THE COGNITIVE ORGANIZATION OF BELIEFS ABOUT BEHAVIORS

THE COGNITIVE ORGANIZATION OF BELIEFS ABOUT BEHAVIORS

BY

ANNE BC DURAN

A dissertation submitted to the Graduate School in partial fulfillment of the requirements for the degree

Doctor of Philosophy

Major Subject: Psychology

New Mexico State University

Las Cruces, New Mexico

December 2003 i

“The Cognitive Organization of Beliefs About Behaviors,” a dissertation prepared by

Anne Duran, in partial fulfillment of the requirements for the degree, Doctor of

Philosophy, has been approved and accepted by the following:

Linda Lacey

Dean of the Graduate School

David Trafimow

Chair of the Examining Committee

Date

Committee in charge:

Dr. David Trafimow, Chair

Dr. W. Larry Gregory

Dr. Laura J. Madson

Dr. Cookie Stephan

Dr. Walter G. Stephan ii

ACKNOWLEDGEMENTS

Thank you to my parents, Jan and Roy Fausnaugh, for their continued and consistent support. Thank you for all of your emergency trips and all of your supportive words.

Thank you to Walter Stephan. You know how they say “without whom this would not have been possible”? I wish I could communicate to you how much your support has meant to me, especially during those lowest of low times. Thanks. You are my hero.

Thanks to Lausanne Renfro-Fernandez—without whom this would not have been possible! iii

VITA

January 10, 1961

1979

1995

1998

2001 - Present

Born in Russell, Kansas

Graduated from Teen Mothers High School,

Arvada, Colorado

Bachelor Degree in Psychology,

Metropolitan State College of Denver,

Magna Cum Laude

Master of Arts Degree in Psychology,


Lecturer, California State University, Bakersfield

Professional and Honorary Societies

Society for Personality and Social Psychology

Western Psychological Association

American Psychological Society

American Psychological Association, Div. 2, Society for the Teaching of Psychology

Publications

Duran, A., & Trafimow, D. (2000). Cognitive organization of favorable and unfavorable beliefs about performing a behavior. Journal of Social

Psychology, 140, 179-187.

Gregory, W.L., & Duran, A. (2000). Scenarios and Acceptance of Forecasts. In J.S.

Armstrong (Ed.), Principles of Forecasting: A Handbook for Researchers and

Practitioners, Kluwer Academic Press, New York, NY.

Renfro, C.L., Duran, A., & Stephan, W.G., & Clason, D. L. The role of threat in attitudes toward affirmative action and its beneficiaries. Manuscript submitted for publication. iv

Stephan, W.G., Diaz-Loving, R., & Duran, A. (2000). Integrated threat theory and intercultural attitudes: Mexico and the United States. Journal of Cross-

Cultural Psychology, 31, 240-249.

Trafimow, D., & Duran, A. (1998). Some tests of the distinction between attitude and perceived behavioral control. British Journal of Social Psychology, 37,

1-14.

Duran, A. (1998). The Effect of Trait Type on Recall. Unpublished master's thesis, New Mexico State University, Las Cruces.

Presentations

Allen, T., Cole, D., & Duran, A. (2002, April). Comparing measures of attitudes toward outgroup members. Poster session presented at the meeting for the

Western Psychological Association, Irvine, CA.

Duran, A., Finlay, K., Stephan, W.G., & Trafimow, D. (2000, April, June). The

Relationship Between Prejudice and Discrimination. Paper presented at the meeting for the Graduate Symposium, Las Cruces, NM (May). Poster session presented at the Rocky Mountain Psychological Association

(RMPA), Tucson, AZ (April) and the American Psychological Society,

Miami, FL (June).

Duran, A., & Madson, L. (2000, June). Teaching the Psychology of Sexual

Orientation. Poster session presented at the meeting for the Rocky Mountain

Psychological Association, Tucson, AZ (April) and the American

Psychological Society Institute on the Teaching of Psychology, Miami, FL.

* 1st place winner, CTUP/STP Teaching Issues Competition

Duran, A., & Stephan, W.G. (1999, June). Perceptions of Threat: Predicting

Attitudes Toward Affirmative Action and its Beneficiaries. Poster presented at the Graduate Symposium, Las Cruces, NM (May). Poster session presented at the American Psychological Society, Denver, CO.

* Winner of the Sigma Xi Best Poster Research Award

Duran, A., Waller, M., & Madson, L. (1999, June). Perceptions of Sexual

Orientation: A Hierarchically Restrictive Trait. In K. T. Schneider and P.

Rhadakrishanan (Co-Chairs), Theoretical perspectives on attitudes toward and perceptions of lesbians and gay men. Symposium conducted at the meeting of the American Psychological Society, Denver, CO (June). v

Duran, A., & Trafimow, D. (1998, April). Attitude and Perceived Behavioral

Control: Is There a Difference? Poster presented at the Western

Psychological Association, Albuquerque, NM.

Duran, A. (1998, April). The Effect of Trait Type on Recall. Paper presented at the meeting for the Dialogue between Aggies and Miners, El Paso, TX, and the

Graduate Research and Arts Symposium, Las Cruces, NM.

Duran, A., & Trafimow, D. (1997, April). Some Tests of the Distinction Between

Attitude and Perceived Behavioral Control. Paper presented at the Dialogue between Aggies and Miners, Las Cruces, NM.

Duran, A., & Trafimow, D. (1996, April). Cognitive Organization of Positive and

Negative Beliefs. Paper presented at the Dialogue between Aggies and

Miners, El Paso, TX.

Teaching Experience

California State University, Bakersfield August 2001-present

New Mexico State University, Las Cruces Fall 1998–Spring 2000

Field of Study

Major field: Psychology vi

ABSTRACT


BY

ANNE BC DURAN, M.A.

Doctor of Philosophy


Las Cruces, New Mexico, 2003

Dr. David Trafimow, Chair

According to the theory of reasoned action, the best predictor of whether a person will perform a behavior is the person’s intentions to perform that behavior.

Researchers have found that when forming intentions to perform a behavior, people access their beliefs in clusters: beliefs that are similar to each other are grouped together. One category of beliefs is affective and cognitive; another is for and against the behavior. Affective and cognitive clustering has consistently been demonstrated, but for and against clustering is less consistent. This set of studies tests four predictions regarding the occurrence of for and against clustering. The first study examined whether the specificity of the behavior was influential in the clustering effects. The second study tested whether salience of different selves was a factor. The third study explored whether the level of morality associated with different behaviors was a factor, and the fourth study investigated the direction of the vii

behavior. Within the four studies, consistent patterns of for and against clustering were not found; however, clustering of affective and cognitive tended to occur.

Possible explanations for the inconsistent results are offered. viii

TABLE OF CONTENTS

Page

LIST OF TABLES ............................................................................................ xii

THE COGNITIVE ORGANIZATION OF BELIEFS

ABOUT BEHAVIORS .........................................................................

Cognitive Organization of Beliefs ....................................................

1

6

The Replication Experiment ............................................................. 12

EXPERIMENT ONE: GENERAL V. SPECIFIC BEHAVIORS .................... 15

Experiment One: Method ................................................................. 17

Pilot Study .................................................................................. 17

Materials and Methods ............................................................... 18

Experiment One: Results .................................................................. 22

For and Against Clustering......................................................... 22

Affective and Cognitive Clustering ............................................ 24

Pleasantness Ratings................................................................... 27

Experiment One: Discussion ............................................................ 27

EXPERIMENT TWO: THE SALIENCE OF THE INGROUP ....................... 32

Experiment Two: Materials and Method.......................................... 36

Experiment Two: Results ................................................................. 37



Proportions of Beliefs ................................................................. 43 ix

Ratings of Beliefs ....................................................................... 44

Sexual Activity ........................................................................... 45

Experiment Two: Discussion ........................................................... 45

EXPERIMENT THREE: THE MORALITY ISSUE ....................................... 50

Experiment Three: Method .............................................................. 54

Pilot Study .................................................................................. 54

Materials and Methods ............................................................... 58

Experiment Three: Results ............................................................... 58



Proportions of Beliefs ................................................................. 64

Experiment Three: Discussion ......................................................... 65

EXPERIMENT FOUR: FRAMING EFFECTS ............................................... 69

Experiment Four: Materials and Method ......................................... 71

Experiment Four: Results ................................................................. 71



Sexual Activity ........................................................................... 74

Experiment Four: Discussion ........................................................... 76

GENERAL DISCUSSION ............................................................................... 78

Individual Differences ...................................................................... 84

Implications and Applications .......................................................... 87 x

CONCLUSION ............................................................................................. 88

REFERENCES ................................................................................................. 89 xi

LIST OF TABLES

Table Page

1. Pleasantness Ratings for General and Specific Behaviors .................... 19

2. Clustering of For and Against Beliefs, General and

Specific Behaviors ............................................................................ 23

3. Clustering of Affective and Cognitive Beliefs, General

and Specific Behaviors ..................................................................... 26

4. Clustering of For and Against Beliefs After Priming ........................... 39

5. Clustering of Affective and Cognitive Beliefs After Priming .............. 42

6. Morality Ratings and T-Tests for Matched Behaviors ......................... 56

7. Clustering of For and Against Beliefs, Non-Moral and

Moral Behaviors ............................................................................... 60

8. Clustering of Affective and Cognitive Beliefs, Non-Moral and

Moral Behaviors ............................................................................... 62

9. Clustering of For and Against Beliefs, Condom Use and

Protected Sex Behaviors ................................................................... 72

10. Clustering of Affective and Cognitive Beliefs, Condom Use and

Protected Sex Behaviors ................................................................... 75

11. Summary of For/Against Clustering and Affective/Cognitive

Clustering Across All Studies .......................................................... 81 xii


The theory of reasoned action is used to predict and explain behaviors (Ajzen

& Fishbein, 1980; Fishbein, 1979; Fishbein & Ajzen, 1975). According to this theory, the best predictor of whether a person will perform a behavior is the person’s intentions to perform that behavior. Intentions to perform the behavior are influenced by two conceptually independent factors: subjective norms and attitudes about the behavior. Subjective norms (SN) are determined by beliefs about what important others think the person should do about the behavior, or normative beliefs

(n), and the person’s motivation to comply with those beliefs (m). If the person believes that most of their important others think that person should perform the behavior, and if the person cares what those others think, this perceived social pressure will influence behavioral intentions in favor of performing the behavior:

SN

  n i m i

Attitudes (A) are determined by the person’s beliefs about the consequences of the behavior, or behavioral beliefs (b), and the evaluation of these consequences

(e). If the benefits of the behavior outweigh the costs, the attitude regarding the behavior will be generally favorable, and this positive attitude will influence behavioral intentions in favor of performing the behavior:

A

  b i e i

Researchers who examine the theory of reasoned action typically use multiple regression analyses to determine the relative contribution of subjective norms and attitudes on different behavioral intentions. For some behaviors, the beta

1

weight for the subjective norm is higher than the beta weight for the attitudes, indicating that the relative contribution of the subjective norm component is larger than the attitude component. These behaviors are said to be under normative control

(NC). For other behaviors, the attitude beta weight is larger than the subjective norm beta weight, indicating that the relative contribution of the attitude component is larger than the subjective norm component. These behaviors are said to be under attitudinal control (AC). Finlay, Trafimow, and Jones (1997) and Finlay, Trafimow, and Moroi (1999) found that many health-related behaviors tend to be NC; for these behaviors, people’s beliefs about what most of their important others think they should do have substantial influence on their intentions. Trafimow and Finlay

(2001) found that attitudes had larger beta weights than subjective norms across thirty different behaviors; for these behaviors, the consequences of the behavior tended to be particularly influential on their intentions.

While some behaviors tend to be NC and some tend to be AC, the relative influence of subjective norms and attitudes on intentions can change, depending on the situation and on the person. Priming is an example of a situational influence.

Ybarra and Trafimow (1998) found that primes can influence the relative beta weights of subjective norms and attitudes. Using private- and collective-self primes, they found that people exposed to the private-self prime placed more weight on attitudes than subjective norms in forming their intentions, and people exposed to the collective-self prime placed more weight on subjective norms than attitudes in forming their intentions. The level of perceived risk in a given situation can also

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influence whether a behavior is NC or AC: Trafimow and Fishbein (1994) and

Stasson and Fishbein (1990) found that in low-risk situations, intentions to wear seat belts are AC; in high-risk situations, intentions to wear seat belts are NC.

Additionally, there is evidence that individuals also may be more or less AC or NC.

Trafimow and Finlay (1996, 2001) and Finlay et al. (1999) found that most individuals are under AC: the correlation between attitudes and intentions is higher than the correlation between subjective norms and intentions. However, a significant minority of participants (18% to 34%) was under NC. For NC participants, the correlation between subjective norms and intentions was higher than the correlation between attitudes and intentions.

One area of research involving the theory of reasoned action is directed toward finding other beliefs, besides normative and behavioral beliefs, which influence intentions. For example, Azjen’s theory of planned behavior (1988) expanded the theory of reasoned action by including control beliefs — beliefs regarding whether performing a behavior is under the actor’s volitional control.

Control beliefs influence perceived behavioral control, which influences intentions to perform some behaviors. Other factors, such as beliefs about morals (Gorsuch &

Ortberg, 1983; Manstead, 2000), affect (Triandis, 1980), confidence in normative beliefs (Trafimow, 1994), expected affect after performing the behavior (Richard, van der Pligt, & de Vries, 1996), perceived difficulty (Trafimow, Sheeran, Conner,

& Finlay, 2002), affective and cognitive properties (Breckler, 1984; Breckler &

Wiggins, 1989; Crites, Fabrigar, & Petty, 1994; Trafimow, Sheeran, Lombardo,

3

Finlay, & Brown, in press) and habit (Trafimow, 2000) have also been found to influence behavioral intentions.

