Adult age differences in DMC
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Running head: Adult age differences in DMC
Explaining adult age differences in decision-making competence
Journal of Behavioral Decision Making, in press
Wändi Bruine de Bruin 1
Andrew M. Parker 2
Baruch Fischhoff 1
1
Department of Social and Decision Sciences and Department of Engineering
and Public Policy, Carnegie Mellon University
2
RAND Corporation, Pittsburgh PA
Author note
This research was funded by NSF # SES-0213782 and #EEC-0540865. The authors
thank Kirstin Appelt, Steve Atlas, Shahzeen Attari, Martine Baldassi, Bernd Figner,
David Hardisty, Eric Johnson, Maria Konnikova, Ye Li, Jenn Logg, Annie Ma, Fabio Del
Missier, Juliana Smith, Katherine Thompson, Elke Weber, Julie Zelmanova, and three
anonymous reviewers for their comments, as well as Jónína Bjarnadóttir, Jacob Chen,
YoonSun Choi, Rebecca Cornelius, Mandy Holbrook, Mark Huneke, Kathleen Pinturak,
and Alanna Williams for their research assistance. Please direct correspondence to Wändi
Bruine de Bruin, Carnegie Mellon University, Dept. of Social and Decision Sciences,
5000 Forbes Ave, Pittsburgh PA 15213; [email protected] (email).
Adult age differences in DMC
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Abstract
Studies on aging-related changes in decision making report mixed results. Some
decision-making skills decline with age, while others remain unchanged or improve.
Because fluid cognitive ability (e.g., reasoning, problem solving) deteriorates with age,
older adults should perform worse on decision-making tasks that tap fluid cognitive
ability. However, performance on some decision-making tasks may also require
experience, which increases with age. On those tasks, older adults should perform at
least as well as younger adults. These two patterns emerged in correlations between age
and component tasks of Adult Decision-Making Competence, controlling for
demographic variables. First, we found negative relationships between age and
performance on two tasks (Resistance to Framing, Applying Decision Rules), which were
mediated by fluid cognitive ability. Second, performance on other tasks did not decrease
with age (Consistency in Risk Perception, Recognizing Age-group Social Norms) or
improved (Under/Overconfidence, Resistance to Sunk Costs). In multivariate analyses,
performance on these tasks showed independent positive relationships to both age and
fluid cognitive ability. Because, after controlling for fluid cognitive ability, age becomes
a proxy for experience, these results suggest that experience plays no role in performing
the first set of tasks, and some role in performing the second set of tasks. Although not
all decision-making tasks showed age-related declines in performance, older adults
perceived themselves as worse decision makers. Self-ratings of decision-making
competence were related to fluid cognitive ability and to decision-making skills that
decreased with age – but not to decision-making skills that increased with age.
Key words: Decision-making competence, fluid cognitive ability, aging, experience.
Adult age differences in DMC
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Explaining adult age differences in decision-making competence
As the U.S. population grows older (Day, 1996), understanding the challenges of
aging is essential to helping older adults remain self-reliant. Across the life span,
decision-making skills are related to obtaining good decision outcomes (Bruine de Bruin,
Parker, & Fischhoff, 2007a; Parker & Fischhoff, 2005). Those skills may be particularly
critical to the often-difficult decisions that older adults face regarding their health,
finances, and living situations (Peters, Finucane, MacGregor, & Slovic, 2000).
Aging-related changes in decision-making skills have received relatively little
attention in judgment and decision-making research, with the few studies that have been
conducted revealing mixed results (for recent reviews, see Hanoch, Wood, & Rice, 2007;
Peters & Bruine de Bruin, in press; Peters, Hess, Västfjäll, & Auman, 2007). Some
decision-making skills appear to decrease with adult age. For example, older adults are
more likely than younger adults to use non-compensatory choice strategies, which require
fewer comparisons – thereby reducing cognitive load but also decreasing the chances of
identifying the best available option (Johnson, 1990). Older adults are also more likely to
choose suboptimal options as the number of alternatives increases (Besedes, Deck,
Sarangi, & Shor, 2009), and to make mistakes when applying decision rules (Bruine de
Bruin et al., 2007a). Some studies have found that older adults’ judgments and decisions
are more strongly influenced by how problems are framed (Bruine de Bruin et al., 2007a;
Finucane, Mertz, Slovic, & Schmidt, 2005; Finucane et al., 2002), while others have not
(Mayhorn, Fisk, & Whittle, 2002; Rönnlund, Karlsson, Laggnäs, Larsson, & Lindström,
2005; Weller, Levin, & Denburg, in press). Age-related increases in framing errors seem
to be more common in studies using within-subjects tasks (LeBeouf & Shafir, 2003;
Adult age differences in DMC
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Stanovich & West, 2008), which should be easier for younger participants because they
have better memory skills, and should therefore be better able to remember previously
presented frames.
Other decision-making skills seem to improve with adult age. Older adults are
more likely than younger adults to discontinue investments that are no longer paying off,
thus avoiding the sunk cost bias (Bruine de Bruin et al., 2007a; Strough et al., 2008).
Older adults are better at resisting the influence of irrelevant options on choices (Kim &
Hasher, 2005; Tentori, Osherson, Hasher, & May, 2001). Older adults’ confidence is
sometimes more appropriate than that of younger adults, in terms of reflecting their actual
knowledge (Kovalchik, Camerer, Grether, Plott, & Allman, 2005), sometimes less
appropriate (Crawford & Stankov, 1996; Parker, Yoong, Bruine de Bruin, & Willis,
2009), and sometimes the same (Bruine de Bruin et al., 2007a; Hansson, Rönnlund,
Juslin, & Nilsson, 2008). The relationship between age and the degree to which
confidence is appropriate appears to depend, in part, on how cognitively demanding the
task is. That is, older adults are more overconfident than younger adults on the
demanding task of generating credible intervals for the populations of different countries
(i.e., the range between ____ and ____ million for which you are 80% certain that it
includes the correct estimate for Burma’s population) but perform as well as younger
adults on the less demanding task of assessing the probability that a given interval
includes the correct population estimate (Hansson, Rönnlund, Juslin, & Nilsson, 2008).
Finally, age appears unrelated to some decision-making skills, such as following the rules
of probability theory when judging risks (Bruine de Bruin et al., 2007a; Fisk, 2005).
