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Personality Plasticity After Age 30


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Rank-order consistency of personality traits increases from childhood to age 30. After that, different summaries of the literature predict a plateau at age 30, or at age 50, or a curvilinear peak in consistency at age 50. These predictions were evaluated at group and individual levels using longitudinal data from the Guilford-Zimmerman Temperament Survey and the Revised NEO Personality Inventory for periods of up to 42 years. Consistency declined toward a nonzero asymptote with increasing time interval. Although some scales showed increasing stability after age 30, the rank-order consistencies of the major dimensions and most facets of the Five-Factor Model were unrelated to age. Ipsative stability, assessed with the California Adult Q-Set, also was unrelated to age. These data strengthen claims of predominant personality stability after age 30.
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Personality Plasticity After Age 30
Antonio Terracciano
Paul T. Costa Jr.
Robert R. McCrae
National Institute on Aging, National Institutes of Health,
Department of Health and Human Services
McCrae and Costa (1990) argued that “personality
change is the exception rather than the rule after age 30;
somewhere in the decade between 20 and 30, individuals
attain a configuration of traits that will characterize them
for years to come” (p. 10). Ten years later, two meta-
analyses were published on the rank-order consistency
(or differential stability) of personality traits. Roberts and
DelVecchio (2000) reported that rank-order consistency
increased with age, even in adulthood. They estimated
that 30- and 40-year-olds would show 7-year retest corre-
lations near .60, whereas individuals older than age 50
would show retest correlations more than .70, and they
concluded that recent generations have “stretched the
time it takes to fully develop one’s traits” (p. 18) past age
30. Curiously, another meta-analysis published the same
year (Ardelt, 2000) concluded that rank-order consis-
tency increased up to age 50 but decreased thereafter.
Meta-analyses combine data from different instru-
ments, samples, and historical times and may be subject
to confounds (e.g., studies of 30-year-olds may have used
less reliable instruments than studies of 60-year-olds).
An alternative approach would examine rank-order sta-
bility in different age groups using the same instrument
administered to comparable samples over the same time
interval and in the same historical period. Such a design
was used by Costa, McCrae, and Arenberg (1980), who
examined 6- and 12-year retest coefficients for Guilford-
Zimmerman Temperament Survey (GZTS; Guilford,
Authors’ Note: Paul T. Costa, Jr., and Robert R. McCrae receive royal-
ties from the Revised NEO Personality Inventory. This research was sup-
ported by the Intramural Research Program of the National Institutes
of Health, National Institute on Aging. Correspondence concerning
this article should be addressed to Antonio Terracciano, Box 03,
Gerontology Research Center, 5600 Nathan Shock Drive, Baltimore,
MD 21224-6825; e-mail:
PSPB, Vol. 32 No. 8, August 2006 999-1009
DOI: 10.1177/0146167206288599
In the public domain
Rank-order consistency of personality traits increases from
childhood to age 30. After that, different summaries of the lit-
erature predict a plateau at age 30, or at age 50, or a curvi-
linear peak in consistency at age 50. These predictions were
evaluated at group and individual levels using longitudinal
data from the Guilford-Zimmerman Temperament Survey and
the Revised NEO Personality Inventory for periods of up to
42 years. Consistency declined toward a nonzero asymptote with
increasing time interval. Although some scales showed increas-
ing stability after age 30, the rank-order consistencies of the
major dimensions and most facets of the Five-Factor Model were
unrelated to age. Ipsative stability, assessed with the California
Adult Q-Set, also was unrelated to age. These data strengthen
claims of predominant personality stability after age 30.
Keywords: Five-Factor Model; personality development; long-term
stability; individual differences; life span; older adults
Since data from longitudinal studies appeared in the
1970s (e.g., Block, 1977), it has been clear that individ-
ual differences in personality traits are stable through-
out long periods of time. Helson and Wink (1992)
reported typical results in a sample of 101 women ini-
tially age 43 and retested after 9 years: The median
retest correlation for scales from the California
Psychological Inventory (CPI; Gough, 1987) was .73.
There is also considerable evidence that personality
traits are more stable in adults than in adolescents. For
example, Finn (1986) showed that the median 30-year
retest correlation for factors from the Minnesota
Multiphasic Personality Inventory (MMPI; Hathaway &
McKinley, 1943) was .35 for respondents initially age
17 to 25 and .56 for respondents initially age 43 to 53.
However, there is disagreement about the degree of
rank-order consistency in different portions of adulthood.
Based on their own research and review of the literature,
Zimmerman, & Guilford, 1976) scales in male partici-
pants in the Baltimore Longitudinal Study of Aging
(BLSA; Shock et al., 1984). The mean uncorrected sta-
bility coefficients for initially young (17-44), middle-age
(45-59), and old (60-85) men were .76, .77, and .75,
respectively, over 6 years, and .72 .75, and .73, respec-
tively, over 12 years. Similar results were reported by
Costa and McCrae (1988) in a 6-year study of men and
women assessed with the NEO Personality Inventory
(Costa & McCrae, 1985) and by McCrae (2001), who
analyzed 6-year retest data in spouse ratings and 7-year
retest data in peer ratings of personality. All of these
studies suggest that personality is quite stable among
young and old adults as well as those in midlife.
The present article updates these studies of BLSA
participants using longer retest intervals on the GZTS
and new data from the Revised NEO Personality
Inventory (NEO-PI-R; Costa & McCrae, 1992b). In addi-
tion, longitudinal self-sorts on the California Adult
Q-Set (CAQ; Bem & Funder, 1978; Block, 1961) are
examined to determine the effect of initial age on the
ipsative stability of personality.
The BLSA has both strengths and limitations as a
sample in which to test these hypotheses. It is a large
sample that has followed some participants for more
than 40 years, and the well-educated and cooperative
respondents provide data of high quality. However, they
are clearly not representative of the population as a
whole. In particular, the sample is biased toward older
age, making it less than optimal for examining stability
in the earliest decade of adulthood. There is, however,
general agreement that personality is less stable before
30 than after; the present study therefore examines sev-
eral forms of stability after age 30, where competing
predictions have been made. Group-level analyses (test-
retest correlations) facilitate comparisons with previous
studies and can address the hypothesis that the degree
of stability is related to age. However, we also examine
stability at the individual level, computing a measure of
stability for each individual that can be used in multi-
level analysis to define in greater detail the effect of age
on stability while controlling for other variables.
Fraley and Roberts (2005) have recently emphasized
the importance of a second issue in studying stability or
change in rank-order consistency across the life span:
the shape of the function relating consistency to retest
interval. Retest correlations are typically lower over
longer retest intervals—for example, the median retest
correlation for GZTS scales after 6 years was .77 and
after 24 years it was .65 (Costa & McCrae, 1992c)—and
researchers have typically assumed an exponential decay
of consistency (e.g., Conley, 1984). Using that model,
Costa and McCrae (1992c) projected that the median
stability of GZTS true scores after 50 years would be .60
and concluded that “about three-fifths of the variance in
personality traits is stable across the full adult age range”
(p. 182).
Fraley and Roberts (2005) pointed out that the expo-
nential decay model predicts that eventually rank-order
consistency will decline to zero unless there are devel-
opmental constancy factors (such as one’s unchanging
genetic endowment) that force consistency to a
nonzero asymptote. Such a model is testable only with
data from multiple administrations with widely varying
retest intervals. GZTS data in the BLSA include retest
intervals as long as 42 years and offer a rare opportunity
to test the long-term exponential decay of consistency.
Most previous analyses of differential stability have
examined retest correlations within different age groups.
