 326-333.

Fried, Y. & Ferris, G. R. (1987). The validity of the job characteristicsmodel: A review and meta-analysis. Pegsonnel Psychology, gg,

287-322.

Goldstein, K, M. & Blackman, S. (1978). Cognitiye style: Fiye annroachesand releyant research. NY: Wiley.

Gorsuch, R. L. (1983). Factor analysis. NY: Lawrence Erlbaum Associates.

Hackman, J. R. & Lawler, E. E. (1971). Employee reactions to jobcharacteristics. Journal of Annlied Psychology, gg, 259-286.

Hackman, J. R. & Oldham, G. R. (1975). Development of the Job DiagnosticSurvey. Journal of Annlied Psychology, gg, 159-170.

Hackman, J. R. & Oldham, G. R. (1976). Motivation through the design of

work: Test of a theory. Organizational Behayior and HumanPerformance, lg, 250-279.

112

Harvey, R. J., Billings, R. S., & Nilan, K. J. (1985). Confirmatoryfactor analysis of the Job Diagnostic Survey: Good news and badnews. gournal of Applied Psychology, Z2, 461-468.

Harvey, R. J., Friedman, L., Hakel, M. D., 8 Cornelius, E. T. (1988).Dimensionality of the Job Element Inventory, a simplifiedworker-oriented job analysis questionnaire. Journal of AppliedPsychology, 12, 639-646.

Idaszak, J., Bottom, W., & Drasgow, F. (1988). A test of the measurementequivalence of the revised Job Diagnostic Survey: Past problemsand current solutions. Journal of Applied Psychology, 12, 647-656.

Idaszak, J. & Drasgow, F. (1987). A review of the Job Diagnostic Survey:Elimination of a measurement artifact. Journal of AppliedPsychology, Z2, 69-74.

Jones, A. P. & Butler, M. C. (1980). Influences of cognitive complexityon the dimensions underlying perceptions of the work environment.Motiyation and Emotion, 4, 1-19.

Jurgensen, C. E. (1950). Intercorrelations in merit rating traits.Journal of Applied Psychology, 24, 240-243.

Kelly, G. A. (1955). The psychology of personal constructs. NY: Norton.

Kelly, G. A. (1970). A brief introduction to personal construct theory.In D. Bannister (Ed.), Perspectiyes in personal constructtheory. NY: Academic Press.

Klores, M. S. (1966). Rater bias in forced—distribution ratings.Personnel Psychology, 12, 411-421.

Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodnessof fit to a nonmetric hypothesis. Psychometrika, 22, 1-27.

Kruskal, J. B. & Wish, M. (1978). Multidimensional scaling. Beverly

Hills, CA: Sage.

Kulik, C. T., 01dham, G. R. , & Langer, P. H. (1988). The measurement

of job characteristics: A comparison of the original and the revisedJob Diagnostic Survey. Journal of Applied Psychology, 12, 462-466.

Lahey, M. A. & Saal, F. (1981). Evidence incompatible with a cognitivecompatibility theory of rating behavior. Jpp;na1_p§_App11eQPsychology, pg, 706-715.

Lal, R. & Singh, R. S. (1984). Cognitive complexity, attitude similarityand interpersonal judgment. Perspectiyes in PsychologicalResearches, Z, 4-7.

113

Landy, F. J. & Farr, J. L. (1980). Performance rating. PsychologicalBulletin, 81, 72-107.

Lee, R. & Klein, A. R. (1982). Structure of the job diagnostic surveyfor public sector occupations. Journal of Applied Psychology , 81,515-519.

Leitner, L. M., Landfield, A. W. & Barr, M. A. (1974). Cognitivecomplexity: A review and elaboration within personal constructtheory. Unpublished manuscript, University of Nebraska.

Leventhal, M. (1957). Cognitive processes and interpersonal prediction.Journal of Abnormal and Social Psychology, 88, 176-180.

Lewin, K. (1936). Theories of topologlcal psychology NY: McGraw Hill.

Lord, R. G., Foti, R. J. & Philips, J. S. (1982). A theory of leadershipcategorization. In J. G. Hunt, V. Sekaran, & C. Schriescheim(Eds.), Leadership: Beyond establishment ylews. Carbondale, IL:Southern Illinois University Press.

Mandell, M. M. (1956). Supervisory characteristics and ratings: A summaryof recent research. Personnel Psychology, 88, 435-440.

Murphy, K. R., Garcia, M., Kerkar, S., Martin, C. & Balzer, W. K. (1982).Relationship between observational accuracy and accuracy inevaluating performance. Journal of Applied Psychology, 81, 320-325.

Neimeyer, G. J. & Neimeyer, R. A. (1981). Personal construct perspectiveson cognitive assessment. In T. V. Merluzzi, C. R. Gloss, & M.Genest (Eds.), Cognitiye assessment. (PP- 188-232). NY: GuilfordPress.

O°Keefe, D. J., & Sypher, H. E. (1981). Cognitive complexity measuresand the relationship of cognitive complexity to communication.Human Communication, 8, 72-92.

Osgood, C. E. (1962). Studies on the generality of affective meaningsystems. American Psychologist, ll , 10-28.

Pervin, L. A. (1973). On construing our constructs. QpprgmpprpryPsychology, _@, 110-112.

Peterson, C. P. & Scott, W. A. (1975). Generality and topic specificityof cognitive styles. Journal of Research in Personality , 8,366-374.

Peterson, C. P. & Scott, W. A. (1983). Toward fundamental measurement of

dimensionality. British Journal of Social Psychology , 82,197-202.

114

Pierce, J. L. 8 Dunham, R. B. (1978). The measurement of perceived jobcharacteristics: The Job Diagnostic Survey versus the JobCharacteristics Inventory. Academy of Mapagement Journal,gl, 123-128.

Pokorney, J. J., Gilmore, D. C., 8 Beehr, T. A. (1980). Job DiagnosticSurvey dimensions: Moderating effect of growth needs andcorrespondence with dimensions of job rating form. OrganizationalBehayior and Human Performance, gp, 222-237.

Roberts, K. H. 8 Glick, W. (1981). The job characteristics approach totask design: A critical review. Qourpal pf Applied Psychology, pp,193-217.

Rush, M. C., Thomas, J. C. 8 Lord, R. G. (1977). Implicit leadershiptheory: A potential threat to the internal validity of leaderbehavior questionniares. Organization Behayior and HumagPerformance, gQ, 93-110.

Sauser, W. I. 8 Pond, S. B. III (1981). Effects of rater training and

participation on cognitive complexityz An exploration of Schneier'scognitive reinterpretation. Pegsonnel Psychology, 3p, 563-577.

Schiffman, S. S.., Reynolds, L. M., 8 Young, F. W. (1981). Igtrogugtipgto mutidimensional scaling. NY: Academic Press.

Schneier, C. E. (1977). Operational utility and psychometriccharacteristics of behavioral expectation scales: A cognitivereinterpretation. Journal of Applied Psychology, pg , 541-548.

Schneier, C. E. (1979). Measuring cognitive complexity: Developingreliability, validity, and norm tables for a personalityinstrument. Educatiog and Psypholpgical Measuremegt, 33, 599-612.

Schroder, H. M., Driver, M. J., 8 Streufert, S. (1967). Human informationprocessing. NY: Holt, Rhinehart 8 Winston.

Scott, W. A. (1962). Cognitive complexity and cognitive balance.Sociometgy, gp, 66-74.

Scott, W. A. (1963). Conceptualizing and measuring structural propertiesof cognition. In O. J. Harvey, Motiyation and social interaction.

NY: The Ronald Press, 266-288.

Scott, W. A., Osgood, D. W., 8 Peterson, C. (1979). Cognitiye structure:Theory and measurement of indiyidual differences. Washington, DC:V. H. Winston 8 Sons.

Seaman, J. M. 8 Koenig, F. K. (1974). A comparison of measures ofcognitive complexity. Sociometgy, 3, 375-390.

115

Sechrest, L. B. & Jackson, D. N. (1961). Social intelligence and accuracyof interpersonal predictions. Journal of Personality, 1;, 167-181.

Shepard, R. N. (1972). Introduction to Volume 1. In R. N. Shepard, A.K. Romney, & S. B. Nerlove, Multidimensional Scalingz Vol 1 NY:Seminar Press.

Solomon, D. (1977). Perceptions of similarity between striving tasks andthe generality of task preferences. Motiyation and Emotion, 1,181-192.

Stone, E. F. (1976). The moderating effects of work-related values onthe job scope-job satisfaction relationship. QrganizationalBehayior and Human Eerformance, 1;, 147-167.

Stone, E. F. (1986). Job scope-job satisfaction and job-scope- jobperformance relationships. In E. A. Locke (Ed.), Qeneralizing fromlaboratory to field settings (pp. 189-206). Lexington, MA:Lexington Books.

Stone, E. F. & Gueutal, H. G. (1985). An empirical derivation of thedimensions along which characteristics of jobs are perceived.Academy of Management Journal, 1;, 376-396.

Stone, E. F., Mowday, R. T., & Porter, L. W. (1977). Higher order needstrengths as moderator of the job scope-job satisfactionrelationship. Journal of Applied Psychology, 6;, 466-471.

Stone, E. F. & Ruddy, T. R. (1987). Dimensionality of perceived jobcharacteristics. Paper presented to the Annual Meeting of theSociety of Industrial and Organizational Psychology.

Streufert, S. (1982, August). Cognitive complexity. Paper presented tothe 90th Annual Convention of the American PsychologicalAssociation, Washington, D.C.

Streufert, S. & Streufert, S. (1978). Behayior lg the complexenylronment. NY: Wiley.

Taylor, S. & Crocker, J. (1981). Schematic bases of social informationprocessing. In E.T. Higgins, C.P Herman, & M.P. Zanna (Eds.)Social cognition: The Ontario Symposium (Vol. 1). Hillsdale, NJ:

Erlbaum.

Thompson, E. G., Mann, I. T., & Harris, L. J. (1981). Relationships amongcognitive complexity, sex and spatial task performance in collegestudents. British Journal of Psychology, 1;, 249-256.

Tripodi, T. & Bieri, J. (1963). Cognitive complexity as a function ofown and provided constructs. Psychological Reports, 1;, 26.

116

Tucker, L. R. & Messick, S. (1963). An individual difference model formultimensional scaling. Psychometrica, 2g, 333-365.

Turner, A. N. & Lawrence, P. R. (1965). Industrial jobs and the yorker.Boston, MA: Harvard University Press.

Vannoy, J. S. (1965). The generality of cognitive complexity-simplicityas a personality construct. Journal of Personallty and SocialPsychology, P, 385-389.

Vernon, P. E. (1972). The distinctiveness of field dependence. Journalof Personality, gg, 366-391.

Weinberg, S. L., Carroll, J. D. & Cohen, H.S. (1984). Confidence regionsfor INDSCAL using the jackknife and bootstrap techniques.Psychometrika, gg, 475-491.

Weiss, H. M. & Adler, S. (1981). Cognitive complexity and the structureof implicit leadership theories. Journal of Applied Psychology,gg, 69-78.

Wicker, A. (1969). Cognitive complexity, school size, and participationin school behavior settings: A test of the frequency of interactionhypothesis. Jourhal of Educational Psychology, gg, 200-203.

Witkin, H. A., Dyk, A., Faterson, H. F., Goodenough, D. R., & Karp, S.A. (1974). Psychological differentiation. Potomac, MD: Erlbaum.(Originally published 1962.)

Witkin, H. A. & Goodenough, D. R. (1977). Field dependence andinterpersonal behavior. Psychological Bulletin, gg, 661-689.

Witkin, H. A., Goodenough, D. R., & Karp, S. A. (1967). Stability ofcognitive style from childhood to young adulthood. Journal ofPersonality and Social Psychology, Z, 291-300.

Wrigley, C. S. & Neuhaus, J. O. (1955). The matching of two sets offactors. American Psychologist, lg, 418·419.

Young, F. W. & Lewyckyj, R. (1979), ALSCAL-4 User°s Guide. Chapel Hill,NC: Data Analysis and Theory Associates.

Zaccaro, S. J. & Stone, E. F. (1988). lncremental validity of anempirically based measure of job characteristics. Journal ofApplied Psychology, lg, 245-252.

Zajonc, R. (1960). The process of cognitive turning in communications.Journal of Abnormal and Social Psychology, gl, 159-164.

117

APPENDIX A

FAMILIARITY QUESTIONNAIRE

118

Directions: Please rate your familiarity with the work activitiesof persons holding the jobs listed below. Please use thefollowing scale for your ratings.

