summary of four psychology articles

Research Article

Domain Specificity in Experimental Measures and Participant Recruitment An Application to Risk-Taking Behavior Yaniv Hanoch,1,2 Joseph G. Johnson,3 and Andreas Wilke4

1 UCLA School of Public Health;

2 Max Planck Institute for Human Development, Berlin, Germany;

3 Miami University;

and 4 International Max Planck Research School LIFE, Max Planck Institute for Human Development, Berlin, Germany

ABSTRACT—We challenge the prevailing notion that risk

taking is a stable trait, such that individuals show con-

sistent risk-taking/aversive behavior across domains. We

subscribe to an alternative approach that appreciates the

domain-specific nature of risk taking. More important, we

recognize heterogeneity of risk profiles among experi-

mental samples and introduce a new methodology that

takes this heterogeneity into account. Rather than using

a convenient subject pool (i.e., university students), as is

typically done, we specifically targeted relevant subsam-

ples to provide further validation of the domain-specific

nature of risk taking. Our research shows that individuals

who exhibit high levels of risk-taking behavior in one

content area (e.g., bungee jumpers taking recreational

risks) can exhibit moderate levels in other risky domains

(e.g., financial). Furthermore, our results indicate that

risk taking among targeted subsamples can be explained

within a cost-benefit framework and is largely mediated by

the perceived benefit of the activity, and to a lesser extent

by the perceived risk.

How should researchers study a psychological construct such as

risk-taking propensity? Answering this question might not be as

easy as it seems at first glance. First, an individual might exhibit

risk-taking tendencies in one domain (e.g., financial) but display

more conservative behavior in another (e.g., recreational).

Second, different methodological designs—for example, the

pool of subjects used or the type of analysis conducted—can

yield contradicting results (e.g., Huber, Wider, & Huber, 1997).

In the present study, our methodological focus on the ecological

validity of the experimental design (e.g., Huber, 1997), domain-

specific risk-taking measures, and recruitment of participants in

targeted groups yielded results that allow us to challenge the

tendency to cluster individuals globally as either risk takers or

risk avoiders—thus offering a richer perspective on the psy-

chology of risk taking.

The psychological literature has been largely dominated by

the assumption that risk taking is a stable personality trait, and

thus individuals can be clustered into groups having risk-taking

or risk-aversive styles (e.g., Eysenck & Eysenck, 1977; Lejuez

et al., 2002; for a review, see Bromiley & Curley, 1992). This

simplistic, though appealing, conceptualization has proven to be

inadequate. Researchers have responded by examining sub-

traits and, therefore, the relation between risk taking and con-

structs such as self monitoring (Bell, Schoenrock, & O’Neal,

2000) and sensation seeking (Hansen & Breivik, 2001; Him-

elstein & Thorne, 1985). In recent years, however, a flourishing

corpus of ideas and empirical findings has come to challenge the

notion that individuals fit nicely into one of the two categories.

Zaleskiewicz (2001) suggested his findings ‘‘confirmed the ad-

equacy of going beyond a simple distinction between risk

seeking and risk aversion’’ (p. 113). Indeed, the current zeitgeist

among decision researchers seems to include a domain-specific

approach to risk.

In line with these arguments, Weber and her colleagues have

argued that risk taking can be better understood in a risk-return

framework, in which risk taking is a function of the perceived

risk of the action or choice option, its expected benefits, and the

decision maker’s attitude toward perceived risk (Weber, 2001;

Address correspondence to Yaniv Hanoch, UCLA School of Public Health, Department of Health Services, Los Angeles, CA 90095-1772, e-mail: [email protected].

P S Y C H O L O G I C A L S C I E N C E

300 Volume 17—Number 4Copyright r 2006 Association for Psychological Science

Weber & Milliman, 1997). Perceptions of risk have been shown

to vary by content domain as a function of such factors as fa-

miliarity or framing (Blais & Weber, 2001; Mellers, Schwartz, &

Weber, 1997), and Weber and Hsee (1998, 1999) have shown

that apparent cultural differences in risk taking are mediated by

cultural differences in the perception of risks, rather than true

attitudinal differences toward risk. The theoretical risk-return

trade-off framework and the large body of supporting empirical

results served as Weber, Blais, and Betz’s (2002) motivation for

developing their domain-specific risk-taking (DOSPERT) scale.

