| HOME | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|---|
| ||||||||||||||||||||||||||||||||
RESEARCH ARTICLE |
Laboratory of Personality & Cognition, National Institute on Aging, Baltimore, Maryland.
Address correspondence to Antonio Terracciano, 5600 Nathan Shock Drive, Baltimore, MD 21224. E-mail: terraccianoa{at}grc.nia.nih.gov
 |
Abstract |
|---|
 TOP Abstract MethodsResultsDiscussionReferences |
|---|
AFTER decades of research, the broad outlines of adult personality development are well understood (Caspi, Roberts, & Shiner, 2005
; Helson, Jones, & Kwan, 2002; Jones & Meredith, 1996; McCrae & Costa, 2003; Mroczek & Spiro, 2003; Roberts, Walton, & Viechtbauer, in press; Small, Hertzog, Hultsch, & Dixon, 2003; Steunenberg, Twisk, Beekman, Deeg, & Kerkhof, 2005). However, because maturational changes are generally small in magnitude, a more detailed account requires data from large samples followed over long periods. In the present study we offer new information about personality development by focusing on specific lower order traits and by adopting advanced statistical methods in the analysis of data from a large, long-term longitudinal study.
Factors and Facets of Personality
Personality psychology has made extraordinary advances in the past 20 years (McCrae, 2002), in large part because of the widespread recognition that most personality traits can be interpreted as aspects of five broad factors that constitute the Five-Factor Model (FFM; Digman, 1990). Literature reviews (e.g., Judge, Heller, & Mount, 2002) now routinely organize findings in terms of the five factors, and, in brief, one can summarize personality development by noting that Neuroticism (N), Extraversion (E), and Openness (O) decline, whereas Agreeableness (A) and Conscientiousness (C) increase throughout adulthood (McCrae & Costa, 2003).
Although categorizing traits in terms of the FFM can bring order to a large body of studies, it does so at a cost: Differences in the developmental trends of specific traits within the same factor are overlooked. Helson and Kwan (2000) pointed to a distinction within the E domain, showing that measures of social assurance increased toward middle age, whereas measures of social vitality declined. There may be other useful distinctions to be made, and one way to search for them is by analyzing data at the level of discrete facets. Many personality models are hierarchical, with broad factors defined by more specific facets (e.g., Caspi et al., 2005). The Revised NEO Personality Inventory (NEO-PI-R; Costa & McCrae, 1992b) measures six facets for each factor, and previous facet-level analyses have shown distinct developmental patterns. For example, cross-sectional analyses of self-reports suggest that E5: Excitement-Seeking declines precipitously from early adolescence to middle adulthood, whereas E1: Warmth shows a much slower decline (Costa & McCrae, 1992b), and this distinction is replicated in analyses of observer ratings from around the world (McCrae et al., 2005).
NEO-PI-R scales provide useful assessments of a wide range of specific personality traits, but scientific advances depend on replications using a variety of methods, including different instruments. In this article we describe normative age trajectories (i.e., those that appear to characterize all people, on average) of personality traits assessed by the Guilford–Zimmerman Temperament Survey (GZTS; Guilford, Zimmerman, & Guilford, 1976), interpreted in terms of higher and lower order traits of the FFM.
Multilevel Modeling Studies of Mean-Level Changes in Personality Traits
Recent progress in statistical methods (see Hertzog & Nesselroade, 2003) has provided researchers with new tools to examine longitudinal trajectories. To assess intraindividual growth and describe normative development in personality traits, recent studies have increasingly relied on structural equation modeling (Small et al., 2003), latent curve analysis (Jones & Meredith, 1996), or multilevel modeling approaches (Mroczek & Spiro, 2003; Steunenberg et al., 2005), including longitudinal hierarchical linear modeling (HLM; Helson et al., 2002; Terracciano, McCrae, Brant, & Costa, 2005).
Mroczek and Spiro (2003) followed a sample of 1,600 men from the Normative Aging Study for 12 years and found curvilinear slopes for N, which declined up to age 80, and an overall linear trajectory for E indicating no average change. However, E increased in the young cohort and declined in the older cohort. Furthermore, these researchers found significant individual differences in intraindividual change. Steunenberg and colleagues (2005) found a similar concave curve for N on a random Dutch sample of 2,177 respondents aged 55 to 85 from the Longitudinal Aging Study Amsterdam.