1: 2: 3: 4: 5:

NOT AT ALL SLIGHTLY MODERATELY VERY TOTALLYFAMILIAR FAMILIAR FAMILIAR FAMILIAR FAMILIAR

1. CASHIER

2. JANITOR

l 3. FIRE FIGHTER

4. TELEPHONE OPERATOR

5. BANK TELLER

6. MAIL CARRIER

7. TAXI DRIVER

8. FILE CLERK

9. ACCOUNTANT

10. SHORT-ORDER COOK

11. ACTOR

12. ASSEMBLY-LINE FOREMAN

13. MACHINE OPERATOR

14. ELEMENTARY SCHOOL TEACHER

15. FLIGHT ATTENDANT

16. WAITER/WAITRESS

17. CONSTRUCTION WORKER

18. TAILOR

19. LAWYER

20. RETAIL STORE MANAGER

119

Appendix A (Continued)

Descriptive Statistics:

Familiarity Ratings

Job n Mean S.D. Minimun Maximum Range

Cashier 39 4.231 0.872 2.000 5.000 3.000Janitor 39 3.949 1.169 2.000 5.000 3.000Firefighter 39 3.821 1.144 2.000 5.000 3.000Telephone Op. 39 3.410 1.208 2.000 5.000 3.000Bank Teller 39 3.641 1.246 1.000 5.000 4.000Mail Carrier 39 3.744 1.141 1.000 5.000 4.000Taxi Driver 39 3.667 1.243 1.000 5.000 4.000File Clerk 39 3.333 1.475 1.000 5.000 4.000Accountant 39 3.282 1.395 1.000 5.000 4.000Short·0rder Cook 39 4.051 1.050 1.000 5.000 4.000Actor 39 3.795 1.151 1.000 5.000 4.000Assembly Line 39 2.974 1.442 1.000 5.000 4.000

ForemanMachine Operator 39 3.308 1.454 1.000 5.000 4.000Teacher 39 4.051 1.025 2.000 5.000 3.000Flight Attendant 39 3.385 1.290 1.000 5.000 4.000Waiter/Waitress 39 4.410 0.677 3.000 5.000 3.000

Construct. Work. 39 3.667 1.402 1.000 5.000 4.000Tailor 39 3.282 1.376 1.000 5.000 4.000Lawyer 39 3.590 1.371 1.000 5.000 4.000Retail Manager 39 3.615 1.310 1.000 5.000 4.000

120

APPENDIX B

BIOGRAPHICAL QUESTIONNAIRE

121

SubjectNumber:Job

Similarities Study

Interview, Interviewer, and Interviewee Information

InterviewInformation1.

Date: Month: Day: Year:

2. Time: Starting Ending: AM PM

InterviewInformation3.

Name: Last: First: Mlzl

InterviewInformation4.

Name: Last: First: MI:_

5. Street name and number:

City: State: l____ Zip___

6. Telephones: Home: Work: _l____

7. Occupational/jobtitle:8.

Jobduties:9.

Nature of present job: [ ] non—managerial [ ] managerial

10. Employing organization°s function (e.g., manufacturing,sales, education, government agency):

11. Tenure on current job: Years: __ Months:_

12. Combined tenure on alljobs, past and present: Years: Months:___

13. Years ef formal education: ___l___

122

Page 2

14. Diplomas and/or degrees (check highest level of attainment):

[ ] High school diploma or GED equivalent[ ] Associates degree[ ] Bachelors degree[ ] Masters degree[ ] Doctoral degree

15. Sex: [ ] Male [ ] Female

16. Age in years:

123

APPENDIX C

JOB SIMILARITY QUESTIONNIARE

124

General Instructions for Similarity Ratings

1. Please enter the last four digits of your social securitynumber in the boxes in the upper left corner of both attachedopscan forms.

2. Be SURE that you begin with the opscan marked "Form No. l"in red ink at the bottom of the page. On Form No. 1,please mark your responses to similarity ratings 1-100 inthe spaces designated 1-100.

3. When you reach line 101, you will notice that the "1"circle is coded. Do not erase this code. It tells thecomputer that you have marked Form 1. (It is also a reminderfor you to stop working on this op-scan form and turn to thenext op-scan form.)

4. Then, on Form No. 2 (so marked in Blue Ink at the Bottom of thepage), please mark your responses to items 101-200 in the spacesdesignated 1-100. Please be sure that you are coding your ·op-scans correctly.

5. Again, you will notice that on line 101 of yourop-scan sheet there is a circle darkened. This time the "2"circle will be darkened. This will indicate to the computerthat you have marked Form No. 2.

6. Please turn to the questionnaire, read the instructions at thetop of each page, and complete the questionnaire by darkeningthe appropriate circle on your op-scan sheets.

7. PLEASE MAKE NO MARKS ON THE SIMILARITY QUESTIONNAIRE ITSELF.

8. When you have completed your similarity ratings, please proceedto the remaining portions of the questionniare packet.

THANK YOU FOR YOUR ASSISTANCE.

125

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

1. CASHIER andRETAIL STORE MANAGER

2. BANK TELLER andWAITER/WAITRESS

3. SHORT-ORDER COOK andTAILOR

4. ACTOR andBANK TELLER

5. FILE CLERK andLAWYER

6. ELEMENTARY SCHOOL TEACHER andTELEPHONE OPERATOR

7. MACHINE OPERATOR andTAXI DRIVER

8. FIRE FIGHTER andTELEPHONE OPERATOR

9. CONSTRUCTION LABORER andMAIL CARRIER

10. LAWYER andWAITER/WAITRESS

11. BANK TELLER andFIRE FIGHTER

12. MACHINE OPERATOR andTAILOR

13. ACCOUNTANT andASSEMBLY-LINE FOREMAN

14. FILE CLERK andTAXI DRIVER

126

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

15. ELEMENTARY SCHOOL TEACHER andSHORT-ORDER COOK

16. MACHINE OPERATOR andFILE CLERK

17. ACCOUNTANT andTELEPHONE OPERATOR

18. CONSTRUCTION LABORER andLAWYER

19. FILE CLERK andJANITOR

20. ACCOUNTANT andFIRE FIGHTER

21. LAWYER andRETAIL STORE MANAGER

22. JANITOR andSHORT-ORDER COOK

23. CASHIER andTAILOR

24. FIRE FIGHTER andMAIL CARRIER

25. CASHIER andJANITOR

26. ACTOR andELEMENTARY SCHOOL TEACHER

27. ACCOUNTANT andSHORT-ORDER COOK

28. CONSTRUCTION LABORER andELEMENTARY SCHOOL TEACHER

127

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

29. LAWYER andTAXI DRIVER

30. ASSEMBLY-LINE FOREMAN andRETAIL STORE MANAGER

31. ACCOUNTANT andFLIGHT ATTENDANT

32. CASHIER andTAXI DRIVER

33. BANK TELLER andTAXI DRIVER

34. FILE CLERK andFLIGHT ATTENDANT

35. CASHIER andLAWYER

36. FILE CLERK andTAILOR

37. JANITOR andMAIL CLERK

38. ELEMENTARY SCHOOL TEACHER andTAXI DRIVER '

39. BANK TELLER andTELEPHONE OPERATOR

40. ACCOUNTANT andELEMENTARY SCHOOL TEACHER

41. ASSEMBLY-LINE FOREMAN andJANITOR

42. ACTOR andTAXI DRIVER I

128

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

43. ACCOUNTANT andMAIL CARRIER

44. SHORT ORDER COOK andTAXI DRIVER

45. LAWYER andMACHINE OPERATOR

46. TAILOR andWAITER/WAITRESS _

47. ELEMENTARY SCHOOL TEACHER andFIRE FIGHER

48. JANITOR andLAWYER

49. ACTOR andFIRE FIGHTER

50. ACCOUNTANT andLAWYER

51. FLIGHT ATTENDANT andWAITER/WAITRESS

52. ACCOUNTANT andCONSTRUCTION LABORER

53. ASSEMBLY—LINE FOREMAN andELEMENTARY SCHOOL TEACHER

54. MACHINE OPERATOR andRETAIL STORE MANAGER

55. FLIGHT ATTENDANT andTAXI DRIVER

56. ASSEMBLY-LINE FOREMAN andMAIL CARRIER

129

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILARA MUCH COMPLETELY

SIMILAR SIMILAR

57. CASHIER andELEMENTARY SCHOOL TEACHER

58. ACTOR andFLIGHT ATTENDANT

59. LAWYER andSHORT-ORDER COOK

60. FILE CLERK andTELEPHONE OPERATOR

61. ACTOR andSHORT-ORDER COOK

62. ASSEMBLY-LINE FOREMAN andTAILOR

63. ACTOR andCASHIER

64. LAWYER andTELEPHONE OPERATOR

65. ELEMENTARY SCHOOL TEACHER andRETAIL STORE MANAGER

66. CASHIER andFLIGHT ATTENDANT

67. ACTOR andMACHINE OPERATOR

68. FLIGHT ATTENDANT andMAIL CARRIER

69. TAILOR andTELEPHONE OPERATOR

70. BANK TELLER andCONSTRUCTION LABORER

130

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: S: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

