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University Mootilms International 300 N. Zeeb Road Ann Arbor, Ml 48106
8403491
Breckler, Steven James
VALIDATION OF AFFECT, BEHAVIOR, AND COGNITION AS DISTINCT COMPONENTS OF ATTITUDE
The Ohio State University PH.D. 1983
University Microfilms I PItemcltlOn9.1 300 N. Zeeb Road, Ann Arbor, Ml 48106
VALIDATION OF AFFECT, BEHAVIOR, AND COGNITION
AS DISTINCT COMPONENTS OF ATTITUDE
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Steven James Breckler, B.A., M.A.
* » » » #
The Ohio State University
1983
Reading Committee: Approved By
Anthony G. Greenwald
Thomas K. Ostrom Advisor Robert C. MacCallum Department of Psychology Copyright by Steven James Breckler 1983 Dedicated To My Mother and To My Father
11 ACKNOWLEDGMENTS
The research in this dissertation was supported by a Graduate Alumni Research Award and a Herbert Toops Research Award (both to Steven J. Breckler), and by NSF Grant BNS 82-17006 (to Anthony G. Greenwald).
The Behavioral Sciences Laboratory provided research space.
Dissertation committee members Tom Ostrom and Bud MacCallum provided valuable feedback on an earlier draft of this dissertation.
Anthony Pratkanis put up with the snakes, the mice, and me.
Tony Greenwald has been more than just an advisor. He has been my nentor, friend, and role model. Thanks, Coach.
This dissertation is dedicated to my parents. I don't think they'll understand much of this, but they'll love it anyway.
iii VITA
EDUCATION
1979 B.A. University of California'at San Diego, magna cum laude Major Field: Psychology Minor Field: Econometrics 1981 M.A. Ohio State University (Advisor: Anthony G. Greenwald) Major Field: Social Psychology Minor Field: Statistics 1983 Ph.D. Ohio State University (Advisor: A. G. Greenwald)
FIELDS OF INTEREST
Social Psychology Attitude Theory; Attitude Measurement Personality Experimental Research; Self-Concept Measurement Statistics Multivariate Data Analysis; Structural Equation Modeling Methodology Laboratory Research; Computer Use in Psychology
HONORS
University Fellowship -- Ohio State University Graduate School (1979) Graduate Alumni Research Award -- OSU Graduate School (1982) Herbert Tbops Research Award -- OSU Psychology Department (1982)
THESIS
Self-referent cognition and personality. Unpublished Master's Thesis, 1981.
BOOK CHAPTER
Greenwald, A. G., & Breckler, S. J. (in press). To whom is the self presented? In B. R. Schlenker (Ed.), The self and social life.. New Vork: McGraw-Hill.
IV PAPERS PRESENTED
Breckler, S. J., & Greenwald, A. G. (May, 1981). Favorable self- referent judgments are made faster than nonfavorable ones. Presented at the 53rd annual meeting of the Midwestern Psychological Association, Detroit. (Resources in Education No. ED-205855)
Breckler, S. J., Banaji, M. R., Greenwald, A. G., & Pratkanis, A. R. (August, 1981). An experimental analog of the self as a memory system. Presented at the 89th annual meeting of the American Psychological Association, Los Angeles.
Greenwald, A. G., Banaji, M. R., Pratkanis, A. R., & Breckler, S. J. (November, 1981). A centrality effect in recall. Presented at the 22nd annual meeting of the Psychonomic Society, Philadelphia.
Breckler, S. J., & Greenwald, A. G. (April, 1982). Location of the self in multidimensional trait space. Presented at the 53rd annual meeting of the Eastern Psychological Association, Baltimore.
Breckler, S. J., & Greenwald, A. G. (August, 1982). Individual- differences in the processing of information about oneself. Presented at the 90th annual meeting of the American Psychological Association, Washingtion, D.C. (Resources in Education No. ED-225062)
Breckler, S. J., & Greenwald, A. G. (August, 1982). Charting coordinates for the self-concept in multidimensional trait space. Presented at a symposium on Functioning and Measurement of Self-Esteem at the 90th annual meeting of the American Psychological Association, Washington, D.C. (Resources in Education No. ED-226297)
Breckler, S. J., & Pratkanis, A. R. (May, 1983). Self-referent decision making: A multidimensional representation. Presented at the 55th annual meeting of the Midwestern Psychological Association, Chicago.
Allen, R. B., & Breckler, S. J. (December, 1983). Human factors of telephone-mediated interactive electronic games. Presented at ACM SIGPC/SIGSMALL conference, San Diego.
v TABLE OF CONTENTS
Page
DEDICATION ii
ACKNOWLEDGMENTS iii
VITA iv
LIST OF TABLES viii
LIST OF FIGURES xii
Chapter
I. INTRODUCTION 1
The Tripartite Model of Attitude Structure 2 History of the Tripartite Model 3 Theoretical Underpinnings of the Tripartite Model 6 Empirical Support for the Tripartite Model 8 Evaluation of the Model's Empirical Base 12 Requirement for a Strong Test of the Tripartite Model 14 Purpose of Dissertation 16
II. RE-ANALYSIS OF PREVIOUS VALIDATION STUDIES 18
Overview of the LISREL Model 18 Re-Analysis of Ostrom (1969) 21 Results and Discussion 23 Re-Analysis of Kothandapani (1971) 28 Results and Discussion 29 General Discussion 32
III. STUDY ONE 34
Method 36 Results 48 Discussion 52
VI IV. STUDY TWO 54
The Role of Past Experience 54 The Prediction of Overt Behavior 56 Overview of Study 2 59 Method 60 Results 65 Evaluation of the Tripartite Model 67 Behavioral Prediction Models 73 The Effects of Direct Experience 79 Discussion 82
V. GENERAL DISCUSSION 85
Summary of Results '85 Significance of Results 86 Future Directions 87
REFERENCES 90
APPENDIXES
A. Questionnaire for Generation of Thurstone Scales 97
B. Item Pool for Construction of Thurstone Scales 102
C. Verbal Report Measures for Studies 1 and 2 109
D. Verbal Report Measures Added For Study 2 121
E. Re-Analysis of Fishbein and A]zen (1974) 138
F. Tables 144
G. Figures 186
vii LIST OF TABLES
Table Page
1. Multitrait-Multiraethod Correlation Matrix from Ostrom (1969) 145
2. Oblique Factor Pattern from Exploratory Factor Analysis Data from Ostrom (1969) 146
3. Interfactor Correlations from Exploratory Factor Analysis Data from Ostrom (1969) 146
4. LISREL Estimates for Unknown Parameters of Figure 3
Data from Ostrom (1969) 147
5. Summary of LISREL Models Fit to Ostrom (1969) 148
6. Multitrait-Multimethod Correlation Matrix from Kothandapani (1971) 148 7. Oblique Factor Pattern from Exploratory Factor Analysis Data from Kothandapani (1971) 149
8. Interfactor Correlations from Exploratory Factor Analysis Data from Kothandapani (1971) 149
9. LISREL Estimates for Unknown Parameters of Figure 3
Data from Kothandapani (1971) 150
10. Summary of LISREL Models Fit to Kothandapani 151
11. Descriptive Statistics for Study 1 152
12. Distribution of Single-Item Self-Rating Scale 152
13. Correlation Matrix for Study 1 153
14. Oblique Factor Pattern from Exploratory Factor Analysis Study 1 154 15. Interfactor Correlations from Exploratory Factor Analysis Study 1 154
viii LISREL Estimates for Unknown Parameters of Figure 2
Data from Study 1 155
Summary of LISREL Models Fit to Study 1 156
Comparison of Intercomponent Correlations Across
Three Studies 156
Descriptive Statistics for Study 2 157
Distribution of Single-Item Self-Rating Scale Study 2 158
Estimates of Reliability from Test-Retest Correlations 158
Correlation Matrix for Testing Tripartite Model Study 2: Session 1 159 Oblique Factor Pattern from Exploratory Factor Analysis Study 2: Session 1 160 Interfactor Correlations from Exploratory Factor Analysis Study 2: Session 1 160
LISREL Estimates for Unknown Parameters of Figure 2 Data from Study 2: Session 1 161
Correlation Matrix for Testing Tripartite Model Study 2: Session 2 162
Oblique Factor Pattern from Exploratory Factor Analysis Study 2: Session 2 163
Interfactor Correlations from Exploratory Factor Analysis Study 2: Session 2 163
LISREL Estimates for Unknown Parameters of Figure 2 Data from Study 2: Session 2 164
Correlation Matrix for Testing Tripartite Model Sample Divided by Past Experience 165
LISREL Estimates for Unknown Parameters of Figure 2 Data from Study 2: Low Past Experience Subjects 166
LISREL Estimates for Unknown Parameters of Figure 2 Data from Study 2: High Past Experience Subjects 167
IX Summary of Tripartite Models Fit to Study 2 168
Comparison of Intercomponent Correlations Across Four Studies 169
Correlation Matrix for Evaluating Predictors of Behavior
Data from Study 2 170
Summary of Singe-Factor Behavioral Prediction Models 171
LISREL Estimates-for Unknown Parameters of Figure 8 17?
LISREL Estimates for Unknown Parameters of Figure 9 172
LISREL Estimates for Unknown Parameters of Figure 10 173
Summary of Behavior Models Fit by LISREL 173
LISREL Estimates for Reasoned Action Model RA-Original 174
LISREL Estimates for Modified' Reasoned Action Model RA-1 175
LISREL Estimates for Modified Reasoned Action Model RA-2 176
LISREL Estimates for Modified Reasoned Action Model RA-3 177
LISREL Estimates for Modified Reasoned Action Model RA-4 178
Summary of Reasoned Action Models 179
Correlation Matrix from Fishbein and Ajzen (1974) 180
Oblique Factor Pattern from Exploratory Factor Analysis Self-Reported Behavior Sample (Fishbein & Ajzen, 1974) 181 Interfactor Correlations from Exploratory Factor Analysis Self-Reported Behavior Sample (Fishbein & Ajzen, 1974) 181
Oblique Factor Pattern from Exploratory Factor Analysis Behavioral Intention Sample (Fishbein 6, Ajzen, 1974) 182
Interfactor Correlations from Exploratory Factor Analysis Behavioral Intention Sample (Fishbein & Ajzen, 1974) 182
LISREL Estimates for Unknown Parameters of Figure 2 Self-Reported Behaviors Sample (Fishbein & Ajzen, 1974) 183
x 53. LISREL Estimates for Unknown Parameters of Figure 2 Behavioral Intention Sample (Fiahbein & Ajzen, 1974) 184
54. Summary of LISREL Models Fit to Fiahbein 5. Ajzen (1974) 185
XI LIST OF FIGURES
Figure Page
1. The Tripartite Model of Attitude (after Rosenberg and Hovland, 1960) 187
2. Structural Equation Representation of the Tripartite Model of Attitude 188
3. Structural Equation Model for Evaluating a Multitrait- Multimethod Correlation Matrix 189
4. Schematic Diagram of Experimental Room 190
5. Reasoned Action Model of Attitude (RA-Original) 191
6. Modification of the Reasoned Action Model 191
7. Six Single-Factor Behavioral Prediction Models 192
8. Behavior-Related Variables as Measures of a Single Latent Variable 193
9. Behavior-Related Variables Divided into Behavioral Predictors and Overt Behavior 193
10. Behavior-Related Variables as Measures of Four
Distinct Constructs 194
11. Modified Reasoned Action Models 195
12. Model BP-A: A Simplified Behavioral Prediction Model 196
13. Model BP-A-BP: Modeling the Effects of Direct Experience 196
xii CHAPTER ONE
INTRODUCTION
Attitude is a term that is used everyday. People generally understand what it Beans to say one has a negative attitude toward, say, the draft. It aeans that one does not like it. An attitude is an evaluative disposition toward an object. Attitudes vary fron pro to con, like to dislike, favorable to unfavorable, positive to negative.
Allport (1935) wrote that "The concept of attitude is probably the Bost distinctive and indispensable concept in contemporary
Anerican social psychology" (p. 798). Allport defined attitude as "a
Bental and neural state of readiness, organized through experience, exerting a directive or dynaaic influence upon the individual's response to all objects and situations with which it is related" (p.
810).
In what ways do people respond to attitude objects? Affect is one class of response. Consider a person who opposes the draft.
Mention of the draft say be enough to elicit strong emotional reactions, for example feelings of disgust and sickness. Action or behavior is a second class of response. Soneone who opposes the draft say join in an anti-draft rally, or burn his draft card. Finally, one nay have cognitive responses to an attitude object. For exaaple,
1 Chapter One 2 one's spontaneous thoughts in response to the draft nay link it to an increased probability of war.
The Tripartite Model of Attitude Structure
The distinction between affect, behavior, and cognition as three components of attitude fores the basis for the tripartite nodel of attitude structure. Figure 1 suggests a useful way for conceptualizing this aodel. Attitude is defined as a response to an antecedent stimulus (attitude object). The stimulus is observable, and can be manipulated as an independent variable. Affect, behavior, and cognition are three hypothetical, unobservable classes of response to that stimulus. Observable, dependent variables are associated with each of the three attitude components.
Affect refers to an emotional response, a gut reaction, or sympathetic nervous activity. It can be measured by monitoring physiological responses (e.g., heart-rate or galvanic skin responses), or by collecting verbal reports of feelings or mood. Behavior refers to overt actions, behavioral intentions, and verbal statements regarding behavior. Cognition refers to beliefs, knowledge structures, perceptual responses, and thoughts.
In operationalizing measures of the three attitude components, it is assuaed that all three can be placed on a common evaluative continuum (e.g., from pro to con). Affect can vary from pleasurable
(feeling good, happy) to unpleasurable (feeling bad, unhappy).
Behavior can range from favorable and supportive (e.g., keeping. Chapter One 3 protecting) to unfavorable and hostile (e.g., discarding, destroying).
Finally, cognitions Bay vary froa favorable (e.g., the draft will ensure peace) to unfavorable (e.g., the draft is unfair).
Figure 1 specifies a unique referent for each of the three components. Affect refers to the feelings or emotions elicited by an attitude object. Here, affect refers to the person holding an attitude, and not to a property of the attitude object. Cognition refers to one's thoughts regarding attributes of the object.
Cognition therefore refers to a property of the attitude object.
Behavior refers to actions or action tendencies on the part of the person in relationship to the attitude object. All three attitude components involve an object/person interaction. Nevertheless, each component involves a different object/person relationship. The distinctions are important; many attitude researchers fail to distinguish them, leading to possible theoretical and empirical confusions. (This problem is taken up again in Chapter Five).
History of the Tripartite Model
The distinction between affect, behavior, and cognition is an old one. The trichotomy of feeling, acting, and knowing as three facets of human experience can be traced to the Greek philosophers, and was considered in some of the earliest social psychological writings (cf.
Allport, 1954; Hilgard, 1980). HcDougall (1908) wrote that "every instance of instinctive behavior involves a knowing of some thing, a feeling in regard to it, and a striving towards or away from that Chapter One 4 object" (p. 23). Similarly, Bogardus (1920) divides conscious reactions Into three characteristics: affective, cognitive, and volitional (which included action).
Interestingly, the concept of attitude was not formally explicated in teres of the tripartite model until the late 1940'a.
The earliest reference is Smith (1947), who distinguished between affective, cognitive, and policy orientation (conatlve) aspects of attitude. Krech and Crutchfield (1948) defined attitude in terns of enotional and cognitive processes, the object of which demanded action. In an extensive analysis of prejudice, Kraaer (1949) organized attitudes in teres of three najor levels: cognitive orientation, enotional orientation, and action orientation. The emerging emphasis on the aultifaceted nature of attitude represented a najor departure froa Thurstone'a (1928) focus on the affective component (Ostroa, 1968).
Based largely on Kraaer's (1949) organizational scheme, Cheln
(1951) proposed that "the aajor differentiae of attitudes toward a given object Care]: how the object is perceived and thought about; the subject's feelings toward it; [and] the action implications of cognizing it and feeling toward it in a certain way" (p. 386).
Harding, Kutner, Proahansky, and Chein (1954) asserted that there was consensus aaong investigators that "attitudes can best be described in teras of their cognitive, affective, and conatlve components" (p.
1023). Chapter One 5
By 1960, the tripartite model appeared to have become the de facto model of attitude structure. The model began to play a central role in major treatments of attitude theory and attitude change (Insko s. Scholper, 1967; Katz 6. Stotland, 1959; Rosenberg et al., 1960). The model's impact is also evidenced by its inclusion in introductory social psychology texts, often without reference to original sources
(e.g., Baron & Byrne, 1977; Krech, Crutchfield, 6. Ballachey, 1962;
Lambert & Lambert, 1973; Newcomb, Turner, & Converse, 1965; Secord &
Backraan, 1964; Shaver, 1977; Worchel & Cooper, 1979; Wrightsman &
Deaux, 1981). Special topic books on attitude theory typically emphasize the tripartite model, and most include reproductions of
Rosenberg and Hovland's (I960) schematization (Figure 1) (Ajzen &
Fishbein, 1980; Oskanp, 1977; Rajecki, 1982; Triandis, 1971; Zimbardo,
Ebbesen, & Maslach, 1977).
Despite the tripartite model's acceptance by textbook writers, the multicoaponent view appears to have had little impact on attitude researchers. Oatrom (1968) concluded that "the bulk of attitude research and, consequently, the theory developed to understand the attitude change process, continues to focus primarily on affect to the detriment of understanding the other characteristics of attitude" (p.
27). Chapter One 6
Theoretical Underpinnings of the Tripartite Model
Greenwald (1968a) analyzed the tripartite model in terms of each component's distinguishing antecedents. Action tendencies (conation) aay be built up through processes of instrumental learning, for example past reinforcement for a particular response to an attitude object. Cognitions (beliefs, thoughts) may develop through prior exposure to communications or educational materials. Affect (emotion) may be the product of classical conditioning — the past pairing of an attitude object with an affective stimulus. (See also Insko &
Schopler, 1967, and Triandis, 1971, for similar analyses of the developmental roots of affective, behavioral, and cognitive components of attitude.)
According to this view, not all attitude components are built up through cognitive processes. Attitudinal affect may not have verbal or cognitive antecedents. Similarly, many behaviors and action tendencies may be established through nonverbal or noncognitive mechanisms. Thus, the three components of attitude are distinguishable in terms of their developmental roots.
Recently, Greenwald (1982) suggested the utility of analyzing the person in terms of distinguishable, and only partially overlapping systems (an approach called personalysis). According to this view, one's verbal system may only partially overlap with, say, one's bodily system. As in Greenwald's (1968a) earlier treatment, personalysis suggests a basis for distinguishing among affect, behavior, and cognition as three separable components of attitude. Greenwald (1982) Chapter One 7 T
aade the point that the cognitive (verbal knowledge) component of attitude aay indicate something very different than the behavioral
(bodily reaction) component with this illustration:
Suppose you are asked if you are afraid of snakes and you say, with the conviction of belief, that you aren't. Surreptitiously, the questioner arranges for a harmless snake to appear crawling toward you on the aria of your chair — and you leap in haste out of the chair. Does this mean that you were lying when you said you weren't afraid of snakes? Not necessarily — it could be that your verbal knowledge included no fear of snakes, whereas your bodily reaction was controlled emotionally und reflexively by genetically transmitted knowledge, (p. 161)
In discussions of the tripartite model, it is typically implied that affect, behavior, and cognition are three correlated components of attitude. For example, consistency among the components is implied by Allport's (1935) definition of attitude as being "organized."
Consistency should be expected since all three components, represent the experience of a single individual. Also, the distinguishing antecedents of affect, behavior, and cognition can be satisfied by the same learning situation (Greenwald, 1968a), thereby producing triadic consistency. Finally, people may be motivated or strive to maintain consistency in their attitudinal responses (cf. Festinger, 1957;
Heider, 1958; McGuire, 1966)
Two bases for positive correlations have different implications.
One view is that the three components are simply alternative measures of the same underlying construct (attitude). Accordingly, measures of affect, cognition, and behavior should, in theory, be perfectly Chapter One 8
correlated. In practice, of course, random measurement error precludes that possibility (Lord & Novick, 1968). Nevertheless, very high correlations should be expected. A second view holds that the three components have both unique and common determinants. This view implies moderate correlations (greater than zero but less than one) between the components.
The most extreme version of the tripartite model views affect, behavior, and cognition as three independent classes of response.
Greenwald's (1968a) treatment of component antecedents, as well as personalysis (Greenwald, 1982) provides the theoretical basis for assualng independence among attitude components. Zajonc (1980) makes a similar point in his suggestion that affective reactions can be independent of cognitive processes.
Empirical Support for the Tripartite Model
The principal objectives in tests of the tripartite model have been to determine the degree to which components are correlated and the extent to which affect, behavior, and cognition diverge as separable components of attitude. The earliest tests were concerned with the correspondence between pairs of components (e.g., Harding et al., 1954; Rosenberg, 1956; Vidulich & Krevanick, 1966), or with the , - - multifaceted nature of individual components (e.g., Scott, 1969;
Triandis, 1967). Only four studies have examined all three attitude components with the goal of validating the tripartite model Chapter One 9
(Kothandapanl, 1971; Mann, 1959; Oatroa, 1969; Woodnanaee & Cook,
1967).
Mann (1959) examined the relationship anong cognitive, affective, and behavioral aspects of racial prejudice. White subjects and black subjects set in snail discussion groups over a three-week period, after which all subjects were adiainistered the attitude aeasures.
Hann expected to observe positive correlations between the three components. For both racial groups combined, moderate correlations were observed: affect-behavior r = 0.22, affect-cognition r = 0.26, and behavior-cognition r = 0.51. However, a different pattern emerged when the correlations were computed for each racial group separately.
Component intercorrelatlons for the black subjects were all high and positive (0.55 to 0.57). For the white subjects, the affect-cognition and behavior-cognition correlations were near zero, while the affect- behavior correlation was -0.54.
Hann'a (1959) study was not a good test of the tripartite nodel.
Measures of affect, behavior, and cognition were not properly operationalized. The affect measure required each subject to rank order the other group aenbera on the criterion "Whom would you most like to continue to be friends with at the end of the suaaer session?"
(p. 225). This neaaure is not one of affect as feeling or emotion; it appears sore closely related to behavior or behavioral intention.
Indeed, it bears close reseablance to a social distance measure
(Bogardus, 1925), which is typically interpreted as part of the behavioral coaponent. The measure of behavior involved each subject Chapter One 10
rank ordering the other group members according to their degree of racial prejudice. This measure relates store to cognition than behavior. Finally, the measures of affect, behavior, and cognition did not lie on a common evaluative continuum.
Uoodnansee and Cook (1967) also examined dimensions of racial attitudes. Their white college-student subjects rated a large set of statements designed to aeasure attitudes toward blacks. A factor analysis of subjects' ratings failed to produce dimensions that could be identified with affect, behavior, and cognition. Instead, a large number of "content-defined" diaensions were observed. It must be noted that the statements were never classified on an a priori basis as aeaauros of affect, behavior, or cognition. Since all measures were based on verbal reports, it is best to assume that the cognitive component of attitude was the principal component being measured.
Perhaps the strongest conclusion is that the cognitive component of attitude is aulti-dimensional itself. Nevertheless, this study did not provide a good test of the tripartite model.
The first strong test of the model was made by Ostrom (1969), who examined attitudes toward the church. Ostrom tested the, convergent and discriminant validity of the model by using the multitrait- nultimethod technique advocated by Campbell and Fiske (1959). This technique first requires an a priori classification of the theoretically discriminable "traits" (attitude components in this case). Each attitude component is measured via multiple, independent Chapter One 11 measurement techniques. In order to demonstrate discriminant validity, an attitude component must correlate higher with itself
(when measured via two methods) than with a different component (when measured with the same nethod). In order to demonstrate convergent validity, two methods mist produce higher correlations when measuring the same component than when measuring two different components.