Roberts and DelVecchio (2000) suggested that these cor-
relations are higher among adults older than age 50 than
among younger adults, including those in their 30s and
40s. Ardelt (2000) reported a curvilinear relation to age,
with stability coefficients peaking around age 50. To test
those hypotheses, we divided the BLSA sample by initial
age into groups age 30 to 50, 50 to 65, and 65+. McCrae
and Costa (1990) would expect no differences in rank-
order consistency between these groups, Roberts and
DelVecchio (2000) would hypothesize higher consistency
in the two older groups, and Ardelt (2000) would predict
higher consistency in the middle group than in the oldest
group. We analyze data from the GZTS and the NEO-PI-R
and, to maximize time interval, we also examine data
from the NEO Inventory, a precursor of the NEO-PI-R.
Previous analyses (Costa & McCrae, 1988) showed
no systematic differences between men and women of
different age groups on retest stability in NEO-PI scales,
but no such comparisons have been made for the
GZTS. We therefore report GZTS data separately for
men and women in this study.
Too few individuals (n = 19) retested on the NEO-PI-R
were initially younger than age 30 to study that group,
but larger numbers of adults younger than age 30 were
retested with the GZTS, and we report supplementary
analyses on that instrument to test the common view
that stability is lower among adults younger than 30.
Participants and procedure. The BLSA is a multidisci-
plinary study of normal aging. Participants have agreed
to return for repeated assessments of biomedical and
psychosocial variables. Recruitment has been continu-
ous throughout the course of the study, so long-term
participants have been assessed more frequently than
more recently recruited participants. GZTS data were col-
lected during regularly scheduled visits, starting for men
in October 1958 and for women in January 1978 and con-
tinuing until May 2002. The GZTS was administered to all
participants at their first or second visit and, subsequently,
approximately every 6 and then 12 years. Retest stability
throughout 6 and 12 years has previously been reported
for GZTS scales in the BLSA sample (Costa et al., 1980;
Costa & McCrae, 1992a, 1998). The present study exam-
ined data for individuals whose first and last administra-
tions were separated by at least 6 and as much as 42 years.
The NEO-PI-R was administered by computer
between September 1989 and July 2004 during regu-
larly scheduled visits (Terracciano, McCrae, Brant, &
Costa, 2005). To obtain long-term stability coefficients,
retest correlations were computed between the first
and last administration for individuals with a time inter-
val between administrations of at least 6 years. Note that
there is no overlap between these data and those used
in Costa and McCrae’s (1988) study of the NEO-PI; fur-
thermore, most of the individuals who took the NEO-
PI-R (about 60%) joined the BLSA after the data
collection for the earlier study.
Finally, to maximize the time interval, we also com-
pared domain scores from individuals who had com-
pleted the NEO Inventory by mail in 1980 (McCrae,
1982) with domain scores from their latest administra-
tion of the NEO-PI-R. A description of the subsamples
that completed each instrument is given in Table 1.
Measures. The GZTS (Guilford et al., 1976) is a factor-
based personality questionnaire composed of 300 items,
30 for each of the 10 GZTS scales. For each item, par-
ticipants choose between yes, no, and ?. Any scale with
more than three ? responses was considered missing, a
procedure suggested by Guilford and colleagues.
Therefore, there are small variations in the number of
participants for different scales. Data were standardized
as T-scores using the grand means and standard devia-
tions across all administrations. In the BLSA (McCrae,
Costa, & Arenberg, 1980), the structural stability of the
GZTS has been shown across age, cohort, and time of
The NEO-PI-R (Costa & McCrae, 1992b), designed to
measure the Five-Factor Model (FFM) of personality,
consists of 240 items answered on a 5-point Likert format
ranging from strongly disagree to strongly agree. The NEO-
PI-R assesses 30 facets, 6 for each dimension of the FFM:
Neuroticism (N), Extraversion (E), Openness to Experi-
ence (O), Agreeableness (A), and Conscientiousness
(C). T-scores for facets are standardized scores (M = 50,
SD = 10) based on combined-sex adult norms in the man-
ual; factor scores combining information from each of
the 30 facets also are expressed as T-scores. Evidence on
reliability and validity is presented in the manual (Costa
& McCrae, 1992b). The NEO Inventory was a precursor
of the NEO-PI-R that assessed only N, E, and O domains.
Revisions introduced with the NEO-PI-R changed 10 of
the 144 N, E, and O items, but these minor changes are
unlikely to distort the long-term stability of the scales and
should have no effect on the comparison of age differ-
ences in consistency.
Baseline comparison. We compared individuals included
in the rank-order correlation analyses (see Tables 2 and
3) with those who had taken the GZTS or NEO-PI-R at
least once but were not included because they had
dropped out of the study, had a retest interval of less than
6 years, were younger than 30 at their first visit, or had not
been in the study long enough to be retested. For the
GZTS, those excluded were about 2 years younger, some-
what less educated, and slightly more likely to be women.
After controlling for those differences, the excluded
respondents scored significantly lower on Emotional
Stability, Friendliness, Thoughtfulness, and Personal
Relations, but none of the differences exceeded about 0.4
standard deviations in magnitude. Similarly, those
excluded from the NEO-PI-R analyses were younger, less
educated, and more likely to be women; after controlling
for those differences, respondents who were excluded
were about 2 T-score points higher on O. In general, it
appears that respondents included in the analyses were
similar to BLSA participants in general.
Results and Discussion
Retest correlations from first and last administra-
tions in the total sample and in various age groups are
reported in Table 2 for the GZTS and in Table 3 for the
NEO-PI-R. Retest intervals for each group are given at
the bottom of each column. All correlations are signifi-
cant, with GZTS scales and NEO-PI-R facets near .70
and NEO-PI-R factors near .80, consistent with previous
literature. The coefficients in Tables 2 and 3 are under-
estimates of the true score stability given that personality
Terracciano et al. / PERSONALITY PLASTICITY 1001
TABLE 1: Description of the Samples
N Initial Age 1st Administration
Instrument Men Women M Range M Range
GZTS, men 737 50.9 30-87 1969 1959-1996
GZTS, women 326 52.8 30-82 1983 1978-1996
NEO-PI-R 367 309 60.6 30-89 1992 1989-1998
NEO Inventory 186 71 51.7 30-80 1980
CAQ 322 195 48.2 30-83 1986 1981-1994
NOTE: Initial age is given in years. GZTS = Guilford-Zimmerman Tem-
perament Survey; NEO-PI-R = Revised NEO Personality Inventory;
CAQ = California Adult Q-Set.
scales are not perfectly reliable. To correct for attenuation
of retest coefficients due to measurement error, esti-
mated stability coefficients can be obtained by dividing
the observed coefficients by the short-term retest relia-
bility. Short-term (e.g., 2-week) retest reliability coeffi-
cients have not been reported for the full NEO-PI-R,
but using the 2-year retest correlations reported by
McCrae, Yik, Trapnell, Bond, and Paulhus (1998), cor-
rected stability coefficients for the five factors of the
NEO-PI-R were .94, .91, .96, .92, and .92 for N, E, O, A,
and C, respectively. Throughout a 2-year interval, some
real change may have occurred, so the McCrae et al.
(1998) values underestimate reliability and lead to an
overcorrection: These corrected coefficients might be
interpreted as upper-bound estimates of the true stabil-
ity of personality traits.
Heise (1969) proposed an alternative method to esti-
mate true score stability coefficients that can be used
when data from three administrations are available. We
applied it in a subsample of 520 individuals with at least
three administrations and a minimum of 6 years
between first and last administration. In this subsample,
the mean interval between the first and the intermedi-
ate administration was 5.3 years (SD = 1.9); between the
first and the last it was 10.1 years (SD = 2.3). Heise’s for-
mula calculates retest stability as s
= r
* r
which yielded coefficients of .91, .89, .95, .90, and .95
for N, E, O, A, and C factors, respectively.
