A Comparison Of Collaborative And Individual Everyday Problem Solving In Younger And Older Adults

INT’L. J. AGING AND HUMAN DEVELOPMENT, Vol. 58(3) 167-195, 2004
A COMPARISON OF COLLABORATIVE AND
INDIVIDUAL EVERYDAY PROBLEM SOLVING
IN YOUNGER AND OLDER ADULTS*
SULING CHENG
California State University, Los Angeles
JONELL STROUGH
West Virginia University
ABSTRACT
To understand the conditions under which age differences in everyday
problem-solving performance occur, this study investigated individual and
collaborative problem solving. Younger (24 females, 24 males; M age =
19.98, SD = 1.43) and older adults (25 females, 24 males; M age = 71.14,
SD = 6.65) worked either alone or with a same-sex friend to plan a cross-
country trip to attend a wedding. Age differences favoring younger adults
were found on three of eight performance measures: two component com-
pletion accuracy variables (i.e., city completion, Mt. Rushmore completion)
and a composite variable that assessed overall performance. Younger adults
also completed the task faster than did older adults. Collaborators out-
performed individuals on the composite measure of overall performance
and frequency of planning errors. Females committed more planning errors
than males did. Younger and older adults’ performances were predicted by
different variables; however, basic abilities were not consistently significant
predictors of performance for either younger or older adults. The results of
the study suggest that task characteristics may differentially influence older
and younger adults’ collaborative performance.
*This article is based on a dissertation submitted to the faculty in the Department of Psychology at
West Virginia University in partial fulfillment of Suling Cheng’s doctoral degree, and was funded in
part by dissertation funds from the Department of Psychology and the Eberly College of Arts and
Sciences at West Virginia University. Portions of this article were presented at the Gerontological
Society of America, November 2000, Washington, D.C.
167
Ó 2004, Baywood Publishing Co., Inc.

INTRODUCTION
Much of the existing research on collaborative cognition in later adulthood
investigates whether working with a partner allows older adults to offset age-
related declines in performance on memory tasks (e.g., Dixon, 1996; Dixon
& Gould, 1996; Gould, Kurzman, & Dixon, 1994; Johansson, Andersson,
& Roennberg, 2000). On memory tasks, familiar partners such as spouses or
friends often are found to have an advantage over unfamiliar partners (e.g.
Andersson & Roennberg, 1996; Dixon & Gould, 1998; cf. Gould et al., 2002), and
collaborating pairs outperform individuals (Dixon, 1992). Recently, researchers
have begun to investigate older adults’ collaboration in solving everyday
problems (Berg, Johnson, Meegan, & Strough, 2003; Margrett & Marsiske,
2002; Strough, Cheng, & Swenson, 2002). On everyday problem-solving
tasks, individuals who work with familiar partners (spouses) outperform
individuals who work with unfamiliar partners (Margrett, 1999). To date, no
studies have investigated whether collaborative everyday problem-solving
performance is better than individual performance, and it is not clear whether
the reported benefits of familiar partners for collaboration on memory tasks
extend to friends’ everyday problem solving. Moreover, no studies have
investigated whether the effects of collaboration on everyday problem-solving
performance are similar for younger and older adults. In the current study,
we compared older and younger adults’ individual problem-solving perform-
ances to their collaborative problem-solving performances with friends on an
everyday task.
Older adults’ effective use of other people as a memory aid is of considerable
interest to developmental researchers who study collaboration. Older adults often
perform worse than younger age groups on memory tasks (see Park, 1992 for a
review). Collaboration is thought to be a mechanism whereby age differences in
memory performance may be attenuated (Dixon & Gould, 1996). Due to their
shared history, familiar partners such as long-term married couples are thought
to be “expert collaborators” (Dixon, 1996) because they have developed a shared
or “transactive” memory (Wegner, Guiliano, & Hertel, 1985). Dixon and Gould
(1998) reported that although age differences in memory performance were
apparent when partners were strangers, when married couples were compared,
there were no age differences in recall.
In accord with research indicating benefits of familiar partners for collaboration
on memory tasks (e.g., Johansson et al., 2000; cf. Gould et al., 2002), Margrett’s
(1999) research on collaborative everyday problem solving indicates that benefits
are more apparent when older adults collaborate with a familiar partner (i.e.,
their spouse) than with a stranger. Comparisons of younger and older married
couples’ performances on everyday problem-solving and decision-making tasks
indicate age similarities rather than differences (Berg et al., 2003; Gould et al.,
1994). These findings parallel results on memory collaboration which indicate age
168 / CHENG AND STROUGH

similarities in performance when younger and older married couples collaborate
(Dixon & Gould, 1998).
The focus on married couples in the current literature limits the applicability
of collaborative everyday problem-solving research. Due to death, divorce, or
because the individual never married, a spouse may not be available for
collaboration. For those who are married, spouses may not be ideal partners if
discord exists in the marital relationship, or if the spouse is incapacitated. Older
adults draw from a variety of members of their social networks to solve everyday
problems (Strough, Patrick, Swenson, Cheng, & Barnes, 2003). Because friends
are also familiar partners, some of the advantages of collaborating with a spouse
may extend to collaborating with friends. Investigating friends’ collaborative
everyday problem solving may help to address whether or not younger and older
adults benefit similarly from collaboration.
Both younger and older adults have friends, and each group reports that
friendships play an important role in their lives (Wagner, Schuetze, & Lang,
1999). Friends serve supportive functions, including emotional support, care-
giving support, and support for solving everyday problems (Himes & Reidy,
2000; Rawlins, 1995; Strough et al., 2003). Friends may be particularly likely
to provide support when a spouse or adult child is not available. Because
both married and unmarried individuals have friends, investigation of friends’
collaboration may have wide applicability for understanding older adults’
collaboration in everyday contexts.
On collaborative memory tasks, younger adults perform better when they
collaborate with a friend than with a stranger (Andersson & Roennberg, 1995,
1996, 1997). In adolescence, collaborating with a friend on a cognitively challeng-
ing task is more beneficial than collaborating with an acquaintance (Azmitia
& Montgomery, 1993). In the current study, we investigated whether working
with a friend to solve an everyday problem was more advantageous than working
alone for both younger and older adults.
Comparing collaborative and individual everyday problem solving in younger
and older adults may provide information relevant to understanding the age
trajectory of everyday problem-solving performance. Although some researchers
report stability in older adults’ abilities to solve everyday problems as compared to
younger age groups (Camp, Doherty, Moody-Thomas, & Denney, 1989), others
report a U-shaped function such that younger and older adults perform similarly to
each other but worse than middle-aged adults (Denney, 1989). Cornelius and
Caspi’s (1987) results suggest improvement in problem-solving performance
with age. The mixed findings regarding the age trajectory of everyday problem
solving may be attributable to the methods used to assess everyday problem
solving, theoretical conceptions of everyday problem solving, and aspects of
the tasks themselves.
Researchers who use traditional methods to assess everyday problem solving
place participants in situations where they solve problems alone; input and
COLLABORATION AND EVERYDAY PROBLEM SOLVING / 169