Ostrom (1969) used four different attitude measurement techniques to measure each attitude component. These were (i) equal appearing intervals (Thurstone & Chave, 1929), (ii) summated ratings (Likert,
1932), (ill) scalograra analysis (Guttaan, 1954), and (iv) a self- rating scale (Guilford, 1954). The resulting attitude scales were administered in booklet form. Results supported the convergent and discriminant validity of the tripartite model. The intercoaponent correlations were estimated as 0.64 (behavior-cognition) and 0.65
(affect-behavior and affect-cognition). However, results, indicated that the amount of unique variance associated with each component was relatively small. Thus, the three components were distinguishable, but their overlap was considerable.
Kothandapani (1971) examined the affective, behavioral, and cognitive components of attitudes toward birth control. Like Ostrom, he used the multitrait-multimethod approach. Results were interpreted as confirming the tripartite model of attitude. In comparison to
Ostrom's study, Kothandapani found greater unique variance associated with each component, and lower intercomponent correlations. These differences in results were attributed to the controversial topic Chapter One 12
(birth control) and to the use of a heterogeneous population (users and nonusers).
Conclusion. Of the four tests discussed above, two supported the tripartite model (Kothandapani, 1971; Oatroa, 1969), and two failed to support it (Mann, 1959; Woodmansee & Cook, 1967). The two failures should be given less weight, however, since they did not include proper operationalizations of the three components. Thus, the model appears to have empirical support.
Evaluation of the Model,a Empirical Base
One of the principal goals in tests of the tripartite model is to determine the extent to which affect, behavior, and cognition diverge as separable components of attitude. In this respect, Oatroa's data indicated minimal divergence among the components (high interconponent correlations and low unique variances). The strongest support for the nodel comes from Kothandapani's study, in which greater separability among components was observed.
Until recently, there has been no formal procedure for using the raultitrait-multimethod technique to test for convergent and discriminant validity. Campbell and Fiske proposed "rules of thumb" for evaluating the correlation matrix. The informal guidelines, however, are inherently subjective. There are 66 correlations in a 12
X 12 correlation matrix (the rank of Oatroa's and of Kothandapani's matrices). To test for discriminant validity, 72 (non-independent) comparisons among those correlations are required. How many of those Chapter One 13 * comparisons must be consistent with the expected pattern before the investigator can claim (with any confidence) that discriminant validity has been observed? It is left to the investigator to decide.
More recently, the LISREL model (Joreskog & Sorbom, 1981) has been developed, and it can be used for evaluating a multitrait- multiraethod matrix. Bagozzi (1978) reanalyzed both Ostrom's and
Kothandapani's data using an early version of the LISREL computer prograa (Joreskog & Van Thillo, 1972). For Ostrom's data, Bagozzi's conclusions agreed with Ostrom's: discriminant validity was demonstrated, but very little unique variance was associated with each component. Bagozzi's reanalysis of Kothandapani's data led to conclusions different from Kothandapani'at He found no statistical support for the discriminant validity of the tripartite model.
Bagozzi's (1978) reanalysis weakened the base for the tripartite model. Of the two originally supporting studies, only Ostrom's provides statistical support, and the three components were only minimally divergent in that study. Some attitude theorists, in summarizing an earlier literature, appear to have anticipated these results. Thus, Harding et al. (1954) concluded that "the relationship among the various attitude components is so close that it does not make much difference in practice whether we use cognitive, affective, or conative [measures]" (p. 1030). NcGuire (1968) anticipated an even stronger conclusion when he wrote that "the three components have proven to be so highly intercorrelated that theorists who insist on Chapter One 14 distinguishing then should bear the burden of proving that the distinction is worthwhile" (p. 157).
Requirements for a Strong Test of the Tripartite Model
Five conditions are essential for a strong test of the tripartite model. The following discussion of those conditions will show that none of the four previous validation efforts have satisfied all of then simultaneously.
(1) Dependent measures of affect, behavior, and cognition must take the form of responses to en attitude object. By definition, each of the- three attitude components represent alternative classes of response. If the attitude object is not physically present, then one can only respond to a symbolic or mental representation of the object.
Because such responses are (presumably) mediated by one's cognitive system, observed measures may assess primarily the cognitive component, and may therefore produce inflated estimates of intercomponent consistencies. The previous validation efforts by
Kothandapani (1971), Ostrora (1969), and Woodmansee and Cook (1967). involved subjects responding to a symbolic representation of the attitude object, rather than the real thing.
(2) Verbal and nonverbal measures of affect and behavior are required. In analyzing the distinguishing antecedents of the three attitude components, it was noted that they are not all built up through cognitive processes (Greenwald, 1968a). Thus, one's cognitive system cannot be assumed to have complete access to emotional and Chapter One 15 behavioral experience. Greenwald (1982) argues that intercomponent correlations are likely overestimated when all three components are indexed soley by verbal report measures. All four of the previously reviewed studies relied exclusively on verbal self-reports. Nonverbal measures sight include physiological responses of affect and recordings of overt behavior.
(3) Multiple, independent measurements of affect, behavior, and cognition are needed. The three attitude components are hypothetical, unobservable constructs. They are represented by observable measures.
According to classical test theory (Lord & Novick, 1968), each observed variable's variance can be partitioned into variance due to the underlying, hypothetical construct and variance due to randoia measurement error. As the number of independently measured variables for a given construct increases (i.e., variables that share little or no common method variance), the relative percentage of measurement error is assumed to decrease (random error cancels out), producing a better overall assessment of the unobservable construct. The relative percentage of measurement error will not decrease as much when the multiple measurements are not independent. The study by Mann (1959) included only one measure of each attitude component. Kothandapani
(1971) and Ostrom (1969) use multiple measures, but they were independent only in a very limited sense.
(4) A confirmatory, rather than exploratory, approach to validation should be used. This approach requires an a priori method for classifying measures of affect, behavior, and cognition. The Chapter One 16
confirmatory approach is easily accomplished with structural equation modeling (Bentler, 1980; Joreskog & Sorbom, 1981). One advantage to this approach is that it provides a foraal, statistical Beans for evaluating a model. The interpretive ambiguity associated with an exploratory approach is illustrated by Uoodmansee and Cook's (1967) evaluation of the tripartite model.
(5) All dependent'measures must be scaled on a common evaluative continuum. Since the attitude construct is defined in terms of evaluation, it follows that all dependent measures should reflect an evaluative disposition (response) toward the attitude object. The atudy by Hann (1959) illustrates the resultant ambiguity when the attitude measures are not scaled on a common evaluative continuum.
Purpose Of Dissertation
Based on the foregoing analysis, it can be concluded that no previous studies have satisfied all the conditions essential for a strong test of the tripartite model. The purpose of this dissertation is to evaluate the validity of the tripartite model in light of the conditions set forth in the previous section. Two approaches are taken.
First (Chapter Two), the data collected by Ostrom (1969) and by
Kothandapani (1971) are re-analyzed with the most recent version of the LISREL computer program (Joreskog & Sorbom, 1981). The purpose of the re-analysis is (i) to evaluate the two strongest data bases in a more comprehensive manner than did Bagozzi (1978), and (ii) to Chapter One 17
demonstrate the application of the LISREL technique in evaluating the tripartite raodel.
The second approach is represented by two new data collection efforts designed specifically to test the tripartite aodel. Both sets of data satisfy the conditions outlined in the previous section. The first study (Chapter Three) exanines the affective, . behavioral, and cognitive cosponents of attitudes toward snakes. The second study
(Chapter Four) examines intercomponent consistency as a function of theoretically iaportant aediating variables (e.g., degree of past experience). The second study also applies the aethod developed in study one to the problem of predicting overt behavior froa other attitudinal measures. CHAPTER TWO
RE-ANALYSIS OF PREVIOUS VALIDATION STUDIES
The purpose of this chapter is to present the results from a re- analysis of Ostrora (1969) and of Kothandapani (1971). (A third re- analysis [of Fishbein and Ajzen, 1974] is described in Appendix E.)
The new analyses will serve (i) to illustrate the use of structural equation modeling (LISREL) in evaluating the tripartite model, and
(ii) to test the tripartite nodel'a validity using the best available data. A similar re-analysis was undertaken by Bagozzi (1978).
However, the early version of the LISREL computer program (Joreskog and Van Thillo, 1972) used by Bagozzi did not perait u direct evaluation of the tripartite model.
Overview of the LISREL Model
LISREL (Analysis of Linear Structural Relationships. Joreskog &
Sorbom, 1981) is the name of a computer program that can be used to evaluate the tripartite model. LISREL allows the specification of relationships among unobserved, hypothetical constructs (referred to as latent variables). Each latent variable is associated with
(represented by) one or more observable, measured variables. LISREL includes a structural model and a measurement model. The measurement model specifies the relationships between measured variables and latent variables. The measurement model is used to define each latent
18 Chapter Two 19 variable in terms of its associated measured variable(s). The structural aodel specifies the relationships anong latent variables.
For example, the tripartite model Bight assume correlations among affect, behavior, and cognition (all latent variables). The correlations would be specified in the structural aodel.
Figure 2 shows the measurement and structural models that correspond to the tripartite aodel of attitude structure. This special case of LISREL is a confirmatory factor analysis model (cf.
Rummel, 1970). Three latent variables (affect, behavior, and cognition) are indicated in circles. Nine measured variables (Xi through Xg) are indicated in boxes. (By convention, latent variables will always be enclosed in circles and measured variables will always be enclosed in boxes.) Each path connecting a latent variable to a measured variable represents a factor loading. It is significant that a given measured variable "loads" on one latent variable and not on the others. The e'a represent the unique variance associated with each measured variable (that is, variance not shared with the common factor). The nine factor loadings plus the nine unique variances constitute the measurement model. The three double-headed curved paths that interconnect affect, behavior, and cognition represent the correlations among those three latent variables. They correspond to interfactor correlations in an oblique factor analysis solution
(Rummel, 1970). The interfactor correlations constitute the structural model. Chapter Two 20
The LISREL approach to model fitting is a confirmatory one. The
investigator first specifies the measurement and structural models.
Input to the computer program consists of a sample variance-covariance matrix. LISREL then uses a maximum likelihood procedure for estimating all of the model's unknown parameters. The resultant model implies a certain covarlance matrix. If that implied matrix is sufficiently similar to the sample (input) covarlance matrix, the model will not be rejected. When the implied matrix is very different than the sample covarlance matrix, the model will be rejected. That is, a given model will either be confirmed or not confirmed. A chi- square (X2 statistic indicates whether a given model is a plausible representation of the data. Unlike traditional statistical procedures, a nonsignificant X? indicates a relatively good fit (i.e., small discrepancy between implied and sample covarlance matrices).
There are several disadvantages in relying solely on the X2 statistic to evaluate a model's goodness of fit. First, the statistic tends to increase with increasing sample size. Thus, most models will be rejected with large samples. Second, a model may provide a good representation of the data, even though the X2 statistic leads to rejection of the model. Therefore, other statistics must be used in conjunction with the X2 statistic to evaluate a model's goodness of fit. One such statistic has been proposed by Bentler and Bonnett
(1980). This statistic (described in more detail below) indicates how well a model fits relative to a worst-case model. Chapter Two 21
Analyzing a multitrait-multimethod matrix involves a slight modification of the Figure 2 model (Kenny, 1979). Any given measured variable's variance can be partitioned into several sources. One source derives from its association with a latent variable. Another source derives from its association with a method of measurement. The remaining variability is attributed to random disturbances. In Figure
2, the G'S represent a combination of method variance and random disturbances. Suppose that X]., X4, and X7 derive from a single measurement technique (e.g., a Thurstone measure of attitude). Those three measured variables can be assumed to share common method variance. One way to model that situation is to allow correlations among the unique variances. Rotationally, that would be represented by double-headed arrows connecting EI to £4, EI to E7, and E4 to G7.
That is, some portion of each variable's unique variance is assumed to be shared with the unique variance of other similarly measured variables. Figure 3 diagrams the tripartite model in which correlated errors have been included in the measurement model.
RE-ANALYSIS OF OSTROM (1969)
Ostrom examined the affective, behavioral, and cognitive components of attitudes toward the church. The objective was to
"assess the theoretical value of maintaining the tripartite classification of evaluative responses" (p. 13). As described earlier, Ostrom used the multitrait-multimethod technique (Campbell &
Flake, 1959) to evaluate convergent and discriminant validity. Four Chapter Two 22 verbal report measures were constructed to measure each of the three attitude components. Ostrom collected data on three separate samples, and reported the correlation matrix for the largest (n = 189).
The four attitude scales were (i) equal-appearing intervals
(Thurstone & Chave, 1929), (ii) summated ratings (Likert, 1932), (ill) scalogram analysis (Guttman, 1944), and (iv) a self-rating scale
(Guilford, 1954) (see also Edwards, 1957). The four scales were constructed independently of each other, and no attitude statement appeared on more than one scale. All of the verbal report scales were assembled into 3 single booklet that included instructions and additional measures of "overt behavior." The overt behavior measures included questions regarding church attendance, participation in church activities, and so on. The data froa these questions were not included in the re-analysis.
The 12 X 12 multitrait-nultimethod correlation matrix from
Ostrom's largest sample is in Table 1. Following Campbell and Flake's
(1959) rules of thumb, Ostrom determined that the monotrait- heteromethod correlations were generally greater than the corresponding heterotrait-heteromethod correlations. That is, the discriminant validity of the tripartite classification appears to have been confirmed. Ostrom also reported that the mean monotrait- heteromethod correlation was .624, while the mean heterotrait- heteromethod correlation was .588. That is, each attitude component had a very small portion of unique variance — an average of 4.4X
(.6242 - .5882). Ostrom concluded that "determinants common to all Chapter Two 23
components were the most potent in producing . . . response[3] to the verbal measures" (p. 22). Finally, Ostrom estimated the mean interconponent correlations; the affect-behavior correlation was .65, the affect-cognition correlation was .65, and the behavior-cognition correlation was .64.
Results and Discussion
A sample correlation matrix is all that is needed to re-analyze
Ostroa's data. The present re-analysis was therefore limited to the largest sample since that was the only correlation matrix reported.
Two approaches were taken.
First, the 12 X 12 correlation matrix was analyzed with an exploratory common factor analysis procedure with oblique rotation of factors (Ruaael, 1970). Oblique rotation permits (but does not force) the factors (e.g., attitude components) to be correlated. The purpose of an exploratory factor analysis is to determine whether factors that can be identified as affect, behavior, and cognition "emerge" from an analysis of the correlations.
The second approach was a confirmatory factor analysis using the
LISREL computer program (Joreskog & Sorbora, 1981). This analysis will lead to either confirmation or rejection of the tripartite model.
Ideally, the two approaches should converge on the same structure.
The exploratory and confirmatory factor analyses of Ostrom's data did not converge on the same structure. The exploratory analysis did not produce factors that could be identified as affect, behavior, and Chapter Two 24
cognition. The confirmatory analyses indicated statistical support for the tripartite model. However, the three attitude components were very highly correlated.
Exploratory factor analysis. The SAS (1979) computer program was used to calculate the principal-axis common-factor solutions. The correlations in Table 1 were used as input to the computer prograa.
Squared multiple correlations were calculated as initial estimates of communality. The first step was to determine the number of factors.
The eigenvalues were 7.529, 0.326, 0.212, 0.103, 0.042, and 0.011 for factors one through six respectively. The first factor accounted for
97.2* of the variance, and all subsequent factors each accounted for less than 5X of the common variance. By these criteria, one factor appears to be sufficient in accounting for the connon variance. In the one-factor solution, all 12 variables had large, positive loadings
(from 0.63 to 0.89). This analysis fails to confirn the validity of the tripartite classification, in favor of a single construct
(attitude) model.
A three-factor solution was also calculated. It did not produce factors that could be identified as affect, behavior, and cognition.
An oblique (PR0HAX) rotation was applied. The rotated factor pattern can be found in Table 2, and the interfactor correlations are in Table
3. Factor one can be identified as behavior, although the self-rating measure of behavior does not load on this factor. Factor two appears to represent a method factor, with all three self-rating measures Chapter Two 25
loading on it. Factor three la not easily interpretable; three of the four affect measures load on this factor as do two of the cognition neasures. These three factors are highly correlated (fron 0.69 to
0.75). This analysis again fails to confira the validity of the tripartite model.
Confiraatory factor analysis. The LISREL V computer prograa
(Joreskog s. Sorboa, 1981) was used for this analysis. The approach suggested by Kenny (1979, p. 150) for analyzing multitrait-aultiaethod correlation matrices was used. Figure 3 represents the aodel that was tested. Table 4 includes the LISREL estimates for all of the aodel'a unknown paraaeter3. The overall X2 (39 df) = 53.82
The confiraatory analyses also supported the tripartite aodel'a discriainant validity. To evaluate discriainant validity, the aodel of Figure 3 was coapared to a aore restricted one-factor aodel. If Chapter. Two 26
the three attitude components are distinguishable, then a three-factor model should fit better than a one-factor model. If a one-factor model fits just as well, then there is no evidence in support of discriminant validity. In can be noted that the one-factor nodel is a special case of a three-factor model in which all interfactor correlations are fixed at 1.0, and in which each measured variable
loads on only one factor. The overall X2 for the one-factor model (42 df) = 72.62 (p_ < .01). Thus, the one-factor model is rejected. Since these two models are nested (one is a restricted case of the other), a difference in their X2'a would indicate the relative increment in fit.
The difference X2 (3 df) = 18.80 (p_ < -01), indicating that the three- factor model fit better than the one-factor model. This analysis supports the model's discriminant validity. These results are summarized in Table 5.
Convergent validity implies that each measured variable loads significantly on its respective factor. For Ostrom's data, all measured variables had significant loadings, supporting convergent validity for the affect, behavior, and cognition measures.
Bentler and Bonnett (1980) proposed an additional method for evaluating a model's goodness of fit. They derived a statistic that provides a- comparison between a given model and its corresponding
"null" model. The null model represents a restricted case in which the variables are assumed to be mutually independent. It represents a worst-case model in which there is no inherent structure among the Chapter Two 27 correlations. The null model provides a baseline against which aodels of interest can be coapared. The noraed fit index (A) is defined as follows:
(1) A = (Fnun - FBOdel> / Fnun , where F represents the applicable X2 statistic. This index ranges froB 0 to 1. Zero would indicate no improvement in fit, and 1 would indicate a perfect fit to the data. A value greater than 0.90 generally indicates a good fit (Bentler & Bonnett, 1980, p. 600). A modification of equation 1 produces an incremental fit index:
(2) Ainc = (Fk - Fi) / Fnull where Fj« is the X2 for a given aodel (k) and Fi is the X2 for a less restricted version of that model. The incremental fit index provides a comparison between two hierarchical aodels relative to a coaaon null model. (Two nodels are,hierarchical when one of then contains all of the parameters of the other, plus some others.) This index will be large when model 1 provides a substantially better fit than aodel k.
It can be observed that when model k is the null aodel, equation 2 reduces to equation 1.
The value of A for the Figure 3 aodel applied to Ostroa's data was 0.971, indicating a very good fit. A for the one-factor aodel was
0.961, also indicating a very good fit. Aj.nc» coaparing the one- factor and three-factor aodels, was 0.01 indicating a very saall iaproveaent in fit relative to the null aodel. Thus, the fit indices indicate minimal discriainant validity in these data. Chapter Two 28
To suanarize: The re-analysis does not permit firs conclusions regarding validity of the tripartite classification. Results from the exploratory factor analysis did not produce factors ' that could be identified as affect, behavior, and cognition. The confirmatory analyses produced ambiguous results. On purely statistical grounds, the tripartite model fit well. This was evidenced by a X2 for the three-factor model that was nonsignificant and substantially lower than the X2 for the one-factor nodel. However, analyses of fit (A) indicated that the one-factor nodel fit almost as well as the three- factor model. Finally, the three attitude components -ware observed to be very highly correlated (all r'a > .9). Even if it is concluded that discriminant validity was demonstrated, it is clear that each component exhibited very little unique variance.
RE-ANALYSIS OF KOTHANDAPANI (1971)
Kothandapani examined the affective, behavioral, and cognitive components of attitudes toward birth control. This study followed very closely from the design of Ostrom (1969). Kothandapani constructed four attitude scales (Thuratone, Likert, Guttman, and
Guilford) for each of the three components. The mult.itrait- multimethod technique (Campbell & Flake, 1959) was used to analyze the data.
The black, low-income, married respondents in Kothandapani's atudy were interviewed individually in their own hoses. The interviewer read the instructiona and attitude statements aloud, and Chapter Two 29 recorded responses. The interview included background questions (age, number of children, etc.), 12 multiple-item attitude scales, and self- reports of actual contraceptive use. Only the data fron the 12 nultiple-iten attitude scales will be re-analyzed. Illustrative affect attitude statements are "I am happy to learn about the benefits of birth control," and "The very thought of birth control disgusts ae." Sample behavior statements are "I would volunteer to speak about the merits of birth control," and "I would walk a nile to get ay birth control supplies." Sample cognition statements are "Birth control will help ae postpone childbirth as long as I want," and "I believe that birth control causes many birth defects."
Applying the Campbell and Fiske rules of thumb, Kothandapani concluded that his data confirmed the discriminant validity of the tripartite classification. In contrast to Ostrom (1969), intercorrelations between the attitude components were small (from -
0.27 to 0.22). Also, the average percentage of unique variance was substantial (approximately 0.21). Kothandapani attributed the lower intercomponent correlations and greater unique variance to the use of a controversial attitude topic (birth control rather than the church) and to a heterogeneous sample (users and nonusers rather than college students).
Results and Discussion
As in the re-analysis of Ostrom, both exploratory and confirmatory factor analyses were conducted. Kothandapani's data Chapter Two 30 supported the tripartite model. Three factors Identified as affect, behavior, and cognition emerged from the exploratory analyses. The confirmatory analysis indicated relatively good support for the three- component distinction. The tripartite model was rejected on purely statistical criteria, but nevertheless fit quite well according to the normed fit index (A). The re-analyses also indicated moderate correlations among the three attitude components.
Exploratory factor analysis. The SAS (1979) computer program was used to calculate the principal-axis common-factor solutions. The correlations in Table 6 were used as input to the computer program.
Squared multiple correlations were calculated as initial estimates of communality. The eigenvalues were 4.243, 1.682, 1.073, 0.754, 0.695, and 0.379 for factors one through six respectively. The first three factors accounted for more than 85* of the common variance (51.6X,
20.5X, and 13cOX respectively). Thus, three factors appear to be sufficient in accounting for the common variance.
The rotated (PROMAX) factor pattern can be found in Table 7.
Factor one can be identified as affect, although the Guttman measure of cognition also loads (marginally) on this factor. Factor two clearly represents behavior. Factor three can be identified as cognition, with the exception of the Guttman cognition measure.
Finally, the interfactor correlations (Table 8) are moderate in magnitude (ranging from 0.28 to 0.34). In comparison to Ostrom, this factor structure from the exploratory analysis provides strong support for the tripartite model's discriminant validity. Chapter Two 31
Conflraatory factor analysis. The LISREL V coaputer prograa
(Joreskog & Sorboa, 1981) was used for this analysis. Just as for
Ostroa, the Figure 3 model was tested. Table 9 includes the LISREL estimates for all of the nodel's unknown parameters. The overall "J&
(39 df) = 71.56 (p_ < .01). Thus, on statistical grounds,
Kothandapani's data significantly diverge froa the Figure 3 aodel.
However, a noraed fit index (A) of 0.91 represents an acceptable fit.
Exaaination of the factor loadings (Table 9) indicates that all aeasured variables loaded highly on their respective factors. The
LISREL estlaates of covarlances between siailarly-aeasured variables
indicate relatively high correlations. In this aodel, the three attitude components are moderately correlated (ranging froa 0.11 to
0.59).
To evaluate discriainant validity, the aodel of Figure 3 was coapared to the aore restricted one-factor model. The overall X2 for the one-factor aodel (42 df) = 263.51 (p_ < .001), indicating a very poor fit. The noraed fit index (A) of 0.67 also indicates a very poor fit. The difference between X2's for the one- and three-factor aodels
(3 df) = 191.95 (p_ < .001), indicating a very austantial improvement of the three-factor aodel over the one-factor aodel. The increaental fit index (coaparing the one- to the three-factor aodel) of 0.23 was also substantial. This analysis supports the tripartite aodel'a discriainant validity. These results are suanarized in Table 10. Chapter Two 32
All measured variables had significant factor loadings on their respective factors, supporting convergent validity.