Another major area of research relating to the theory of reasoned action is directed toward determining how well subjective norms and attitudes predict or explain behavioral intentions. There have been several meta-analyses on the predictive ability of the theory of reasoned action or its related expansion, the theory of planned behavior (e.g., Albarracin, Johnson, Fishbein, & Muellerleile, 2001;

Hausenblas, Carron, & Mack, 1997; Sheeran & Taylor, 1999; Sheppard, Hartwick, &

Warshaw, 1988; Sutton, 1998). The findings in this research indicate that subjective norms, attitudes, and perceived behavioral control are good proximal predictors of behavioral intentions, and behavioral intentions are good proximal predictors of behaviors. These components explain on average between 40% and 50% of the variance in intentions, and between 19% and 38% of the variance in behavior

(Sutton, 1998). The explained variance is higher when the measures of attitudes, subjective norms, and perceived behavioral control correspond to the behavior (or the behavioral intentions) regarding the target, time, context, and object (Ajzen &

Fishbein, 1977; Ajzen, 1991).

Some research on the theory of reasoned action combines both the identification of different belief types and the ability of different belief types to predict intentions. In this area, researchers work to distinguish whether different belief types are distinct from each other. For example, Trafimow and Fishbein

(1995) found that people distinguish between behavioral and normative beliefs, and

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Trafimow and Duran (1998) found that people distinguish between behavioral beliefs and control beliefs. Trafimow, Finlay, Sheeran, and Conner (2002) found that perceived behavioral control is a function of the perception of control and the perception of difficulty. They also found that control beliefs and difficulty beliefs are distinct, and each of these belief types can be independently manipulated to predict behavioral intentions. The results of these studies indicate that there are essential differences between different belief types, and different belief types may make different predictions of behavioral intentions. Understanding differences between belief types can increase the predictive ability of the theory of reasoned action.

Less attention has been directed to research regarding the manner in which different beliefs are organized and accessed to form attitudes and subjective norms.

To better understand and predict behavior, it is important to begin with an understanding of how people cognitively organize and access their beliefs about behaviors. In previous research (e.g., Ybarra & Trafimow, 1998), the relative weight of attitudes and subjective norms was changed after exposure to different primes.

Because attitudes and subjective norms are determined by behavioral and normative beliefs, respectively, it is possible that the primes activated different beliefs, and it was the accessing of these beliefs that influenced intentions. If the beliefs that are accessed influence intentions, it is important to investigate how these beliefs are accessed. Are there heuristics that guide which beliefs will be accessed for a particular behavior, in a particular situation? Or are beliefs randomly accessed?

5

According to the theory of reasoned action and related research, there are several different types of beliefs that influence intentions to behave. Investigating how these beliefs are organized can be a complex matter. The first step is to narrow the focus of attention. Generally, attitudes are the most important predictors of behavioral intentions (Ajzen & Fishbein, 1980; Finlay et al., 1999; Fishbein &

Ajzen, 1975; Miniard & Cohen, 1981; Trafimow, 2000; Trafimow & Finlay, 1996;

Trafimow & Finlay, 2001). The beliefs that influence attitudes, behavioral beliefs, can be classified in many different ways. For example, a behavioral belief can be either affective or cognitive (Crites et al., 1994; Trafimow & Sheeran, 1998) or for or against the behavior (Duran & Trafimow, 2000). Because attitudes tend to be the most important predictors of intentions, and because behavioral beliefs can be classified in many different ways, it would be unmanageable to examine all beliefs that influence intentions (e.g., normative, control, difficulty). The current research will be limited to two subsets of behavioral beliefs: affective/cognitive and for/against.

Cognitive Organization of Beliefs

The theory of reasoned action implies that when people make a decision about a behavior, they do so after thoroughly weighing the pros and cons of beliefs about that behavior (Thogersen, 1996). However, people have limited cognitive resources, and may not be able to consider a large set of beliefs when determining an attitude (Fishchoff, Goitein, & Shapira, 1982). It seems unlikely that all of the pros and cons, or costs and benefits, of a particular behavior are accessed each time a

6

behavioral decision is made. Situational influences may affect the accessibility of different beliefs, and as a result, which beliefs are used to form behavioral intentions.

For example, imagine a student is considering the behavior “turning on the air conditioning during studying.” The student might access beliefs such as “it will help me relax” and “it will keep me cool.” Imagine the same student considering the same behavior, but on the day that the electricity bill arrived. The belief that

“electricity is expensive” may become salient, and may then be used to form attitudes and behavioral intentions.

If behavioral intentions are based on a subset of relevant beliefs that are accessed, rather than all of the potentially relevant beliefs about the behavior, then changing the beliefs that are accessed might also change behavioral intentions. The assumption that accessing different beliefs may influence intentions has not been directly tested; however, there is indirect supporting evidence. Although differences in intentions due to accessing different beliefs was not a key issue in their study,

Ybarra and Trafimow (1998) found that priming different selves altered the weights given to attitudes and subjective norms. Additionally, Verplanken and Holland

(2002) found that when values related to the environment were activated, behavioral intentions for environmentally friendly behaviors increased. Because specific beliefs about behaviors were not directly measured in these studies, the explanation for the differences between the relative influence of different belief types on intentions is not clear. One possibility is that the primes increased the accessibility of one type of beliefs (e.g., behavioral or normative) about the behavior, and the increased

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accessibility of these beliefs increased the influence of their corresponding component (e.g., attitudes or subjective norms) on behavioral intentions. A second possibility is that, when primes are used, the same beliefs are accessed, but the prime motivates people to place more relative weight on either attitudes or subjective norms. A third possibility is that the use of primes changed the order in which relevant beliefs were accessed, and the different order of beliefs influences intentions. For example, if people access groups of similar beliefs together, the ease with which clusters are formed may influence their perception of the importance of the issue, which may then influence their intentions to behave. These possibilities imply that the ways in which beliefs are accessed can influence behavioral intentions.

If accessing different beliefs influences behavioral intentions, then it is important to understand how this accessing of beliefs occurs. Perhaps when people form intentions regarding a behavior, they randomly access accessible beliefs and compare each belief to the others until some limit is reached and a decision is made.

However, the social world is complex, and demands attention, and the cognitive resources available to individuals are limited. Finding meaning in randomly accessed beliefs would take time and cognitive resources. It would be more efficient to access beliefs according to some organizational heuristic.

One organizational heuristic is to access several relevant beliefs which are similar to each other on some dimension, form an aggregate on that dimension, and then access several beliefs which are similar to each other on a different dimension

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and form an aggregate on that dimension. The two aggregates could then be compared. For example, a person could access several beliefs in favor of performing the behavior, and several beliefs against performing the behavior, and compare these aggregates to form their intentions. Another relevant dimension is whether the belief is affective or cognitive: a person could form their intentions by accessing several affective beliefs and several cognitive beliefs, and compare these aggregates to each other. By using aggregates rather than individual beliefs, people would not need to hold all of the relevant beliefs about a behavior in working memory to form their intentions. They would not need to compare each relevant belief against each other belief. Instead, only the aggregate judgments need to be simultaneously held in working memory and compared.

If people do form aggregate judgments and compare them in order to form a behavioral intention, then similar beliefs would be considered in conjunction with each other. After accessing a relevant belief of one type, people should access another belief of that same type. As they compare beliefs of the same type, associative links should be formed between the similar beliefs. Each time the beliefs are compared, the links between the beliefs are reinforced. To form a general “for” concept, people need to compare for beliefs with other for beliefs. Analogously, to form a general “against” concept, people compare against beliefs with other against beliefs. As they do so, links between for beliefs and between against beliefs are reinforced. At the same time, there is not much reason to form or reinforce

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associative links between beliefs on one dimension and beliefs on another dimension.

These associative links between beliefs can be used as retrieval routes when people are asked to retrieve their beliefs. After accessing a belief of one type, people may be more likely to traverse the associative pathways to access other beliefs of the same type. If beliefs are linked to similar beliefs and not linked to dissimilar beliefs, people would be unlikely to access a belief of a different type. Thus, if people are asked to list their beliefs about a behavior, beliefs that are similar on some dimension should tend to be listed together, and beliefs that are similar on another dimension should be listed together; there should be clusters of beliefs that are similar on some dimension.

Trafimow and Sheeran (1998) used this associative network approach and found that participants clustered their behavioral beliefs according to whether the beliefs were affective or cognitive. For example, in one of their studies, participants were asked to list six beliefs they had about “having unprotected sex next weekend.”

Each of the beliefs were coded as either affective or cognitive. Throughout this set of studies, participants tended to list their beliefs in affective or cognitive clusters.

Duran and Trafimow (2000) also used the associative network approach to examine the organization of behavioral beliefs about condom use and about a novel behavior (asking the experimenter for candy). They found that when participants listed behavioral beliefs, these beliefs were organized into clusters of “for” and

“against.” For example, with regard to condom use, beliefs that were consistent with

10

the use of condoms (e.g., avoid pregnancy, avoid sexually transmitted diseases) tended to be grouped together, and beliefs that were consistent with not using a condom (e.g., breaks the mood, is expensive) tended to be grouped together. When participants were asked to list beliefs about a novel behavior (asking the experimenter for candy), the pattern was repeated: beliefs were listed in for/against clusters. The implication of this set of studies is that when people are forming their intentions to perform a behavior, they tend to access “for” behavioral beliefs together and form an overall “for” concept, and access “against” behavioral beliefs together to form an overall “against” concept.

The for and against clustering that was demonstrated in the Duran and

Trafimow (2000) studies is not always present. In one of the Trafimow and Sheeran

(1998) studies (experiment 5), statements about “having unprotected sex” were coded as for or against. They found that no clustering effects were present. In two of the Duran and Trafimow (2000) studies, statements about “using a condom” did show for and against clustering. Because these two behaviors—“having unprotected sex” and “using a condom”—seem quite similar, they should have had similar patterns of clustering. However, the for and against clustering was only present in the “using a condom” condition.

It is possible that the findings of the “having unprotected sex” and “using a condom” studies were anomalous, and there is no difference in how people access their beliefs for both of these behaviors. To test whether the results of these studies were anomalous, a replication was completed using both of these conditions.

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The Replication Experiment

In the replication, participants (N = 45) were asked to list eight beliefs about

“you or your partner using a condom the next time you have sex” or “your having unprotected sex next weekend.” Beliefs were coded according to two dimensions.

First, beliefs were coded as either for or against the behavior. Then beliefs were coded as either affective or cognitive. These ratings were analyzed using the

Adjusted Ratio of Clustering (ARC) index, proposed by Roenker, Thompson, and

Brown (1971) and recommended by Srull (1984). The ARC index is computed using the formula:

ARC = [R – E(R)] / [maxR – E(R)], where R equals the total number of observed category repetitions, maxR equals the maximum possible category repetitions, and E(R) equals the expected (chance) number of category repetitions. The formula for maxR is N – k, where N is the total number of items recalled, and k is the number of categories in the protocol. The equation to find E(R) is (

 n i

2

/N) – 1, where n is the number of items recalled from category i, and N is the total number of items recalled. An ARC index of 1 indicates perfect clustering: that is, all beliefs of one type are listed together, and all beliefs of another type are listed together. A score of 0 indicates chance clustering. A negative score indicates less than chance clustering; that is, a belief of one type tends to be followed by a belief of another type.

In this replication study, participants in the “using a condom” condition (N =

20) wrote their beliefs in clusters of for and against (mean ARC = .27, t = 2.7, p <

12

.02), but participants in the “having unprotected sex” condition (N = 25) did not demonstrate the for and against clustering (mean ARC = .04, t = .70, p = .49). When statements were coded as affective or cognitive, clustering occurred for both the

“using a condom” (mean ARC = .30, t = 2.6, p < .02) and the “having unprotected sex” (mean ARC = .23, t = 2.7, p < .02) conditions.

The replication experiment produced two of the same findings as the previous studies. First, affective and cognitive clustering occurred across conditions. People access groups of affective beliefs together, and groups of cognitive beliefs together.

This affective/cognitive clustering occurred across both conditions, whether or not the for/against clustering was significant.

The second finding that was replicated in this study was that, in some cases, beliefs about a behavior clustered according to whether they were for or against performing the behavior. In other cases, for and against beliefs did not cluster.

These mixed findings between two seemingly similar conditions are certainly puzzling. Why would people sometimes access beliefs according to whether they are for and against, but not always? What is the difference between “having unprotected sex” and “using a condom” that changes how people access their beliefs?

The goal of this research was to test possible explanations for the mixed findings of for/against clustering. The first experiment was based on Fishbein’s

(1979) articulation of the theory of reasoned action, and his proposal that people think about general behaviors differently from specific behaviors. The second study tested whether salience of different selves was a factor. The third study explored

13

whether the level of morality associated with different behaviors was a factor, and the fourth study investigated the direction of the behavior.

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EXPERIMENT ONE: GENERAL V. SPECIFIC BEHAVIORS

Fishbein (1979) points out that there are differences in how people think about general and specific behaviors. General behaviors, such as dieting or auto maintenance, are not behaviors per se; instead, they are behavioral categories. Many behaviors may be included in dieting, such as eating healthy meals, exercising daily, and not eating high calorie foods. A behavioral category cannot be observed; instead, the category is inferred from specific behaviors. For example, after seeing a person take a diet pill, the inference may be made that that person is dieting.

One problem that can occur when investigators ask about general behaviors

(or behavioral categories) rather than specific behaviors is that participants may think about many behaviors within that category, rather than just one behavior. “Having unprotected sex” is a behavioral category, rather than a specific behavior. When people consider this category, their beliefs might be about several specific behaviors within that category, such as not using a condom, not knowing about a partner’s sexual history, going against societal expectations, and not using birth control.

When a person is asked about “having unprotected sex,” they may list the highest cost and the highest benefit for one behavior within the category, then the highest cost and the highest benefit for another behavior within the category, and so on.

Alternatively, they may list the most salient belief about each behavior within the category. In either case, clustering of for and against beliefs is unlikely to occur.