Adult age differences in DMC
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Recent reviews (Bruine de Bruin & Peters, in press; Hanoch et al., 2007; Peters et
al., 2007) speculate that these mixed patterns of results may reflect age-related decreases
in fluid cognitive ability and age-related increases in experience. Fluid cognitive
abilities, such as reasoning, pattern recognition, and problem solving, show linear agerelated declines starting in the early 20s (Park et al., 2002; Salthouse, 1991, 2004; Wilson
et al., 2002). Thus, decision-making skills that rely on these abilities should decrease
with age.
Decision-making skills that do not decrease with age may tap both fluid cognitive
ability, which decreases with age, as well as other, cognitive and affective, abilities that
increase with the experience that comes with age. Cognitive abilities that are acquired
with experience and age, also called crystallized intelligence, are typically domainspecific, pragmatic, and idiosyncratic (Horn & Cattell, 1967). Examples include
vocabulary knowledge (Salthouse, 2004; Verhaeghen, 2003) and complex job
performance (Sturman, 2003), as well as expertise in chess (Ericsson & Lehman, 1999;
Roring & Charness, 2007), music composition (Hayes, 1981), sports, arts, and science
(Ericsson, Krampe, & Tesch-Römer, 1993). Similarly, good performance on some
decision-making tasks may require experience with normative decision rules (Stanovich
& West, 2008). For example, individuals who have been exposed to the normative sunkcost rule are better at implementing it in hypothetical decisions (Larrick, Nisbett, &
Morgan, 1993).
Age may also bring emotion-related abilities, such as recognizing emotion states
(Labouvie-Vief, DeVoe, & Bulka, 1989), emotion regulation (Mather & Carstensen,
2003), and ignoring interpersonal stressors (Neupert, Almeida, & Charles, 2007). Older
Adult age differences in DMC
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adults are more likely to base decisions on positive affective information (Charles &
Carstensen 2007), which is less arousing and easier to process (Charles & Carstensen
2007). Doing so may help them to avoid the sunk-cost bias (Strough et al., 2008), and
errors due to loss aversion (Mikels & Reed, 2009) although these latter findings have not
been consistently replicated (Mayhorn et al., 2002; Weller et al., in press).
The relative roles of fluid cognitive ability and experience likely vary across
decision-making tasks. For example, Stanovich and West (2008) speculate that making
normatively appropriate decisions requires having sufficient experience to recognize
which normative decision-making rule applies, along with the fluid cognitive ability to
apply it, while overriding any competing heuristics. They note that experience and fluid
cognitive ability are interdependent, because (a) fluid cognitive ability may reduce the
experience needed to master new decision-making rules and (b) fluid cognitive ability
will facilitate the application of a rule only if individuals have sufficient experience to
recognize that the rule applies. Other researchers have also noted that experienced
individuals may have automated normative rules, thereby reducing the need for fluid
cognitive ability when applying it (Hanoch et al., 2007; Peters et al., 2007; Reyna, 2004;
Reyna, Lloyd, & Brainerd, 2003; Yates & Patalano, 1999).
Based on this task analysis, Stanovich and West (2008) predict two patterns in the
relationships between experience, fluid cognitive ability, and specific decision-making
skills. The first pattern pertains to decision-making tasks that require no experience to
detect the normative rule, either because the rule is widely known or because it is
described by the task instructions. For example, no experience should be needed to
perform well on Resistance to Framing tasks, because most adults know the rule that
Adult age differences in DMC
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consistent responses should be given to decision problems with equivalent frames
(Stanovich & West, 2008). Neither should experience play a role in Applying Decision
Rules, a task that explicitly presents participants with the specific decision rules (e.g.,
select the option with the highest average rating) to apply to their choices (Bruine de
Bruin et al., 2007). Even when no experience is needed to detect the rule that applies,
individuals with higher fluid cognitive ability should be better at applying the rule
correctly. Because fluid cognitive ability declines with age, older adults should perform
worse on these tasks.
The second pattern pertains to decision-making tasks for which good performance
requires more extensive experience to detect the normative rule. On those tasks,
increases in both experience and fluid cognitive ability should contribute to better
performance.1 Because experience increases with age, it may help older adults to
counteract or even overcome age-related declines in fluid cognitive ability. As a result,
older adults may perform at least as well as younger adults. According to Stanovich and
West (2008), experience is important to understand the sunk-cost rule, which prescribes
discontinuing investments that are no longer paying off, and the conjunction probability
rule, which refers to judging lower probabilities for compound events (e.g., dying from
any cause) than to each of their constituents (e.g., dying in a terrorist attack).
Thus, decision-making tasks that require fluid cognitive ability but no experience
should show a negative relationship between performance and age, with fluid cognitive
mediating that relationship. Tasks for which performance relies on fluid cognitive ability
as well as on experience should show no decreases in performance with age – and
possibly even improvements with age. In the analyses that follow, we use a direct
Adult age differences in DMC
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measure of fluid cognitive ability. After controlling for fluid cognitive ability, which
decreases with age, age should be a proxy for the abilities that improve with experience.
Hence, on tasks for which performance is hypothesized to benefit from both fluid
cognitive ability and experience, semipartial correlations are expected to reveal that both
fluid cognitive ability and age are independently correlated to task performance.
The few studies that have examined the relationships among decision-making
performance, age, and fluid cognitive ability suggest support for the two proposed
patterns. An example of the first pattern is seen in a study by Hansson and colleagues
(2008), who reported a negative relationship between age and using more appropriate
(e.g., wider) credible intervals, which was mediated by fluid cognitive ability. That result
is consistent with individuals needing no experience to understand the normative rule, but
greater fluid cognitive ability to apply it correctly. Strough and colleagues (2008) report
an example of the second pattern. They found that older adults were better than younger
adults at avoiding the sunk-cost bias, with performance showing only a weak correlation
with fluid cognitive ability, in both zero-order and semipartial correlations (Strough et al.,
2008). That result is consistent with individuals needing both experience and fluid
cognitive ability to perform the task. As mentioned, after controlling for fluid cognitive
ability, which decreases with age, age should be a proxy for abilities that improve with
experience.