To sueaarize: The re-analysis of Kothandapani provides strong support for the tripartite model of attitude. Results from the exploratory factor analysis produced factors that could be identified as affect, behavior, and cognition. The confirmatory analyses also supported the tripartite model. Although the X2 statistic led to rejection of both one-factor and three-factor models, the three- factor model fit substantially better than did the one-factor model.
The nor'med fit index also indicated that the three-factor model accounted very well for the data. Finally, the three attitude components were observed to be only moderately correlated (all r's <
.6). i,
GENERAL DISCUSSION
It is interesting to compare the present re-analysis with a similar one made by Bagozzi (1978). Bagozzi concluded that Ostroe's data, but not Kothandapani's, demonstrated discriminant validity for the tripartite model. One source for the difference in conclusions is that Bagozzi evaluated the data sets solely on the basis of their chi- square significance tests, not on the basis of goodness of fit. Also,
Bagozzi concentrated on the partitioning of variance into attitude component, method, and error components. In comparison, the present re-analysis stressed the magnitude of intercomponent correlations. It should be noted that Bagozzi used an earlier version of the LISREL Chapter Two 33 conputer program (Joreskog & Van Thillo, 1972) and also excluded the
Guilford self-rating neasures, and ao the re-analyaes may not be directly comparable.
An Important difference between Ostrom's and Kothandapanl's attitude domains is the extent of past experience subjects may have had. At the tine of Kothandapanl's study (pre-1971), low-income black women were not likely to have been exposed to extensive educational materials, nor to have discussed the topic with others. In contrast, the college-student respondents in Ostrom's study are likely to have had extensive experience with the church. With increasing past experience, one's verbal system can acquire knowledge regarding
affective and behavioral responses, thereby producing very high
intercomponent consistencies.
According to the, present view, Kothandapanl's data represents the strongest support for the tripartite model. Ostrom's data should have been expected to produce very high intercomponent correlations because of the attitude domain, and because all responses were verbal. In
this regard, it is impressive that the three components were statistically distinguishable in Ostrom's data. Nevertheless, no
single study has produced confirmation of the tripartite model in terms of both atatistical fit and goodness of fit. The study to be reported next (Chapter III) was designed to produce the strongest possible test of the tripartite model. CHAPTER THREE
STUDY ONE
As developed in Chapter One, a strong test of the tripartite model should use (1) dependent aeasures of affect, behavior, and cognition in the fora of responses to an attitude object, (2) dependent measures scaled on a common evaluative continuum, (3) verbal as well as nonverbal aeasures of affect and behavior, (4) nultiple independent aeasures of affect, behavior, and cognition, and (5) a confirmatory, rather than exploratory, approach to validation.
No tests of the tripartite model have satisfied more than two of these five conditions. The strongest tests to date (Kothandapani,
1971; Ostrom, 1969) relied exclusively on verbal report measures.
Also, the multiple measurements of each attitude component in those studies should not be considered mutually independent since they almost certainly shared common method variance.
One consequence of relying exclusively on verbal report measures is that intercomponent consistencies are likely to be overestimated.
This was evident in the re-analysis of Ostroa's (1969) data. Even though discriminant validity was demonstrated for those data, the three attitude components were very highly correlated. The true magnitude of the intercomponent correlations is of great importance.
If they are as highly correlated as Ostrora's data would suggest, then
34 Chapter Three 35
It aay not be necessary In practice to distinguish among them.
A second consequence of using only verbal reports is that many characteristics of the affective and behavioral components are not properly operationalized. As noted in Chapter One, one's verbal system cannot be assumed to have complete access to emotional and behavioral experience. While verbal report measures may measure some characterstics of the affective and behavioral components, they most likely do not capture other important (nonverbal) features. Thus, a strong test of the tripartite model must include verbal and nonverbal measures.
The attitude domain of snakes was selected for the present study becauae (1) the attitude object can be (literally) placed in the presence of subjects (so that subjects can respond to the actual object, rather than a symbolic or mental representation of it), (2) there are relatively obvious and easy-to-collect measures of affective and behavioral responses to snakes, (3) an extremely negative attitude toward snakes (i.e., a phobia) may be characterized by independence between the behavioral and cognitive components (Coleman, 1976), affording the possibility of estimating a lower bound for the intercomponent correlations, and (4) understanding the interrelationships among affect, behavior, and cognition in the context of a phobia may have useful clinical applications (cf.
Bandura, Blanchard, & Ritter, 1969). Chapter Three 36
METHOD
Subiects
Subjects were 138 undergraduates who participated in fulfillment of a course requirement. Subjects were recruited for a study on
"attitude structure," and were informed only after arrival at the laboratory that the study involved snakes. As described below, all subjects were provided an opportunity to withdraw from the study after having learned that it involved snakes. No subject chose to do so.
Overview of Procedure
Subjects were tested individually in a session that lasted under one hour. Upon arrival at the laboratory, subjects were seated at a table and allowed to rest for a minimum of five minutes. The investigator then introduced himself and described the study as concerned with people's attitudes toward animals. Several baseline measures of affect were administered, after which subjects were informed that the animal for that day's study was a snake. Subjects were given an opportunity to withdraw from the study (none did), after which the dependent measures were collected. Finally, subjects were debriefed, thanked for their participation, and asked not to mention details of the study to others who might later participate in the same study.
Apparatus and Physical Facility
Figure 4 shows the layout of the experimental room and the placement of apparatus. Subjects remained seated at one corner of a Chapter Three 37
large table In the center of the room. Straight-ahead and to the right of the subject's seat (at a distance of 3.6 m) was a 71 (1) X 56
(w) cm white projection screen that hung from the ceiling. A Kodak- brand carousel slide projector was located on a cart to the subject's right. A numbered cardboard strip (Ideal-brand number line #7800, plastic coated) hung froa the ceiling and ran perpendicularly from the projection screen past the subject's seated location. A Sears-brand digital electronic exercise/pulse monitor (catalog number 6-29151) was located diagonally across the table froa the subject's seated location at a distance of 163 cm.
Two snakes were used during the course of the study, although each subject saw only one of the snakes. The snakes were identical juvenile red rat snakes (also called corn snakes), approximately 65 cm in length. The snakes ordinarily resided out of the subject's view. A single snake was presented to subjects in a 40 (1) X 20 (w) X 25 (h) cm clear plastic container.
Summary of Measured Variables
Ten measures were used to estimate the structural model of Figure
2. There were four measures of affect, and three measures each of behavior and cognition. These measures are introduced here, and described in detail in a later section.
Affect measures. The four measures of affect were taken while the subject was in the presence of a live snake. The measures were
(1) heart-rate (HR), (2) a mood adjective checklist measure of Chapter Three 38
positive affect (MACL+), (3) a mood adjective checklist measure of negative affect (MACL-), and (4) a Thurstone equal-appearing interval measure of affect (Thurstone Mood).
Behavior measures. The three measures of behavior were (1) a
Thurstone equal-appearing interval measure of behavior (Thurstone
Behavior), (2) the average distance to which the subject would be willing to approach a variety of pictured snakes (Distance), and (3) the extent of contact in which the subject was willing to engage with a live snake (Sequence).
Cognition neaaurea. The three measures of cognition were (1) a
Thurstone equal-appearing interval measure of belief (Thurstone
Cognition), (2) ratings of snakes on scales representing the evaluative dimension of the semantic differential (SD), and (3) the net proportion of favorable-to-snake listed thoughts given in the presence of a snake (Listed Thoughts).
Construction of Thurstone Scales
The three Thurstone (Thurstone & Chave, 1929; Edwards, 1957) equal-appearing interval attitude scales were constructed by the following procedure: First, a group of 40 subjects generated attitude statements with the aid of provided forms (reproduced in Appendix A). .
Second, a pool of attitude statements obtained by editing these elicited statements and adding investigator-generated items (see also
Ostrom, 1971) was administered to a second group of 35 judges, who rated each statement for degree of favorability toward snakes. The Chapter Three 39
judged pool of statements, along with a sample rating scale and instructions, are given in Appendix B. Lastly, attitude statements were selected for inclusion in the final attitude scales such that the entire range of the attitude continuum was represented by items with low interquartile ranges (see Edwards, 1957).
Measures of the Affective Component
Thurstone affect. This scale included 16 statements such a3 "I feel anxious," "I feel tense," and "I feel happy." This measure was calculated as the median Thurstone scale value of checked items. The
Thurstone scale of affect, along with all scale values, can be found in Appendix C. The ordering of itens was randomly determined.
HACL* and MACL-. These two measures of affect were derived from the Mood Adjective Check-List (HACL) (Nowlis, 1965). The MACL form is shown in Appendix C. The measure of positive affect (MACL+) was calculated by summing responses to nine adjectives representing the surgency, elation, and social affection scales: carefree, elated, affectionate, playful, overjoyed, kindly, witty, pleased, and warmhearted. The measure of negative affect (MACL-) was calculated by summing responses to nine adjectives representing the anxiety, sadness, and aggression scales: angry, tense, regretful, defiant, fearful, sad, rebellious, jittery, and sorry. Items were scored +2 if the (vv> was circled, +1 if the Heart-rate. The fourth measure of affect was derived from heart- rate measurements. As described below, each subject's pulse-rate was recorded four separate times during the course of the experimental session. Each of the four heart-rate measurements involved recording the subject's pulse (beats per ninute) for a period of three minutes. Twelve recordings were made, each separated by IS seconds. The highest and lowest recordings were discarded, and the remaining ten recordings were averaged to produce a single heart-rate measurement. This procedure was repeated four times during the experimental session, thereby producing four measures of heart-rate. The four heart-rate measures were standardized in relation to their overall mean. One of the four heart-rate measurements was taken while the subject was in the presence of a live snake. The heart-rate measure of affect was the z-score corresponding to the latter measure of heart-rate. Thus, the other three heart-rate measures served as baselines. Measures of the Behavioral Component Thurstone behavior. This scale included 14 statements such as "I scream whenever I see a snake" and "I like to handle snakes." This measure was calculated as the median Thurstone scale value of checked items. Appendix C includes a reproduction of this scale. The statements were randomly ordered. Sequence. A second measure of behavior was an overt action sequence. Subjects were asked to engage in a series of actions that Chapter Three 41 brought them into increasingly closer physical contact with the live snake. When this measure was taken the snake was in its cage on the table in front of the subject. The investigator said, "For the next part of the study, I'd like to find out whether you would be willing to do a variety of things involving the snake. I'm not going to ask you to do anything that would make you feel uncomfortable. If what I ask would make you feel at all uneasy, just say so and I won't ask you to do it. First, would you prefer that I take the snake away or leave it here for the rest of the experiment? If you would really prefer that I take it away, jtit say so." The subject's response was noted, but regardless of the reply the snake remained in its cage on the table. The experimenter then said, "The next question is whether it is alright with you if I take the snake out of the cage? I'll hold on to it, but if that would make you feel even slightly uncomfortable, just say so." If the subject agreed to the request, the snake was removed from its cage and held by the investigator in the subject's presence. If the subject declined, the snake was left in its cage. The experimenter then said, "Will you touch the snake while I hold it? If you really prefer not to, please let me know." If the subject agreed, the experimenter held out the snake for petting. Finally, the experimenter said, "Will you hold the snake by yourself. If you don't want to, just say so." The experimenter waited for a reply, but placed the snake back in the cage regardless of the answer. There were four steps in this action sequence (allow snake to stay, remove snake from cage, pet snake, hold snake). The score for this measure Chapter Three 42 was the number of actions agreed to by the subject. Hence, its value ranges from 0 to 4. Distance. The third measure of behavior was a preferred distance measure. Subjects were shown twelve color slides of snakes that varied in size, color, dangerousness, etc. The slides were projected on a screen placed to the subject's front and right. Subjects were instructed to "imagine as if each snake is a real live snake, and that it is sitting right there where the screen is, just as it is shown in the picture." Subjects were then asked to indicate "the closest they would be willing to get to each snake, if it was a real live snake." Subjects indicated the distance by pointing to the corresponding physical location along a numbered line that ran perpendicularly front the projection screen. The line was numbered in integers from one to fifty at intervals of 8.5 cm. The subject was told to "indicate the number that corresponds to the closest you would stand if the snake was right where the screen ia. For example, if you would get right next to it, or even touch it, you can indicate a zero or one. If the closest you would get is where you are right now, you would indicate a 36 or 37 (the subject's location]. If the closest you would get is even further, you can go back to 50. If you would stay past that, you can just say 'more than 50'." Following these instructions, the lights were dimmed and the slides were displayed one at a time. The ordering of slides was different for each subject. The twelve color slides were a subset of Blackhawk Films' set of 'Snakes of the Eastern Chapter Three 43 United States'. The scale value for this measure was the average of the twelve distances. Measures of the Cognitive Component Thurstone cognition. This scale included statements such as "Snakes are soft and smooth," "Snakes control the rodent population," and "Snakes will attack anything that moves." This measure was calculated as the median Thurstone scale value of checked items. Items were randomly ordered. The scale and scale values can be found in Appendix C. Semantic differential (SD). Subjects rated snakes on each of these six bipolar scales representing the evaluative dimension of the semantic differential (Osgood, Suci, & Tannenbaum, 1957): good/bad, friendly/unfriendly, kind/cruel, clean/dirty, beautiful/ugly, and important/unimportant. The scale value for this measure was the mean rating (larger numbers attached to the more favorable pole) across all six scales. This scale is reproduced in Appendix C. Listed thougths. The listed thoughts (cognitive response) procedure (Greenwald, 1968b; Petty, Ostrora, & Brock, 1981) required subjects to list all of their thoughts while they were in the presence of the snake. A special form for this task is reproduced in Appendix C. (Subjects were provided with two pages of "thought boxes.") After listing their thoughts, subjects were instructed to indicate which of them were favorable toward snakes and which were unfavorable. This measure was the difference between proportion of favorable and Chapter Three 44 unfavorable thoughts (net positive thoughts). (The proportions were first converted via an arcsin transformation [Winer, 1971] before their differences were calculated). Procedure Subjects were scheduled individually for sessions that lasted under one hour. Upon arrival at the laboratory, the subject was seated at a large table and was left alone for a minimum of five minutes to allow stabilization of heart-rate (see Figure 4). The experimenter then sat down across from the subject, introduced himself, and said, "In this study we are interested in people's attitudes about animals. I am sure you can imagine that people have all kinds of different attitudes toward animals. For example, some people are dog-lovers, while others are cat-lovers. We're interested in various aspects of your attitudes toward animals. One aspect in which are interested is your beliefs. For example, you might believe that dogs are friendly, or that cats are independent. We are also interested in your behaviors. For example, if you like dogs then you might like to pet them. Finally, we are interested in certain physiological reactions that may occur in the presence of an animal. For example, if you are walking down the street, and a dog suddenly barks at you, your heart may start to beat faster. For that reason, I'll be taking measurements of your heart-rate at various times during today's session. Are there any questions?" Chapter Three 45 Heart-rate baseline. The experimenter then explained that it was necessary to take a baseline recording of heart-rate. The subject was told that the heart-rate measurement procedure was simple and harmless, and the operation of the electronic recorder was explained. A photoplethysraograph was attached to the subject's right earlobe, and the experimenter made recordings for three minutes (see above). The heart-rate monitor's display was out of the subject's view, as were the experimenter's recordings. Mood baseline. After recording heart-rate, the experimenter gave the subject a two-page questionnaire to complete. One page contained the mood adjective check-list (MACL), and the other page contained the Thurstone measure of affect (see Appendix C). The order of pages was counterbalanced across subjects. The subject was left alone to complete the two .scales. These two measurements served as baseline indicators of affect. Introduction of the live snake. When the subject had completed the baseline affect measures, the experimenter returned, and said "The kind of animal for which we would like to find out about your attitudes is a snake. Now, we have a real live snake here, and I'm going to bring it out in a moment. However, you should know that you won't be asked to do anything that you don't wish to do. The snake is in a cage, and cannot escape. The kind of snake we have is a small, harmless corn snake. Again, I won't ask you to do anything that would nake you feel uncomfortable. Is it alright with you if I bring out the snake?" Chapter Three 46 Every subject agreed to have the snake placed in their presence. Before bringing out the snake, the experimenter explained that the subject's consent was required before continuing with the experiment. The experimenter then read aloud the informed consent form in Appendix C. Every subject agreed to sign the form. At that point, the caged snake was taken from behind a partition (outside of the subject's view) and placed on the table approximately 60 cm in front of the subject. The subject was then informed that another heart-rate measurement was to be taken. The photoplethysmograph was re-attached to the subject's earlobe, and a second recording was taken. This recording formed the basis for the to-be-analyzed heart-rate measurement (see above for the calculation of that value). The experimenter then asked the subject to complete a questionnaire booklet. The booklet (reproduced in Appendix C) included (a) the MACL and Thurstone measures of affect, (b) the listed thoughts, semantic differential, and Thurstone measures of cognition, and (c) the Thurstone measure of behavior. The booklet also included a single-item self-rating scale and several background questions (see Appendix C). Booklet format. The booklet was divided into three sections. The first section included the two affect scales and the listed thoughts procedure (the latter measure consisted of four pages). The ordering of these three measures was counterbalanced across subjects. Chapter Three 47 These measures always appeared first because they depended most on initial spontaneous responses to the snake's presence and to avoid contamination from the other self-report scales. The second section included the semantic differential scale, the Thurstone measure of belief, and the Thurstone measure of behavior. The ordering of these three scales was counterbalanced across subjects. The third section consisted of a single page that included a single-item self-rating scale and various background questions (e.g., the subject's age, gender, college major). Sequence and Distance measures. When the subject had completed the booklet, a third heart-rate neasure was taken. The experimenter then delivered the instructions for the action sequence neasure (see above). After the last step in that sequence had been completed, the experimenter removed the caged snake from the subject's presence. The experimenter then delivered the instructions for the preferred distance measure (see above). Subjects then completed the HACL and Thurstone affect measures for a third time. Finally, a fourth heart- rate measure was taken. Subjects were then debriefed, requested not to mention details of the study to other potential participants, and asked to complete a general evaluation of their experience in the experiraent. No subject indicated prior knowledge regarding the use of a snake in the study. Chapter Three 48 RESULTS Description of the Data Of the 138 subjects, 63* (87) were female. . Descriptive statistics for the ten measured variables plus the single-itea self- rating scale can be found in Table 11. Examination of Table 11 confirms that the variables possess adequate variability for correlational analyses. The single-itea self-rating is useful in characterizing the distribution of subjects' attitudes. Table 12 shows the frequency of responses for each of the seven possible values associated with that scale. Although the distribution for this scale is approximately normal, it deviates statistically from normality (Kolmogorov-Sairnov D = 0.126, p_ < .01). The distribution has a slight, positive skew (measure of skewness = 0.18). Nevertheless, it would appear that subjects' attitudes were adequately distributed for the correlational analyses to be reported below. The action sequence variable was examined to determine whether it satisfied the conditions of a Guttnan (1944) scale (cf. Edwards, 1957). This variable was assumed to do so since a positive response at any given step in the sequence implies a positive response to all previous- steps. Examination of this variable's distribution confirmed the assumption. Indeed, the coefficient of reproducibility was 1.0, indicating perfect adherence to the Guttman criteria. That is, for every subject a positive response at any given step in the sequence was associated with positive responses to all previous steps. Also, a Chapter Three 49 negative response at any given step was associated with negative responses to all subsequent steps. The heart-rate measure was not distributed as anticipated. In particular, there were no large changes in pulse-rate relative to the baseline measures. Rather, the distribution was characterized by moderate increases and decreases relative to baselines. Of course, heart-rate is not a simple measure of affect. For example, a stress- arousing stimulus aay induce parasyapathetic activation, which would be accompanied by a decrease in heart-rate (cf. Buck, 1976). Alternatively, a fear response aay be accompanied by the release of epinepherine into the bloodstream, which produces an increase in heart-rate (Buck, 1976). Also, a moderate increase or a moderate decrease may indicate pleasurable arousal associated with interest in a novel stimulus. Nevertheless, a large increase in heart-rate is assumed to be indicative of negative affect. Factor Analyses As in the re-analyses of Ostrom (1969) and of Kothandapani (1971), both exploratory and confirmatory factor analyses were conducted. The results from both sets of analyses supported the tripartite model. Exploratory factor analysis. The SAS (1979) computer program was used to calculate the principal-axis common-factor solutions. The correlations in Table 13 were used as input to the computer program. (The signs