TABLE 2: Rank-Order Consistency Coefficients for GZTS Scales
for the Full Sample and Three Age Groups
Age Group
Scale/Factor Total 30-50 50-65 > 65
General activity .75 .71 .78 .75
Restraint .68 .62 .70 .72
Ascendance .78 .77 .77 .79
Sociability .73 .68
.79 .74
Emotional stability .64 .59
.70 .66
Objectivity .67 .63 .72 .68
Friendliness .66 .63 .71 .68
Thoughtfulness .65 .60
Personal relations .67 .66 .72 .68
Masculinity .70 .67 .74 .69
Mdn .68 .65 .73 .69
n 600-682 315-350 189-215 96-117
M retest interval 16.6 (6-42) 20.2 (6-41) 14.7 (6-37) 9.5 (6-21)
General activity .76 .69
.82 .78
Restraint .70 .72 .63 .75
Ascendance .78 .72
.85 .81
Sociability .78 .75 .82 .80
Emotional stability .69 .59
.76 .76
Objectivity .69 .67 .67 .77
Friendliness .66 .64 .57
Thoughtfulness .71 .69 .70 .75
Personal relations .68 .66 .69 .72
Masculinity .76 .68
.82 .83
Mdn .71 .69 .73 .77
n 252-305 116-139 82-101 54-68
M retest interval 10.5 (6-24) 10.8 (6-24) 11.2 (6-21) 9.1 (6-15)
NOTE: All coefficients are significant at p < .001. GZTS = Guilford-
Zimmermann Temperament Survey.
a. Correlation for age 30-50 differs from correlation for age 50-65, p < .05.
b. Correlation for age 50-65 differs from correlation for age > 65, p < .05.
c. Correlation for age 30-50 differs from correlation for age > 65, p < .05.
TABLE 3: Rank-Order Consistency Coefficients for NEO-PI-R
Scales for the Full Sample and Three Age Groups
Age Group
Total 30-50 50-65 > 65
Factor/Facet (n = 676) (n = 151) (n = 259) (n = 266)
N: Neuroticism .78 .79 .79 .78
E: Extraversion .83 .84 .85
O: Openness .85 .82 .87 .86
A: Agreeableness .80 .79 .80 .80
C: Conscientiousness .81 .76 .82 .83
Mdn .81 .79 .82 .80
N1: Anxiety .71 .76 .69 .71
N2: Angry hostility .66 .68 .63 .69
N3: Depression .62 .62 .63 .64
N4: Self-consciousness .67 .64 .71 .65
N5: Impulsiveness .62 .59 .62 .64
N6: Vulnerability .65 .61 .65 .67
E1: Warmth .71 .73 .71 .70
E2: Gregariousness .77 .81
E3: Assertiveness .79 .76 .80 .80
E4: Activity .74 .68 .74 .73
E5: Excitement-seeking .74 .71 .77 .71
E6: Positive emotions .71 .64 .73 .71
O1: Fantasy .73 .70 .73 .71
O2: Aesthetics .82 .81 .83 .81
O3: Feelings .69 .75
.65 .64
O4: Actions .73 .71 .72 .73
O5: Ideas .79 .78 .78 .80
O6: Values .68 .56
.67 .73
A1: Trust .67 .65 .71 .64
A2: Straightforwardness .66 .61 .71 .63
A3: Altruism .65 .60 .64 .69
A4: Compliance .71 .70 .72 .70
A5: Modesty .70 .72 .69 .71
A6: Tender-mindedness .64 .68 .66 .61
C1: Competence .66 .61 .66 .64
C2: Order .75 .75 .76 .74
C3: Dutifulness .57 .59 .55 .58
C4: Achievement striving .72 .68 .75 .70
C5: Self-discipline .70 .65 .70 .72
C6: Deliberation .67 .63 .71 .65
Mdn .70 .68 .71 .70
M retest interval 10 (6-15) 10.1 (6-15) 10.3 (6-14) 9.6 (6-14)
NOTE: All coefficients are significant at p < .001. NEO-PI-R = Revised
NEO Personality Inventory.
a. Correlation for age 50-65 differs from correlation for age > 65, p < .05.
b. Correlation for age 30-50 differs from correlation for age > 65, p < .05.
Of primary interest are the comparisons of the last
three columns of Tables 2 and 3. Consistent with Roberts
and DelVecchio’s (2000) report, adults age 30 to 50
showed significantly lower rank-order consistency (using
Fisher’s z test) than adults older than age 50 on GZTS
Sociability, Emotional Stability, and Thoughtfulness in
men and on General Activity, Ascendance, Emotional
Stability, and Masculinity in women, with small effect
sizes ranging from q = .10 to .17 (q = | Fisher’s z
Cohen, 1988). The hypothesis from Ardelt (2000) that
stability declines after age 50 was supported for
Thoughtfulness in men (q = .13), but women initially
older than age 65 were more rather than less consistent
than women age 50 to 65 on Friendliness (q = .20). No
significant differences were found for GZTS Restraint,
Objectivity, or Personal Relations. These data thus pro-
vide mixed support for the hypothesis of continued
increase in rank-order consistency between 30 and 50
but no consistent support for the hypothesis that stability
declines after age 50. Furthermore, the mean retest
interval is substantially longer for the male group age 30
to 50 compared to the other groups (see Table 2), which
has the effect of reducing the retest correlation coeffi-
cients and might explain some of the above effects.
Analyses at the individual level will address this issue.
Among the five NEO-PI-R factors, significant differ-
ences among groups can be seen only for Extraversion,
with adults age 50 to 65 showing higher consistency
than adults older than age 65 (q = .06). Among the 30
NEO-PI-R facets, the group older than age 65 showed
lower rank-order stability on E2: Gregariousness (q = .13)
and O3: Feelings (q = .11) but higher on O6: Values
(q = .16). For the remaining 27 facets, no significant
effects were found, providing no support for the
hypotheses that rank-order stability of individual differ-
ences varies systematically with age after age 30.
In 1980, the NEO Inventory was completed by 257
respondents age 30 and older who subsequently took
the NEO-PI-R an average of 19.0 years later (range = 9-
24 years). Rank-order consistency for the N, E, and O
domains were .73, .74, and .77, respectively, for the full
group. Not surprisingly, too few of these individuals
were initially older than age 65 to allow meaningful
analyses. The 112 respondents initially age 30 to 50 had
retest correlations of .76, .74, and .81 for N, E, and O,
respectively, whereas the 145 respondents initially older
than age 50 had retest correlations of .71, .74, and .74.
These data provide no support for the view that long-
term stability is higher among those older than 50.
We conducted a supplementary analysis in which we
examined rank-order consistency for GZTS scales in
respondents initially younger than 30 years old (ns = 101-
117 men, 39-46 women) and retested after an average of
17.9 (men) or 10.2 (women) years. As expected, they
tended to show lower retest correlations, ranging from .38
to .73, with median values of .58 in men and .64 in women.
One limitation of the retest correlation analyses is
that the retest interval differed for different age groups,
and consistency generally declines with longer retest
intervals. It would be preferable to control statistically
for the retest interval, but the optimal form of that con-
trol depends on the shape of the function relating rank-
order consistency to retest interval. Is decline linear,
exponential, or does it approach a nonzero asymptote?
Fraley and Roberts (2005) developed a model based
on retest correlations in groups, a strategy that was fea-
sible for them because their meta-analysis provided
coefficients from many samples. In our single sample,
we can address these issues by conducting analyses at
the individual level using Asendorpf’s (1992) individual
stability coefficients, defined for each individual as
1 – [(z
where z
and z
are scores for a trait standardized across
the full sample at the first and second administrations.
