debiasing of pessimistic judgments associated with anxiety

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Journal of Psychopathology and Behavioral Assessment (JOBA) pp1164-joba-484414 March 27, 2004 8:57 Style file version June 25th, 2002

Journal of Psychopathology and Behavioral Assessment, Vol. 26, No. 3, September 2004 (C© 2004)

Debiasing of Pessimistic Judgments Associated With Anxiety

Bret G. Bentz,1,4 Donald A. Williamson,2 and Susan F. Franks3

Accepted December 20, 2003

This study tested the Consider-An-Alternative debiasing procedure and its ability to reduce pessimisticjudgmental predictions associated with anxiety. Study participants were randomly assigned to either acontrol group or a debiasing group and were asked to rate the likelihood of hypothetical events. Levelof trait anxiety was assessed to assign participants to a “normal” and a highly anxious group. Theresults showed that the debiasing procedure was effective in the reduction of pessimistic judgmentalpredictions for participants with both high and “normal” levels of trait anxiety. Specifically, thegeneration of alternative positive information significantly reduced pessimistic predictions of futureevents. The results were discussed in terms of availability of information in memory and cognitivebiases.

KEY WORDS: judgmental bias; debiasing; anxiety; cognitive bias.

Cognitive research pertaining to the developmentand maintenance of emotional disorders has historicallyfocused on attentional and memory biases (Mineka &Sutton, 1992). A judgmental bias in the prediction offuture negative events associated with anxiety (Bentz &Williamson, 1998; Bentz, Williamson, & Smith, 1999) anddepression (Butler & Mathews, 1983; MacLeod & Byrne,1996) has also been reported. These studies have foundthat the judgment of individuals with higher levels of anx-iety or depression is biased toward a pessimistic predic-tion of the likelihood of future negative events. This biasedjudgment of future events may be the result of the acces-sibility of similarly pessimistic information within mem-ory (Hirt & Markman, 1995; Mumma & Wilson, 1995;Vaughn & Weary, 2002).

An availability heuristic explanation of a judgmentalbias fits well with cognitive theories of emotional disor-ders. For example, Bower (1981), Lang (1985), and Foaand Jaycox (1999) have proposed theories which hypoth-esize that threat-related information may be encoded into

1Department of Psychology and Philosophy, Texas Woman’s University,Denton, Texas.

2Pennington Biomedical Research Center, Baton Rouge, Louisiana.3University of North Texas Health Sciences Center, Fort Worth, Texas.4To whom correspondence should be addressed at Department of Psy-chology and Philosophy, Texas Women’s University, P.O. Box 425470,Denton, Texas 76204-5470; e-mail: [email protected].

organized systems that are easily accessed and serve toactivate fear.

The reduction of judgmental biases has been investi-gated in studies of risk perception, clinical judgment, anddecision making. Debiasing strategies such as Consider-An-Alternative, Consider-The-Opposite, note taking,educational techniques, and exposure to life events havebeen studied. In general, the investigation of debiasinghas supported the hypothesis that techniques that serve toactivate alternative information within memory result inpositive debiasing effects (Hirt & Markman, 1995; Lim& Benbasat, 1997; McKenna & Albery, 2001; Mumma& Wilson, 1995). Most studies of debiasing have focusedon simple strategies, common judgmental errors, andoptimistic biases in normal populations. Debiasing tech-niques have yet to be applied to a pessimistic judgmentalbias associated with high levels of depression or anxiety.

There is a small body of research investigating thecognitive changes that result from the treatment of emo-tional disorders (Mathews, Mogg, Kentish, & Eysenck,1995; Mogg, Bradley, Millar, & White, 1995; Westling& Ost, 1995). These studies have shown a reduction incognitive bias as a result of treatment. The specific com-ponent of the intervention procedures that resulted in thebias reduction has not been studied, however.

The present investigation applied the Consider-An-Alternative debiasing strategy (Hirt & Markman,1995) to the reduction of a pessimistic judgmental bias

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174 Bentz, Williamson, and Franks

associated with anxiety (Bentz et al., 1999; Bentz &Williamson, 1998). The primary goal of the study was toinvestigate the Consider-An-Alternative debiasing proce-dure for the reduction of a pessimistic judgmental bias. Asecondary goal was to replicate previous studies that haveshown a judgmental bias associated with anxiety (Bentzet al., 1999; Bentz & Williamson, 1998). To evaluate thesegoals, a 2× 2 × 2 × 2 (Anxiety × Gender× Inter-vention group× Trial) repeated measures experimentaldesign was used for the study.