Helson and associates (2002) conducted HLM analyses on data from two small samples of individuals followed for about 40 years. These researchers found that Dominance and Independence (measures of social assurance) peaked in middle age, whereas measures of social vitality declined with age. Some measures of norm adherence such as Self-Control increased with age. Somewhat similar results are reported by Jones, Livson, and Peskin (2003).
Terracciano and colleagues (2005) used HLM to analyze NEO-PI-R data collected between 1989 and 2004 in the Baltimore Longitudinal Study of Aging (BLSA). They found a decline up to age 80 in N, stability and then decline in E, a decline in O, increase in A, and increase up to age 70 in C. These researchers also described trends for the facets of each factor. Although most facets followed the pattern of the factor they define, some facets did not. This was particularly evident for some facets of E, which showed clearly distinct patterns.
Using HLM, in this study we attempt to replicate and extend the trends reported in the 15-year longitudinal study by Terracciano and colleagues (2005) by using a different personality questionnaire, the GZTS, and assessment points that span a much longer time interval, up to 42 years. On the basis of the results obtained by Terracciano and colleagues, we can formulate specific hypotheses for each GZTS scale, provided that we can interpret the GZTS scales in terms of NEO-PI-R factors and facets. To do so, we take advantage of the fact that the BLSA participants have completed several other personality questionnaires, including the NEO-PI-R. In Table 1 we report correlations between the 10 GZTS scales and the NEO-PI-R factor scores in a sample of 900 individuals who were administered both tests during the same visit. Table 1 reports the specific NEO-PI-R facet with which each GZTS scale is most highly correlated and the correlation, and the last column describes the age trajectory observed for that facet by Terracciano and colleagues.
|
BLSA Studies of the GZTS
The GZTS was administered from the inception of the BLSA in 1958 until 2002, and several previous studies have reported longitudinal analyses of mean-level changes in men (Costa, McCrae, & Arenberg, 1980; Costa, Metter, & McCrae, 1994; Douglas & Arenberg, 1978) and women (Costa & McCrae, 1992a, 1998). In several respects, the present study represents an advance. First, more data have been collected, increasing the number of assessment points and expanding the time interval, at least for a few respondents, to as long as 42 years. Since the last analyses of BLSA men's data were presented in 1994, 390 additional administrations of the GZTS have been gathered from men; since the last analyses of women's data were published in 1998, 237 additional administrations have been gathered from women. Second, HLM analyses offer more refined estimates of maturational trends, based on a more complete use of data. In fact, HLM analyses use all available data, despite variations in retest interval and number of administrations per individual. Previously published GZTS studies used relatively smaller subsamples because there were missing data and variations in time interval, for both men (Costa et al., 1994, N = 205, retest interval = 20–30 years, M = 24.4 years) and women (Costa & McCrae, 1998, Sample A: N = 114, retest interval = 12–16 years, M = 13.4 years; Sample B: N = 211, retest interval = 6–10 years, M = 7.8 years). Finally, unlike previous reports on the GZTS from the BLSA, this study includes both men and women, making possible a direct test of gender differences in personality change, as suggested by other researchers (Viken, Rose, Kaprio, & Koskenvuo, 1994; Wink & Helson, 1993).
Terracciano and colleagues (2005) reported a longitudinal HLM study of changes on NEO-PI-R scales in the BLSA. The GZTS is a different instrument, but the samples used in that and the present study overlap. The two studies differ in the time interval covered: The GZTS was administered from 1958 to 2002, the NEO-PI-R from 1989 to 2004, and 72% of GZTS administrations were gathered before the introduction of the NEO-PI-R. The two studies also examined different subsets of BLSA participants: 1,443 participants were included in both studies, but 501 had only NEO-PI-R data and 916 had only GZTS data.
|
METHODS |
|---|
|
TOPAbstractMethodsResultsDiscussionReferences |
|---|
|
At their first GZTS administration, individuals who subsequently dropped out of the study were younger [43 vs 52 years; t(1, 2357) = 11.0; p <.01, d = 0.59], more likely to be women [45% vs 36%, 
2(1) = 12.1, p <.01], and less educated [15.9 vs 16.3 years; t(1, 2236) = 3.1; p <.01, d = 0.16] than were participants who did not drop out of the study. After age, year tested, sex, and education were controlled for, individuals who dropped out of the study did not differ from those who did not on the 10 GZTS scales at their first administration.