This scale allows for an assessment of the relative contributions

of individual, group, and domain differences in risk perception,

perception of benefits or returns, and attitude toward perceived

risk, and the resulting differences in risk taking. In the present

study, we used the German version of the DOSPERT scale

(DOSPERT-G; see Johnson, Wilke, & Weber, 2004) to provide

further support of the notion that risk taking is domain-specific.

Our methodology diverged in an important way from the

methods in previous research. A growing literature has ques-

tioned the use of aggregate analyses to study individual be-

haviors, showing how such procedures can provide misleading

results (Maddox, 1999). However, individual analyses often

sacrifice power or introduce unwanted statistical dependencies.

As a compromise of sorts, instead of using a heterogeneous group

(i.e., university students) and exploring how their scores on a

risk scale cluster into categories, we studied distinct but inter-

nally homogeneous groups, most of which were chosen precisely

because of their extreme risk-taking behavior. Furthermore, we

wanted to include a group that would be less likely to engage in

domain-specific risky behaviors. To our knowledge, this is the

first study to employ a domain-specific approach to investigate

populations specifically for their behavioral tendencies. This is

an important methodological advance with respect to the se-

lection of experimental participants (who are typically drawn

from the same underlying population).

We recruited individuals who were known to be risk takers

(e.g., sky divers, smokers, and gamblers) or risk avoiders (i.e.,

gym members) in one domain. Although our study might seem to

resemble previous research, such as investigations of stock-

brokers’, bankers’, and laypeople’s risk attitudes in the financial

domain (MacCrimmon & Wehrung, 1990), ours is the only study

to look simultaneously at multiple subpopulations and multiple

risk-taking domains. That is, not only did we examine domain-

specific behaviors, but we employed ‘‘domain-specific’’ partic-

ipants, who provided another, novel way to test the validity of the

DOSPERT scale (Weber et al., 2002).

METHOD

Participants

Participants (N 5 146; mean age 5 28.1, SD 5 8.86) were

recruited from the recreational domain (e.g., sky divers, bungee

jumpers, hang gliders, scuba divers; n 5 39), the health-seeking

domain (i.e., gym members; n 5 24), the health-risk domain

(i.e., smokers; n 5 50), the gambling domain (i.e., casino

gamblers; n 5 19), and the investment domain (i.e., members of

stock-trading clubs; n 5 14).

Materials and Procedure

The full version of the DOSPERT-G, containing 40 items,

was administered using each of three response scales. The

DOSPERT-G contains 8 items each for recreational, health,

social, and ethical risks and 4 items each for the gambling and

investment domains. For the risk-behavior scale, participants

indicated their likelihood of engaging in each of the risky ac-

tivities; the risk-perception scale assessed how risky participants

perceived these activities to be; and the expected-benefit scale

asked participants to indicate how much benefit they would

expect to obtain from engaging in each activity. All judgments

were made on 5-point Likert scales, whose endpoints and mid-

point were labeled: Higher values indicated greater likelihood

of engaging in the behavior, greater perceived risk associated

with the activity, and greater expected benefit for engaging in the

activity. After initial telephone contact with relevant clubs and

institutions, paper questionnaires were given personally to

participants. The instructions were general, indicating only that

a survey about various risky behaviors and perceptions of those

behaviors would be given and that individuals who participated

(anonymously) would be paid (h8; about $9 at the time) upon

completion of the questionnaire.

RESULTS AND DISCUSSION

The present research had two primary goals, namely, to dem-

onstrate further the utility of a domain-specific approach to

studying risk and to illustrate the benefit of focusing analyses on

homogeneous subsamples in experimental studies. We hypoth-

esized that, as in previous studies, we would observe domain-

specific differences in behaviors and perceptions of risky ac-

tivities. Furthermore, we hypothesized that these trends would

be best elucidated by clustering our sample a priori into ho-

mogeneous subsamples. These hypotheses predicted that within

each domain, the target subsample of risk takers (e.g., gamblers

for the gambling domain) would show greater propensity for

engaging in risky behaviors compared with the other subsam-

ples, but that each subsample would not necessarily exhibit

strong risk-seeking tendencies outside its domain. Finally, be-

cause gym members are likely to be health-conscious, we hy-

pothesized that they would show a lower degree of risky behavior

(compared with the other subsamples) in the health domain.