Note that the use of z-scores eliminates the effects of
mean-level differences. The mean of Asendorpf’s coef-
ficient across all respondents is equal to the retest cor-
relation, so each coefficient represents the individual’s
contribution to overall rank-order consistency.
Hierarchical regression analysis with Asendorpf’s indi-
vidual stability coefficients as dependent variable was per-
formed on the combined male and female GZTS sample
(n = 1,063), controlling for initial age at Step 1 (which
had minor effects) and then introducing time interval
and its square at Steps 2 and 3. As in Table 2, analyses
were limited to respondents initially older than age 30,
with a minimum retest interval of 6 years. All 10 GZTS
scales showed some decline in individual stability with
increasing retest interval, but it was significant only for
General Activity, Ascendance, Sociability, and Personal
Relations, which all declined at a decelerating rate. For
these four scales, linear and quadratic terms explain
between 0.6% and 2.3% of the variance in individual sta-
bility beyond that accounted for by initial age.
Because all GZTS scales showed a similar form of
decay, we created an average individual stability score
(Cronbach’s α = .58) across the 10 GZTS scales. As
shown in Figure 1, this mean was best predicted by a con-
cave curve; the linear and quadratic terms accounted for
2.7% of the variance. It is notable, however, that the
curve does not resemble exponential decay; instead of
Terracciano et al. / PERSONALITY PLASTICITY 1003
tending toward zero consistency, it turns up after a retest
interval of about 20 years. This should not be interpreted
as evidence that stability increases with longer intervals;
it is instead simply a quadratic approximation to a
plateau. In the language of Fraley and Roberts (2005),
developmental constancy factors seem to operate such
that retest stability of GZTS scales does not decline fur-
ther after retest intervals exceed 20 years; there appears
to be a nonzero asymptote.
Exponential decay to a nonzero asymptote can be
modeled with the Nonlinear Estimation program of
Statistica (SoftStat, 1995) using the equation
Individual Stability = c + (b
* t
where c is the asymptote, b
and b
are exponential
decay coefficients, and t is the time interval in years. It
is clear from Figure 1 that c must be between .6 and .7,
and we varied it systematically across this range. The
optimal value was .655, leading to the equation
Individual Stability = .655 + (.413
–.187 * t
which is plotted in Figure 1. This model accounted for
3.0% of the variance in mean individual stability scores,
somewhat more than the quadratic model. Because the
mean of the individual stability coefficients is equal to
the retest correlation, the asymptote also can be regarded
as an estimate of the long-term stability for GZTS scales:
The lower bound for observed coefficients is about .65,
and (using reliability estimates from Costa et al., 1980)
the lower bound for true scores is close to .80.
The retest correlations in Tables 2 and 3 are easily
understood but they are not optimal tests of the effects
of age on rank-order consistency for two reasons. First,
they utilize only a portion of the available data, namely,
the first and last administrations for individuals whose
retest interval is at least 6 years. Second, they do not
control for the time interval. For example, the retest
interval for the GZTS was substantially longer in men
age 30 to 50 (M = 20.2 years) than in men older than 65
(M = 9.5 years), and stability declines over longer time
intervals, at least up to 20 years.
To address these problems, for each scale we calcu-
lated a measure of individual consistency, the stan-
dard deviation for each individual across all available
administrations. Because SDs are a measure of incon-
sistency, we reflected the values (1–SD) to obtain an
absolute measure of consistency.
Note that this is a
conservative measure of rank-order stability because
normative changes in mean level, which do not affect
rank order, do lower these consistency scores. Mean
level changes, however, are generally modest in these
data (Terracciano et al., 2005; Terracciano, McCrae, &
Costa, 2006). All respondents with at least two data
points were included in this analysis, and data from
more than two administrations were used when avail-
able to obtain a best estimate of individual consis-
tency. We also calculated the maximum retest interval
as a control variable.
Analyses of individual consistency were based on
3,281 administrations of the GZTS from 1,194 individu-
als and 4,217 administrations of the NEO-PI-R from the
1,178 individuals who had at least two administrations.
Note that all the data reported in Tables 2 and 3 are
included here, but they are supplemented by data from
additional administrations and retest intervals outside
the limits used in the retest correlation analyses. Examina-
tion of scatterplots showed a small number of outliers
(from 0 to 6 per scale), which were recoded as 3 standard
deviations above the mean. For each scale, we conducted
hierarchical multiple regressions in which we successively
5 1015202530354045
Time interval (years)
Asendorpf’s (1992) individual stability
Figure 1 Mean individual stability coefficients (N = 1,063) across 10
GZTS scales as a function of retest interval in years.
NOTE: Significant quadratic (dotted line; R
= .027) and exponential
decay (solid line; R
= .030) curves are superimposed. Data from 15
individuals whose mean coefficients are less than 0.0 do not appear in
the figure but were used in the analyses. GZTS = Guilford-Zimmerman
Temperament Survey.
entered maximum retest interval and interval-squared,
age, and age-squared as predictors.
Because gender does not have systematic effects on
either rank-order consistency or the relation of rank-
order consistency to age (e.g., Table 2), data from men
and women were combined. Only data from respon-
dents initially older than age 30 were included.
Results and Discussion
Maximum retest interval and its square were entered
as a block that was a significant predictor of consistency
for all GZTS scales and NEO-PI-R factors, accounting
for 0.4% to 4.1% of the variance in individual consis-
tency scores. As expected, longer retest intervals were
modestly associated with lower consistency.
In the next block, age was a significant linear predictor
of stability for five GZTS scales: Consistency on Restraint,
Ascendance, and Personal Relations increased cross-
sectionally with age, whereas consistency on General
Activity declined.
All of these effects were less than 1%,
accounting for 0.4% to 0.7% of the variance in consis-
tency scores. There were no significant linear age effects
on Emotional Stability, Objectivity, Friendliness,
Thoughtfulness, or Masculinity. Only Sociability showed
significant curvilinear effects, accounting for 0.7% of the
variance in consistency scores.
The only GZTS scale that showed increasing consis-
tency with older age, at both the group (Table 2, women
only) and individual level (Analysis 3), was Ascendance.
Multiple regression showed that individual consis-
tency of the five NEO-PI-R factors was unrelated to age
or to age-squared. After age 30, consistency appears to
have reached a plateau, a result at the individual level
that confirms the group-level findings. Of the 30 NEO-
PI-R facets, only E3: Assertiveness, which is strongly cor-
related with Ascendance (Terracciano et al., 2006),
showed a significant linear increase in consistency,
explaining 0.4% of variance. Curvilinear trends were
found for O2: Openness to Aesthetics, which increased
up to age 60, and for O4: Openness to Actions and A4:
Compliance, which both showed the lowest consistency
values in middle adulthood. Age and age-squared com-
bined accounted for 0.4% to 1.0% of the variance.
Ipsative stability (or “person centered continuity”;
Caspi & Roberts, 2001, p. 52) refers to the stability of
the configuration of personality traits in each individ-
ual. Concern for ipsative stability or change was inau-
gurated by Block (1971), who believed that it more
adequately captured the integrated functioning of traits
within the individual. The interpersonal activities of a
sociable person may be inhibited by self-consciousness;
a small change in the relative balance of these two dis-
positions might lead to large changes in social behavior.
From childhood to adolescence, ipsative stability
appears to be rather limited, and there are substantial
individual differences (Caspi & Roberts, 2001).
Ipsative stability is most frequently assessed using lon-
gitudinal Q-sort data. Q-sort instruments require that a
set of items be ordered from most to least characteristic
of the person, usually with a fixed distribution. Block’s
(1961) CAQ was originally intended for use by expert
raters but was modified by Bem and Funder (1978) for
use as a self-sort. Ipsative stability is usually quantified as
a Q- or inverse correlation, that is, the correlation for
each individual of first and second sort across the 100
items. Costa, McCrae, and Siegler (1999) reported
Q-correlations for 273 BLSA participants retested on
average after 6.6 years. These values ranged from .12 to
.86, with a median of .71 for men and .72 for women. In
the present study, we report data from an expanded
BLSA sample, stratified by age group.