It was hypothesized that highly anxious participantswould report higher probability estimates of future threat-related events relative to participants with a “normal”level of anxiety, a main effect for anxiety. It was also hy-pothesized that a two-way interaction of sex and anxietygroup upon threat-related ratings would be found. Specif-ically, it was expected that highly anxious female partici-pants would report higher pessimistic predictions of futureevents in comparison to all male participants and femaleparticipants with a lower level of anxiety. The interactionof sex and anxiety was expected because of previous re-search that has shown similar results (Bentz et al., 1999;Bentz & Williamson, 1998).

Finally, to test the primary goal of the study, a three-way interaction of intervention group, trial, and anxi-ety group upon threat-related ratings was hypothesized.Specifically, highly anxious participants in the debiasingintervention group were hypothesized to have a significantreduction in pessimistic judgment ratings from the pre- toposttest.

METHOD

Participants

A sample of 476 (197 male and 279 female) under-graduate students was screened for participation in thestudy. The participants were screened for inclusion in highand “normal” trait anxious groups using the trait formof the State–Trait Anxiety Inventory (STAI: Spielberger,Gorsuch, & Lushene, 1970). The criterion for inclusionwithin the highly anxious group was set at a STAI traitanxietyT-score of 65 or greater. Participants within the“normal” anxiety group were then chosen from the re-maining sample based upon several criteria. For inclusionin the “normal” group, a STAI trait anxietyT-score of 60or below was required. Second, all participants within the“normal” group were matched to the high anxiety partic-ipants on age, sex, and race to control for variability dueto these demographic variables.

All participants who had STAI trait anxiety scoresthat fell between 61 and 64, or were not matched to an

equivalent participant on age, sex, and race and were notincluded in any subsequent analysis. Since a smaller per-centage of participants were found with aT-score of 65 orabove on the STAI, a much larger pool of participants werefound who had STAI scores below aT-score of 60. Oncecomplete matching of participants was completed, any re-maining subjects were excluded from further analysis. Atotal of N = 132 undergraduate students met the criteriafor inclusion in the study (high anxietyn = 66, “normal”anxietyn = 66). The sample sizes in each of the four cellsof the pretest phase of the experiment for highly anxiousparticipants weren = 15, n = 16, n = 15, andn = 16,for male debiasing, female debiasing, male control, andfemale control participants, respectively. The sample sizesin each of the four cells of the pretest phase of the ex-periment for the “normal” participants were equal to thehighly anxious group. The racial composition of the studysample was primarily Caucasian (n = 106, 80.3%), withthe remaining participants identified as African American(n = 16, 12.1%), Hispanic (n = 6, 4.5%), and other ethnicorigin (n = 4, 3.0%). The average age of the participantswas 22.07 years (SD= 4.08 years, Range= 18–43 years).Finally, the average STAI trait anxietyT-score for the par-ticipants within the highly anxious group was 69.41 (SD=4.70, Range= 65–82). For the “normal” control group,the average STAI trait anxietyT-score was 49.77 (SD=6.01, Range= 35–60).

Assessment Measures

State–Trait Anxiety Inventory (STAI)

The STAI (Spielberger et al., 1970) is a paper andpencil self-report inventory designed to measure both stateand trait anxiety. The trait form was used for the mea-surement of the level of anxiety in all participants for thegrouping of subjects into highly anxious and “normal”groups. Evidence for the reliability of the STAI has beenreported in previous studies (Martuza & Kallstrom, 1974).

Experimental Stimuli

A total of 20 experimental stimuli were constructed.Ten stimuli presented threat-related situations and 10 stim-uli presented positive situations with content matched tothe threat-related stimuli. For each of the 20 stimuli, theparticipant made a judgment as to how probable a hy-pothetical outcome was based on the situation that wasjust presented. Participant probability ratings were madeon the number line rating system employed by Bentzand Williamson (1998) which used a deliminated num-ber line with anchors of 0 and 100%. In addition, the

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Debiasing of Pessimistic Judgments 175

stimuli themselves were constructed, with modifications,from the threat-related stimuli used and validated by Bentzand Williamson (1998). The threat and positive stimuli arepresented in Appendix.

