Running Head: Judgment of Riskiness · The anchoring-and-adjustment heuristic is another cognitive...

Running Head: Judgment of Riskiness

Psychology & Health, Volume 25 Issue 2 2010

Pages 131 – 147

Judgment of Riskiness: Impact of Personality, Naive Theories, and Heuristic Thinking among

Female Students

Kamel Ganaa, Marcel Lourelb, Raphaël Trouilletc, Isabelle Fortd, Djamila Mezreda,

Christophe Blaisona, Valérian Boudjemadia, Pascaline K'Delanta, & Julie Ledricha

a Department of Psychology, Nancy University, France b Department of Psychology, University of Rouen, France c Department of Psychology, University of Montpellier, France d Department of Psychology, University of Provence, France

Corresponding author:

[email protected]

Pr. Kamel Gana

2

Abstract

Three different studies were conducted to examine the impact of heuristic reasoning in the

perception of health-related events: lifetime risk of breast cancer (Study 1, n = 468),

subjective life expectancy (Study 2, n = 449), and subjective age of onset of menopause

(Study 3, n = 448). In each study, three experimental conditions were set up: control,

anchoring heuristic, and availability heuristic. Analyses of Covariance (ANCOVA)

controlling for optimism, depressive mood, Locus of Control, hypochondriac tendencies and

subjective health, indicated significant effect of experimental conditions on perceived breast-

cancer risk (p=.000), subjective life expectancy (p=.000) and subjective onset of menopause

(p=.000). Indeed, all findings revealed that availability and anchoring heuristics were being

used to estimate personal health-related events. The results revealed that some covariates,

hypochondriac tendencies in study 1, optimism, depressive mood, and subjective health in

study 2, internal locus of control in study 3 had significant impact on judgments of riskiness.

Key words: Heuristic, Lay theories, Perceived risk, Breast cancer, Subjective life expectancy,

Onset of menopause

Judgment of riskiness 3

How people think about, estimate, and respond to future risks is a major concern for

preventive action and educational intervention. For example, health-risk perception is

believed to have an impact on early cancer detection behavior (Brewer et al., 2007; Gross et

al., 2006; Jacobsen et al. 2004; Moser at al., 2007). To optimize health-promotion efforts, it is

therefore important to understand how people construe the risk of health threats. It seems

difficult to gain an accurate understanding of risk perception because perceived health risks

can incorporate beliefs about disease etiology, the disease history of friends and family

members, and personality traits (locus of control, optimism, need for cognition, health

anxiety, hypochondriac tendencies, and so on).

Like any other perception, health-risk perception is influenced by both rational (theory-

based judgments) and irrational (inference-based judgment) mechanisms for processing

information. Lay knowledge of disease inheritance, naive theories, illness representations, and

also cognitive heuristics intervene massively in our perception of health risks (Davison,

Frankel, & Smith, 1989; Marteau, 1999; Quillin et al., 2006; Rees, Fry, & Cull, 2001).

Greening, Dollinger, and Pitz (1996) claimed that cognitive heuristics mediate the relationship

between personal experience and risk perception. Tversky and Kahneman (1974) proposed

that people make use of a limited number of cognitive heuristics, i.e., learned shortcuts for

judging the probability or frequency of uncertain events. A heuristic is a cognitive strategy

that allows one to reduce complex problem solving to simpler judgmental operations, but

heuristics can yield incorrect solutions. "In general, these heuristics are quite useful, but

sometimes they lead to severe and systematic errors" (Tversky & Kahneman, 1974, p. 1124).

The most common heuristics are availability, representativeness, and anchoring-and-

adjustment.

The availability heuristic is a cognitive shortcut used for judging the frequency or

likelihood of events based on the ease with which instances or occurrences can be brought to

mind (Tversky & Kahneman, 1974). Here, we make judgments based on information the

mind can imagine or retrieve, rather than on complete data. For example, people overestimate

the divorce rate if they can quickly find instances of divorced friends and relatives.

The anchoring-and-adjustment heuristic is another cognitive strategy in which decisions

are made based on an initial "anchor". This initial value or starting point "may be suggested

by the formulation of the problem, or it may be the result of a partial computation" (Tversky

& Kahneman, 1974). People who have to make judgments under uncertainty use this heuristic

by starting with a certain reference point (anchor) and then adjusting it until a plausible

estimate is reached. However, the adjustment tends to be insufficient because it is effortful

4

and stops once a plausible solution is found (Epley & Gilovich, 2006). Yamagishi (1997)

noted that participants rated cancer as riskier when it was described as "kills 1,286 out of

10,000 people" than as "kills 24.14 out of 100 people. Facione (2002) reported that women

who had relatives with breast cancer were more likely to perceive themselves as being at a

higher risk. According to Facione (2002), this result provides evidence of the impact of the

availability heuristic on risk estimation. Montgomery et al. (2003) reported that having a

family history of breast or colon cancer, heart disease, or diabetes affected the perceived risk

of the disease. They also found that having a friend diagnosed with the disease contributed to

perceived risk for breast and colon cancers, as well as heart disease and diabetes among

women, but not among men. Helzlsouer et al. (1994) found that employees in an oncology

center felt that their lifetime cancer risk was greater than 40%.

Katapodi et al. (2005) showed that experiences with family members and friends were

integrated into risk estimates by way of the availability, simulation, representativeness, and

affect heuristics. They noted that women with a positive family history of breast cancer

entertained the stereotype that they were more prone to breast cancer than women with no

such history. Consequently, women who themselves did not have a positive family history

believed they were not at risk for this disease. Katapodi et al. (2005) argued that women use

personal experiences to create a "dominance structure" around different diseases that

potentially could be a threat to their health. According to Katapodi et al. (2005), the search for

a dominance structure (Montgomery, 1989) is a cognitive mechanism that relates new

information to pre-existing knowledge by activating schemata and mental images, and can

lead to severe and systematic errors. Such schemata and mental images involve lay beliefs

about the role of genes in disease.