At their first GZTS administration, individuals who died in the course of the study were older [64 vs 42; t(1, 2357) = –38.1; p <.01, d = 1.64], more likely to be men [82% vs 51%, 2(1) = 229.2, p <.01], and tested earlier in time [1970 vs 1984; t(1, 2357) = 29.4; p <.01, d = 1.26] compared with participants who were still alive at end of this study (2002). After age, year tested, sex, and education were controlled for, individuals deceased in the course of the study differed on only 1 of the 10 GZTS scales at their first administration from those who had not died. Estimated marginal means indicated that participants who died were lower on Emotional Stability than were those who were still alive in 2002 (47.9 vs 49.9; p <.05, partial 
2 =.004).
Measure
The GZTS (Guilford et al., 1976) is a factor-based personality questionnaire consisting of 300 items, 30 for each of the 10 GZTS scales. For each item, participants choose between "yes," "no," and "?" We considered any scale with more than three question-mark responses to be missing, a procedure suggested by Guilford and Zimmerman (1949). Therefore, small variations in the number of participants will be seen in the analyses for different scales. We standardized raw scores as T scores (M = 50, SD = 10) by using the mean and standard deviation of all 4,739 administrations to obtain the same metric for each scale and a clear measure of effect size. (Means and standard deviations for the first assessment for all participants and by age groups are available from A. Terracciano, as are tables showing means and standard deviations contrasting first assessments with second, M interval = 7.5 years, third, M interval = 14.9 years, and fourth, M interval = 22.6 years, assessments.)
The GZTS scales are valid and reliable (Guilford et al., 1976). Internal consistency reliability coefficients range from.75 to.87 (Mdn =.80; Guilford et al.) In the BLSA (McCrae, Costa, & Arenberg, 1980), the structural stability of the GZTS has been shown across age, cohort, and time of measurement. Test–retest reliability estimates for the 10 scales ranged from.75 to.91 (Mdn =.83; Costa et al., 1980). Retest stability coefficients over a 24-year interval ranged from.61 to.71 (Mdn =.65; Costa & McCrae 1992a).
Data Analysis: HLM
HLM (Raudenbush & Bryk, 2002) is a flexible approach that can be applied to evaluate within-individual change or growth trajectories. In HLM analyses the number and spacing of measurement observations may vary across persons, given that the time-series observations in each individual are used to estimate each individual's trajectory (Level 1), and those individual parameters are the basis of group estimates (Level 2). Even data from individuals who were tested only on a single occasion can be used to stabilize estimates of mean and variance. In this way, all available data can be included in the analyses. This is a major advantage of conducting analysis within the HLM framework; by contrast, missing data and varying timing pose major problems in conventional repeated measures analyses of variance (ANOVA). Furthermore, longitudinal HLM can estimate age trajectories over a broad age span (from 20 to 90) with data collected in a shorter time interval (up to 42 years).
We conducted the analyses by using the program HLM Version 6 (Raudenbush, Bryk, & Congdon, 2004). For each trait, we adopted a stepwise procedure to evaluate longitudinal trajectories. First we tested Level 1 linear models, and then we tested quadratic models. We chose the quadratic model when it provided a better fit, according to the chi-square test of deviance at p <.01. After we determined the Level 1 model, we entered sex (male = 0, female = 1) and cohort (year of birth) in the model as Level 2 variables. We retained Level 2 variables in the final model if they improved the fit of the model, or in other words, explained a significant amount of the variance in mean intercept or slope. Finally, we examined possible effects of attrition and death on the intercept and slopes of the 10 scales.
We centered age in decades on the grand mean (M = 54.9 years) to minimize the correlation between the linear and quadratic terms. At Level 2, we centered year of birth (in decades) on the sample mean year of birth (1929).
|
RESULTS |
|---|
|
TOPAbstractMethodsResultsDiscussionReferences |
|---|
, p. 24) to estimate the proportion of the stable variance in personality traits, as the ratio of between-subjects variance (u0: intercept variance) to the total variance (u0: intercept variance + 
2: within-subject variance). This ratio, which is an intraclass correlation, indicates that the proportion of variance that was stable over the course of this study ranged from 67% for thoughtfulness to 86% for masculinity; Mdn = 71%. (Estimates of variance components from the basic one-way ANOVA with random effects model are not shown in the Table 3. If the variance components from the final models reported in Table 3 are used, the proportion of stable variance would have Mdn =.74 for the 10 scales.)
|
, p. 24). The within-subject variance accounted for by age ranged from 4% for restraint to 26% for General Activity (Mdn = 14%). Thus, age accounts on average for about 14% x 30% = 4% of the total variance, a figure consistent with cross-sectional estimates (Terracciano et al., 2005).