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In contrast, when people consider performing a specific behavior, they are likely to access only beliefs that are associated with that behavior. For example, in considering “using a condom,” beliefs specifically about using a condom, such as

“the rubber smells funny” or “it helps stop pregnancy,” should be accessed. To form intentions regarding a specific behavior, it would be logical to weigh the pros and cons regarding that behavior in order to determine the most beneficial – or least detrimental – course of behavior. In this case, it would be cognitively efficient to form an aggregate judgment based on the relevant beliefs in favor of performing the behavior and another aggregate judgment based on the accessible beliefs against performing the behavior, and then compare the two aggregates. If people perceive

“using a condom” as a specific behavior, this would explain the findings of the replication study, in which participants clustered their beliefs in clusters of for and against.

In the replication study, it is possible that participants perceived “having unsafe sex” as a general behavior, and “using a condom” as a specific behavior. If the difference between the responses for these two behaviors is because one behavior was general and one specific, then when participants are asked about other general and specific behaviors, the same patterns should result. That is, for and against clustering should occur in specific behaviors, but not for general behaviors.

Additionally, because the clustering of affective and cognitive beliefs is a robust finding, it should occur for both general and specific behaviors.

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Experiment One: Method

Pilot Study

A possible confound regarding general and specific behaviors is the perceived pleasantness of each type of behavior. It is possible that general behaviors are perceived as more pleasant than specific behaviors, or vice versa. If this is true, the different clustering effects may be due to perceived pleasantness rather than the specificity of behaviors. For example, perhaps “using a condom” is perceived as pleasant, while “having unprotected sex” in general is perceived as unpleasant.

Pleasant behaviors may motivate people to access beliefs in favor of performing the behavior together, because these “for” beliefs are easily accessible. If “against” beliefs are accessed, they should be accessed together, after accessing “for” beliefs.

On the other hand, unpleasant behaviors may motivate people to randomly access their beliefs; people may alternate accessing beliefs about the unpleasantness of the behavior with beliefs about the positive consequences of the behavior. Because the pleasantness of the behavior may influence the accessing of beliefs, it is important to be aware of the pleasantness of different behaviors. A pilot study assessed the pleasantness of general and specific behaviors. For the pilot study, eight general behaviors and 20 related specific behaviors were generated. Because some of the specific behaviors were very closely related (e.g., “attending class every day this week” and “attending class most of the time”), two questionnaires were created; behaviors that were quite similar appeared on either one or the other, but not both.

Behaviors replicating the previous literature and the pilot study were also included;

17

one version included “using a condom” and “having unprotected sex”; the other included “not using a condom” and “having protected sex.”

Forty-four participants completed the questionnaires by rating each of the behaviors on a seven-point pleasantness scale, with 0 = “Extremely Pleasant” and 6

= “Extremely Unpleasant” (see Table 1 for the complete list of behaviors and their corresponding pleasantness means). Overall, general behaviors (M = .90, SD = .59) were rated as significantly more pleasant than specific behaviors (M = 2.28, SD =

.65), t (43) = -17.73, p < .01.

Materials and Methods

Six pairs of behaviors (one general, one corresponding specific) were included from the pilot study: maintaining your car/washing your car this week; attending a sporting event/going to an Aggies football game on Saturday afternoon

(the Aggies is the football team for the college at which the data were collected); performing a charitable behavior/donating money to the Salvation Army bellringers at Christmas; getting good grades/taking notes in class; keeping your house or apartment clean/washing your dishes immediately after dinner tonight; and living a healthy lifestyle/taking the stairs instead of the elevator.

Two of the general behaviors were not included in the study. “Being honest” may have presented a confound: it is generous, but it is also a moral behavior (see

Experiment Three). “Eating healthy” was dropped because it is quite similar to another general behavior, “living a healthy lifestyle.”

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Table 1

Pleasantness Ratings for General and Specific Behaviors

____________________________________________________________________

Behavior Type N Mean SD

____________________________________________________________________

Maintaining your car

Washing your car this week

General

Specific

44

44

1.73

2.25

1.34

1.33

Getting a tune-up on your car every six months Specific 44 2.52 1.15

Having your oil changed in your car

Getting good grades

Specific 44 2.23 1.12

General 44 0.18 0.54

Taking notes in class

Studying for 3 hours tonight

Attending class every day this week

Specific 44 2.59 1.09

Specific 44 3.41 1.60

Specific 20 1.80 1.24

Attending class most of the time

Attending a sporting event

Specific 24 1.21 1.32

General 44 1.59 1.44

Going to an Aggies football game on

Saturday afternoon

Being honest

Specific 44 1.91 1.83

General 44 0.66 0.89

Telling the instructor that points were added wrong and you should receive a higher grade Specific 20 2.10 1.52

Telling the instructor that points were added wrong and you should receive a lower grade Specific 24 3.75 1.96

A cashier gives you too much change and you keep it

A cashier gives you too much change and you return it

Specific

Specific

20

24

3.55

2.00

1.93

1.50

19

Table 1 (Continued)

____________________________________________________________________

Behavior Type N Mean SD

____________________________________________________________________

Keeping your house or apartment clean General 44 1.30 1.30

Washing your dishes immediately after dinner tonight

Taking out your trash when it gets full

Specific 44 2.68 1.60

Specific 44 2.34 1.43

Living a healthy lifestyle General 44 0.82 0.97

Exercising for 30 minutes 3 times this week Specific 44 1.45 1.61

Taking the stairs instead of the elevator Specific 44 2.48 1.68

General 44 0.98 1.09 Eating healthy

Eating eight servings of fruits or vegetables every day

Performing a charitable behavior

Specific

General

44

44

2.32

0.89

1.44

0.99

Donating money to the Salvation Army bellringers at Christmas

Donating blood to United Blood Services within the next 2 weeks

Donating your old clothes to the Association for Retarded Citizens

Using a condom

Specific 44 1.48 1.42

Specific 44 3.05 1.79

Specific 44 1.20 1.11

20 2.40 1.54

Not using a condom 24 4.17 2.24

Having protected sex

Having unprotected sex

20

24

4.05

1.33

Note . Pleasantness values range from 0, Extremely Pleasant, to 6, Extremely

Unpleasant.

20

2.01

1.74

The specific behaviors relating to each of the general behaviors were chosen to obtain a range of differences in pleasantness ratings. Including a range of pleasantness differences between the general and specific behaviors would make it possible to examine whether pleasantness did influence the accessing of behaviors.

“Attending a sporting event” was rated as almost as pleasant as “going to an Aggies football game on Saturday afternoon” (M = 1.59 and 1.91, respectively). On the other extreme, “getting good grades” was rated as much more pleasant than “taking notes in class” (M = .18 and 2.59).

Packets for Experiment One consisted of three behaviors, each on a separate page. Each packet included two beliefs of one type (specific or general) and one of the other. General behaviors were always followed by specific, and specific behaviors were always followed by general. Therefore, each participant completed either two general behaviors and one specific, or one general and two specific.

Additionally, within each questionnaire, the three behaviors were not related. For example, one participant might have been asked to respond to “maintaining your car”

(a general behavior), “donating money to the Salvation Army bellringers at

Christmas” (a specific behavior), and “living a healthy lifestyle” (a general behavior). Another participant might have been asked to respond to “going to an

Aggies football game on Saturday afternoon” (a specific behavior), “getting good grades” (a general behavior), and “washing your dishes immediately after dinner tonight” (a specific behavior). The twelve possible behaviors were ordered according to a Latin square, so that each behavior occurred in each of the three

21

positions (first, second, and third). Counterbalancing general and specific behaviors was used to control for order effects.

For each behavior, participants were asked to “write down your beliefs about your [behavior]. Write them down in the order that you think of them.” The participants were instructed not to turn back to previous pages, and they were allowed to complete the packet at their own rate. A total of 243 participants volunteered to complete the study. An average of 60 participants responded to each behavior.

Experiment One: Results

Each of the statements was coded on two dimensions: for or against performing the behavior, and affective or cognitive. Two people coded the statements, with 89% agreement. There were no significant differences between behaviors based on the order in which they appeared in the packet. The adjusted ratio of clustering (ARC) index was used to test for the clustering of beliefs.

For and Against Clustering

Because the goal of this set of studies was to determine whether people cluster their different types of beliefs, participants who only listed one type of belief—either for or against—were excluded from the for/against ARC analyses. Of the 725 lists of beliefs, 274 (38%) were usable. Table 2 lists the original N, the usable n, the ARC, t, and significance levels for the clustering of for and against beliefs.

22

Table 2

Clustering of For and Against Beliefs, General and Specific Behaviors

____________________________________________________________________

Behavior

Maintaining your car

Type N n ARC t p

_____________________________________________________________

General 67 15 .48 1.87 .08

Specific 38 21 .13 1.25 .23 Washing your car this week

Getting good grades General 61 16 .08 .53 .60

Specific 50 22 .31 .33 .74 Taking notes in class


Going to an Aggies football game on

Saturday afternoon

General

Specific

42

67

15

30

.10 .72

.05 .38

.49

.71

Keeping your house or apartment clean General 68 17 .07 .48 .64

Washing your dishes immediately after

dinner tonight Specific 76 37 .31 2.98 .01

Living a healthy lifestyle General 91 19 .26 1.57 .13

Taking the stairs instead of the elevator Specific 43 22 .06 .42 .68

Performing a charitable behavior General 53 21 .25 1.46 .16

Donating money to the Salvation Army bellringers at Christmas Specific 69 35 .68 2.70 .01

Note . N indicates the number of participants who completed the materials; n indicates the number of participants who listed more than one belief type and were therefore included in the analyses.

23

T-tests were run on the ARC scores of each of the behaviors separately. The mean ARC score for one of the six general behaviors, “maintaining your car,” was marginally significant, M = .48, t (14) = 1.87, p < .08. Two of the six specific behaviors showed significant clustering: the mean ARC score for “donating money to the Salvation Army bellringers at Christmas” was .34, t (34) = 2.70, p < .02. The mean ARC score for “washing your dishes immediately after dinner tonight” was

.31, t (36) = 2.98, p < .01. For all of the other behaviors, the mean ARC score was positive, although not significant.

On the surface, it seemed as though general behaviors cluster more than specific behaviors. To determine whether participants clustered more for general behaviors than for the specific behaviors, behaviors were coded as either general or specific. An ANOVA to determine differences between these groups was nonsignificant, F (1, 268) = .08, p > .77. There was no difference in clustering based on whether the behavior was general or specific.

To determine if participants had a broad tendency to cluster their for and against beliefs, the data were collapsed across all behaviors. The mean ARC across all of the participants was .19, which was significantly different from zero, t (269) =

4.51, p < .01. Across all behaviors, participants tended to cluster their for and against beliefs.

Affective and Cognitive Clustering

As before, participants who only listed one type of belief—either affective or cognitive—were excluded from the affective/cognitive ARC analyses. Table 3 lists

24

the original N, the usable n, the ARC, t, and significance levels of the analyses of affective and cognitive clustering. Of the 725 lists of beliefs, 374 (52%) were included in the analysis.

T-tests were run on all of the ARC scores for each behavior separately. In regard to the general behaviors, all of the ARC scores were positive, and demonstrated at least marginally significant clustering. The significance levels ranged from .08 to .01 (see Table 3). Regarding the specific behaviors, three showed significant affective and cognitive clustering. The mean ARC for “donating money to the Salvation Army bellringers at Christmas” was .22, t (47) = 2.48, p < .02. The mean ARC for “taking notes in class” was .36, t (17) = 2.67, p < .02. The mean

ARC for “going to an Aggies football game on Saturday afternoon” was .36, t (50) =

4.42, p < .01. To summarize, the ARC scores for affective and cognitive beliefs about general behaviors tended to be significant; beliefs about specific behaviors were less consistent.

Again, it appeared as if participants clustered more for the general behaviors than for the specific behaviors. To determine if affective and cognitive clustering occurred more for general behaviors than for specific behaviors, behaviors were collapsed across groups of general or specific. An ANOVA comparing the two groups was nonsignificant, F (1, 369) = .60, p > .43. There was no difference in affective and cognitive clustering based on whether the behavior was general or specific.

25

Table 3

Clustering of Affective and Cognitive Beliefs, General and Specific Behaviors

____________________________________________________________________

Behavior Type N n ARC t p

_____________________________________________________________

Maintaining your car General 67 21 .26 1.84 .08

Washing your car this week

Getting good grades

Specific 38

General 61

13

35

.19

.31

.98 .35

3.01 .01

Taking notes in class


Specific 50

General 42

Going to an Aggies football game on Saturday afternoon Specific 67

Keeping your house or apartment clean General 68

Washing your dishes immediately

18

30

51

23

.36

.40

.36

.26

2.57 .02

3.82 .01

4.42 .01

1.83 .08 after dinner tonight

Living a healthy lifestyle

Specific 76

General 91

Taking the stairs instead of the elevator Specific 43

Performing a charitable behavior General 53

Donating money to the Salvation Army bellringers at Christmas Specific 69

30

53

16

33

.19

.25

.02

.32

1.58 .13

2.78 .01

-.08 .94

2.45 .02

48 .22 2.48 .02

All General Behaviors 382 195 .30 6.41 .01

All Specific Behaviors 343 176 .25 5.12 .01


26

To determine if participants had a broad tendency to cluster their affective and cognitive beliefs, data were collapsed across all behaviors. The mean ARC, .27, was significantly different from zero, t (370) = 8.18, p < .01. Participants did have a tendency to cluster their affective and cognitive beliefs across all behaviors.

Pleasantness Ratings

The pilot study indicated that the general behaviors used in this study were more pleasant than the specific behaviors. In order to determine whether pleasantness influenced the clustering of beliefs, each behavior was coded on pleasantness using the pleasantness ratings obtained from the pilot study. Two

ANOVAs were performed. The first used the pleasantness ratings to predict for/against ARC scores. The second used the pleasantness ratings to predict affective/cognitive ARC scores. The results were not significant. Pleasantness did not predict clustering of for and against beliefs, F = .90, p > .54, or clustering of affective and cognitive beliefs, F = .63, p > .80. Because pleasantness did not influence clustering of beliefs, it was not measured in the next three experiments.