associated with correlations for the MACL- and Distance Chapter Three 50 measures have been reversed, so that all expected directions of correlation would be positive.) Squared multiple correlations were calculated as initial estimates of coraraunality. The eigenvalue's were 3.301, 0.625, 0.415, 0.194, and 0.067 for factors one through five, respectively. Three factors were retained. The factors could be identified as affect, behavior, and cognition, strongly supporting the tripartite model. The rotated (PROMAX) factor pattern can be found in Table 14. Factor one can be identified as behavior, factor two as cognition, and factor three as affect. The only exception to this pattern of loadings is heart-rate, which does not load on any of the three factors. Its highest loading is on affect, but that loading is too low to be given serious consideration. The interfactor correlations (Table 15) are moderate in magnitude (ranging from 0.37 to 0.64). Thus, with the exception of heart-rate, the tripartite model is strongly supported by the exploratory analysis. Confirmatory factor analysis. The LISREL V computer program (Joreakog & Sorbom, 1981) was used to estimate the unknown parameters of Figure 2, applied to the data of study one. The three-component model of Figure 2 fits well both in terms of statistical criteria and goodness of fit (A), also showing moderate correlations among the three attitude components. Table 16 gives the LISREL estimates. The overall X2 (32 df) = 37.51 (p. > .20), statistically confirming the tripartite model. The normed fit index (A) value = 0.92, also indicating' that the fit is an Chapter Three 51 excellent one. Examination of the factor loadings (Table 16) indicates that all measured variables loaded highly on their respective factors, again with the exception of heart-rate. A replication of these analyses excluding the heart-rate measure produced the same pattern of results. The test of the tripartite model's discriminant validity was obtained by comparison of the three-factor model of Figure 2 with the sore restricted one-factor model. The overall X2 (35 df) = 113.45 (p. < .01), indicating rejection of the one-factor model. A normed fit index of 0.74 also indicates that the one-factor model fits poorly. The difference between X2s for the one-factor and three-factor models (3 df) = 75.94 (p_ < .01), indicating a very substantial improvement of the three-factor model over the one-factor model. The incremental fit index (comparing the one- and three-factor models) is also substantial With the exception of the heart-rate measure, convergent validity was supported. That is, the measured variables had significant factor loadings only on their respective factors. Summary Study one indicates strong support for the tripartite model of attitude structure. Results from the exploratory factor analysis produced factors that could be identified as affect, behavior, and cognition. The confirmatory analyses also supported the tripartite Chapter Three 52 model. Finally, the three attitude components were observed to be only moderately correlated (.52 < r < .71). DISCUSSION The results from study one provide unequivocal support for the tripartite model, validating it both in terms of statistical criteria and goodness of fit. When conditions suitable for a rigorous test of the triparite model were metp affect, behavior, and cognition emerged as three distinct components of attitude. The generality of this conclusion is limited, however, to the single attitude domain of snakes. The attitude domain of snakes was purposely selected as one that had a strong a priori likelihood of allowing independence among attitude components. Many responses to snakes may be controlled reflexively, with little or no cognitive mediation. One's cognitive system may therefore produce responses that are different from,' or independent of, those produced by other control systems. Relative independence of attitude components should be expected when responses to an object are mediated by multiple control systems. For example, consider the case of allergic reactions. Those automatic allergic reactions may control affective and behavioral responses, independently of one's cognitive response system. Thus, one may (cognitively) like dogs very much, but nevertheless avoid them or have adverse physiological reactions in their presence because of the Chapter Three 53 allergy. Less independence is expected when measures of the three attitude components derive from, or are largely mediated by, a single response system. The exclusive use of paper-and-pencil measures is one example of responses mainly under control of one's verbal knowledge system. In some attitude domaina, subjects can only respond to a cognitive representation, and ao responses are necessarily controlled by that single response system. Abstract attitude objects — for example, love, peace, God, religion — are of this sort. Consistency among components should also increase as one gaina experience interacting with an attitude object. With increasing experience, one gaina verbal knowledge of one's affective and overt behavioral reactions to the object. In support of this interpretation, recent research has indicated greater attitude-behavior consistency when the attitude has been formed on the basis of direct experience (Fazio s. Zanna, 1978; 1981; Fazio, Zanna, & Cooper, 1978). CHAPTER FOUR STUDY TWO The second study includes a verbal report analog of study one, allowing a direct assessment of the importance of nonverbal measures of affect and behavior. Also, nultiple measures of noat variables are collected, allowing test-retest correlations to be calculated as estinates of reliability. An .additional concern in study two was the role of past experience in the attitude domain. Fazio and his colleagues (Fazio & Zanna, 1978; 1981; Fazio, Zanna, & Cooper, 1978) have stressed the importance of considering the role of past experience in attitude formation, and in evaluating the attitude-behavior relationship. A related issue is the problem of predicting overt behavior froa prior knowledge of other attitudinal properties (e.g., affect or cognition). In this regard, Fishbein and Ajzen's theory of reasoned action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) provides a framework for evaluating the attitude-behavior relationship. The Role of Past Experience In the Chapter Two discussion of the Ostron and Kothandapani studies, it was noted that their attitude doaains (the church; birth control) differed along several dimensions. One difference was the extent to which respondents were assumed to have had prior experience. 54 Chapter Four 55 In the case of the church, it was argued that college-student subjects were likely to have had extensive past experience. With extensive past experience one may have much practice in verbally describing one's affective reactions to and behavioral interactions with the object. Consistency is then especially likely when measurements of all three attitude components derive from verbal reports. Fazio and Zanna (1981) identify three sources for increased attitude-behavior consistency when the attitude is formed on the basis of direct prior experience. (1) More information about the attitude object is available. (2) Past behaviors become very salient when the individual has actually performed them. (3) Attitudes based on direct prior experience may be more accessible from memory than attitudes based on indirect prior experience. The common thread in the three sources of consistency is that all involve an increased overlap between one's verbal knowledge system and one's bodily response system. More generally, direct prior experience may increase the overlap between many response systems, thereby producing increased consistency. Bern (1968; 1972) has proposed that people make inferences regarding their attitudes by examining their own past behavior. Thus, one may infer a negative attitude toward snakes by noting one's own past refusals to visit the zoo's snake exhibit. This view implies the absence of an attitude in domains for which one has had no past experience. As with Fazio and Zanna's (1981) analysis, this position predicts low attitude-behavior correspondence when there has been Chapter Four 56 little past experience. This analysis inplies higher intercomponent correlations when subjects have had past experience with the attitude domain. Study Two tests this hypothesis by dividing subjects on the basis of their past experience with snakes. Thus, intercoraponent correlations can be compared for subjects with past experience to those who have had little past experience. The Prediction of Overt Behavior One of the classic problems in social psychology is that of the attitude-behavior relationship. In the context of attitude theory, and the tripartite model sore specifically, the problem is one of predicting the behavioral component of attitude fron prior knowledge of other variables. Affect and cognition would seem to be good predictors since they are presuned to share variance with behavior. Measures of the behavioral component, however, should be the best predictors of subsequent overt behavior since they can be assuned to share the greatest variance. (It should be noted that the terra "attitude" in "attitude-behavior relationship" soaetiiaes refers to the cognitive coaponent of attitude, and soaetiraes to the affective component, and only rarely to the entire three-component structure. This issue of interpretation is taken up in greater depth in Chapter Five.) A substantial literature has been interpreted to indicate little attitude-behavior correspondence (e.g., LaPiere, 1934j see Wicker, Chapter Four 57 1969, for a summary of this view). However, recent investigators have been critical of the past interpretations (e.g., Dillehay, 1973), and have focused attention on the conditions essential to producing strong attitude-behavior correlations (A^zen & Fishbein, 1973; 1977), and/or strong behavior predictions (Wicker, 1971). Ajzen and Fishbein (1977) identified a number of conditions for producing correspondence between attitude and behavior. The attitude must (i) have obvious behavioral implications, and (ii) be represented by multiple measures. The behavior should be (iii) overt, (iv) represented by multiple acts, and (v) measured on a methodologically sound scale. Finally, (vi) attitude and behavior must be measured at the same level of specificity. For example, if the to-be-predicted behavior is touching a snake, ' then measures of affect and behavior must be in the specific context of that action. Very recently, investigators have been focusing on the assessment of causal relationships between attitude and behavior (Bagozzi, 1981a; Bagozzi & Burnkrant, 1979; Bentler s, Speckart, 1979; 1981; Kahle & Berman, 1979). Frequently, Fishbein and A3zen's (1975; Ajzen 6. Fishbein, 1980) theory of reasoned action is used to evaluate such attitude-behavior relationships. According to Ajzen and Fishbein, "a person's intention to perform (or not to perform) a behavior [is] the immediate determinant of the action" (1980, p. 5). Behavioral intention (BI) is determined by two variables: attitude toward the behavior and subjective norm. Attitude toward the behavior (AACT) Chapter Four 58 refers to one's evaluation of the action (e.g., rating the act of touching a snake on a "good/bad" scale). Subjective norm (SN) refers to one's perception of social pressures to perform the behavior, weighted by one's motivation to comply with the pressure. This theory is diagrammed (in the form of a structural equation model) in Figure 5. The Fishbein-Ajzen model introduces three potential predictors of behavior (AACT, BI, and SN). In terms of the tripartite model, attitude toward the act shares elements with both behavior and cognition, since it is defined as an evaluation of the behavior. (It shares little with affect, since the measures do not refer to the atttitude holder's feelings or mood.) Behavioral intention also overlaps with cognition and behavior, referring to one's verbal t; statements regarding the likelihood of performing a given action. Subjective norm does not directly map on to affect, behavior, or cognition, although the construct is cognitive in origin (referring to one's cognitions of social pressures). Behavioral intention and attitude toward the act should be very good predictors of overt behavior since they overlap with the behavioral component of attitude. Affect, cognition, and subjective norm should be less powerful predictors since they presumably share less variance with the behavioral component of attitude. Bentler and Speckart (1979) used LISREL to evaluate the Fishbein/Ajzen model. They rejected the Figure 5 model, but found evidence in support of the modified model in Figure 6. In the Chapter Four 59 modified model, past behavior is introduced, and is assumed to have an effect on both behavioral intention and the target action. This is consistent with Fazio and Zanna's (1981) treatment of past experience, and with Bern's (1968; 1972) theory of self-perception. In addition, attitude toward the act is hypothesized to have a direct effect on behavior. Overview of Study 2 There were three objectives in the second study. (1) To evaluate the extent to which verbal report measures overestinate correlations among affect, behavior, and cognition. (2) To evaluate the role of past experience in the context of the tripartite model. Greater overlap among attitude components is expected for subjects with past experience. (3) To examine the power of theoretically based alternative predictors of overt behavior. It is expected that the behavior-related variables (BI, AACT, PB) will be better predictors of overt behavior than will affect- and cognition-related variables (A, C, SN). The procedure of Study 1 was modified to test these hypotheses. Subjects participated in two sessions. The first session included verbal report measures of affect, cognition, past behavior, behavioral intentions, attitude toward the act, and subjective norms relating to attitudes toward snakes. The second session was an exact replication of Study 1, and included the measures of overt behavior. Chapter Four 60 METHOD Subiects Subjects were 114 undergraduates who participated in fulfillment of a course requirement. Subjects were recruited for a study on "attitude structure," and were informed only after arrival at the laboratory that the study involved snakes. Nine subjects failed to return for a second session, and their data were discarded. Hence, 105 subjects provided complete data in Study 2. Overview of Procedure Subjects participated in two sessions, each lasting under one hour. The first session took place in a classroom, and subjects were tested in groups of three to ten. During the first session, subjects completed a questionnaire, and were then scheduled for the second session. Except as noted below, the second session was identical to the procedure of Study 1. Verbal report measures that have been introduced in study two, or that have been modified from study one, are reproduced in Appendix D. Measures of Affect The MACL (which produces two scores — HACL+ and MACL-) and the Thurstone affect scales were administered during session one. Subjects were instructed to imagine as if they were in the presence of a live snake. Other than this modification in instructions, these neasures were the same as in Study 1. Chapter Four 61 Measures of Cognition The Thurstone measure of belief and the semantic differential scales were administered just as in Study 1. The listed thought measure was modified in that subjects were asked to think about snakes (since one was not present). In all other respects, these three measures were the same as in Study 1. Measures of Behavioral Intention There were two measures of behavioral intention. One measure mapped on to the action sequence, and another mapped on to the distance measure. BI: Action sequence. Subjects were instructed to rate the likelihood of their (i) staying in the same room with a live, caged snake, (ii) letting someone else hold a live snake while in their presence, (iii) touching a live snake while someone else holds it, and (iv) holding a live snake. The four actions correspond directly to the four action-sequence steps of study one. The likelihood ratings were scored from -3 (very unlikely) to +3 (very likely). The four ratings were summed to produce a single score. BI: Distance. Subjects were instructed to rate the likelihood of their getting close to, or touching (i) a small snake, (ii) a dangerous snake, (iii) a harmless snake, and (iv) a large snake. The likelihood ratings were scored from -3 (very unlikely) to +3 (very likely). The four ratings were summed to produce a single score. Chapter Four 62 Measures of Attitude Toward the Act For each of the eight behavioral intention scales described above, subjects were asked to rate the described action on a single bipolar scale, anchored at one end with "BAD" and at the other with "GOOD." These scales were scored from -3 (bad) to +3 (good). Two scores were produced by summing ratings for the four action-sequence descriptions (AACT: Sequence) and for the four distance descriptions (AACT: Distance). respectively. Measures of Subiective Norm Subjective norm is measured by sunning the product of normative beliefs (NB) and motivation to comply (MO. Two measures were constructed corresponding to the action sequence and preferred distance, respectively. For measures of normative beliefs, subjects rated each action description in regard to whether most people who were important to them thought they should perform the action (see Appendix D). The scales were scored from -3 (should not) to +3 (should). Motivation to comply was measured by scales requesting subjects to indicate how much they wanted to do what those who were important to them thought they should do in regard to each of the action descriptions. These scales were scored from 1 (not at all) to 7 (very much). Single scores for the action sequence (SN: Action) and distance (SN: Distance), respectively, were constructed by summing the NB X MC products. Chapter Four 63 Measures of Past Behavior PB: Action sequence. For each of the four action-sequence steps, subjects indicated whether they had ever performed the described action (yes or no). Each "yes" was scored as 1; scores for this variable can therefore range from 0 to 4. PB: Distance. Subjects indicated the closest distance they had ever been to a live snake that was not in a cage. Seven response options ranged from "close enough to touch" to "more than 10 feet." PB: Overall. A third measure was a single-item rating of past experience with snakes (from "none at all" to "very much"). Procedure Subjects were initially scheduled in groups of three to ten for a first session that lasted under one hour. At the time they signed up, subjects were informed that two sessions were involved, and that a second session would be scheduled for a later time. Session 1 was conducted in a large classroom. The experimenter distributed booklets containing all measures, and instructed subjects to coraplete the questionnaire at their own pace. After completing the questionnaire, subjects were individually scheduled to return to the laboratory at a later date. The booklet was divided into three sections. The first section included the two affect scales (MACL and Thurstone) and the listed thoughts procedure (which included two pages of "thought boxes"). The ordering of these three measures was counterbalanced across subjects. Chapter Four 64 These measures appeared first to avoid contamination from the other scales. The second section included measures of behavior, behavioral intention, attitude toward the act, subjective norms, and past behavior. The ordering of these scales was counterbalanced across subjects, with the following constraints: The scales for each of the two behavioral intention measures appeared on separate pages (one for the action sequence and one for preferred distance). Likewise, the scales for each of the two measures of attitude toward the act appeared on two separate pages. The AACT scales always followed the corresponding BI scales (just as they appear in Appendix D). Also, the normative belief scales always immediately preceeded the notivation to comply scales. The third section of the booklet consisted of a single-item self-rating scale along with background questions (e.g., subject's age, gender, college major). The final page reminded subjects of their need to schedule a time for the second session, and requested their name and phone number. The experimenter telephoned each subject the night before their scheduled return to remind them of the appointment. The second session was identical to the procedure of study one, with an added final questionnaire that included second measures of behavioral intention, attitude toward the act, subjective norms, and past behavior. Subjects were then debriefed, requested not to mention details of the study to other potential participants, and asked to complete a general evaluation of their experience in the experiment. Chapter Four 65 RESULTS Description of the Data Of the 105 subjects, 56.2% (59) were female. The mean lapse between session one and session two was 16.9 days, and ranged frost 0 to 42 days (sd = 7.68). Descriptive statistics for the measured variables can be found in Table 19. Examination of the table confirms that the variables possess adequate variability for correlational analyses. As in study one, the single-item self-rating scale is useful in characterizing the distribution of subjects' attitudes. Table 20 shows the frequency of responses for each of the seven possible values associated with that scale, for each of the two administrations (session one and session two). Subjects' attitudes appear to be evenly distributed over the entire range. The action sequence variables were examined to determine whether they satisfied the conditions of a Guttman (1944) scale (cf. Edwards, 1957). The four relevant variables were (1) the ratings of past behavior in the action sequence (session one), (2 & 3) ratings of behavioral intention (sessions one and two), and (4) the overt behavior measure of session two. The coefficient of reproducibility for the past behavior measure was 0.97, with seven classification errors. For the session one behavioral intention measure, the coefficient of reproducibiliyt was 0.91 (38 classification errors), and for session two was 0.97 (12 classification errors). The coefficient of reproducibility for the session two measure of overt Chapter Four 66 behavior was 0.99, with two classification errors. By these criteria, all four scales adequately satisfy the requirements for a Guttman scale. As in Study 1, there were no large changes in heart-rate. Examination of the data indicated very low correlations between heart- rate and the other measured variables (range from .04 to .16). Since the heart-rate measure had no impact on the pattern of results in study one, it was dropped from the majority of analyses to be reported in connection with Study 2. Eatlaates of Reliability One feature of Study 2 was the inclusion of multiple measures for most variables. This allows for estimates of reliability based on the test-retest correlations. Because the second round of measurements was taken while subjects were in the presence of a snake; however, there is a possibility that some of the test-retest correlations are attenuated. The reliability estimates are summarized in Table 21. It can be observed that reliability is generally very high. The lowest correlations are for the three affect measures. This suggests that affect is more labile than cognition or behavior. The lower reliability coefficients may also be due to the difference between imagined or anticipated affect and actual affective responses. That is, the first measures had subjects imagine as if they were in the presence of a snake, while the second measures actually had subjects in the presence of a snake. The lower correlations for the affect Chapter Four 67 measures underscores the usefulness of having subjects respond to the actual attitude object, rather than (or in addition to) a symbolic representation of it. Evaluation of the Tripartite Model As in Study 1, the tripartite model was tested through the use of an exploratory factor analysis procedure and a confirmatory factor analysis procedure (LISREL). The confirmatory analysis included tests of discriminant validity by comparing a three-factor model to a one- factor model. These analyses were conducted separately for the Session 1 data and for the Session 2 data. To test hypotheses regarding past experience, parallel analyses were conducted on high and low past experience subgroups, respectively. The three attitude components did not emerge "cleanly" from the exploratory analysis of the Session 1 verbal report measures. The confirmatory analysis produced results very similar to those observed in the re-analysis of Ostroa's data (Chapter Two): The three-factor model fit only minimally better than the one-factor model, and the three attitude components were highly correlated. Several differences emerged when subjects were divided on the basis of their past experience with snakes. High past experience subjects had high intercomponent correlations, and the one-factor model fit nearly as well as the three-factor model. In contrast, low past experience subjects had a lower behavior-cognition correlation, and the three- factor model fit substantially better than the one-factor model. Chapter Four 68 Exploratory factor analysis; Session 1. The SAS (1979) computer prograia was used to calculate the principal-axis common-factor solutions. The correlations in Table 22 were used as input to the compter program. (As in Study 1, the signs associated with correlations for the MACL- and Distance measures have been reversed, so that all expected directions of correlation would be positive.) Squared multiple correlations were calculated as initial estimates of communality. Three factors were retained. The exploratory analysis did not indicate strong support for the tripartite nodel. The rotated (PROMAX) factor pattern for the Session 1 data can be found in Table 23. Factor one can be identified as affect, however the MACL- measure did not load on this factor, and the listed thoughts measure did. Factor two can be identified as .