Here, we examined age-related changes in a comprehensive set of judgment and
decision making tasks comprising the Adult Decision-Making Competence (A-DMC)
measure, performance on which is correlated with good life decision outcomes (Bruine
de Bruin et al., 2007a). The six component tasks of A-DMC reflect skills identified by
Adult age differences in DMC
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normative decision-making theories (e.g., Edwards, 1954; Raiffa, 1968): (1) Resistance
to Framing, (2) Applying Decision Rules, (3) Consistency in Risk Perception, (4)
Recognizing Social Norms, (5) Under/Overconfidence, and (6) Resistance to Sunk Costs.
Following Stanovich and West’s (2008) task analysis, explained above, performance on
two of these tasks should require fluid cognitive ability to apply the rule correctly, but
little to no experience to detect the rule, either because it is widely known or explicitly
provided. This is the case for (1) Resistance to Framing, which requires giving consistent
responses across related items, and thus relies on a consistency rule that is understood by
most adults (Stanovich & West, 2008) and (2) Applying Decision Rules, which explicitly
describes the rules that need to be applied. On those tasks, performance should show a
negative relationship with age, mediated by fluid cognitive ability. The four remaining
tasks should require more experience to detect their more complex rules, although fluid
cognitive ability should additionally be needed to implement them correctly. On those
tasks, performance should show independent positive relationships with age and fluid
cognitive ability.
Bruine de Bruin et al. (2007) report that, while overall A-DMC scores were
unrelated to age, component tasks had positive, negative, or no significant correlations.
We extended those analyses here by controlling for fluid cognitive ability. Age can be
interpreted as a proxy for abilities that improve with experience, after factoring out agerelated declines for fluid cognitive ability. We tested the following hypotheses.
Hypothesis 1: For A-DMC component scores that decrease with age, fluid
cognitive ability mediates the relationship. We expect to find that pattern for (a)
Resistance to Framing and (b) Applying Decision Rules.
Adult age differences in DMC 10
Hypothesis 2: For A-DMC component scores that do not decrease with age, age
and fluid cognitive ability are independent predictors. We expect to find that
pattern for (a) Consistency in Risk Perception, (b) Recognizing Social Norms, (c)
Under/overconfidence, and (d) Resistance to Sunk Costs.
We also examined whether perceived decision-making competence is related to
age. If adults are aware of the age-related decreases in fluid cognitive ability as well as
age-related improvements in other abilities, then, like overall performance on the Adult
Decision-Making Competence measure (Bruine de Bruin et al., 2007), these self-ratings
should be unrelated to age. However, if adults pay more attention to age-related declines
in fluid cognitive ability than to abilities that they may gain with age, then their perceived
decision-making competence will decrease with age. That finding would be consistent
with the finding that older adults are more likely than younger adults to agonize over
choices and surrender their autonomy to others (Yates & Palatino, 1999). If the opposite
is found, older adults will be more likely than younger adults to exaggerate their abilities,
make rash decisions, and fail to seek support when they need it.
Method
Sample
We present previously unpublished secondary analyses of data collected by
Bruine de Bruin et al. (2007). In that study, 360 participants were recruited through
community groups in Pittsburgh, Pennsylvania, with 46.1% from areas with low socioeconomic status (SES) and the remainder from areas with relatively higher SES. Table 1
describes participant age, gender, education, and SES, among those who reported each of
Adult age differences in DMC 11
these variables.2 It also shows demographic information for each age group and Pearson
correlations with the demographic variables. The only significant correlation was that
older participants were less likely to have gone to school beyond a high school diploma.
The analyses below control for demographic variables, and hence include only those
participants reporting each.
Measures
Adult-Decision-Making Competence (A-DMC). The component A-DMC tasks
were adapted from ones used in published studies to represent skills central to normative
theories of decision making (Edwards, 1954; Raiffa, 1968). Task selection, development,
and validation are described elsewhere (Bruine de Bruin et al., 2007a; Parker &
Fischhoff, 2005).3 Resistance to Framing measured the extent to which preferences are
consistent across normatively irrelevant variations in how options are described (see
Levin et al., 1998). It presented fourteen pairs of positively and negatively framed items.
Seven pairs involved attribute framing. For example, one pair (from Levin & Gaeth,
1988) asked for ratings of the attractiveness of ground beef described as “20% fat”
(negative frame) or “80% lean” (positive frame.) The other seven pairs involved risky
choices, including the well-known Asian disease problem (Tversky & Kahneman, 1981).
Applying Decision Rules assessed the ability to apply designated decision rules (from
Payne, Bettman, & Johnson, 1993) in ten hypothetical choices among DVD players. For
example, one item required selecting the DVD player with the highest average rating
across features. Consistency in Risk Perception assessed the ability to make internally
consistent risk judgments (e.g., giving probabilities that add up to 100% for getting in an
accident while driving and driving accident-free), in 20 paired judgments (Mandel, 2005).
Adult age differences in DMC 12
Under/Overconfidence measured the appropriateness of individuals’ confidence in their
knowledge (Keren, 1991; Yates, 1990), using a 34-item true/false test, with questions
selected from 17 Complete Idiot’s guides. After answering each question, participants
judged the probability that their choice was correct, on a 50%-100% scale. The overall
score reflected the absolute difference between the mean probability judgment and the
percentage of correct answers. Recognizing Age-group Social Norms assessed how well
participants estimated social norms held by members of their age group (Jacobs,
Greenwald, & Osgood, 1995; Loeber, 1989).4 Items asked “out of 100 people your age,
how many would say it is sometimes OK” to engage in each of 16 potentially
objectionable behaviors (e.g., “steal under certain circumstances”). These estimates were
compared to the percentage of study participants in their age group (i.e., <20, 21-29, 3039, etc.) saying that “it is sometimes OK” to engage in each behavior. Each individual’s
score was the Spearman rank correlation between their judgment and the endorsement
rate among the study participants in their age group. Resistance to Sunk Costs measured
the ability to ignore prior investments that do not affect future decision outcomes (Arkes
& Blumer, 1985). It was tested with ten vignettes adapted from the sunk-cost literature.
Fluid cognitive ability. Participants completed a shortened version of Raven’s
Standard Progressive Matrices (see Bruine de Bruin et al., 2007a), a measure of fluid
cognitive ability for which scores have been found to decrease with age (Raven, Raven,
& Court, 2003).
Perceived decision-making competence. Participants answered on a scale from 0100%, “what percent of other people do you think are worse decision makers than you?”