Experiment One: Discussion

The primary goal of this experiment was to determine if people access their beliefs about general behaviors differently from specific behaviors. Specifically, the hypothesis was that specific behaviors should result in for and against clustering, and general behaviors should not. This hypothesis was not supported. The type of

27

behavior did not influence the clustering of either for/against beliefs or of affective/cognitive beliefs.

The mean ARC scores of for/against clustering for each of the twelve behaviors were positive, but rarely significant, regardless of behavior type. When for/against ARC scores were collapsed across groups, there was no significant difference between the two groups, indicating that the tendency to cluster for and against beliefs was not influenced by the type of behavior. Additionally, when the data were collapsed across all of the behaviors, the mean ARC was significantly different from zero. This indicates that there is a general tendency for people to cluster their for and against beliefs about behaviors.

The mean ARC scores of affective/cognitive beliefs for all twelve of the behaviors were positive, and usually significant. When affective/cognitive scores were collapsed across groups, there was no significant difference between the two groups, indicating that participants tended to cluster affective and cognitive beliefs regardless of behavior type. Again, when the data were collapsed across all of the beliefs, the mean ARC was significant; it appears that participants did have a general tendency to cluster their beliefs in groups of affective and cognitive.

Two cautions should be presented before any firm conclusions can be made about this data. First, for some of the behaviors, the degrees of freedom were quite small. The degrees of freedom for individual behaviors ranged from 12 to 52. For those behaviors with smaller degrees of freedom (e.g., “washing your car,” with 12 degrees of freedom in the test for affective/cognitive clustering), the likelihood of a

28

Type I error is increased. That is, clustering of beliefs could be occurring, but the sample size could be too small to detect the effects.

The other caution is related to the collapsing of data to compare the two groups (e.g., general and specific). In order to compare the two groups, the ARC scores were collapsed across behaviors. While this approach can be helpful in determining whether differences between groups are significant, caution should be used in interpreting the data because of the large degrees of freedom. The degrees of freedom for individual behaviors ran from 12 to 52; by collapsing across groups, the degrees of freedom increased to 268 for the for/against test and 369 for the affective/cognitive clustering test. With larger degrees of freedom comes a larger possibility of a Type II error, in which a very small difference is likely to be incorrectly interpreted as significant. In the current study, the difference between the two groups was not significant, so the Type II error did not occur; however, it is important to keep in mind that this approach needs to be interpreted with caution.

Similarly, data were collapsed across all behaviors to determine whether clustering generally occurs with for and against beliefs and with affective and cognitive beliefs. The same caution is in order for this approach. Because collapsing across behaviors increased the degrees of freedom, the likelihood of a

Type II error increased. Clustering was significant for both for/against and affective/cognitive beliefs; however, this result could be a function of the increased degrees of freedom.

29

A better way to determine whether clustering tended to occur may be to conduct a binomial test. Because the ARC scores were positive for all of the behaviors (although not necessarily significant), it appears as if people generally do cluster their beliefs. It is unlikely that 12 out of 12 ARC scores would be positive unless there was some sort of pattern going on in the data. Each behavior can be coded as either positive or not-positive. For both for/against clustering and affective/cognitive clustering, the likelihood of having twelve out of twelve behaviors as positive is very small, z = 3.46, p < .001. Using a binomial approach indicates that clustering of similar beliefs (for/against or affective/cognitive) does occur, regardless of behavior type.

In general, participants in this study were more likely to list only for or only against beliefs than to list beliefs of both types. Of those who were excluded, 93.7% listed only for beliefs. Similarly, a significant portion of the data in the affective/cognitive clustering analyses needed to be disregarded because people listed only one type of belief. Of those who were excluded, 93.1% listed only cognitive beliefs. It is possible that the majority of participants are such cognitive misers that they consider only one type of belief, rather than weighing both sides.

Additionally, it appears as if the one type of belief that most of these participants depended on was a cognitive/for belief.

Experiment One did not find support for the hypothesis that people cluster their for and against beliefs when considering specific behaviors, and do not cluster their for and against beliefs when considering general behaviors. Instead, whether

30

people cluster their for and against beliefs was not related to the specificity of the behavior. Additionally, participants had a strong tendency to cluster their affective and cognitive beliefs across all behaviors. This implies that the difference in the accessing of for and against beliefs about “using a condom the next time you have sex with another person” and “having unprotected sex next weekend” was not due to the specificity of one behavior over another. Instead, perhaps the difference is in the perception of what is socially acceptable. This hypothesis is investigated in the next experiment.

31

EXPERIMENT TWO: THE SALIENCE OF THE INGROUP

In the replication study, one of the most interesting differences was that participants in the “having unprotected sex” condition mentioned people other than their sex partner. Examples include “my parents would kill me,” “people should be respectful of their bodies,” and “if I don’t want other people to have unprotected sex,

I better practice what I preach.” In fact, ten of the 25 participants in this condition included references to others. In the “use a condom” condition, only three out of 24 participants made these references: two wrote that “people stare at me when I buy condoms,” and one wrote, “my mom uses them more than I do.” Something about the “having unprotected sex” condition seems to have motivated people to access information about other people. Perhaps the phrasing of the behavior made social expectations more accessible, which influenced the behavioral beliefs that participants listed.

For humans, the need to belong to social groups is strong (Baumeister &

Leary, 1995). To belong to a group, it is important to do as the group does, even if the individual disagrees with the group’s behavior (Asch, 1955). Because people are motivated to behave consistently with their group, it would be important for an individual to know what behaviors are socially expected for each of the groups they belong to, as well as which beliefs are important to different groups. For example, a person’s family group and their club group may believe that it is important to use condoms, but their sexual partner and close friends group may believe that using condoms is unnecessary. Accessing different beliefs that are relevant to the behavior

32

may depend on the group that is most salient. When primed to consider themselves as a group member, without specifying any particular group, people may retrieve the most important or most salient belief that is associated with each of the many groups they belong to. At the same time, there is no real reason to expect that beliefs should be clustered in groups of for and against. Instead, people may access beliefs that are associated with their most important groups. If their most important groups have different expectations for the behavior, then clustering of beliefs should not necessarily occur; clustering should be no different from chance.

In comparison, when group membership is not activated, people may consider beliefs about behaviors only in regard to the direct consequences to themselves. They may access beliefs with the goal of weighing the costs and benefits of the behavior in order to form behavioral intentions that maximize their benefits and minimize their costs. If people access beliefs about the behavior with the intent to compare the costs and benefits, then beliefs in favor of performing the behavior (benefits) should have associative links between them, and beliefs against performing the behavior (costs) should have associative links between them. People should access their beliefs in clusters of for and against performing the behavior. In the replication study, when participants were asked about “using a condom,” they may have been more likely to consider themselves as individuals, separate from others: for these people, the private self may have been activated. As individuals, separate from others, they may have been motivated to access information about the immediate costs and benefits to themselves about using (or not using) a condom.

33

The proportion of for and against beliefs may also depend on the salience of group membership. If the person believes that most of the groups they belong to think the behavior should be performed, proportionally more of the beliefs they access should be in favor of performing the behavior. In the replication study, the phrase “unprotected sex” might have motivated people to be thinking about their group membership. They listed more beliefs consistent with general societal expectations—in this case, against having unprotected sex—than those in the “using a condom” condition. In the “having unprotected sex” condition, 96 of the 139 beliefs that participants listed were against the behavior (that is, they would not have unprotected sex). In comparison, in the “using a condom” condition, only 56 of the

102 beliefs that were listed were consistent with the social norm (that is, they would use condoms). This difference in listed beliefs is significant, x 2 (1) = 13.96, p < .01.

I propose that the phrase “having unprotected sex” acted as a prime for social norms, and priming social norms motivated people to access beliefs that were consistent with the many different groups they belong to. If this is so, then using another prime to activate social norms should motivate people to list proportionately more beliefs that are consistent with the social norm. Additionally, because different groups may hold different beliefs about specific behaviors, they should not necessarily access their beliefs in clusters of for and against the behavior; clustering should not occur at a rate greater than chance. Alternatively, the phrase “using a condom” may have acted as a prime for people to consider themselves as individuals, and this priming motivated participants to weigh the costs and benefits

34

of the behavior. If this is so, then using another prime to activate the private self should motivate people to cluster their beliefs in groups of for and against performing the behavior. I expect that activating group memberships with a collective self prime will make clustering of for and against beliefs less likely, and activating the private self will make the clustering of for and against beliefs more likely.

Two more general questions were addressed in this experiment. The first focused on the importance of beliefs. In listing beliefs, it is possible that the most important beliefs are listed first, and the less important beliefs are listed later. On the other hand, they may list irrelevant or unimportant beliefs first, and save the most important beliefs for the end. Alternatively, they may perceive each listed belief as equally important, and each belief may weigh into their decisions equally. The importance of each belief may influence the forming of intentions. A manipulation was included to determine if there was a pattern of importance regarding the listed beliefs.

The second general question dealt with the relationship between whether the performance of the behavior influenced the organization of beliefs. Holland,

Verplanken, and Knippenberg (2003) found that attitude accessibility influences attitude strength, and attitude accessibility is increased with the increased number of times the attitude is expressed. If a person is sexually active, they may have had more opportunities to express their attitudes regarding condom use. More opportunities to express their attitudes may be correlated with a higher likelihood of

35

for and against clustering. A manipulation was included to assess the level of sexual activity of the participants to determine if sexual activity was correlated with for/against clustering.

Experiment Two: Materials and Method

To test whether the increased accessibility of social norms decreases for and against clustering, a three (priming) by two (behavior) between-subjects design experiment was performed. Two behaviors that had previously shown significant clustering of for and against beliefs were used. The first, “you or your partner using a condom the next time you have sex,” had shown significant clustering of for and against beliefs in the Duran and Trafimow (2000) work and in the replication study.

The second, “returning a watch that you had found in the library in order to collect the reward,” had shown significant clustering of for and against beliefs in a separate study (Experiment Three, this manuscript). These two behaviors were chosen because it was important to use behaviors that had previously elicited for and against clustering to determine if activating social norms makes for/against clustering less likely.

The primes were based on the work of Ybarra and Trafimow (1998) and

Trafimow, Triandis, and Goto (1991). For the private-self prime, participants read,

“For the next two minutes, you will not need to write anything. Please think of what makes you different from your family and friends.” For the collective-self prime, participants read, “For the next two minutes, you will not need to write anything.

Please think of what you have in common with family and friends.” The third

36

condition was a neutral condition. Participants read, “For the next two minutes, you will not need to write anything. Please think about a typical day at school.” After completing the prime, participants were asked to list eight beliefs about one of the two behaviors. A total of 203 participants completed the questionnaire, with an average of 33 participants in each condition.

After listing eight beliefs about the behavior, participants were instructed to

“Go back to your list of beliefs. Rate each of the beliefs according to the scale below. Write the corresponding number next to EACH belief. Consider each belief separately.” The scale was a five-point “extremely unimportant” to “extremely important” scale. This task was included to identify whether participants tended to list their most important beliefs first.

In this study, participants also completed a demographics page, indicating their gender, year in school, age, and ethnicity. Additionally, in the “using a condom” conditions, they responded to an item regarding their sexual activity. The item read, “I have had sex with another person,” and the four response items were

“in the last six months,” “in the last year,” “in the last five years,” and “I am not sexually active.” This item was included to determine whether their sexual activity influenced their organization of beliefs about condom use, and was not included in the “return a watch” condition.

Experiment Two: Results

ARC scores were obtained as before for each of the two beliefs in each of the three conditions. Additionally, ARC scores were obtained for each of the three

37

conditions, collapsed across both behaviors. Scores were computed for both for/against clustering and affective/cognitive clustering, as before. (See Tables 4 and

5.)


Table 4 lists the initial N, the useable n, the ARC scores, and the t and p values for the clustering of for and against beliefs for this experiment. As before, lists of beliefs of only one type—either all for or all against—were not included in the analyses. Of the total 203 lists of beliefs, 89 (44%) were useable.

To determine if clustering of for and against beliefs occurred in any of the six cells, t-tests were computed on the obtained ARC scores. Clustering of beliefs was significant only in the collective self/condom use condition, t (14) = 2.86, p < .01.

For the first time in this set of studies, negative mean ARC scores occurred.

Both occurred in the condom use behavior. With the private-self prime, the mean

ARC was -.09; with the neutral prime, the mean ARC was -.05. Neither of these were significantly different from zero. However, a negative ARC score indicates that beliefs may have been accessed in an alternating pattern: for example, for beliefs are followed by against, and against beliefs are followed by for. To determine if the negative clustering of this behavior was significantly different from chance, the negative ARC (NARC) statistic was performed. This statistic uses the formula:

NARC = [R – E(R)]/[E(R) – minR], and yields scores that have the same properties as the ARC scores: that is, chance is set at zero, and the lower boundary is –1. A NARC that is significantly different

38

Table 4

Clustering of For and Against Beliefs After Priming

____________________________________________________________________

Prime Behavior N n ARC t p

____________________________________________________________________

Private Self

Condom Use

Return Watch

27

39

17

15

-.09

.26

-.86

1.53

.40

.15

Collective Self

Both Behaviors

Condom Use

Return Watch

Neutral Prime

Both Behaviors

66 32 .08

27

0

67

15

15

30

.34

.13

.24

.75 .46

2.86

.90

2.50

.01

.39

.02

Condom Use 29 14 -.05 -.35 .73

Return Watch 41 13 .10 .70 .50

Both Behaviors 70 27 .20 .20 .84


39

from zero indicates clustering below chance. The NARC for condom use in the private-self prime condition was -.32, which was not significantly different from zero, t (16) = -1.77, p > .10. The NARC for condom use in the neutral prime condition was -.19, which was not significantly different from zero, t (14) = -.57, p >

.58. The clustering of for and against beliefs was not different from chance for these two conditions.