*, cognition, with the discrepancy of the low loading of listed thoughts. Factor three corresponds to behavior, with an additional loading of HACL-. The interfactor correlations (Table 24) indicate highly correlated factors (ranging from .62 to .71). Thus, although affect, behavior, and cognition can be identified in this factor pattern, the analysis does not indicate strong, clear support for the tripartite model. Confirmatory factor analysis: Session 1. The LISREL V computer „ program (Joreskog & Sorbora, 1981) was used to estimate the unkown parameters of Figure 2. The tripartite model fit well. However, unlike Study 1, affect, behavior, and cognition were very highly correlated. Chapter Four 69 Table 25 shows the LISREL estimates for the Session 1 data. The overall X2 (24 df) = 42.29 (p. < .05). Thus, the tripartite Hodel is rejected on the basis of the statistical test. Nevertheless, the noraed fit index (A) = .94, indicating a very good fit. Examination of the factor loadings in Table 25 i that all measured variables loaded significantly on their respective factors. To evaluate discriminant validity, the one-factor model was estimated for the Session 1 data. The overall X2 (27 df) = 84.25 (p. < .05), and the normed fit index (A) = .88. The X2 difference between the one-factor and three-factor models (3 df) = 41.96 (p_ < .05), indicating a substantial improvement. However, a Ainc value of .06 indicates only modest improvement. The interfactor correlations ranged from .81 to .86; indicating substantial overlap among components. Thus, the tripartite model is supported. However, the three-factor model does not represent a substantial improvement over the one-factor model, and the three components are intercorrelated more strongly than in Study 1. Exploratory factor analysis: Session 2. To evaluate the tripartite model for the Session 2 data, the correlations in Table 26 were analyzed. The rotated (PROMAX) factor pattern can be found in Table 27, and the interf actor correlations are in Table 28. This factor pattern does not reveal factors that can be identified as affect, behavior, or cognition. Thus, the exploratory factor analysis for Session 2 does not support the tripartite model. Chapter Four 70 Conflraatorv factor analysis: Session 2. The estimated parameters for Session 2 are summarized in Table 29. The overall X2 (24 df) = 46.21 (p_ < .05) and A = .92. Thus, the model is rejected on statistical grounds, but nevertheless provides an adequate fit. Examination of the factor loadings indicates that all measured variables loaded highly on their respective factors. To evaluate discriminant validity, a one-factor model was also estimated for the Session 2 data. The overall X2 (27 df) = 58.95 (p_ < .05), and A = .90. The X2 difference between the one-factor and three-factor models (3 df) = 12.74 (p_ < .05), and Aj.nc = '02. These results indicate very weak (albeit statistically significant) discriminant validity. The interfactor correlations are very high, ranging from .88 to .96. As for Session 1, the tripartite model t represents only a small improvement over the one-factor model. Division of sample based on past experience. To evaluate the effects of past experience, the tripartite model was evaluated separately for subjects high in past experience and for subjects low in past experience. Subjects were divided as follows: Each of the three past behavior measures (PB: Sequence, PB: Distance, and PB: Overall) was standardized across subjects. A composite past behavior score was then constructed by summing these three standardized measures. The 53 subjects with the lowest scores were classified as low in past experience, and the remaining 52 subjects were classified as high in past experience. This analysis was limited to the Session 1 data. Summaries of correlations for the two subsamples can be found Chapter Four 71 in Table 30. Confirmatory factor analysis: Low past experience. LISREL V was used to estimate the unknown parameters of Figure 2. Table 31 gives the parameter estimates. The tripatite model fit reasonably well. The overall X2 <24 df) = 35.05 (The smaller sample size probably reduced the X2 for the null model. For that reason, A is lower than usual.) The intercomponent correlations were relatively high (ranging from .71 to .92). The one-factor model produced a X2 (27 df) = 54.31 (p. < .05), and A of .75. The difference X2 (3 df) = 19.26 (p_ < .05) indicates significant discriminant validity. Confirmatory factor analysis: High past experience. The parameter estimates for subjects high in past experience are given in Table 32. The overall X2 (24 df) = 41.72 (p_ < .05), and A = .86. Thus, the tripartite model is rejected on statistical grounds, but still fits relatively well. The interfactor correlations were very high (ranging from .81 to .90). The one-factor model produced a X2 (27 df) = 51.24 (p_ < .05), and A of .83. The difference X2 (3 df) = 9.52 (p_ < .05) indicates small, but significant, discriminant validity. Summary of Tripartite Model Tests Table 33 shows a summary of the confirmatory analyses. The Session 1 data yielded a pattern of results very similar to that Chapter Four 72 observed in the re-analysis of Ostrom (1969). The tripartite model did not emerge "cleanly" from an exploratory factor analysis. While the three-factor model fit quite well, it was only minimally better than the one-factor model. Finally, as for Ostrom, the three factors were very highly correlated. Table 34 allows a comparison of intercomponent correlations across four studies. The average intercomponent correlation in Study 2 (Session 1) was .83, which is substantially greater than the average correlation of .55 observed in Study 1. Thus, the use of entirely verbal reports appears to have increased the average common variance by more than 38*. A different pattern of results was observed for the Session 2 data. That session was an exact replication of Study 1, with the exception that subjects had previously answered many questions regarding their attitudes toward snakes. Affect, behavior, and cognition did not emerge from the exploratory analysis. Although the confirmatory analysis indicated some support for the tripartite model, it was not strong support. The divergent results can be attributed, in part, to the prior experience of Session 1. Subjects may have made an effort to appear consistent both within Session 2, and relative to their previous experience in Session 1. Thus, subjects may have behaved in accordance with their previous statements of behavioral intention in order to appear consistent The analyses indicated several differences between subjects high and those low in past experience. The behavior-cognition correlation was lower for subjects low in past experience than for those high in past experience (r's = .71 and .90, respectively). This difference of 31% in the variance shared between behavior and cognition confirms the prediction of increased overlap between the verbal knowledge system and bodily response system for individuals with greater past experience. This conclusion is further supported in the comparisons between the one-factor and three-factor models. For low past experience subjects, the three-factor model fit substantially better than the one-factor model. In contrast, the two models fit nearly as well for the high past experience subjects. t, Behavioral Prediction Models The analyses in this section evaluate the extent to which various predictors account for the variance in overt behavior. Relatively simple single-predictor models are tested — ones that examine single latent variables (e.g., affect, cognition, subjective norm) as individual predictors of behavior. Multiple-predictor models are also tested, including the model of reasoned action and its derivatives. Single-predictor models. Study 2 allows for the evaluation of six latent variables (measured at Session 1) as predictors of overt behavior (measured at Session 2): affect, cognition, behavioral intention, attitude toward the act, subjective norm, and past Chapter Four 74 behavior. The correlations among the measured variables can be found in Table 35. Structural diagrams for the six single-predictor models, in which each latent variable is an individual predictor of behavior, are shown in Figure 7. LISREL V was used to estimate each of the six single-predictor models. The latent variables of affect and cognition were each represented by three measured variables. The latent variables of BI, AACT, SN, PB, and B were each represented by two measured variables. This classification of the measured variables is presented in Table 35." The LISREL parameter estimate used to evaluate the single- predictor models is the standardized path coefficient between each predictor latent variable and the behavior latent variable. That ?, coefficient represents an estimate of the correlation between the latent predictor variable and the latent behavioral component. As an alternative estimate of those correlations, canonical correlations were calculated for each of the Figure 7 models. A canonical correlation is the correlation between linear combinations of the predictor variables and criterion variables (i.e., behavior), respectively (see Pedhazur, 1982). LISREL-estimated models ordinarily provide additional information regarding goodness of fit — A. For the single-predictor models, however, A will not serve as a useful goodness of fit measure. This is because the models of Figure 7 can be regarded as measurement models. As long as each of the latent variables 'have been properly Chapter Four 75 specified in terms of their measured variables, the models will "fit" extremely well, regardless of the magnitude of the path from the predictor latent variable to the criterion latent variable (behavior). Therfore, an alternative method was used to evaluate the relative power of the six predictors in accounting for variance in behavior. The alternative analysis was to constrain the path coefficients to be equal to 1.0. This constraint tests the hypothesis that the prediction is a perfect one. To the extent that the prediction is a good one, the constrained model will fit well and A will be large. To the extent that the prediction is a bad one, the constrained model will fit poorly and A will be small. Results from these analyses of single-predictor models are summarized in Table 36.- All six behavioral prediction models were accepted (by the chi-square test). Evaluation of the path coefficients and canonical correlations indicates that a large portion of overt behavior was accounted for by the latent predictor variables. The three best predictors were behavioral intention, attitude toward the act, and past behavior, each of which accounted for more than 50* of the variance in behavior. Affect and cognition were the next best predictors, each accounting for more than 40x of the variance in behavior. Subjective norm was the worst (but still significant) predictor, accounting for less than 36* of behavioral variance. This ordering of variables on the basis if their path coefficients and canonical correlations confirms the prediction of behavior-related Chapter Four 76 variables as the best predictors of overt behavior. Table 36 also shows the results for the constrained single-factor nodels (i.e., the ones in which the path coefficients have been fixed to 1.0). The conclusions are the same as above: the behavior-related variables (AACT, BI, and PB) produce the best fit when they are evaluated against the hypothesis of perfect prediction (all three values of A 'are relatively high). The worst single-factor model (subjective norm) has a relatively low value of A. Distinguishing among latent behavioral measures. The preceding analyses demonstrated that BI, AACT, and PB are very good predictors of overt behavior. While these behavior-related variables can be distinguished on theoretical grounds, it is not clear whether they are empirically distinguishable constructs. Several confirmatory factor analyses.were conducted to determine the extent to which BI, AACT, PB, and B diverge as distinct latent variables. The tested structural nodels are shown in Figures 8, 9, and 10. Model BI (Figure 8) represents an extreme case in which all the behavior-related variables are assumed to represent the single latent variable of behavior. Model B2 (Figure 9) separates the measured variables into behavioral predictors (verbal measures) and overt behavior. Model B3 (Figure 10) specifies four distinct behavior-related latent variables. Tables ' 37, 38, and 39 show the LISREL estimates for models BI, B2, and B3, respectively. Table 40 summarizes this analysis. All three models are rejected on the basis of the statistical (chi-square) test. However, examination of the A values indicates that model B3 Chapter Four 77 provides the best fit. This is confirmed by comparisons among the three models, summarized in the lower half of Table 40. The comparisons indicate that model B3 fits significantly better than both models Bl and B2, and that the latter two models are statistically indistinguishable. However, examination of the Ainc values in Table 40 indicates that the improvement in fit is relatively small in nagnitude. Examination of Table 39 reveals very high interfactor correlations for model B3 (ranging from .77 to .97). Thus, the four latent variables are statistically distinguishable, but share a very large portion of common variance. Testing the model of reasoned action. Several analyses were conducted as tests of the Fishbein and A}zen (1975; Ajzen 6. Fishbein, 1980) model of Figure 5, and of recent variations on that model (Bagozzi, 1981a; Bentler & Speckart, 1979). These analyses appear in Tables 41 through 45, and are summarized in Table 46. Three models were initially tested: (i) the model of reasoned action (Figure 5), (ii) the model of Figure 5 with an added path from AACT to behavior (Bentler 6. Speckart, 1979), and (iii) the model of Figure 6, in which past behavior has been introduced as a direct cause of behavioral intention and behavior (Bentler & Speckart, 1979). The LISREL estimates for the model of Figure 5 are given in Table 41. The X2 (16 df) for this model = 59.80 (p_ < .05), and A = .92. These results indicate that the model is statistically rejected, but that it represents a relatively good fit. Examination of the Table 41 Chapter Four 78 parameter estimates reveals an insignificant path coefficient from subjective norm to behavioral intention. This indicates that, in the case of attitudes toward snakes, normative influences may have little bearing on one's actions. The LISREL estimates for the modified Figure 5, and for the Figure 6 models produced negative variances for some latent variables, a condition indicating that the models were not evaluable. However, four modified reasoned-action models (shown in Figure 11) were evaluable. These models did not include subjective norm as a behavioral predictor. These analyses indicated that all four Figure 11 models represent plausible representations of the data. Furthermore, it is not possible to choose among the four models either in terms of chi-square significance tests or in terras of the A { goodness of fit indexes. (See Tables 42 through 45.) A simple behavioral prediction model (BP-A). The preceding analyses examined several relatively complex behavioral prediction nodeis. It was difficult to distinguish among the various models in terras of their power in predicting behavior. It was nevertheless evident that many of the behavioral prediction variables individually accounted for a substantial portion of the variance in overt behavior. These results suggest a relatively simple behavioral prediction model, Model BP-A shown in Figure 12. Here, there is one latent behavioral predictor variable that is composed of the four measured variables representing behavioral intention and attitude toward the act. Chapter Four 79 LISREL V was initially used to estimate Model BP-A shown in Figure 12. However, that analysis produced uninterpretable solutions (in most cases, LISREL estimated negative variances, indicating that the model was not evaluable). An alternative (and approximately equivalent) analysis using canonical correlations was therefore designed. In the alternative analysis, canonical correlations were computed among the measured variables representing the behavioral predictors and overt behavior. The canonical correlation for the Figure 12 model was .85. That is, a linear combination of the four behavioral predictor variables accounted for 72X of the variance in overt behavior. Parallel analyses based on a division of subjects high and low in past experience were also conducted. For low past experience { subjects, the canonical correlation for the Figure 12 model was .71. For high past experience subjects, the canonical correlation was .82. Thus, 16X more variance in overt behavior was accounted for in the actions of high past experience subjects. The Effects of Direct Experience Evaluation of the tripartite model above indicated greater consistency among attitude components for subjects who had prior experience with snakes. The repeated measurement, 2-session procedure of Study 2 affords a different view on the effects of direct experience. That is, after subjects completed their interactions with a snake in Session 2, measures of attitude toward the act and Chapter Four 80 behavioral intention were administered for a second time (the first was during Session 1). This procedure provided subjects with direct experience in the attitude domain. If direct experience increases intercoraponent consistency, then correlations between behavior and subsequent attitude measures should be higher than between behavior and prior attitude measures. Further, this contrast should be stronger for subjects who initially had little prior experience, since the correlations between Session 1 measures and behavior should already be high for subjects with prior experience. To test these hypotheses, LISREL V was used to estimate the model shown in Figure 13 (Model BP-A-BP). This model is a modification of the Figure 12 model, in which measures of behavioral intention and attitude toward the act have been added after the measures of overt •; behavior. The path from the behavioral predictors (time 1) to behavior should be weaker than the path from behavior to the behavioral predictors (time 2). Again, the model was not evaluable by LISREL V (the program estimated negative variances for some latent variables). As before, canonical correlations were computed among the measured variables representing the behavioral predictors and overt behavior. The canonical correlation between the first behavioral prediction measures and behavior was reported above (.85). The canonical correlation between overt behavior and the second behavioral prediction measures is .94. Thus, there was an increase of 18* of variance shared between behavioral predictors and behavior. The difference is a significant one (t (102) = 7.16, 'p. < .05). (This test Chapter Four 81 requires the comparison of two non-independent correlations. The significance test described by Bruning & Kintz, 1977, was used. However, that teat aaaumea Pearaon product-moment correlations, and not canonical correlations. Therefore, this aignificance teat -- and the following similar onea — must be interpreted with caution.) Parallel analyses were conducted separately for subjects low and high in past experience. For low experience subjects, the canonical correlation between the initial behavioral predictora and behavior waa (as above) .71. The canonical correlation between behavior and the final behavioral predictors was .92. The increase of 33* shared variance is a signifcant one (t (50) = 5.45, p_ < .05). For high experience subjects, the canonical correlation between the initial behavioral"predictors and behavior was (as above) .82. The canonical correlation between behavior and the final behavioral predictors waa .90. The increaae of 15* ahared variance ia alao a significant one (t (49) = 3.54, p_ < .05). For subjects both high and low in prior experience, there was an observed increase in the variance shared between behavioral predictors and overt behavior. It was predicted that the increaae would be greater for low experience subjects. The difference of 18* is, indeed, a significant one (z = 2.IS, p_ < .05). It should also be noted that the initial canonical correlation between the behavioral predictors and action was, as expected, lower for low than for high past experience subjects. However, the direct experience of Session 2 Chapter Four 32 erased that initial difference, producing approximately equal canonical correlations between action and behavioral predictors for subjects high and low in prior experience (.90 and .92, respectively). DISCUSSION There were three major objectives in Study 2. (1) To evaluate the extent to which verbal report measures overestimate correlations among affect, behavior, and cognition. The tripartite model analyses indicated an average intercomponent correlation of .83, which was substantially higher than the average correlation of .55 observed in Study 1. (2) To evaluate the role of past experience in the context of the tripartite model. Greater overlap among attitude components was observed for subjects with past experience. (3) To examine the ( power of theoretically based alternative predictors of overt behavior. The behavior-related variables (A, C, SN). The verbal report measures from Session 1 indicated discriminant validity for the affect-behavior-cognition distinction. At the same tine, the three components were observed to be more highly correlated than in Study 1. The higher intercomponent correlations may be attributed to the use of exclusively verbal reports and to the absence of a real snake to which subjects could respond. Thus, Study 2 helps to confirm the importance of using nonverbal measures, where possible, in evaluating the tripartite model. Chapter Four 83 Past experience was observed to play an important role in the tripartite model. The separate analyses of the Session 1 data for subjects high and low in past experience indicated 31* greater overlap between behavior and cognition for subjects high in past experience. Also, behavioral intention and attitude toward the act were initially better predictors of behavior for high experience subjects. However, participation in Study 2 served to provide low experience subjects with direct experience. This was manifest by an increase in the correspondence between behavior and subsequent measures of BI and AACT. These effects confirm the predictions derived fron Fazio and Zanna (1981) in showing greater attitude-behavior consistency when subjects have had direct prior experience. The results are also consistent with Bern's (1968; i.972) self-perception theory, which assumes that subjects sake inferences regarding their attitude after observing their own behavior. The single-predictor-of-behavior analyses clearly demonstrated that very substantial correlations can be obtained between attitude and behavior. This is not surprising since study two purposely incorporated conditions that are theorized to produce high attitude- behavior correspondence (cf. Ajzen & Fishbein, 1973; 1977). Nevertheless, the attitude measures were not equally good predictors of behavior. The best predictors involved measures of the behavioral component (BI, AACT, PB). This is consistent with earlier findings of Ostron (1969) and Kothandapani (1971), who found that verbal measures Chapter Four 84 of the behavioral component provided better prediction of verbal reports regarding- overt behavior than did measures of either the affective or cognitive components. The analyses regarding the decomposition of the behavior construct (Figures 8 through 10) indicated that BI, AACT, PB and overt behavior, while distinguishable on a theoretical level, were not distinguishable on an empirical level. It therefore seems reasonable to regard all of the behavioral predictors (BI, AACT, PB) as alternative (and approximately equivalent) measures of the behavioral component of attitude. CHAPTER FIVE GENERAL DISCUSSION Summary of Results The tripartite model la empirically validated. Study 1 Indicated strong support for affect, behavior, and cognition as distinct attitude components. That is, the tripartite model was not rejected by the chi-square test, and it3 empirical fit (the A index) was very good. Further, the three-component model fit substantially better than the one-component model, indicating its discriminant validity. Nonverbal measures are Important. The re-analysis of Ostrom's (1969) data indicated very high intercomponent correlations (.94 < r < .98). One explanation for those high correlations was that the verbal J, report format artificially inflated intercomponent consistencies. Study 2 established that the exclusive use of verbal report measures does, indeed, produce increased (and presumably inflated) estimates of intercomponent correlations. Verbal measures of behavior predict overt behavior. Study 2's analyses of behavioral predictors indicated that behavior-related measures (BI, AACT, PB) accounted for more variance in subsequent measures of overt behavior than did measures of other attitude components (A, C, SN). When the behavior-related constructs were combined into a single "behavioral prediction" construct, they 85 Chapter Five 86 accounted for a very high 72X of the variance in overt behavior. Experience Increases intercomponent correlations. Subjects in Study 2 were grouped on the basis of their past experience with snakes. In Study 2's confirmatory factor analysis of the tripartite model, the behavior-cognition correlation was greater for subjects who had past experience (r = .90) than for those who had not (r = .71). Also, importantly, the experience of participating in Study 2 increased attitude/behavior correspondence for all subjects, and especially for those who were initially low in prior experience. Significance of Results The results from Studies 1 and 2 indicate that affect, behavior, and cognition are distinguishable components of attitude. Correlations araong these three components were moderate, suggesting the practical importance of discriminating' among them. Thus, attitude researchers are advised either to measure each of the three components or to specify which of the three is of focal concern. To say a researcher is measuring "attitude" is ambiguous, since it does not specify which of the three components is being measured. Prior to the present studies, only Ostrom (1969) and Kothandapani (1971) had attempted to validate the tripartite model. Given the model's prominent treatment in social psychology and attitude theory texts, it is surprising that so few studies have investigated it. Perhaps this reflects a pre-occupation in social psychology with determining causation (e.g.. Cook & Campbell, 1979), rather than with Chapter Five 87 the logically prior problem of identifying, measuring, and validating the theoretical constructs that participate in causal relations. Future Directions Generalizing to other attitude domains. One limitation in the present studies was the focus on a single attitude domain. As noted previously (Chapter Three), snakes were used in the present studies to allow for the possibility of low correlations among — and, therefore, discriminant validity of -- the three attitude components. The conclusion in favor of the utility of distinguishing among affective, behavioral, and cognitive components of attitude can use support from similar validation studies using other attitude objects. Constructing a taxonomy of attitude domains. It would be desirable to construct a"method for identifying, on a priori grounds, those attitude domains that should be associated with high or low intercomponent consistency. Attitude dbmains in which respondents can be assumed to have had extensive past experience (e.g., the church) are likely to be associated with very high intercomponent correlations. In comparison, attitude domains for which respondents are likely to have had minimal prior experience (e.g., snakes) should produce low intercomponent consistency. Low intercomponent correlations might be expected for attitude objects in which responses are mediated by more than one response system. This might be the case for food preferences. One may like certain foods very much, but nevertheless have intense allergies to Chapter Five 88 then. Thus, cognitive responses may be generally favorable, while affective responses indicate extreme aversion. Intercoaponent consistency may also be related to the abstractness versus concreteness of the attitude object. People respond to abstract attitude objects only at a conceptual or symbolic level. Therefore, most responses are likely to be mediated almost exclusively by one's verbal knowledge or cognitive systems. In contrast, responses to concrete attitude objects may be mediated by bodily control systems (e.g., reflexes, physiological reactions) in addition to one's verbal knowledge system. Application to persuasion research. One of the major applications of the tripartite model is in the area of persuasion research (Rosenberg et al., 1960). One issue of concern is whether (, change in one component leads to change in other components. For example, an attitude change campaign may concentrate on the cognitive component by using appeals based on information or logical arguments. The desired result is (i) to produce a corresponding change in other components (e.g., behavior), and (ii) to produce a lasting change in attitude. Host persuasion studies concentrate on changing only the cognitive component. 