HLM results for the final models of the 10 GZTS scales are reported in Table 3. We first describe the average trajectories with the fixed effects of the intercept, linear, and quadratic terms respectively (
00, 01, 02); then the associated random effects (u0, u1, u2); and finally the effects of predictors (i.e., gender, cohort, attrition, and death) of intercept and slope variability.
Fixed Effects
Of most interest are the linear and quadratic fixed effects, which determine the shape of the developmental trajectory. Estimated age trajectories for the 10 scales are depicted in Figure 1, setting cohort as equal to the sample mean date of birth (1929), and separately for men and women for the scales that showed significant gender differences.
|
Random Effects
The random effects terms (u0: variance) associated with the intercepts, which reflect between-individual differences in personality traits, were all statistically significant and substantially higher than the within-individual variance (2). General Activity, Ascendance, and Sociability scales showed less within-individual variability than other scales. In other words, on these scales, individuals are relatively more consistent across assessments. Masculinity shows markedly smaller values of both within- and between-individuals variance values (2 and u0), setting it apart from the other GZTS scales, perhaps because Masculinity is a marker of gender-role attributes more than a personality trait.
None of the variances of the quadratic slopes in Table 3 was significant. However, for seven scales, variances associated with the linear slopes were significant, indicating that for those scales, there are individual slopes departing from the overall trends. Next, we attempt to explain such variability with individual difference variables.
Level 2 Predictors of Intercept and Slope Variability
Gender was a significant predictor of the intercept for six scales. In the terminology of the NEO-PI-R, women were higher on A (Friendliness and low Masculinity), lower in assertiveness (Ascendance), and higher on N (i.e., lower on Emotional Stability, Objectivity, Personal Relations, and Masculinity), which is consistent with cross-cultural patterns of sex differences (Costa, Terracciano, & McCrae, 2001). Gender explains 55% of the variance in the intercept of the Masculinity scale, 3.3% for the Ascendance scale, and about 1% for the other scales.
Cohort (year of birth centered on 1929) was a significant predictor of the intercept for 7 of the 10 GZTS scales. Later-born cohorts have lower intercepts (about 1 T-score point per decade) on Restraint, Friendliness, and Personal Relations scales, and higher intercepts on General Activity, Ascendance, Emotional Stability, and Thoughtfulness scales. These effects are net of the estimated effects of age (the Level 1 predictor), and they explain 7.6% of the variance in the intercept of the Personal Relations scale and 2.6% for the Ascendance scale, but less than 1% for the other scales.
As indicated by the table note in Table 3, gender was a significant predictor of the slope variability for the Thoughtfulness scale, with women declining in young adulthood more, and increasing in old age less, than men (see Figure 1). This gender by age effect explained less than 1% of the slope variance. Birth cohort was a significant predictor of the linear slope of the Sociability and Masculinity scales, explaining 1% and 3.7% of the slopes' variance, respectively. In both cases, later-born cohorts decline less.
Attrition and Death
Although there is little evidence that attrition influences the results of longitudinal studies of personality traits (Roberts et al., in press), we examined whether the trajectories of individuals who dropped out of the study differed from the trajectories of those who remained in this study. We also examined whether individuals who died during the course of this study had different levels (intercept) or slopes compared with individuals still alive. We created a dummy variable, attrition, that contrasted those who dropped out with those who remained in the study, and another dummy variable, death, that contrasted those who died with those still alive. We entered the dummy variables as Level 2 predictors of intercept, linear, and quadratic terms in the models presented in Table 3.
Overall, HLM analyses indicate that attrition and death had limited effects on the trajectories of the GZTS scales. We found a significant attrition effect only on the Emotional Stability scale: Individuals who dropped out of the study had an intercept that was lower by 1.3 T-score points (p <.05; see also Terracciano et al., 2005). The trajectories of participants who died differed significantly from those who were still alive on 2 of the 10 GZTS scales. Those who died had an intercept that was 2.4 T-score points lower on the Emotional Stability scale (p <.001). For the Friendliness scale, those who later died showed a decline of less than 2.5 T-score points from age 20 to 90 (p <.05), whereas those still alive were substantially stable or slightly increased.