Half of each of the positive and threat-relatedsituations used questions that were reverse scored forboth of the trial pre- and posttest. The questions werecounter balanced for the pre- and posttest stimuli such thatthe positive and threat stimuli each received a pessimisticquestion and a reverse scored optimistic question. Thus,the 20 experimental stimuli included five stimuli withpositive situations and optimistic questions, five stimuliwith positive situations and pessimistic questions, fivestimuli with threat-related situations and optimistic ques-tions, and finally five stimuli with threat-related situationsand pessimistic questions. The purpose of the inclusion ofpositive situations and reverse scored questions was to dis-guise the objective of the experimental stimuli to minimizethe demand characteristics inherent within the stimulithemselves.

The probability ratings from the positive situationswere not used in the analysis because the study only in-vestigated the reduction of threat-related probability rat-ings. Only threat-related ratings were used and reversescored questions were scored in terms of their pessimisticoutcome. Pilot data were collected prior to the actual ex-periment that demonstrated the stimuli themselves werenot creating response set or halo effects.

Debiasing Intervention

In the intervention phase of the repeated measuresdesign, the debiasing group received the same 20 threat-related and positive scenarios as presented in the pretest.However, after reading each of the 20 scenarios, the par-ticipants generated three alternative positive outcomes foreach of the situations. This Consider-An-Alternative inter-vention procedure was constructed as a modification of thedebiasing procedure used by Hirt and Markman (1995).The participants generated outcomes by writing threeshort positive alternative outcomes for each situation.

Control Intervention

In the intervention phase of the repeated measuresdesign, the control group received the same 20 experi-mental stimuli as presented to the debiasing interventiongroup. However, instead of generating positive alterna-tive outcomes, the participants wrote all of the nouns andverbs found in each situation paragraph. The recording ofthe parts of speech in the control condition was includedto ensure that the participants completely read each situa-tion in order to replicate the activity of participants within

the debiasing group. The identical presentation of the 20experimental situations was completed to control for anyeffects due to habituation to the scenarios.

Experimental Procedures

Undergraduate participants were recruited throughsign-up sheets and solicitations in psychology classes.Participants were volunteers recruited through middleand upper division undergraduate psychology classes ata large north Texas university. Recruitment of volunteersoccurred at the end of regular class times with the exper-iment beginning just after class in the same room or anadjacent room. Participants responding to sign-up sheetadvertisements were instructed to arrive at classes whererecruitment was scheduled with the class instructor. Ex-tra credit was given for participation with the amount andtype of credit determined by each class instructor.

The experiment began with all participants com-pleting an informed consent. After consent was obtained,participants then completed a short demographic ques-tionnaire and the trait form of the STAI (Spielberger etal., 1970). Participants were then randomly assigned toeither the debiasing or control intervention.

After group assignment, all participants completedthe randomly presented experimental stimuli by readingthe situations and making their probability judgments.There was no time limit for completion of the probabilityratings. Following this pretest, each of the two experimen-tal groups completed their respective tasks, the debiasingor control intervention. Then, after the experimental ma-nipulation all participants again completed the randomlypresented experimental stimuli, the posttest.

Participants completed the experiment in group ses-sions with group sizes that ranged from 10 to 100. Thegroup sizes depended upon the class size, number of vol-unteers, and number of participants from posted sign-upsheets. Intervention condition assignment was made ran-domly within each experimental session. Instructions andquestions about each condition were handled individuallyby multiple experimenters, the first author and researchassistants, at each session.

RESULTS

Measurement Checks5

Measures of internal consistency and test-retest reli-ability were calculated in order to test for the validity of

5The Measurement Checks and Manipulation Check analyses were in-cluded at the suggestion of reviewers of the manuscript during the peerreview process.