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Toward an analysis of the components of interpersonal attitudes. In C. W. Sherif & M. Sherif (Eds.), Attitude, ego- involvemeni:, and change. New York: Wiley, 1967. Triandis, H. C. Attitude and attitude change. New York: Wiley, 1971. Wicker, A. W. Attitudes versus actions: The relationship of verbal and overt behavioral reponses to attitude objects. Journal of Social Issues. 1969, 25, 41-78. Wicker, A. W. An examination of the "other variables" explanation of attitude-behavior inconsistency. Journal of Personality and Social Psychology. 1971, 19, 18-30. Winer, B. J. Statistical principles in experimental design (2nd ed.>. New York: McGraw-Hill, 1971. Horchel, S., & Cooper, J. Understanding social psychology (Revised). Homewood, I.L.: Dorsey, 1979. Wrightsman, L. S., & Deaux, K. Social psychology in the 80s (3rd edition). Monterey, C.A.: Brooks/Cole, 1981. Zajonc, R. B. Feeling and thinking: Preferences need no inferences. American Psychologist. 1980, 35, 151-175. ti Zimbardo, P. G., Ebbesen, E. B., & Maalach, C. Influencing attitudes and changing behavior (second edition). Reading, M.A.: Addison- Hesley, 1977. APPENDIX A QUESTIONNAIRE USED TO GENERATE POOL OF ITEMS FOR THURSTONE SCALES Instruction page for generation of belief statements 98 Form for generating belief statements 99 Instruction page for generation of behavior statements 100 Form for generating behavior statements 101 97 Appendix A 98 We would like you to think about snakes. You are probably aware that some people like snakes very much, others dislike them very much, and still others are neutral (they neither like them nor dislike them). Right now we would like you to think about the kinds of beliefs people have about snakes, For example, someone who dislikes snakes might believe that they are slimy. In comparison, someone who likes snakes might think that they are friendly. Of course opinions about snakes may range from extreme dislike at one end to extreme like at the other end. We have identified below five categories describing attitudes toward snakes. These are! extremely dislike, moderately dislike, neutral, moderately like, and extremely like. These categories represent varying degrees of attitude that people hold about snakes. Your task is to write down a one-sentence statement of belief for each category that reflects the views of a person whose attitude would be described by that category. The kinds of belief statements we would like should represent various characteristics and attributes of snakes. Example belief statements are! "I believe snakes are dangerous" or "I think snakes make great pets." Appendix A 99 Please write down a one-sentence statement of belief for each category that reflects the beliefs of a person whose attitude would be described by that category. A person who extremely dislikes snakes would make this belief statement! A person who moderately dislikes snakes would make this belief statement' A person who is neutral about snakes would make this belief statement* A person who moderately likes snakes would make this belief statement! A person who extremely likes snakes would make this belief statement! Appendix A 100 We would like you to think about snakes. You are probably aware that some people like snakes very much, others dislike them very much, and still others are neutral (they neither like them nor dislike them). Right now we would like you to think about the kinds of things people are likely to do with respect to snakes. For example, someone who dislikes snakes might run away from them. In comparison, someone who likes snakes might keep one as a pet. Of course opinions about snakes may range from extreme dislike at one end to extreme like at the other end. We have identified below five categories describing attitudes toward snakes. These are! extremely dislike, moderately dislike, neutral, moderately like, and extremely like. These categories represent varying degrees of attitude that people hold about snakes. Your task is to write down a one-sentence statement of behavior for each category that reflects the actions of a person whose attitude would be described by that category. Behavior statements should include supportive to hostile actions. These statements should reflect personal action tendencies. They should be statements of past action, future intentions, and predicted behavior in hypothetical situations. Example behavior statements are! "I like to handle snakes" or "I scream everytime I see a snake." Appendix A 101 Please write down a one-sentence statement of behavior for each category that reflects the actions of a person whose attitude would be described by that category. A person who extremely dislikes snakes would make this behavioral statement' A person who moderately dislikes snakes would make this behavioral statement' A person who is neutral about snakes would make this behavioral statement! A person who moderately likes snakes would make this behavioral statement; A person who extremely likes snakes would make this behavioral statement! APPENDIX B RATING SCALE AND ITEM POOL USED TO CONSTRUCT THURSTONE SCALES Instruction Page 103 Statements relating to the cognitive component 104 Statements relating to the behavioral component 105 Statements relating to the affective component 106 102 Appendix B 103 Introduction' We are interested in determining the favorability of various statements people might make about snakes. Your task is to rate each statement on a scale from very nonfavorable statement about snakes, through neutral, to very favorable statement about snakes. We are not seeking your own agreement or disagreement with the statements. We simply want you to estimate how favorable or nonfavorable each statement is about snakes. Procedure! Statements are listed on the following pages. Read each statement and judge where it belongs on a scale from very nonfavorable about snakes to very favorable about snakes. Mark your judgment on the seven interval scale that appears below each statement. Interval one should be marked with an "X" if the statement appears to be very nonfavorable about snakes. Interval four should be marked with an "X" if the statement appears to be neutral about snakes. Interval seven should be marked if the statement appears to be very favorable about snakes. In like mannert mark intervals 3 and 2 as the statements suggest more and more extreme attitudes that are negative about snakes! mark intervals 5 and 6 as the statements suggest more and more extreme attitudes that are positive about snakes. Example? Snakes are kind of nice. Vers Nonfavorable i J J J i t X 1 5 Very favorable About Snakes 12 3 15 6 7 About Snakes An "X" is placed in category 6 to indicate that the example judge rated the statement as being moderately favorable about snakes. Reminder! We are not interested in whether you agree or disagree with each statement. We are interested in how favorable or nonfavorable each statement is about snakes. Please do not skip any statements. Appendix B 104 Original pool of items for construction of Thurstone scales. Note that rating scales have been removed. Items were presented in the following order. Median scale value and Q-value are indicated after each statement. Statements relating to the cognitive component! 1. Snakes are clever. 6 2. 2. Snakes can be timid. 4 1 3. Snakes are sneaky. 5 5 4. Snakes are slimy. 2 3 5. Snakes are cold-blooded. 4 3 6. Snakes are dangerous. 3 4 7. Snakes are ugly. 2 3 8. Snakes are quiet reptiles with no emotions. 4 2 9. Snakes are predators. 5 2 10. Snakes are mysterious. 5 3 11. Snakes are unpredictable. 4 3 12. Snakes are patient. 5 2 13. Snakes are cautious. 5 2 14. Snakes are sinister. 3 2 15. Snakes are harmless. 5 2 16. Snakes bite or strangle their prey to death. 3 3 17. Snakes are nice. 5 2 18. Snakes slither. 4 4 19. Snakes control the rodent population. 6 2 20. Snakes will attack anything that moves. 2 2 21. Snakes are quiet. 5 1 Appendix B 22. Snakes are unfriendly. 3 2 23. Snakes won't hurt you as long as they are not provoked. 6 24. Snakes symbolize evil. 2 3 25. Snakes make good pets. 5 2 26. Snakes have long, sharp fangs. 3 2 27. Snakes are very moody and don't like to be disturbed. 3 2 28. Snakes are friendly. 5 2 29. Most snakes hiss just before they strike. 4 2 30. Snakes serve little purpose. 2 2 31. Snakes are useful in keeping insects out of gardens. 6 1 32. Only poisonous snakes are mean. 4 1 33. Snakes use their eyes to hypnotize their prey. 3 2 34. Snakes are soft and smoothi 5 1 35. Snakes like to keep-to themselves. 4 2 36. Snakes are lovable and kind creatures. 6 3 37. The only thing snakes ever do is eat and sleep. 4 2 38. Snakes are independent. 5 2 39. Snakes like to be handled. 5 1 Statements relating to the behavioral component' 40. Everytime I see a snake» I try to kill it. 2 2 41.1 like to handle snakes. 6 3 42.1 would like to become a snake trainer. 4 5 43. If I saw a snake, I would run away. 2 2 44.1 avoid snakes at all costs. 2 3 Appendix B 106 45.1 refuse to see the snakes when visiting a zoo. 1 3 46.1 like to play with snakes. 5 4 47.1 would never keep a snake as a pet. 3 3 48. If I found a snake, I would chop off its head. 1 2 49.1 throw things at snakes to scare them away. 2 3 50.1 like to watch snakes swallow their prey. 5 1 51.1 would let a snake crawl over my body. 5 5 52.1 like to catch snakes. 4 3 53. If a snake doesn't bother me, I won't bother him. 5 3 54.1 always visit the snake house when at the zoo. 6 2 55.1 scream whenever I see a snake. 2 2 56.1 would not stay in the same room with a snake. 2 3 57.1 jump whenever I see a snake. 2 2 58.1 have handled snakes in the past. 5 2 59.1 would pet a snake if someone else held it. 5 2 60. If I came across a snake, I would ignore it. 4 2 Statements relating to the affective component! 61.1 feel happy when in the presence of a snake. 5 2 62.1 feel cheerful when in the presence of a snake. 5 2 63.1 feel alert when in the presence of a snake. 5 3 64.1 feel good when in the presence of a snake. 5 2 65.1 feel energetic when in the presence of a snake. 5 2 66.1 feel optimistic when in the presence of snake. .4 3 67.1 feel adventurous when in the presence of a snake. 5 2 Appendix B 107 68.1 feel relaxed when in the presence of a snake. 5 3 69.1 feel self-confident when in the presence of a snake. 5 3 70.1 feel normal when in the presence of a snake. 5 2 71.1 feel excited when in the presence of a snake. 5 3 72.1 feel lonesome when in the presence of a snake. 3 2 73.1 feel restless when in the presence of a snake. 3 2.25 74.1 feel lonely when in tlr.- presence of a snake. 3 2 75.1 feel anxious when in the presence of a snake. 4 2 76.1 feel dissatisfied when in the presence of a snake. 3 2 77.1 feel troubled when in the presence of a snake. 3 2 78.1 feel tense when in the presence of a snake. 2 2.25 79.1 feel fearful when in the presence of a snake. 2 3 80.1 feel sad when in the presence of a snake. 3 2.25 81.1 feel unhappy when in the presence of a snake. 2.5 2.25 82.1 feel indifferent when in the presence of a snake. 4 2 83.1 feel insecure when in the presence of a snake. 2 1.25 84.1 feel nervous when in the presence of a snake. 2 3.25 85.1 feel frustrated when in the presence of a snake. 3 3 86.1 feel depressed when in the presence of a snake. 3 3 87.1 feel pessimistic when in the presence of a snake. 3 2 88.1 feel weak when in the presence of a snake. 2 2 89.1 feel irritable when in the presence of a snake. 3 3 90.1 feel gloomy when in the presence of a snake. 3 3 91.1 feel grouchy when in the presence of a snake. 3 3 92.1 feel bad when in the presence of a snake. 3 2 Appendix B 108 93.1 feel afraid when in the presence of a snake. 2 3 94.1 feel angry when in the presence of a snake. 3 2 95.1 feel disgusted when in the presence of a snake. 3 2 96.1 feel disliking when in the presence of a snake. 3 3 97.1 feel uneasy when in the presence of a snake. 3 2 98.1 feel hopeful when in the presence of a snake. 4 2 99.1 feel liking when in the presence of a snake. 5 1 100.1 feel proud when in the presence of a snake. 5 2 101.1 feel sympathetic when in the presence of a snake. 4 2 APPENDIX C VERBAL REPORT MEASURES USED IN STUDIES 1 AND 2 Cover page and instructions 110 Mood adjective check-list 111 Thurstone scale of affect 112 Instructions for listed thoughts measure 113 Form for listing thoughts (thought boxes) 114 Instructions for rating listed thoughts 115 Thurstone scale of belief (cognition) 116 Thurstone scale of behavior . 117 Semantic differential measure of cognition 118 Single-item self-rating and background questions 119 Consent form 120 109 . Appendix C 110 Inside this booklet are a variety of questions we would like you to answer. You shouldn't spend too much time on any particular question. We are most interested in your first impressions. You should answer the questions in the order they appear. Please do not go back to questions once you have answered them, unless specifically instructed to do so. Please notify the experimenter when you have finished. Appendix C 111 This page has been reduced by 64%; Each of the following words describes feelings or mood. Please use the list to describe your feelings at the moment you read each word. If the word definitely describes how you feel at the moment you read it, circle the double check (vv) to the right of the word. For examplei if the word is relaxed and you are definitely feeling relaxed at the moment, arde the vv as follows! relaxed Q£) v " no. (This means you definitely feel relaxed at the moment.) If the word only slightly applies to your feelings at the moment, circle the single check as follows: relaxed w(v)'? no. (This means you feel slightly relaxed at the moment.) If the word is not clear to you or you cannot deade whether or not it applies to your feelings at the moment, circle the question mark as follows! relaxed vv vQ no. (This means you cannot deade whether you are relaxed or not.) If you definitely decide the word does not apply to your feelings at the moment, circle the no as follows! relaxed vv v ^ (nm (This means you are definitely not relaxed at the moment.) Work rapidlyt Your first reaction is best. Work down the first column, then go to the next. Please mark all words. This should take only a few minutes. Please begin. angry Virdly tense sad carefree statical elated egotistic concentrating energetic drowsy rebellious affectionate jittery regretful witty dubious pleased boastf"l intent active tired defiant uarnhearted fearful sorry Playful vspicics overjoyed self-centered engaged in Ihot-jht vigoro"S sluggish leis"rely nonchalant Appendix C 112 Below are several statements that indicate how you might be feeling at the moment. Please mark those statements that describe how you are feeling right now by placing a check mark {•/) next to the statement. You should check only those statements that describe how you are feeling at the present time. CThurstane scale value indicated after each statement} ( ) 1.1 feel likino. 5 ( ) 2.1 feel anxious. 4 ( ) 3.1 feel sympathetic. 4 ( ) 4.1 feel uneasy. 3 ( ) 5.1 feel unhappy. 2.5 ( ) 6.1 feel hopeful. 4 ( ) 7.1 feel tense. 2 ( ) 8.1 feel happy. 5 ( ) 9.1 feel troubled. 3 ( ) 10,1 feel good. 5 ( ) 11.1 feel weak. 2 ( ) 12.1 feel indifferent. 4 ( ) 13.1 feel disgusted. 3 ( ) 14,1 feel bad. 3 ( > 15.1 feel insecure. 2 ( ) 16.1 feel cheerful. 5 Appendix C 113 We would like you to indicate what you are thinking about the snake right now. The next few pages contain a form we have prepared for you to record your thoughts and ideas. Simply write down the first thought that occurs to you in the first box, the second thought in the second box, and so on. Please put only one idea or thought in a box. You might have positive thoughts, negative thoughts, or neutral thoughts, all are fine. You can use short phrases or statements. You may ignore spelling, grammar, and punctuation. Don't worry if you don't fill every box. Just put down as many thoughts as occur to you. I Appendix C 114 This page has been reduced by £4%i PLEASE WRITE ONLY ONE IDEA OR THOUGHT PEP BOX ( Appendix C 115 For each of the statements or phrases yau wrote down on the previous pages, we would like you to indicate whether the statement is favorable or nonfavorable about snakes. If a statement is favorable about snakes, then put a "+" sign next to the box. If a statement is nonfavorable about snakes, then put a "-" sign next to the box. If a statement is neither favorable nor nonfavorable (it is neutral), then put a "0" sign next to the box. You should puta"+", "-", or "0" next to every statement or phrase you wrote down on the previous pages. Appendix C 116 Below are several statements about snakes. Please indicate the statements with which you agree by placing a check mark (\/) next to the statement. You should check only those statements with which you agree*. CThurstone scale values indicated after each statement} ( ) 1. Snakes can be timid. 4 ( ) 2. Snakes are soft and smooth. 5 ( ) 3. The only thing snakes ever do is eat and sleep. 4 ( ) 4. Snakes are very moody and don't like to be disturbed. 3 ( ) 5. Snakes are unfriendly. 3 ( ) 6. Snakes serve little purpose. 2 ( ) 7. Snakes will attack anything that moves. 2 ( ) 8. Snakes like to be handled. 5 ( ) 9. Snakes won't hurt you as long as they are not provoked. 6 ( ) 10. Only poisonous snakes are mean. 4 ( ) 11. Snakes are sinister. 3 ( ) 12. Snakes control the rodent population. 6 ( ) 13. Snakes are quiet. 5 ( ) 14. Snakes are useful in keeping insects out of gardens. 6 Appendix C 117 Below are several statements about snakes. Please indicate the statements with which you agree by placing a check mark (V) next to the statement. You should check only those statements with which you agreei iThurstone scale values indicated after each statement} ( ) 1.1 scream whenever I see a snake. 2 ( ) 2.1 like to catch snakes. 4 ( ) 3.1 always visit the snake house when at the zoo. 6 ( > 4. If I saw a snake, I would run away. 2 ( ) 5. If I came across a snake, I would ignore it. 4 ( ) 6.1 like to handle snakes. 6 ( ) 7.1 jump whenever I see a snake. 2 ( ) 8. If I found a snake, I would chop off its head. 1 ( ) 9.1 refuse to see the snakes when visiting a zoo. 1 ( ) 10.1 would pet a snake if someone else held it. 5 ( ) 11.1 have handled snakes in the past. 5 ( ) 12.1 would never keep a snake as a pet. 3 ( ) 13.1 like to watch snakes swallow their prey. 5 ( ) 14.1 avoid snakes at all costs. 2 Appendix C 118 For each of the scales below, please indicate your beliefs about snakes. If you believe snakes are most like the label at the left end of the scale, then place an "X" near that end, If you believe snakes are most like the label at the right end of the scale, then place an "X" near that end. Use the remaining parts of the scale to indicate gradations between the two extremes. For example, if you think snakes are moderately fast, you would mark the scale as follows' FAST 5 t X '. J J ! J J SLOW Please mark the following scales to indicate your beliefs about snakes. Snakes are. BAD : ! : : J J : J GOOD ! UNFRIENDLY J 5 J J ! J J ! FRIENDLY CRUEL : j J 5 ! J : : KIND CLEAN : : J 5 J : : : DIRTY BEAUTIFUL ! J ! 