Supplementary Analyses
The data structure for this study was unusual because data were collected for the first 20 years of the study only from men. To examine whether this feature affected results, we attempted to replicate the results reported in Table 3 on the assessments collected from 1978 to 2002 only, when both men and women were tested. This analysis removes possible Gender x Time-of-measurement interaction effects. These analyses produced essentially the same results as those reported in Table 3, with only 4 of the 54 significant coefficients reported in Table 3 becoming nonsignificant.
Next, we repeated the analyses in separate samples of men and women. These analyses cannot be compared with the full model, where gender was a Level 2 variable, but the major concern is with the shape of the developmental curves. Of the 14 significant linear and quadratic fixed effects, all except the linear effects for General Activity in men and Sociability in women remained significant, and in the same direction, in the new analyses. In addition, Personal Relations showed a significant linear effect in men. Overall, however, these analyses suggest similar results for independent analyses of men and women.
Finally, we examined whether we could replicate the findings in the study by Terracciano and colleagues (2005) by using the subset of 916 individuals who had not completed the NEO-PI-R. In this subset we can test hypotheses about developmental curves that were derived from a different instrument administered to a nonoverlapping sample (see Table 1). We used overlapping versus nonoverlapping with the NEO-PI-R sample as a Level 2 variable in analyses of the full GZTS sample. Those analyses showed that personal relations declined more steeply in the nonoverlapping group, but there were no other significant difference between slopes. Thus, consistency of results for the NEO-PI-R and the GZTS is not due simply to overlapping samples.
|
DISCUSSION |
|---|
|
TOPAbstractMethodsResultsDiscussionReferences |
|---|
). For all scales except General Activity and Emotional Stability, cumulative effects over the entire adult life span are less than 0.5 SD, which is a medium effect size (Cohen), but would not reach the threshold for clinically significant change in an individual (Jacobson & Truax, 1991). Note that these effects are found over the age range from 20 to 90, which reaches from adolescence to advanced old age. Effects are still smaller between ages 30 and 70, the usual age range in discussions of adult personality (McCrae & Costa, 2003).
However, the observed changes followed expected patterns for most scales. We found distinct trajectories for the four GZTS scales related to E: Scores for the Ascendance scale showed a peak when an individual reached middle age, replicating the findings of Helson and colleagues (2002). Scores for the Sociability scale declined linearly. In addition, we found that the score for the Activity scale was essentially stable until a person reached the age of 50, and then it declined at an accelerating rate as the person reached old age. This finding is consistent with a biologically based decrease in energy and tempo with age, and with previous reports from the BLSA (Douglas & Arenberg, 1978; Terracciano et al., 2005). The score on the scale for Restraint, which is inversely related to E5: Excitement Seeking and positively related to C6: Deliberation, increased linearly throughout adulthood, which is consistent with declines in risk taking and increased impulse control with aging. Although these findings are consistent with the broad generalization that E declines with a person's age, it is clear that at least for this factor, age trajectories are more appropriately described and researched at the facet level.
Emotional Stability, Objectivity, and Personal Relations increased in a person's young adulthood and decline slightly in later adulthood. These findings mirror previous reports on N (Mroczek & Spiro, 2003; Robins, Fraley, Roberts, & Trzesniewski, 2001; Small et al., 2003; Terracciano et al., 2005; but see Weiss et al., 2005), and they suggest that although in young and middle adulthood a person's changes are in the direction of greater maturity (e.g., Caspi et al., 2005), that gain does not necessarily continue in old age. However, despite the decline in activity, ascendance, and emotional stability, these small changes probably do not reflect meaningful declines of functionality in old age.
As in previous reports (Douglas & Arenberg, 1978), Masculinity declined with age for men and women, with men substantially higher than women on average throughout the entire age range. Thoughtfulness was expected to show little change (like O5: Ideas in Terracciano et al., 2005), but it increased slightly after a person reached the age of 50, a finding that contrasts with the overall decline of the O factor in previous studies.
One scale, Friendliness, did not change longitudinally. This finding is somewhat puzzling, because both cross-sectional (McCrae et al., 1999; Weiss et al., 2005) and longitudinal (Terracciano et al., 2005) studies have found increases with age in A, the factor that includes friendliness. However, one large-scale 6-year longitudinal study of men and women at midlife found no longitudinal change in A (Costa, Herbst, McCrae, & Siegler, 2000). Clearly, this is a question that requires further research.