As stated by Henderson and Maguire (2000), "Most people have an idea of what

characteristics run in their family and presumably some implicit theory about how they are

passed down from one generation to another." Thus, people could hold popular views of

heredity that they consciously or unconsciously use to estimate their lifetime health risks. The

lay understanding of genetics (Richards & Ponder, 1996) constitutes a kind of "implicit theory

of heredity" which conflicts with scientific knowledge in a number of respects (Singer,

Corning, & Lamias, 1998) and thereby prevents assimilation of scientific data. For instance, if

someone has had one child affected by a genetic disorder, people think that his/her future

children should be healthy (Henderson & Maguire, 2000). Denes-Raj and Ehrlichman (1991)

reported that participants with at least one parent who died prematurely (age 55 or younger)

estimated that their lifespan would be shorter than others in their age cohort. A variety of


studies have demonstrated the existence of public understandings of genetics (Bates, 2005)

and have reported that lay views of the role of genes in the development of various human

traits contribute to perceived health risks and personal health practices (Condit et al., 2004;

Bates et al., 2003; Lock et al., 2006; Parrott et al., 2003, 2004; Santos, 2006; Walter &

Britten, 2002).

The purpose of the present research was to examine the use of heuristic reasoning in

health-related perceptions. Three different and separate studies were designed to determine

the influence of the availability and anchoring heuristics on perceived risk of breast cancer,

subjective personal life expectancy,1 and subjective onset of menopause. Perceived risk of

breast cancer is the perceived likelihood of contracting this disease in one's lifetime. It was

assessed using the question "On a scale from 0% (not at all likely) to 100% (extremely likely),

how likely do you think it is that you will develop breast cancer in your lifetime?" (Rowe et

al., 2005). Subjective life expectancy is the age to which an individual expects to survive,

assessed using the question "To what age do you expect to live?" (Ross & Mirowsky, 2002).

Subjective age of onset of menopause is the age at which a woman expects to start

menopause, assessed using the question "At what age do you expect the onset of your

menopause" (Lawlor, Adamson, & Ebrahim, 2002). Breast cancer, life expectancy, and onset

of menopause are assumed to be partly influenced by genes, so people may have lay

perceptions of the role played by genes in these life events. The activation of lay beliefs about

inheritance could lead to the use of inferential shortcuts in decision-making, such as the

availability heuristic.

In each study, the participants were randomly assigned to three different experimental

conditions: a control condition (C1), an anchoring-heuristic condition in which participants

received objective information that could serve as an "anchor" (C2), and an availability-

heuristic condition in which participants activated their personal family history regarding the

target event (C3). The participant's personal family history of a health event, as well as his/her

"implicit theories of inheritance", could act as the availability heuristic. Objective and

experiential components of family history contribute to perceived risk because they activate

the distinctiveness that increases availability (Folkes, 1988). In their longitudinal study, Hurd

and McGarry (1997) found that respondents modified their subjective life expectancy on the

basis of new information. Accordingly, the onset of a new disease condition or the death of a

parent between two testing times was associated with a reduction in subjective survival

1 Subjective longevity and subjective life expectancy are interchangeable here.

6

probability. These authors also found that participants with surviving parents felt they had a

higher probability of living until the age of 75 or 85 than did other individuals, and

participants whose parents had died during the study period anticipated lower chances of

surviving to age 75 or 85 (Hurd & McGarry, 1995, 1997).

Furthermore, because personality traits and mood may intervene in health perception

and decision-making processes, it seems necessary to control these variables. For the present

research, we thus included the locus of control (Rowe et al., 2002), optimism (McGregor et

al., 2004; Weinstein & Klein, 1996), hypochondriac tendencies, subjective health (Ross &

Mirowski, 2002), and depressive mood. Based on the health-risk perception and heuristic-

reasoning literature, we hypothesized that (1) participants in the availability condition,

particularly those with a family history, would report higher personal risk than the other

participants; (2) participants in the availability condition, particularly those with no family

history, would report lower personal risks than the other participants because they may

"naively" think they are "genetically protected"; (3) participants in the control and anchoring-

heuristic conditions should make optimistic judgments about their personal risk; and (4)

participants in the anchoring-heuristic condition should, however, make more realistic

estimations than those in the control condition. We should recognize that using true values as

anchors would facilitate correct reasoning.

Data analyses

Differences between groups were examined using one-way analysis of covariance

(ANCOVA) with experimental conditions as fixed factors and optimism, depressive mood,

LOC, hypochondriac tendencies and subjective health as covariates. One-way analysis of

variance (ANOVA) was performed to examine differences in each covariate. Once a

significant F-value was obtained in ANCOVA or ANOVA, post-hoc comparisons using

Bonferroni's test were performed. We set alpha level at .05. Missing data was replaced my

mean values. All analyses were computed using the statistical software SPSS 9.0.1.

Study 1

This study was aimed at examining the effect of heuristic reasoning on women's perceived

risk of developing breast cancer. After controlling for optimism, locus of control,

hypochondriac tendencies, subjective health, and depressive mood, we hypothesized that (1)


women who had relatives who developed breast cancer would overestimate their personal

risk; (2) women without any relatives who developed breast cancer would underestimate their

personal risk, due to the optimistic bias and because they may believe they are genetically

protected; and (3) women in the anchoring-heuristic condition should be more realistic than

those in the control condition, who should make optimistic judgments about their personal

risk.