feedback from others are not available (e.g., Allaire & Marsiske, 1999; Marsiske
& Willis, 1995). Performance on everyday problem-solving tasks often is timed
(e.g., Allaire & Marsiske, 1999), and one response or answer usually is con-
sidered superior to others (e.g., Diehl, Willis, & Schaie, 1995). These methods of
assessment do not capture the problem-solving process as it occurs in indi-
viduals’ daily lives. Problem solving in the real world is characterized by a range
of acceptable response times (Cornelius, Willis, Nesselroade, & Baltes, 1983).
Multiple outcomes also are possible, and oftentimes no single correct solution to
the problem exists (Luszcz, 1989; Sinnott, 1989). Problem-solving in everyday
life is rarely a solitary activity (Berg, Meegan, DeViney, 1998; Dixon, 1992).
Individuals frequently use others as problem-solving aids (Crovitz, 1970), turn
to others for advice when solving both important life problems and everyday
problems (Baltes & Staudinger, 1996; Berg, Strough, Calderone, Sansone, &
Weir, 1998), and include other people in their problem-solving goals (Strough,
Berg, & Sansone, 1996). Depending on the type of problem and their perceived
ability to solve it, individuals may prefer to include other people in their problem-
solving activities rather than solving the problem alone (Strough et al., 2002).
Thus, older adults’ everyday competence may be related to interpersonal resources
that often are not available in traditional laboratory settings.
The mixed pattern of results regarding the age trajectory of everyday problem--
solving performance also may reflect different theoretical conceptions of everyday
problem solving. Research on everyday problem solving arose from dissatis-
faction with the ecological validity of traditional psychometric measures of
intellectual functioning (Schaie, 1978; Sinnott & Guttman, 1978; see Berg
& Klaczynski, 1996 for a review). Performance on psychometric measures of
intellectual functioning declines with age (see Willis & Schaie, 1993 for a review).
Some researchers suggest that age differences in everyday problem solving are
due to age differences in basic cognitive abilities (Allaire & Marsiske, 1999;
Diehl et al., 1995; Willis & Schaie, 1986). Other researchers assert that problem
solving in the everyday world requires different intellectual abilities such as
practical intelligence (Berg & Sternberg, 1985; Sternberg, Wagner, & Okagaki,
1993). Denney’s (1989) theory of optimally exercised abilities suggests that age
declines are less likely to be found for skills that are exercised. Social problem
solving may be one such domain (see Heidrich & Denney, 1994). Berg and
Sternberg’s (1985) theory of adult intellectual functioning posits that experiential
and contextual aspects of intelligence may be less likely to show age-related
declines. Collaboration with others to solve everyday problems involves both
social skills for working with others and knowledge of specific task domains.
Thus, collaborative everyday problem solving may be representative of contextual
and experiential aspects of intelligence, aspects of intellectual functioning thought
to be less likely to show age-related declines.
In addition to different theoretical conceptions, mixed results regarding the
age trajectory of everyday problem-solving performance may reflect the types of

tasks investigated. Denney (1989) describes three classes of problem-solving
tasks that can be categorized as everyday problems: 1) problems with novel stimuli
and a realistic task, 2) problems with realistic stimuli and a novel task, and
3) problems with realistic stimuli and a realistic task. Within the existing literature,
task familiarity has been accomplished by making the stimuli more meaningful
or realistic, and by using problems people may encounter in their everyday lives
(e.g., Allaire & Marsiske, 1999, 2002; Cornelius & Caspi, 1987; Denney &
Pearce, 1989). The meaningfulness of the everyday tasks, however, has often been
arbitrarily defined (Berg & Klaczynski, 1996), and although the stimuli are
ecologically valid, the process whereby the problem is solved is not. In everyday
life, memory tasks (e.g., when to take a medication, potential side effects of
medications) are performed in the presence of the to-be-remembered stimuli
(e.g., the prescription label).
Because collaborative everyday problem solving only recently has gained
attention as a distinct area of inquiry, the dyadic characteristics that may deter-
mine the effectiveness of the collaborative effort have yet to be established.
One dyadic characteristic that may influence performance is dyad gender.
Women, in comparison to men, view seeking support from others as a more
effective problem-solving strategy (Watson & Blanchard-Fields, 1998). Work
with young adults indicates that women’s performances are superior to men’s
when collaborative tasks require discussion to consensus (Wood, 1987). Thus,
female collaborators may outperform male collaborators.
Collaborators’ cognitive abilities also may influence performance. Age differ-
ences exist in performance on intelligence-test type tasks that measure basic
abilities such as memory and processing speed (Hultsch & Dixon, 1990; Light,
1991). It is well established that older adults experience declines in these areas
(see Park, 1992 for a review). Thus, older adults may derive greater benefits from
collaborative work than younger adults, because collaborating with a peer may
offset some of these age-related declines.
Denney and Palmer’s (1981) work indicates that education is associated
with performance on both everyday problem-solving tasks and traditional
problem-solving tasks. In addition, age-cohort differences often are found for
years of education, with younger adults having, on average, more education than
older adults (U.S. Census Bureau, 2000). Thus, when considering associations
between age and individual and collaborative problem solving, it may be impor-
tant to consider education.
Current Investigation
Four issues were addressed in the current investigation. First, performance
differences between collaborators and individuals were examined. It was expected
that both younger and older adult collaborators would outperform younger and
older individuals on the everyday problem-solving task. Second, age differences

in collaborative and individual problem solving were examined. In line with
research indicating age-related declines in problem-solving performance in
later adulthood, younger adults were expected to outperform older adults in the
individual condition. In the collaborative condition, however, younger collab-
orators and older collaborators were expected to perform similarly on the every-
day problem-solving task. That is, an age group by problem-solving condition
interaction was expected. Third, potential interactions among gender, age, and
problem-solving condition were explored. Previous research had suggested
potential gender differences in collaborative benefits such that female collab-
orators outperform male collaborators. Fourth, the extent to which basic abilities
such as memory, math ability, and education predicted performance on an
everyday task was explored.
METHODS
Participants
Older participants were recruited in the community. All were living inde-
pendently in their own homes. Only participants who had a valid driver’s license
were invited to participate. Those who agreed to participate were contacted in
advance of the study and were asked to bring a same-gender friend with them.
They were not told about the specific purpose of the study before they arrived.
Participants who brought friends to the study were randomly assigned to either
the individual or collaborative condition.1
The experiment took place in a laboratory in the psychology building of a
university. The principal investigator administered the task in participants’ homes
for those who were unwilling or unable to travel to the testing site (42% of the
older adult sample).
Ninety-seven individuals from a mid-Atlantic state participated in the study.
The sample size was based on a power analysis with .85 power and a large effect
size of .40 (Keppel, 1991). Thirty-three participants (16 males, 17 females) were
in the individual condition. Thirty-two dyads were formed by pairing 64 same-
gender friends (16 male dyads, 16 female dyads). Approximately half of the par-
ticipants (n = 48) were young adult college students (M = 19.98 years, SD = 1.42;
1
Some participants arrived at the testing site alone and were automatically assigned to the individual
condition. The following reflects the percentage of participants who were automatically assigned to
the individual condition because the friend they identified did not participate in the study: 8.3%
(2 participants) younger adult males; 0% younger adult females; 16.7% (4 participants) older adult
males; and 16.7% (4 participants) older adult females. To determine if participants who were randomly
assigned to the individual condition were different from those who were assigned to the individual
condition because they arrived alone, one-way ANOVAs were conducted on each of the basic abilities
(i.e., Digit Symbol Substitution, Memory Score, Math). The analyses did not reveal any significant
differences between the two groups.

age range 18–24 years) and half (n = 49) were community-dwelling adults aged
60 years and older (M = 71.13 years; SD = 6.64; age range 61–92 years).
Ninety percent of the participants were Caucasian Americans and 10% were
Asian Americans. Ninety-seven percent of the younger adult sample identified
their relationship status as “single.” The majority of the older adult sample
reported that they were married (55%); 24% of older adults identified their
relationship status as “other,” 16% were divorced, and 4% were widowed. None of
the older adults reported that they were single. Average years of education were
not significantly different for younger (M = 14.11 years, SD = 1.25; range 12–16
years) and older adults (M = 14.32 years, SD = 3.18; range 8–21 years). The older
adults had known their partners longer (M = 16.03 years, SD = 20.14; range .71–63
years) than the younger adults had known theirs (M = 1.51 years, SD = 2.36;
range .01–7 years).
The college students were recruited in undergraduate classes and participated
in exchange for class extra credit. The older adults were recruited in the com-
munity and were paid $20 each for their efforts.
Procedures
Participants were briefed about the general purpose of the study and were
asked to give written consent. Next, each participant individually completed the
demographic questionnaire that assessed the participant’s age, years of education,
and marital status. Participants then engaged in the problem-solving task either
individually, if they were in the individual condition, or with their friends, if they
were in the collaborative condition. Participants in the collaborative condition
were placed in separate rooms after the problem-solving task and there completed
questionnaires to assess their experiences with the task and collaboration. Next,
participants were administered the Digit Symbol Substitution (DSS) subtest of the
WAIS-3 (Wechsler, 1997), the memory subtlest of the WAIS-3 (Wechsler), and a
math test extracted from Behavioral Assessment of the Dysexecutive Function
(BADS) (Wilson, Alderman, Burgess, Emslie, & Evans, 1996). All procedures
were completed in one session. Each session lasted approximately 90 minutes.
Problem-Solving Task
The everyday problem-solving task was a trip-planning task derived from
previous research on planning and problem solving (e.g., Gauvain & Rogoff,
1989; Hayes-Roth & Hayes-Roth, 1979). The task was representative of an
everyday problem because planning a route to take when driving is part of one’s
day-to-day life and is required when traveling between two points along U.S.
roads and highways (Liu, 1997). Multiple correct solutions to the task were
possible because a number of different routes could complete the task success-
fully. The task stimulus, an American Automobile Association (AAA) “United
States Driving Distance Chart” (American Automobile Association, 1998) is