Mean DOSPERT-G scores for each risk domain (excluding the

social and ethical domains) are given in Table 1, separately for

each subsample. The table is organized such that domain spe-

cificity can be viewed across columns, whereas differences de-

pendent on subsample membership can be viewed across

Volume 17—Number 4 301

Yaniv Hanoch, Joseph G. Johnson, and Andreas Wilke

rows—variance across both columns and rows indicates the

combined dependency on domains and subsamples. We propose

that the subsample is a useful level of analysis, especially when

subsample clustering can be theoretically, empirically, or in-

tuitively performed prior to analyses (see Lee & Webb, in press,

for another method). Here, we report several indicators of this

advantage.

First, we examined differences in the mean behavioral risk-

taking propensity in each domain. A repeated measures analysis

of variance (ANOVA) indicated that there was a main effect of

domain, F(3, 423) 5 45.9, prep > .999, Zp 2 ¼ :25, as well as an

interaction between domain and subsample, F(12, 423) 5 8.74,

prep > .999, Zp 2 ¼ :199: According to our hypotheses, the in-

teraction would occur because in each domain, the target sub-

sample of risk takers (aside from gym members) would exhibit a

greater risk-taking propensity than other subsamples, and the

gym members would exhibit lower risk taking in the health

domain. This is indeed the pattern evident in Table 1 and Figure

1. Furthermore, in the recreation, gambling, and investment

domains, t tests showed that the mean score of the associated

subsample was significantly higher than the mean across all

remaining subsamples (all ps < .01). In the health domain, t

tests indicated that the subsample of smokers had a higher mean

score ( p < .01), and the subsample of gym members a lower

mean score ( p 5 .05), than the mean across the remaining

subsamples. These results support the validity of the DOSPERT

scale in revealing tendencies to engage in risky behaviors within

specific domains.

Next, we performed similar analyses on the mean expected

benefit of risk taking in each domain (Fig. 1b). The results in-

dicated that the differences in behavioral tendency may be in

part explained by differences in the perception of expected

benefit. A repeated measures ANOVA indicated a main effect of

domain on expected benefits, F(3, 423) 5 75.4, prep > .999,

Zp 2 ¼ :35, and an interaction between domain and subsample,

F(12, 423) 5 8.15, prep > .999, Zp 2 ¼ :19: Figure 1b shows that

within each domain, the target subsample of risk takers ex-

pected to receive greater benefits from risky behaviors than the

other subsamples did, thus mirroring the differences in behav-

ioral tendencies. Again, t tests showed a significant difference

between the target subsample and all remaining samples in the

TABLE 1

Mean Scores on the German DOSPERT Subscales, by Subsample

Subsample

Subscale (risk-taking domain)

Recreation Gambling Investment Health

Behavior subscale

Males 3.04 1.91 2.69 2.88

Females 2.57 1.65 2.47 2.64

Athletes 3.25 a

1.54 2.69 2.82

Gamblers 2.66 2.99 a

2.51 2.57

Investors 2.92 1.70 3.20 a

2.54

Smokers 2.90 1.74 2.47 3.04 a

Gym members 2.33 1.56 2.52 2.54 b

All 2.87 1.82 2.61 2.79

Risk-perception subscale

Males 2.97 3.61 2.60 3.22

Females 3.27 3.86 2.73 3.64

Athletes 3.07 3.85 2.70 3.54

Gamblers 2.78 3.12 b

2.61 2.86

Investors 3.16 4.21 2.61 3.42

Smokers 2.98 3.67 2.44 3.35

Gym members 3.47 3.70 3.04 3.51

All 3.08 3.70 2.65 3.37

Expected-benefits subscale

Males 2.75 1.77 2.77 1.97

Females 2.53 1.54 2.74 1.71

Athletes 3.02 a

1.38 2.73 1.81

Gamblers 2.36 2.54 a

2.26 1.89

Investors 2.65 1.86 3.27 a

1.71

Smokers 2.75 1.57 2.94 2.04 a

Gym members 2.21 1.67 2.52 1.74

All 2.67 1.72 2.68 1.84

Note. N 5 146. The ns for the subsamples were as follows: males, 94; females, 52; athletes, 39; gamblers, 19; investors, 14; smokers, 50; gym members, 24. aValue is significantly higher than the mean across the remaining four sub- samples (comparison does not include male and female subsamples).

b Value is

significantly lower than the mean across the remaining four subsamples (comparison does not include male and female subsamples).