71. FIRE FIGHTER andFLIGHT ATTENDANT

72. BANK TELLER andTAILOR

73. MACHINE OPERATOR andMAIL CARRIER

74. CONSTRUCTION LABORER andRETAIL STORE MANAGER

75. BANK TELLER andMACHINE OPERATOR

76. ACCOUNTANT andCASHIER

77. ELEMENTARY SCHOOL TEACHER andFLIGHT ATTENDANT

78. CONSTRUCTION LABORER andTAILOR

79. ACTOR andLAWYER

80. ACCOUNTANT andWAITER/WAITRESS

81. CASHIER andFILE CLERK

82. FLIGHT ATTENDANT andLAWYER

83. ACCOUNTANT andTAXI DRIVER

84. FLIGHT ATTENDANT andRETAIL STORE MANAGER

131

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

85. BANK TELLER andLAWYER

86. ACTOR andTELEPHONE OPERATOR

87. ACCOUNTANT andTAILOR

88. ELEMENTARY SCHOOL TEACHER andWAITER/WAITRESS

89. TAXI DRIVER andTELEPHONE OPERATOR

90. ACTOR andMAIL CARRIER

91. ASSEMBLY-LINE FOREMAN andLAWYER

92. FIRE FIGHTER andWAITER/WAITRESS

93. ACTOR andCONSTRUCTION LABORER

94. ASSEMBLY—LINE FOREMAN andMACHINE OPERATOR

95. CASHIER andSHORT-ORDER COOK

96. FLIGHT ATTENDANT andJANITOR

97. ACCOUNTANT andRETAIL STORE MANAGER

98. BANK TELLER andJANITOR

132

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

99. MAIL CARRIER andWAITER/WAITRESS

100. CONSTRUCTION LABORER andFLIGHT ATTENDANT

' 101. JANITOR andTAXI DRIVER

102. ACTOR andASSEMBLY-LINE FOREMAN

103. FILE CLERK andSHORT—ORDER COOK

104. RETAIL STORE MANAGER andTAXI DRIVER

105. CONSTRUCTION LABORER andFIRE FIGHTER

106. ASSEMBLY-LINE FOREMAN andTELEPHONE OPERATOR

107. FIRE FIGHTER andTAXI DRIVER

108. ACTOR andTAILOR

‘

109. CONSTRUCTION LABORER andWAITER/WAITRESS

110. MAIL CARRIER andTAXI DRIVER

111. MACHINE OPERATOR andTELEPHONE OPERATOR

112. CONSTRUCTION LABORER andFILE CLERK

133

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

113. ACTOR andWAITER/WAITRESS

114. MAIL CARRIER andRETAIL STORE MANAGER

115. ACCOUNTANT andMACHINE OPERATOR

116. FIRE FIGHTER andLAWYER

117. CONSTRUCTION LABORER andMACHINE OPERATOR

118. ACTOR andJANITOR

119. CASHIER andMACHINE OPERATOR

120. BANK TELLER andRETAIL STORE MANAGER

121. ACCOUNTANT andBANK TELLER

122. JANITOR andRETAIL STORE MANAGER

123. CASHIER andWAITER/WAITRESS

124. CONSTRUCTION LABORER andTELEPHONE OPERATOR

125. MAIL CARRIER andTAILOR

126. BANK TELLER andSHORT-ORDER COOK

134

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

127. RETAIL STORE MANAGER andTAILOR

128. ELEMENTARY SCHOOL TEACHER andMACHINE OPERATOR

129. FIRE FIGHTER andSHORT—ORDER COOK

130. JANITOR andMACHINE OPERATOR

131. FLIGHT ATTENDANT andTAILOR

132. ACCOUNTANT andACTOR

133. CONSTRUCTION LABORER andSHORT-ORDER COOK

134. ASSEMBLY-LINE FOREMAN andBANK TELLER

135. ELEMENTARY SCHOOL TEACHER andFILE CLERK

136. ASSEMBLY—LIINE FOREMand andCASHIER

137. RETAIL STORE MANAGER andSHORT-ORDER COOK

138. LAWYER andMAIL CARRIER

139. ASSEMBLY-LINE FOREMAN andFLIGHT ATTENDANT

140. FILE CLERK andFIRE FIGHTER

135

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

141. CASHIER andCONSTRUCTION LABORER

142. FLIGHT ATTENDANT andMACHINE OPERATOR

143. ASSEMBLY-LINE FOREMAN andSHORT-ORDER COOK

144. FIRE FIGHTER andTAILOR

145. ASSEMBLY-LINE FOREMAN andTAXI DRIVER

146. ACTOR and .FILE CLERK

147. MAIL CARRIER andTELEPHONE OPERATOR

148. MACHINE OPERATOR andWAITER/WAITRESS

149. ACTOR andRETAIL STORE MANAGER

150. ELEMENTARY SCHOOL TEACHER andJANITOR

151. ASSEMBLY-LINE FOREMAN andFIRE FIGHTER

152. FLIGHT ATTENDANT andTELEPHONE OPERATOR

153. CASHIER andFIRE FIGHTER

154. BANK TELLER andMAIL CARRIER

136

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELYl

SIMILAR SIMILAR

155. CONSTRUCTION LABORER andTAXI DRIVER

156. JANITOR andTELEPHONE OPERATOR

157. TAXI DRIVER andWAITER/WAITRESS

158. RETAIL STORE MANAGER andTELEPHONE OPERATOR

159. BANK TELLER andCASHIER

160. ELEMENTARY SCHOOL TEACHER andLAWYER

161. SHORT-ORDER COOK andTELEPHONE OPERATOR

162. FIRE FIGHTER andMACHINE OPERATOR

163. FILE CLERK andWAITER/WAITRESS

164. TAXI DRIVER andTAILOR

165. FIRE FIGHTER andJANITOR

166. RETAIL STORE MANAGER andWAITER/WAITRESS

167. ACCOUNTANT andJANITOR

168. BANK TELLER andFILE CLERK

137

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

169. FIRE FIGHTER andRETAIL STORE MANAGER

170. MACHINE OPERATOR andSHORT-ORDER COOK

171. BANK TELLER andELEMENTARY SCHOOL TEACHER

172. CASHIER andTELEPHONE OPERATOR

173. SHORT-ORDER COOK andWAITER/WAITRESS

174. FILE CLERK andMAIL CARRIER

175. BANK TELLER andFLIGHT ATTENDANT

176. ASSEMBLY-LINE FOREMAN andCONSTRUCTION LABORER

177. JANITOR andWAITER/WAITRESS

178. FLIGHT ATTENDANT andSHORT-ORDER COOK ~

179. CASHIER andMAIL CARRIER

180. LAWYER andTAILOR

181. ASSEMBLY-LINE FOREMAN andWAITER/WAITRESS

182. MAIL CARRIER andSHORT-ORDER COOK

138

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR183. ACCOUNTANT and

FILE CLERK

184. ELEMENTARY SCHOOL TEACHER andMAIL CARRIER

185. JANITOR andTAILOR

186. FILE CLERK andRETAIL STORE MANAGER

187. ELEMENTARY SCHOOL TEACHER andTAILOR

188. TELEPHONE OPERATOR andWAITER/WAITRESS

189. CONSTRUCTION LABORER andJANITOR

190. ASSEMBLY-LINE FOREMAN andFILE CLERK

191. SHORT—ORDER COOK andELEMENTARY SCHOOL TEACHER

192. RETAIL STORE MANAGER andASSEMBLY-LINE FOREMAN

193. MACHINE OPERATOR andLAWYER

194. TELEPHONE OPERATOR andFILE CLERK

195. MACHINE OPERATOR andBANK TELLER

196. MAIL CARRIER andACTOR

139

Use the following numeric values to indicate how similar the job dutiesare for the pairs of jobs shown below:

1: 2: 3: 4: 5: 6:

NOT AT ALL SLIGHTLY SOMEWHAT MODERATELY VERY ALMOSTSIMILAR SIMILAR SIMILAR SIMILAR MUCH COMPLETELY

SIMILAR SIMILAR

197. JANITOR andELEMENTARY SCHOOL TEACHER

198. RETAIL STORE MANAGER andBANK TELLER

' 199. FILE CLERK andELEMENTARY SCHOOL TEACHER

200. FIRE FIGHTER andCONSTRUCTION LABORER

140

APPENDIX D

REP GRID MEASURE

141

I.D.

JOB DESCRIPTOR QUESTIONNAIRE

Directions: Listed below are ten sets of job titles. For each set ofthree job titles, please think about what makes any two of the jobssimilar to one another, but different from the third. First, write adescriptive word that indicates what is similar about the two jobs in the"Descriptive Word" column. Now write down the word you believe to be theopposite of that descriptive word in the "Opposite" column. Continue thisprocess for each group of three jobs until you have written ten (10) wordsand their opposites. Please don't repeat any words; use a diffferent wordfor each of the ten sets of job titles. When you are finished, pleaseturn to the instructions for the grid task.

Job Titles Descriptive Word OppositeCa) Cb)

1. cashiertailorlawyer

2. fire fighteractorelementary school teacher

3. taxi driveractorassembly—line foreman

4. cashierbank tellerwaiter/waitress

5. actormachine operatortailor

6. short-order cookelementary school teacherflight attendant

7. file clerkassembly-line foremanretail store manager

8. telephone operatorbank tellerconstruction worker

142

9. telephone operatoractorretail store manager

10. fire fighterbank tellerfile clerk

l&3

JOB GRID TASK

Instructions:

l. Please turn to the GRID Form on the next page.

2. Carefully copy the ten descriptive words and their opposites from theprevious questionnaire onto the grid form. Copy words from column(a) on the Job Descriptor Questionniare to column (a) on the GridForm. Copy words from column (b) on the Job Descriptor Questionniareto column (b) on the Grid Form.

3. Make sure that you position the next page so that the words "GridForm" appear centered at the bottom of the page (and Code No. appearsin the upper right column).

A. Look at the job CASHIER listed in the first column (upper left handcorner of the grid). Using a scale of +3 (very high on thedescriptive word) to -3 (very high on its opposite), assign the jobof cashier one number for the pair of terms (descriptive word and itsopposite) in Row 1. In other words, your rating for the job CASHIERfor the pair of words in Row l will be be ONE of the following: +3,+2, +1, -l, -2, -3. Place your rating in the square on the Grid thatcorresponds to column one, row one (i.e. the first square in the upperleft corner).

5. Next, moving DOWN the first column, rate the job CASHIER for thesecond word/opposite terms (in Row No. 2) and so on until you haveentered ratings for the first job on all ten descriptors/opposites.Be careful to place your ratings in the grid square that correspondsto the appropriate job column and descriptor row.

6. Now rate the job JANITOR in the same manner that you did the firstjob, and so on until all twenty jobs are rated on all tendescriptor/opposite terms.

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M5

APPENDIX E

BIOGRAPHI CAL QUESTIONNAIRE

146

I.D. No.

PARTICIPANT INFORMATION

1. Have you held a paying job?

2. If you have held a paying job, for how long?

Years: Months:

‘ 3. What is/was your most recent occupational/job title?

4. What are/were your job duties?

S. Nature of most recent job:

[ ] non—managerial [ ] managerial

6. If you have EVER been a manager, for how long have you been one?

Years: Months:

7. Employing organization's function (eg. manufacturing, sales,education, government agency):

8. Tenure (length of service) on current job:

Years: Months:

9. What is your age in years?

10. What is your year in college? (eg. Freshman, Sophomore, Junior,Senior).

ll. Are you a male or a female?

147

APPENDIX F

PERFORMANCE RATING QUESTIONNAIRE

148

PERFORMANCE RATING TASK

Instructions:

You are being asked to participate in a teacher evaluation study.

In a few minutes you will view videotapes of four instructors. Please

watch these carefully because you will be asked to respond to questions

concerning the performance of the instructors.

After viewing each instructor's presentation, you will complete a

two-page questionnaire. Please use the orange op—scan sheets for your

responses. There is a separate sheet for each lecturer.

When you have completed the ratings for the first instructor, please

wait quietly for the researcher to begin the next tape. This process will

be repeated until all four instructors have been rated.

Your ratings will be used for feedback and research purposes only.

They will not affect the 1ecturer's employment status or salary.

Individual responses will be strictly confidential. Only group data will

be reported. If you have any questions, please direct them to the

researcher. Thank you for your participation. Your assistance will

enable us to better understand the performance appraisal process.

149

I.D.

Teacher Evaluation Form

Directions: How often did the instructor you just observed on thetape demonstrate the behaviors listed below? Please use thefollowing scale to indicate the frequency. For each of the twelvebehaviors listed below, place your rating on the appropriateop-scan sheet.

1 2 3 h 5 6 7

Never Almost A Few About Often Most AllNever Times Half of of the of the

the Time Time Time

l. Examples were presented which were clearlyrelated to the central topic. -----··-----·

2. Lecturer used purposeful nonverbal behaviors(smiled, pointed) to emphasize points. ----··-······

3. Lecturer stopped in midsentence. —-----·-—-··—

4. Lecturer was hesitant (said "eh" or num"). --—-··---····

5. Lecturer lost eye contact with audience. ----··-······

6. Lecturer provided facts or evidence to supportbroad generalizations. ·-···········

7. Lecturer spoke fast. ·············

8. Lecturer acted nervous. ······—······

9, Lecturer spoke in a monotone for a Sustained period. ··•••········

10. Lecturer gave clear answers to questions. ·············

11. Lecturer varied his facial expressions. ·············

12. Lecturer appeared unsure of what he was saying. -—···········

150

Page two

I.D.

Teacher Evaluation Form

Directions: Using the following scale, please rate the instructoryou just observed on the tape. For each of the nine categorieslisted below, place your rating on on the appropriate line on youropscan sheet.1 2 3 4 5

Very Poor Average Good VeryBad Good

113. Thoroughness of preparation ------------

14. Grasp of material --------—---

15. Organization and clarity --·—------—--

16. Poise and demeanor ------~------

17. Responsiveness to questions ---------·-··

18. Educational value of lecture —---·········

19. Rapport with audience ··-·—·——·····

20. Speaking ability -·---········

21. Overall rating ·············

151

APPENDIX G

PLOTS OF GROUP STIMULUS SPACE: INDSCAL AND ALSCAL

152

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APPENDIX I

SUBJECT WEIGHTS MEASURING THE IMPORTANCE OF EACH DIMENSION

189

SUBJECT NEIGHTS

DIMENSIONSUBJECT PLOT HEIRO— 1 2 3 4

NUPBER SYFBOL NESS

1 1 0.1877 0.3716 0.2503 0.4464 0.32822 2 0.1767 0.4793 0.3403 0.5799 0.40913 3 0.2084 0.5110 0.2687 0.4130 0.28144 4 0.1988 0.4216 0.3176 0.5415 0.47325 5 0.0650 0.3590 0.2926 0.3526 0.30146 6 0.2590 0.3282 0.6232 0.3559 0.44447 7 0.1156 0.4973 0.4876 0.4996 0.32298 8 0.0632 0.4052 0.3391 0.3866 0.36569 9 0.0665 0.4579 0.5095 0.4763 0.4559

10 A 0.1127 0.3443 0.4140 0.4482 0.367511 8 0.0891 0.5161 0.3954 0.4328 0.339412 C 0.0684 0.4040 0.3602 0.4138 0.386013 0 0.1459 0.3981 0.5373 0.3883 0.362014 E 0.0911 0.3813 0.3442 0.4297 0.370915 F 0.1014 0.3868 0.3792 0.4717 0.351916 G 0.1770 0.4817 0.3720 0.5530 0.311017 H 0.1963 0.5765 0.2922 0.4108 0.385118 I 0.2055 0.5252 0.3363 0.3041 0.266519 J 0.0706 0.5075

A0.5353 0.4345 0.4282

20 K 0.1228 0.3102 0.3693 0.3409 0.372521 L 0.1476 0.3666 0.4057 0.4518 0.471322 M 0.1321 0.3653 0.4232 0.3624 0.434523 N 0.1662 0.5047 0.3095 0.3410 0.292924 0 0.0913 0.4850 0.4662 0.3884 0.458125 P 0.3411 0.6803 0.4484 0.2086 0.321226 G 0.2145 0.3622 0.5614 0.3255 0.411727 R 0.0993 0.4240 0.3477 0.4616 0.385328 S 0.0415 0.4560 0.4028 0.3859 0.386629 T 0.1764 0.4733 0.3667 0.5372 0.299630 U 0.3011 0.6885 0.2493 0.3911 0.397131 V 0.1761 0.4577 0.5165 0.5772 0.312632 N 0.2338 0.4153 0.5937 0.3045 0.471933 X 0.0495 0.3516 0.2853 0.2977 0.279034 Y 0.0736 0.4036 0.4459 0.4110 0.329535 Z 0.3258 0.5980 0.2989 0.5917 0.226736 1 0.1555 0.4197 0.5226 0.3548 0.459437 2 0.3107 0.6402 0.2707 0.3869 0.257238 3 0.1628 0.5400 0.5247 0.4082 0.299739 4 0.1299 0.4600 0.4352 0.5916 0.422840 5 0.1838 0.2539 0.4160 0.3183 0.296241 6 0.1551 0.4747 0.2897 0.4534 0.374242 7 0.3024 0.2757 0.4437 0.4521 0.610443 8 0.0401 0.4248 0.3873 0.4219 0.383144 9 0.0747 0.4312 0.4022 0.4753 0.4150

