Running Head: Judgment of Riskiness · The anchoring-and-adjustment heuristic is another cognitive...
Transcript of 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:
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
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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
Judgment of riskiness 5
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.
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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)
Judgment of riskiness 7
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.
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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.
Judgment of riskiness 9
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
Judgment of riskiness 11
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
Participants and Procedure
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
Results and Discussion
First of all, it is important to note that 142 (69.6%) of the 204 women in the availability-
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
Judgment of riskiness 13
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.
INSERT TABLE 2 ABOUT HERE
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
Participants and Procedure
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.
Results and Discussion
First of all, it is important to note that 82 (41.6%) of the 197 women in the availability-
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
Judgment of riskiness 15
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.
INSERT TABLE 3 ABOUT HERE
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
Judgment of riskiness 17
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).
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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
Judgment of riskiness 23
Table 2. Means (and standard deviations) of measures used in Study 2.
Variable Control condition
(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)**
Subjective health 4.07 (0.69) 3.98 (0.63) 4.13 (0.68) 4.00 (0.68) 1.16 (.32)
Hypochondriac tendencies 28.14 (8.28) 29.80 (9.93) 28.27 (9.42) 28.98 (8.75) 0.85 (.46)
Optimism 14.15 (4.80) 13.27 (4.39) 14.63 (4.54) 12.50 (4.60) 4.00 (.00)
Internal control 28.46 (5.68) 27.80 (5.85) 28.03 (5.90) 26.82 (6.25) 1.10 (.34)
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
Table 3. Means (and standard deviations) of measures used in Study 3.
Variable Control condition
(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)***
Subjective health 4.05 (0.69) 4.00 (0.63) 4.15 (0.67) 3.94 (0.72) 1.72 (.15)
Hypochondriac tendencies 28.54 (8.48) 29.91 (9.90) 28.20 (8.51) 28.18 (9.92) 0.83 (.47)
Optimism 13.94 (4.92) 13.14 (4.43) 13.84 (5.00) 13.40 (4.80) 0.72 (.53)
Internal control 28.30 (5.64) 27.66 (5.87) 28.52 (5.89) 26.72 (6.14) 2.04 (.10)
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