Method

Participants and Procedure

Four hundred and seventy-three female undergraduate students in the Arts and Humanities2

(mean age: 20.92 years, SD = 2.19) participated in the study. They completed the

questionnaire in classrooms. They were randomly assigned to three experimental conditions

(i.e., questionnaires were randomly distributed). In the first condition (C1), the participants

(N = 128, mean age: 21.08, SD = 2.61) had to estimate the likelihood of contracting breast

cancer in their lifetime by answering the question "On a scale from 0% (not at all likely) to

100% (extremely likely), how likely do you think it is that you will develop breast cancer in

your lifetime?" (control condition). In the second condition (C2), the participants (N = 119,

mean age: 20.58, SD = 1.80) had to estimate the likelihood of getting breast cancer in their

lifetime after having been informed of its prevalence ("Knowing that the risk of breast cancer

for women is 9%, on a scale from 0% (not at all likely) to 100% (extremely likely), how

likely do you think it is that you will develop breast cancer in your lifetime?). This was the

anchoring-heuristic condition. In the third condition (C3), the participants (N = 226, mean

age: 21.03, SD = 2.19) were asked to report their family history of breast cancer (mother,

grandmother, sisters, aunts). This recall task was expected to have higher personal relevance

for women with a family history of breast cancer than for women with no such history, once

this information was rendered salient (distinctiveness). These participants were then told to

estimate the likelihood of contracting breast cancer in their lifetime by answering the question

"On a scale from 0% (not at all likely) to 100% (extremely likely), how likely do you think it

is that you will develop breast cancer in your lifetime?". This was the availability-heuristic

condition because distinctiveness was assumed to increase availability (Folkes, 1988).

2 We deliberately chose students in the Arts and Humanities and not in the Life Sciences or Medicine.

8

Participants were informed that their responses would remain anonymous. Several

pretests had been carried out in order to evaluate the clearness of the questionnaire items and

instructions.

Measures

Participants were assessed on following measures:

- A French version of the Life Orientation Test-Revised (LOT-R), validated by Sultan

and Bureau (1999), was administered to assess optimism. Optimism, defined as the

fact of having positive expectancies about the future even in the face of adversity, was

used as a control variable because it could have an effect on risk perception. This is a

ten-item measure that includes four fillers, and the scale is in five-point Likert format

(ranging from Disagree a lot to Agree a lot). High scores imply optimism. The internal

consistency of the scores obtained by our participants was satisfactory (α = .80).

- A French version of the Internal, Powerful Others and Chance Scales (IPC)

(Levenson, 1981), validated by Loas et al. (1994), was administered to assess locus of

control, used here as a control variable because it could affect risk perception. This

scale has three eight-item subscales with a six-point Likert format. The internal part of

the scale (I) measures the extent to which people believe they have control over their

own lives; the powerful-others part (P) deals with beliefs about the control exerted by

powerful others; the chance part (C) assesses perceptions of control over chance.

Alpha reliability levels were .49 for the I scale, .74 for the P scale, and .70 for the C

scale.

- A French version of the Whitely Index (WI) (Pilowski, 1967) was administered to

assess hypochondriac tendencies, used here as a control variable because

hypochondriacal attitudes and beliefs could affect health-risk perception. This is a 14-

item scale with a five-point Likert format ranging from Not at all to A great deal. High

scores indicate high hypochondriac attitudes and beliefs. The original version of WI

was translated into French by two psychologists and a professional translator. The

final French version selected after comparison of the proposals was pre-tested

(N = 67) to evaluate its clarity. The internal consistency of the scores obtained by our

participants was very satisfactory (α = .82).

- Subjective health was assessed by a single item asking participants to evaluate their

own health status on a five-point scale ranging from Very poor to Very good.


Subjective health was introduced as a control variable because it could have an effect

on health-risk perception.

- A French version of the HADS-Depression Subscale (Zigmond & Snaith, 1983),

validated by Razavi et al. (1989), was administered to assess depressive mood, which

we used as a control variable because depressive mood could have an impact on risk

perception. This scale is a seven-item measure presented in four-point Likert format.

The answers to each item range from zero (Not at all typical) to 3 (Very typical).

Given the small number of items, the internal consistency of the subscale in our

sample was acceptable (α = .64) (see Streiner, 2003).

Results and Discussion

First of all, it is important to note that 56 (24.77%) of the 226 women in the availability-

heuristic condition (C3) reported having a relative that had developed breast cancer. In our

analyses, participants with a family history of breast cancer were thus distinguished from

those with no family history.

Means and standard deviations of the measures used in this study are given in Table 1.

As we can see, except for the anchoring group, the perceived lifetime risk of breast cancer

was much higher than the true risk. An analysis of covariance (ANCOVA) was performed to

analyze differences across experimental conditions; optimism, depressive mood, LOC,

hypochondriac tendencies and subjective health were incorporated as covariates. The results

indicated a significant effect of experimental condition on perceived breast-cancer risk after

controlling for the effect of the covariates, F(3, 462) = 28.70, p = .000; eta²= .188. Only one

covariate, hypochondriac tendencies, was significantly related to participant’s perceived

breast-cancer risk (p=.031). However, as we can see in table 1, the Anova results revealed no

differences between experimental groups in any control variable. The ANCOVA post-hoc

comparison of adjusted means using Bonferroni's test indicated that participants in the

availability-heuristic condition, particularly those who had relatives with breast cancer,

overestimated their own risk of breast cancer as compared to participants with no family

history (34.82% vs 23.36%, p=.000) and with those in the other experimental conditions.

Indeed, they expressed higher levels of breast cancer expectancy than the controls (34.76% vs

27.38%, p=.052), and believed themselves to be three times more at risk than women in the

anchoring condition (34.76% vs 11.32%, p=.000). The latter were more accurate about their

10

lifetime risk of breast cancer. They anchored the estimates of their own lifetime risk to the

initial information given (i.e., "The risk of breast cancer for women is 9%"). These results

provide evidence that the anchoring and availability heuristics have an effect on estimates of

breast-cancer risk. However, women who had no relatives with breast cancer underestimated

significantly (p=.000) their risk of cancer as compared to women whose relatives had breast

cancer (23.40% vs 34.76%). No difference between them and women in the control condition

(23.40% vs 27.38%, p=.280). This result may be due to a kind of "implicit theory of genetics"

based on lay knowledge of inherited family characteristics like physical features, character,

and temperament, as well as health and proneness to illness (Richards & Ponder, 1996). One

can assume here that the availability condition activated such knowledge, in such a way that

participants with a family history of breast cancer perceived their personal risk for this disease

to be higher than those with no breast cancer among their relatives. Even, if this result is in

line with other studies (Mouchawar et al., 1999; Montgomery et al., 2003), one should be

cautious because we do not know family history for the other experimental groups.