19 O

45 A 0.1782 0.5352 0.5772 0.4462 0.300646 8 0.1602 0.4102 0.5874 0.4174 0.398947 C 0.0554 0.4109 0.4046 0.4315 0.399148 0 0.1958 0.3821 0.5872 0.3810 0.468449 E 0.1477 0.4894 0.3184 0.3152 0.336850 F 0.1401 0.2927 0.3600 0.4093 0.353351 G 0.0771 0.4600 0.4883 0.4449 0.348852 H 0.1709 0.2972 0.4013 0.4707 0.365253 •L 0.2180 0.2888 0.4214 0.5397 0.399954 J 0.1429 0.5488 0.3921 0.3458 0.347255 K 0.1286 0.3283 0.2467 0.3548 0.248356 L 0.0477 0.3732 0.3967 0.3908 0.343657 M 0.2458 0.3585 0.6090 0.3297 0.401258 N 0.0568 0.3753 0.2984 0.3115 0.283459 0 0.2372 0.6464 0.3264 0.3497 0.384560 P 0.1741 0.4547 0.3610 0.3920 0.512061 O 0.1320 0.3544 0.3958 0.4744 0.322262 R 0.1376 0.5456 0.3360 0.4224 0.403663 S 0.1363 0.4267 0.5433 0.3785 0.398064 T 0.2200 0.3447 0.5278 0.3917 0.532665 U 0.3848 0.3123 0.6753 0.3703 0.216466 V 0.1362 0.6053 0.5053 0.3759 0.413167 H 0.1234 0.4015 0.3146 0.4495 0.371268 X 0.0835 0.4047 0.3487 0.4109 0.394369 Y 0.1412 0.4224 0.4791 0.3949 0.280070 Z 0.2301 0.3742 0.5006 0.3934 0.220071 1 0.1769 0.5584 0.3769 0.5852 0.351472 2 0.2601 0.5396 0.2482 0.4987 0.294673 3 0.2185 0.4838 0.2596 0.4391 0.469974 4 0.2610 0.6445 0.4116 0.3239 0.279575 5 0.1907 0.3656 0.5001 0.5222 0.303976 6 0.2161 0.5706 0.3570 0.5147 0.273377 7 0.1007 0.4381 0,4982 0.3744 0.401078 8 0.2347 0.5762 0.5555 0.2990 0.324979 9 0.1368 0.3263 0.3593 0.3972 0.406780 A 0.1203 0.3529 0.3113 0.4206 0.290381 8 0.1570 0.3999 0.4953 0.3376 0.304782 C 0.1755 0.3233 0.4089 0.5061 0.425383 0 0.2143 0.4764 0.4907 0.4694 0.226884 E 0.0793 0.3986 0.4036 0.3612 0.285585 F 0.0815 0.4393 0.3881 0.4683 0.339786 G 0.2275 0.3375 0.3997 0.6040 0.471487 H 0.1438 0.5641 0.3568 0.3831 0.390688 I 0.1077 0.4912 0.5724 0.4671 0.382889 J 0.0919 0.4846 0.4256 0.3690 0.320990 K 0.1834 0.4693 0.5790 0.4722 0.296591 L 0.0443 0.4252 0.4192 0.4147 0.404192 H 0.1108 0.2822 0.2479 0.3338 0.249593 N 0.1957 0.5362 0.6149 0.3551 0.368794 O 0.1838 0.3556 0.2277 0.4017 0.321095 P 0.2651 0.5085 0.3602 0.3348 0.597596 0 0.1249 0.4486 0.5536 0.4182 0.369597 R 0.1506 0.4212 0.3164 0.4056 0.445198 S 0.0561 0.3602 0.3180 0.2846 0.288099 T 0.1028 0.2579 0.2907 0.3085 0.2901

100 U 0.1686 0.5113 0.4438 0.5328 0.2851101 V 0.2529 0.6176 0.4376 0.3505 0.2478102 N 0.2161 0.3484 0.6057 0.4131 0.3807103 X 0.1977 0.3758 0.2730 0.4898 0.3351104 Y 0.3390 0.3729 0.5947 0.2513 0.6050

191

105 Z 0.2039 0.5890 0.2993 0.4487 0.3460

106 1 0.2073 0.5818 0.3080 0.4768 0.3218

107 2 0.0807 0.4050 0.4656 0.4481 0.4222

108 3 0.1605 0.4391 0.2964 0.4214 0.4358

109 4 0.0604 0.4335 0.4025 0.3486 0.3688

110 5 0.1951 0.5125 0.2747 0.3451 0.3001

111 6 0.2411 0.5718 0.3645 0.5343 0.2503

112’7°

0.1339 0.5787 0.4204 0.3681 0.3875

113 8 0.3165 0.3665 0.2782 0.3769 0.5817

114 9 0.0958 0.5382 0.4335 0.3994 0.3506

115 A 0.0808 0.3089 0.3327 0.3614 0.2780

116 8 0.0562 0.4701 0.3824 0.4262 0.3483

117 C 0.3582 0.5287 0.6520 0.1752 0.3788

118 D 0.2185 0.4663 0.5594 0.2830 0.4564

119 E 0.1593 0.5862 0.3428 0.4151 0.3868

120 F 0.1195 0.3496 0.4428 0.3372 0.3587

121 G 0.1547 0.6023 0.3570 0.4261 0.4156

122 H 0.1405 0.3608 0.2292 0.3196 0.3056

123 I 0.1425 0.5652 0.3426 0.4566 0.3872

124 J 0.1356 0.3992 0.2774 0.3680 0.2495

125 K 0.2010 0.3523 0.5290 0.6186 0.4441

126 L 0.1213 0.4789 0.3617 0.4400 0.4670

127 M 0.0831 0.2960 0.3097 0.3518 0.2766

128 N 0.0870 0.4610 0.3760 0.3650 0.2998

129 0 0.0255 0.3989 0.3799 0.3657 0.3162

130 P 0.1373 0.3541 0.4075 0.4574 0.2932

131 Q 0.1547 0.3186 0.4571 0.3868 0.2984

132 R 0.2276 0.5291 0.2990 0.5036 0.2773

133 S 0.1917 0.3465 0.3544 0.3822 0.4788

134 T 0.1200 0.4960 0.3520 0.3425 0.3257

135 U 0.0510 0.4845 0.4152 0.4355 0.4305

136 V 0.2446 0.3334 0.5552 0.4098 0.2663

137 H 0.0966 0.5174 0.3965 0.3884 0.3377

138 X 0.0863 0.3880 0.4155 0.4643 0.3573

139 Y 0.1075 0.4323 0.4108 0.3866 0.4574

140 Z 0.2610 0.3875 0.5695 0.2932 0.2876

141 1 0.1110 0.3828 0.4950 0.4375 0.3684

142 2 0.1135 0.4015 0.3824 0.4870 0.3348

143 3 0.2186 0.3757 0.3724 0.4863 0.5536

144 4 0.0404 0.3834 0.3896 0.3571 0.3488

145 5 0.2484 0.3779 0.3848 0.5611 0.2477

146 6 0.0318 0.5046 0.4971 0.4647 0.4508

147 7 0.1422 0.4758 0.3207 0.4885 0.3614

148 8 0.1812 0.5162 0.5894 0.3677 0.3470

149 9 0.1996 0.5228 0.5661 0.3238 0.3420

150 A 0.0577 0.4518 0.3902 0.4014 0.3191

151 8 0.0765 0.4376 0.4713 0.3571 0.3996

152 C 0.2754 0.2588 0.6000 0.4904 0.5128

153 D 0.1800 0.5712 0.4107 0.4094 0.2769

154 E 0.3631 0.6762 0.2393 0.3584 0.2570

155 F 0.1291 0.2992 0.3059 0.3232 0.3573

156 G 0.1780 0.4150 0.5448 0.3840 0.3032

157 H 0.1437 0.4561 0.3961 0.4056 0.5055

158 I 0.1367 0.4816 0.5139 0.4974 0.3110

159 J 0.2918 0.3299 0.4534 0.4285 0.6457

160 K 0.2590 0.5080 0.4211 0.2960 0.5854

161 L 0.1396 0.4949 0.5997 0.4149 0.3981

162 H 0.0529 0.4468 0.4671 0.4020 0.3842

163 N 0.2357 0.5953 0.3034 0.3828 0.2915

164 0 0.0529 0.5143 0.4123 0.4510 0.4163

192

165 P 0.1144 0.4298 0.5505 0.4324 0.4280166 Q 0.2459 0.6507 0.4423 0.3294 0.2984167 R 0.1341 0.5775 0.4012 0.3877 0.3621168 S 0.2454 0.5374 0.3215 0.3046 0.5022169 T 0.2875 0.4582 0.6791 0.3496 0.2962170 U 0.0880 0.3812 0.3698 0.3931 0.4037171 V 0.1004 0.3530 0.3728 0.4406 0.3378172 N 0.0903 0.5112 0.4085 0.3688 0.4003173 .X 0.1944 0.5746 0.3033 0.4423 0.3381174 Y 0.1522 0.3955 0.5840 0.4623 0.4411175 Z 0.1702 0.6052 0.3656 0.3896 0.3661176 1 0.0766 0.4432 0.5118 0.4353 0.4025177 2 0.1019 0.4742 0.3354 0.4374 0.3832178 3 0.2082 0.3014 0.5008 0.4514 0.4814179 4 0.0619 0.4507 0.3833 0.4405 0.3387180 5 0.2993 0.3492 0.4169 0.3862 0.6386181 6 0.1168 0.5406 0.3622 0.4225 0.4267182 7 0.1205 0.5695 0.3728 0.4439 0.3884183 8 0.1950 0.6392 0.3886 0.4265 0.3221184 9 0.2486 0.4525 0.2164 0.3216 0.2174185 A 0.0429 0.4421 0.3826 0.3908 0.3261186 8 0.1276 0.5111 0.5031 0.3806 0.5019187 C 0.1135 0.3180 0.3004 0.3943 0.2980188 D 0.0352 0.4798 0.4065 0.4312 0.3728189 E 0.2006 0.4111 0.6391 0.4341 0.3744190 F 0.1219 0.4489 0.3378 0.4874 0.3774191 G 0.0876 0.3520 0.3764 0.4024 0.2914192 H 0.1255 0.3660 0.2625 0.3713 0.2651193 I 0.0762 0.3607 0.3070 0.3761 0.3301194 J 0.1334 0.5184 0.3270 0.4356 0.3525195 K 0.2682 0.3358 0.4364 0.3108 0.5424196 L 0.3327 0.6474 0.3015 0.4742 0.2066197 M 0.3530 0.4135 0.6431 0.1977 0.5257198 N 0.1529 0.4547 0.5353 0.3404 0.3722199 O 0.0314 0.4928 0.4408 0.4221 0.3822200 P 0.0864 0.4397 0.3521 0.3573 0.3907201 G 0.1343 0.4498 0.5753 0.4091 0.4547202 R 0.1117 0.4535 0.3307 0.4590 0.3546203 S 0.0907 0.4357 0.3622 0.4307 0.4226204 T 0.1852 0.4471 0.5814 0.3404 0.3868205 U 0.2108 0.3979 0.5904 0.3364 0.3874206 V 0.1339 0.3900 0.4814 0.4761 0.4903207 H 0.0565 0.5092 0.4100 0.4716 0.4199208 X 0.2995 0.3187 0.6860 0.4499 0.3306209 Y 0.1348 0.3822 0.3060 0.3948 0.4053210 Z 0.2814 0.4269 0.4508 0.2897 0.5973211 1 0.0390 0.4472 0.4385 0.4658 0.3886212 2 0.1456 0.4934 0.4021 0.4923 0.2898213 3 0.0468 0.3843 0.3842 0.3345 0.3152214 4 0.2767 0.2937 0.5805 0.3645 0.2982215 5 0.1348 0.4957 0.3278 0.4603 0.4320216 6 0.0963 0.3716 0.3827 0.4543 0.3754217 7 0.1569 0.3779 0.4266 0.5321 0.3351218 8 0.1096 0.4281 0.3065 0.4023 0.3743219 9 0.0412 0.4511 0.4517 0.4427 0.4254220 A 0.0660 0.5168 0.4015 0.4189 0.3992221 B 0.1875 0.3865 0.4935 0.3403 0.4886222 C 0.2888 0.2965 0.5602 0.3494 0.5497223 0 0.2552 0.2644 0.3052 0.4906 0.4132224 E 0.1001 0.4143 0.4919 0.4283 0.4514