available in the real world. The chart is used to determine the distance and
drive time between cities. It was thought that the task materials would be familiar
to both younger and older adults.
Participants were given a map, a sheet summarizing the task and requirements,
and a record sheet. They were required to sequence a variety of required and
optional destinations in the most efficient (e.g., shortest) route. They used the
“United States Driving Distance Chart” to chart the most efficient round-trip route
between Denver, Colorado and San Francisco, California. A set of instructions
regarding the purpose of the trip and the constraints for completing the task
were provided. The instructions emphasized that the trip was to be planned for
somebody else in order to minimize the potential for personal preferences in
the planning.
Participants were informed that the purpose of the trip was to attend a wedding,
and that during the two-week trip across the country, four specified destinations
(wedding in San Francisco, California; Salt Lake City, Utah; Las Vegas, Nevada;
and Mt. Rushmore, South Dakota) and one additional destination selected
from two options (Yellowstone National Park, Wyoming or Yosemite National
Park, California) were required. The task had several constraints: required sights
(Mt. Rushmore, and either Yellowstone or Yosemite National Park) were open for
limited hours, and some activities had to be accomplished by a certain date
(e.g., date of wedding). Instructions also indicated that participants were to plan
the trip so as to spend three nights in Las Vegas, two nights in Salt Lake City,
a minimum of six hours in the national park, a minimum of three hours at
Mt. Rushmore, and to set aside one hour per day for meals. Other constraints
included the maximum number of hours allowed for travel each day (17 hours)
and the hours during the day in which travel was not permitted (between the
hours of 11 p.m. and 6 a.m.). Due to the complexity of the task, the instructions
emphasized that participants could consider committing smaller errors (e.g.,
being at a national park for four rather than the required six hours) to avoid
larger errors (e.g., missing the wedding). The task was designed with numerous
constraints so that participants had to simultaneously consider various pieces
of information to complete the task efficiently. The list of places to visit and the
time and date constraints for each site were available to participants at all times
as they completed the task, to simulate real-world trip planning.
Materials
Map
A large, 11 × 17 inch laminated copy of the western half of the “United States
Driving Distance Chart” from the American Automobile Association (1998) was
used. The map was enlarged to make the numbers indicating time and distance
between cities more visible, to help eliminate poor vision as a source of age
differences in task performance. A magnifying glass also was provided.

Daily-Driving Itinerary Recording Sheet
Participants were given a record sheet to track their daily itinerary. The sheet
had columns to record the date, departure time (e.g., 6 a.m.), route traveled
(e.g., Denver, Colorado to Salt Lake City, Utah), time to travel between the cities
(e.g., 9 hours and 45 minutes), time to participate in the activities (e.g., six hours
at Yosemite National Park), and time of arrival at destination (e.g., 9:45 p.m.).
This sheet indicated the specific routes participants planned.
Dependent Measures
Overall Completion Accuracy
Overall completion accuracy was a combined measure of the four component
completion accuracy variables (described below). This measure was an index of
participants’ planning performance for the entire trip. Hours or days spent at each
of the destinations were transformed into z-scores so that the destinations were
on a comparable scale.
Destinations were weighted differently depending on the emphasis placed on
attending or visiting each destination in the instructions. Fulfilling requirements of
the wedding (wedding completion) was weighted .60 because this was the stated
purpose of the trip and thus the most critical destination. Failing to attend the
wedding would have constituted a major planning error. Fulfilling the required
five nights in the cities (city completion), including three nights in Las Vegas and
two nights in Salt Lake City, was weighted .30. Though the two cities were not
main destinations, completing the five nights comprised a sizeable portion of the
trip and was thus given a rather considerable weight of .30. The requirements of
spending six hours at the national park (national park completion) and three hours
at Mt. Rushmore (Mt. Rushmore completion) were weighted .05 each, because
these destinations were described as sites to see along the way rather than primary
objectives of the trip. Overall completion accuracy was a sum of the weighted
z-scores that reflected the importance of each destination. For example, a par-
ticipant who completed all requirements associated with the wedding in San
Francisco (z-score = .38), designated only four of the five required days in
Las Vegas and Salt Lake City (z-score = –1.30), satisfied four out of the six
hours at the national park (z-score = –.87), and completed the required three
hours at Mt. Rushmore (z-score = .49) would receive a score as follows on the
overall completion accuracy measure:
.6 (.38) + .3 (–1.30) + .05 (–.87) + .05 (.49) = –.18
Component Completion
To better understand participants’ performances on the task, the individual
requirements of the task also were examined. The trip-planning task provided

four measures of task component completion accuracy. The first was wedding
completion. Wedding completion reflected whether participants fulfilled all
requirements associated with the wedding: attending the two-hour ceremony,
the six-hour reception, and spending the wedding night in San Francisco. To create
the wedding completion score, the amount of time participants spent at each
component of the wedding was transformed into z-scores. The z-scores of each
component were then weighted depending on the component’s relative impor-
tance; attending the ceremony and the reception were weighted equally at .42
each, and spending the night in San Francisco was weighted .16. Attending the
wedding was the key component of the task, since the stated purpose of the
cross-country trip was to attend the wedding. Wedding completion was the sum
of each weighted component.
The second measure of component completion accuracy was city completion,
the percentage of city requirements completed. To accurately fulfill all of the city
completion requirements, at least two of 12 possible evenings in Salt Lake City
and three of 12 possible evenings in Las Vegas had to be designated for the trip.
Visiting the two cities was considered one dependent measure because the cities
had similar requirements and were geographically close to one another. This
measure was computed by determining the percentage of required total nights (5)
that participants completed. For example, if one allotted two nights in Salt Lake
City and one night in Las Vegas, the participant received a score of .60.
The third measure of component completion accuracy was national park
completion. National park completion reflected the percentage of the six required
hours that was spent at either Yellowstone National Park or Yosemite National
Park. The fourth measure of component completion accuracy was Mt. Rushmore
completion and reflected the percentage of the three required hours spent at
the location.
The four measures of component completion accuracy (wedding, city, national
park, and Mt. Rushmore) were retained as separate indices of performance.
Intercorrelations of variables were moderate (see Table 1), indicating that each
variable represented a unique aspect of component completion accuracy.
Requirements Completed
This dependent measure reflected the number of seven requirements (the
wedding ceremony, wedding reception, night in San Francisco, Las Vegas, Salt
Lake City, Mt. Rushmore, and one of the national parks) participants fulfilled
either in full or in part. It did not take into account whether the requirements of a
given destination were completed in their entirety, but rather it measured whether
participants arrived at the designated destination at the appropriate time or hour.
For example, if a participant planned to arrive at Yosemite National Park at 9 a.m.
and stayed for only one of the required six hours, he or she received credit for
attending the park. However, if the participant planned to arrive at the park past its