Higher numbers reflected better self-ratings.
Adult age differences in DMC 13
Demographic variables. Respondents reported their age, gender, and highest
level of education completed. We also recorded whether each respondent was recruited
from a community with lower or higher socio-economic status.
Procedure.
After completing a reading comprehension task (not analyzed here), participants
received the self-paced DMC tasks, in the order: (a) positive version of Resistance to
Framing, (b) Recognizing Age-group Social Norms questions asking if “it is sometimes
OK” to engage in 16 behaviors, (c) Under/Overconfidence, (d) Applying Decision Rules,
(e) Consistency in Risk Perception, (f) Resistance to Sunk Costs, (g) negative version of
Resistance to Framing, and (h) Recognizing Age-group Social Norms questions asking
about others’ reports. Next, participants gave self-ratings of their perceived decisionmaking competence and completed measures of decision-making styles (not analyzed
here). Finally, participants completed Raven’s Standard Progressive Matrices and a
demographic form. Participants received $35, with the option of donating all, half, or
none to the community organization through which they were recruited.
Results
Decision-making skills that decrease with age.
We found significant negative zero-order correlations between age and
performance on Resistance to Framing and Applying Decision Rules, which held after
additionally controlling for demographic variables (Table 2). Hypothesis 1 holds that
these negative relationships should be mediated by fluid cognitive ability. Table 2 shows
Adult age differences in DMC 14
the results of linear regressions predicting performance on these tasks from (1) age, (2)
fluid cognitive ability, (3) age and fluid cognitive ability, both before and after
controlling for the demographic variables of gender, education, and SES. Before adding
demographic controls, regressions for (1) and (2) are bivariate, with the standardized beta
coefficients showing the same value as corresponding zero-order correlations. To test for
simple mediation, we followed the three-step procedure outlined by Baron and Kenny
(1986) as well as Sobel tests (Preacher & Hayes, 2004; Sobel, 1982).
As predicted, both methods found significant mediation, before and after
controlling for demographic variables. The first step of the Baron and Kenny (1986)
procedure revealed a significant negative relationship between the independent variable,
age, and the presumed mediator, fluid cognitive ability (r=-.33, β=-.33, p<.001), which
held after controlling for demographics (β=-.27, p<.001). The second step revealed
significant positive relationships between the presumed mediator, fluid cognitive ability,
and the outcome variables, performance on Resistance to Framing and Applying Decision
Rules (Table 2). The third step found that, for both decision-making tasks, the negative
correlation between performance and age was no longer significant after additionally
controlling for fluid cognitive ability, in models with and without demographic controls.
As seen in Table 2, Sobel (1982) tests confirmed that fluid cognitive ability significantly
mediated the relationship between age and performance on each of these two tasks. In
contrast, Sobel (1982) tests found that controlling for age did not significantly affect the
significance of the relationship between fluid cognitive ability and performance on these
tasks.
Adult age differences in DMC 15
Decision-making skills that do not decrease with age.
Table 2, shows that there were no significant zero-order correlations between age
and performance on Consistency in Risk Perception, Recognizing Age-group Social
Norms and Under/Overconfidence, while performance on Resistance to Sunk Costs
significantly improved with age. After adding demographic controls, the positive
correlation between Under/Overconfidence and age also became significant.
According to Hypothesis 2, performance on these four tasks should have
independent relationships with age and fluid cognitive ability. Indeed, performance on
three of these four tasks had a significant positive relationship with age after controlling
for fluid cognitive ability and a significant positive relationship with fluid cognitive
ability after controlling for age, both before and after controlling for the demographic
variables (Table 2). The fourth task, Consistency in Risk Perception, had a similar
pattern, but the positive relationship between age and performance become only
marginally significant after controlling for fluid cognitive ability.
Yet, for each of the four tasks, Sobel tests suggested significant suppression
effects (Cohen & Cohen, 1983; MacKinnon, Krull, & Lockwood, 2000; Preacher &
Hayes, 2004), with relationships between age and performance on each of the three tasks
becoming more positive after controlling for age-related declines in fluid cognitive ability
(except for Under/Overconfidence when controlling for demographic variables). Sobel
tests also found that age suppressed the relationship between fluid cognitive ability and
performance on Under/Overconfidence and Resistance to Sunk Costs, both before and
after adding demographic controls.
Adult age differences in DMC 16
Perceived decision-making competence.
The final row of Table 2 shows the results of linear regressions predicting selfratings of decision-making competence from (1) age, (2) fluid cognitive ability, (3) age
and fluid cognitive ability, both before and after controlling for the demographic
variables of gender, education, and SES. Before controlling for demographic variables,
there was a negative relationship between age and self-ratings of decision-making
competence, which was mediated by fluid cognitive ability, according to both Baron and
Kenny’s (1986) three-step procedure and a Sobel test. Although these results were not
significant after controlling for demographic variables, it appears that relationships
among self-ratings, age, and fluid cognitive ability follow the pattern found for A-DMC
tasks on which performance decreased with age (Table 2). These results suggest that
older adults may have assessed these self-ratings based on perceived declines in fluid
cognitive ability, rather than on perceived improvements in experience.
Self-ratings of decision-making competence were positively related to
performance on the two tasks that showed significant age-related declines (β=.17, p<.05
for Applying Decision Rules; β=.11, p=.09 for Resistance to Framing), but not to
performance on the other tasks (β=.09, p=.15 for Consistency in Risk Perception, β=.03,
p=.60 for Recognizing Age-group Social Norms; β=-.03, p=.69 for
Under/Overconfidence; β=.04, p=.57 for Resistance to Sunk Costs). These relationships
were no longer significant after controlling for demographic variables (p>.10).
Adult age differences in DMC 17
Discussion
We examined age-related changes in performance on six tasks that comprise the
validated measure of Adult Decision-Making Competence (A-DMC). After controlling
for demographic variables (gender, education, SES), performance on two tasks decreased
with age (Resistance to Framing, Applying Decision Rules), while performance on the
four other tasks remained unchanged (Consistency in Risk Perception, Recognizing Agegroup Social Norms) or improved with age (Under/Overconfidence, Resistance to Sunk
Costs). Thus, as in previous studies (Hanoch et al., 2007; Peters et al., 2007), we found a
mixed pattern of results.