Knowledge of disease inheritance seems to have an impact on perceived risk: women who

have a family history of cancer overestimate their own risk. However, those with no cancer

history did not underestimate it. They did not feel “genetically protected”. But both

overestimate their personal risk of contracting the disease in comparison to objective

epidemiological data. This overestimation behavior is consistent with the findings in the

literature (Lipkus et al., 1996; Quillin et al., 2004; Skinner et al., 1998), but does not seem to

be the expression of pessimism nor of a depressive mood. It seems to be influenced by

hypochondriac tendencies.

INSERT TABLE 1 ABOUT HERE

Study 2

The purpose of this study was to investigate the effect of heuristic reasoning on

women's subjective longevity ratings. After controlling for optimism, locus of control,

hypochondriac tendencies, subjective health, and depressive mood, we predicted that (1)

women who believe they are descended from a long-lived family would expect to live longer

because they may believe that longevity is genetically determined (Robbins, 1988a, 1988b);

(2) women who believe they are not descended from a long-lived family would underestimate

their subjective life expectancy because they may believe that longevity is genetically


determined (Denes-Raj & Ehrlichman, 1991); and (3) women in the anchoring-heuristic

condition should have more accurate expectations than those in the control condition, who

should make optimistic ratings about their subjective longevity (Mirowski, 1999).

Method


Four hundred and forty-nine female undergraduate students in the Arts and Humanities (mean

age: 20.90 years, SD = 2.15) participated in this study. They completed the questionnaire in

classrooms. They were randomly assigned to three experimental conditions. In the first

condition (control condition, C1), the participants (N = 123, mean age: 21.13, SD = 2.66)

were asked to estimate their subjective life expectancy ("To what age do you expect to live?).

In the second condition (anchoring-heuristic condition, C2), the participants (N = 122, mean

age: 20.59, SD = 1.78) had to estimate their subjective life expectancy after being informed

that the female actuarial life expectancy in our country is 83 years ("Knowing that the actual

female life expectancy in our country is 83 years, to what age do you expect to live?"). In the

third condition (availability-heuristic condition, C3), the participants (N = 204, mean age:

20.94, SD = 2.00) were first asked whether they believed they belonged to a long-lived

family. Then they had to report the age of each maternal and paternal grandparent, and to

indicate the age at which each of their great grandparents had died.3 This recall task was

expected to have higher personal relevance for those who belonged to a long-lived family

than for those who did not, once this information was rendered salient (distinctiveness).

Finally, participants had to estimate their subjective longevity.

Participants were informed that their answers to the questionnaire would remain

anonymous. Several pretests had been carried out to evaluate the clarity of the questionnaire

items and instructions.

Measures

The measures included in this study were the same as in Study 1: LOT-R (α = .80), IPC

(α = .51, .75, and .69 for I, P, and C, respectively), HADS-Depression Scale (α = .62), WI

(hypochondriasis, α = 82), and subjective health.

3 A significant number of answers were missing, undoubtedly due to the fact that the participants were unaware

of exactly how old their great grandparents were at death. Thus, this variable was excluded from the analysis.

12



heuristic condition (C3) believed they descended from a long-lived family. Thus, in our

analyses, participants belonging to a long-lived family were distinguished from those whose

family was not long-lived. Maternal grandmothers (MGM) of participants belonging to a

long-lived family were significantly (at p < .05) older than those of the other participants (age

75.87 vs 73.18), and their maternal and paternal grandmothers (PGM) died at a significantly

(at p < .05) older age than those of other participants (age 73.68 vs 65.70 for MGM; age 78.54

vs 64.17 for PGM). No differences were found for the grandfathers.

The means and standard deviations of the measures used in this study are given in

Table 2. An ANCOVA was performed to analyze differences across experimental conditions.

The results showed a significant effect of experimental condition on subjective life

expectancy after controlling for the effect of optimism, depressive mood, LOC,

hypochondriac tendencies and subjective health, F(3, 438) = 6.71, p = .000; eta²= .191. Of

the covariates examined, only subjective health (p= .000), depressive mood (p= .040), and

optimism (p=.007) were significant predictors of subjective life expectancy. However the

ANOVA results revealed only a significant effect of optimism, F(3, 445)= 4.00, p= .008.

There were only one significant difference between groups, participants who believed they

belonged to a long-lived family were significantly (p<.05) more optimistic than those who did

not believe this (M = 14.63 vs M = 12.50). However, note that participants in the anchoring-

heuristic condition (C2) underestimated their subjective life expectancy relative to the female

actuarial life expectancy. They expected to live about one and a half years less than predicted

by the actuarial estimate. In spite of the fact that they were informed of the actuarial life

expectancy estimate, these participants underestimated their own longevity. Instead of the

optimistic bias, this is a case of a pessimistic bias. However, these women were neither more

pessimistic nor more depressed than participants of the control group (see Table 2), but

simply thought they would not live as long as one can hopefully expect. Were they more

superstitious, more realistic? The ANCOVA post-hoc comparison of life expectancy

estimates using Bonferroni's test (p < .05) revealed significant differences between

participants in the availability-heuristic condition (C3) and those in the other experimental

conditions. Participants who believed they did not belong to a long-lived family


underestimated their own life expectancy (age 75.93). On the other hand, participants who

believed their family was long-lived overestimated their subjective life expectancy (age

84.70). It seems that the use of the availability heuristic activated a kind of "implicit theory of

genetics" (lay knowledge of inheritance) resulting in an overestimation of the impact of

heredity on longevity.