An ANOVA was performed to determine if the mean for/against ARC score for the collective self prime was significantly different from the other primes within the “use a condom” condition. The results were significant, F (2, 43) = 3.91, p < .03.

Planned contrasts revealed that the difference between the collective self prime and the private self prime was significant, t = -2.58, p < .02, as was the difference between the collective self prime and the no-prime condition, t = 2.23, p < .03.

There was no significant difference between the private self prime and the no-prime condition, t = .23, p > .81.

Regarding the “return a watch” behavior, all of the mean ARCs were positive, but none were significant. In the private self prime condition, the mean

ARC was .26, p > .14; in the collective self prime condition, the mean ARC was .12, p > .38; in the neutral prime condition, the mean ARC was .09, p > .49. Because there was no clustering of for/against beliefs for any of the conditions given the

“return watch” behavior, it is not surprising that an ANOVA was not significant, F

(2, 40) = 3.41, p > .71. Priming did not influence the clustering of for/against beliefs when asked about returning a watch.

40

To determine if there were main effects for behavior, condition, or an interaction between behavior and condition, an ANOVA was computed. There were no main effects for behavior, F (1, 83) = .71, p > .40. There were no main effects for priming across both behaviors, F (2, 83) = 1.25, p > .29. Finally, there were no significant interaction effects, F (2, 83) = 2.27, p > .11.

Because neither main nor interaction effects were significant, the data were collapsed across groups. The grand mean ARC was .11, which was significantly different from zero, t (88) = 1.99, p < .05. Across all conditions, participants tended to list their beliefs in clusters of for and against beliefs.


Table 5 lists the N, the useable n, the average ARC score, and the t and p values for affective/cognitive clustering for this experiment. Of the 203 lists of beliefs, 170 (84%) were useable.

While no specific predictions were made regarding affective and cognitive clustering, because there is a general tendency for people to cluster affective and cognitive beliefs, it was expected that clustering should occur across all conditions.

Separate t-tests on each behavior revealed that this expectation was almost completely met: in all cells except one, the ARC was significantly different from zero. For those who were considering returning a watch, the mean ARC given the private self prime was .23, p < .04; the mean ARC given the collective self prime was .19, p < .05; the mean ARC given the neutral prime was .24, p < .02. For those who were considering condom use, the mean ARC given the private self prime was

41

Table 5

Clustering of Affective and Cognitive Beliefs After Priming

____________________________________________________________________

Prime Behavior N n ARC t p

____________________________________________________________________

Private Self

Condom Use 27

Return Watch 39

Collective Self

Both Behaviors 66

Condom Use 27

21

34

55

23

.47

.23

.32

.25

4.99

2.20

4.31

2.40

.01

.04

.01

.03

Return Watch 40

Neutral Prime

Both Behaviors 67

Condom Use 29

Return Watch 41

Both Behaviors 70

35

58

25

32

.19

.21

.05

.24

2.02

3.07

.49

2.47

.05

.01

.63

.02

57 .16 2.24

Note . N indicates the number of participants who completed the materials; n

.03 indicates the number of participants who listed more than one belief type and were therefore included in the analyses.

42

.47, p < .01; the mean ARC given the collective self prime was .25, p < .03. The sole difference in this pattern of significance was in the no prime/condom use condition; the mean ARC for this cell was .05, t (24) = .49, p > .63.

In considering the data between groups, there was no main effect for behavior. An ANOVA determined that those asked about condom use (mean ARC =

.24) were as likely to cluster as those asked about returning a watch (mean ARC =

.22), F (1, 168) = .10, p > .75.

There was no main effect for primes. An ANOVA revealed that there was no difference in clustering of affective and cognitive beliefs for those exposed to the private-self prime (mean ARC = .32), the collective-self prime (mean ARC = .21), nor the neutral prime (mean ARC = .16), F (2, 167) = 1.31, p > .27.

Because there were no significant main or interaction effects, data were collapsed across primes and behaviors. For affective and cognitive clustering, the grand mean ARC was .23, and was significantly different from zero, t (169) = 5.55, p

< .01. Across all conditions, participants tended to list in clusters of affective and cognitive beliefs.

Proportions of Beliefs

One of the hypotheses in this experiment was that those exposed to a collective-self prime should list more beliefs consistent with social expectations, compared to those exposed to a private-self prime or a neutral prime. They should also list fewer beliefs that are inconsistent with social expectations. To test this hypothesis, the proportion of beliefs in favor of performing the behavior was

43

computed; in both the “condom use” and “return a watch” condition, it was assumed that the social expectation is in favor of performing the behavior. The priming condition did not influence the proportion of beliefs: there was no significant difference in the number of for beliefs between the private-self prime (M = 5.28), collective-self prime (M = 5.37), or the neutral prime (M = 5.0), F (1, 86) = .34, p >

.56. The difference in the number of against beliefs was not significant for the private-self prime (M = 2.03), collective-self prime (M = 2.07), or the neutral prime

(M = 2.30), F (1, 86) = .42, p > .52. The behavior did influence the proportions of beliefs: on average, participants in the return a watch condition listed more for beliefs (M = 5.77) than those in the condom use condition (M = 4.72), F (1, 86) =

7.02, p < .01. This pattern was reversed with the listing of against beliefs: on average, participants in the return a watch condition listed fewer against beliefs (M =

1.77) than those in the condom use condition (M = 2.46), F (1, 86) = 4.23, p < .05.

Ratings of Beliefs

Participants listed an average of 7.44 beliefs. They rated each belief using the five-point “extremely unimportant” (1) to “extremely important” (5) scale. The average rating of the first belief listed was 4.55; the average rating of the eighth belief was 3.68. This difference was significant, t (97) = 5.38, p < .01. The difference between each consecutive pair of beliefs (e.g., 2/3, 3/4, 4/5) was not significant (ps > .06). Participants rated their first beliefs as the most important, and the other beliefs were gradually less important.

44

Sexual Activity

Pearson’s correlations were run to determine if participants’ responses on the sexual activity question were associated with the level of for/against and affective/cognitive clustering. The correlation between sexual activity and for/against clustering was -.09 (p > .59), and between sexual activity and affective/cognitive clustering was -.12 (p > .37). This implies that the level of sexual activity does not influence the cognitive organization of beliefs about condom use.

Experiment Two: Discussion

The main hypothesis in this experiment was that people would show clustering of for and against beliefs when their private-self concepts were primed, because they would be motivated to weigh the direct positive and negative consequences of the behavior. When their collective-self concepts were primed, they should not necessarily cluster their beliefs into groups of for and against; instead, they would be motivated to list the most salient beliefs consistent with the expectations of their social groups, which may be either for or against the behavior.

When participants were exposed to a neutral prime, their beliefs were expected to reflect previous research and reveal a clustering of beliefs for and against the behavior. Instead, the findings revealed that participants clustered their for and against beliefs only when primed with a collective-self prime, and only when asked about condoms; they did not significantly cluster their for and against beliefs about returning a watch, regardless of the prime.

45

It is interesting to note that in previous research and in the replication study, participants had a tendency to cluster their for and against beliefs about condom use; yet in this study, the clustering of for and against beliefs about condoms occurred only when participants were exposed to a collective-self prime. Exposure to the private-self prime and the neutral prime decreased the clustering of for and against beliefs about condom use. On the surface, it appears as if being asked about condom use in general is similar to being exposed to a collective-self prime; if this is true, then being exposed to a collective-self prime should increase clustering of for and against beliefs. However, those who were exposed to a collective-self prime and asked about returning a watch did not cluster their beliefs. This difference between two behaviors given the same prime seems irreconcilable; however, this peculiar finding may be explained with the low degrees of freedom. In this study, the degrees of freedom in the for and against analyses ranged from 12 to 16; and, as previously mentioned, any firm conclusions given these findings should be considered with caution.

Additionally, it is important to remember the cautions against the increased degrees of freedom when the data were collapsed across the groups and primes. In this study, when the for and against data were collapsed, the degrees of freedom changed from a low of 12 to a total of 88. When the affective and cognitive data were collapsed, the degrees of freedom changed from a low of 20 to a total of 169.

In both of these collapsed analyses, participants did cluster their beliefs. However, because the large degrees of freedom increases the risk of a Type II error, it is

46

possible that these significant findings may simply be a function of large sample size.

Once again, a more appropriate way to look at whether people tend to cluster is the binomial test. The mean ARC scores for each of the conditions and both behaviors can be coded as positive or non-positive. In the case of for and against clustering, four of the six cells were positive. The likelihood of having four of six cells be positive is quite small, z = 1.64, p < .01. In regard to the affective and cognitive clustering, six of the six cells were positive, z = 2.46, p < .01. This indicates that participants tend to cluster both their for/against beliefs and their affective/cognitive beliefs.

A further hypothesis in this study was that in the collective-self prime, in comparison with the other primes, people would access proportionately more beliefs that are consistent with the socially acceptable response. In this experiment, the socially acceptable responses were expected to be in favor of both behaviors (e.g., using a condom the next time you have sex, and returning an expensive watch found in the library). While the results of the study did not reveal significant differences in the proportion of beliefs in favor of the behavior due to the priming condition, there was a difference due to the behavior. Those who listed beliefs about returning a watch listed proportionately more beliefs in favor of the behavior, and they listed proportionately more beliefs against using a condom. These findings indicate that participants may have found it easier to access beliefs against condom use; alternatively, they may have fewer beliefs against returning a watch.

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In this study, participants evaluated the importance of each of their beliefs, and they rated the first beliefs as more important than later beliefs. This implies at least two conclusions. It is possible that they tended to list their more important beliefs first. Alternatively, participants could have rated their first listed beliefs as more important because they listed them first. Future experiments could investigate which of these hypotheses is supportable by having participants list their beliefs, then rate the importance when the beliefs are randomly presented back to them. The current study did not address whether either of these alternatives might be true; all that can be said about the current findings and the rating of importance is that beliefs at the beginning of the list were rated as more important, and beliefs at the end of the list were rated as less important.

Information gathered in Experiment Two also included participant’s sexual activity. The data indicate that participants’ reported sexual activity did not influence the clustering of their beliefs about condom use, with regard to for and against and affective and cognitive clustering. The response indicated most often was that they had had sex within the last six months (58.7%). A small number

(13%) indicated that they were not sexually active. The lack of correlation between sexual activity and clustering of beliefs is not necessarily surprising, given that the population sampled was college students. It is likely that those who were not sexually active had had opportunities to make decisions about their own sexual activity, and had already created links between relevant beliefs about the behavior.

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Generally, the results of Experiment Two support the hypothesis that people do cluster in groups of affective and cognitive beliefs, and they do not usually cluster in groups of for and against, regardless of whether they have been exposed to a self prime. Therefore, the different clustering effects when asked about condom use or safe sex are not a function of the salience of different selves. Perhaps the clustering is less dependent on the social world, and more dependent on what people perceive to be the “right thing to do.” The next experiment addresses whether the perception of a behavior as a moral issue influences accessing of beliefs.

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EXPERIMENT THREE: THE MORALITY ISSUE

The concept of morality is difficult to articulate. Schwartz (1970) proposed that morality involves three factors. First, moral decisions lead to interpersonal actions having consequences for the welfare of others. Second, a behavior can only be considered moral if the actor is a responsible agent. Third, the action and the person are evaluated as good or bad depending on an impacted other’s welfare. Kant

(as cited in Trafimow & Trafimow, 1999) proposed that there are two classes of morality: perfect duties, which must always be obeyed, and imperfect duties, which do not always have to be obeyed, as long as they are sometimes obeyed. While these researchers have tried to describe relevant factors and different types of morality, it is unlikely that most people have articulated a definition of morality. However, people do have a feel for what a moral behavior is; they may have a general feeling of “the right thing to do.”

There is evidence that people conceptualize moral traits and behaviors differently from those that are not moral. Reeder and Brewer (1979) propose that although people generally expect others to behave consistently with their traits, some traits allow for more variability in behavior than other traits. Partially restrictive

(PR) trait dimensions allow for a range of behaviors, including some that are inconsistent with the trait, without disconfirming that trait expectation. For example, a person who is perceived to be kind is expected to behave in a kind manner, but can perform some unkind behaviors without changing the perception of being kind; similarly, an unkind person can perform some kind behaviors and still be perceived

50

as unkind. Hierarchically restrictive (HR) trait dimensions allow for this same variability, but only for one end of the trait continuum. At the other end of the continuum, people are expected to behave consistently with the trait, and only a very few inconsistent behaviors will disconfirm trait expectancies. For example, a dishonest person can perform both honest and dishonest behaviors, and will still be considered dishonest; however, an honest person can perform only honest behaviors.

Only a very few inconsistent behaviors would change the perception of honesty: the honest person who performs only a few dishonest behaviors would be perceived of as dishonest. In general, traits associated with morality are HR traits (but see

Trafimow & Trafimow, 1999, for exceptions); however, not all HR traits are associated with morality.

Most of the work in this area is primarily directed toward the perception of others (e.g., Reeder & Coovert, 1986; Rothbart & Park, 1986; Skowronski &

Carlston, 1987; Trafimow & Schneider, 1994). In one exception, Trafimow,

Armendariz, and Madson (in press) examined how people make trait attributions regarding themselves; however, they did not examine the variability in those attributions. It is logical to assume that people hold the same concepts about their own traits and behaviors. They should allow themselves variability of behaviors for

PR traits. If they believe they hold a trait that is on the restricted pole of an HR trait dimension, they should not allow themselves much variability. Instead, they should think of themselves as performing only behaviors that are consistent with that trait.