Q-correlations based on raw CAQ data represent the
degree to which individuals report the same ordering of
characteristics across two time points and, to the extent
that the reports are accurate, gives a straightforward esti-
mate of the stability of the trait configuration. However,
there is a sense in which such Q-correlations are inflated
because the items of the CAQ have different normative
values. At any given time, almost all individuals are likely
to claim that “is genuinely dependable” is more charac-
teristic of them than “is guileful, deceitful” (McCrae,
Terracciano, Costa, & Ozer, 2006). Because of these dif-
ferences in item desirability, Q-correlations between any
two people are usually positive and often substantial
(Ozer & Gjerde, 1989). It is not clear from an analysis of
raw CAQ items how much of the observed stability is
due to the relative permanence of trait configurations in
the individual and how much is due to enduring social
norms about item endorsement.
McCrae et al. (2006) therefore recommended that
CAQ items first be standardized across persons to remove
differences due to item endorsement norms. Q-correla-
tions can then be calculated on the standardized items,
and age differences in standardized ipsative stability can
be examined. These values are likely to be substantially
less than the median values of .70 typically reported in
adult samples because they exclude stability attributable
to enduring social norms of item endorsement.
However, lower standardized ipsative stability coeffi-
cients would not necessarily imply that there are major
changes in the configuration of traits across adulthood
because single items are apt to be unreliable. More reli-
able assessments of personality are given by factors from
Terracciano et al. / PERSONALITY PLASTICITY 1005
the CAQ items, which can be interpreted in terms of the
dimensions of the FFM. Q-correlations can be com-
puted across these five factor scores on two occasions.
Participants and procedures. BLSA participants com-
pleted the Bem and Funder (1978) modification of
Block’s (1971) CAQ during their regular visits to the
Gerontology Research Center. Sorting 100 items into
nine fixed categories is a challenging task, especially for
older participants, and a first longitudinal analysis
(Costa et al., 1999) suggested that a few participants
had been confused about the direction of the sort,
putting most characteristic items in the least characteristic
bin. In subsequent analyses (McCrae et al., 2006), we
correlated each Q-sort with the normative values of the
100 items, defined by their means across all administra-
tions. Of 2,289 administrations, 31 showed a negative
correlation with the normative values and were dis-
carded. The CAQ was readministered approximately
every 6 years; for the present study, we analyzed data
from the first two administrations, with a minimum retest
interval of 4 years. Characteristics of this subsample are
given in the last line of Table 1. A comparison of indi-
viduals with and without a second CAQ sort showed
that those not retested were more likely to be women
(50% vs. 38%), were initially tested about 6 years later,
and curiously, were about one-eighth standard devia-
tion higher in Conscientiousness. There were no dif-
ferences on the other personality factors.
Instrument. The CAQ was developed to provide a
comprehensive description of personality traits, with a
particular emphasis on clinical description (Block,
1961). In the self-sort version, respondents arrange the
100 items into nine categories, with a fixed number of
items in each, approximating a normal distribution.
Analyses of some of the first administration data in the
BLSA (McCrae, Costa, & Busch, 1986) showed that the
CAQ included items from all five factors of the FFM
and that a five-factor structure captured most of the
common variance in CAQ items (cf. Lanning, 1994).
Five varimax-rotated factors were extracted from the
intercorrelation of the CAQ items on both occasions,
yielding the expected N, E, O, A, and C factors. Retest
correlations for these factors ranged from .70 for C to
.80 for E. Q-correlations across the two occasions were
computed across these five factor scores.
Results and Discussion
In this subsample of BLSA participants, Q-correla-
tions based on raw CAQ items ranged from –.06 to .89;
the median was .70 for men and .73 for women. Table 4
reports mean ipsative stability coefficients for the total
and by age groups. The rows report results for raw CAQ
items, standardized CAQ items, and CAQ factors. As
expected, the standardized items showed lower levels of
ipsative stability than the raw items because stability
coefficients are not inflated by item response norms.
The clearest evidence of ipsative stability is given by the
analysis of the more reliable CAQ factors, which are not
inflated by item response norms, and provide a mea-
sure of the degree to which the relative ordering (i.e.,
profile) of the five factor scores within an individual
persists over time.
Of primary interest here are the comparisons of
ipsative stability across age groups. Preliminary analyses
including gender as a classifying factor showed that
there were no Gender × Age Group interactions, so fur-
ther analyses were conducted on the combined sample.
Ipsative stability based on standardized CAQ items was
significantly higher in middle-age men and women
than in older adults, which is consistent with Ardelt’s
(2000) claim that stability declines after age 50. Here, age
accounted for 1.8% of the variance in Q-correlations.
However, there were no significant age differences
between younger and middle-age adults, and there
were no significant differences between any of the
groups when ipsative stability was based on raw CAQ
items or CAQ factors.
Results were unchanged when
retest interval was used as a covariate.
The retest correlations in Tables 2 and 3 confirm the
well-established fact that the rank-order consistency of
personality traits in adulthood is quite high. For the
total group, the median retest correlation across all
scales is .70. This value is almost as high as that esti-
mated by Roberts and DelVecchio (2000) for their most
stable age group (.75 at age 50-59), despite the fact that
the retest interval in the present study ranged from 10
to 16 years, whereas Roberts and DelVecchio’s was only
TABLE 4: Mean Q-Correlations for Three Age Groups
Age Group
Q-Correlation for Total 30-50 50-65 > 65
Raw CAQ items .69 .69 .70 .68
Standardized .45 .45 .46
CAQ items
CAQ factors .72 .71 .73 .75
n 463 245 138 80
M retest interval 6.7 (4-11) 6.9 (4-11) 6.6 (5-10) 6.4 (4-10)
NOTE: CAQ = California Adult Q-Set.
a. Groups are significantly different by Scheffé post hoc test (p < .05).
6.7 years. High rank-order consistency in adulthood is
equally characteristic of men and women.
Consistency as a Function of Age
The focus of interest in the present study was the rela-
tion of age to rank-order consistency after age 30. McCrae
and Costa (1990) and meta-analyses by Roberts and
DelVecchio (2000) and Ardelt (2000) all suggested that
stability should be lower in the decade of the 20s than in
later age periods, and that view was supported by supple-
mentary analyses of the GZTS scales. The main issue the
present study hoped to resolve was whether rank-order
consistency reached a plateau by age 30 or continued to
increase after that age. The evidence was mixed: For
some GZTS scales, there were significant differences
between retest correlations for respondents younger than
and older than 50, and in every case, the lower correla-
tions were found in the younger group. However, most
scales, including all NEO-PI-R factors and most facets,
failed to show significant differences between these age
groups. Analyses of individual consistency, which con-
trolled for retest interval, provided partial support for
Roberts and DelVecchio’s (2000) hypothesis of increasing
consistency for four GZTS scales but not for the other
GZTS scales or for any of the NEO-PI-R factors.
Ardelt (2000) reported a curvilinear relation of age to
stability, with declines after age 50. We found no support
for that view, despite the large number of old and very old
participants in the BLSA. Provided that they remain cog-
nitively intact (Siegler et al., 1991), older individuals show
remarkable rank-order consistency in personality traits.
Adults of all ages also show considerable ipsative sta-
bility of personality. There were no consistent age dif-
ferences in ipsative stability, and the most accurate and
reliably assessed measure, based on the profile of five
factor scores derived from the CAQ, showed no signifi-
cant age differences. The stable “configuration of
traits” predicted by McCrae and Costa (1990, p. 10) was
clearly found here for adults of all ages.