Fig. 1. Mean risk propensity (a) and mean expected benefit (b) by domain and subsample. The target subsample in the recreation domain included bungee jumpers, hang gliders, and scuba divers; the target subsample in the gambling domain was casino gamblers; the target subsample in the investment domain was members of stock-trading clubs; and the target subsample in the health domain was smokers. Additionally, for the health domain, a subsample of non-risk takers (gym members) was included. The members of these subsamples were pooled outside of their target domain (dark bars). Error bars represent standard errors.

302 Volume 17—Number 4

Domain Specificity

recreation, gambling, and investment domains (ps � .01), and between the smokers and remaining subsamples in the health

domain (p 5 .01).

For perceived riskiness, a repeated measures ANOVA again

suggested a main effect of domain, F(3, 423) 5 52.98, prep > .999,

Zp 2 ¼ :27, and a domain-subsample interaction, F(12, 423) 5

2.11, prep 5 .99, Zp 2 ¼ :06: However, planned contrasts exam-

ining differences between the target subsample of risk takers

(excluding gym members) and the remaining subsamples

(including gym members) were significant only in the gambling

domain, where gamblers had lower perceptions of risk than the

other subsamples did. Thus, it seems the benefits of engaging in

risky activities are better than the costs in explaining patterned

subsample differences in behavioral propensities.

We performed several regression analyses to explore this

claim further, as well as to identify the contribution of subsample

membership in predicting behavioral propensities. Specifically,

for each domain, we predicted behavioral scores first with a

linear model including the perceived-risk and expected-benefit

scores as predictors, and second with a linear model that in-

cluded these predictors plus variables reflecting subsample

membership. 1

In the first set of models, the standardized coef-

ficients for perceived risk were negative in all domains except

investment, and those for expected benefits were all positive,

consistent with the intuitive impact of these variables on risk-

taking propensity (see also Weber et al., 2002). The magnitude

(absolute value) of the coefficients was much larger for expected

benefits (.64, .63, .72, and .47 for the recreation, gambling, in-

vestment, and health domains, respectively; mean b 5 .62) than for perceived risk (�.26, �.13, .03, and �.20 for the four do- mains, respectively; mean b 5 �.14), suggesting that expected benefits, indeed, were more important in determining risk pro-

pensity than perceived risk was. Finally, including subsample as

a predictor increased the adjusted R 2

value in each domain, by

an average of .026.

A final indicator of the utility of focusing on subsamples is the

variance that can be explained. Specifically, if we predict an

individual’s behavioral score by using the overall mean across

all subsamples and domains (2.63), we obtain a sum of squared

errors (SSE ) of 473.66. Using the mean of all respondents across

all scale items ignores the context (risk domain) and individual

differences (subsample membership). If the subsample means

from Table 1 are used to predict individual scores in each do-

main, the explained variance increases, SSE 5 307.55, R 2

5

.34, adjusted R 2

5 .24. Note that gender, a ubiquitous sub-

sample variable in other work, does not produce the same level

of improvement, SSE 5 349.54, R 2

5 .25, adjusted R 2

5 .21.

The data of the current study are best summarized in two

ways—across subsamples within a domain (differences across

rows within each column of Table 1) and across domains within a

subsample (differences across columns within each row of Table

1). Although these results indicate main effects of subsample

and domain (domain specificity), respectively, the interaction

between domain and subsample can be observed in Figure 1

because the highest values for risk propensity and expected

benefit (the white bars) were obtained for a different subsample

in each domain. Specifically, it was the target subsample of risk

takers in each domain (e.g., extreme athletes in the recreation

domain) that received the highest scores for these variables.