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The difference in HR and PR trait dimensions should be reflected in how people access their beliefs about their own behavior. If a trait is on the restricted pole of an HR trait dimension, then the behaviors relevant to that trait should be similar to each other on that trait dimension. Because these behaviors are similar to each other on that HR trait dimension (e.g., honesty), it is likely that the same beliefs influence many related behaviors. For example, if people perceive themselves to be honest, then the relevant behaviors they perform should all be honest. Whether forming intentions to tell a superior a difficult truth or intentions to return too much change to a cashier, some of the same beliefs (e.g., “if I do this, I will be proud of myself,” “this is the right thing to do”) should be accessed. As people access the same beliefs when considering many relevant behaviors, associative links between those beliefs are formed. When asked to consider other behaviors that are relevant to the HR dimension, these associative links can be used as retrieval routes. If people tend to think of themselves as honest, then when asked about performing any behavior relevant to honesty (e.g., telling the truth to a superior), they will be likely to retrieve beliefs that are consistent with being an honest person.

On the other hand, beliefs about PR traits are not necessarily linked to each other. Behaviors may be on different ends of the trait dimension, and beliefs associated with those behaviors may be both for and against the behavior. For example, imagine a person is deciding whether to be kind (a PR trait) to their neighbor. They may weigh the costs and benefits—e.g., “it will take my time,” “the neighbor will owe me”—in order to form their behavioral intentions.

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Several researchers have contended that there are different reasoning processes underlying decisions about moral and non-moral behaviors. For example,

Thogersen (1996) points out that performing a moral behavior is a question of right or wrong rather than costs and benefits. Therefore, if a behavior is presented as moral, such as “recycling aluminum in order to have a cleaner environment,” there is no question about whether or not the behavior will be done; there may be an assumption that the “right” decision—the moral decision—will be made. If this is true, then when people consider moral behaviors, they may be motivated to consider only their beliefs in favor of performing the behavior, and they may not consider beliefs against the behavior. Alternatively, if the same behavior is re-framed without the implication of morality, such as “recycling aluminum to collect the money,” people may be motivated to weigh the costs and benefits of performing the behavior.

If people who are forming intentions about a moral behavior only consider beliefs that are consistent with that behavior, and people who are forming intentions about a non-moral behavior weigh the costs and benefits of that behavior, then the beliefs that they access for each of these intentions should reflect those considerations. Specifically, when asked about a non-moral behavior, people should access for and against beliefs in clusters in order to weigh the differences. On the other hand, when asked about a moral behavior, most—if not all—of the beliefs that people access should be consistent with the morally consistent behavior.

If moral and non-moral decisions are considered differently, perhaps the different clustering effects in the replication study are due to one behavior (having

53

unprotected sex) being perceived of as a moral decision and the other (using a condom) as not a moral decision. If this is true, then when participants are presented with a behavior framed as a non-moral decision, their results should replicate findings of the “using a condom” condition: that is, they should cluster their beliefs in groups of for and against the behavior. When participants are presented with the same behavior framed as a moral decision, they should show the same results as in the “having unprotected sex” condition: that is, they should not have clusters of for and against beliefs. Additionally, participants should list a higher proportion of morally consistent beliefs when they are considering a moral behavior than when considering a non-moral behavior. Finally, because affective and cognitive clustering seems to be a robust finding, it should occur again in this experiment, regardless of behavior type.

Experiment Three: Method

Pilot Study

Eleven behaviors were generated. Caution was taken to be sure that the behaviors included in the pilot study could be presented as either moral or cost/benefit. Behaviors that most participants were likely to consider significantly immoral, such as clear cases of theft, were not included. Each behavior was framed in a moral and a non-moral sentence. For example, the moral framing of “becoming a vegetarian” was “to avoid cruelty to animals,” and the non-moral framing was

“because it is cheaper than eating meat.”

54

Two questionnaires were created. Each questionnaire had half of the behaviors in moral framing, and the other half in non-moral framing. To avoid contrast effects, no behavior was presented twice; participants received the behavior presented as either moral or non-moral, but not both. The sentences were randomly presented among the behaviors listed in the Pilot Study, Experiment One.

Participants rated each behavior on a seven point “extremely moral” (0) to

“extremely immoral” (6) scale. Fifty-four participants volunteered to complete the survey. A t-test was run on each corresponding pair. Ten of the eleven sets of behaviors were significantly different on the morality scale (p < .01), indicating that within each pair, one of the behaviors was rated as significantly more moral than the other. The highest rating was 4.74 for “a cashier gives you too much change and you keep it,” indicating that while this behavior was not moral, it was not completely immoral. The non-moral behaviors that were included had an average morality rating of 2.85. See Table 6 for a complete list of behaviors, their mean morality ratings, and t-tests within each pair.

Along with the generated behaviors, participants also rated either “having unprotected sex” and “using a condom” or “having protected sex” and “not using a condom.” The mean morality rating for using a condom (M = 1.74) was significantly different from the mean for not using a condom (M = 4.26), t (52) = -

6.30, p < .01, and the mean for having protected sex (M = 2.22) was significantly different from having unprotected sex (4.44), t (52) = 5.74, p < .01.

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Table 6

Morality Ratings and T-Tests for Matched Behaviors

____________________________________________________________________

Behavior Mean t df p

____________________________________________________________________

Donating blood for money

Donating blood because blood supplies are low

3.00

1.15

-5.31 52 0.00

Becoming a vegetarian to avoid cruelty to animals 2.11 3.85 52 0.00

Becoming a vegetarian because it is cheaper than eating meat 3.37

Recycling for a cleaner environment

Recycling for money

Donating your old clothes to the Salvation Army for a tax break

Donating your old clothes to the Salvation Army so that another person can benefit

Calling to chat with your parents because it's the right thing to do

Calling to chat with your parents because they're paying your expenses

Telling the instructor that points were added wrong and you should receive a higher grade

Telling the instructor that points were added wrong and you should receive a lower grade


A cashier gives you too much change and you keep it

Explaining to your professor about why you missed an exam

Lying to your professor about why you missed an exam

1.15

3.00

3.31

0.74

1.96

3.96

2.44

0.85

0.85

4.74

2.19

4.44

5.54

-7.08

-5.93

3.57

-6.02

52

51

52

52

11.62 52

52

0.00

0.00

0.00

0.01

0.00

0.00

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Table 6 (Continued)

____________________________________________________________________

Behavior Mean t df p

____________________________________________________________________

Returning an expensive watch found in the library

Returning an expensive watch found in the library

0.56 -6.40 52 0.00 in order to collect the reward

Wearing a seat belt so that you don't get a ticket

Wearing a seat belt for your safety

Withholding information from a friend because

2.59

2.11 -0.12 52

2.15

0.90 it might hurt that person

Withholding information from a friend because it might benefit you

Having protected sex

Having unprotected sex

3.22

4.59

2.22

4.44

-3.89

5.73

52

52

0.00

0.00

Using a condom

Not using a condom

1.74 -6.30 52

4.26

Note: Morality ratings range from 0, Extremely Moral, to 6, Extremely Immoral.

0.00

57

Materials and Methods

Five pairs of behaviors from the pilot study were used in this study. Five behaviors with the lowest ratings on the morality scale (that is, the most moral behaviors) and their corresponding non-moral behaviors were selected: donating blood for money or because blood supplies are low; donating your old clothes to the

Salvation Army for a tax break or so that another person can benefit; telling the instructor that points were added wrong and you should get a higher grade or a lower grade; a cashier gives you too much change and you keep it or you return it; and returning an expensive watch found in the library or returning an expensive watch in the library in order to collect the reward.

Questionnaires consisted of two behaviors, one moral and one non-moral.

Within each questionnaire, the two behaviors were not related. Each behavior was listed on a separate page. Participants were asked to “write down your beliefs about your [behavior]. Write them down in the order that you think of them.” The participants were allowed to complete the survey at their own pace. A total of 235 participants volunteered to complete the study. An average of 47 people completed each behavior.

Experiment Three: Results

Each belief was coded as either for or against, and as either affective or cognitive. There were no order effects; whether participants completed a moral or a non-moral behavior first did not influence the results.

58


Participants who listed only one belief type were excluded from the ARC analyses. Of the 470 lists of beliefs, 207 (44%) were included in the analyses of for and against clustering.

T-tests were run on the ARC scores of each behavior separately (see Table 7 for the original N, the usable n, the ARC scores, the t-score and significance level of each behavior). All of the mean ARC scores were positive. Three of the five mean

ARCs cores for non-moral behaviors were significantly different from zero:

“donating blood for money,” M = .44, t (26) = 3.87, p < .01; “returning an expensive watch found in the library in order to collect the reward,” M = .46, t (17) = 3.29, p <

.01; and “telling the instructor that points were added wrong and you should get a higher grade,” M = .56, t (11) = 3.51, p < .01. Two of the five moral behaviors showed significant clustering of for and against beliefs: “a cashier gives you too much change and you return it,” M = .36, t (25) = 3.38, p < .01; and “returning an expensive watch found in the library,” M = .47, t (19) = 3.41, p < .01. One moral behavior, “telling the instructor that points were added wrong and you should get a lower grade,” was marginally significant, M = .24, t (24) = 1.82, p < .08.

Across the two groups, there was no significant difference between moral and non-moral behaviors in clustering of for and against beliefs. Both moral (M = .38) and non-moral (M = .26) behaviors demonstrated similar levels of clustering, F (1,

202) = 1.62, p > .20. Because there was no difference between moral and non-moral behaviors, these groups were collapsed. After collapsing across behaviors, the mean

59

Table 7

Clustering of For and Against Beliefs, Non-Moral and Moral Behaviors

____________________________________________________________________

Behavior N n ARC t p

____________________________________________________________________

Donating blood for money 49 27 .44 3.87 .01

Donating your old clothes to the Salvation

Army for a tax break 36 13 .27 1.49 .16

Telling the instructor that points were added wrong and you should receive a higher grade 35 12 .56 3.51 .01

A cashier gives you too much change and you keep it 33 23 .19 1.21 .24

Returning an expensive watch found in the library in order to collect the reward

All non-moral behaviors

48

201

18

93

.46

.38

3.29 .01

5.61 .01

Donating blood because blood supplies are low 88 28 .16 1.28 .21

Donating your old clothes to the Salvation Army so that another person can benefit 38 12 .02 .07 .95

Telling the instructor that points were added wrong and you should receive a lower grade 33 25 .24 1.82 .08

A cashier gives you too much change and you return it 50 26 .36 3.38 .01

Returning an expensive watch found in the library 60 20 .47 3.41 .01

All moral behaviors 269 111 .26 4.34 .01


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ARC was .31, indicating that participants did show clustering of for and against beliefs, t (203) = 7.00, p < .01.


Table 8 lists the original N, the usable n, the ARC, t-score, and significance level of affective and cognitive beliefs about moral and non-moral behaviors. Of the

470 lists of beliefs, 258 (55%) were included in the analyses of affective and cognitive clustering.

At least marginal significance of clustering of affective and cognitive beliefs occurred with four of the five non-moral behaviors: “donating blood for money,” M

= .29, t (28) = 2.18, p < .04; “telling the instructor that your points were added wrong and you should receive a higher grade,” M = .54, t (7) = 2.39, p < .05; “a cashier gives you too much change and you keep it,” M = .27, t (19) = 2.04, p < .06, and

“returning an expensive watch in the library in order to collect the reward,” M = .43, t (34) = 3.48, p < .01. For the fifth non-moral behavior, “donating your old clothes to the Salvation Army for a tax break,” the mean ARC was -.09, which was not significantly different from zero, t (18) = -.54, p > .59. Because this ARC was negative, the NARC statistic was run on this behavior. The NARC was not significant (M = -.10, t (19) = .40, p > .69), indicating that for this behavior, the manner in which participants accessed their beliefs was not different from chance; they did not necessarily alternate between affective and cognitive beliefs.

Affective/cognitive clustering was significant for one moral behavior:

“donating blood because supplies are low,” M = .39, t (55) = 3.55, p < .01. Two

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Table 8

Clustering of Affective and Cognitive Beliefs, Non-Moral and Moral Behaviors

____________________________________________________________________


____________________________________________________________________

Donating blood for money


Army for a tax break

Telling the instructor that points were added

50

36

29

19

.29

-.09

2.18 .04

-.54 .60 wrong and you should receive a higher grade 35 8 .54 2.39 .05

A cashier gives you too much change and you keep it 33 20 .27 2.04 .06

Returning an expensive watch found in the library in order to collect the reward 48 35 .43 3.48 .01

All non-moral behaviors 202 101 .27 3.92 .01

Donating blood because blood supplies are low 88 56 .39 3.55 .01


Army so that another person can benefit

Telling the instructor that points were added w rong and you should receive a lower grade


38 24

33 11

50 28

-.29

-.02

.29

-4.42 .01*

-.16 .88

1.59 .12

Returning an expensive watch found in the library 60 35 .18 1.16 .26

All moral behaviors 269 154 .19

* Negative Adjusted Ratio of Clustering Index was significant, p < .01.

2.84 .01

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moral behaviors resulted in negative mean ARC scores. Because the mean ARCs were negative, the NARC statistic was computed for these behaviors. A negative mean ARC score occurred for the behavior “telling the instructor that points were added wrong and you should receive a lower grade,” M = -.02, t (10) = -.16, p >.88.

The NARC for this behavior was not significant, t (10) = -.02, p > .98. This indicates that the clustering of beliefs was not different from chance. The other negative mean ARC score occurred for the behavior “donating your old clothes to the Salvation Army so that another person can benefit.” Using the NARC statistic, the average NARC, -.63, was significantly different from zero, t (34) = -5.94, p <

.01. This indicates that for this behavior, participants tended to alternate between affective and cognitive beliefs: an affective belief tended to be followed by a cognitive belief and vice versa.

To compare the clustering of affective and cognitive beliefs when considering a moral behavior versus a non-moral behavior, the data were collapsed within groups. There was no clustering difference between the two groups. Both moral (M = .19) and non-moral (M = .27) behaviors showed similar clustering effects, F (1, 253) = .71, p > .40. Because there was no difference between the two groups, the beliefs were collapsed across all of the behaviors. The mean ARC for all behaviors was .22, which was significantly different from zero, t (254) = 4.56, p <

.01. Across all behaviors, participants tended to cluster affective and cognitive beliefs.