Study 3

The objective of the present study was to investigate the effect of heuristic thinking on

women's prediction of the age at which they would begin menopause. After controlling for

optimism, locus of control, hypochondriac tendencies, subjective health, and depressive

mood, we hypothesized that (1) women whose mother had reached menopause would expect

to experience an earlier menopause; (2) women whose mother had not yet reached menopause

would expect to experience a later menopause; and (3) women in the anchoring-heuristic

condition would be more realistic than those in the control condition, who should make

optimistic ratings about when they would start menopause.

Method


Four hundred and forty-eight female undergraduate students in the Arts and Humanities

(mean age: 21.04 years, SD = 2.14) participated in the study. They completed the

questionnaire in classrooms. They were randomly assigned to three experimental conditions.

In the first condition (control condition, C1), the participants (N = 127, mean age: 21.05,

SD = 2.61) were asked to estimate the age of onset of their menopause by answering the

question "At what age do you think you will begin menopause?" In the second condition

(anchoring-heuristic condition, C2), the participants (N = 124, mean age: 20.57, SD = 1.78)

had to estimate the age of onset of their menopause after having been informed that

menopause usually begins anytime between the ages of 50 and 55. The question was:

"Knowing that menopause usually begins anytime between the ages of 50 and 55, at what age

you think you will begin menopause?" In the third condition (availability-heuristic condition,

C3), the participants (N = 197, mean age: 21.05, SD = 2.09) were asked whether their mother

14

had gone through menopause and if so, at what age. This recall task was expected to have

higher personal relevance for women whose mother had gone through menopause than for

those whose mother had not, once this information was rendered salient (distinctiveness).

Lastly, they had to estimate the age of onset of their own menopause.

Participants were informed that their answers would remain anonymous. Several

pretests had been carried out in order to evaluate the clarity of the questionnaire items and

instructions.

Measures

The measures used in this study were the same as in Studies 1 and 2: LOT-R (α = .81), IPC

(α = .50, .73, and .70 for I, P, and C, respectively), HADS-Depression Scale (α = .65), WI

(α = .83), and subjective health.



heuristic condition (C3) said that their mother had reached menopause. The mean age of

menopause onset among these mothers was 47.85 (SD = 4.42). Thus, in our analyses,

participants whose mother had or had not reached menopause were distinguished.

The means and standard deviations of the measures included in this study are given in

Table 3. An ANCOVA was performed to analyze differences across experimental conditions

with optimism, depressive mood, LOC, hypochondriac tendencies and subjective health

introduced as covariates. The results revealed a significant effect of experimental condition on

estimated age of onset of participant’s own menopause after controlling for the effect of the

covariates, F(3, 437) = 10.30, p = .000; eta²= .080. Only the covariate, internal control, was

significantly related to the estimated age of menopause onset (p=.030). However the ANOVA

results revealed only a significant effect of age, F(3, 444)= 3.2., p= .022. A post-hoc

comparison of participant age showed that participants in the availability-heuristic condition

whose mother had reached menopause were significantly older than participants in the

anchoring condition (21.47 years vs 20.56 years; p=.031). The ANCOVA post-hoc

comparison revealed a significant difference in the adjusted means between the participants in

the availability-heuristic condition (C3) whose mother had reached menopause and the


participants in the other experimental conditions. Indeed, women whose mother had reached

menopause significantly underestimated the age of onset of their own menopause compared to

women in the control (p= .004) and anchoring-heuristic (p=.000) conditions, and to those

whose mother had not reached menopause (p=.000). Participants in the anchoring-heuristic

condition used prior information related to the average age of menopause as a cognitive

anchor around which they estimated when they would begin menopause (M = 52.26 years). In

short, they neither overestimated nor underestimated the age at which they thought they

would experience menopause, but simply adjusted their estimate around the age 50-55 anchor

by choosing its average (52.26 years). The availability heuristic led the C3 participants to use

the information retrieved about their mother's menopause to underestimate the age of onset of

their own menopause.


Conclusion

Risk perception refers to the subjective estimation of the likelihood of an adverse event.

This type of judgment involves cognitive and emotional processes, and implements both

rational and irrational information-processing mechanisms. To make a judgment about the

likelihood of an event, people use a number of learned cognitive shortcuts as well as naive

theories. Accordingly, risk perception is influenced by the data an individual is given, as well

as by how he/she processes information.

The three studies conducted in this research fall within this theoretical framework. We

used lifetime risk of breast cancer in the first study, expected longevity in the second, and

subjective age of onset of menopause in the third. In each study, three experimental

conditions were set up: control, anchoring heuristic, and availability heuristic. Let us

summarize the main results. First, although there were no differences between the

experimental groups in personality variables such as hypochondriac tendencies, locus of

control, subjective health, and depressive mood, some of them showed significant impact on

judgments of riskiness. Thus, hypochondriac tendencies had a significant effect on perceived

risk of breast cancer. This finding is in line with studies demonstrating that hypochondriasis is

often accompanied by a heightened sense of risk of disease (Barsky et al. 2001). Subjective

health, depressive mood and dispositional optimism showed a significant effect on subjective

16

life expectancy. And internal locus of control was significantly related to the estimated age of

menopause onset. Second, except in the anchoring group, the perception of lifetime risk of

breast cancer was much higher than the true risk. The women in this study greatly

overestimated their risk of developing breast cancer, believing themselves to be three times

more at risk than in reality. Although this finding is similar to that obtained in other studies, it

is possible that the large amount of TV coverage on breast-cancer prevention during our study

accounts for this result. Thus, not only did we not observe an optimistic bias in women's

perceived risk of breast cancer (Clarke et al., 2000; Facione, 2002; Fontaine & Smith, 1995),

but we think that risk-prevention campaigns should be conducted very cautiously, because

awareness of a disease does not automatically lead to accurate risk perceptions. On the

contrary, such awareness could generate useless worry and distress. Third, no matter what

adverse event was at stake (breast cancer, longevity, or onset of menopause), our results

replicated the anchoring and availability effects. Indeed, compared to participants in the

control condition, women with a positive family history — used here as the availability

condition — overestimated their risk. This finding is consistent with prior studies and can be

explained by a kind of implicit theory of genetics based on lay knowledge of heredity. This

popular knowledge concerns the inheritance of family characteristics, including physical

features, character, and temperament, as well as health and proneness to illness (Richards &

Ponder, 1996). A strong relationship is known to exist between family history and perceived

vulnerability (Jacobsen et al., 2004). A person's family history may generate a belief of being

genetically protected or genetically vulnerable, and this in turn would influence judgments of

riskiness. However, to be effective, this belief needs to be recalled and activated. We can

legitimately assume that, among the participants in our control condition, some had a positive

family history. Also, we can legitimately assume that “friend effect” (Montgomery et

al.,2003) and “context effect” (Council, 1993) exist, and they were not controlled in our study.