Some large cross-sectional and longitudinal studies (Srivastava, John, Gosling, & Potter, 2003; Viken et al., 1994) have reported different developmental trends for men and women, especially for N. With the exception of Thoughtfulness, in the present study we found no evidence that gender influences personality development. The general absence of gender effects on developmental curves is consistent with other longitudinal (e.g., Helson et al., 2002; Roberts et al., in press) and cross-sectional studies in the United States and around the world (McCrae & Costa, in press) using self-report and observer perspectives (McCrae et al., 2004).
In the HLM analyses in the present study, we found significant cohort effects on seven scales. We found the largest effect for the Personal Relations scale, with later-born cohorts declining more than 1 T-score point per decade, or about 0.5 SD over four decades. Personal Relations is related to trust, which Robinson and Jackson (2001) found to be declining among Americans born after the 1940s. Personal relations is also weakly and inversely related to N, which shifted toward substantially higher levels (about 1 SD) during recent decades (from 1952 to 1993) according to one meta-analysis (Twenge, 2000). However, in the present study we did not find corresponding cohort effects for Emotional Stability or Objectivity, which are both strongly related to N. Later-born cohorts are instead higher in the Ascendance scale, which is related to E. A study by Twenge (2001) on American college students assessed from 1966 to 1993 found similar increases for E, but with a larger effect size of slightly less than 1 SD over 20 to 25 years. In the present study, we found over the same time period an estimated effect of one third of 1 SD for the Ascendance scale. Other GZTS scales related to E did not show consistent cohort effects.
The present study has several positive features, including sample size, long duration, and analytical method used, but it also has several limitations. Although the GZTS is a valid and reliable instrument, it is an older personality inventory that does not comprehensively cover the full range of traits in the FFM. The GZTS scales tap the N and E dimensions well, but not O, A, or C. Another limitation is the demographics of the sample. BLSA participants are not representative of the U.S. population, because they are highly educated and have high socioeconomic status. However, similar trajectories for N and E were found in the Normative Aging Study (Mroczek & Spiro, 2003), which differs in education and social-economic status from the BLSA (see also Steunenberg et al., 2005). Furthermore, Small et al. (2003) found years of education to be unrelated to longitudinal change in FFM traits.
GZTS data indicate that over 70% of the variance in personality traits is between individuals (stable individual differences). The within-individual variance is in part accounted for by age (about 14%), but most might be explained by measurement error or biological or environmental factors, which should all be addressed by future research. The present study confirms previous reports showing different trajectories for different components of the Extraversion factor, and it suggests that the analysis of lower order traits can provide a more nuanced picture of developmental change. HLM analyses indicate that the trends in middle adulthood are in the direction of greater maturity, but changes among the oldest adults are not necessarily positive. Overall, the changes in adulthood seen in this exceptionally long-running longitudinal study are consistent with trends seen in other studies that used cross-sectional (Srivastava et al., 2003) and accelerated longitudinal designs (Steunenberg et al., 2005). Long-term longitudinal studies are crucial for predicting important outcomes and explaining nonnormative trajectories, but it is good news for gerontologists that briefer studies give similar results in describing normative developmental trends.
|
|
Acknowledgments |
|---|
|
Footnotes |
|---|
Received for publication January 11, 2005. Accepted for publication September 19, 2005.
|
References |
|---|
|
TOPAbstractMethodsResultsDiscussionReferences |
|---|
ebí
ková, M., Urbánek, T., Martin, T. A., Oryol, V. E., et al. (2004). Age differences in personality traits across cultures: Self-report and observer perspectives. European Journal of Personality, 18,143-157.This article has been cited by other articles:
 |
|
 |
|
  A. Terracciano, C. E. Lockenhoff, A. B. Zonderman, L. Ferrucci, and P. T. Costa Jr Personality Predictors of Longevity: Activity, Emotional Stability, and Conscientiousness Psychosom Med, July 1, 2008; 70(6): 621 - 627. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Terracciano, P. T. Costa Jr., and R. R. McCrae Personality Plasticity After Age 30 Pers Soc Psychol Bull, August 1, 2006; 32(8): 999 - 1009. [Abstract] [PDF]
|
|
| ||||||||||||||||||||||||||||||||
| HOME | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|---|