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Proportions of Beliefs

One hypothesis in this study was that participants should list a higher proportion of morally consistent beliefs when they are considering a moral behavior than when considering a non-moral behavior. To determine if this hypothesis was supported, the number of for beliefs was compared to the number of against beliefs.

When the behavior was moral, participants listed an average of 3.60 for beliefs and

2.71 against beliefs. This difference was significant, t (111) = 2.58, p < .02. When the behavior was not moral, participants listed an average of 3.89 for beliefs and 2.48 against beliefs. This difference was also significant, t (94) = 4.32, p < .01.

Participants listed significantly more for beliefs than against beliefs when considering both moral and immoral behaviors. There was no difference between behavior type: the amount of for beliefs listed was statistically similar for both moral and immoral behaviors, and the amount of against beliefs listed was statistically similar for both moral and immoral behaviors, ps > .30.

If participants listed only one type of belief—for example, only for beliefs— they were excluded from the study. If considering a moral behavior influences people to access only beliefs that are consistent with morality (in this case, for beliefs), then participants who were considering a moral behavior should have been more likely to be excluded from the analyses; that is, more people should have been dropped from the moral group than from the non-moral group. Of the 269 participants who responded to a moral behavior, 158 (59%) were excluded because they listed only one type of belief. Of the 201 who responded to a non-moral

64

behavior, 108 (54%) were excluded. This difference was not significant, x

2

= 1.17, p

> .10. Neither group was more likely than the other to list only for beliefs or only against beliefs; considering a moral behavior was not different from considering a non-moral behavior. For both behaviors, participants were equally likely to consider both for and against beliefs.

Experiment Three: Discussion

The primary hypothesis in this study was that when people are considering behaviors framed morally, they should access relevant for and against beliefs in a pattern that is different from the same behaviors presented in a non-moral frame.

Specifically, moral behaviors should not cluster in for and against beliefs, and nonmoral behaviors should show clustering of for and against beliefs. This hypothesis was not supported. Regardless of whether behaviors were presented morally or not, participants were equally likely to cluster in groups of for and against beliefs. The framing of the behaviors did not influence how participants accessed their for and against beliefs about the behaviors.

The second major hypothesis was that regardless of the framing of the behavior, participants should cluster their affective and cognitive beliefs into groups.

Across all behaviors, participants did tend to cluster their beliefs into these groups.

These results indicate that there is a tendency to access beliefs in affective and cognitive clusters.

However, the problem of the large degrees of freedom rises again. And once again, to determine whether participants have a tendency to cluster their beliefs, a

65

binomial approach can be used. Regarding for and against clustering, 10 out of the

10 behaviors resulted in a positive ARC, z = 3.16, p < .01. For the affective and cognitive clustering, seven out of the 10 behaviors resulted in a positive ARC, z =

1.27, p < .01. Generally, participants tend to use for/against clustering and affective/cognitive clustering.

Another hypothesis addressed the proportions of beliefs. Participants who were considering moral behaviors were expected to list more for beliefs than against, and were expected to list more for beliefs than those who were considering nonmoral behaviors. The data indicate that participants did tend to list more for beliefs, but the behavior under consideration did not influence that proportion. Participants who were considering moral behaviors were also expected to be more likely than those considering non-moral behaviors to consider only beliefs that were consistent with morality. If this were true, then more people would be dropped from the moral behavior analyses because they had listed only one belief type. Instead, people from both groups were equally likely to be excluded. Additionally, across both groups,

90.9% of those who were dropped listed all for beliefs.

One confound with this study may involve differences in perceived morality.

As described earlier, Kant described perfect duties as those which must always be performed, and imperfect duties are those that must be performed at least sometimes

(as cited in Trafimow & Trafimow, 1999). Other differences between types of moral behaviors may be associated with the expected emotional reaction if the behavior is performed. For example, Lindsay-Hartz (1984) proposes that violations of morality

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might result in either guilt or shame. With guilt, the actor perceives that they did something wrong; they are motivated to set things right. With shame, the actor perceives that they are bad; the act is internalized, and the actor is motivated to hide.

In both Kant’s and Lindsay-Hartz’s descriptions, there is more than one interpretation of what constitutes a moral behavior—or a violation of morality. If some people perceive a particular behavior as moral and others view it as unintelligent (e.g., “a cashier gives you too much change and you return it”), then the hypothesis that clustering occurs for non-moral behaviors may not have been tested after all. In the pilot study for this experiment, participants rated behaviors on a moral/immoral scale. It might have been more appropriate to ask participants to rate each behavior on a perfect/imperfect morality scale, or it might have been appropriate to ask participants to rate whether they would feel guilt or shame when violating that behavior. Differences in the type of morality involved may be influential in how participants access relevant beliefs about these behaviors.

On the surface, the two behaviors that initially inspired this set of studies,

“using a condom” and “having unprotected sex,” are closely related. However, previous studies and the replication study described earlier have indicated that people access beliefs about these two behaviors differently. The first three experiments in the current set of studies addressed whether the difference in accessing beliefs about behaviors was correlated with the behaviors’ specificity, the inherent capacity to act as primes for different self-concepts, or the associated level of morality. None of these experiments have found consistent results explaining the difference in

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accessing beliefs about these behaviors. Perhaps the framing of the behavior made associated risks and benefits more or less salient. This is the focus of the final study.

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EXPERIMENT FOUR: FRAMING EFFECTS

There is a significant body of research indicating that the way a decision is presented influences decision making. Tversky and Kahneman (1981) found that when a decision is presented as a question of obtaining gains, people tend to be riskaversive; when the same decision is presented as a question of avoiding losses, people become more risk-taking. It is possible that when the behavior was framed as

“using a condom,” people perceived it as a possibility of obtaining gains (e.g., having sex). When the behavior was framed as “having unprotected sex,” people might have thought about the behavior as a question of avoiding losses (e.g., avoiding sexually transmitted diseases and unwanted pregnancies). The framing of the beliefs might have influenced how people accessed relevant beliefs.

Another possibility is that the behaviors “using a condom” and “having unprotected sex” were processed differently because the implied decisions in each case were different. Levin, Schneider, and Gaeth (1998) expanded on Tversky and

Kahneman’s work and proposed that there are three kinds of framing of decisions; two of these framings are relevant here. The first is “risky choice,” which is consistent with Tversky and Kahneman’s original approach. The second is “attribute framing,” in which some characteristic of an object is the focus of the decision.

According to Levin et al., object attributes can be positive or negative, and the valence of the attitude object motivates people to access beliefs that are similar in valence. The differences in decision making are a result of the accessing of valenceconsistent information in memory. Similarly, van Schie and van der Pligt (1995)

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compared “risky choice” and “outcome salience” behaviors, and found that the decision making processes were different, given the different framing of decisions.

The different processes associated with framing effects in decision-making may be related to how beliefs about the decision are accessed. For example, the increased risk-taking that is associated with avoiding losses may be a result of randomly accessed for and against beliefs. If these beliefs are randomly accessed, people may be less able to weigh the costs and rewards, and may take more risks. If

“having unprotected sex” is perceived as a risky behavior, then people may be randomly accessing relevant beliefs.

In comparison, “using a condom” may be a type of attribute framing.

Perhaps people perceive this behavior as positive, which would motivate them to access primarily positive beliefs. According to Levin et al. (1998), these positive beliefs should be linked to each other in memory because of their shared valence. If the valence of the attribute is positive, then positive beliefs should be listed together, and if negative beliefs are accessed, they should be listed together.

If the different clustering of beliefs about behaviors is based on the framing of the behavior, and both “having unprotected sex” and “not using a condom” are perceived as equally risky, then beliefs should be listed in the same way – that is, no clustering of for and against beliefs should occur. Similarly, if both “using a condom” and “having protected sex” act as attribute framing, the clustering of beliefs for both of these behaviors should be similar: there should be clusters of for and against beliefs for both of these behaviors.

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Experiment Four: Materials and Method

Participants were asked to list eight beliefs they had about one of four behaviors. These behaviors were taken from the Duran and Trafimow (2000) and

Trafimow and Sheeran (1998) studies. The first behavior was “not using a condom the next time you have sex with another person.” The second was “having protected sex next weekend.” The third, “using a condom the next time you have sex with another person,” and the fourth, “having unprotected sex next weekend,” were also included as further replications of the previous studies. The first behavior was completed by 22 participants, the second by 37, the third by 20, and the fourth by 45.

Participants also completed a demographics page on which they indicated their sexual activity, as in Experiment Two.

Experiment Four: Results

Participant responses were coded as either for or against the behavior, and as either affective or cognitive, as before, and ARC statistics were computed.


Participants who listed only one belief type were excluded from the ARC analyses. Of the 124 lists of beliefs, 67 (54%) were included in the analyses of for and against clustering.

T-tests were run on the ARC scores of each behavior separately (see Table 9 for the original N, the usable n, the ARC scores, the t-score and significance level of each behavior). Consistent with previous literature and the replication study, participants who were asked about using a condom tended to cluster their for and

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Table 9

Clustering of For and Against Beliefs, Condom Use and Protected Sex Behaviors

____________________________________________________________________


____________________________________________________________________

Using a condom the next time you have sex with another person

Not using a condom the next time you have sex with another person

20

22

14

15

.33

.07

2.53 .03

.45 .66

Having protected sex next weekend

Having unprotected sex next weekend

37

45

19

19

.10

.12

.47 .64

.96 .35


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against beliefs, M = .33, t (13) = 2.53, p < .03. And, consistent with previous literature and the replication study, participants who were asked about having unprotected sex next weekend did not cluster their beliefs in groups of for and against, M = .12, t (18) = .96, p > .35. Neither of the two new behaviors resulted in participants grouping their beliefs in clusters of for and against. When asked about not using a condom, the mean ARC score was .07, t (14) = .45, p > .66. When asked about having protected sex next weekend, the mean ARC score was .10, t (18) = .47, p > .64. While all of the ARC scores were positive for these four behaviors, the only significant condition was when participants were asked about using a condom.

An ANOVA comparing risky framing (unprotected sex and no condom) to attribute framing (protected sex and using a condom) was not significant, F (1, 65) =

.35, p > .55. The framing of the behavior with the emphasis on either risk or attribute did not make a significant difference in how participants accessed their for and against beliefs.

Because there was no significant difference between the two belief types, data were collapsed across all behaviors. The mean ARC score, .19, was significantly different from zero, t (60) = 2.30, p < .03. Participants had an overall tendency to cluster their for and against beliefs.


Participants who listed only one belief type were excluded from the ARC analyses. Of the 124 lists of beliefs, 96 (77%) were included on the analyses of affective and cognitive clustering.

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T-tests were run on the ARC scores of each behavior separately (see Table 10 for the original N, the usable n, the ARC scores, the t-score and significance level of each behavior). In all of the behaviors, the mean ARC scores were positive. In three of the four behaviors, clustering of affective and cognitive beliefs was significant.

For participants in the using a condom condition, the mean ARC score was .36, t

(17) = 2.75, p < .01. In the protected sex condition, the mean ARC score was .48, t

(20) = 3.19, p < .01. In the unprotected sex condition, the mean ARC score was .22, t (34) = 2.21, p < .04. Clustering of affective and cognitive beliefs was not significant in the not using a condom condition, M = .05, t (21) = .30, p > .76.

An ANOVA comparing risky framing (unprotected sex and no condom) to attribute framing (protected sex and using a condom) was significant, F (1, 94) =

4.42, p < .04. Attribute framing (M = .42) resulted in more affective/cognitive clustering than risky framing (M = .14).

To determine if participants had a general tendency to cluster their affective and cognitive beliefs, the data was collapsed across all behaviors. The mean ARC,

.26, was significantly different from zero, t (95) = 4.03, p < .01. Participants had a tendency to cluster their affective and cognitive beliefs.

Sexual Activity

Pearson’s correlations were run to determine if participant’s responses on the sexual activity question were associated with the level of for/against and affective/cognitive clustering. The correlation between sexual activity and for/against clustering was .08 (p > .49), and between sexual activity and affective/cognitive

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Table 10

Clustering of Affective and Cognitive Beliefs, Condom Use and Protected Sex

Behaviors

____________________________________________________________________


____________________________________________________________________

Using a condom the next time you have sex with another person

Not using a condom the next time you have sex with another person

Having protected sex next weekend

20

22

18

22

.36

.05

2.75 .01

.30 .76

37 21 .48 3.19 .01

Having unprotected sex next weekend 45 35 .22 2.21 .03


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clustering was -.07 (p > .44). This implies that the level of sexual activity does not influence the cognitive organization of beliefs about either condom use or protected sex.

Experiment Four: Discussion

This experiment tested the hypothesis that both “having protected sex” and

“using a condom” would show similar for and against clustering effects, because in both of these cases, the behaviors were framed as gains to be obtained. By the same token, because “having unprotected sex” and “not using a condom” are both framed as consequences to be avoided, they should show similar clustering effects. This hypothesis was not supported. Only “using a condom” showed significant clustering of for and against beliefs.

There was a difference in how participants clustered their affective and cognitive beliefs, given the different framing. Participants demonstrated more clustering in the attribute framing conditions. This result may be driven by the nonsignificant clustering in the “not using a condom” condition, or the increased degrees of freedom (from a low of 17 for individual behaviors to a total of 94) may have resulted in a Type II error. On the other hand, it is possible that when considering attribute framing, participants were more likely to cluster their affective and cognitive beliefs. Perhaps when the consequences of the behavior are positive, participants employ affective and cognitive clustering of beliefs, and when the consequences are mixed, for and against clustering is more informative.

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Looking at the data collapsed across all behaviors revealed that participants do cluster their for/against beliefs and their affective/cognitive beliefs. Again, however, it is important to keep in mind the caution about the increased degrees of freedom when collapsing across all of the data. It is possible that the findings of these t-tests were due to a Type II error.

For both for/against clustering and affective/cognitive clustering, the mean

ARC scores were positive. The likelihood that all four of the four conditions are positive is quite small, z = 2, p < .03. This binomial test indicates that participants tended to cluster their for/against beliefs and their affective/cognitive beliefs.