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aggregating probability judgment scores for the 10 threat-related stimuli and to ascertain the stability of the measure.Cronbach’s alpha coefficient for the pretest threat-relatedprobability ratings was .75. The aggregate test-retest re-liability using a Pearson correlation was calculated forthe control group participants across all 10 threat-relatedstimuli. The correlation was very high,r = .82, p < .01,indicating that the probability judgments were highly re-lated across the pretest to posttest interval.

Finally, a 2× 2 Analysis of Variance was conductedon the threat-related pretest ratings that compared the in-tervention groups as a function of anxiety level. The re-sults showed a significant main effect for anxiety group,F(1, 128)= 11.55, p < .001, and no effect for interven-tion group,F(1, 128)= 2.85, p > .10 or interaction ofthe two variables,F(1, 128)= 0.05, p > .10.

Manipulation Check6

Two graduate level students completed blind Likertscale ratings of the emotional valence level of all alter-natives generated during the debiasing intervention. TheLikert rating scale used anchors of negative one to indi-cate alternatives with a negative valence, zero for neutralalternatives, and positive one for positive alternatives. In-terrater reliability was found to be high with a Pearsoncorrelation ofr = .83, p < .001. Mean ratings for raterone and two were 0.91 and 0.95, respectively, indicatingthat participants in the debiasing condition did indeed gen-erate positive alternatives.

Finally, the valence rating for each participant wasaveraged and used in an independent samplest-test toevaluate the degree of positive alternatives generated asa function of anxiety group. The statistic was found tobe nonsignificant,t(31)= 0.93, p > .10, indicating thatthe highly anxious and “normal” groups generated equallypositive alternatives.

Threat Ratings Results

Probability ratings for each of the 10 threat-relatedsituations were scored and averaged to yield a mean threatprobability rating for the pre- and posttest of each partic-ipant. All participant ratings were scored in terms of thepessimistic probability outcome. These data were used asthe repeated measure-dependent variable.

First, an analysis was conducted on the threat-relatedprobability ratings using pretest data to investigate the

6See footnote 5.

first two hypotheses: (1) the initial threat probability rat-ings of the highly trait anxious group would be greaterthan the normal trait anxious group, and (2) the two-way interaction between sex and anxiety group. A 2×2 (High vs. “Normal” Anxiety× Male vs. Female Sex)analysis of variance was conducted on the average threat-related probability ratings. Significant main effects werefound for anxiety group,F(1, 128)= 62.25, p < .001,and for sex,F(1, 128)= 17.41, p < .001. The interactionof anxiety group and sex was found to be nonsignificant,F(1, 128)= 0.70, p > .10.

Second, a mixed factorial 2× 2 × 2 × 2 (Pre vs.Posttest× Control vs. Debiasing Intervention× Highvs. “Normal” Anxiety× Male vs. Female Sex) repeatedmeasures analysis of variance was conducted. The designincluded one within subject independent variable, the re-peated measure, and three between subject independentvariables. Results are presented in Table I.

Significant main effects were found for three of thefour independent variables. As hypothesized, a main effectfor anxiety group was found. The average threat probabil-ity ratings for the highly anxious group (M = 49.79%,SD = 13.20) were found to be significantly greaterthan the “normal” group (M = 42.41%,SD = 14.58).

Table I. Summary of the Mixed Factorial Anova Results on theTreat-Related Probability Ratings and Effect Sizes

Source df F p η d

Between subjectsAnxiety (A) 1 13.35∗ .00 .31 .45Intervention (I) 1 12.70∗ .00 .30 .44Sex (S) 1 47.00∗ .00 .57 .85A × I 1 0.05 .83 .00A × S 1 0.34 .56 .06I × S 1 0.44 .51 .06A × I × S 1 0.74 .39 .08Between error 124 (265.47)

Within subjectsTrial (R) 1 0.58 .45 .07R× A 1 0.85 .36 .08R× I 1 22.89∗ .00 .39 a

R× S 1 2.42 .12 .14R× A × I 1 0.07 .80 .03R× A × S 1 0.23 .63 .04R× I × S 1 1.01 .32 .09R× A × I × S 1 0.83 .37 .08Within error 124 (48.08)

Note. Values enclosed in parentheses are mean square error. Trial (R)source represents the repeated measure independent variable, pre- andposttest.aEffect sizes for the interaction are reported with the simple effectsttests.∗ p < .01.