After controlling for fluid cognitive ability, we found the two predicted patterns
of results. First, for the two tasks on which performance decreased with age, the negative
relationships were mediated by age-related declines in fluid cognitive ability (Hypothesis
1). Second, for three of the four decision-making tasks on which performance did not
decrease with age, performance was positively and independently correlated with both
fluid cognitive ability and age (Hypothesis 2). The fourth, Consistency in Risk
Perception showed a similar pattern, with the relationship between age and performance
becoming more positive after controlling for fluid cognitive ability – although without
reaching significance. Yet, each of the four decision-making tasks for which
performance did not decrease with age showed significant suppression effects, such that
controlling for fluid cognitive ability increased the correlation between performance and
age, while controlling for age increased the correlation between performance and fluid
Adult age differences in DMC 18
cognitive ability. These suppression effects would be expected if two predictors (i.e.
fluid cognitive ability and age) are negatively correlated, with each also being positively
correlated to the predicted variable (performance) (Cohen & Cohen, 1983). As a result of
the suppression effects reported here, improvements in decision-making performance that
are due to the experience acquired with age mask simultaneous reductions in decisionmaking performance that are due to age-related declines fluid cognitive ability, and vice
versa.
Mediation and suppression effects are similar in the sense that both reflect the
indirect effects of a third variable on a correlation (MacKinnon, Krull, & Lockwood,
2000; Preacher & Hayes, 2004). Mediation is said to occur when controlling for the third
variable drives the correlation towards zero. Suppression is said to occur when
controlling for the third variable drives the correlation to be more positive or more
negative. Mediation and suppression are difficult to distinguish when they drive
correlations in the same direction – as seen in the present study. For Resistance to
Framing and Applying Decision Rules, controlling for fluid cognitive ability drove the
negative relationship between age and performance to non-significance, suggesting
mediation. However, that pattern could also be interpreted as suppression, such that
controlling for fluid cognitive ability made the relationship between age and decisionmaking performance more positive (i.e., more non-negative) – as seen here. Thus, the
same conclusion holds for all decision-making tasks examined in our study: Age-related
declines in fluid cognitive ability systematically reduce the decision-making performance
of older adults – even on tasks for which zero-order correlations show no age-related
declines in performance.
Adult age differences in DMC 19
The reported results provide new insights into the roles of fluid cognitive ability
and experience in performance on A-DMC tasks. We found (1) age-related declines in
performance on Resistance to Framing and Applying Decision Rules being mediated by
fluid cognitive ability, and (2) no age-related declines in performance on the other tasks,
with independent positive contributions of age and fluid cognitive ability. Because, after
controlling for fluid cognitive ability, age becomes a proxy for experience, these two
patterns suggest that experience plays no role in performing the first set of tasks, and
some role in performing the second set of tasks. As argued by Stanovich and West’s
(2008), it requires no experience to detect the widely known consistency rule that applies
to the item pairs presented with Resistance to Framing (e.g., describing a condom as 95%
effective or 5% ineffective), or to follow the explicit description of the decision rules in
Applying Decision Rules. By comparison, it should require at least some experience to
detect the more complex rules that apply to the second set of A-DMC tasks.
Moreover, closer inspection of Table 2 reveals two distinct patterns in the second
set of tasks, with (2a) performance on Consistency in Risk Perception and Recognizing
Age-group Social Norms showing a modest positive relationship with age after
controlling for fluid cognitive ability and (2b) performance on Under/Overconfidence and
Resistance to Sunk Costs showing a positive relationship with age that was strong enough
to suppress the relationship with fluid cognitive ability. Thus, although all four tasks may
require experience, these results suggest that the Consistency in Risk Perception and
Recognizing Age-group Social Norms require less experience than
Under/Overconfidence and Resistance to Sunk Costs. Possibly, moderate exposure to
life events and peers may be sufficient to learn how to judge risks (e.g., getting in an
Adult age differences in DMC 20
accident or driving accident-free) and age-group social norms (e.g. percent of peers who
think it is sometimes OK to steal). Indeed, adolescents are already able to judge valid
probabilities for the life events that are familiar to them, such as being in school the next
year (Bruine de Bruin et al.). In contrast, experience with more specialized feedback may
be needed to understand that appropriate confidence should reflect one’s knowledge
levels (Gonzalez-Vallejo & Bonham, 2007; Stone & Opel, 2000) and that sunk costs
should not affect decisions (Larrick et al., 1993).
One limitation of this study is the absence of specific measures of abilities that
improve with experience and age. As noted in the introduction, these might include
crystallized cognitive abilities, such as specialized knowledge and decision strategies, as
well as emotion-related abilities, such as improved processing of affective information.
Validated measures of these abilities would be valuable for future study.
Another limitation of this study is its cross-sectional design, comparing the
decision-making skills of different cohorts of adults. Although cohort studies are
common in aging research (Mayhorn et al., 2002; Salthouse, 2004), members of different
cohorts may differ in characteristics other than age. We controlled statistically for the
lower educational attainment of the older adults in this study, but had no measures of
other potentially relevant differences between cohorts.
The mixed pattern of age-related changes in decision-making skills observed here
is consistent with the view that compensatory changes allow most older adults to function
effectively and independently (Salthouse, 1990, 2004). Nonetheless, older adults in our
sample rated themselves as worse decision makers than did younger adults. These agerelated declines in self-ratings were mediated by fluid cognitive ability, suggesting that
Adult age differences in DMC 21
decreases in fluid cognitive ability are more readily noticed than increases in experiencebased abilities, perhaps because the latter make processing more automatic and less
cognitively demanding (Reyna, 2004; Reyna, Lloyd, & Brainerd, 2003; Yates &
Patalano, 1999). This differential sensitivity may lead older adults to surrender their
decision-making autonomy (Yates & Palatino, 1999) and be fueled by others’ perceptions
of older adults as being less competent (Cleveland & Landy, 1981). In either case,
accurate assessments of decision-making competence will likely require considering
experience as well as fluid cognitive ability (Del Missier, Mäntylä, & Bruine de Bruin,
2009; Friedman, Miyake, Corely, Young, & DeFries, 2006; Salthouse, 2004).
References
Arkes, H. R., & Blumer, C. (1985). The psychology of sunk cost. Organizational
Behavior and Human Decision Processes, 35, 124–140.
