Long-Term Correlated Change in Personality Traits in Old Age

Mathias Allemand, Daniel Zimprich, and Mike MartinUniversity of Zurich

The present study examines long-term correlated change in personality traits in old age across a timeperiod of 12 years. Data from the Interdisciplinary Study on Adult Development were used to investigatedifferent aspects of personality change and stability. The sample consisted of 300 adults ranging from 60to 64 years of age at Time 1. Personality was measured with the NEO Five-Factor Inventory. Longitu-dinal structural stability, differential stability, change in interindividual differences, mean-level change,and correlated change of the 5 personality traits were examined utilizing structural equation modeling.After having established strict measurement invariance, factor variances in Openness to Experience andConscientiousness were found to be different across testing occasions, implying variant covariationpatterns over time. Stability coefficients were around .70, indicating high but not perfect differentialstability. The amount of interindividual differences increased with respect to Openness to Experience andConscientiousness. Both mean-level change and stability in personality were observed. Eventually,except for Neuroticism, a number of medium effect-sized correlations among changes in personality traitsemerged, implying that personality changes share a substantial amount of commonality.

Keywords: personality traits, personality change, correlated change, aging, life span development

There is now growing evidence that both stability and changemark personality trait development across the adult lifespan(e.g., Allemand, Zimprich, & Hendriks, 2008; Caspi, Roberts, &Shiner, 2005; McCrae et al., 1999; Roberts & DelVecchio, 2000;Roberts, Walton, & Viechtbauer, 2006; Srivastava, John, Gosling, &Potter, 2003; Terracciano, McCrae, Brant, & Costa, 2005). Gen-erally, personality change and stability can be evaluated frommultiple perspectives. For example, structural stability (i.e., con-stant correlations among personality factors within measurementoccasions) implies that the positioning of traits relative to eachother remains stable and is unaffected by age and aging. Differ-ential stability indicates perfect correlations within personalityfactors across measurement occasions, implying that individualskeep their ranking in a reference group over time. Mean-levelchange suggests that the average trait score of the group haschanged. Contrasting these sample- or population-oriented per-spectives of change, the concept of individual differences in in-traindividual change (e.g., Alwin, 1994; Nesselroade, 1991) im-plies that individuals change differentially; also the degree anddirection or pattern of change may vary across people. Regardingpersonality traits, there is growing evidence for the existence of

interindividual differences in personality trait change in youngadulthood (e.g., Robins, Fraley, Roberts, & Trzesniewski,2001), middle age (e.g., Allemand, Zimprich, & Hertzog, 2007;Roberts, Helson, & Klohnen, 2002), and old age (e.g., Alle-mand et al., 2007; Mroczek & Spiro, 2003; Small, Hertzog,Hultsch, & Dixon, 2003). To summarize, interindividual differ-ences in intraindividual change speak to the unique develop-mental patterns particular to individual lives.

The purpose of the present study was to extend previous re-search on personality trait development by examining the afore-mentioned aspects of stability and change in old age over a 12-yeartime period, augmented by two additional aspects of change.Specifically, we were interested in change in interindividual dif-ferences in personality traits, and, particularly, in intraindividualcorrelated change in personality.

Change in Interindividual Differences

Irrespective of the level of differential stability and mean-levelchange, the amount of interindividual differences in personalitytraits might change across time (e.g., Biesanz, West, & Kwok,2003; Martin & Zimprich, 2005). In the sequel, we will use thephrase change of divergence to describe change in individualdifferences in personality traits. Empirically, this aspect of changecan be examined by comparing personality factor variances cross-sectionally and, preferably, longitudinally. An increase or decreaseof personality factor variances would indicate that the amount ofchange is different for different persons. Indeed, with respect tocognitive functions, there is empirical evidence for increasingvariability with age regarding cognitive variables such as reactiontime, memory, or fluid intelligence (cf. Morse, 1993; Nelson &Dannefer, 1992). If we borrow from the literature on cognitivedevelopment, different amounts of individual differences in per-sonality traits might be indicative of the variables governingchange and development. Horn (1988) has argued that relatively

Mathias Allemand, Daniel Zimprich, and Mike Martin, Department ofPsychology, University of Zurich, Zurich, Switzerland.