193

225 F 0.0740 0.4214 0.3339 0.3922 0.5040

226 G 0.1092 0.5869 0.4996 0.4525 0.4097

227 H 0.1471 0.5775 0.3405 0.4962 0.5661

228 I 0.1777 0.5459 0.5231 0.4287 0.2802

229 J 0.2484 0.6116 0.5059 0.3547 0.2549

250 K 0.5085 0.2806 0.5489 0.5941 0.6098

251 L 0.1960 0.3589 0.2843 0.4926 0.5542

252 M- 0.5562 0.6510 0.2871 0.5758 0.2105

255 N 0.1079 0.4084 0.3256 0.4457 0.5514

234 0 0.2098 0.6505 0.3230 0.4143 0.5615

255 P 0.2052 0.6288 0.3999 0.4200 0.2965

256 Q 0.0707 0.4186 0.4175 0.5598 0.5605

257 R 0.2542 0.4402 0.2516 0.5085 0.2676

258 S 0.0568 0.4511 0.4485 0.4947 0.4171

259 T 0.4575 0.6939 0.2568 0.2571 0.2291

240 U 0.0911 0.4066 0.5715 0.4599 0.4001

241 V 0.0668 0.4845 0.4507 0.4745 0.5514

242 N 0.2199 0.4059 0.5022 0.5715 0.4991

245 X 0.1595 0.5551 0.5271 0.4168 0.5684

244 Y 0.0955 0.4554 0.5214 0.4327 0.5777

245 Z 0.5624 0.8398 0.1949 0.2616 0.1956

246 1 0.0982 0.4294 0.5421 0.4581 0.5656

247 2 0.1947 0.2959 0.5006 0.4648 0.4168

248 5 0.1251 0.2844 0.2796 0.5620 0.3054

249 4 0.1021 0.5042 0.5606 0.4654 0.4240

250 5 0.2597 0.5537 0.5995 0.4105 0.5519

251 6 0.1055 0.4868 0.5409 0.3753 0.5765

252 7 0.0975 0.5505 0.2627 0.2815 0.5024

255 8 0.1017 0.5797 0.5621 0.4109 0.2725

254 9 0.5152 0.5015 0.6251 0.5878 0.2694

255 A 0.0409 0.4517 0.3658 0.4089 0.3505

256 B 0.2541 0.4059 0.4972 0.5954 0.6128

257 C 0.2596 0.6815 0.4050 0.5590 0.5299

258 D 0.1247 0.4056 0.5857 0.5005 0.4591

259 E 0.1565 0.4125 0.5589 0.4712 0.2887

260 F 0.5645 0.7046 0.2678 0.5209 0.2776

261 G 0.0761 0.5094 0.4134 0.5908 0.5545

262 H 0.2046 0.3075 0.4894 0.5459 0.4017

265 I 0.1165 0.2697 0.2145 0.2685 0.2711

264 J 0.0981 0.4950 0.5476 0.4404 0.5788

265 K 0.1065 0.2978 0.3407 0.5822 0.5052

266 L 0.1968 0.4044 0.5725 0.4096 0.5056

267 M 0.1452 0.5055 0.4700 0.5471 0.4854

268 N 0.1550 0.5862 0.5876 0.4098 0.5529

269 0 0.1868 0.4282 0.6525 0.4971 0.5574

270 P 0.1255 0.3526 0.2559 0.2599 0.5074

271 Q 0.2442 0.6291 0.5551 0.5061 0.2859

272 R 0.0725 0.4612 0.4807 0.5145 0.5886

275 S 0.1107 0.5854 0.2976 0.4055 0.5559

274 T 0.2407 0.2800 0.5617 0.4223 0.4475

275 U 0.1103 0.4255 0.2849 0.5589 0.2954

276 V 0.5556 0.5505 0.6859 0.5072 0.5467

277 N 0.2448 0.5566 0.2464 0.5500 0.5507

278 X 0.0885 0.5166 0.5506 0.5795 0.2871

279 Y 0.1565 0.3542 0.5315 0.4759 0.5945

280 Z 0.1598 0.5771 0.4420 0.5416 0.5441

281 1 0.1717 0.4739 0.5462 0.5202 0.3996

282 2 0.1475 0.5470 0.5264 0.4407 0.5718

285 5 0.0652 0.3478 0.5048 0.5519 0.2650

284 4 0.1559 0.5546 0.4002 0.4159 0.5151

191+

285 5 0.0939 0.4632 0.4339 0.4330 0.3045286 6 0.0865 0.3652 0.3183 0.4036 0.3335287 7 0.2090 0.3674 0.5429 0.3542 0.4955288 8 0.1070 0.3670 0.4121 0.4395 0.4193289 9 0.2270 0.2386 0.3214 0.4293 0.3773290 A 0.3094 0.2594 0.6574 0.4242 0.3844291 B 0.0434 0.3713 0.3253 0.3587 0.3288292 C 0.1428 0.3370 0.3498 0.3898 0.4197293 .0 0.0828 0.4738 0.4621 0.5481 0.4134294 E 0.1337 0.4504 0.2953 0.4211 0.3170295 F 0.1187 0.4876 0.3834 0.4466 0.2967296 G 0.1263 0.3786 0.3859 0.4536 0.2852297 H 0.1596 0.4137 0.4397 0.5752 0.5137298 I 0.1113 0.3746 0.3354 0.4494 0.3457299 J 0.1114 0.4138 0.5262 0.4632 0.3751300 K 0.1402 0.5374 0.4055 0.3341 0.3466301 L 0.1049 0.4626 0.4871 0.3908 0.4690302 H 0.2009 0.4514 0.6283 0.4736 0.3193303 N 0.1605 0.3241 0.3133 0.4547 0.3455304 O 0.1018 0.4812 0.3688 0.3849 0.4290305 P 0.1118 0.5787 0.3980 0.4298 0.4401

OVERALL IMPORTANCE OF EACH DIMENSION 0.2051 0.1812 0.1742 0.1431

195

APPENDIX J

PLOTS OF INDIVIDUAL SUBJECT WEIGHTS

FOUR-DIMENSIONAL INDSCAL SOLUTION

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APPENDIX L

COMPUTER PROGRAM FOR DETERMINING ACCURACY SCORES

205

DATA ONE;INPUT IDA 1-5 ID 6-9 TAPE1 10 RATEE1 11F1 12 FZ 13 F3 14 F4 15 F5 16 F6 17F7 18 F8 19 F9 20 F10 21 F11 22 F12 23 P1 24 P2 25 P3 26 P4 27 P5 28P6 29 P7 30 P8 31 GLOBAL1 32 IDZ 33-41 TAPEZ 42 RATEE2 43 F13 44 F14 45F15 46 F16 47 F17 48 F18 49 F19 50 F20 51 F21 52 F22 53 F23 54 FZ4 55P9 56 P10 57 P11 58 P12 59 P13 60 P14 61 P15 62 P16 63 GLOBALZ 64 Q2ID3 1-9 TAPE3 10 RATEE3 11 FZ5 12 FZ6 13 FZ7 14 F28 15 F29 16 F30 17F31 18 F32 19 F33 Z0 F34 21 F35 Z2 F36 23 P17 24 P18 25 P19 26 P20 27PZ1 28 PZZ 29 PZ3 30 P24 31 GLOBAL3 32 ID4 33-41 TAPE4 42 RATEE4 43F37 44 F38 45 F39 46 F40 47 F41 48 F42 49 F43 50 F44 51 F45 52 F46 53F47 54 F48 55 P25 56 PZ6 S7 PZ7 58 P28 59 P29 60 P30 61 P31 62 P32 63GLOBAL4 64;t1=5.4; t2=3.4; t3=5.3; t4=2.9; t5=3.S; t6=3.3: t7=3.2; t8=3.2;t9=4.6; t10=4.4; t11=4.3; t1Z=4.7; t13=4.0s t14=4.0; t15=4.2;t16=4.5; t17=4.1; t18=4.0;t19=4.1; tZ0=3.9; t21=3.3; t2Z=3.4;t23=3.3; t24=3.6; t25=Z.4; t26=Z.5; t27=2.0; t28=1.5; tZ9=2.2;t30=2.3; t31=1.6; t32=1.7sTF1=5.1; TF2=4.3; TF3=4.4; TF4=4.2; TF5=4.1; TF6=5.3; TF7=6.2;TF8=4.4; TF9=5.8; TF10=4.4s TF11=3.9; TF12=4.4;TF13=5.7; TF14=5.9;TF15=6.1; TF16=6.7;TF17=6.1; TF18=5.8;TF19=3.9; TF20=6.4; TF21=6.7sTFZZ=3.1;TF23=5.4; TFZ4=6.4;TF2S=5.6;TF26=4.0; TF27=6.1; TFZ8=6.1;TF29=5.0; TF30=5.3s TF31=6.5; TF32=6.0; TF33=5.2; TF34=4.6; TF35=3.3;TF36=5.6; TF37=4.3; TF38=2.0; TF39=3.4;TF40=4.1; TF41=3.3; TF4Z=4.1;TF43=6.1; TF44=2.3; TF45=3.6; TF46=2.2; TF47=2.6; TF48=3.0;If tapel= 0 then tape=1;If ta¤e1=1 then tape=2;If ratee1=0 then r1=1;If ratee1=1 then r1=2;If ratee1=2 then r1=3;If ratee1=3 then r1=4;If ratee2=0 then r2=1sIf ratee2=1 then r2=2;if ratee2=2 then r2=3;If ratee2=3 then r2=4;If ratee3=0 then r3=1;if r¤tee3=1 then r3=2;if ratee3=2 then r3=3;if ratee3=3 then r3=4;If ratee4=0 then r4=1;If ratee4=1 then r4=2;If ratee4=2 then r4=3;If ratee4=3 then r4=4;ARRAY A P1-P32 F1—F48;

do over A;A = A + 1;end;

206

ARRAY REVERSEF5 F4 F5 F7 F8 F9 F12 F15 F16 F17 F19 FZ9 F21 FZ4FZ7 FZ8 F29 F31 FSZ F35 F56 F39 F40 F61 F45 F44 F45 F48TF3 TF4 TF5 TF7 TF8 TF9 TF12 TF15 TF16 TF17 TF19 TFZ0 TFZ1 TFZ4TF27 TFZ8 TFZ9 TF31 TF32 TF33 TF36 TF39 TF40 TF41 TF43 TF44 TF45 TF48;DO OVER REVERSE;REVERSE=8—REVERSE; END;

XPERFORMANCE RATING MODIFICATIONS HERE;XFOLLONING ARE MEANS (PERF RATINGS) FOR EACH RATEE;PMRATEE1=MEAN(P1„P2,P5•P4;P5„P6„P7;P8);PMRATEEZ=MEAN(P9„P10;P11„P1Z,P13,P14„P15,P16);PMRATEE3=MEAN(P17,P18;P19;PZO;PZ1„PZZ„PZ5;PZ4);PMRATEE4=MEAN(PZ5;PZ6;PZ7,P28„PZ9;P30,P31;P32);¥FOLLONING ARE MEANS (PERF RATINGS) FOR EACH DIMENSION;PMDIM1=MEAN(P1,P9;P17,P25);PMDIMZ=MEAN(PZ;P10;P18;P26);PMDIM3=MEAN(P3,P11;P19,P27);PMDIM4=MEAN(PQ„P1Z;P20„PZ8);PMDIM5=MEAN(P5,P15,PZ1,P29);PMDIM6=MEAN(P6;P14;P22„P30);PMDIM7=MEAN(P7„P15;P23;P31);PMDIM8=MEAN(P8;P16;PZ4;PSZ);¤TRUE SCORE (PERF) MODIFICATIONS HERE;TMRATEE1=MEAN(T1;TZ;T3;T4;T5;T6„T7;T8);TMRATEEZ=MEAN(T9;T10;T11;T1Z;T15;T14;T15;T16);TMRATEE3=MEAN(T17,T18„T19;T20;TZ1„TZZ,TZ3;TZ4);TMRATEE4=MEAN(TZ5„TZ6;TZ7,TZ8;TZ9„T30;T31;T3Z);TMDIM1=MEAN(T1,T9;T17;TZ5);TMDIM2=MEAN(T2,T10„T18;TZ6);TMDIM3=MEAN(T3,T11;T19;TZ7);TMDIM4=MEAN(T4;T12,TZO;T28);TMDIM5=MEAN(T5;T13;T21,TZ9);TMDIM6=MEAN(T6;T14„TZ2„T50);TMDIM7=MEAN(T7,T15;TZ5;T31);TMDIM8=MEAN(T8;T16„TZ4;T3Z);PMOVERAL=MEAN(P1;PZ;P3;P4;P5;P6„P7;P8;P9;P10;P11;P12,P13,P14,P15;P16;P17;P18,P19;P20„PZ1;PZZ;PZ$;PZ4„P25„PZ6;PZ7;PZ8;PZ9„P50,P31,P3Z);TMOVERAL=MEAN(T1;TZ;T3;T4,T5;T6;T7;T8„T9„T10;T11;T12„T13;T14,T15;T16;T17„T18•T19„TZO;TZ1;TZZ;TZ5;TZ4,TZ5;TZ6;TZ7„T28,T29„T30„T31,T3Z);XNEXT COMPUTE P' AND T' FOR DIFFERENTIAL ACCURACY;PPRIME1=(P1—PMRATEE1—PMDIM1+PMOVERAL);TPRIME1=(T1—TMRATEE1-TMDIM1+TMOVERAL);PPRIME2=(PZ—PMRATEE1-PMDIMZ+PMOVERAL);TPRIMEZ=(TZ—TMRATEE1—TMDIMZ+TMOVERAL);PPRIME3=(P3—PMRATEE1-PMD1M5+PMOVERAL);TPRIME3=(T3-TMRATEE1—TMDIM3+TMOVERAL);PPRIME4=(P4—PMRATEE1-PMDIM6+PMOVERAL);TPRIME4=(T4—TMRATEE1—TMDIM6+TMOVERAL);PPRIME5=(P5-PMRATEE1—PMDIM5+PMOVERAL);TPRIME5=(T5—TMRATEE1—TMDIM5+TMOVERALI;PPRIME6=(P6—PMRATEE1—PMDIM6+PMOVERAL);TPRIME6=(T6-TMRATEE1-TMDIM6+TMOVERAL);PPRIME7=(P7-PMRATEE1-PMDIM7+PMOVERAL);TPRIME7=(T7—TMRATEE1—TMDIM7+TMOVERAL);PPRIME8=(P8—PMRATEE1·PMDIM8+PMOVERAL);