5 i ', J 5 UGLY IMPORTANT : 5 : : : : i : UNIMPORTANT Appendix C 119 Overall, how would you rate your attitude about snakes? I Dislika Snakes i 5 J J J J J ! I Like Snakes Very Much -3 -2 -1 Neutral +1 +2 +3 Vers Much How old are you! Are you male or female (circle one). What is your college major! Did you grow up in a (check one or more)? Large Town Small Town Farm {, Do you now own a pet snake (circle one)t yes no Have you ever owned a pet snake (circle one)' yes no Before today, have you ever touched a live snake (circle one)! yes no Please describe any bad experiences you may have had involving a snake! Appendix C 120 This page has been reduced by 64%* THE OHIO STATE UNIVERSITY PROTOCOL NUMBER *2Bul20 CONSENT FOR PARTICIPATION IN SOCIAL AND BEHAVIORAL RESEARCH I consent to participating in research entitled! Attitudes About Animals. Steven Breckler or his authorized representative has explained the purpose of the study, the procedures to be fallowed, and the expected duration of my participation. Possible benefits of the study have been described as have alternative procedures, if such procedures are applicable and available. I acknowledge that I have the opportunity to obtain additional information regarding the study and that any questions I have raised have been answered to my full satisfaction. Furtheri I understand that I am fr.ee to withdraw consent at any time and to discontinue participation in the study without prejudice to me. The information obtained from me will remain confidential unless I specifically agree otherwise by placing initials here . Finally, I acknowledge that I have read and fully understand the consent farm. I sign it freely and voluntarily. A copy has been given to me. Signed! (Participant) Signed! , (Principal Investigator or his authorized representative) APPENDIX D VERBAL REPORT MEASURES ADDED FOR STUDY 2 Cover page for Study 2, Session 1 122 Mood adjective check-list (expected mood) 123 Thurstone scale of affect (expected) 124 Instructions for listed thoughts (imagined snake) 125 Scales of behavioral intention for action sequence 126 Scales of attitude toward the act for action sequence 127 Scales of past behavior 128 Scales of behavioral intention for distances 129 Scales of attitude toward the act for distances 130 Scales of normative beliefs for action sequence 131 Scales of normative beliefs for distances 132 Scales of motivation to comply 133 Single-item scale and background questions (Study 2, Session 1) 135 Final instructions (Study 2, Session 1) 136 Perceptual measures of cognition (Study 2, Session 2) 137 121 Appendix D 122 In this study, we are interested in your attitudes toward snakes. Inside this booklet you will find a variety of questions that we would like you to answer. You shouldn't spend too much time on any particular question. We are most interested in your first impressions. You should answer the questions in the order they appear. Please do not go back to questions once you have answered them, unless specifically instructed to do so. Please notify the experimenter when you have finished. Thank-you in advance for your cooperation. Appendix D 123 This page has been reduced by 64%t Imagine that you are in the presence of a live snake. Each of the following words describes feelings or moods that you might be feeling while in the presence of a snakei Please use the list to describe how yau would feel if vou were in the presence of a live snake. If the word definitely describes how you would feeli circle the double check (vv) to the right of the ward. For example, if the word is relaxed and you would definitely feel relaxed in the presence of a snake, circle the vv as follows! relaxed {vv) v " no. (This means you would definitely feel relaxed.) If the word only slightly applies to your feelings in the presence of a snake* circle the single check as follows! relaxed vv(v)^ no. (This means you would feel slightly relaxed.) If the ward is not clear to you or you cannot deade whether or not it would apply to your feelings in the presence of a snake, circle the question mark as follows: relaxed vv v 0 no. (This means you cannot decide whether yau would be relaxed or not.) If yau definitely decide the ward would not apply to yaur feelings in the presence of a snake( circle the no as follows! relaxed vv v *> (ng) (This means you would definitely not be relaxed.) Work rapidly. Your first reaction is best. Work dawn the first column, then go to the next. Please mark all words. This should take only a few minutes. Please begin. anqry kindly tense sad carefree sreptical elated egotistic concentrating energetic drousy rebellion affectionate jittery regretful uitty d"bious pleased boastful intent active tired defiant warnhearted fearful sorry playful yrtpiciorjs overjoyed self-centered engaged in thouqht vigorous sluggish leisurely rcnchalant Appendix D 124 Imagine that you are in the presence of a live snake. Below are several statements that indicate how you might feel while in the presence of a snake. Please mark those statements that describe how you would feel in the presence of a live snake by placing a check mark (V) next to the statement. You should check only those statements that describe how you would feel in the presence of a snake. ( ) It I would feel liking. ( ) 2.1 would feel anxious. ( ) 3.1 would feel sympathetic. ( ) 4.1 would feel uneasy. ( ) 5t I would feel unhappy. ( ) 6.1 would feel hopeful. ( ) 7t I would feel tense. ( ) 8.1 would feel happy. ( ) 9.1 would feel troubled. ( ) 10.1 would feel good. ( ) 11.1 would feel weak. ( ) 12.1 would feel indifferent. ( ) 13.1 would feel disgusted. ( ) 14.1 would feel bad. ( ) 15.1 would feel insecure. ( ) 16.1 would feel cheerful. Appendix D 125 Think about snakes. Take a minute or so to collect your thoughts. We would like you to indicate what you are thinking about snakes right now. The next few pages contain a form we have prepared for you to record your thoughts and ideas. Simply write down the first thought that occurs to you in the first box, the second thought in the second box, and so on. Please put only one idea or thought in a box. You might have positive thoughts, negative thoughts, or neutral thoughts, all are fine. You can use short phrases or statements. You may ignore spelling, grammar, and punctuation. Don't worry if you don't fill every box. Just put down as many thoughts as occur to you. ( Appendix D 12& Below are descriptions of several things a person might do while in the presence of a snake. Please indicate how likely it is that you would perform each of these actions. You can indicate how likely it is that you would do each of these things by marking the scale below each statement. For example, if you think it would be fairly unlikely that you would catch a snake, you would mark the scale as follows? VERY UNLIKELY 5. J X I. J VERY LIKELY 1.1 would stay in the same room with a live, caged snake. VERY UNLIKELY J. .5 VERY LIKELY h I would let someone else hold a live snake while in my presence. 3.1 would touch a live snake while someone else holds it. VERY UNLIKELY i_ J VERY LIKELY 4.1 would hold a live snake. VERY UNLIKELY 5. .5 VERY LIKELY Appendix D 127 * Please rate your attitude toward each of the activities listed below. If you would rate the activity as very bad, then place an "X" near the left end of the scale. If you would rate the activity as very good, then place an "X" near the right end of the scale. Use the remaining parts of the scale to indicate gradations between the two extremes. For example, if you think catching snakes is moderately good, you would mark the scale as follows* BAD *. : 5 J : t x ; J GOOD 1. Being in the same room with a live, caged snake is! BAD J J J 5 5 5 ! ! GOOD 2. Being in the same room while someone else holds a live snake isi BAD ! 5, J 5 ! J 5 5 GOOD 3. Touching a live snake while someone else holds it is! BAD J : : : 5 : J 5 GOOD 4. Holding a live snake is! BAD ! ! ! ! ! 5 ! ! GOOD Appendix D 128 Please indicate which of the following things you have done. Circle "yes" if you have ever performed the stated activity} circle "no" if you have never performed the stated activity. 1. Have you ever been in the same room with a live* caged snake? YES NO 2. Have you ever let someone else hold a live snake while in your presence? YES NO 3. Have you ever touched a live snake while someone else held it? YES NO 4. Have you ever held a live snake? YES NO 5. What is the closest distance you have ever been to a live snake that was not in a cage? (Check only one) Close enough to touch 2 feet 4 feet 6 feet 8 feet 10 feet More than 10 feet 6. How much experience have you had with snakes? NOfE AT All J } ! 5 J 5 J i VERY HUGH Appendix D 129 Please indicate how likely it is that you would get close to or touch each of the snakes described below. You should assume that these snakes are live, and that they are not in a cage. You can indicate how likely it is that you would do each of these things by marking the scale below each statement. For example» if you think it would be fairly unlikely that you would get dose to a snake, you would mark the scale as follows? VERY UNLIKELY J i X t J 5 5 J ! VERY LIKELY 1.1 would get dose to or touch a small snake (e.g., a garter snake). VERY UNLIKELY J ! I J 5 J 5 J VERY LIKELY 2.1 would get dose to or touch a dangerous snake (e.g., a rattlesnake). VERY IMJKELY 5 J J i 5 ! J J VERY LIKELY 3.1 would get dose to or touch a harmless snake (e.g., a kingsnake). VERY UNLIKELY 5 ! ! J '. i ! J VERY LIKELY 4.1 would get close to or touch a large snake (e.g., a boa). VERY UNLIKELY t t J i J ! J J VERY LIKELY Appendix D 130 Please rate your attitude toward each of the activities listed below. If you would rate the activity as very bad, then place an "X" near the left end of the scale. If you would rate the activity as very good, then place an "X" near the right end of the scale. Use the remaining parts of the scale to indicate gradations between the two extremes. For example, if you think catching snakes is moderately good, you would mark the scale as followst BAD : j : : j J_X__: ; GOOD 1. Getting close to or touching a dangerous snake (e.g., a rattlesnake) is{ BAD 5 J J J i J ! ! GOOD 2. Getting close to or touching a harmless snake (e.g., a kingsnake) is' BAD ! J J J 5 5 J J GOOD 3. Getting close to or touching a large snake (e.g., a boa) is5 BAD : 5 ; J j : : : GOOD 4. Getting close to or touching a small snake (e.g., a garter snake) is? BAD : j j : j i J : GOOD Appendix D 131 Below are descriptions of several things a person might do while in the presence of a snake. Please indicate whether people who are important to you think you should perform each of these actions. Important people might include members of your family, close friends, teachers, and so on. For example, if you believe that most people who are important to you think you probably should catch snakes, you would mark the scale as follows! Most people who are important to me think! I SHOULD .!_X. .! I SHOULD NOT catch snakes, 1. Most people who are important to me think! I SHOULD .! I SHOULD NOT stay in the same room with a live, caged snake. 2. Most people who are important to me think! I SHOULD 5. .! I SHOULD NOT let someone else hold a live snake while in my presence. 3. Most people who are important to me think! I SHOULD 5 5 ! ! ! !_ .! I SHOULD NOT touch a live snake while someone else holds it. 4. Most people who are important to me think! I SHOULD .! I SHOULD NOT hold a live snake. Appendix D Please indicate whether people who are important to you think you should get close to or touch each of the snakes described below. Important people might include members of your family* close friends, teachers, and so on. For example, if you believe that most people who are important to you think you probably should get close to or touch snakes, you would mark the scale as follows; Most people who are important to me think! i SHOULD : t x ; s : .5 I SHOULD NOT get close to or touch snakes. 1. Most people who are important to me think! I SHOULD .5 I SHOULD NOT get close to or touch a large snake (e.g., a boa). 2. Most people who are important to me think! I SHOULD 5. .! I SHOULD NOT get close to or touch a dangerous snake (e.g., a rattlesnake). 3. Most people who are important to me think! I SHOULD !. .! I SHOULD NOT get close to or touch a small snake (e.g., a garter snake). 4. Most people who are important to me think! I SHOULD 5. .5 I SHOULD NOT get close to or touch a harmless snake (e.g., a kingsnake). Appendix D 133 For each of the questions below, please indicate how much you want to do what those who are important to you think you should do. People who are important to you might include members of your family, close friends, teachers, and so. For example, if you want very much to do what those who are important to you think you should do, then you would mark the scale as follows? NOT AT ALL 5 ! i 5 . J i X . VERY HUGH If Do you want to do what those who are important to you think you should do in regard to staying in the same room with a live, caged snake? NOT AT ALL . l J J . J _. J VERY HUGH 2. Do you want to do what those who are important to you think you should do in regard to letting someone else hold a live snake while in your presence? t NOT AT ALL . J J 5 J 5 ! i VERY HUCH 3f Do you want to do what those who are: important to you think you should do in regard to touching a live snake while someone else holds it? NOT AT ALL 5 J 5 5 5 J J ! VERY HUCH 4. Do you want to do what those who are important to you think you should do in regard to holding a live snake? NOT AT ALL 5 J . J 5 J 5 ! VERY HUCH Appendix D 134 5. Do you want tD do what those who are important to you think you should do in regard to getting close to or touching a large snake (e.g., a boa)? NOT AT ALL 2 J J 5 ! 2 J J VERY HUGH 6. Do you want to do what those who are important to you think you should do in regard to getting close to or touching a dangerous snake (e.g., a rattlesnake)? NOT AT ALL J 2 2 J 2 2 2 J VERY HUGH 7. Do you want to do what those who are important to you think you should do in regard to getting close to or touching a small snake (e.g., a garter snake)? NOT AT ALL 5 2 *. 2 2 _'. 2 2 VERY HUGH 8. Do you want to do what those who are important to you think you should do in regard to getting close to or touching a harmless snake (e.g., a kingsnake)? Appendix D 135 Overall, how would you rate your attitude about snakes? I Dislike Snakes J 5 5 5 } J 5 5 I Like Snakes Very Much -3 -Z -1 Neutral +1 +2 +3 Very Much How old are you? Are you male or female (tirde one). What is your college majort Did you grow up in a (check one or more)' Large Town Small Town Farm Do you now own a pet snake (tirde one)! yes no Have you ever owned a pet snake (tirde one)5 yes no Have you ever touched a live snake (tirde one)5 yes no Appendix D 136 Thank-you very much for your cooperation. The second session of this study will be scheduled for a time at your convenience. Please write down your name and telephone number so that the experimenter can get in touch with you if the need arises. Name! Telephone Number? At this time you should check through the booklet to make sure you didn't miss any pages. Then you should notify the experimenter that you are finished, and he will schedule a time for you to return and complete the study. Finally, we would like to ask that you not discuss the details of this study with other people who might be participating. Again, thank-you for your cooperation. Appendix D 137 Below are several questions about today's experiment. 1. In your opinion, what was the size of the Hve snake that you saw today? VERY SHALL J 5 5 J 5 5 5 J VERY LARGE 2. How many color slides of snakes did you see today?' 3. For how many minutes was the live snake in your presence? I APPENDIX E RE-ANALYSIS OF FISHBEIN AND AJZEN (1974) This re-analysis concerns data originally collected by Fishbein and Ajzen (1974), and subsequently re-analyzed by Bagozzi and Burnkrant (1979). The present re-analysis has been relegated to an appendix because the original data were never intended to test the tripartite model. As a result, the re-analysis serves to demonstrate the ambiguity that can arise when attitude measures are classified as affect, behavior, or cognition on a post-hoc basis. Fishbein and Ajzen's (1974) main purpose was to demonstrate the advantage of using multiple behavioral measures (rather than single measures) to evaluate attitude-behavior correlations. The attitude { domain was religion. All subjects completed five "traditional" attitude scales. These were: (i) a Guilford self-rating scale, (ii) a semantic differential scale (consisting of five bipolar scales), (iii) a Likert scale, (iv) a Guttman scale, and (v) a Thurstone scale. Fishbein and Ajzen also constructed a set of 100 behavior descriptions. One sample of 62 subjects indicated which of those behaviors they had performed (self-reported behavior sample). A second sample of 63 subjects indicated which of the behaviors they would perform (behavioral intention sample). Several scales of the behavioral criteria were constructed, including (i) a Thurstone scale. 138 Appendix E 139 (11) a Likert scale, (111) and a Guttman scale. Fishbein and Ajzen (1974) intended the five attitude scales to serve as five alternative measures of attitude. They never distinguished among them as measures of affect or cognition. Nevertheless, Bagozzl and Burnkrant (1979) suggested that the data could be used to distinguish between affect and cognition as distinct behavioral predictors. On a post-hoc basis, they identified the self- rating and semantic differential scales as measures of affect, and the Likert, Guttman, and Thurstone scales as measures of cognition. In light of the affect and cognition definitions outlined in Chapter One, that division of measures is questionable. Bagozzi and Burnkrant (1979) used LISREL to test models that consisted of "affect" and "cognition" as predictors of the scaled j, behaviors. Those models are statistically equivalent to the tripartite models evaluated in the Chapter Two re-analyses of Ostrom (1969) and Kothandapani (1971). They conducted separate analyses for the self-reported behavior and behavioral intention samples, respectively. Their results supported a distinction between affect, behavior, and cognition. However, Bagozzi and Burnkrant reported results only for the self-reported behavior sample. They did not report results for the behavioral intention sample "because of problems of interpretational confounding" (p. 925). The present re-analysis is based on Fishbein and Ajzen's (1974) data. The tested models are the same ones tested by Bagozzi and Burnkrant (1979). Despite the latter investigators' interpretational Appendix E 140 problems, the results from both samples will be reported. RESULTS Exploratory factor analysis: Self-reported behavior sample. The SAS (1979) computer program was used to calculate the principal-axis common-factor solutions. The correlations above the diagonal in Table 47 were used as input to the computer program. Squared multiple correlations were calculated as initial estimates of communality. The . eigenvalues were 4.75, 0.41, and 0.20 for factors one through three, respectively. On this basis, one factor appears to be sufficient in accounting for the correlations. Nevertheless, a three-factor solution was estimated. The rotated (PROMAX) factor pattern can be found in Table 48. | Factor one can be identified as cognition. Factor two corresponds to affect. Factor three represents behavior. The interfactor correlations (Table 49) are moderately high. This factor structure supports the tripartite model. Exploratory factor analysis: Behavioral intention sample. The correlations below the diagonal in Table 47 were used for this analysis. The eigenvalues were 4.98, 0.34, 0.12 for factors one through three, respectively. As for the previous sample, one factor appears sufficient. However, a three-factor solution was also calculated. The rotated (PROMAX) factor pattern for the behavioral intention sample can be found in Table 50. The highest loadings on factor one Appendix E 141 are for the affect measures, however Likert cognition also has a moderate loading on this factor. v Factor two represents behavior. Factor three can be identified as cognition, with a low loading for the Likert measure. The interfactor correlations (Table 51) are moderately high. While behavior emerges in the factor solution, affect and cognition do not emerge as clearly distinct factors. Confirmatory factor analysis; Self-reported behavior sample. The LISREL V computer program (Joreskog & Sorbora, 1981) was used for this analysis. The procedure was similar to that used in the re-analyses of Chapter Two. Table 52 shows the LISREL estimates for all of the model's unknown parameters. The overall X2 (11 df) = 13.11 (p. > .05), and A = .97. Thus, the tripartite model is strongly supported by these data. -The three attitude components are also highly correlated (ranging from .77 to .84). To evaluate discriminant validity, the three-factor model was compared to a one-factor model. The overall X2 (14 df) for the one- factor model is 55.65 (p_ < .05), and A = .85. The difference X2 (3 df) = 42.54 (p_ < .05). Thus, the model's discriminant validity is strongly supported. One additional model was estimated to evaluate Fishbein and Ajzen's (1974) intention for the five attitude measures to represent a single latent variable. The two-factor model produced a X? (13 df) = 39.20 (p. < .05), and A = .90. This analysis indicates that the three- factor model substantially improves upon both the one-factor and two- Appendix E 142 factor models. Confirmatory factor analysis: Behavioral Intention sample. Table 53 contains the LISREL estimates for this analysis. The overall X2 (11 df) = 12.22 (p_ > .05), and A = .97. Thus, the tripartite model is again strongly supported. The three attitude components are also highly correlated (ranging from .75 to .93). A one-factor model was estimated to evaluate discriminant validity. The overall X2 (14 df) = 40.13 (p_ < .05), and A = .91. The difference X2 (3 df) between the one-factor and three-factor models is 27.91 (p_ < .05). Thus, discriminant validity is supported, although the one-factor model still fits relatively well. As in the previous analysis, a two-factor model was also estimated. That model produced a X2 (13 df) = 18.03 (p_ > .05), and A | = .96. Furthermore, the three-factor model does not provide a statistically better fit than the two-factor model (difference X2 (2 df) = 5.81, p_ > -05). Thus, the factor structure intended by Fishbein and Ajzen (1974) is supported in this sample. The results from these analyses are summarized in Table 54. DISCUSSION The re-analysis of Fishbein and Ajzen's (1974) self-reported behavior sample supports the tripartite model, if it is assumed that Bagozzi and Burnkrant's (1979) post-hoc division of measures is proper. The re-analysis of the behavioral intention sample suggests that the division was not proper. That analysis indicated, as Appendix E 143 Fishbein and Ajzen intended, an "attitude" factor and a behavior factor. It is not clear why the two samples produced different factor structures. The five attitude measures were the same for both samples. Presumably, the only difference was instructions associated with the behavior descriptions (indicating past behavior versus behavioral intention). Perhaps it is best to conclude that these data support Fishbein and Ajzen's intentions of separating the measures according to two constructs (attitude and behavior). Using the data as support for the tripartite model is not justified on two grounds. First, the data were never intended to test that model. Second, Bagozzi and Burnkrant's post-hoc division of measures is difficult to justify. APPENDIX F TABLES I 144 Appendix F (Tables) 145 TABLE 1 MULTITRAIT-MULTIMETHOD CORRELATION MATRIX FROM OSTROM (1969) Thurstone Likert Guttman Self-rating ABCABCABCABC Equal- A — appearing B 57 — intervals C 63 62 — Sunmated A 71 59 68 . — ratings B 67 67 71 79 — C 69 62 72 79 81 — Scalograa A 54 39 49 58 51 56 — analysis B 59 61 58 60 69 60 43 C 63 50 63 69 67 71 49 56 — Self-rating A 65 63 69 72 73 72 54 66 65 -- B 61 51 56 62 68 64 53 52 56 76 C 56 48 60 63 66 66 46 48 62 77 68 Notes: Decimal points omitted. N = 189. A indicates the affective, B indicates the behavioral, and C indicates the cognitive component. Appendix F (Tables) 146 TABLE 2 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS DATA FROM OSTROM (1969) FACTOR ONE TWO THREE Thurstone Affect 0.245 0.122 0.499" Likert Affect 0.187 0.104 0.652" Guttnan Affect 0.030 0.224 0.497" Guilford Affect 0.300 0.646" 0.044 Thurstone Behavior 0.697" 0.040 0.053 Likert Behavior 0.496" 0.145 0.339 Guttman Behavior 0.675" 0.061 0.073 Guilford Behavior 0.069 0.661" 0.144 Thurstone Cognition 0.401" 0.128 0.351 Likert Cognition 0.231 0.126 0.604" Guttman Cognition 0.123 0.185 0.541* Guilford Cognition 0.010 0.692" 0.186 Note: Largest loading in each row is starred. TABLE 3 INTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS DATA FROH OSTROM (1969) 0.69 0.75 0.71 Appendix F (Tables) 147 TABLE 4 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE DATA FROM OSTROM (1969) Parameter Estimate Standard Error Factor Loadings* Thurstone Affect 0.800 (0.359) 0.062 (0.043) Likert Affect 0.865 (0.254) 0.059 (0.035) Guttman Affect 0.639 (0.591) 0.067 (0.064) Guilford Affect 0.842 (0.285) 0.059 (0.036) Thurstone Behavior 0.735 (0.459) 0.064 (0.053) Likert Behavior 0.898 (0. 195) 0.058 (0.032) Guttman Behavior 0.763 (0. 418) 0.063 (0.049) Guilford Behavior 0.742 (0.442) 0.064 (0.051) Thurstone Cognition 0.813 (0.340) 0.061 (0.042) Likert Cognition 0.883 (0.219) 0.058 (0.032) Guttman Cognition 0.798 (0.364) 0.062 (0.044) Guilford Cognition 0.739 (0.448) 0.064 (0.051) Correlated Errors: Thurstone A/B 0.006 0.034 Thurstone A/C -0.007 0.030 Thurstone B/C 0.060 0.034 Likert A/B 0.051 0.025 Likert A/C 0.044 0.025 Likert B/C 0.062 0.024 Guttman A/B -0.030 0.040 Guttman A/C -0.006 0.038 Guttman B/C 0.005 0.033 Guilford A/B 0.139 0.033 Guilford A/C 0.159 0.033 Guilford B/C 0.152 0.038 Interfactor Correlations: Affect/Behavior 0.962 0.016 Affect/Cognition 0.976 0.014 Behavior/Cognition 0.939 0.018 Notes: "Each measured variable loads only on its respective factor (affect, behavior, or cognition.) Values in parentheses are unique variances. Approximate T-values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 148 TABLE 5 SUMMARY OF LISREL MODELS FIT TO OSTROM (1969) Model X2 df A Null 1871.92 66 One-Factor 72.62 42 .961 Three-Factor 53.82* 39 .971 Note: *D > .05 TABLE 6 MULTITRAIT-MULTIMETHOD CORRELATION MATRIX FROM KOTHANDAPANI (1971) Thur3tone Likert Guttnan Self-rating Equal- A — appearing B 19 -- intervals C 26 43 — Susunated A 58 05 34 -- ratings B -03 49 08 31 — C 20 22 50 66 65 -- Scalogram A 49 -06 06 60 04 20 — analysis B 18 42 -05 05 47 07 28 -- C 20 09 28 41 24 44 48 32 -- Self-rating A 45 12 20 59 31 41 50 13 30 -- B 17 49 12 15 60 34 14 50 24 55 C 25 32 39 31 39 51 13 13 35 65 61 Notes: Decimal points omitted. N = 100. A indicates the affective, B indicates the behavioral intention, and C indicates the cognitive component. Appendix F (Tables) 149 TABLE 7 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS DATA FROM KOTHANDAPANI (1971) FACTOR ONE TWO THREE Thurstone Affect 0.653* -0.058 0.043 Likert Affect 0.724* -0.161 0.381 Guttman Affect 0.869* -0.040 -0.203 Guilford Affect 0.613* . 