Table 1. Intercorrelations among Performance Measures
Dependent measures
1 2 3 4 5 6 7 8 9 10
1. Overall completion accuracy
2. Planning errors
3. Route efficiency
4. Requirements completed
5. Number of calculation errors
6. Task-completion time
Component Completion Accuracy Variables
7. Wedding completion
8. City completion
9. National park completion
10. Mt. Rushmore completion
—
–.28*
.10
.89**
–.16
.03
.89**
.54**
.56**
.49**
—
.01
–.30*
.08
.02
–.32*
–.03
–.20
–.12
—
.21
–.04
–.03
.32**
.30*
.32*
.36**
—
–.13
.01
.84**
.45**
.55**
.60**
—
–.07
–.06
–.01
–.16
.01
—
.05
.12
.12
.05
—
.10
.41*
.31*
—
.36**
.38**
—
.28* —
*p < .05. **p < .01.
COLLABORATION
AND
EVERYDAY
PROBLEM
SOLVING
/
177

hours of operation, credit was not given because the destination was not visited.
Similarly, if the participant arrived in San Francisco a day before the wedding,
they did not receive credit for attending the wedding unless they remained in
San Francisco for the wedding.
Planning Errors
Planning errors constituted errors in planning where the time restrictions of
the task were violated. Examples of this type of error included continuing to
drive past 11 p.m., leaving a destination prior to 6 a.m., or omitting a minimum
of one hour for a meal each day. Each violation constituted one planning error.
Number of Calculation Errors
Calculation errors were errors associated with adding the hours and minutes
of travel time together. This measure of performance was derived from the
number of calculation errors on the recording sheet.
Route Efficiency
Route efficiency was the number of driving hours that would have been required
to drive the routes participants planned. Route efficiency was considered to
be a key measure (along with attending the wedding) of performance because
participants were instructed to plan the most time-efficient route for the trip.
Task-completion Time
This measure of performance was the number of minutes participants took
to complete the task. Participants were not told that their problem solving would
be timed; they were given unlimited time to complete the task. Though the utility
of time as a performance measure is questionable, task-completion time was
included as a means to compare this study with previous research findings.
Basic Abilities Measures
Digit Symbol Substitution Test
Following completion of the demographic questionnaire, each participant
completed the DSS portion of the WAIS-3 (Wechsler, 1997). The DSS test was
used because the trip-planning task required participants to continuously transfer
between the list of constraints, the record sheet, and the map. A percentage score
on the DSS test reflected the number of correct responses divided by the total
number of possible responses.

Memory Test
Next, each participant was administered the Digit Forward and Digit Backward
subtest from the WAIS-3 (Wechsler, 1997). The Digit Forward score and the Digit
Backward score were then added together to compute a global memory score
(Wechsler, 1997).
Math Test
The mathematics test was administered next. It involved addition, subtraction,
division, and multiplication, and had to be conducted by hand. This test was
administered because the trip-planning task required the use of mathematical
abilities. Math scores were the percentage of correct answers out of a possible
60 on the math measure.
Task Questionnaire
The Task Questionnaire was administered to all participants immediately after
the trip-planning task was completed. Participants’ experiences with trip planning
were assessed; experience with trip planning was assessed using a Likert-type
scale (1 = Never; 5 = Very Often) with the question, “Have you planned a trip
similar to this before?” The questionnaire also assessed individuals’ frequency of
collaboration and the types of situations in which they collaborate with another
individual. Frequency of collaboration was assessed via two questions using a
Likert-type scale: “Do you normally ask other people for help when solving
problems?” (1 = Never; 5 = Very Often) and “Do you normally work with
someone to solve problems?” (1 = Never; 5 = Very Often). Responses to these last
two questions were correlated positively, r (53) = .47, p < .01, thus they were
summed to create one variable, frequency of collaboration.
Design
The study was a 2 × 2 × 2 factorial design with age (young, old), problem-
solving condition (individual, collaborative), and gender (male, female) as between-
subjects variables. The main dependent variables were overall completion accur-
acy, component completion accuracy (wedding, national park, Mt. Rushmore,
and cities), route efficiency, requirements completed, number of calculation
errors, and task-completion time.
RESULTS
Unit of Analysis
For participants in the collaborative condition, dyadic indices of performance
(i.e., component completion accuracy, overall completion accuracy, requirements
completed, number of calculation errors, task-completion time, and route
efficiency) were used. Dyadic indices of performance were used because dyad

members worked together to create a single product; individuals’ contributions
were indistinguishable from each other. For participants in the collaborative
condition, dyadic indices of age, education, DSS, and memory were created
by averaging the two individual scores. Intraclass correlations indicated dyad
members’ ages, r (32) = .99, p < .01; education, r (32) = .72, p < .01; DSS,
r (32) = .80, p < .01; and global memory scores, r (32) = .56, p < .01, were
positively correlated. Dyad members’ math abilities were moderately, but not
significantly, correlated, r (32) = .32, p = .08. Paired sample t-tests did not
reveal any significant differences between the partners’ ages, education, DSS
Scores, Global Memory Scores, or math scores. For participants in the individual
condition, individual indices of performance were used because participants
worked alone throughout the trip-planning task. Individual indices of age, edu-
cation, DSS scores, and memory scores also were used.
Overview of Analyses
The four component completion accuracy variables (wedding, city, national
park, and Mt. Rushmore) were considered as a set in a 2 (age: young, old) ×
2 (condition: collaborative, individual) × 2 (gender: male, female) MANOVA.
Intercorrelations among these variables were moderate (see Table 1).
Overall completion accuracy, requirements completed, planning errors, route
efficiency, number of calculation errors, and task-completion time were analyzed
with 2 × 2 × 2 ANOVAs. Intercorrelations among these variables generally were
low, indicating that the variables assessed unique aspects of task performance
(see Table 1). Although conducting a MANOVA would have helped to control
for Type I error, the low intercorrelations of the variables could have resulted
in low power in MANOVA (see Tabachnick & Fidell, 1996).
Age, Problem-Solving Condition, Gender,
and Performance Analyses
The results of the ANOVA indicated a significant effect of age group on overall
completion accuracy, F(1, 56) = 4.11, p < .05, 02 = .07. Younger adults were more
accurate than were older adults (see Table 2). The effect of condition was
significant, F (1, 56) = 4.06, p < .05, h2 = .07.Collaborators were more accurate
than were individuals (see Table 2).
Component Completion accuracy
The results of MANOVA revealed a significant main effect of age group on the set
of component completion accuracy variables, F (4, 53) = 5.78, p < .01, h2 = .30. To
examine the main effect of age group, follow-up tests were conducted for each of

Table 2. Mean Scores and Standard Deviations on Trip-Planning Task
Performance by Age Group and Problem-Solving Condition
Performance Younger adults Older adults Total
Individual
Collaborative
Total
Component Completion Accuracy
Wedding Completion
Individual
Collaborative
Total
City Completion
Individual
Collaborative
Total
National Park Completion
Individual
Collaborative
Total
Mt. Rushmore Completion
Individual
Collaborative
Total
Route Efficiency
Individual
Collaborative
Total
Completed Requirements
Individual
Collaborative
Total
Planning Errors
Individual
Collaborative
Total
Individual
Collaborative
Total
Task-Completion Time
Individual
Collaborative
Total
–.08 (.97)
.45 (.11)
.18 (.73)a
–.27 (1.24)
.38 (.00)
.05 (.92)
86.25 (27.05)
85.00 (27.81)
91.88 (20.86)a
77.95 (35.49)
84.38 (34.14)
81.16 (34.41)
84.88 (33.12)
100.00 (0.00)
92.44 (24.29)a
4430.75 (384.00)
4449.38 (280.18)
4440.06 (330.79)
6.19 (1.33)
6.88 (.34)
6.53 (1.02)
70.63 (145.76)
8.06 (17.72)
39.34 (106.97)
3.44 (2.39)
2.19 (1.56)
2.81 (2.09)a
44.28 (13.57)
39.68 (19.66)
41.98 (16.78)a
–.22 (.76)
–.09 (.52)
–.15 (.64)b
–.19 (1.10)
.08 (.81)
–.05 (.96)
67.50 (30.88)
67.50 (29.10)
67.50 (29.51)b
82.04 (32.47)
83.34 (36.00)
82.69 (33.73)
82.29 (38.23)
62.50 (50.00)
72.40 (44.92)b
4599.69 (370.58)
4415.56 (454.93)
4507.63 (418.74)
6.25 (1.06)
6.38 (.96)
6.31 (1.00)
67.69 (88.91)
35.50 (59.54)
51.59 (76.21)
4.06 (3.42)
3.93 (2.57)
4.00 (2.97)b
50.21 (24.61)
56.86 (18.36)
53.54 (21.66)b
–.15 (.86)c
.18 (.46)d
.02 (.70)
–.23 (1.15)
.23 (.59)
.00 (.93)
76.88 (30.10)
82.50 (26.27)
79.69 (28.17)
80.00 (33.53)
83.86 (34.51)
81.93 (33.81)
83.58 (35.21)
81.25 (39.66)
82.41 (37.22)
4515.22 (381.00)
4432.47 (372.05)
4473.84 (375.87)
6.22 (1.18)
6.63 (.75)
6.42 (1.00)
69.16 (118.77)c
21.78 (45.41)d
45.46 (92.34)
3.75 (2.92)
3.06 (2.27)
3.40 (2.62)
47.15 (19.59)
48.00 (20.66)
47.57 (19.97)
Notes: ab indicates that the means in a row differ significantly at p £ .05. cd indicates
that the means in a column differ significantly at p £.05.