Also, further research with a fourth experimental condition is needed, combining availability

and anchoring heuristics, where we asked about family history, but we also give the objective

information on the average risk of breast cancer to determine which information most

contributes to perceived risk. Nevertheless, except for breast cancer, the estimates of the

participants were more accurate in the control condition. Is there more uncertainty about the

risk of breast cancer than about longevity and the onset of menopause? For longevity, the

correspondence in the control condition between subjective and actuarial life expectancy was

far from perfect: these women expected to live two years less than current mortality rates

indicate (age 81 vs 83). One possible account of this lack of an optimistic life expectancy is


the impact of chronological age on future time perspectives. According to Socioemotional

Selectivity Theory, perception of time is malleable, and future time perspectives are related to

social motivation, which changes with age (Carstensen, 2006; Carstensen, Isaacowitz, &

Charles, 1999; Lang & Carstensen, 2002). Recall that our participants were young (mean

age: 21). Fourth, the anchoring effect resulted in a more realistic perception of personal risk.

Participants made estimates that were closer to the anchor values. Thus, we did not observe an

optimistic bias. Participants in the anchoring condition believed they were 2.5% more at risk

than the actual risk stated, and they expected to live about one and a half years less than

indicated in the current mortality tables (information given as the anchor value).

The findings obtained in this research demonstrate that risk assessment is influenced by

the information an individual is given as well as by how he/she processes probabilistic data.

Accurate risk assessment thus seems to rely on the dissemination and accessibility of

scientific information. To be more effective, risk-communication efforts should try to combat

certain lay beliefs and naive theories. For example, it is hard for many women to understand

that breast cancer can be inherited from a father (Green et al., 1997).

References

Barsky, A.J., Ahern, D.K., Bailey, E.D., Saintfort, R., Liu, E.B., & Peekna, H.M. (2001).

Hypochondriacal patients' appraisal of health and physical risks. American Journal of

Psychiatry, 158, 783-787.

Bates, B.R. (2005). Public culture and public understanding of genetics: A focus group study.

Public Understanding of Science, 14, 47-65.

Bates, B.R., Templeton, A., Achter, P.J., Harris, T.M., & Condit, C.M. (2003). What does "a

gene for heart disease" mean? A focus group study of public understandings of genetic

risk factors. American Journal of Medical Genetics, 119, 156-161.

Brewer, N.T., Chapman, G.B., Gibbons, F.X., Gerrard, M., McCaul, K.D., Weinstein, N.D.

(2007). Meta-Analysis of the Relationship Between Risk Perception and Health

Behavior: The Example of Vaccination. Health Psychology, 26, 136-145.

Carstensen, L.L.(2006). The Influence of a Sense of Time on Human Development. Science,

312, 1913-1915.

Carstensen, L.L., Isaacowitz, D., & Charles, S.T. (1999). Taking time seriously: A theory of

socioemotional selectivity. American Psychologist, 54, 165—181.

Clarke, V.A., Lovegrove, H., Williams, A., & Machperson, M. (2000). Unrealistic optimism

and the Health Belief Model. Journal of Behavioral Medicine, 23, 367-376.

18

Condit, C., Parrott, R., Harris, T.M., Lynch. J., & Dubriwny, T. (2004). The role of "genetics"

in popular understandings of race in the United States. Public Understanding of

Science, 13, 1-24.

Council, J.R. (1993). Context effects in personality research. Current Directions in

Psychological Science, 2, 31-34

Davison, C., Frankel, S., & Smith, G.D. (1989). Inheriting heart trouble: The relevance of

common-sense ideas to preventive measures. Health Education Research 4: 329-340.

Denes-Raj, V., & Ehrlichman, H. (1991). Effects of premature parental death on subjective

life expectancy, death anxiety, and health behavior. Omega: Journal of Death-and-

Dying, 23, 309-321.

Epley, N. & Gilovich., T. (2006).The Anchoring-and-Adjustment Heuristic. Why the

adjustments are insufficient. Psychological Science, 17, 311-318.

Facione, N.C. (2002). Perceived risk of breast cancer. Influence of heuristic thinking. Cancer

Practice, 10, 256-262.

Folkes, V.S. (1988). The availability heuristic and perceived risk. Journal of Consumer

Research, 15, 13-23.

Fontaine, K.R., & Smith, S. (1995). Optimistic bias in cancer risk perception: A cross-

national study. Psychological Report, 77, 143-146.

Greening, L., Dollinger, S.J., & Pitz, G. (1996). Adolescents’ perceived risk and personal

experience with natural disasters: An evaluation of cognitive heuristics. Acta

Psychologica, 91, 27-38.

Gross, C.P., Filardo, G., Singh, H.S., Freedman, A.N., Farrell, M.H. (2006). The Relation

Between Projected Breast Cancer Risk, Perceived Cancer Risk, and Mammography

Use: Results from the National Health Interview Survey. Journal of General Internal

Medicine, 21, 158-164.

Helzlsouer, K., Ford, D., Hayward, R., Midzenski, M., & Perry, H. (1994). Perceived risk of

cancer and practice of cancer prevention behaviors among employees in an oncology

center. Preventive Medicine, 23, 302-308.