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A significant main effect for the intervention group wasalso found. The average threat probability ratings for thecontrol group (M = 49.70%,SD= 14.38) were found tobe significantly greater than the debiasing group (M =42.51%,SD= 13.07). Finally, a significant main effectfor sex was found. The average threat probability ratingsfor female participants (M = 53.03%,SD= 13.07) werefound to be significantly greater than male participants(M = 39.18%,SD= 11.65). The main effect for trial wasfound to be nonsignificant.

All possible interaction effects involving the fourindependent variables were tested in the repeated mea-sures analysis of variance and are presented in Table I.The hypothesized three-way interaction of interventiongroup, trial, and anxiety group was not found to be sig-nificant. However, a significant interaction of interven-tion group and trial upon threat probability ratings wasfound. The average threat probability ratings of each ofthe four groups involved in the two-way interaction wereas follows; Control Group: Pretest (M = 47.97%,SD=14.05) and Posttest (M = 51.43%,SD= 16.07); Debi-asing Group: Pretest (M = 44.89%,SD = 13.86) andPosttest (M = 40.12%,SD = 14.28). The interactionof intervention group and trial is illustrated in Fig. 1.All other interaction effects were tested and found to benonsignificant.

Post hoc analysis of the simple effects within this sig-nificant interaction was conducted. A total of fourt-testswere conducted which were adjusted using the Bonferronicorrection, resulting in an alpha level ofp= .0125. Two in-dependent samplest-tests were conducted to examine theintervention group ratings. In addition, two paired sam-

Fig. 1. Plot of the interaction effect of the debiasing condition and trialon average threat-related ratings.

Table II. Simple Effects and Effect Size Results of the Interactionof Intervention Group and Trial Upon Threat-Related Ratings

d

Independent samplesIntervention group

Pretest t(130)= −1.63Posttest t(130)= 4.58∗ .80

Paired samplesTrial

Control t(61)= 3.08∗ .54Debiasing t(69)= −3.67∗∗ .64

∗ p < .001.∗∗ p < .005.

ples t-tests were conducted to examine the trial ratings.The results of the post hoc analysis and effect sizes arepresented in Table II.

The difference between the control and debiasinggroup ratings of the pretest was found to be nonsignifi-cant. However, at posttest the control versus the debiasingratings differed significantly. The paired samples analysisrevealed that the debiasing group ratings decreased signifi-cantly and the control group ratings increased significantlyfrom the pre- to posttest.

DISCUSSION

The study found that the pretest scores of highlyanxious participants were rated with a likelihood offuture threat-related events as more probable than partici-pants with a “normal” level of anxiety. As hypothesized, ahigh level of anxiety was associated with higher ratings offuture threat. This result is consistent with several previousstudies that found similar pessimistic predictions associ-ated with anxiety (Bentz et al., 1999; Bentz & Williamson,1998; Butler & Mathews, 1983; MacLeod & Byrne,1996).

The study also was found that the pretest scores offemale participants were rated with a likelihood of futurethreat as more probable than male participants. All femaleparticipants, regardless of anxiety level, were found tomake more pessimistic ratings. It was hypothesized thata two-way interaction of sex and anxiety group wouldbe found due to the previous investigations of Bentz andWilliamson (1998) and Bentz et al. (1999).

The two previous studies that found a sex by anxi-ety interaction utilized a passive-observational cross sec-tional design with a wide range of anxiety scores analyzedusing multiple regression. It is likely that the regressionapproach of the two previous studies (Bentz et al., 1999;Bentz & Williamson, 1998) found an interaction because

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participants with extremely low levels of anxiety showedno differences in their prediction ratings.

The primary goal of this study was to test theefficacy of one debiasing technique in the reduction ofjudgmental bias associated with anxiety. The associationbetween the pessimistic judgment of future threat-relatedevents and anxiety was replicated (Bentz et al., 1999;Bentz & Williamson, 1998). In addition, the Consider-An-Alternative debiasing procedure (Hirt & Markman,1995) was shown to be an effective technique in thereduction of these judgmental biases. However, contraryto prediction, this reduction in pessimistic predictionsoccurred not only in the highly trait anxious group,but also in the “normal” group. It is important to notethat statistically equivalent baseline threat was foundbetween the two intervention groups at pretest. Therefore,subsequent changes in probability ratings can be reliablyattributed to the debiasing intervention.