All of the indicators of rank-order stability are lowered
by unreliability, and several previous studies (e.g., Costa
et al., 1980; Costa & McCrae, 1992c) have demonstrated
that disattenuation for retest unreliability substantially
increases coefficients, with estimates of the true score sta-
bility as high as .87 throughout a 24-year interval (Costa
& McCrae, 1992c). Personality traits are indeed enduring
dispositions. Nevertheless, it is also true that these esti-
mated consistency coefficients rarely attain 1.0. The pre-
sent analyses of retest interval confirmed earlier findings
that stability decays slowly with the passage of time
(although never approaching zero). These declines in
consistency may have two sources: They may represent
small accumulated effects due to random processes that
affect all individuals, such as the gradual atrophy of the
brain (Resnick, Pham, Kraut, Zonderman, & Davatzikos,
2003), or they may be due to the presence in the sample
of a small number of individuals who show substantial
personality change, perhaps as a result of traumatic
events or an episode of depression. (Costa & McCrae,
1994, described these two kinds of change as the “crum-
bling” and “cracking” of the set plaster of personality.)
The existence of a small subset of individuals with signifi-
cant change can be seen in Figure 1 and has been sug-
gested by HLM analyses of NEO-PI-R and GZTS data,
where significant individual variations in longitudinal
slopes were found (Terracciano et al., 2005; Terracciano
et al., 2006). In this study, we used a novel, simple,
absolute measure of consistency at the individual level
that could be related to biological and environmental
variables in future studies.
The present data provide at least some support for
the view that there are subtle increases in rank-order
consistency between the ages of 30 and 50, especially for
measures of ascendance or assertiveness. Roberts and
DelVecchio (2000) interpreted such findings as evi-
dence of cultural change in the century since William
James opined that personality was “set like plaster” after
age 30 (James, 1890/1981, p. 126). Data from that era
are not available and so subtle a change might simply
have escaped James’s notice. Roberts and DelVecchio’s
hypothesis could be tested, however, in cross-cultural
longitudinal studies. If personality development is
paced by the demands and opportunities of the culture,
then stability should be reached more quickly in under-
developed nations, where full adult responsibilities are
assumed at earlier ages. At present, there are no longi-
tudinal studies of personality traits in underdeveloped
nations; testing the Roberts and DelVecchio hypothesis
would be one of many reasons to inaugurate them.
Consistency as a Function of Time Elapsed
Although a decline in stability with increasing time
interval is generally found over relatively short time
intervals (10 years or less), few longitudinal studies have
examined the effect of elapsed time on the consistency
of personality traits over several decades. The exception-
ally long time interval between GZTS assessments was
used to examine the effect of time on stability at the indi-
vidual level. Surprisingly, the expected pattern of contin-
uing exponential decay was not observed: For six of the
GZTS scales, individual stability coefficients were not sig-
nificantly related to the length of the retest interval; for
four scales, and for mean stability, the data were best
modeled by a concave quadratic curve, suggesting a
decelerating pattern of decline up to about a 20-year
interval and then a plateau.
Fraley and Roberts (2005) attributed nonzero asymp-
totic declines in rank-order consistency to developmental
Terracciano et al. / PERSONALITY PLASTICITY 1007
constancy factors such as enduring DNA. They argued
that other factors, including person-environment trans-
actions and stochastic-contextual processes, also were
needed to account for long-term patterns of continuity
and claimed that “a model that excludes the role of envi-
ronmental experiences . . . simply cannot account for
the empirical patterns of stability and change that we
have presented” (p. 71). But their interpretation goes
beyond the data examined. It is true that there must be
sources of medium-term stability that affect trait levels
for periods of a few years and then fade away, but these
sources need not be environmental. Genes are activated
and silenced during the course of development (Fraga
et al., 2005; Plomin, 1986); perhaps a subset of person-
ality-related genes are switched on for several years and
then switched off. The data Fraley and Roberts exam-
ined, like ours, do not speak to this issue.
These GZTS data are certainly not definitive. They are
based on a single instrument, and relatively few of the
respondents (none of the women) had retest intervals
throughout 30 years (see Figure 1). A further complica-
tion is introduced by the fact that many participants had
taken the GZTS more than twice, and repeated “practice”
on the same instrument might conceivably inflate stabil-
ity. However, if these GZTS data accurately reflect the
developmental course of personality traits, then previous
estimates of lifetime stability must be revised upward.
Costa and McCrae (1992c), using a model of exponential
decay, estimated that 60% of true score variance was con-
stant throughout the full 50-year adult life span. The pre-
sent data suggest that perhaps as much as 80% is constant.
1. Fifteen respondents had mean individual stability coefficients
less than 0.0, suggesting major and pervasive changes in personality
trait levels. The largest of these, marked by dramatic decreases in
scales measuring Emotional Stability and Extraversion, was coinci-
dent with an episode of depression at the time of retest (cf. Costa,
Bagby, Herbst, & McCrae, 2005).
2. The correlations of Asendorpf’s (1992) individual stability coef-
ficient with our measure of consistency in the subsample with first
and last administration at least 6 years apart ranged from .67 to .80
for the Guilford-Zimmerman Temperament Survey (GZTS) scales
and Revised NEO Personality Inventory (NEO-PI-R) factors.
3. The declining consistency in General Activity may seem puz-
zling given the high retest correlations for that scale in Table 2. This
effect is probably due to the accelerated decline in the mean level of
General Activity in old age (Terracciano, McCrae, Brant, & Costa,
2005), to which our measure of consistency is sensitive.
4. A supplementary analysis including a group of 54 men and
women age 17 to 30 showed that they did not differ from any of the
older groups on the three measures of ipsative stability.
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Revision accepted March 1, 2006
... Should it stay relatively the same over increasing time periods? Most work with longer testretest intervals (i.e., decades long; Atherton et al., 2021;Jones & Meredith, 1996;Roberts & DelVecchio, 2000;Terracciano et al., 2006) stems from between-person, rank-order stability. Rank-order stability in personality traits increases throughout early and middle adulthood, peaks in midlife, and decreases again in old age and very old age (Ardelt, 2000;Bleidorn & Hopwood, 2019;Briley & Tucker-Drob, 2014;Lucas & Donnellan, 2011;Roberts & DelVecchio, 2000;Wortman et al., 2012). ...
... This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 4 thus leading to lower stability over time McCrae, 1993;Ozer & Benet-Martínez, 2006;Terracciano et al., 2006). Alternatively, it could lead to enhanced stability across time due to increased levels of person-environment fit (Beck & Jackson, 2021a;Caspi & Moffitt, 1993). ...
... Lastly, another important consideration is the length of the testretest intervals. Stability estimates will decrease with longer time intervals (Fraley & Roberts, 2005;Roberts & DelVecchio, 2000;Terracciano et al., 2006). Notably, this factor is important to note not only for comparisons across individuals but also for a single individual. ...