Besedes, T., Deck, C., Sarangi, S., & Shor, M. (2009). Age effects and heuristics in
decision making. Working paper 2009-03: Louisiana State University: Department of
Economics.
Bruine de Bruin, W., Downs, J.S., Murray, P.M., & Fischhoff, B. (in press). Can female
adolescents tell whether they have a Chlamydia infection? Medical Decision Making.
Bruine de Bruin, W., Parker, A. M., & Fischhoff, B. (2007a). Individual differences in
Adult Decision-Making Competence. Journal of Personality and Social Psychology,
92, 938-956.
Adult age differences in DMC 22
Bruine de Bruin, W., Parker, A.M., & Fischhoff, B. (2007b). Individual differences in
Adult Decision-Making Competence. Journal of Personality and Social Psychology,
92, 938-956.
Carstensen, L., & Turk-Charles, S. (1994). The salience of emotion across the adult life
span. Psychology and Aging, 9, 259-264.
Charles, S.T, & Carstensen, L. (2007). Emotion regulation and aging. In J. J. Gross (Ed.),
Handbook of emotion regulation (pp. 307-327). New York: Guilford Press.
Cleveland, J. N., & Landy, F. J. (1981). The influence of rater and ratee age on two
performance judgments. Personnel Psychology, 34, 19-29.
Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the
behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.
Crawford, J.D., & Stankov, L. (1996). Age differences in the realism of confidence in
judgments: A calibration study using tests of fluid and crystallized intelligence.
Learning and Individual Differences, 8, 83-103.
Day, J.C. (1996). Population projections of the United States by age, sex, race, and
hispanic origin: 1995 to 2050. U.S. Bureau of the Census, Current Population
Reports, U.S. Government Printing Office, Washington, DC.
Del Missier, F., Mäntylä, T., & Bruine de Bruin, W. (2009). Executive functions in
decision making: An individual differences approach. Paper presented at Subjective
Probability and Utility in Decision Making, Rovereto, Italy.
Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51, 380417.
Adult age differences in DMC 23
Ericsson, K.A., Krampe, R.T., & Tesch- Römer, C. (1993). The role of deliberate
practice in the acquisition of expert performance. Psychological Review, 102, 363406.
Ericsson, K.A., & Lehman, A.C. (1996). Expert and exceptional performance: Evidence
on maximal adaptations on task constraints. Annual Review of Psychology, 47, 273305.
Finucane, M. L., Mertz, C. K., Slovic, P., Schmidt, E. S. (2005). Task complexity and
older adults’ decision-making competence. Psychology and Aging, 20, 71-84.
Finucane, M. L., Slovic, P., Hibbard, J. H., Peters, E., Mertz, C. K., & Macgregor, D. G.
(2002). Aging and decision-making competence: An analysis of comprehension and
consistency skills in older versus younger adults. Journal of Behavioral Decision
Making, 15, 141-164.
Fisk, J.E. (2005). Age and probabilistic reasoning: Biases in conjunctive, disjunctive,
and Bayesian judgments in early and late adulthood. Journal of Behavioral Decision
Making, 18, 55-82.
Friedman, N. P., Miyake, A.., Corley, R. P., Young, S. E., DeFries, J. C., & Hewitt, J. K.
(2006). Not all executive functions are related to intelligence. Psychological Science,
17, 172-179.
González-Vallejo, C. & Bonham, A. (2007). Aligning confidence with accuracy:
Revisiting the role of feedback. Acta Psychologica, 125, 221-239.
Hansson, P., Rönnlund, M., Juslin, P., & Nilsson, L.G. (2008). Adult age differences in
the realism of confidence judgments: Overconfidence, format dependence, and
cognitive predictors. Psychology and Aging, 23, 531-544.
Adult age differences in DMC 24
Hanoch, Y., Wood, S., & Rice, T. (2007). Bounded rationality, emotions and older adult
decision making: Not so fast and yet so frugal. Human Development, 50, 333-358.
Hayes, J.R. (1981). The complete problem solver. Philadelphia, PA: Franklin Institute
Press.
Horn, J.L., & Cattell, R.B. (1967). Age differences in fluid and crystallized intelligence.
Acta Psychologica, 26, 107-129.
Jacobs, J. E., Greenwald, J. P., & Osgood, D. W. (1995). Developmental differences in
baserate estimates of social behaviors and attitudes. Social Development, 4, 165–181.
Jepson, C., Krantz, D. H., & Nisbett, R. E. (1983). Inductive reasoning: Competence or
skill? Behavioral and Brain Sciences, 6, 494–501.
Johnson, M. M. (1990). Age differences in decision making: A process methodology for
examining strategic information processing. Journal of Gerontology, 45, 75–78.
Keren, G. (1991). Calibration and probability judgments: conceptual and methodological
issues. Acta Psychologica, 77, 217–273.
Kim, S., Goldstein, D., Hasher, L., & Zacks, R. (2005). Framing effects in younger and
older adults. Journal of Gerontology, 60B, P215-P218.
Kim, S., & Hasher, L. (2005). The attraction effect in decision making: Superior
performance by older adults. Quarterly Journal of Experimental Psychology, 58A,
120-133.
Kovalchik, S., Camerer, C.F., Grether, D.M., Plott, C.R., & Allman, J.M. (2005). Aging
and decision making: A comparison between neurologically healthy elderly and
young individuals. Journal of Economic Behavior & Organization, 58, 79-94.
Adult age differences in DMC 25
Larrick, R. P., Nisbett, R. E., & Morgan, J. N. (1993). Who uses the cost-benefit rules of
choice? Implications for the normative status of microeconomic theory.
Organizational Behavior and Human Decision Processes, 56, 331–347.
Labouvie-Vief, G., DeVoe, M., & Bulka, D. (1989). Speaking about feelings:
Conceptions of emotion across the life span. Psychology & Aging, 4, 425-437.
LeBoeuf, R. A., & Shafir, E. (2003). Deep thoughts and shallow frames: On the
susceptibility to framing effects. Journal of Behavioral Decision Making, 16, 77–92.
Levin, I.P., & Gaeth, G.J. (1988). Framing of attribute information before and after
consuming the product. Journal of Consumer Research, 15, 374-378.
Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: A
typology and critical analysis of framing effects. Organizational Behavior and
Human Decision Processes, 76, 149–188.