the four component completion accuracy variables (wedding, city, national park,
and Mt. Rushmore). Younger adults and older adults differed significantly on the
city requirements, F(1, 56) = 16.42, p < .01, 02 = .23; younger adults completed a
greater percentage of the city requirements than did older adults (see Table 2).
Younger and older adults also differed significantly on the Mt. Rushmore
requirement, F(1, 56) = 5.01, p < .05, 02 = .08. Younger adults completed a greater
percentage of the Mt. Rushmore requirements than did older adults (see Table 2).
Age differences were not significant for the other two measures of component
completion accuracy (wedding and national park; see Table 2). Thus, of the four
component completion accuracy variables examined, age differences were
observed in two measures: city completion and Mt. Rushmore completion.
Planning Errors
The results of the ANOVA revealed a main effect of condition, F (1, 56) = 4.60,
p < .05, 02 = .07. Collaborators made fewer planning errors than did individuals
(see Table 2). The ANOVA also revealed a main effect of gender, F (1, 56) = 8.25,
p < .01, 02 = .12. Female participants (M = 75.67, SD = 116.67) made more
planning errors than did male participants (M = 13.32, SD = 36.26).
Route Efficiency, Number of Requirements
Completed, and Calculation Errors
ANOVA did not reveal any main effects or interactions for route efficiency,
completed requirements, and number of calculation errors. Thus the performances
of younger and older adults, individuals and collaborators, and men and women
on these variables were similar.
Results indicated a significant main effect of age, F (1, 54) = 5.36, p < .05, 02 =
.07. In comparison to older adults, the younger adults completed the route-
planning task faster (see Table 2).
Summary
Age differences were found on half of the performance measures. Younger
adults outperformed older adults on overall completion accuracy and completed
the task faster. Of the component completion accuracy variables, younger adults
fulfilled more of the city requirements and the Mt. Rushmore requirements.
Differences between individuals and collaborators were found in overall com-
pletion accuracy and planning errors. Males made fewer planning errors than did
females. In contrast to predictions, the results did not indicate a significant age by
problem-solving condition interaction or a gender by problem-solving condition
interaction for any of the performance measures.

Age and Basic Skills, Experience, and
Frequency of Collaboration
Because main effects of age were found for some of the performance measures,
additional analyses were conducted to examine if age differences between
younger and older adults existed on the measures of basic abilities (global
memory, DSS, and math). One-way ANOVAs were conducted to examine if
younger adults’ abilities exceeded those of older adults. Younger adults out-
performed older adults on all basic ability measures (see Table 3).
To further understand differences between younger and older adults that
may have contributed to the observed age differences, frequency of collaboration
and previous experiences with planning a trip were examined. Two one-way
ANOVAs were conducted, with age as the independent variable and frequency
of collaboration and experience with “planning a similar trip” as the dependent
variables. Younger (M = 6.07, SD = 1.01) and older adults (M = 5.48, SD = 1.28)
did not differ on frequency of collaboration F(1, 51) = 3.53, p = .07, nor did
they differ on previous experiences with trip planning F(1, 63) = .40, p = .53
(younger: M = 2.20, SD = 1.08; older: M = 2.36, SD = .96).
Predictors of Problem-Solving Performance
Additional analyses were conducted to determine the extent to which variables
such as basic abilities (DSS, memory, and math) and education were related
to problem-solving performance. Because problem-solving condition and age
effects were found in prior analyses, bivariate correlations were conducted among
basic abilities, education, and the performance measures (i.e., overall completion
accuracy, component completion accuracy, planning errors, route efficiency,
completed requirements, and task-completion time) for both younger and older
collaborators and younger and older individuals (see Table 4).
Table 3. Mean Scores on Tests of Basic Abilities by Age Group
Basic abilities
Younger
adults
Older
adults F df p
Digit Symbol Substitution
Memory Score
Math
81.78
(11.95)
17.81
(3.93)
74.58
(10.69)
51.37
(13.24)
15.32
(3.42)
66.18
(19.00)
91.71
7.17
4.71
1, 61
1, 61
1, 61
.00*
.01*
.03*
*p < .05.

Table 4. Bivariate Correlations between Basic Abilities,
Education, and Performance by Age Group and
Problem-Solving Condition
DSS Memory Math Education
INDIVIDUALS
Younger Adults
Wedding Completion
City Completion
Planning Errors
Route Efficiency
Older Adults
Wedding Completion
City Completion
Planning Errors
Route Efficiency
.14
.17
–.03
.14
.07
–.22
.05
.16
–.09
–.25
.24
.13
.37
.15
–.29
–.06
.32
.26
–.33
.09
–.15
–.19
–.08
.04
.11
.53*
.24
–.24
–.32
.32
.44
.43
.15
.29
–.29
.22
–.05
.27
–.08
.16
.08
.10
–.19
.28
.33
.06
.41
.06
–.33
.07
.61*
.34
.67**
.80**
.06
.16
.16
.61*
–.44
.26
.12
.04
.31
.14
.47
.32
.29
.15
.07
.08
.29
.22
.19
.52*
.32
–.28
.11
.42
–.65**
.23

Table 4. (Cont’d.)
DSS Memory Math Education
COLLABORATORS
Younger Adults
Wedding Completion
City Completion
Planning Errors
Route Efficiency
Older Adults
Wedding Completion
City Completion
Planning Errors
Route Efficiency
.10
—a
.00
.23
—a
–.37
–.17
.29
–.26
–.07
.21
.16
–.05
–.02
.20
.32
.21
.46
–.65**
–.18
–.04
—a
–.10
.11
—a
–.03
–.03
.03
–.22
–.11
–.22
–.32
.11
.50
–.08
–.17
–.12
–.29
.01
–.02
.34
—a
.28
.16
—a
–.63**
.07
.23
.10
–.33
.14
.01
.04
.34
.35
–.17
.50
.27
–.47
.05
–.05
—a
–.13
.16
—a
–.41
.02
.08
–.65**
–.34
.07
–.16
.27
.38
.06
–.26
.13
–.08
–.39
.12
aCorrelation could not be computed because one variable was a constant.
*p < .05. **p < .01.

Individual Condition
For older adults in the individual condition, significant correlations among
math scores and several of the performance measures were found. Better math
scores were significantly correlated with greater overall completion accuracy
scores, r (17) = .61, p < .05; with better performance on two of the component
completion-accuracy variables (city completion, r (17) = .67, p < .01, and national
park completion, r (17) = .80, p < .01); and also with completing a greater number
of requirements, r (17) = .61, p < .05.
For older adults in the individual condition, education was significantly related
to national park completion, r (17) = .52, p < .05, and to the number of calculation
errors committed, r (17) = –.65, p < .01. Greater years of education were related
to completing a greater percentage of the national park requirements and com-
mitting fewer calculation errors on the task. For younger adults in the individual
condition, greater memory scores were related to a greater number of planning
errors, r (16) = .53, p < .05.
Collaborative Condition
For older adult collaborators, better performance on the DSS was related to
fewer calculation errors on the task, r (16) = –.65, p < .01. For younger adult
collaborators, greater years of education were related to fewer calculation errors,
r (16) = –.65, p < .01, and better math scores were related to fewer planning
errors, r (16) = –.63, p < .01.
Age Differences
Fisher’s r to z transformations were conducted to determine if the correlations
among basic abilities, education, and the performance measures differed signifi-
cantly between younger and older individuals, and between younger and older
collaborators. Education predicted the number of calculation errors that older
individuals committed, r (17) = –.63, p < .01, but not the number of errors that
younger individuals committed, r (16) = .07, p = .81, z = 2.17, p < .05.
Math scores were significantly correlated with older individuals’ city comple-
tion performance, r (17) = .67, p < .05, but were not significantly correlated
with younger individuals’ city completion performance, r (16) = –.19, p = .49,
z = –2.42, p < .05. The relationship between the other basic abilities, education,
and other performance measures did not significantly differ between younger
and older individuals. For younger and older adult collaborators, the strength
of the association between the performance measures and basic abilities and
education was not significantly different.