Theoretically, these results support a domain-specific approach

to studying risk within a cost–benefit framework. Methodologi-

cally, the results demonstrate the advantage of domain-specific

measurements, and of using subsamples as a compromise be-

tween aggregate and individual levels of analysis. Admittedly,

these analyses provide only a first glimpse of the utility of this

approach in one area (risk taking); more stringent tests in other

kinds of tasks are imperative.

Although employing new methodology can be risky, our study

does have several broad and exciting ramifications. It illustrates

the utility of investigating homogeneous clusters of risk takers,

instead of a single heterogeneous sample of college students, to

gain further understanding about the psychological processes

that determine and motivate risky behavior, as well as their ef-

fects on experimental results. The level of analysis we utilized

also avoids the pitfalls associated with aggregating data across

participants from different underlying populations and simul-

taneously avoids the dependencies and low power that plague

analyses at the individual level.

The current results suggest that the propensity to take risks is

largely mediated by the perceived benefit of the activity, and to a

lesser extent by the perceived risk. They indicate that sky di-

vers, for example, view skydiving as far more beneficial (and

somewhat less dangerous) than do individuals from other sub-

samples. Needless to say, our data do not allow one to explain

risk-taking behavior solely on the basis of perceived benefit,

riskiness, or both, because people might have many other

motivations to participate in various activities (e.g., monetary

reward, belonging to a group). There also seem to be differences

in the perception of risk and expected benefit across domains, as

evidenced by the variability in beta coefficients. For example, it

seems that perceived risk becomes more important when the

stakes include one’s life or physical well-being (recreation and

health domains) than when they involve money (investment

domain).

Other studies using the DOSPERT scale have found that

perception of risk is better than expected benefits as a predic-

tor of risky behaviors among university students (Weber et al.,

2002). Yet Shapira (1994) demonstrated that another tar-

geted subsample (managers) was more concerned with benefits

(monetary gains) than with risk magnitude (probabilities);

Shapira’s results may be supported by the positive coefficient we

obtained for the contribution of expected benefit toward risk

1 Binary variables were used for contrast-coding membership in each sub-

sample, except for the health subsample, for which a ternary variable repre- sented gym members (�1), smokers (1), and all others (0).

Volume 17—Number 4 303

Yaniv Hanoch, Joseph G. Johnson, and Andreas Wilke

propensity in the investment domain. Furthermore, expected

benefit can serve as an important component in explaining some

risk-taking behaviors, such as willingness to smoke (Sloan,

Smith, & Taylor, 2003) or engage in unprotected sex (Kershaw,

Ethier, Niccolai, Lewis, & Ickovics, 2003). Additional work is

necessary to determine the relative impact of costs and benefits

on risk-taking behavior. The current study has shown how such

work can—and should—take a domain-specific approach, tar-

geting the subsamples of interest.

Acknowledgments—We would like to thank members of the

Berliner Börsenkreis, ELAN Health Club Berlin, Tauchsport-

Club-Berlin, and Aktienclub Berlin for participation. We also

thank Uwe Czienskowski, Yaacov Kareev, Michael Lee, Ann

Renee-Blais, and Elke Weber for constructive comments. This

research was supported by National Research Services Award

T32 HS 0046 from the Agency for Health Care Research to the

first author and a Max Planck Society fellowship to the third

author; the second author was supported by National Institute of

Mental Health Grant 5 T32 MH014257-30 to the University of

Illinois. The first and second authors contributed equally in the

preparation of the article.

REFERENCES

Bell, N.J., Schoenrock, C.J., & O’Neal, K.K. (2000). Self-monitoring

and the propensity for risk. European Journal of Personality, 14, 107–119.

Blais, A.-R., & Weber, E.U. (2001). Domain-specificity and gender

differences in decision making. Risk Decision and Policy, 6, 47– 69.

Bromiley, P., & Curley, S.P. (1992). Individual differences in risk tak-

ing. In J.F. Yates (Ed.), Risk taking behavior (pp. 87–132). Chichester, England: John Wiley.

Eysenck, S.B., & Eysenck, H.J. (1977). The place of impulsiveness in a

dimensional system of personality description. British Journal of Social and Clinical Psychology, 16, 57–68.