Because all four of the behaviors had to do with sexual activity, it would not have been surprising if participants’ sexual activity was correlated with the clustering of their beliefs. Instead, the correlations were all nonsignificant. As in Experiment

Two, levels of sexual activity did not influence the clustering of beliefs. This indicates that whether they are sexually active or not, participants are similar in the way they process the decision making about safe sex and condom use.

It is important to note that the behaviors in this study were assigned to each category based on the assumptions of the researcher. It is possible that all four behaviors may have been perceived as equally risky. If all four behaviors were perceived as equally risky, then the lack of significance of for and against clustering between groups is not surprising. Additionally, because these assumptions about the classifications of the behaviors were made, definitive conclusions regarding differences in clustering due to framing effects cannot be made.

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GENERAL DISCUSSION

This set of studies was motivated by conflicting findings in previous research. People accessed their beliefs differently about two seemingly related behaviors: using a condom and having unprotected sex. What motivated them to group their for and against beliefs about using a condom, but not about having protected sex? Four different hypotheses were tested, and none of them provided a clear answer to this question.

Experiment One addressed the question of general and specific behaviors.

The hypothesis was that for and against clustering should occur for specific behaviors, and not for general behaviors. Instead, there was no difference in the clustering of for and against beliefs for general or specific behaviors; all of the mean

ARC scores were positive, but only two of the twelve were significantly different from zero. Because the majority of both the general and specific behaviors did not demonstrate for and against clustering, there is no reason to believe that the specificity of a behavior influences for and against clustering. Regarding affective and cognitive beliefs, almost all of the behaviors resulted in clustering, regardless of the specificity. In this case, the specificity of behaviors did not influence affective and cognitive clustering.

Experiment Two used a priming paradigm to determine if the salience of different selves influenced the accessing of beliefs. The primary hypothesis in this experiment was that people would cluster their for and against beliefs after exposure to a private-self prime, and would not cluster their for and against beliefs after

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exposure to a collective-self prime or no prime. Instead, the priming did not have a significant effect on clustering of either for/against beliefs or affective/cognitive beliefs. Clustering of for and against beliefs rarely happened, and clustering of affective and cognitive beliefs happened most of the time. Priming different self concepts does not appear to influence the clustering of affective and cognitive beliefs.

In Experiment Three, behaviors were presented as either a question of morality or a question of costs and rewards. The major hypothesis in this experiment was that for and against beliefs would be clustered when a behavior was presented as non-moral, and for and against clustering would not occur when the same behavior was presented as moral. Instead, for half of the behaviors, the mean ARC was significant for both for/against clustering and affective/cognitive clustering; however, some of these behaviors were moral, and some were cost/reward. Overall, there was no difference between the two types of presentations, indicating that whether the behavior is thought of as a moral decision or not does not appear to influence the likelihood of their clustering beliefs.

Experiment Four addressed the question of phrasing. In that study, only the

“using a condom” condition resulted in significant for and against clustering, although clustering for all four behaviors was positive. This implies that the way people access their for and against beliefs about “using a condom” may be different from the way they access their beliefs about “not using a condom,” “having protected sex,” and “having unprotected sex,” but again, the cause of the difference

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regarding these behaviors is not clear. In this experiment, participants almost always accessed their affective and cognitive beliefs in clusters; the exception was for the

“not using a condom” condition.

Looking across all of the experiments, there does not seem to be a predictable pattern of when participants access their for and against beliefs in clusters. (See

Table 11 for a summary of results.) Only nine behaviors resulted in significant for and against clustering, including “returning a watch that was found in the library,”

“donating blood for money,” and “washing your dishes immediately after dinner tonight.” On the other hand, almost all of the for/against mean ARC scores—30 out of 32—were positive, implying a general tendency of for/against clustering, z = 4.95, p > .01. The notable exceptions to this pattern were in the priming study: for participants primed with the private self and with the “neutral” prime, the mean ARC was negative, although not significantly so. If each behavior , rather than each individual, is considered a case, then this set of studies indicates that people do tend to access their beliefs in clusters of for and against.

In comparison, affective and cognitive clustering was a relatively robust finding. Most of the behaviors across all of the experiments resulted in affective/cognitive clustering. Additionally, 29 of the 32 behaviors resulted in positive mean ARC scores; 22 of these were at least marginally significant. The likelihood that 29 of the 32 behaviors were positive is small, z = 4.60, p < .01.

Given this binomial test, this set of studies indicates that people tend to access their affective and cognitive beliefs in clusters.

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Table 11

Summary of For/Against Clustering and Affective/Cognitive Clustering Across All

Studies

____________________________________________________________________

Study For/Against Clustering Affective/Cognitive Clustering

____________________________________________________________________

Specificity of Beliefs

General 0/6 Significant 4/6 Significant

Specific 2/6 Significant 3/6 Significant

Salience of Social Norms

Collective Self

Private/Neutral

Morality

Moral

Non-moral

Direction of Phrasing

Benefit Framing

Risky Framing

1/2 Significant

0/4 Significant

1/5 Significant

3/5 Significant

1/2 Significant

0/2 Significant

2/2 Significant

3/4 Significant

2/5 Significant

4/5 Significant

2/2 Significant

1/2 Significant

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Although this set of studies was rather disappointingly non-significant in regard to the for and against clustering, some of the findings were interesting. One of the most noticeable outcomes was the amount of data that could not be included because only one belief type was listed. Only 43% of the data was usable for the clustering analyses of for and against beliefs. This indicates that participants had a tendency to list only for or only against beliefs. One possible explanation is that in considering their beliefs about the behavior, they may have made a decision about the behavior, and only listed beliefs that would support their decision, perhaps to avoid cognitive dissonance.

Before any definitive statements can be made about whether participants cluster their for and against beliefs, it would be appropriate to increase the amount of useable data. The priming approach used by Duran and Trafimow (2000) could be used to encourage participants to list both for and against beliefs. In that study, participants read about beliefs purportedly listed in a previous study. These purported beliefs included two “for” beliefs and two “against” beliefs. The benefit of encouraging the listing of both for and against beliefs is that clustering effects can be examined more closely; the drawback is that it may not represent the everyday cognitive processes that people employ in decision-making. Because cognitive organization of beliefs about attitudes is an area that is only recently being studied, both questions—whether people access for and against beliefs and whether people tend to cluster these beliefs once they are accessed—are important to address in future research.

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Another possible explanation for the large amount of unusable data in the for/against clustering analyses is that affective and cognitive clustering may be a primary heuristic for accessing beliefs, and for and against clustering is secondary.

If this is true, then participants should have listed several affective beliefs together and several cognitive beliefs together; within these clusters, there should be for and against clusters. In the current set of studies, participants listed a maximum of only eight beliefs. If there were clusters of for and against beliefs within each of the affective and cognitive clusters, the ARC scores should have been positive, but not necessarily significant—which is consistent with the findings across these studies.

To test whether for/against clustering is subsumed by affective/cognitive clustering, a priming paradigm could be utilized. Participants could be affectively or cognitively primed, and then asked to list beliefs about behaviors. If for and against clustering occurred within each of the primes, this would provide support for the hypothesis that affective/cognitive clustering is a primary heuristic.

The difficulty of unusable data was also present in the affective/cognitive analyses, in which only 60% of the data were usable. Across all four experiments, the mean number of affective beliefs was 1.69; the mean number of cognitive beliefs was 5.08. Not surprisingly, this difference was significant at the .001 level, t (1521)

= -34.33. When participants were asked to list their beliefs about behaviors, there was a strong tendency to list cognitive beliefs. This may be evidence that it is easier to list cognitive beliefs than affective beliefs. Participants may have avoided listing affective beliefs because to do so might make them vulnerable to ridicule, or they

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may not have had the vocabulary to express their relevant emotions about the behavior. They also may have been motivated to list more cognitive beliefs because the data were collected at school; cognitive beliefs about behaviors may have been more accessible than affective beliefs.

Individual Differences

Because this set of studies indicates that there is a general trend to cluster in groups of for and against beliefs, but this clustering was not always significant, it is possible that unmeasured individual differences influence how beliefs are accessed.

There may be a general tendency for people to cluster their for and against beliefs, but for a significant minority, some individual differences may influence the accessing of different beliefs. Equally possible is that most people do not cluster their for and against beliefs, but for a significant minority, this type of accessing is used. There is evidence that individual differences influence the relative influence of attitudes, subjective norms, and perceived behavioral control on behavioral intentions (e.g., Sheeran et al., 2002; Trafimow & Finlay, 1996, 2001); it is possible that individual differences can also influence how behavioral beliefs are accessed.

One individual difference that may have influenced beliefs in the current set of studies is the relative influence of cognition and affect. Crites et al. (1994) found that some people base their attitudes more on affective information, while others utilize more cognitive information. Similarly, Trafimow et al. (in press) found that some people are more affectively controlled across several behaviors—that is, affect contributes more to behavioral intentions—and some are cognitively controlled—

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cognition contributes more to behavioral intentions. They found that affect is more relevant than cognition, and that more people are under attitudinal control.

Whether people are more affectively or cognitively controlled is likely to influence whether they access their behavioral beliefs in clusters. For example, affectively controlled people might access beliefs that are associated with stronger emotions, and not whether the belief is for or against the behavior; they may be less likely to cluster their for/against beliefs. Cognitively controlled people might be more likely to systematically access beliefs in order to form an overall “for” evaluation and an overall “against” evaluation. If this is true, then affectively controlled people would form clusters of affective and cognitive beliefs, but not necessarily clusters of for and against beliefs; cognitively controlled people would be likely to access their beliefs in clusters of for and against, but not necessarily affective and cognitive. Because behavioral intentions were not measured in the current studies, testing of the correlation between affective or cognitive control and the accessing of beliefs will have to be performed in the future.

Individuals may also differ in their level of perceived behavioral control

(PBC). Sheeran et al. (2002) found that the correlation between PBC and behavioral intentions can be influenced by individual differences. For a minority of individuals, those who were “PBC controlled,” the predictive weight of PBC is higher across behaviors. People who were PBC controlled scored lower on a measure of selfefficacy and higher on a measure of external locus of control. As Trafimow, Finlay, et al. (2002) found, PBC is a measure of perceived difficulty as well as perceived

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control. Perhaps those who are PBC controlled are not accessing beliefs in clusters of for and against; instead, they may be accessing behavioral beliefs according to how difficult it would be to obtain or avoid the consequences. For example, in considering using condoms and having protected sex, they may perceive beliefs such as “condoms interrupt the mood” and “my partner says we have to use condoms” as similarly difficult, and may access them together. In this case, clustering of for and against beliefs would not necessarily occur. Assuming that the populations used in the current studies are comparable to the populations in the Sheeran et al. (2002) study, 22.9 to 28.2% of the participants are PBC controlled. This percentage is large enough to influence the significance levels of clustering found in the current studies.

While there was a tendency for participants to form clusters of for/against beliefs and affective/cognitive beliefs in the current set of studies, the variability of clustering implies that individuals do not necessarily make decisions in the same way, even for the same behavior. In future research, it would be appropriate to determine whether people are affectively, cognitively, or PBC controlled; each of these dimensions influences the contribution of different factors to behavioral intention, and the differences may be due to how different beliefs are accessed. As

Trafimow, Sheeran, Lombardo, et al. (in press) point out, in order to understand the complete picture of human behavior, the possible influence of individual difference variables should be considered.

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Implications and Applications

This set of studies was conducted in order to determine factors that influence the accessing of behavioral beliefs. If some of the factors could be determined, then one area of future research could focus on how changing the accessing of behavioral beliefs might change behavioral intentions. This information could have practical applications, such as in intervention research. While the results of the four studies were not clear cut, some suggestions for practical applications can still be made.

The first suggestion is directed to the tendency of participants to list only one type of belief. In the current set of studies, more than half of the data (57%) could not be included in the analysis of for and against clustering because participants listed only one belief type – either for or against. While intentions were not measured in these studies, it seems likely that those who intend to perform a behavior would list only for beliefs, and those who do not intend to perform the behavior would list only against beliefs. If this is true, then researchers who seek to change behavior should be aware that many people access only one type of belief. In designing interventions, it may be appropriate to begin with a step that encourages people to access both for and against beliefs.

Similarly, some people tended to list either all cognitive or all affective beliefs; as a result, 40% of the data could not be used in the analyses. The assumption can be made that these people listed only one belief type because that is the belief type on which they base their decisions about behaviors. This is consistent with other researchers who have found that some people are under cognitive control,

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while others are under affective control (e.g., Trafimow, Sheeran, Lombardo et al., in press). More effective interventions would probably be those that match affective arguments with affectively controlled individuals, and cognitive arguments with cognitively controlled individuals.

For those who listed more than one belief type, some formed clusters and some did not. It would be appropriate to determine the correlation between behavioral intentions and clustering, especially the clustering of for and against beliefs. It seems likely that those who do not cluster their beliefs are more ambivalent about the behavior, and their behavioral intentions may be more easily influenced. Those who have formed clusters of for and against beliefs may have more stable intentions, and interventions may take longer.

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CONCLUSION

This set of studies was intended to be the first step in understanding what motivates people to access their beliefs in different ways. The hypotheses that were proposed received mixed support; however, some patterns were revealed. First, people tend to cluster affective and cognitive beliefs. Second, people have a tendency to list beliefs of only one type when considering behaviors. These tendencies should be investigated further to determine factors that influence accessing of beliefs, as well as whether the manner in which people access beliefs influences their behavioral intentions.

Research regarding the theory of reasoned action has most often been directed to the link between attitudes, subjective norms, perceived behavioral control, and behavioral intentions; less has been directed to the underlying cognitive processes. As Hastie (2001) points out, one area that is important to address in the next decade is determining how deliberate decision-making problems are represented cognitively. By eliminating some of the seemingly likely prospects for clustering of beliefs, this set of studies is a first step in addressing this point.

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