Summary
The associations among basic abilities, education, and the performance
measures were significantly different for younger and older individuals, but not
for younger and older collaborators. Years of education were significantly related
to the number of calculation errors committed for older individuals but not for
younger individuals. Older individuals with greater years of education committed
fewer calculation errors than those with fewer years of education. Math scores
were significantly related to city completion for older individuals, but not for
younger individuals. Older individuals with better math scores fulfilled a greater
percentage of the city requirements than did older individuals who had poorer
math scores. The associations among basic abilities, education and performance
measures were not significantly different for younger and older collaborators.
DISCUSSION
By comparing the problem-solving performances of younger and older adults
working alone to their performances when working with a friend, the present study
makes important contributions to the literature on collaborative everyday problem
solving. First, our results suggest that when differences are found between indi-
viduals’ and collaborators’ everyday problem-solving performances, the effect of
collaboration is similar for younger and older adults. Second, our results indicate
both age similarities and age differences in everyday performance favoring young
adults when task materials are taken from the real world (e.g., a road map and
driving distance chart). Together, these findings address how age combines with
task demands in social contexts to affect problem-solving performance.
Problem-Solving Condition and Performance
Collaborators were more successful than individuals in putting the various
requirements of the trip together, as indicated by their superior performance on
the composite measure of overall performance. Collaborators also made fewer
planning errors than did individuals. These findings are in accord with research on
memory collaboration, which indicates that collaborating with a friend can be
beneficial (Andersson & Roennberg, 1996). Thus, the benefits of collaborating
with friends appear to extend to some aspects of everyday problem-solving
performance. However, not all indices of problem-solving performance indicated
superior performance of collaborators as compared to individuals. Indeed, the
performance of collaborators and individuals on most of the measures was the
same. When considering whether or not collaborators outperform individuals, it
may be important to consider aspects of partners’ interpersonal relationships and
aspects of the task. Aside from research by Andersson and his colleagues (1996,
1997) on friends’ collaborative memory performance, studies investigating the
benefits of collaboration over working alone are based on long-term married

couples (Dixon & Gould, 1998). Long-term married couples are thought to have
collaborative expertise due to their extensive shared history, which facilitates the
development of transactive memory and effective communication (Dixon, 1992,
1996). Similar to married couples, long-lasting friends have a shared history and
may also develop transactive memories and effective communication. However,
even when friends are long-lasting, they do not necessarily provide the immediate
social context of daily life that spouses provide. Although older adults may
nominate friends as partners for collaborative problem solving, older adults who
are married overwhelmingly nominate their spouses (Strough et al., 2003). In
addition, for both spouses and friends, specific qualities of the relationship such as
cooperative communication (see Berg et al., 2003) may be more important than
the duration of the relationship for understanding collaborative benefits. Thus,
when predicting whether familiar partners outperform individuals, it may be
important to consider both the type of relationship (e.g., spouses, friends) and
specific qualities (e.g., shared history, communication).
Differences between the task used in the present study and tasks used in
previous research also may help to explain why collaborators did not outperform
individuals on all of the performance measures. Researchers who find that collab-
orators outperform individuals employ memory tasks such as prose recall (Dixon
& Gould, 1998) and remembering digits (Dixon, 1992). The trip-planning task
used in the present study did not have high memory demands because of the
presence of external memory aids: the instructions, map, and daily itinerary.
Indeed, correlations between memory and problem-solving performance were, for
the most part, small and nonsignificant in our sample. In the real world, tasks with
high memory demands often are accomplished with the assistance of external
memory aids such as a list of items to be remembered or asking another individual
to assist with reminders. The current task resembles problem solving in the
real world context because of the presence of external memory aids and the
presence of friends to assist in the problem solving.
Age Differences and Similarities in Performance
When overall task performance was considered using the composite measure,
younger adults’ performance exceeded that of older adults. Closer examination of
the specific components of the task revealed that particular aspects of the task
(i.e., city, Mt. Rushmore requirements) contributed to the age differences on the
overall measure. The task requirements pertaining to visiting Salt Lake City,
Las Vegas, and Mt. Rushmore may have been more complex than other aspects of
the task because of the degree of planning involved. The location of the cities to
be visited was geographically in the middle of the trip, and occupied five of the
12 possible travel days. The remoteness of Mt. Rushmore in comparison to the
other required destinations may have led participants to ignore or disregard that
requirement until after the other requirements were satisfied. Indeed, examination

of participants’ maps indicated that the majority of participants chose to complete
the Mt. Rushmore requirement last, and that 16% of older adults, but only 3.4%
of younger adults, omitted Mt. Rushmore altogether.
Older adults took longer to complete the task than did younger adults. These
results are consistent with those of Berg and colleagues (2003), who reported that
older married couples took longer than younger married couples to make decisions
about a vacation. Quick task-completion times may not be crucial in the everyday
world where a wide range of response times is often acceptable (Cornelius et al.,
1983). In the real world, planning only needs to be completed before the trip. Thus,
the time difference in completing vacation plans between younger and older adults
is probably not a detriment to performance in the everyday world. However, time
differences could be detrimental in cases where a quick response time is important,
such as in driving or in medical emergencies.
Importantly, age differences were not found for three of the primary per-
formance measures. First, older adults’ and younger adults’ performances in terms
of attending the wedding were similar. Both age groups were equally successful
in completing the requirement that was stated as the purpose for the trip. Second,
younger and older adults were equally effective in planning efficient routes.
Third, in terms of the number of requirements completed, older and younger
adults’ performances were similar. The importance of attending the wedding
(since it was the purpose of the trip), planning an efficient route, including as many
of the requirements as possible, and making calculated tradeoffs to include the
most requirements were highly emphasized in the instructions. That older adults
fulfilled a similar number of requirements on the task as younger adults, but
performed more poorly when the accuracy of requirements was considered as an
aggregate, suggests that older adults made more tradeoffs. Overall, these results
suggest that when instructed to pay attention to key features of a problem-solving
task, older adults perform as well as younger adults.
Consistent with previous research, our sample showed that younger adults’
math scores, global memory scores, and DSS scores were superior to those of older
adults (cf. Schaie, 1994). However, scores on these measures of basic abilities
were not overwhelmingly related to performance. For older adults in the individual
condition, better math scores were related to better performance on the composite
measure of overall completion accuracy, completing a greater percentage of
the city requirements and national park requirements, and fulfilling more of the
requirements of the task. For older adults in the collaborative condition, better
DSS scores were related to fewer calculation errors. For younger adults in the
individual condition, better memory scores were related to a greater number of
planning errors. For younger adults in the collaborative condition, better math
scores were related to fewer planning errors. Math scores may have been more
consistently related to older adults’ task performance as compared to that of
younger adults because of the greater range of math abilities found in the older
adult sample.