207

TPRIME8=(T8·TMRATEE1—TMDIM8+TMOVERAL);PPRIME9=(P9-PMRATEE2—PMDIM1+PMOVERAL);TPRIME9=(T9—TMRATEEZ—TMDIM1+TMOVERALl;PPRIME10=(P10—PMRATEE2—PMDIM2+PMOVERAL);TPRIME10=(T10·TMRATEE2·TMDIM2+TMOVERAL):PPRIME11=(P11—PMRATEE2·PMDIM3+PMOVERAL);TPRIME1l=(T11—TMRATEE2—TMDIM3+TMOVERAL);PPRIME12=(P12-PMRATEE2—PMDIM4+PMOVERAL);TPRIME12=(T12—TMRATEE2—TMDIM4+TMOVERAL);PPRIME13=(P15-PMRATEE2—PMDIM5+PMOVERAL);TPRIME13=(T13·TMRATEE2-TMDIM5+TMOVERAL)sPPRIME14=(P14-PMRATEE2—PMD!M6+PMOVERAL);TPRIME14=(T14-TMRATEE2—TMDIM6+TMOvERAL);PPRIME15=(P15—PMRATEE2—PMDIM7+PMOVERAL);TPRIME15=(T15-TMRATEE2-TMDIM7+TMOVERAL);PPRIME16=(P16—PMRATEE2—PMDIM8+PMOVERAL);TPRIME16=(T16·TMRATEE2—TMDlM8+TMOVERAL);PPRIME17=(P17—PMRATEE5—PMDIM1+PMOVERAL);TPRIME17=(T17—TMRATEE5—TMDIM1+TMOVERAL)sPPRIME18=(P18—PMRATEE3—PMDIM2+PMOVERAL);TPRIME18=(T18-TMRATEE3-TMDIM2+TMOVERAL);PPRIME19=(P19—PMRATEE5·PMDIM5+PMOVERAL);TPRIME19=(T19—TMRATEE3—TMDIM3+TMOVERAL);PPRIME20=(P20—PMRATEE}-PMDIM4+PMOVERAL);TPRIME20=(T20—TMRATEE3-TMDIM4+TMOVERAL)sPPRIME21=(P21—PMRATEE5-PMDIM5+PMOVERAL);TPRIME21=(T21—TMRATEE3-TMDIM5+TMOVERAL);PPRIME22=(P22—PMRATEE5-PMDIM6+PMOVERAL);TPRIME22=(T22—TMRATEE3—TMDIM6+TMOVERAL);PPRIME23=(P23—PMRATEES—PMDIM7+PMOVERAL)STPRIME23=(T25—TMRATEE3-TMDIM7+TMOVERAL);PPRIME24=(P24—PMRATEE3-PMDIM8+PMOVERAL);TPRIME24=(T24-TMRATEE3—TMDIM8+TMOVERAL);PPRIME25=(P25-PMRATEE4—PMDIM1+PMOVERAL);TPRIME25=(T25-TMRATEE4—TMDIM1+TMOVERAL);PPRIME26=(P26—PMRATEE4—PMDIM2+PMOVERAL);TPRIME26=(T26-TMRATEEG-TMDIM2+TMOVERAL);PPRIME27=(P27-PMRATEE4—PMDIM3+PMOVERAL):TPRIME27=(T27-TMRATEE4-TMDIM5+TMOVERALl;PPRIME28=(P28—PMRATEE4—PMDIM4+PMOVERAL);TPRIME28=(T28—TMRATEEQ—TMDIM4+TMOVERAL):PPRIME29=(P29—PMRATEE4-PMDIM5+PMOVERAL); ,TPRIME29=(T29—TMRATEE4—TMDIM5+TMOVERAL)sPPRIME30=(P50—PMRATEE4—PMDIM6+PMOVERAL);TPRIME50=(T30—TMRATEE4—TMDIM6+TMOVERALlsPPRIME31=(PS1—PMRATEE4—PMDIM7+PMOVERAL);TPRIME31=(T31-TMRATEE4-TMDIM7+TMOVERAL);PPRIME32=(P32—PMRATEE4-PMDIM8+PMOVERAL);TPRIME32=(T32—TMRATEE6—TMDIM8+TMOVERAL);DIFF1=(PPRIMEl—TPRIME1)¤¤2:DIFF2=(PPRIME2·TPRIME2)X¥2;DIFF3=(PPRIME3-TPRIME3)X¥2;DIFF4=(PPRIMEQ—TPRIME4)¤¤2;

{ DIFF5=(PPRIME5—TPRIME5)¤¥2:DIFF6=(PPRIME6—TPRIME6)¤¥2;

208

DIFF7=(PPRIME7—TPRIME7)XX2;DIFF8=(PPRIME8-TPRIME8)xx2;DIFF9=(PPRIME9-TPRIME9)xx2;DIFF10=(PPRIME10-TPRIME10)¤¥2;DIFF11=(PPRIME11-TPRIME11)X¤2;DIFF12=(PPRIME12-TPRIME12)¤¥2;DIFF13=(PPRIME13—TPRIME13)xx2;DIFF14=(PPRIME14-TPRIME14)**2;DIFF15=(PPRIME15—TPRIME15)¥¤2;DIFF16=(PPRIME16—TPRIME16)**2;DIFF17=(PPRIME17—TPRIME17)¥¤2;DIFF18=(PPRIME18—TPRIME18)¤!2;DIFF19=(PPRIME19—TPRIME19)¤X2;DIFF2U=(PPRIMEZÜ•TPRIME20)*!2iD1FF21=(PPRIME21—TPRIME21)xx2;DIFF22=(PPRIME22—TPRIME22)¤¥2:DIFF23=(PPRIME23-TPRIME25)X¤2;DIFF24=(PPRIME24-TPRIME24)xx2;DIFF25=(PPRIME25—TPRIME25)¥¤2;DIFF26=(PPRIME26—TPRIME26)X¤2;DIFF27=(PPRIME27—TPRIME27)¥¤2;DIFFZ8=(PPRIME28*TPRIME28)X¥Z;DIFFZ9=(PPRIMEZ9—TPRIME29)xx2;DIFF30=(PPRIME30-TPRIME30)¥¥2;DIFF51=(PPRIME31·TPRIME31)§¤2;DIFF32=(PPRIME52—TPRIME32)**2;DAPERF=(DIFF1+DIFF2+DIFF5+DIFF4+DIFF5+DIFF6+DIFF7+DIFF8+DIFF9+DIFF10+DIFF11+DIFF12+DIFF15+DIFF14+DIFF15+DIFFl6+DIFF17+DIFF18+DIFF19+DIFF20+DIFF2l+DIFF22+DIFF23+DIFF24+DIFF25+DIFF26+DIFF27+DIFF28+DIFF29+DIFF30+DIFF31+DIFF52)/52;DAP=SQRT(DAPERF);*COMPUTE DACOR FOR PERF RATINGS AS FOLLONS;¤P=PPRIME1;¤T=TPRIME1;*0UTPUT;¤P=PPRIME2;¤T=TPRIME2;¥0UTPUT;¤P=PPRIME3;¤T=TPRIME3;¤OUTPUT;¥P=PPRIME4;¥T=TPRIME4;¥0UTPUT;¤P=PPRIME5;*T=TPRIME5;*0UTPUT;¥P=PPRIME6;¤T=TPRIME6;¥0UTPUT;¥P=PPRIME7;¥T=TPRIME7;¥0UTPUT;¤P=PPRIME8;¤T=TPRIME8;¥0UTPUT;¤P=PPRIME9;*T=TPRIME9;¥0UTPUT;*P=PPRIME10;*T=TPRIME10;¤0UTPUT;XP=PPRIME11;¥T=TPRIME11;¥OUTPUT;¥P=PPRIME12;¤T=TPRIME12;¤OUTPUT;¥P=PPRIME15;¤T=TPRIME15;¥OUTPUT:¥P=PPRIME14;XT=TPRIME14;XOUTPUT;¤P=PPRIME15;¤T=TPRIME15;¤0UTPUT;¥P=PPRIME16s¤T=TPRIMEl6s*0UTPUT;¤P=PPRIME17;¤T=TPRIME17;*OUTPUT;¤P=PPRIME18;¤T=TPRIME18;XOUTPUT;¤P=PPRIME19;¥T=TPRIME19;¥OUTPUT;¤P=PPRIME20;¤T=TPRIME20;x0UTPUT;xP=PPRIME21;¤T=TPRIME21;¥OUTPUT;*P=PPRIME22;¤T=TPRIME22;¥0UTPUT;¤P=PPRIME23;¤T=TPRIME23;¥0UTPUT;

209

¤P=PPRIME24;XT=TPRIME24;¤0UTPUT;¤P=PPRIME25;¥T=TPRIME25;*0UTPUT;xP=PPRIME26;XT=TPRIME26;*0UTPUT;¤P=PPRIME27;¥T=TPRIME27;*0UTPUT;¤P=PPRIME28;¤T=TPRIME28;¤0UTPUT;¤P=PPRIME29;¤T=TPRIME29;¥0UTPUT;¥P=PPRIME50;¤T=TPRIME30;XOUTPUT;¥P=PPRIME31;*T=TPRIME51;*0UTPUT;¤P=PPRIME32;!T=TPRIME32;*0UTPUT;¥CORRELATE P AND T LATER;*MODIFICATIONS FOR FREQUENCY RATINGS HERE;FMRAT1=MEAN(F1;F2„F3;F4;F5;F6;F7;F8;F9,F10;F11;F12);FMRAT2=MEAN(F13;F14;F15;F16„F17,F18;F19;F20„F21;F22;F23;F26);FMRAT5=MEAN(F25„F26;F27;F28„F29„F30„F51„F32;F33;F36„F35,F36);FMRAT4=MEAN(F57;F38;F39;F40;F41;F42;F43„F44;FQ5„F46;F47,F48);FMDIM1=MEAN(F1;F13;F2S;F37);FMDIM2=MEAN(F2;F14„F26,F38);FMDIM5=MEAN(F3;Fl5;F27,F39);FMDIM4=MEAN(F4„F16;F28;F40);FMDIM5=MEAN(F5;F17;F29;F41);FMDIM6=MEAN(F6„F18;F30,F42);FMDIM7=MEAN(F7„F19„F51;F43);FMDIM8=MEAN(F8,F20„F32;F44);FMDIM9=MEAN(F9;F21,F55,F45);FMDIM10=MEAN(Fl0,F22;FS4;F46);FMDIM11=MEAN(F11,F23,F35„F47);FMDIM12=MEAN(F12,F24;F36„F48);xTRUES FOR FREQUENCIES GO HERE;TFMRAT1=MEAN(TF1,TF2;TF3„TFO,TF5;TF6;TF7;TF8;TF9;TF10;TF11„TF12);¥;g§AT2=MEAN(TF13,TF14„TF15,TF16,TF17,TFl8„TF19,TF20,TF21,TF22,TF25,TE§§ATé;2EAN(TF25,TF26,TF27,TF28,TF29;TF50;TF31„TF32;TF35;TF34;TFMRAT4=MEAN(TF37,TF38„TF39;TF40;TF41;TF42;TF4$„TF44;TF45;TF46;TF47„TF48);TFMDIM1=MEAN(TF1,TF13,TF25;TF37);TFMDIM2=MEAN(TF2;TF14,TF26;TFS8);TFMDIM3=MEAN(TF3;TF15;TF27,TF39);TFMDIM4=MEAN(TF4,TF16„TF28;TF40l;TFMDIM5=MEAN(TF5;TF17;TF29;TF41);TFMDIM6=MEAN(TF6;TF18„TF30,TF42);TFMDIM7=MEAN(TF7,TF19,TF31;TF43);TFMDIM8=MEAN(TF8,TF20;TF52;TF44);TFMDIM9=MEAN(TF9,TF21;TF33,TF45):TFMDIM10=MEAN(TF10;TF22;TF34;TF46);‘TFMDIMl1=MEAN(TF11,TF25;TF35;TF47);TFMDIM12=MEAN(TF12;TF24;TF36;TFQ8);FMOVER=MEAN(F1„F2;F3;F4;F5;F6;F7;F8;F9,F10;F11„F12,F13,F14,F15,F16,F17;F18;F19;F20,F2l;F22,F25,F24„F25;F26;F27;F28,F29,F50,F31,F32,F33,

TFMOVER=MEAN(TF1;TF2;TF5„TF4„TF5„TF6,TF7,TF8„TF9,TF10„TF11,TF12;TF15;TFl4;TF15;TF16;TFl7„TF18;TF19;TF20;TF21,TF22;TF23;TF26;TF25,TF26;TF27„TF28„TF29„TF50,TF31„TF32„TF33„TF34,TF35„TF36,TF37;TF38,TF39,TF40;

*COMPUTE P' AND T' (FREQUENCY RATINGS);