Loeber, R. (1989). Values and attitudes towards delinquent behavior. Pittsburgh, PA:
Pittsburgh Youth Study, Department of Psychiatry, University of Pittsburgh.
MacKinnon, D.P., Krull, J.L., & Lockwood, C.M. (2000). Equivalence of the mediation,
confounding, and suppression effect. Prevention Science, 1, 173-181.
Mandel, D.R. (2005). Are risk assessments of a terrorist attack coherent? Journal of
Experimental Psychology: Applied, 11, 277-288.
Mather, M., & Carstensen, L.L. (2003). Aging and attentional biases for emotional faces.
Psychological Science, 14, 409-415.
Mayhorn, C.B., Fisk, A.D., & Whittle, J.D. (2002). Decisions, decisions: Analysis of
age, cohort, and time of testing on framing of risky options. Human Factors, 44, 515521.
Adult age differences in DMC 26
Mikels, J. A., & Reed, A. E. (2009). Monetary losses do not loom large in later life: Age
differences in the framing effect. Journal of Gerontology: Psychological Sciences,
64B, 457-460.
Neupert, S.D., Almeida, D.M., & Charles, S.T. (2007). Age differences in reactivity to
daily stressors: The role of personal control. The Journals of Gerontology:
Psychological Sciences and Social Sciences, 62, P216-P225.
Park, D.C., Lautenschlager, G., Hedden, T., Davidson, N.S., Smith, A.D., & Smith, P.K.
(2002). Models of visuospatial and verbal memory across the life span. Psychology
and Aging, 17, 299-320.
Parker, A. M., & Fischhoff, B. (2005). Decision-making competence: External
validation through an individual-differences approach. Journal of Behavioral
Decision Making, 18, 1-27.
Parker, A.M., Yoong, J., Bruine de Bruin, W., & Willis, R. (2009). (Over)confidence
and economic behavior. Paper presented at Subjective Probability and Utility in
Decision Making, Rovereto, Italy.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptive decision maker. New
York: Cambridge University Press.
Peters, E., & Bruine de Bruin, W. (in press). Aging and decision skills. In: Dhami, M.,
& Schlottman, A. (Eds.). In: M. K. Dhami, A. Schlottmann, & M. Waldmann.
Judgment and decision making as a skill: Learning, development, and evolution.
Cambridge University Press.
Peters. E., Hess, T.M., Västfjäll, D., & Auman, C. (2007). Adult age differences in dual
information processes. Implications for the role of affective and deliberative
Adult age differences in DMC 27
processes in older adults’ decision making. Perspectives on Psychological Science, 2,
1-23.
Raiffa, H. (1968). Decision analysis. Reading, MA: Addison-Wesley.
Preacher, K.J., & Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect
effects in simple mediation models. Behavior Research Methods, Instruments, &
Computers, 36, 717-731.
Raven, J., Raven, J.C., & Court, J.H. (2003). Manual for Raven’s Progressive Matrices
and Vocabulary Scales, Section 3.
Reyna, V.F. (2004). How people make decisions that involve risk: A dual-processes
approach. Current Directions in Psychological Science, 13, 60-66.
Reyna, V. F., Lloyd, F. J., & Brainerd, C. J. (2003). Memory, development, and
rationality: An integrative theory of judgment and decision making. In S. L.
Schneider & J. Shanteau (Eds.), Emerging perspectives on judgment and decision
research (pp. 201-245). NY: Cambridge, MA.
Rönnlund, M., Karlsson, E., Laggnäs, E., Larsson, L., & Lindström, T. (2005). Risky
decision making across three arenas of choice: Are younger and older adults
differently susceptible to framing effects? The Journal of General Psychology, 132,
81-92.
Roring, R.W., & Charness, N. (2007). A multilevel model analysis of expertise in chess
across the life span. Psychology and Aging, 22, 291-299.
Salthouse, T.A. (2004). What and when of cognitive aging. Current Directions in
Psychological Science, 13, 140-144.
Adult age differences in DMC 28
Salthouse, T.A. (1991). Theoretical perspectives on cognitive aging. Hillsdale, NJ:
Erlbaum.
Salthouse, T.A. (1990). Cognitive competence and expertise in aging. In J.E. Birren &
K.W. Schaie (Eds.) Handbook of the psychology of aging (3rd edition, pp.310-319).
San Diego: Academic Press.
Sobel, M.E. (1982). Asymptotic confidence intervals for indirect effects in structural
equation models. In S. Leinhart (Ed.) Sociological Methodology 1982 (pp. 290-312).
San Francisco: Jossey-Bass.
Stanovich, K.E., & West, R.F. (2008). On the relative independence of thinking biases
and cognitive ability. Journal of Personality and Social Psychology, 94, 672-695.
Stanovich, K. E., & West, R. F. (2000). Individual differences in reasoning: Implications
for the rationality debate? Behavioral and Brain Sciences, 23, 645–726.
Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought.
Journal of Experimental Psychology: General, 127, 161–188.
Stone, E.R., & Opel, R.B. (2000). Training to improve calibration and discrimination:
The effects of performance and environmental feedback. Organizational Behavior
and Human Decision Processes, 83, 282-309.
Sturman, M.C. (2003). Searching for the inverted U-shaped relationship between time
and performance: Meta-analyses of the experience/performance, tenure/performance,
and age/performance relationships. Journal of Management, 29, 609-640.
Strough, J., Mehta, C.M., McFall, J.P., & Schuller, K.L. (2008). Are older adults less
subject to the sunk-cost fallacy than younger adults? Psychological Science, 19, 650652.
Adult age differences in DMC 29
Tentori, K., Osherson, D., Hasher, L., & May, C. (2001). Wisdom and aging: Irrational
preferences in college students but not older adults. Cognition, 81, 87-96.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of
choice. Science, 211, 453–458.
Verhaeghen, P. (2003). Aging and vocabulary scores: A meta-analysis. Psychology and
Aging, 18, 332-339.
Weller, J. A., Levin, I. P., & Denburg, N. (2009). Trajectory of adaptive decision making
for risky gains and losses from ages 5 to 85. Manuscript under review.
Wilson, R.S., Beckett, L.A., Barnes, L.L., Schneider, J.A., Bach, J., Evans, D.A., &
Bennett, D.A. (2002). Individual differences in rates of change in cognitive abilities
of older persons. Psychology and Aging, 17, 179-193.