Hansen, E.B., & Breivik, G. (2001). Sensation seeking as a predictor of

positive and negative risk behaviour among adolescents. Person- ality and Individual Differences, 30, 627–640.

Himelstein, P., & Thorne, S.B. (1985). Relationship between the Sen-

sation Seeking Scale and a biographical inventory designed to

predict risk-taking behavior. Personality and Individual Differ- ences, 6, 121–122.

Huber, O. (1997). Beyond gambles and lotteries: Naturalistic risky

decisions. In R. Ranyard, W.R. Crozier, & O. Svenson (Eds.),

Decision making: Cognitive models and explanations (pp. 145– 162). London: Routledge.

Huber, O., Wider, R., & Huber, O.W. (1997). Active information search

and complete information presentation in naturalistic risky deci-

sion tasks. Acta Psychologica, 95, 15–29.

Johnson, J.G., Wilke, A., & Weber, E.U. (2004). Beyond a trait view of

risk-taking: A domain-specific scale measuring risk perceptions,

expected benefits, and perceived-risk attitude in German-speak-

ing populations. Polish Psychological Bulletin, 35(3), 153–163. Kershaw, T.S., Ethier, K.A., Niccolai, L.M., Lewis, J.B., & Ickovics, J.R.

(2003). Misperceived risk among female adolescents: Social and

psychological factors associated with sexual risk accuracy. Health Psychology, 22, 523–532.

Lee, M.D., & Webb, M.R. (in press). Modeling individual differences in

cognition. Psychonomic Bulletin & Review. Lejuez, C.W., Read, J.P., Kahler, C.W., Richards, J.B., Ramsey, S.E.,

Stuart, G.L., Stong, D.R., & Brown, R.A. (2002). Evaluation of a

behavioral measure of risk taking: The balloon analogue risk task

(BART). Journal of Experimental Psychology: Applied, 8, 75–84. MacCrimmon, K.R., & Wehrung, D.A. (1990). Characteristics of risk

taking executives. Management Science, 36, 422–435. Maddox, T.W. (1999). On the dangers of averaging across observers

when comparing decision bound models and generalized context

models of categorization. Perception & Psychophysics, 61, 354– 375.

Mellers, B.A., Schwartz, A., & Weber, E.U. (1997). Do risk attitudes

reflect in the ‘eye of the beholder’? In A.A.J. Marley (Ed.), Choice, decision, and measurement: Essays in honor of R. Duncan Luce (pp. 59–73). Mahwah, NJ: Erlbaum.

Shapira, Z. (1994). Managerial risk taking. New York: Sage. Sloan, F.A., Smith, K.V., & Taylor, D.H. (2003). The smoking puzzle:

Information, risk perception, and choice. Cambridge, MA: Harvard University Press.

Weber, E.U. (2001). Personality and risk taking. In N.J. Smelser & P.B.

Baltes (Eds.), International encyclopedia of the social and behav- ioral sciences (pp. 11274–11276). Oxford, England: Elsevier Science.

Weber, E.U., Blais, A.-R., & Betz, N.E. (2002). A domain-specific risk-

attitude scale: Measuring risk perceptions and risk behaviors.

Journal of Behavioral Decision Making, 15, 263–290. Weber, E.U., & Hsee, C.K. (1998). Cross-cultural differences in risk

perception but cross-cultural similarities in attitudes towards risk.

Management Science, 44, 1205–1217. Weber, E.U., & Hsee, C.K. (1999). Models and mosaics: Investigating

cross-cultural differences in risk perception and risk preference.

Psychonomic Bulletin & Review, 6, 611–617. Weber, E.U., & Milliman, R. (1997). Perceived risk attitudes: Relating

risk perception to risky choice. Management Science, 43, 122– 143.

Zaleskiewicz, T. (2001). Beyond risk seeking and risk aversion: Per-

sonality and the dual nature of economic risk taking. European Journal of Personality, 15, 105–122.

(RECEIVED 3/7/05; REVISION ACCEPTED 7/21/05; FINAL MATERIALS RECEIVED 9/20/05)

304 Volume 17—Number 4

Domain Specificity