It was hypothesized that a three-way interaction of in-tervention group, trial, and anxiety group would be found.Specifically, highly anxious participants in the debiasinggroup were hypothesized to have a significant reductionin threat ratings from the pre- to posttest. Only a two-wayinteraction was found. The debiasing group did show a re-duction in ratings from the pre- to posttest, however, thisreduction in ratings did not significantly differ betweenthe high versus “normal” anxiety groups. Thus, the re-duction of pessimistic judgmental bias occurred for bothhighly anxious and “normal” participants equally. The in-crease in threat probability ratings of the control groupwas unexpected and requires further discussion. The con-trol intervention procedure involved the reading of eachscenario and the recording of the nouns and verbs withineach paragraph. It is possible that participants within thecontrol group actually read each paragraph more than onceduring their recording of the parts of speech. The repeti-tion of the negative scenarios may have then resulted infurther activation of threat-related information in memory,ultimately leading to increased probability estimates forthe control group.

This explanation of the increased probability ratingsin the control group due to the repetition of the stimuliis supported by the previous research of Weinstein andKlien (1995). They reported that procedures that actuallyforced participants to focus on risky stimuli resulted in anexaggeration of the bias. Similarly, the control interven-tion procedure may have forced participants to repeatedlyfocus on the threat-related scenarios leading to the exag-geration of the judgmental bias. This explanation fits wellwith the proposed availability interpretation of the mech-anism of the judgmental bias (Hirt & Markman, 1995;Mumma & Wilson, 1995).

In summary, the primary findings of this study in-cluded (1) the replication of pessimistic judgmental pre-dictions associated with anxiety (Bentz et al., 1999; Bentz& Williamson, 1998); (2) female participants were foundto have higher future threat-related judgments than maleparticipants; (3) the Consider-An-Alternative debiasingprocedure was found to be effective in the reduction ofpessimistic judgmental predictions; finally, (4) debiasingwas found to occur for both highly anxious and “normal”participants.

This study was not without its limitations, however.First, demand characteristics were not controlled or eval-uated. Because of the obvious nature of the stimuli andexperimental procedures, it could be argued that any sig-nificant findings were due to experimental cues given tothe participants clueing them to respond in particular ways.It could be argued that the reduction of threat ratings forthe debiasing group was due to a systematic response bythe participants to the production of positive alternativeoutcomes, and not due to the availability of alternativeinformation in memory.

Second, in the present study there was no way toevaluate the accuracy of participant judgments against an“objective truth.” As a result, the study failed to distin-guish between a criterion of “truth” and accuracy in judg-ments, that is, a bias. However, it is important to notethat the purpose of the study was to investigate a judg-mental bias for which a criterion of accuracy was notestablished as compared to the evaluation of judgmentalerrors.

Third, the present investigation failed to conclusivelydemonstrate that the Consider-An-Alternative debiasingstrategy generalizes to pessimistic probability judgmentson stimuli other than the stimuli used in the debiasingcondition. This is an important point because the samestimuli used for both the pre- and posttest, were alsoused for the debiasing intervention. However, past em-pirical evidence has shown that identical debiasing pro-cedures have resulted in positive reductions in judgmen-tal bias (Hirt & Markman, 1995; Mumma & Wilson,1995).

Future investigations should attempt to use novel sit-uations and stimuli to empirically demonstrate the abil-ity of the Consider-An-Alternative debiasing procedureto generalize beyond the stimuli used for the debiasingtraining to stimuli other than those used for the debiasingtraining. In addition, given the relevance for a clinicallyanxious population, future investigations should attemptto replicate the current study with a sample of clinicallyanxious participants. Finally, it would be beneficial to in-vestigate the stability of the debiasing effect by including afollow-up component. Investigation of altered judgments

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over time would demonstrate changes in the judgmentalbias beyond a single experimental session.

APPENDIX

Threat-Related Stimuli

1. You are late for an important meeting acrosstown so you are driving above the speed limit. Itstarts to rain heavily and the traffic around youis hard to see clearly.