Yates, J. F. (1990). Judgment and decision making. Englewood.
Yates, J.F., & Patalano, A.L. (1999). Decision making and aging. In D.C. Park & R.W.
Mortell (Eds.) Processing of medical information in aging patients (pp. 31-54).
Mahwah, NJ: Erlbaum
Adult age differences in DMC 30
Footnotes
1
In samples that consist solely of inexperienced younger adults, neither experience
or fluid cognitive ability will show significant correlations to performance on
these tasks. After all, these individuals will not have sufficient experience to
detect the rule, leaving their levels of fluid cognitive ability immaterial.
2
As part of our consent procedure, participants were explicitly told that they did
not have to report demographic information. They were also allowed to leave
after 2 hours, even if they had not completed all of the measures. Demographic
questions appeared near the end of the survey packet. Of all participants, 87.2%
reported age, 88.9% reported gender, 87.8% reported education, and 78.9%
reported race. SES had no missing data, because it reflected whether or not
participants were recruited through social service organizations targeting low-SES
communities. Among participants who self-reported race, SES was highly
correlated with race, χ(1)=102.57, p<.001, with 90.7% of high-SES participants
being white, and 67.5% of low-SES participants being of racial minority status.
To reduce the amount of missing data, our analyses controlled for age, gender,
education, and SES, which served as a proxy for race. Overall, demographic
information (i.e., age, gender, education, and SES) was complete for 85.8% of
participants. Participants who reported demographics had better performance on
measures of fluid cognitive ability (r=.24, p<.001), Applying Decision Rules (r=.22, p<.001), Recognizing Age-group Social Norms (r=-.13, p<.001), and
Under/Overconfidence(r=-.13, p<.05). However, the correlations among these
Adult age differences in DMC 31
measures were not affected by excluding participants who did not report
demographic information.
3
The full measure is available at http://sds.hss.cmu.edu/risk/ADMC.htm. Except
for one task, Path Independence, all component tasks correlated with reporting
better life decision outcomes (Bruine de Bruin et al., 2007). Hence, Path
Independence was not included in the analyses of overall A-DMC (Bruine de
Bruin et al., 2007), or in the analyses of component tasks presented here.
4
Here, we refer to this A-DMC component task as Recognizing Age-group Social
Norms, and compared each individual’s judged norm was compared with the
observed norm in their age group. By comparison, in our previous work (Bruine
de Bruin et al., 2007) we referred to this A-DMC component task as Recognizing
Social Norms and compared judged norms with observed norms in the entire
study sample. Although the two scores (for Recognizing Age-group Social
Norms and Recognizing Social Norms) are highly correlated to each other (r=.91,
p<.001), the computation by age group was deemed more appropriate for the
present study.
Adult age differences in DMC 32
Biographical sketches
Wändi Bruine de Bruin is an Assistant Professor at Carnegie Mellon University’s
Departments of Social and Decision Sciences and of Engineering and Public Policy in
Pittsburgh, PA. Her research focuses on judgment and decision making, risk perception
and communication, as well as individual differences in decision-making competence.
Andrew M. Parker is a Behavioral Scientist at the RAND Corporation in Pittsburgh, PA.
His research focuses on individual differences in decision making, risk perception and
communication, and crisis decision making.
Baruch Fischhoff is Howard Heinz University Professor, in the Department of Social and
Decision Sciences and of Engineering and Public Policy, at Carnegie Mellon University.
His research interests include environment, adolescence, national security, and risk
analysis and communication.
Adult age differences in DMC 33
Table 1: Demographic variables and A-DMC performance by participant age.
Age group
All
Percent
Gender
Education
SES
(% female)
(% beyond high-school)
(% low)
100.0%
73.8%
52.5%
46.1%
<20 years old
5.7%
52.9%
5.6%
38.9%
20-29 years old
9.2%
79.3%
51.7%
75.9%
30-39 years old
10.2%
84.4%
65.6%
53.1%
40-49 years old
39.8%
72.0%
67.5%
26.4%
50-59 years old
14.3%
73.3%
62.2%
40.0%
60-69 years old
5.4%
58.8%
41.2%
35.3%
70-79 years old
10.8%
82.4%
25.0%
55.9%
80-89 years old
4.5%
64.3%
7.1%
85.7%
Correlation with age
-
.01
-.14*
.05
Adult age differences in DMC 34
Table 2: Zero-order correlations and standardized beta coefficients a with performance on component tasks of Adult Decision-Making
Competence (A-DMC) and self-ratings of A-DMC.
Before controlling for demographic variables
Age
Fluid
cognitive
ability
Age,
controlling
for fluid
cognitive
ability
A-DMC task
Resistance to Framing
Applying Decision Rules
Consistency in Risk Perception
Recognizing Age-group Social Norms
Under/Over-confidence
Resistance to Sunk Costs
-.20**
-.18**
-.05
.05
.09
.26***
.36***
.66***
.41***
.27***
.27***
.18**
-.09b
.02b
.10+b
.15*b
.20***b
.35***b
Self-ratings of A-DMC
-.13*
.17**
-.08b
Outcome variable
***
Fluid
cognitive
ability,
controlling
for age
After controlling for demographic variables
Age,
controlling
for fluid
cognitive
ability
Fluid
cognitive
ability,
controlling
for age
Age
Fluid
cognitive
ability
.34***
.67***
.45***
.32***
.34***b
.29***b
-.16**
-.16**
-.01
.08
.14*
.27***
.24***
.52***
.25***
.18*
.10
.16*
-.11b
-.02b
.07+b
.13*b
.18**
.35***b
.20**
.51***
.27***
.23**
.17*b
.30***b
.14*
-.11
.15*
-.08
.12
p< .001, ** p< .01, * p< .05, + p< .10, two-sided
Before controlling for demographic variables, zero-order correlations and standardized beta coefficients have the same values.
Relationships controlling for demographic variables reflect standardized beta coefficients.
b
Sobel test showed that the relationship between the predictor and the A-DMC component was mediated/suppressed by the control variable
(two-sided p<.05)
Note: The relationship between age and cognitive ability was -.33 (p<.001) before controlling for demographic variables, and -.27 (p<.001)
after controlling for demographic variables.
a