2lO

FPR1=(F1—FMRAT1-FMDIM1+FMOVER);FPR2=(F2—FMRAT1—FMDIM2+FMOVER);FPR3=(F3—FMRAT1—FMDIM5+FMOVER);FPR4=(F4-FMRAT1-FMDIM4+FMOVER)SFPR5=(F5—FMRAT1—FMDIM5+FMOVER);FPR6=(F6·FMRAT1—FMDIM6+FMOVER);FPR7=(F7—FMRAT1—FMDIM7+FMOVER);FPR8=(F8—FMRAT1—FMDIM8+FMOVER);FPR9=(F9—FMRAT1—FMDIM9+FMOVER);FPR10=(F10—FMRAT1—FMDIM10+FMOVER);FPR11=(F11-FMRAT1—FMDIM1l+FMOVER);FPR12=(F12—FMRAT1—FMDIM12+FMOVER);FPR13=(F15'FMRAT2'FMÜIM1+FMÜVER)BFPR14=(F14—FMRAT2·FMDIM2+FMOVER);FPR15=(F15—FMRAT2—FMDIM3+FMOVER);FPR16=(F16-FMRAT2~FMDIMQ+FMOVER)sFPR17=(F17—FMRAT2—FMDIM5+FMOVER)sFPR18=(F18—FMRAT2—FMDIM6+FMOVER);FPR19=(F19'FMRÄT2'FMDIM7+FMÜVER)BFPR20=(F20—FMRAT2-FMDIM8+FMOVER)2FPR21=(F21—FMRAT2—FMDIM9+FMOVER);FPR22=(F22-FMRAT2—FMDIM10+FMOVER);FPR23=(F25—FMRAT2—FMDIM11+FMOVER);FPR24=(F24-FMRAT2—FMD1Ml2+FMOVER);FPRZ5=(FZ5-FMRAT5—FMDIM1+FMOVER);FPR26=(F26—FMRAT5—FMDIM2+FMOVER);FPR27=(F27-FMRAT3-FMDIM3+FMOVER)sFPR28=(F28-FMRAT5—FMDIM4+FMOVER);FPR29=(F29—FMRAT3-FMDIM5+FMOVER)sFPR30=(F30-FMRAT3·FMDIM6+FMOVER);FPR31=(F51-FMRAT5—FMDIM7+FMOVER);FPR52=(F52-FMRAT3-FMDIM8+FMOVER);FPR35=(F33—FMRAT3—FMDIM9+FMOVER);FPR34=(F34—FMRAT5·FMDIM10+FMOVER);FPR35=(F55·FMRAT5—FMDIM11+FMOVER);FPR56=(F36—FMRAT3—FMDIMl2+FMOVER);FPR57=(F57-FMRAT4-FMDIM1+FMOVER);FPR58=(F38-FMRAT4-FMDIM2+FMOVER);FPR59=(F39—FMRAT4—FMDIM3+FMOVER);FPR40=(F40-FMRAT4—FMDIM4+FMOVER);FPR41=(F41-FMRAT4—FMDIM5+FMOVER)sFPR42=(F42-FMRAT4—FMDIM6+FMOVER);FPR63=(F43-FMRAT4-FMDIM7+FMOVER);FPR44=(F44-FMRAT4—FMDIM8+FMOVER);FPR45=(F45-FMRAT4—FMDIM9+FMOVER);_FPR46=(F46·FMRAT4—FMDIMl0+FMOVER);FPR47=(F47-FMRAT4—FMDIM11+FMOVER);FPR48=(F48—FMRAT4—FMDIM12+FMOVER)ETFPR1=(TF1—TFMRAT1—TFMDIMl+TFMOVER);TFPRZ=(TF2—TFMRAT1-TFMDIM2+TFMOVER);TFPR5=(TF3-TFMRAT1-TFMDIM3+TFMOVER);TFPR4=(TF4-TFMRAT1—TFMDIM4+TFMOVER);TFPR5=(TF5—TFMRAT1—TFMDIM5+TFMOVER)E_TFPR6=(TF6—TFMRAT1·TFMDIM6+TFMOVER);.TFPR7=(TF7—TFMRAT1—TFMDIM7+TFMOVER);

2ll

TFPR8=(TF8—TFMRAT1—TFMDIM8+TFMOVER);TFPR9=(TF9—TFMRAT1-TFMDIM9+TFMOVER);TFPR10=(TF10-TFMRAT1—TFMDIM10+TFMOVER);TFPR11=(TF11-TFMRAT1-TFMDIM11+TFMOVER)zTFPR12=(TF12-TFMRAT1—TFMDIM12+TFMOVER);TFPR13=(TF15·TFMRAT2-TFMDIM1+TFMOVER);TFPR14=(TF14-TFMRAT2—TFMDIM2+TFMOVER);TFPR15=(TF15-TFMRAT2-TFMDIM5+TFMOVER):TFPR16=(TF16—TFMRAT2-TFMDIM4+TFMOVER);TFPR17=(TF17-TFMRAT2—TFMDIM5+TFMOVER)sTFPR18=(TF18—TFMRAT2—TFMDIM6+TFMOVER);TFPR19=(TF19-TFMRAT2—TFMDIM7+TFMOVER);TFPR20=(TF20—TFMRAT2—TFMDIM8+TFMOVER);TFPR21=(TF21—TFMRAT2—TFMDIM9+TFMOVER);TFPR22=(TF22—TFMRAT2—TFMDIM10+TFMOVER);TFPR25=(TF23-TFMRAT2—TFMDIM11+TFMOVER);TFPR24=(TF24—TFMRAT2-TFMDIM12+TFMOVER);TFPR25=(TF25-TFMRAT3-TFMDIM1+TFMOVER);TFPR26=(TF26—TFMRAT3—TFMDIM2+TFMOVER);TFPR27=(TF27-TFMRAT5-TFMDIM3+TFMOVER);TFPR28=(TF28—TFMRAT3—TFMDIM4+TFMOVER);TFPR29=(TF29—TFMRAT3—TFMDIM5+TFMOVER);TFPR50=(TF30-TFMRAT3—TFMDIM6+TFMOVER);TFPR31=(TF31—TFMRAT5-TFMDIM7+TFMOVER);TFPR52=(TF32—TFMRAT5-TFMDIM8+TFMOVER1;TFPR53=(TF33·TFMRATS—TFMDIM9+TFMOVER);TFPR54=(TF34—TFMRAT3—TFMDIMl0+TFMOVER);TFPR35=(TF35—TFMRAT3—TFMDIM11+TFMOVER);TFPR36=(TF56—TFMRAT3—TFMDIM12+TFMOVER);TFPR37=(TF57—TFMRAT4-TFMDIM1+TFMOVER);TFPR58=(TF38-TFMRAT4-TFMDIM2+TFMOVER);TFPR39=(TF39—TFMRAT4—TFMDIM5+TFMOVER);TFPR60=(TF40-TFMRAT4—TFMDIM4+TFMOVER);TFPRQI=(TF61—TFMRAT4-TFMDIM5+TFMOVER):TFPR42=(TF42—TFMRAT4-TFMDIM6+TFMOVER);TFPR43=(TF43—TFMRAT4—TFMDIM7+TFMOVER);TFPR44=(TF44—TFMRAT4-TFMDIM8+TFMOVER);TFPR45=(TF45—TFMRAT4—TFMDIM9+TFMOVER);TFPR46=(TF46—TFMRAT4—TFMDIM10+TFMOVER);TFPR47=(TF47-TFMRAT4·TFMDIM11+TFMOVER);TFPR48=(TF68-TFMRAT4-TFMDIMl2+TFMOVER);FDIFF1=(FPR1—TFPR1)xx2;FDIFF2=(FPR2-TFPR2)xx2:FDIFF3=(FPR3—TFPR3)xx2;FDIFF4=(FPR4-TFPR4)xx2s

_FDIFF5=(FPR5-TFPR5)¥¤2;FDIFF6=(FPR6-TFPR6)xx2:FDIFF7=(FPR7—TFPR7)xx2:FDIFF8=(FPR8-TFPR8)x¤Z;FDIFF9=(FPR9—TFPR9)x¤2;FDIFF1U=(FPR1U'TFPR1U)¥¥2;FDIFF11=(FPR11-TFPR11)¤*2;FDIFF12=(FPR12—TFPR12)**2:FDIFF15=(FPR13—TFPR15)Xx2;FDIFF14=(FPR14-TFPR14)xx2;

212

FDIFF15=(FPR15—TFPR15)¤¥2§FDIFF16=(FPR16—TFPR16)XX2:FDIFF17=(FPR17-TFPR17)xx2sFDIFF18=(FPR18—TFPR18)x¥2;FDIFF19=(FPR19—TFPR19)xx2;FDIFF20=(FPR20—TFPR20)xx2;FDIFF21=(FPR21-TFPR21)xx2;FDIFF22=(FPR22-TFPR22)xx2;FDIFF25=(FPR25—TFPR23)¤x2;FDIFF24=(FPR24-TFPR24)xx2;FDIFF25=(FPR25-TFPR25)xx2;FDIFF26=(FPR26—TFPR26)xx2;FDIFF27=(FPR27—TFPR27)xx2;FDIFF28=(FPR28—TFPR28>xx2;FDIFF29=(FPR29-TFPR29)**Z;FDIFF50=(FPR30-TFPR30)¥§2;FDIFF31=(FPR$1—TFPR31)¥x2;FDIFF32=(FPR32-TFPR32)**2;FDIFF53=(FPR53-TFPR55)¤¤2:FDIFF34=(FPR54—TFPR34)xx2;FDIFF$5=(FPR55'TFPR55)¥¥Z§FDIFF36=(FPR56-TFPR56)¥*2;FDIFF37=(FPR37-TFPR37)xx2;FDIFF38=(FPR38-TFPR58)xx2iFDIFFS9=(FPR39·TFPR59)¤¤2;FDIFF40=(FPRQO-TFPR40)¥x2;FDIFF41=(FPR41-TFPR41uxxz;FDIFF42=(FPR42—TFPR42)xx2;FDIFFQ3=(FPR43—TFPR63)xx2;FDIFF44=(FPR44—TFPR44)xx2;FDIFF45=(FPR45-TFPR45)X¥2§FDIFF46=(FPRQ6—TFPR46)x¥2sFDIFFQ7=(FPR47-TFPR67)¤*2;FDIFF48=(FPR48-TFPR48Jxx2:DAFREQ=(FDIFF1+FDIFF2+FDIFF3+FDIFF4+FDIFF5+FDIFF6+FDIFF7+FDIFF8+FDIFF9+FDIFF10+FDIFF11+FDIFF12+FDIFF13+FDIFF14+FDIFF15+FDIFF16+FDIFF17+FDIFFl8+FDIFF19+FDIFF20+FDIFF21+FDIFF22+FDIFF25+FDIFF2Q+FDIFF25+FDIFFZ6+FDIFF27+FDIFF28+FDIFF29+FDIFF30+FDIFF31+FDIFF32+FDIFF33+FDIFF34+FDIFF55+FDIFF36+FOIFF37+FOIFF58+FDIFF39+FDIFF40+FDIFF41+FDIFF42+FDIFF43+FDIFF44+FDIFF45+FDIFF46+FDIFF47+FDIFF48)/48;DAF=SQRT(DAFREQ);XCOMPUTE DACOR FOR FREQ RATINGS AS FOLLONS;¥F=FPR1;¤FT=TFPR1;¤OUTPUT;¥F=FPR2;*FT=TFPR2;¥OUTPUT;¤F=FPR3;¤FT=TFPR5;¤OUTPUT;¤F=FPR4;¤FT=TFPR4;¥OUTPUT;¤F=FPR5;¤FT=TFPR5;¤0UTPUT;¤F=FPR6;¤FT=TFPR6;¤0UTPUT;¤F=FPR7;¥FT=TFPR7;XOUTPUT:¤F=FPR8;¥FT=TFPR8;*OUTPUT;¤F=FPR9;*FT=TFPR9;*OUTPUT;*F=FPR10;*FT=TFPRlU;*0UTPUT;¤F=FPR11;¤FT=TFPR11;*0UTPUT;¤F=FPR12;¥FT=TFPR12;*0UTPUT;¤F=FPR15;*FT=TFPR13;*OUTPUT;

213

¤F=FPR14;¥FT=TFPR14;¥0UTPUT;¥F=FPR15;*FT=TFPR15;¤0UTPUT;¤F=FPR16;¥FT=TFPR16;¥0UTPUT;¤F=FPR17;¤FT=TFPR17;¤OUTPUT;¤F=FPR18;¥FT=TFPR18;¤0UTPUT;¤F=FPR19;¤FT=TFPRl9;*0UTPUT;¤F=FPR20;*FT=TFPR20;¥0UTPUT:¥F=FPR21;*FT=TFPR2l;XOUTPUT;¤F=FPR22;¤FT=TFPR22;¤0UTPUT;¤F=FPR25;XFT=TFPR25;¤0UTPUT;¤F=FPR24;*FT=TFPR26;XOUTPUT;¤F=FPR25;¤FT=TFPR25;*OUTPUT;¤F=FPR26;*FT=TFPR26;*0UTPUT;¥F=FPR27;¥FT=TFPR27;XOUTPUT;¤F=FPR28;¤FT=TFPR28;¤OUTPUT;¤F=FPR29;¤FT=TFPR29;¤0UTPUT;¥F=FPR30;¥FT=TFPR50;x¤UTPUT;¥F=FPR31;*FT=TFPR31;¥0UTPUT;¤F=FPR52;*FT=TFPR32;¤0UTPUT;¤F=FPR53;¥FT=TFPR35;*0UTPUT;¤F=FPR34;¥FT=TFPR54;¥OUTPUTs¤F=FPR55;¥FT=TFPR35;¤0UTPUT;¤F=FPR56;¤FT=TFPR36;*0UTPUT;xF=FPR57;XFT=TFPR37;¤OUTPUT;*F=FPR38;¤FT=TFPR38;¥0UTPUT;¤F=FPR59;¤FT=TFPR39;¥0UTPUT;¤F=FPR40;¤FT=TFPR40;¤0UTPUT;¤F=FPR41;*FT=TFPR41;*0UTPUT:!F=FPR42;¤FT=TFPR42;*0UTPUT;¥F=FPR45;¤FT=TFPR45;¤0UTPUT:¤F=FPR44;¥FT=TFPRü4;¤0UTPUT;¤F=FPR45;¤FT=TFPR45;¤0UTPUT;XF=FPRG6;¥FT=TFPR46;XOUTPUT;*F=FPR47;¥FT=TFPR47;¤0UTPUT;¥F=FPR48;¤FT=TFPR48;*0UTPUT;XCORRELATE F AND FT LATER;äelevation is calculated as follows;EL=(PMOVERAL-TMDVERAL)¤¤2;EP=sqrt(e1);ädifferential slevation is calculatad as followssDELEV1=((PMRATEE1-PMOVERAL)-(TMRATEEl—TMOVERAL))x¥2:DELEV2=((PMRATEE2—PMOVERAL)—(TMRATEE2—TMOVERAL))¤¤2;DELEV3=((PMRATEE3—PMOVERAL)—(TMRATEE5—TMOVERAL))x¥2;DELEV4=((PMRATEE4—PMOVERAL)—(TMRATEE4—TMOVERAL))xx2sDIFELEV=MEAN(DELEVl,DELEV2,DELEV3,DELEV4);‘DEP=SQRT(DIFELEV);*stereotypic accuracy is calculated as follows;STER1=((PMDIM1—PMDVERAL)-(TMDIM1—TMDVERAL))¤x2;STER2=((PMDIM2—PMOVERAL)-(TMDIM2—TMOVERAL))¥x2;STER3=((PMDIM5-PMOVERAL)—(TMDIM3-TMOVERAL))!¥2;STER6=((PMDIM4—PMOVERAL)—(TMDIM4-TMOVERAL))¤¤2;STER5=((PMDIM5-PMOVERAL)-(TMDIM5—TMOVERAL))¥¤2:STER6=((PMDIM6—PMOVERAL)—(TMDIM6-TMOVERAL))**2;STER7=((PMDIM7*PNOVERAL)—(TMDIM7—TMOVERAL))XX2§STER8=((PMDIM8—PMOVERAL)—(TMDIM8—TMOVERAL))**2;