What is the probability that you (will have/willavoid) a car accident?

2. You are riding a bicycle down a large hill whenyou realize that the brakes of the bike are notworking and a sharp turn is just ahead.

What is the probability that you (will wreck/willavoid wrecking) the bicycle?

3. Late at night, you are driving on a highway thatis totally deserted. One of your tires blows outand you pull off the road to check the damage.

What is the probability that you (will be/willavoid being) stranded on the highway?

4. You are in a large auditorium with hundreds ofpeople watching a movie. You have a faint smellof smoke when an alarm goes off and peoplebegin running to the exits.

What is the probability that you (will be/willavoid being) injured trying to leave the audito-rium?

5. You have a job that you enjoy but the companyis having financial problems and will lay offseveral employees in the near future.

What is the probability that you (will/will not)keep your job?

6. You are out in the ocean deep sea fishing withsome friends when a large storm begins to rollin. You try to start the engine but mechanicalproblems prevent the engine from starting.

What is the probability that you (will/will not)get home safe?

7. You are failing one of your classes and it is al-ready half way through the semester. There areonly two tests remaining to pull your grade up toa passing level.

What is the probability that you (will/will not)pass the class?

8. Late at night you are walking to your car in apart of town that is known for a high crime rate.Your car is parked in an area that has very poorlighting.

What is the probability that you (will be/willavoid being) mugged?

9. It has been raining very hard and windy all dayand there has been a flood and tornado advisoryreported on the news. Your home is built in a lowarea with a history of water and wind damage.

What is the probability that your home (willsustain/will avoid) damage from the storm?

10. You have just graduated from college and takena job that will move you away from your hometown. This job will take you to a city with a highcrime rate and you will be living in an unsafepart of the town.

What is the probability that you (will be/willavoid being) a crime victim?

Positive Stimuli

11. You are driving to a meeting across town, butyou don’t expect to be late. The weather is fineand traffic is average.

What is the probability that you (will have/willavoid) a car accident?

12. You are riding a bicycle on a relatively flat roadwith no other cars or bicycles in sight. Thebrakes of your bike are working just fine.

What is the probability that you (will wreck/willavoid wrecking) the bicycle?

13. During the day, you are driving on a highwaythat has few other cars. You know that one ofyour tires has a slow air leak, but you checkedthe air pressure in the morning.

What is the probability that you (will be/willavoid being) stranded on the highway?

14. You are in a large auditorium with very fewother people watching a movie. At the end of themovie people being to move toward the exits.

What is the probability that you (will be/willavoid being) injured trying to leave the audito-rium?

15. You have a job that you enjoy and the companyis financially having no problems. There is littlerisk that the company will lay off any employeesin the near future.

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What is the probability that you (will/will not)keep your job?

16. You are out in the ocean deep sea fishing withsome friends and it is sunny with few clouds inthe sky. Your boat has never had any mechanicalproblems.

What is the probability that you (will/will not)get home safe?

17. You are passing one of your classes but it is earlyin the semester. There are several more testsremaining for your grade to change.

What is the probability that you (will/will not)pass the class?

18. During the day, you are walking to your car in apart of town that is familiar to you. Your car isparked in an area that often has others aroundbut at this time you do not see anyone.

What is the probability that you (will be/willavoid being) mugged?

19. It has been a sunny day with very few clouds inthe sky. You live in a home that has never had ahistory of water or wind damage from a storm.

What is the probability that your home (willsustain/will avoid) damage from the storm?

20. You have just graduated from college and takena job that will move you away from your hometown. This job will take your to a city with a lowcrime rate and you will be living in a safe part oftown.

What is the probability that you (will be/willavoid being) a crime victim?

ACKNOWLEDGMENTS

This study was conducted in partial fulfillment ofthe Doctor of Philosophy degree for the first author. Theauthors thank Jason Hicks, PhD, William F. Waters, PhD,and Alan Baumeister, PhD, Louisiana State University,for their contributions to the study and work as membersof the first author’s dissertation committee. In addition,the authors thank Jennifer E. Sorlie and Neetha R. Devdas,Texas Woman’s University, for their contributions asblind raters in completing the manipulation check.

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