Henderson, B. J., & Maguire, B. T. (2000). Three lay mental models of disease inheritance.

Social Science & Medicine, 50, 293—301.

Hurd, M.D., & McGarry, K.M. (1995). Evaluation of the Subjective Probabilities of Survival

in the Health and Retirement Study. Journal of Human Resources 30, S268-S292.

Hurd, M. D., & McGarry, K.M. (1997). The predictive validity of subjective probabilities of

survival. NBER Working Paper No. W6193.

Jacobsen, P.B., Lamonde, L.A., Honour, M., Kash, K., Hudson, P.B., & Pow-Sang, J. (2004).

Relation of family history of prostate cancer to perceived vulnerability and screening

behavior. Psycho-Oncology, 13, 80-85.


Katapodi, M.C., Facione, N.C., Humphreys, J.C., & Dodd, M.J. (2005). Perceived breast

cancer risk: Heuristic reasoning and search for a dominance structure. Social Science

& Medicine, 60, 421-432.

Lang, F.R., & Carstensen, L.L. (2002). Time counts: Future time perspective, goals, and

social relationships. Psychology and Aging, 17, 125-139.

Lawlor, D.A., Adamson, J., & Ebrahim, S. (2002). Lay perceptions of a 'natural' menopause.

Cross-sectional study of the British Women's Heart and Health Study. British Journal

of Obstetrics and Gynaecology, 109, 1398-1400.

Levenson, H. (1981). Differentiating among internality, powerful others, and chance. In:

Lefcourt, H.M. (Ed.), Research with the Locus of Control Construct: Assessment

Methods (Volume 1), pp. 15-63. New York: Academic Press.

Loas, G., Dardennes, R., Dhee-Perot, P., Leclerc, V., & Fremaux, D. (1994).

Operationnalisation du concept de lieu de controle: traduction et premiere etude de

validation de l'echelle de controle de Levenson (IPC: The internal powerful others and

chance scale). Annales Medico-Psychologiques, 152, 466-469.

Lock, M., Freeman, J., Sharples, R., & Lloyd, S. (2006). When it runs in the family: Putting

susceptibility genes in perspective. Public Understanding of Science, 15, 277-300.

Marteau, T.M. (1999). Communicating genetic risk information. British Medical Bulletin, 55,

414-428.

Mirowsky, J. (1997). Age, subjective life expectancy, and the sense of control: The horizon

hypothesis. Journal of Gerontology-B, 52, 125-134.

Mirowsky, J. (1999). Subjective life expectancy in the US: Correspondence to actuarial

estimates by age, sex and race. Social Science & Medicine, 49, 967-979.

Montegomery, G.H. (1989). From cognition to action: The search for dominance in decision.

In G. H. Montegomery (Ed.), Process and structure in human decision-making (pp.23-

49). Chichester, UK: Wiley.

Montegomery, G.H., Erblich, J., DiLorenzo, T., & Bovbjerg, D.H. (2003). Family and friends

with disease: Their impact on perceived risk. Preventive Medicine, 37, 242-249.

Moser, R.P., McCaul, K., Peters, E., Nelson, W., Marcus, S.E. (2007).Associations of

Perceived Risk and Worry with Cancer Health-protective Actions: Data from the

Health Information National Trends Survey (HINTS). Journal of Health Psychology,

12, 53-65.

Mouchawar, J., Byers, T., Cutter, G., Dignan, M., & Michael, S. (1999). A study of the

relationship between family history of breast cancer and knowledge of breast cancer

genetic testing prerequisites. Cancer Detection & Prevention, 23, 22-30.

Nezlek, J.B., & Zebrowski, B.D. (2001). Implications of the dimensionality of unrealistic

optimism for the study of perceived health risks. Journal of Social and Clinical

Psychology, 20, 521-537.

20

Parrott, R., Silk, K., & Condit, C. (2003). Diversity in lay perceptions of the sources of human

traits: Genes, environments, and personal behaviors. Social Science & medicine, 56,

1099-1109.

Parrott, R., Silk, K., Weiner, J., Condit, C., Harris, T., & Bernhardt, J. (2004). Deriving lay

models of uncertainty about genes' role in illness causation to guide communication

about human genetics. Journal of Communication, 54, 105-122.

Persson, G.R., Persson, R.E., MacEntee, C.I., Wyatt, C.C.I.I., Hollender, L.G., & Kiyak, H.A.

(2003). Periodontitis and perceived risk for periodontitis in elders with evidence of

depression. Journal of Clinical Periodontology, 30, 691-696.

Pilowsky, I. (1967). Dimensions of hypochondriasis. British Journal of Psychiatry, 113, 89-93

Quillin, J.M., McClish, D.K., Jones, R.M., Burruss, K., & Bodurtha, J.N. (2006). Spiritual

coping, family history, and perceived risk for breast cancer - Can we make sense of it?

Journal of Genetic Counseling, 15, 449-460.

Razavi, D., Delvaux, N., Farvacques, C., Robaye, E. (1989). Validation de la version

française du HADS dans une population de cancéreux hospitalisés. Revue de

Psychologie Appliquée, 39, 295-308.

Rees, G., Fry, A. & Cull, A. (2001). A family history of breast cancer: Women's experiences

from a theoretical perspective. Social Science & Medicine, 52, 1433-1440.

Richards, M.P.M., & Ponder, M. (1996). Lay understanding of genetics: A test of a

hypothesis. Journal of Medical Genetics, 33, 1032-1036.

Robbins, R.A. (1988a). Objective and subjective factors in estimating life expectancy.

Psychological Reports, 63, 47-53.

Robbins, R.A. (1988b). Subjective life expectancy as a correlate of family life expectancy.

Psychological Reports, 62, 442.

Ross, C.E., & Mirowski, J. (2002). Family Relationships, Social Support and Subjective Life

Expectancy. Journal of Health and Social Behavior, 43, 469-489.