0.201 0.161 Thurstone Behavior 0.223 0.636* 0.216 Likert Behavior 0.104 0.764* 0.218 Guttman Behavior 0.196 0.734* -0.400 Guilford Behavior 0.092 0.791* 0.005 Thurstone Cognition 0.017 -6.083 0.742* Likert Cognition 0.148 0.131 0.755* Guttman Cognition 0.436* 0.146 0.135 Guilford Cognition 0.154 0.357 0.435* Mote; Largest loading in each row is starred. TABLE 8 INTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS DATA FROM KOTHANDAPANI (1971) 1 2 0.28 3 0.34 0.34 Appendix F (Tables) 150 TABLE 9 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 3 DATA FROM KOTHANDAPANI (1971) Parameter Estimate Standard Error Factor Loadings": Thurstone Affect 0.713 (0.498) 0.092 (0.086) Likert Affect 0.884 (0.221) 0.077 (0.072) Guttraan Affect 0.663 (0.541) 0.086 (0.087) Guilford Affect 0.580 (0.644) 0.076 (0.098) Thurstone Behavior 0.764 (0.532) 0.089 (0.100) Likert Behavior 0.707 (0.485) 0.075 (0.090) Guttman Behavior 0.696 (0.523) 0.090 (0.094) Guilford Behavior 0.600 (0.516) 0.076 (0.087) Thurstone Cognition 0.684 (0.530) 0.088 (0.099) Likert Cognition 0.714 (0.467) 0.074 (0.096) Guttman Cognition 0.605 (0.731) 0.100 (0.120) Guilford Cognition 0.515 (0.686) 0.083 (0.108) Correlated Errors: Thurstone A/B 0.128 0.066 Thurstone A/C 0.011 0.064 Thurstone B/C 0.335 0.082 Likert A/B 0.251 0.062 Likert A/C 0.276 0.067 Likert B/C 0.445 0.082 Guttman A/B 0.239 0.068 Guttman A/C 0.288 0.078 Guttman B/C 0.226 0.080 Guilford A/B 0.383 0.077 Guilford A/C 0.462 0.088 Guilford B/C 0.327 0.078 Interfactor Correlations: Affect/Behavior 0.111 0.119 Affect/Cognition 0.591 0.080 Behavior/Cognition 0.381 0.109 Notes: "Each measured variable loads only on its respective factor (affect, behavior, or cognition.) Values in parentheses are unique variances. Approximate T-values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 151 TABLE 10 SUMMARY OF LISREL MODELS FIT TO KOTHANDAPANI (1971) Model X2 df A Null 785.54 66 One-Factor 263.51 42 .665 Three-Factor 71.56 39 .909 Appendix F (Tables) 152 TABLE 11 DESCRIPTIVE STATISTICS FOR STUDY 1 Measured Variable Mean ad Minimum Maximum Thurstone Mood 4.15 0.86 2.0 5.0 MACL Positive 3.04 3.24 0 14 HACL Negative 1.56 1.87 0 10 Heart-rate -0.14 0.82 -1.65 1.70 Action Sequence 3.05 1.12 0 4 Preferred Distance 30.68 10.93 3.9 50.0 Thurstone Behavior 3.93 1.25 2 S Thurstone Belief 4.77 0.87 3 6 Senantic Differential 4.01 1.15 1.3 6.7 Listed Thoughts 0.04 1.19 -3.14 3.14 Single Item Rating -0.39 1.60 -3 3 TABLE 12 DISTRIBUTION OF SINGLE-ITEM SELF-RATING SCALE Scale Value Frequency Percentage -3 13 9.4 -2 26 18.8 -1 27 19.6 0 32 23.2 • 1 22 15.9 +2 12 8.7 • 3 6 4.4 Totals: 138 100 Appendix F (Tables) 153 TABLE 13 CORRELATION MATRIX FOR STUDY 1 10 1. Thurstone Mood 2. MACL Positive 45 — 3. MACL Negative" 50 13 — 4. Heart-rate 17 04 04 — 5. Action Sequence 35 25 37 02 -_ 6. Preferred Distance" 23 26 15 -07 52 -- 7. Thurstone Behavior 38 26 28 03 66 50 — 8. Thurstone Belief 18 13 21 05 29 27 33 -- 9. Semantic Diff. 30 23 29 05 45 47 58 61 10. Listed Thoughts 30 17 28 -01 38 25 44 36 55 Notes: "Signs have been reversed. Decimal points omitted. N = 138. Appendix F (Tables) 154 TABLE 14 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS STUDY 1 ONE TWO THREE Thurstone Mood 0.14 -0.01 0.67* MACL Positive 0.30 -0.11 0.32* MACL Negative 0.01 0.16 0.51* Heartrate -0.11 0.01 0.15* Action Sequence 0.71* 0.04 0.04 Preferred Distance 0.68" 0.06 -0.15 Thurstone Behavior 0.68" 0.16 -0.01 Thurstone Belief -0.08 0.71* -0.01 Semantic Diff'l 0.17 0.74* -0.02 Listed Thoughts 0.08 0.50* 0.12 Mote: Largest loading in each row is starred. TABLE 15 IHTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS STUDY 1 1 2 0.37 3 0.64 0.49 Appendix F (Tables) 155 TABLE 16 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 2 DATA FROM STUDY 1 Parameter Estimate Standard Error Factor Loadings: AFFECT Thurstone Mood 0.920 (0.153) 0.099 (0.140) MACL Positive 0.474 (0.775) 0.091 (0.102) MACL Negative 0.543 (0.705) 0.092 (0.099) Heart-rate 0.164 (0.973) 0.092 (0.118) BEHAVIOR Action Sequence 0.769 (0.409) 0.078 (0.069) Preferred Distance 0.627 (0.607) 0.083 (0.083) Thurstone Behavior 0.846 (0.284) 0.076 (0.066) COGNITION Thurstone Belief 0.638 (0.592) 0.082 (0.081) Semantic Differential 0.947 (0.103) 0.076 (0.080) Listed Thoughts 0.591 (0.651) 0.083 (0.086) Interfactor Correlations: Affect/Behavior 0.495 0.087 Affect/Cognition 0.378 0.089 Behavior/Cognition 0.701 0.063 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 156 TABLE 17 SUMMARY OF LISREL MODELS FIT TO STUDY 1 Model X2 df A With Heart-rate: Null 440.96 45 One-Factor 113.45 35 .743 Three-Factor 37.51" 32 .915 Without Heart-rate: Null 433.25 36 One-factor 105.99 27 .755 Three-factor 32.90" 24 .924 Note: *D > .05 TABLE 18 COMPARISON OF INTERCOMPONENT CORRELATIONS ACROSS THREE STUDIES Correlation Data Source A/B A/C B/C Average* Ostrom (1969) .96 .98 .94 .96 Kothandapani (1971) .11 .59 .38 .38 Study 1 .52 .40 .70 .55 Note: "Correlations were converted to Fisher's ZJ averaged, and reconverted to r. A is the affective, B is the behavioral, and C is the cognitive component. Appendix F (Tables) 157 TABLE 19 DESCRIPTIVE STATISTICS FOR STUDY 2 Measured Variable Mean ad Minimum Maximum Session 1: Thurstone Mood 3.14 0.74 2.0 5.0 MACL Positive 1.78 3.04 0 11 MACL Negative 5.17 3.60 0 16 Action Sequence: BI 2.48 8.13 -12 12 Action Sequence: AACT 0.34 7.46 -12 12 Action Sequence: SN -2.91 32.72 -84 84 Action Sequence: PB 2.92 1.18 0 4 Preferred Distance: BI -3.53 6.98 -12 11 Preferred Distance: AACT -2.82 5.93 -12 12 Preferred Distance: SN -17.99 31.36 -84 84 Preferred Distance: PB 1.99 1.91 1 7 Thurstone Behavior 3.53 1.38 2.0 5.5 Past Experience 3.07 1.65 1 7 Thurstone Belief 4.46 0.95 2.5 6.0 Semantic Differential 3.61 1.39 1.0 6.7 Listed Thoughts -1.13 ' 1.39 -3.14 3.14 Single Item Rating -0.75 1.89 -3 +3 Session 2: Thurstone Mood 3.73 0.96 2.0 5.0 MACL Positive 1.83 2.63 0 12 MACL Negative 2.02 2.55 0 14 Heart-Rate 0.28 1.02 -1.4 1.4 Action Sequence 2.75 1.43 0 4 Preferred Distance 31.43 12.82 1.9 50.0 Thurstone Behavior 3.78 1.40 1 6 Thurstone Belief 4.49 0.94 2.5 6.0 Semantic Differential 3.84 1.29 1.0 6.5 Listed Thoughts -0.41 1.63 -3.14 3.14 Single Item Rating -0.41 1.78 -3 +3 Note: BI i3 behavioral intention, AACT is attitude toward the act, SN is subjective norm, and PB is past behavior. Appendix F (Tables) 158 TABLE 20 DISTRIBUTION OF SINGLE-ITEM SELF-RATING SCALE STUDY 2 Frequency Percentage Scale V 'alue Session 1 Session 2 Session 1 Session 2 -3 25 16 23.8 15.2 -2 20. 18 19.0 17.1 -1 14 16 13.3 15.2 0 21 21 20.0 20.0 +1 5 17 4.8 16.2 +2 15 11 14.3 10.5 +3 5 6 4.8 5.7 Totals: 105' 105 100 99.9 TABLE 21 ESTIMATES OF RELIABILITY FROM TEST-RETEST CORRELATIONS Variable Correlation Thurstone Mood .41 MACL Positive .58 MACL Negative .53 Thurstone Behavior .89 Thurstone Belief .67 Semantic Differential .87 Listed Thoughts .69 Single Item Rating .92 Action Sequence: BI .85 Action Sequence: AACT .83 Action Sequence: SN .66 Preferred Distance: BI .83 Preferred Distance: AT .80 Preferred Distance: SN .76 Note.: Correlation is between measured variable at session one and measured variable at session two. BI is behavioral intention, AACT is attitude toward the act, and SN is subjective norm. Appendix F (Tables) 159 TABLE 22 CORRELATION MATRIX FOR TESTING TRIPARTITE MODEL STUDY 2: SESSION 1 1. Thurstone Mood 2. MACL Positive 57 — 3. MACL Negative" 54 42 -- 4. Preferred Distance (BI) 57 43 58 -- 5. Action Sequence (BI) 57 44 67 82 -- 6. Thurstone Behavior 58 42 63 77 85 — 7. Thurstone Belief 33 22 40 45 42 49 -- 8. Semantic Differential 60 46 60 71 70 72 69 — 9. Listed Thoughts 62 48 51 65 61 63 44 69 Motes: "Signs have been reversed. Decimal points omitted. N = 105. Appendix F (Tables) 160 TABLE 23 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS STUDY 2: SESSION 1 ONE TWO THREE Thurstone Mood 0.69* -0.01 0.13 MACL Positive 0.68* -0.07 0.03 MACL Negative 0.24 0.08 0.46* Action Sequence Thurstone Belief 0.10 0.79* 0.01 Semantic Diff'l 0.20 0.63* 0.18 Listed Thoughts 0.47* 0.23 0.18 Mote: Largest loading in each row is starred. TABLE 24 IHTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS STUDY 2: SESSION 1 1 2 0.62 3 0.71 0.69 Appendix F (Tables) 161 . TABLE 25 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE DATA FROM STUDY 2: SESSION 1 Parameter Estimate Standard Error Factor Loadings: AFFECT Thurstone Mood 0.758 (0.426) 0.089 (0.077) MACL Positive 0.608 (0.631) 0.095 (0.097) MACL Negative 0.760 (0.423) 0.089 (0.077) BEHAVIOR Action Sequence (BI) 0.931 (0.133) 0.075 (0.032) Preferred Distance (BI) 0.872 (0.240) 0.079 (0.042) Thurstone' Behavior" 0.901 (0.188) 0.077 (0.036) COGNITION Thurstone Belief 0.694 (0.519) 0.088 (0.078) Semantic Differential 0.962 (0.075) 0.076 (0.048) Listed Thoughts 0.735 (0.459) 0.086 (0.071) Interfactor Correlations: Affect/Behavior 0.862 0.048 Affect/Cognition 0.823 0.056 Behavior/Cognition 0.814 0.043 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 162 TABLE 26 CORRELATION MATRIX FOR TESTING TRIPARTITE MODEL STUDY' 2: SESSION 2 1. Thurstone Mood 2. MACL Positive 52 -- 3. MACL Negative" 53 23 -- 4. Preferred Distance* 50 49 39 — 5. Action Sequence 60 37 60 66 — 6. Thurstone Behavior 68 49 48 59 66 -- 7. Thurstone Belief 46 25 45 50 59 51 -- 8. Semantic Differential 61 53 58 61 70 70 71 -- 9. Listed Thoughts 49 41 42 56 56 53 51 66 Notes: "Signs have been reversed. Decimal points omitted. N = 105. Appendix F (Tables) 163 TABLE 27 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS STUDY 2: SESSION 2 ONE TWO THREE Thurstone Mood 0.46 -0.20 0.49* MACL Positive 0.85" -0.17 -0.10 MACL Negative -0.09 -0.21 0.91* Heart-rate -0.09 0.43* -0.10 Action Sequence 0.16 0.14 0.62* Preferred Distance 0.54* 0.20 0.13 Thurstone Behavior 0.49* -0.07 0.42 Thurstone Belief 0.01 0.35 0.48* Semantic Diff'l 0.37 0.15 0.48" Listed Thoughts 0.37* 0.26 0.21 Mote: Largest loading in each row is starred. TABLE 28 INTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS STUDY 2: SESSION 2 0.50 0.72 0.62 Appendix F (Tables) 164 TABLE 29 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 2 DATA FROM STUDY 2: SESSION 2 Parameter Estimate Standard Error Factor Loadings: AFFECT Thurstone Mood 0.784 (0.385) 0.089 (0.076) MACL Positive 0.577 (0.667) 0.095 (0.100) MACL Negative 0.651 (0.576) 0.093 (0.090) BEHAVIOR Action Sequence 0.835 (0.303) 0.082 (0.056) Preferred Distance 0.739 (0.453) 0.087 (0.072) Thurstone Behavior 0.818 (0.330) 0.083 (0.058) COGNITION Thurstone Belief 0.745 (0.445) 0.086 (0.070) Semantic Differential 0.937 (0.121) 0.077 (0.047) Listed Thoughts 0.711 (0.494) 0.088 (0.076) Interfactor Correlations: Affect/Behavior 0.956 0.048 Affect/Cognition 0.878 0.054 Behavior/Cognition 0.902 0.038 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 165 TABLE 30 CORRELATION MATRIX FOR TESTING TRIPARTITE MODEL SAMPLE DIVIDED BY PAST EXPERIENCE 1. Thurstone Mood — 61 56 48 52 40 30 54 62 2. MACL Positive 09 -- 35 35 47 38 28 53 44 3. MACL Negative" 22 32 — 54 67 65 43 56 48 4. Preferred Distance (BI) 32 22 24 -- 67 60 63 70 57 5. Action Sequence (BI) 40 17 42 69 -- 70 56 66 59 6. Thurstone Behavior 59 14 29 58 71 — 64 6.3 54 7. Thurstone Belief 30 05 29 27 32 36 -- 73 50 8. Semantic Differential 47 06 42 49 59 63 68 — 68 9. Listed Thoughts 40 35 25 48 37 46 32 54 — Notes: "Signs have been reversed. Decimal points omitted. Below diagonal correlations for low past experience subjects (n = 53); above diagonal correlations for high past experience subjects (n = 52). Appendix F (Tables) 166 TABLE 31 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE DATA FROM STUDY 2: LOW-PAST EXPERIENCE SUBJECTS Parameter Estimate Standard Error Factor Loadings: AFFECT Thurstone Mood 0.569 (0.677) 0.165 (0.179) MACL Positive 0.179 (0.968) 0.149 (0.191) MACL Negative 0.472 (0.777) 0.158 (0.174) BEHAVIOR Action Sequence (BI) 0.876 (0.232) 0.116 (0.082) Preferred Distance (BI) 0.739 (0.455) 0.124 (0.106) Thurstone Behavior 0.827 (0.315) 0.119 (0.088) COGNITION Thurstone Belief 0.677 (0.541) 0.128 (0.119) Semantic Differential 0.994 (0.011) 0.114 (0.113) Listed Thoughts 0.540 (0.709) 0.132 (0.143) Interfactor Correlations: Affect/Behavior 0.923 0.184 Affect/Cognition 0.831 0.180 Behavior/Cognition 0.707 0.090 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) TABLE 32 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 2 DATA FROM STUDY 2: HIGH PAST EXPERIENCE SUBJECTS Parameter Estimate Standard Error Factor Loadings: AFFECT Thurstone Mood 0.736 (0.459) 0.130 (0.117) MACL Positive 0.609 (0.629) 0.137 (0.140) HACL Negative 0.771 (0.406) 0.128 (0.112) BEHAVIOR Action Sequence (BI) 0.851 (0.275) 0.116 (0.076) Preferred Distance' (BI) 0.795 (0.368) 0.120 (0.089) Thurstone Behavior 0.791 (0.375) 0.120 (0.090) COGNITION Thurstone Belief 0.773 (0.402) 0.121 (0.094) Semantic Differential 0.921 (0.151) 0.111 (0.067) Listed Thoughts 0.740 (0.452) 0.124 (0.102) Interfactor Correlations: Affect/Behavior 0.886 0.074 Affect/Cognition 0.807 0.086 Behavior/Cognition 0.901 0.054 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) TABLE 33 SUMMARY OF TRIPARTITE MODELS FIT TO STUDY 2 Model X2 df Session 1: Hull 681.68 36 One-Factor 84.25 27 .876 Three-Factor 42.29 24 .938 Session 2: Null 563.52 36 One-factor 58.95 27 .895 Three-factor 46.21 24 .918 Low Past Experience Ss: Null 213.94 36 One-factor 54.31 27 .746 Three-factor 35.05* 24 .836 High Past Experience Ss: Null 303.66 36 One-factor 51.24 27 .831 Three-factor 41.72 24 .863 Note: *p_ > .05 Appendix F (Tables) 169 TABLE 34 COMPARISON OF INTERCOMPONENT CORRELATIONS ACROSS FOUR STUDIES Correlation Data Source A/B A/C B/C Average* Ostrom (1969) .96 .98 .94 .96 Kothandapani (1971) .11 .59 .38 .38 Study 1 .52 .40 .70 .55 Study 2 Session 1 .86 .82 .81 .83 Session 2 .9G .88 .90 .92 Low Exp. Subjects .92 .83 .71 .84 High Exp. Subjects .89 .81 .90 .87 Note:. "Correlations were converted tc> Fisher's z, averaged, and the cognitive component. Appendix F (Tables) 170 TABLE 35 CORRELATION MATRIX FOR EVALUATING BEHAVIORAL PREDICTORS DATA FROM STUDY 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 1. Thurstone Hood — 2. KACL Positive 57 - 3. MACL Negative* 54 42 - 4. Thurstone Belief 33 22 40 — 5. Semantic Dfrntl 60 46 60 69 - .6. Listed Thoughts 62 48 51 44 69 - 7. Distance (BI) 57 43 58 45 71 65 - 8. Sequence (BI) 57 44 67 42 70 61 82 - 9. Distance (AACT) 49 45 50 34 64 53 83 75 - 10. Sequence (AACT) 55 47 61 38 69 58 76 87 S3 - 11. Distance (SN) 32 38 33 37 43 43 40 31 43 34 - 12. Sequence (SN) 44 44 50 31 50 47 40 43 48 47 73 - 13. Distance (PB) 19 21 34 25 39 29 46 54 45 52 20 29 - 14. Sequence (PB) 48 36 52 28 47 47 68 73 60 67 28 39 62 - 15. Distance* 48 45 47 33 56 50 68 63 61 58 41 37 34 51 - 16. Sequence 47 35 55 47 65 56 77 78 74 74 42 47 57 72 66 - Notes: tSigns have been reversed. Decioal points omitted. N = 105. Variables 1 through 14 Here oeasured at session one; variables 15 and 16 were measured at session two. Appendix F (Tables) 171 TABLE 36 SUMMARY OF SINGLE-FACTOR BEHAVIORAL PREDICTION MODELS Test of Relevant Predictive Predictive Path Null Model Path Free Fixed at :L. O path canonical Model X2 df X2 df X2 df A coefficient correlation BI 303.15 6 1.78 1 10.68 2 .965 .97 .84 AACT 283.29 6 0.72 1 2.79 2 .990 .89 .79 PB 193.17 6 1.15 1 24.56 2 .873 .83 .74 C 266.83 10 7.33 4 40.92 5 .847 .76 .69 A 195.91 10 8.56 4 43.33 5 .779 .78 .63 SN 170.95 6 1.70 1 53.56 2 .687 .60 .50 Notes: Tests are for six aodels shown in Figure 7. Each model's tests are given in one line of the table. Appendix F (Tables) 172 TABLE 37 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 8 Parameter Estimate Standard Error Factor Loadings: Sequence: BI 0.920 (0.154) 0.075 (0,.028 ) Distance: BI 0.891 (0.205) 0.077 (0,.035 ) Sequence: AACT 0.899 (0.192) 0.076 (0..033 ) Distance: AACT 0.865 (0.251) 0.078 (0,.040 ) Sequence: PB 0.771 (0.405) 0.083 (0..060 ) Distance: PB 0.584 (0.659) 0.091 (0,.094 ) Sequence: Overt 0.862 (0.257) 0.079 (0..041 ) Distance: Overt 0.702 (0.507) 0.086 (0,.074 ) Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. TABLE 38 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 9 Parameter Estimate Standard Error Factor Loadings: Sequence: BI 0.922 (0.150) 0.075 (0.028) Distance: BI 0.889 (0.209) 0.077 (0.035) Sequence: AACT 0.903 (0.185) 0.076 (0.032) Distance: AACT 0.865 (0.251) 0.078 (0.040) Sequence: PB 0.771 (0.406) 0.083 (0.060) Distance: PB 0.584 (0.658) 0.091 (0.094) Sequence: Overt 0.907 (0.177) 0.080 (0.056) Distance: Overt 0.731 (0.466) 0.087 (0.072) Interfactor Correlation: Predictors/Overt Behavior 0.944 0.032 Notes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 173 TABLE 39 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 10 Parameter Estimate Standard Error Factor Loadings: Sequence: BI 0.922 (0.151) 0.075 (0.031) Distance: BI 0.888 (0.212) 0.077 (0.037) Sequence: AACT 0.932 (0.131) 0.075 (0.033) Distance: AACT 0.891 (0.205) 0.077 (0.038) Sequence: PB 0.917 (0.160) 0.085 (0.078) Distance: PB 0.678 (0.540) 0.091 (0.085) Sequence: Overt 0.912 (0.169) 0.080 (0.055) Distance: Overt 0.727 (0.471) 0.087 (0.073) Interfactor Correlations: BI/AACT 0.970 021 BI/PB 0.847 052 BI/B 0.949 034 AACT/PB 0.766 060 AACT/B 0.886 041 PB/B 0.848 057 Motes: Values in parentheses are unique variances, Approximate T- values can be calculated by dividing an estimate by its standard error. TABLE 40 SUMMARY OF BEHAVIOR MODELS FIT BY LISREL Model X2 df Null 775.61+ 28 - One-Factor (BI) 79.66+ 20 .897 Two-Factor (B2) 76.39+ 19 .902 Four-Factor (B3) 50.48+ 14 .935 BI versus B2 3.27 1 .005 B2 versus B3 25.91+ 5 .033 BI versus B3 29.18+ 6 .038 Note: +p < .05. Appendix F (Tables) 174 TABLE 41 LISREL ESTIMATES FOR REASONED ACTION MODEL RA-ORIGINAL (FIGURE 5) Parameter Estimate Approximate T-Value Measurement Model: Sequence: AACT 1.291 (8.251) 14.89 (4.39) Distance: AACT 1.000 (6.653) - (5.18) Sequence: SN 1.295 (99.37) 5.97 (0.70) Distance: SN 1.000 (404.3) - (4.00) Sequence: BI 1.187 (10.41) 14.87 (4.96) Distance: BI 1.000 (9.222) - (5.42) Sequence: Overt 0.134 (0.404) 9.07 (3.51) Distance: Overt 1.000 (74.18) - (6.34) Structural Model: AACT ) BI 1.150 11.30 SN > BI -0.007 -0.43 BI ) B 1.437 8.78 AACT/SN Correlation 0.546 3.75 Error in Equation: BI 2.953 1.95 Error in Equation: B 8.669 1.50 Note: Values in parentheses are unique variances. Parameter estimates of 1.000 have been fixed to set the unit of measurement. Appendix F (Tables) 175 TABLE 42 LISREL ESTIMATES FOR MODIFIED REASONED ACTION MODEL RA-1 (FIGURE 11) Parameter Estimate Approximate T-Value Measurement Model: Sequence: AACT 1.305 (7.739) 14.79 (4.12) Distance: AACT 1.000 (6.957) - (5.26) Sequence: BI 1.190 (10.85) 14.67 (4.95) Distance: BI 1.000 (9.706) - (5.53) Sequence: Overt 0.134 (0.414) 9.10 (3.59) Distance: Overt 1.000 (73.65) - (6.32) Structural Model: AACT ) BI 1.143 " 12.73 AACT ) B -1.231 -0.75 BI ) B 2.487 1.74 Error in Equation: BI 2.205 1.35 Error in Equation: B 3.956 0.44 Note: Values in parentheses are unique variances. Parameter estimates of 1.000 have been fixed to set the unit of measurement. Appendix F (Tables) 176 TABLE 43 LISREL ESTIMATES FOR MODIFIED REASONED ACTION MODEL RA-2 (FIGURE 11) Parameter Estimate Approximate T-Value Measurement Model: Sequence: AACT 1.302 (7.854) 14.78 (4.16) Distance: AACT 1.000 (6.889) (5.22) Sequence: 81 1.192 (10.13) 14.91 (4.89) Distance: BI 1.000 (9.325) (5.45) Sequence: Overt 0.135 (0.403) 9.05 (3.48) Distance: Overt 1.000 (74.27) (6.33) Structural Model: AACT BI 1.135 12. .61 BI B 1.435 8. .73 Error in Equation: BI 3.005 2. .01 Error in Equation: B 9.060 1,.5 5 Note: Values in parentheses are unique variances. Parameter estimates of 1.000 have been fixed to set the unit of measurement. Appendix F (Tables) 177 TABLE 44 LISREL ESTIMATES FOR MODIFIED REASONED ACTION MODEL RA-3 (FIGURE 11) Parameter Estimate Approximate T-Value Measurement Model: Sequence: AACT 1.317 (7.301) 14.77 (3.94) Distance: AACT 1.000 (7.212) - (5.38) Sequence: PB 1.000 (0.223) - (2.03) Distance: PB 1.195 (1.967). 7.26 (6.32) Sequence: BI 1.209 (9.957) 14.74 (4.79) Distance: BI 1.000 (10.32) - (5.79) Sequence: Overt 0.139 (0.344) 9.00 (3.08) Distance: Overt 1.000 (77.42) - (6.49) Structural Model: AACT > BI " 0.910 7.44 AACT T-) B -0.915 -0.51 PB —» BI 1.445 2.53 PB —» B 0.460 0.16 BI » B 2.116 1.09 AACT/PB Correlation 0.766 5.53 Error in Equation: BI 1.287 0.97 Error in Equation: B 6.939 0.93 Note: Values in parentheses are unique variances. Parameter estimates of 1.000 have been fixed to set the unit of measurement. Appendix F (Tables) 178 TABLE 45 LISREL ESTIMATES FOR MODIFIED REASONED ACTION MODEL RA-4 (FIGURE 11) Parameter Estimate Approximate T-Value Measurement Model: Sequence: AACT 1.311 (7.522) 14.76 (4.02) Distance: AACT - 1.000 (7.084) - (5.31) Sequence: PB 1.000 (0.209) - (1.87) Distance: PB 1.182 (1.986) 7.17 (6.32) Sequence: BI 1.211 (9.623) 14.84 (5.08) Distance: BI 1.000 (10.23) - (5.89) Sequence: Overt 0.138 (0.358) 9.01 (3.17) Distance: Overt 1.000 (76.70) - (6.46) Structural Model: AACT ) BI 0.864 7.33 PB ) BI 1.666 2.97 BI ) B 1.429 8.50 AACT/PB Correlation 0.762 5.54 Error in Equation: BI 1.588 1.42 Error in Equation: B 9.130 1.66 Note: Values in parentheses are unique variances. Parameter estimates of 1.000 have been fixed to set the unit of measurement. Appendix F (Tables) 179 TABLE 46 SUMMARY OF REASONED ACTION MODELS Teat of Relevant Test of Theoretical Null Model Model Model X2 df X2 df A Figure 5 (RA-Original) 749.52 28 59.80 16 .92 Figure 11: RA-1 622.09 15 40.77 6 .94 Figure 11: RA-2 622.09 15 41.98 7 .93 RA-1 versus RA-2* 1.21 1 -.01 Figure 11: RA-3 775.61 28 50.48 14 .94 Figure 11: RA-4' 775.61 28 52.91 16 .93 RA-3 versus RA-4* 2.43 2 -.01 Note: "Indicates a comparison of nested models. Appendix F (Tables) 180 TABLE 47 CORRELATION MATRIX FROM FISHBEIN AND AJZEN (1974) SRA SDA GUB LIB GUC LIC THC Sslf-Report Affect — 800 451 582 519 652 584 SD Affect 765 591 688 644 762 685 Guttman Behavior 444 517 776 531 660 575 Likert Behavior 594 640 656 647 656 624 Guttman Cognition 688 773 501 656 790 744 Likert Cognition 743 837 563 727 878 785 Thurstone Cognition 672 666 483 649 818 786 Notes: Decimal points omitted. Above diagonal is self-reported behavior sample (n = 62); below diagonal is behavioral intention sample (n = 63). Appendix F (Tables) 181 TABLE 48 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS SELF-REPORTED BEHAVIOR SAMPLE (FISHBEIN & AJZEN, 1974) FACTOR ONE TWO THREE Semantic Differential Affect 0.243 0.617* 0.140 Self-Reported Affect 0.045 0.741* 0.113 Thuratone Behavior 0.028 0.305 0.667' Likert Behavior 0.242 0.020 0.677' Thurstone Cognition 0.753* 0.123 0.016 Likert Cognition 0.737* 0.219 0.011 Guttman Cognition 0.793* -0.059 0.152 Note: Largest loading in each row is starred. TABLE 49 INTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS SELF-REPORTED BEHAVIORS SAMPLE (FISHBEIN £• AJZEN, 1974) 0.67 0.64 0.69 Appendix F (Tables) 182 TABLE 50 OBLIQUE FACTOR PATTERN FROM EXPLORATORY FACTOR ANALYSIS BEHAVIORAL INTENTION SAMPLE (FISHBEIN 5. AJZEN, 1974) FACTOR ONE TWO THREE Semantic Differential Affect 0.797" 0.057 0.073 Self-Reported Affect 0.684* 0.086 0.093 Thurstone Behavior 0.007 0.758* 0.119 Likert Behavior 0.155 0.750* 0.017 Thurstone Cognition 0.192 0.160 0.580* Likert Cognition 0.507* 0.168 0.358 Guttman Cognition 0.290 0.082 0.625* Mote: Largest loading in each row is starred. TABLE 51 INTERFACTOR CORRELATIONS FROM EXPLORATORY FACTOR ANALYSIS SELF-REPORTED BEHAVIORS SAMPLE (FISHBEIN 6. AJZEN, 1974) 67 76 0.69 Appendix F (Tables) 183 TABLE 52 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 2 SELF-REPORTED BEHAVIORS SAMPLE (FISHBEIN & AJZEN, 1974) Parameter Estimate Standard Error Factor Loadings*: SD Affect 0.955 (0.088) 0.099 (0.057) Self-Report Affect 0.838 (0.298) 0.106 (0.068) Thurstone Behavior 0.901 (0.188) 0.104 (0.067) Likert Behavior 0.879 (0.227) 0.105 (0.069) Thurstone Cognition 0.853 (0.273) 0.105 (0.063) Likert Cognition 0.930 (0.135) 0.099 (0.048) Guttnan Cognition 0.849 (0.278) 0.105 (0.063) Interfactor Correlations: Affect/Behavior 0.840 0.056 Affect/Cognition 0.834 0.053 Behavior/Cognition 0.769 0.056 Motes; "Each measured variable loads only on its respective factor (affect, behavior, or cognition.) Values in parentheses are unique variances. Approximate T-values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 184 TABLE 53 LISREL ESTIMATES FOR UNKNOWN PARAMETERS OF FIGURE 2 BEHAVIORAL INTENTION SAMPLE (FISHBEIN 6, AJ2EN, 1974) Parameter Estimate Standard Error Factor Loadings*: S,D Affect 0.920 (0.154) 0.100 (0.057) Self-Report Affect 0.832 <0.308) 0.106 (0.069) Thurstone Behavior 0.869 (0.245) 0.105 (0.072) Likert Behavior 0.913 (0.167) 0.103 (0.070) Thurstone Cognition 0.840 (0.294) 0.103 (0.059) Likert Cognition 0.957 (0.084) 0.094 (0.030) Guttnan Cognition 0.920 (0.154) 0.097 (0.037) Interfactor Correlations: Affect/Behavior 0.750 0.074 Affect/Cognition 0.928 0.035 Behavior/Cognition 0.821 0.055 Notes: "Each measured variable loads only on its respective factor (affect, behavior, or cognition.) Values in parentheses are unique variances. Approximate T-values can be calculated by dividing an estimate by its standard error. Appendix F (Tables) 185 TABLE 54 SUMMARY OF LISREL MODELS FIT TO FISHBEIN AND AJZEK (1974) Model X2 df A Self-Reported Behaviors: Null 372.37 21 One-Factor 55.65 14 .851 Two-Factor 39.20 13 .895 Three-Factor 13.11* 11 .965 Behavioral Intentions: Null 421.17 21 One-Factor 40.13 14 .905 Two-Factor 18.03* 13 .957 Three-Fadtor 12.22* 11 .971 Note: *E. > -05 APPENDIX G FIGURES 186 Appendix G (Figures) 187 STIMULI Individuals; social issues; social groups; objects fATTITUDE] ( AFFECT ] (BEHAVIOR) [COGNITION] Sympathetic Overt Actions Perceptual Nervous Responses Responses Verbal Statements Verba] Statements Verbal Statements of Affect About Behavior of Belief Self is Referent Self/Object Object is Referent Interaction Figure 1. The tripartite model of attitude (after Rosenberg & Hovland, 1960). Appendix G (Figures) 188 / 1 \ E7 E8 Eg Figure 2. Structural equation representation o£ the.tripartite model of attitude. Appendix G (Figures) 189 Figure 3. Stuctural equation model for evaluating a multitrait- nultinethod correlation matrix. (Correlated errors are shown for only one set of similarly-measured variables.) Appendix G (Figures) 190 Home cage for snakes Projection Table Screen <- 0 + HR Monitor # -> 1 -» ===l i -» l=== n -> Snake e -i Subject's Seat Projecto r t- 50 * Collapsible Partition l( 6 ra )l Figure 4. Schematic diagram of experimental room (not drawn to scale). Appendix G (Figures) 191 Figure 5. Model RA-Original: Fishbein and Ajzen's (1975) reasoned action model of attitude (AACT is attitude toward the act, SN is subjective norm, BI is behavioral intention, and B is behavior). Figure 6. Modification of the reasoned action model (after Bentler & Speckart, 1979). CPB is past behavior] Appendix G (Figures) 192 Session 1 Session 2 0 (AACTJ © Figure 7. Six single-factor behavioral prediction models. Appendix G (Figures) 193 Sequence: BI Distance: BI Sequence: AACT Distance: AACT Sequence: PB Distance: PB Sequence: Overt Distance: Overt Figure 8. Behavior-related variables as measures of a single latent variable (Model BI). Sequence: BI Distance: BI Sequence: AACT Distance: AACT Sequence: PB Distance: PB Sequence: Overt •£ Distance: Overt Figure 9. Behavior-related variables divided into behavioral predictors and overt behavior (Model B2). Appendix G (Figures) 194 Sequence: BI Distance: BI Sequence: AACT Distance: AACT Sequence: PB Distance: PB -t Sequence: Overt Distance: Overt <• Figure 10. Behavior-related variables as measures of four distinct constructs (Model B3). Appendix G (Figures) 195 MODEL RA-l: ( AACT J- -0. MODEL RA-3: MODEL RA-4: ( AACT ). 1 0. 0- Figure 11. Modified reasoned action models. Appendix G (Figures) 196 AACT: Distance Distance AACT: Sequence BI: Distance Sequence BI: Sequence Figure 12. Model BP-A: A simplified behavioral-prediction model. AACT:D Sequence AACT:D AACT:S AACT:S BUD BI:D BI:S BI:S Figure 13. Model BP-A-BP: Modeling the effects of direct experience.