Personality traits are relatively consistent across time, as indicated by test-retest correlations. However, ipsative consistency approaches suggest there are individual differences in this consistency. Despite this, it is unknown whether these differences are due to person-level characteristics (i.e., some people are just more consistent) or exogenous forces (i.e., lack of consistency is due to environmental changes). Moreover, it is unclear whether the processes promoting long-term consistency are the same across people. We examine these two questions using item-level profile correlations across four to nine waves of data with four data sets (N = 21,616) with multilevel asymptotic growth models. Results indicated that there were, on average, high levels of profile consistency. However, there were notable individual differences in initial profile correlation values as well as in changes in levels of consistency across time, indicating that some people are more stably consistent than others. Moreover, the directions of people's trajectories across increasing time intervals suggest that the mechanisms responsible for reinforcing personality consistency vary across people. These effects were typically moderated by age at 30 years old, maturity-related traits, and education level. Overall, findings indicate some people are more consistent than others, such that this stable level of (in)consistency is a dispositional factor. Additionally, individual differences in profile consistency are shaped by different levels of three processes. On average, stochastic factors are not impactful for most individuals, and transactional processes have an important role in increasing consistency for a sizable amount of people-nuances not previously revealed when focusing on rank-order stability. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
... This model proposes that a person's overall "personality" is constructed by the degree to which they experience the core features of openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism (i.e., emotional instability; Costa et al., 2019). Test-retest intervals of 6-15 years have shown little change in the endorsement of the Big Five personality traits over time (Terracciano et al., 2006), providing support for their enduring nature. Furthermore, Big Five personality traits have been found to predict a wide range of positive and negative outcomes across the lifespan, and more recently have begun to be linked to diagnosable personality disorders (DSM-5; American Psychiatric Association, 2013). ...
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As anthropogenic climate change progresses, there is an increasing need for individuals to make appropriate decisions regarding their approach to extreme weather events. Natural hazards are involuntary risk environments (e.g., flooded roads); interaction with them cannot be avoided (i.e., a decision must be made about how to engage). While the psychological and sociocultural predictors of engagement with voluntary risks (i.e., risk situations that are sought out) are well-documented, less is known about the factors that predict engagement with involuntary risk environments. This exploratory study assessed whether mental health (depression, anxiety, and stress symptoms), personality traits, and cultural worldviews combine to predict engagement with involuntary risk, using the situation of floodwater driving. An Australian sample (N = 235) was assessed via questionnaire and scenario measures. Results were analyzed in a binomial logistic regression assessing which individual factors predicted decision-making in a proxy floodwater driving scenario. Agreeableness and gender were individually significant predictors of floodwater driving intention, and four factors (named “affect,” “progressiveness,” “insightfulness,” and “purposefulness”) were derived from an exploratory factor analysis using the variables of interest, though only two (“progressiveness” and “insightfulness”) predicted floodwater driving intention in an exploratory binomial logistic regression. The findings highlight the need for further research into the differences between voluntary and involuntary risk. The implication of cultural worldviews and personality traits in interaction with mental health indicators on risk situations is discussed.
... The Big Few domains are partly stable over time (Terracciano et al., 2006), agreed-upon by informants (De Vries et al., 2008;Connelly & Ones, 2010), and heritable (Briley & Tucker-Drob, 2014;De Vries et al., 2021;Kandler, Richter et al., 2019, Vukasović & Bratko, 2015. They are also pervasivelyalbeit generally weaklycorrelated with a host of life outcomes (Ozer & Benet-Martínez, 2006;Soto, 2019;Stewart et al., 2021;Zettler et al., 2020), and have crossculturally replicable associations with demographic factors like sex and age (Allik et al., 2013;. ...
Most Five-Factor Model questionnaire items contain unique variance that 1) is heritable, stable, and observable; 2) demonstrates consistent associations with age and sex, and 3) is predictive of life outcomes over and above higher-order factors. This is consistent with items indexing a unique level of the personality trait hierarchy—nuances. Extending these findings to the six-trait HEXACO model, we meta-analyzed cross-rater agreement, heritability, and two-year stability using samples from Canada, Croatia, Finland, Germany, the Netherlands, and the United Kingdom. Analyses were conducted on both raw item scores and their unique variance, and estimates were corrected for short-term test-retest reliability. Median cross-rater agreement, heritability and stability estimates were, respectively, .30,.30 and .57 for raw items and .10,.16 and .39 for item residuals. After reliability correction, the respective medians rose to .46 and .25 for cross-rater agreement, .46 and .39 for heritability, and .87 and 1.00 for stability. These results are strikingly consistent with FFM-based findings, providing further evidence that personality nuances constitute a unique personality hierarchy level with trait properties comparable to those of higher-order traits.
... Moreover, when assessing the rank-order stability of a psychological construct, such as relationship satisfaction, it is essential to account for the time lag between assessments. As noted above, theory and empirical findings clearly suggest that rank-order stability is often large when the time lag is short (e.g., one year), but rank-order stability decreases as the time lag increases (e.g., Ardelt, 2000;Fraley & Roberts, 2005;Kuster & Orth, 2013;Terracciano et al., 2006). Specifically, as the time lag increases, rank-order stability typically levels off at medium values, and this nonzero asymptote suggests that there is an enduring component of individual differences in a given construct even across very long periods. ...
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Satisfaction with a romantic relationship often changes over time, and individuals differ in how satisfied they are in their relationship. However, no systematic review is available regarding the stability of individual differences in relationship satisfaction. Therefore, this meta-analysis synthesizes the available longitudinal data on rank-order stability of relationship satisfaction, as a function of age and relationship duration. Analyses were based on 148 samples including 153,396 participants reporting on their relationship over time. Mean age associated with the effect sizes ranged from 19 to 71 years, and mean relationship duration from 3 months to 46 years. On average, individual differences in relationship satisfaction were highly stable over time (r = .76, corrected for attenuation due to measurement error and based on an average time lag of 2.30 years). Rank-order stability varied systematically as a function of age, increasing from young to late adulthood with a slight decline during middle adulthood. Rank-order stability also varied as a function of relationship duration, increasing over the course of the relationship with a slight decline around 20 years of relationship duration. Moderator analyses suggested that relationship transitions shortly before Time 1 and sample type explained variance in rank-order stability. However, except for these two moderators, the pattern of findings was robust across all characteristics tested. In sum, this meta-analysis indicates that relationship satisfaction is a relatively stable construct, with lower stabilities in young adulthood and in the first years after beginning a relationship. This knowledge may stimulate future research on developmental processes within romantic relationships.
... Cependant, la stabilité des traits de personnalité en tant que tels reste très importante tout au long de la vie, y compris dans les périodes les plus concernées par les changements (Terracciano et al., 2006), ce qui rend problématique le fait de les intégrer au sein des soft skills (Caspi et al., 2005) : les traits de personnalité, non ou peu évolutifs, ne peuvent prétendre à être considérés comme des « skills » (Matteson et al., 2016). Nous retrouvons également cette distinction entre traits de personnalité et « skills » dans le modèle de performance de Motowidlo, Borman et Schmidt (1997). ...
The links between soft skills, adaptation and performance are widely recognized, but the psychological mechanisms underlying these links have not yet been examined precisely, especially given that the definition and content of soft skills remain unclear. This research begins by gathering the consensual elements on soft skills to propose an updated definition, emphasizing the fact that they are transversal, non-technical competencies and that they can include an implicit part in their implementation and development, underlining the interest of making them explicit. Then we establish a taxonomy of soft skills based on a systematic review of the literature. This taxonomy allows us to corroborate the potential implicit part of soft skills and to highlight the need to clarify their definition. Combined with a survey of experts in organizational transformation, this study conceptually and empirically documents the links between soft skills and adaptive performance. Our second empirical study highlights the positive impact of a soft skills awareness workshop on self-efficacy and adaptive performance in a sample of employees and managers of a large industrial group undergoing transformation. This study supports also the mediating role of self-efficacy on the impact that soft skills awareness has on adaptive performance. Our third study addresses the indirect positive impact that soft skills awareness can have in supporting self-efficacy in transformational stressful periods. It shows that general self-efficacy and self-efficacy concerning soft skills remain stable, are positively related to positive mood and adaptive performance, and negatively linked to negative mood during a longitudinal study of eight weeks of confinement considered as a period of stress and transformation. This study also highlights the mediating effect of positive affect on the positive impact of self-efficacy on adaptive performance. In conclusion, this work stresses the interest of circumscribing soft skills to competencies in order to keep their conceptual and socio-economic relevance. The possibility of a hierarchic taxonomy of soft skills in order to underscore the functional and structural links of this network of skills is discussed. The overlap between soft skills and other psychological constructs, especially constructs that broaden the scope of intelligence (emotional, social and practical intelligence) are discussed as well. The ways in which soft skills awareness can contribute to reduce students’ and professionals’ soft skills deficit are developed. Finally, the contribution of soft skills to the different dimensions of work performance is conceptually synthesized.