Together, our results suggest that performance on an everyday problem-solving
task may be distinct from basic cognitive abilities (see also, Berg & Sternberg,
1985; Sternberg et al., 1993). Had the older adults performed worse in comparison
to younger adults on all aspects of the task, and had basic abilities been important
for predicting performance, one interpretation would be that the older adults’ poor
performance was due to their declining cognitive competencies. However, basic
abilities, with the exception of math scores, were not any more predictive of older
adults’ problem-solving performance than younger adults’ performance.
Education also was an important predictor of performance. In the individual
condition, older adults with more years of education completed a greater per-
centage of the national park requirements, and also committed fewer calculation
errors than participants with fewer years of education. In the collaborative
condition, younger adult collaborators with more years of education committed
fewer calculation errors. Individuals with more education may have learned to
double-check their own work, and thus were careful with their calculations.
Limitations and Future Directions
Because we employed a cross-sectional design, it is impossible to determine
whether the age differences and similarities we found are due to development
per se, or to the specific cohorts we investigated. Given that education was
equivalent for the older and younger adults in our study, our sample is not
representative of the larger population of older adults. In addition, the task we
used did not index the association between age and experience with social and
experiential aspects of everyday problem solving to the extent that was expected.
That is, younger and older adults did not differ in their reported frequency of
collaboration, and both reported that they had planned a similar trip only once
or twice. Thus, the type of planning we examined may not completely capture
planning as it occurs in day-to-day life. Often, it is only necessary to get an
approximate estimate of the time when one might arrive at a destination, rather
than an exact time as was required in the current study. When planning an actual
vacation one might decide to omit destinations that are out of the way, as
Mt. Rushmore was in the current study.
The task we examined was ill-structured. There were multiple routes that
satisfied the requirements of planning the cross-country trip. Because the task was
ill-structured, some of the measures of performance, such as overall completion
accuracy, had to be logically derived by the researcher. The derived measures were
based on the constraints of the task as they were presented to participants;
however, these measures were somewhat subjective. Determining how to best
represent the success or failure of individuals’ problem-solving efforts is one
challenge faced by researchers who investigate problem solving on ill-structured
tasks (see also Margrett & Marsiske, 2003). Considering the type of everyday
problem (e.g., memory, trip-planning, instrumental, interpersonal) and the extent

to which tasks tap basic, componential versus contextual, experiential aspects of
intelligence may be useful in understanding conditions under which benefits exist
for younger and older collaborators.
To better understand age differences and similarities in collaborative everyday
problem solving, future investigations could examine the interpersonal processes
that are associated with collaborative gains and losses. Berg and colleagues
(2003) found that younger and older married couples who expressed greater
affiliation during their interactions performed better on decision-making and
errand-planning tasks. Other research indicates that older adults engage in off-
target speech (Gold, Andres, Arbuckle, & Schwartzman, 1988) and are more
concerned with socializing with their partners than with instrumental task activity
(Boden & Bielby, 1983; Gould, Trevithick, & Dixon, 1991). Consideration of
process loss in collaborative pairs and groups (Basden, Basden, Bryner, & Thomas,
1997; Basden, Basden, & Henry, 2000) as well as compensatory gains may lead
to a more complete understanding of collaborative everyday problem solving in
adulthood. Such investigations will contribute to a greater understanding of how
older adults’ everyday competencies are related to their social contexts.
CONCLUSION
Our results highlight the importance of considering the conditions under
which age differences and similarities in performance emerge, rather than
focusing on whether declines, maintenance, or improvements best characterize
age trajectories of collaborative everyday problem solving. The current study
provides evidence that older adults are competent problem solvers in the everyday
world. Using older adults’ declining basic abilities to explain these findings paints
an incomplete picture, and may inadvertently contribute to some of the negative
stereotypes about aging. Our findings indicate that, in many respects, older and
younger adults’ everyday problem solving do not greatly differ. Future research
should continue to investigate the social mechanisms by which older adults
effectively adapt to their environments in order to age successfully despite aging-
related losses.
ACKNOWLEDGMENTS
The authors thank Rick Briggs, Barry Edelstein, Julie Patrick, and Hayne Reese
(members of the dissertation committee), and Lisa Swenson for her assistance
with data collection.
REFERENCES
Allaire, J. C., & Marsiske, M. (1999). Everyday cognition: Age and intellectual ability
correlates. Psychology and Aging, 14, 627-644.

Allaire, J. C., & Marsiske, M. (2002). Well- and ill-defined measures of every-
day cognition: Relationship to older adults’ intellectual ability and functional status.
Psychology and Aging, 17, 101-115.
American Automobile Association (1998). United States Road Atlas. Heathrow, FL:
American Automobile Association.
Andersson, J., & Roennberg, J. (1995). Recall suffers from collaboration: Joint recall
effects of friendship and task complexity. Applied Cognitive Psychology, 9, 199-211.
Andersson, J., & Roennberg, J. (1996). Collaboration and memory: Effects of dyadic
retrieval on different memory tasks. Applied Cognitive Psychology, 10, 171-181.
Andersson, J., & Roennberg, J. (1997). Cued memory collaboration: Effects of friend-
ship and type of retrieval cue. European Journal of Cognitive Psychology, 9, 273-287.
Azmitia, M., & Montgomery, R. (1993). Friendship, transactive dialogues, and the
development of scientific reasoning. Social Development, 2, 202-221.
Baltes, P. B., & Staudinger, U. M. (1996). Interactive minds: Life-span perspectives
on the social foundations of cognition. New York: Cambridge University Press.
Basden, B. H., Basden, D. R., Bryner, S., & Thomas, R. L. III, (1997). A comparison
of group and individual remembering: Does collaboration disrupt retrieval strategies?
Journal of Experimental Psychology: Learning, Memory, & Cognition, 23, 1176-1191.
Basden, B. H., Basden, D. R., & Henry, S. (2000). Costs and benefits of collaborative
remembering. Applied Cognitive Psychology, 14, 497-507.
Berg, C. A., Johnson, M. M., Meegan, S. P., & Strough, J. (2003). Collaborative
interaction in young and old married couples. Discourse Processes, 15, 33-58.
Berg, C. A., & Klaczynski, P. (1996). Practical problem solving during adolescence
and adulthood: Searching for perspectives. In F. Blanchard-Fields & T. M. Hess
(Eds.), Perspectives on cognition in adulthood and aging (pp. 323-357). New York:
McGraw Hill.
Berg, C. A., Meegan, S. P., & DeViney, F. P. (1998). A social-contextual model
of coping across the life span. International Journal of Behavioral Development, 22,
239-261.
Berg, C. A., & Sternberg, R. J. (1985). A triarchic theory of intellectual development
during adulthood. Developmental Review, 5, 334-370.
Berg, C. A., Strough, J., Calderone, K. S., Sansone, C., & Weir, C. (1998). The role of
problem definitions in understanding age and context effects on strategies for solving
everyday problems. Psychology and Aging, 13, 29-44.
Boden, D., & Bielby, D. D. (1983). The past as resource: A conversational analysis
of elderly talk. Human Development, 26, 308-319.
Camp, C. J., Doherty, K., Moody-Thomas, S., & Denney, N. (1989). Practical problem
solving in adults: A comparison of problem types and scoring methods. In J. D. Sinnott.
(Ed.), Everyday problem solving: Theory and applications (pp. 211-228). New York:
Praeger Publishers.
Cornelius, S. W., & Caspi, A. (1987). Everyday problem solving in adulthood and
aging. Psychology and Aging, 2, 144-153.
Cornelius, S. W., Willis, S. L., Nesselroade, J. R., & Baltes, P. B. (1983). Convergence
between attention variables and factors of psychometric intelligence in older adults.
Intelligence, 7, 253-269.
Crovitz, H. H. (1970). Galton’s walk: Methods for the analysis of thinking, intelligence,
and creativity. New York: Harper & Row.