214

STEREOT=MEAN(STER1„STER2„STER3,STER4„STER5„STER6,STER7,STER8)ESAP=SORT(STEREOT);XFOLLONING ARE THE CALCULATIONS FOR FREQUENCY RATINGS;äelevation is calculated as follows;ELEVA=(FMOVER-TFMOVER)X*2;EF=sqrt(e1eva);¥differentia1 elevation is calculated as follows;DEL1=((FMRAT1—FMOVER)—(TFMRAT1-TFMOVER))xx2;DEL2=((FMRAT2-FMOVER)—(TFMRAT2—TFMOVER))¤x2;DEL3=((FMRAT3-FMOVER)—(TFMRAT5—TFMOVER))xx2;DEL4=((FMRAT4—FMOVER)—(TFMRAT4—TFMOVER))xx2;DIFEL=MEAN(DEL1,DEL2,DEL5,DEL4);DEF=SORT(DIFEL);lstareotypic accuracy is calculated as follows;STE1=((FMDIMI-FMOVER)—(TFMDIM1-TFMOVER))¤x2;STE2=((FMDIM2—FMOVER)-(TFMDIM2-TFMOVER))¤¤2;STE3=((FMDIM3-FMOVER)-(TFMDIM5—TFMOVERl)x¤2;STE4=((FMDIM4—FMOVER)—(TFMDIM4-TFMOVER))¤X2;STE5=((FMDIM5—FMOVER)—(TFMDIM5—TFMOVER))**2;STE6=((FMDIM6-FMOVER)—(TFMDIM6-TFMOVER))xx2;STE7=((FMDIM7—FMOVER)—(TFMDIM7—TFMOVER))x¤2;STE8=((FMDIM8—FMOVER)—(TFMDIM8—TFMOVER))**2;STE9=((FMDIM9—FMOVER)-(TFMDIM9—TFMOVER))xx2;STE10=((FMDIM10-FMOVER)—(TFMDIM10—TFMOVER))**2;STE11=((FMDIM11-FMOVER)-(TFMDIM11—TFMOVER))xx2:STE12=((FMDIMl2—FMOVER)—(TFMDIM12—TFMOVER))**2;5TEREO=MEAN(STE1,STE2„STE3,STE6„STE5„STE6„$TE7,STE8,STE9,STE10„STE11„STE12);SAF=SQRT(STEREO);XCOMPUTE SACOR FOR BOTH PERF AND FREQ RATINGS AS FOLLONS;¤SP=PMDIM1;XST=TMDIMl;¥OUTPUT;¥SP=PMDIM2;¥ST=TMDIM2;¥0UTPUT;*SP=PMDIM5;¥ST=TMDIM3;*OUTPUT;¤SP=PMDIM4;¤ST=TMDIM4:XOUTPUT;¥SP=PMDIM5;¤ST=TMDIM5;¥OUTPUT;¤SP=PMDIM6;*ST=TMDIM5;¥0UTPUT;¤SP=PMDIM7;XST=TMDIM7;¤OUTPUT;¤SP=PMDIM8;¤ST=TMDIM8;*OUTPUT;*SF=FMDIM1;¥STF=TFMDIM1;¤OUTPUT;¥SF=FMDIM2;¤$TF=TFMDIM2;¥0UTPUT;¥SF=FMDIM3;¥STF=TFMDIM3;*0UTPUT:¤SF=FMDIM4;¥STF=TFMDIM4;¤OUTPUT;¤SF=FMDIM5;XSTF=TFMD!M5;*OUTPUT;¤SF=FMDIM6;XSTF=TFMDIM5;*OUTPUT;¥SF=FMDIM7;¤STF=TFMDIM7;*OUTPUT;

_*SF=FMDIM8;*STF=TFMDIM8;¤0UTPUT;¥SF=FMDIM9;*STF=TFMDIM9;XOUTPUT;XSF=FMDIM10;*STF=TFMDIM10;*OUTPUT;XSF=FMDIM11;¤5TF=TFMDIM11;¤OUTPUT;¤SF=FMDIMl2;*STF=TFMDIMl2;*0UTPUT;¤COMPUTE DECOR FOR BOTH PERF AND FRE0 RATINGS AS FOLLOHS;XDECP=PMRATEE1;¥DECT=TMRATEE1;¤0UTPUT;*DECP=PMRATEE2;*DECT=TMRATEE2;¤OUTPUT;¤DECP=PMRATEE3;%DECT=TMRATEE5;¤0UTPUT;XDECP=PMRATEE4;¤DECT=TMRATEE4;¤OUTPUT;

215

¤DECF=FMRAT1;¤DECFT=TFMRAT1;¤0UTPUT;¤DECF=FMRAT2;¥DECFT=TFMRAT2;¤0UTPUT;xDECF=FMRAT3;¤DECFT=TFMRAT5;¤OUTPUT;¤DECF=FMRAT4;¤DECFT=TFMRAT4;¤0UTPUT;¤COMPUTE OVERAL ACCURACY AS FOLLOHS;GLOB1=(P1—T1)¤¤2;GLOB2=(P2—T2)¤¤2;GLOB3=(P5—T3)¤¥2;GLOB4=(P4-T4)¤¥2;GLOB5=(P5—T5)¤¥2;GLOB6=(P6—T6)¤x2:GLOB7=(P7—T7)¤¥2;GLOB8=(P8-T8)¤¥2sGLOB9=(P9-T9)x¥2sGLOB10=(P10—T10)¤¤2:GLOB11=<P11-T11)xx2;GLOB12=(P12—T12)x¤2;GLOB13=(P13—T15)xx2;GLOB14=(P14-T14)¤x2;GLOB15=(P15—T15)¥¤2;GLOB16=(P16-T16)¥¥2;GLOB17=(P17—T17)x¤2;GLOB18=(P18—T18)x¤2;

UGLOB19=(P19—T19)¤x2;GLOB20=(P20-T20>xx2;GLOB21=(P21—T21)xx2;GLOB22=(P22—T22)¥¤2;GLOB23=(P23—T25)xx2;GLOB24=(P24—T24)¤¤2;GLOB25=(P25—T25)¤¤2;GLOB26=(P26-T26)*¤2;GLOB27=(P27·T27)xx2;GLOB28=(P28—T28)xx2;GLOB29=(P29—T29)¤x2;GLOB50=(P50—T30)xx2;GLOB51=(P51—T51)¤*2;GLÜB52=(P52'T$2)§XZ}TOTACCP=MEAN(GLOB1,GLOB2,GLOB3,GLOB4,GLOB5,GLOB6,GLOB7,GLOB8,

GLOB9,GLOB10,GLOB11.GLOB12.GLOB15,GLOB14,GLOB15,GLOB16,GLOB17,GLOB18„GLOB19,GLOB20,GLOBZI.GLOB22,GLOB25,GLOB24,GLOB25„GLOB26,GLOBZ7„GLOB28,GLOB29.GLOB30,GLOBSI,GLOB52l;

CKPERF=TOTACCP—(STEREOT+DIFELEV+EL+DAPERF);GLOBAL1=(F1-TF1)xx2;GLOBAL2=(F2—TF2)¤x2;GLOBAL5=(F3—TF3)xx2;GLOBAL4=(F4—TF4)xx2;GLOBAL5=(F5-TF5)xx2;GLOBAL6=(F6-TF6)xx2;GLOBAL7=(F7—TF7)xx2;GLOBAL8=(F8—TF8)x¤2sGLOBAL9=(F9—TF9)xx2;GLOBAL10=(F10—TF10)xx2;GLOBAL11=(F11—TF11)xx2;UGL0BAL12=<Fl2—TF12)xx2;.GLOBAL13=(F13—TF15)xx2;

216

GLOBAL14=(F14-TF14)xx2;GLOBAL15=(F15-TF15)xx2;GLOBAL16=(F16—TF16)xx2;GLOBAL17=(F17—TF17)¤x2;GLOBAL18=(F18-TF18)x¤2;GLOBAL19=(F19·TF19)¥¥2;GLOBAL20=(F20-TF20)xx2;GLOBAL21=(F21—TF21)¤x2;GLOBAL2Z=(F22-TF22)¤x2:GLOBAL23=(F23-TF23)**2;GLOBAL24=(F24—TF24)x¤2;GLOBAL25=(FZS—TF25)¤¥2;GLOBAL26=(F26-TF26)xx2sGLOBAL27=<F27-TF27)xx2sGLOBAL28=(F28—TF28)x¤2;GLOBAL29=(F29—TF29)¤¤2;GLOBAL30=(F30-TF30)xx2;GLOBAL31=(F31—TF51)xx2;GLOBAL32=(F32—TF32)xx2;GLOBAL35=(F53—TF33)¤§2;GLOBAL34=(F34—TF34)x¤2;GLOBAL55=(F35—TF35)xx2;GLOBAL56=(F36-TF36)x¤2;GLOBAL37=(F37·TF37)xx2;GLOBALS8=(F38-TF38)xx2;GLOBAL39=(F39-TF59)¤¤2;GLOBAL40=(F40—TF40)xx2;GLOBAL41=(F41—TF41)xx2;GLOBAL42=(F42-TF42)¤¤2;GLOBAL43=(F45-TF45)x¤2;GLOBAL44=(F44-TF46)¤x2;GLOBAL45=(F45—TF4S)x¤2;GLOBAL46=(F46-TF46)xx2;GLOBAL47=(F47-TF47)x¤2:GLOBAL48=(F48-TF48)¤¤2;TOTACCF=MEAN(GLOBAL1„GLOBAL2,GLOBAL5„GLOBAL4,GLOBAL5,GLOBAL6;GLOBAL7,GLOBAL8;GLOBAL9,GLOBAL10,GLOBAL11,GLOBAL12,GLOBAL13;GLOBAL14,GLOBAL15,GLOBAL16„GLOBAL17,GLOBAL18„GLOBAL19,GLOBAL20;GLOBAL21;GLOBAL22;GLOBAL23„GLOBAL24,GLOBAL25,GLOBAL26,GLOBAL27;GLOBAL28,GLOBAL29„GLOBAL30,GLOBAL31,GLOBAL32,GLOBAL33,GLOBALSG;GLOBAL55;GLOBAL36,GLOBAL57;GLOBAL38,GLOBAL39„GLOBAL40,GLOBAL41,GLOBAL62;GLOBAL43„GLOBAL44,GLOBAL45,GLOBAL46,GLOBAL47;GLOBAL48)SCKFREO=TOTACCF—(STEREO+DIFEL+ELEVA+DAFREQ);KEEP ID DAPERF DAFRE0 ELEVA EL DIFELEV DIFEL STEREOT STEREODAP DAF SAP SAF EP EF DEP DEF TOTACCP TOTACCFCKPERF CKFREO;cards;

2l7

PROC SORT DATA=THREE; BY ID;DATA BOTH;MERGE ONE TNO THREE; BY ID;PROC PRINT; VAR REP DIMS DAP DAF SAP SAF EP EF DEP DEFDARP DARF SARP SARF DERP DERF TOTACCP TOTACCF CKPERF CKFREO;BY D;PROC FREQ;TABLES PAYJOB TITLE MGNOMG ORGFUNC SEX DIMS;PROC CORR DATA=BOTH;VAR PAYJOB YRSNORK MGNOMG AGE YRCOLL SEXREP DIMS DAP DAF SAP SAF EP EF DEP DEF DARP DARF SARP SARFDERP DERF TOTACCP TOTACCF;PROC RANK DATA=BOTH OUT=LAST GROUPS=5;VAR YRSNORK;RANKS EXPER;PROC ANOVA DATA=LAST;CLASS EXPER;MODEL DEP DEF=EXPER;¥MODEL DAP DAF SAP SAF EP EF DEP DEF=EXPER;MEANS EXPER/TUKEY;PROC SORT;BY EXPER;PROC MEANS;VAR DAP DAF SAP SAF EP EF DEP DEF;BY EXPER;XPROC CORR DATA=ONE;*VAR P T;*BY ID;XPROC CORR DATA=ONE;*VAR F FT;XBY ID;¤PROC CORR DATA=ONE;*VAR SP ST;*BY ID;¤PROC CORR DATA=ONE;*VAR SF STF;*BY ID;*PROC CORR DATA=ONE;*VAR DECP DECT;*BY ID;¥PROC CORR DATA=ONE;¥VAR DECF DECFT;*BY ID;x///

218