... Adolescence is a stage of rapid and extensive physical, psychological, and biological change (Gray et al., 2012). Personality tends to stabilise by the age of 30 and remain relatively stable thereafter (Terracciano et al., 2006). However, during adolescence, personality, although still relatively stable, can be a little more flexible. ...
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This review focused on three main areas, "Social Media Influencers (SMIs) in Context, The Impact of SMIs on Adolescents, and Consumer Behaviour in a Digital Era – Generation Z in Perspective". This article aims to further the overall understanding of SMIs and outlines the impact of SMIs on adolescents‘ lives. Thus, the main objective of this literature review is to raise awareness within the marketing field about the influence of social media influencers on adolescents and how brands promote their products and content through social media influencers. The review comprised a regular search using electronic journal databases and secondary data from reports, surveys, and empirical research. The main findings from this review are insights about who social media influencers are, how to recognise them, how they impact adolescents‘ lives, how brands and SMIs are partnering.
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L’incertitude, la complexité et l'ambiguïté sont de plus en plus présentes dans nos sociétés. Les transformations s'accélèrent et représentent un défi incontournable pour toutes les organisations, publiques comme privées. La solution est souvent recherchée à travers les outils, les processus et les organigrammes alors qu’il s'agit avant tout d'une démarche commune de femmes et d'hommes. Ainsi, une meilleure compréhension de l'innovation et de la transformation dans les organisations nécessite d’une part de saisir les caractéristiques individuelles, collectives et environnementales des acteurs qui y contribuent, et d’autre part tout particulièrement de comprendre quelles sont les compétences spécifiques des innovateurs. L'objectif de cet ouvrage est de comprendre quelles sont les compétences transversales, ou soft skills, mobilisées par ces hommes et ces femmes afin d’innover et de conduire la transformation de leurs organisations. Fruit de six années de recherche et de l'analyse de 364 profils de managers, les travaux présentés associent les points de vue de la psychologie de la personnalité, des sciences de gestion et de la sociologie. Ces travaux identifient et évaluent les compétences et les contextes de travail qui favorisent ou freinent les innovations et les transformations, à des niveaux individuels comme collectifs. Former les femmes et les hommes, nos talents, à savoir reconnaître et développer leurs soft skills, engager et préparer les équipes à être moteurs de la transformation sont des enjeux majeurs, car vecteurs de croissance, de compétitivité et d’emploi.
Our next topic is that of personality development. In this chapter we will consider the typical person changes in personality through different stages of life. We will also examine the extent to which people's levels of personality characteristics—relative to those of other people of their own age cohort—tend to be stable across long periods of time. In a separate section, we will investigate the same questions in relation to children's personalities, whose measurement poses some special challenges for the researcher.
Personality traits are familiar to laypersons as enduring characteristics of individuals that distinguish them from others. Jane may be exuberant, energetic, and overbearing, whereas Jack is sober, steady, and reserved. Such distinctions are routinely made in everyday life and in literature, and they are facilitated by an enormous vocabulary of trait terms in English and other languages. Trait psychology consists of the scientific study of these characteristics, and trait theories of personality attempt to explain the development and functioning of the person primarily in terms of traits. Personality research is currently dominated by trait approaches, and much has been learned about traits in the past 30 years.
Publisher Summary The dominant paradigm in current personality psychology is a reinvigorated version of one of the oldest approaches, trait psychology. Personality traits are “dimensions of individual differences in tendencies to show consistent patterns of thoughts, feelings, and actions.” In this context, trait structure refers to the pattern of co-variation among individual traits, usually expressed as dimensions of personality identified in factor analyses. For decades, the field of personality psychology was characterized by competing systems of trait structure; more recently a consensus has developed that most traits can be understood in terms of the dimensions of the Five-Factor Model. The consensus on personality trait structure is not paralleled by consensus on the structure of affects. The chapter discusses a three-dimensional model, defined by pleasure, arousal, and dominance factors in which it is possible to classify such state-descriptive terms as mighty, fascinated, unperturbed, docile, insolent, aghast, uncaring, and bored. More common are two-dimensional systems with axes of pleasure and arousal or positive and negative affect. These two schemes are interpreted as rotational variants—positive affect is midway between pleasure and arousal, whereas negative affect lies between arousal and low pleasure.
Costa and McCrae maintain that personality is basically stable after age 30. Other researchers, however, find that personality tends to change over time and that personality stability depends on the stability of the social environment and the instruments used to test personality stability and change. A meta-analysis of 206 personality stability coefficients reported in the literature fails to support personality stability theory. Personality tends to be less stable if the retest interval is large, if age at first measurement is low or over 50, and if a change in individual aspects of personality rather than overall personality is measured. Moreover, studies assessing any of the "big five NEO" personality traits and studies by Costa, McCrae, and colleagues tend to find higher personality stability coefficients. It is suggested that personality stability and change cannot be studied meaningfully without simultaneously examining stability and change in the social environment.
• In the last half-generation or so there has been increased emphasis on an understanding of personality functioning. It is asked what, if anything, is known or agreed to in this field. Is there a typical mother of schizophrenics, for example? In all the talk about the "creative personality" or the "authoritarian personality" just what is meant by these terms? What really is "hysteria"? Doctor Jack Block's monograph introduces the California Q-set—a method for describing comprehensively in contemporary psychodynamic terms an individual's personality. This method for encoding personality evaluation will prove highly useful in research applications by psychiatrists, psychologists, and sociologists, for it permits quantitative comparisons and calibrations of their evaluations of patients. He compares the Q-sort procedure with conventional rating methods and adjective check lists. He considers in detail the various forms of application of Q-sort procedure and appropriate statistical procedures to employ for these applications. Included in the Appendices are conversion tables for calculation of Q-sort correlations, California Q-set descriptions of various clinical concepts to be employed for calibration purposes, and an adjective Q-set for use by non-professional sorters. (PsycINFO Database Record (c) 2015 APA, all rights reserved) • In the last half-generation or so there has been increased emphasis on an understanding of personality functioning. It is asked what, if anything, is known or agreed to in this field. Is there a typical mother of schizophrenics, for example? In all the talk about the "creative personality" or the "authoritarian personality" just what is meant by these terms? What really is "hysteria"? Doctor Jack Block's monograph introduces the California Q-set—a method for describing comprehensively in contemporary psychodynamic terms an individual's personality. This method for encoding personality evaluation will prove highly useful in research applications by psychiatrists, psychologists, and sociologists, for it permits quantitative comparisons and calibrations of their evaluations of patients. He compares the Q-sort procedure with conventional rating methods and adjective check lists. He considers in detail the various forms of application of Q-sort procedure and appropriate statistical procedures to employ for these applications. Included in the Appendices are conversion tables for calculation of Q-sort correlations, California Q-set descriptions of various clinical concepts to be employed for calibration purposes, and an adjective Q-set for use by non-professional sorters. (PsycINFO Database Record (c) 2015 APA, all rights reserved)
Formulas are developed for estimating the true reliability of a measure from data collected at three points in time. The procedure can be applied to a single question, and unlike traditional test-retest reliabilities, this measure is not reduced in value when changes occur during the testing interval. A related coefficient of stability also is introduced, and a procedure is presented for examining the credibility of required assumptions.