Rowe, J.L., Montgomery, G.H., Duberstein, P.R., & Bovbjerg, D.H. (2005). Health locus of

control and perceived risk for breast cancer in healthy women. Health Medicine, 31,

33-40.

Santos, S. (2006). The diversity of everyday ideas about inherited disorders. Public

Understanding of Science, 15, 259-275.

Singer, E., Corning, A., & Lamias, M. (1998). The polls — trends: Genetic testing,

engineering and therapy. Public Opinion Quarterly, 62, 633—664.

Streiner, D.L. (2003). Starting at the Beginning: An Introduction to Coefficient Alpha and Internal Consistency. Journal of Personality Assessment, 80, 99-103.

Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.

Science, 185, 1124-1130.


Walter, F.M., & Britten, N. (2002). Patients' understanding of risk: A qualitative study of

decision-making about the menopause and hormone replacement therapy in general

practice. Family Practice, 19, 579-586.

Weinstein, N.D., & Klein, W.M. (1996). Unrealistic optimism: present and future. Journal of

Social & Clinical Psychology,15, 1—8.

Yamagishi, K. (1997). When a 12.86% mortality is more dangerous than 24.14%:

Implications for risk communication. Applied Cognitive Psychology, 11, 495-506.

Zigmond, A.S., & Snaith, P.R. (1983). The Hospital Anxiety and Depression Scale. Acta

Psychiatrica Scandinavica, 67, 361-370.

22

Table 1. Means (and standard deviations) of measures used in Study 1.

Variable Control condition

(C1)

(n = 128)

Anchoring-heuristic

condition (C2)

(n = 119)

Availability

Condition (n = 226)

F-test (p)

Family history

(C3) (n = 56)

No family history

(C4) (n = 170)

Age 21.06 (2.62) 20.58 (1.80) 21.05 (1.99) 21.01 (2.14) 1.43 (.23)

Participant's estimate of

personal risk of developing

breast cancer (%)

27.38 (1.54)*

C2, C3***

11.32 (1.60)

C1, C3, C4

34.76 (2.34)

C1, C2, C4

23.40 (1.33)

C2,C3

28.70 (.000)**

Subjective health 4.03 (0.70) 4.00 (0.62) 4.09 (0.65) 4.07 (0.69) 0.39 (.75)

Hypochondriac tendencies 28.42 (8.46) 29.95 (10.06) 28.47 (7.99) 28.33 (9.47) 0.83 (.47)

Optimism 13.98 (4.91) 13.03 (4.45) 12.78 (4.83) 13.94 (4.87) 2.17 (.09)

Internal control 28.43 (5.68) 27.59 (5.82) 27.49 (5.91) 27.52 (5.98) 0.64 (.58)

Powerful others 15.35 (7.80) 15.11 (7.30) 16.14 (7.41) 15.18 (7.52) 0.52 (.66)

Chance 20.08 (8.17) 19.67 (7.34) 20.84 (7.32) 19.56 (7.38) 0.92 (.42)

Depressive mood 4.72 (3.21) 4.96 (3.21) 4.85 (2.97) 4.51 (3.01) 0.39 (.76)

* Adjusted mean (Standard error)

** Adjusted F

*** significant pairwise comparisons at p<.05




(C1)

(n = 123)

Anchoring condition

(C2)

(n = 122)

Availability condition

(n = 229)

F-test (p)

Long-lived

family (C3)

(n = 142)

Not long-lived

family (C4)

(n = 62)

Age 21.13 (2.66) 20.59 (1.78) 20.93(1.97) 20.97 (2.06) 1.34 (.25)

Participant's estimate of

subjective life expectancy

80.85 (0.97)*

C3***

81.77 (0.98)

C4

84.39 (0.91)

C1, C4

77.13 (1.38)

C2, C3

6.67 (.000)**



Optimism 14.15 (4.80) 13.27 (4.39) 14.63 (4.54) 12.50 (4.60) 4.00 (.00)


Powerful others 15.07 (7.66) 15.09 (7.24) 15.66 (7.93) 15.15 (6.40) 0.19 (.90)

Chance 19.72 (7.99) 19.77 (7.39) 19.46 (7.34) 20.53 (6.96) 0.29 (.82)

Depressive mood 4.61 (3.06) 4.64 (2.59) 4.20 (2.66) 5.34 (3.16) 2.37 (.07)

* Adjusted mean (Standard error)

** Adjusted F

*** significant pairwise comparisons at p<.05

24



(C1)

(n = 127)

Anchoring-heuristic

condition (C2)

(n = 124)

Availability

Condition (n = 197)

F-test (p)

Mother reached

menopause*

(C3) (n = 82)

Mother not

reached

menopause (C4)

(n = 115)

Age 21.05 (2.61) 20.56 (1.77) 21.47(2.39) 20.65 (1.79) 3.22 (.02)

Participant's estimate of the

age of onset of own

menopause

51.65 (.403)**

C3****

52.23 (.409)**

C3

49.43 (.503)**

C1, C2, C4

52.99 (.425)**

C3

10.30 (.000)***



Optimism 13.94 (4.92) 13.14 (4.43) 13.84 (5.00) 13.40 (4.80) 0.72 (.53)


Powerful others 15.36 (7.92) 15.20 (7.26) 15.31 (7.20) 15.49 (8.17) 0.04 (.98)

Chance 20.02 (8.18) 19.83 (7.35) 19.78 (7.44) 20.24 (7.62) 0.07 (.97)

Depressive mood 4.74 (3.20) 4.74 (2.73) 4.33 (2.95) 4.92 (3.08) 0.57 (.63)

* Mean age of onset of menopause among mothers: 47.85 (SD = 4.42)

** Adjusted mean (Standard error)

*** Adjusted F

**** significant pairwise comparisons at p<.05

Running Head: Judgment of Riskiness · The anchoring-and-adjustment heuristic is another cognitive...

Documents

Transcript of Running Head: Judgment of Riskiness · The anchoring-and-adjustment heuristic is another cognitive...