Denney, N. W. (1989). Everyday problem solving: Methodological issues, research
findings, and a model. In L. W. Poon, D. C. Rubin, & B. A. Wilson (Eds.), Everyday
cognition in adulthood and late life (pp. 330-351). New York: Cambridge University
Press.
Denney, N. W., & Palmer, A. M. (1981). Adult age differences on traditional and
practical problem-solving measures. Journal of Gerontology, 36, 323-328.
Denney, N. W., & Pearce, K. A. (1989). A developmental study of practical problem
solving in adults. Psychology & Aging, 4, 438-442.
Diehl, M., Willis, S. L., & Schaie, K. W. (1995). Everyday problem solving in older
adults: Observational assessment and cognitive correlates. Psychology and Aging, 10,
478-491.
Dixon, R. A. (1992). Contextual approaches to adult intellectual development. In
R. J. Sternberg & C. A. Berg (Eds.), Intellectual development (pp. 350-380). New York:
Cambridge University Press.
Dixon, R. A. (1996). Collaborative memory and aging. In D. J. Herrmann, C. McEvoy,
C. Hertzog, P. Hertel, & M. K. Johnson (Eds.), Basic and applied memory research theory
in context (pp. 359-383). Mahwah, NJ: Lawrence Erlbaum Associates.
Dixon, R. A., & Gould, O. N. (1996). Adults telling and retelling stories collaboratively.
In P. B. Baltes & U. M. Staudinger (Eds.), Interactive minds: Life-span perspectives on
the social foundation of cognition. New York: Cambridge University Press.
Dixon, R. A., & Gould, O. N. (1998). Younger and older adults collaborating on
retelling everyday stories. Applied Developmental Science, 2, 160-171.
Gauvain, M., & Rogoff, B. (1989). Collaborative problem solving and children’s
planning skill. Developmental Psychology, 25, 139-151.
Gold, D., Andres, D. Arbuckle, T., & Schwartzman, A. (1988). Measurement and
correlates of verbosity in elderly people. Journal of Gerontology: Psychological Sciences,
43, P27-P33.
Goodnow, J. J., Lawrence, J. A., Ryan, J., Karantzas, G., & King, K. (2002). Extending
studies of collaborative cognition by way of care giving situations. International Journal of
Behavioral Development, 26, 16-25.
Gould, O. N., Kurzman, D., & Dixon, R. A. (1994). Communication during prose recall
conversations by young and old dyads. Discourse Processes, 17, 149-165.
Gould, O. N., Osborn, C., Krein, H., & Mortenson, M. (2002). Collaborative recall
in married and unacquainted dyads. International Journal of Behavioral Development,
26, 36-44.
Gould, O. N., Trevithick, L., & Dixon, R. A. (1991). Adult age differences in elabora-
tions produced during prose recall. Psychology and Aging, 6, 93-99.
Hayes-Roth, B., & Hayes-Roth, F. (1979). A cognitive model of planning. Cognitive
Science, 3, 275-310.
Heidrich, S. M., & Denney, N. W. (1994). Does social problem solving differ from
other types of problem solving during the adult years? Experimental Aging Research,
20, 105-126.
Himes, C. L., & Reidy, E. B. (2000). The role of friends in caregiving. Research on
Aging, 22, 315-336.
Hultsch, D. F., & Dixon, R. A. (1990). Learning and memory in aging. In J. E. Birren
& K. W. Schaie (Eds.), Handbook of the psychology of aging (3rd ed.) (pp. 258-274).
San Diego, CA: Academic Press.

Johansson, O., Andersson, J., & Roennberg, J. (2000). Do elderly couples have a better
prospective memory than other elderly people when they collaborate? Applied Cognitive
Psychology, 14, 121-133.
Keppel, G. (1991). Design and analysis (3rd ed.). Englewood Cliffs, NJ: Prentice Hall.
Light, L. L. (1991). Memory and aging: Four hypotheses in search of data. American
Review of Psychology, 42, 333-376.
Liu, B. (1997). Route finding by using knowledge about the road network. IEEE–
Transactions on Systems, Man, and Cyberetics Part A: Systems and Humans, 27, 436-448.
Luszcz, M. A. (1989). Theoretical models of everyday problem solving in adulthood.
In J. D. Sinnott (Ed.), Everyday problem solving: Theory and application (pp. 24-39).
New York: Praeger.
Margrett, J. A. (1999). Collaborative cognition and aging: A pilot study. Unpublished
doctoral dissertation, Wayne State University, Detroit.
Margrett, J. A., & Marsiske, M. (2002). Gender differences in older adults’ everyday
cognitive collaboration. International Journal of Behavioral Development, 26, 45-59.
Margrett, J. A. & Marsiske, M. (2003). Collaboration on everyday problem solving
tasks by older adults. Manuscript submitted for publication.
Marsiske, M., & Willis, S. L. (1995). Dimensionality of everyday problem solving with
older adults. Psychology and Aging, 10, 269-281.
Park, D. C. (1992). Applied cognitive aging research. In F. Craik & T. Salthouse
(Eds.), Handbook of cognition and aging (pp. 449-493). Hillsdale, NJ: Lawrence Erlbaum
Associates.
Rawlins, W. K. (1995). Friendships in later life. In J. F. Nussbaum & J. Coupland
(Eds.), Handbook of communication and aging research (pp. 227-257). Mahwah, NJ:
Lawrence Erlbaum Associates.
Schaie, K. W. (1978). External validity in the assessment of intellectual development
in adulthood. Journal of Gerontology, 33, 695-701.
Schaie, K. W. (1994). The course of adult intellectual development. American
Psychologist, 49, 304-313.
Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for
everyday and abstract problem solving. In J. D. Sinnott (Ed.), Everyday problem solving:
Theory and applications (pp. 72-99). New York: Praeger.
Sinnott, J. D., & Guttman, D. (1978). Piagetian logical abilities and older adults’
abilities to solve everyday problems. Human Development, 21, 327-333.
Sternberg, R. J., Wagner, R. K., & Okagaki, L. (1993). Practical intelligence: The
nature and role of tacit knowledge in work and at school. In J. M. Puckett & H. W. Reese
(Eds.), Mechanisms of everyday cognition (pp. 205-227). Hillsdale, NJ: Lawrence Erlbaum
Associates.
Strough, J., Berg, C. A., & Sansone, C. (1996). Goals for solving everyday problems
across the life span: Age and gender differences in the salience of interpersonal concerns.
Developmental Psychology, 32, 1106-1115.
Strough, J., Cheng, S., & Swenson, L. (2002). Preferences for collaborative and indi-
vidual everyday problem solving in later adulthood. International Journal of Behavioral
Development, 26, 26-35.
Strough, J., Patrick, J. H., Swenson, L. M., Cheng, S., & Barnes, K. (2003). Collab-
orative everyday problem solving: Interpersonal relationships and problem dimensions.
International Journal of Aging and Human Development, 56, 43-56.

Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics (3rd ed.).
New York: HarperCollins.
U.S. Bureau of the Census (2000). Educational attainment in the United States. Current
population reports (P20-536). Washington, DC: Author.
Wagner, M., Schuetze, Y., & Lang, F. R. (1999). Social relationships in old age. In
P. B. Baltes & K. U. Mayer (Eds.), The Berlin aging study: Aging from 70 to 100
(pp. 282-301). New York: Cambridge University Press.
Watson, T. L., & Blanchard-Fields, F. (1998). Thinking with your head and your
heart: Age differences in everyday problem-solving strategy preferences. Aging, Neuro-
psychology, and Cognition, 5, 225-240.
Wechsler, D. (1997). Wechsler Adult Intelligence Scale (3rd ed.). San Antonio:
Harcourt Brace & Company.
Wegner, D. M., Guiliano, T., & Hertel, P. T. (1985). Cognitive interdependence in
close relationships. In W. J. Licks (Ed.), Compatible and incompatible relationships
(pp. 253-276). New York: Springer-Verlag.
Willis, S., & Schaie, I. W. (1986). Practical intelligence in later adulthood. In
R. Sternberg & R. Wagner (Eds.), Practical intelligence (pp. 236-270). New York:
Cambridge University Press.
Willis, S. L., & Schaie, K. S. (1993). Everyday cognition: Taxonomic and methodo-
logical considerations. In J. M. Puckett & H. W. Reese (Eds.), Life-span developmental
psychology: Mechanisms of everyday cognition (pp. 33-53). Hillsdale, NJ: Erlbaum.
Wilson, B. A., Alderman, N., Burgess, P., Emslie, H., & Evans, J. S. (1996).
Behavioural assessment of the dysexecutive syndrome. Lutz, FL: Psychological Assess-
ment Resources.
Wood, W. (1987). Meta-analytic review of sex differences in group performance.
Psychological Bulletin, 102, 53-71.
Direct reprint requests to:
Suling Cheng
Department of Child and Family Studies
California State University
5151 State University Drive
Los Angeles, CA 90032
e-mail: scheng4@calstatela.edu

A Comparison Of Collaborative And Individual Everyday Problem Solving In Younger And Older Adults

Recommended

Recommended

More Related Content

Similar to A Comparison Of Collaborative And Individual Everyday Problem Solving In Younger And Older Adults

Similar to A Comparison Of Collaborative And Individual Everyday Problem Solving In Younger And Older Adults (20)

More from Andrew Parish

More from Andrew Parish (20)

Recently uploaded

Recently uploaded (20)

A Comparison Of Collaborative And Individual Everyday Problem Solving In Younger And Older Adults