This publication is based on data from the Interdisciplinary LongitudinalStudy on Adult Development (ILSE), funded by the Federal Ministry ofFamily Affairs, Senior Citizens, Women and Youth, Germany (AZ: 301-1720-295/2). The order of the first two authors is strictly alphabetical; bothcontributed equally.

Correspondence concerning this article should be addressed to MathiasAllemand or Daniel Zimprich, Department of Psychology, Gerontopsy-chology, University of Zurich, Binzmuhlestrasse 14/24, CH-8050 Zurich,Switzerland. E-mail: m.allemand@psychologie.uzh.ch or d.zimprich@psychologie.uzh.ch

Psychology and Aging Copyright 2008 by the American Psychological Association2008, Vol. 23, No. 3, 545–557 0882-7974/08/$12.00 DOI: 10.1037/a0013239

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homogeneous developmental trajectories might characterize amore biologically driven developmental process, whereas increas-ing variances might denote changes triggered by external influ-ences that are socially driven. Based on this line of reasoning, theamount of interindividual differences should be relatively stable ifperson variables such as personality, cognition, or attitudes aremore genetically based (Johnson, McGue, & Krueger, 2005). Bycontrast, increasing differences among individuals might arisethrough a number of reasons such as the combined effects ofindividuals’ unique experiences over more years producing in-creasing differences among them or significant changes in physi-ological and functional status in later adulthood.

To the best of our knowledge, only three studies have rigorouslytested change of divergence in the five personality traits. Small etal. (2003) found that the Big Five personality factor variances wereequal across a 6-year period in a sample of older adults, implyingperfect stability of divergence across time. Allemand et al. (2007)reported that, cross-sectionally, but not longitudinally, the Open-ness to Experience variance in middle-aged participants (aged42–46) was significantly larger than in older participants (aged60–64) at two measurement occasions across 4 years. Recently, ina large and representative Dutch sample, Allemand et al. (2008)found that personality factor variances were cross-sectionallyequal across six age groups. To summarize, examination of age-related changes in variances in the five personality traits across thelifespan represents an important complement to the examination ofcorrelational and mean structures of personality.

Correlated Change

An important developmental question is whether changes indifferent personality traits are related over time. Therefore, theexamination of specific versus general change (Allemand et al.,2007; Martin & Zimprich, 2005; Zimprich, 2002a, 2002b; Zim-prich & Martin, 2002) that can be examined through correlatedchange on the latent level by means of latent change models(Hertzog & Nesselroade, 2003; McArdle & Nesselroade, 1994)focuses on the question whether changes in the Big Five person-ality traits are related across individuals. It might be that the sameunderlying causes of change such as social roles, life events, andsocial environments (cf. Caspi & Roberts, 2001; Roberts & Caspi,2003) operate simultaneously on multiple personality constructssuch as the Big Five. Recently, by investigating correlated changein middle-aged and older adults across a 4-year period, Allemandet al. (2007) found a number of statistically significant changecorrelations with average absolute correlations of .36 and .32 formiddle-aged and older participants, respectively. This implies thatpersonality trait change over 4 years seems to occur in a concertedmanner. A more restrictive variant of a correlated change modelwould test for equality of correlations at the first measurementoccasion, Time 1 (T1), that is, latent level factor correlations andlatent change factor correlations. Should equality hold, this wouldimply “intercorrelations stationarity,” that is, stability of the asso-ciations among personality factors over time.1 Specifically, if thecorrelation between change factors is used as an estimate ofcorrelated linear change that would emerge if the longitudinal timespan tended to infinity, it can be shown that the cross-sectionalcorrelations among personality factors approach the change factorcorrelations (cf. Hofer, Flaherty, & Hoffman, 2006). An indication

of intercorrelations stationarity would imply neither differentiationnor dedifferentiation (e.g., Baltes, Cornelius, Spiro, Nesselroade,& Willis, 1980). Intercorrelations stationarity is similar to but notthe same as structural stability. Whereas structural stability isestablished in a step-by-step fashion in comparing pairs of mea-surement occasions, intercorrelations stationarity is directly basedon change but has implications for the individual measurementoccasions.

Longitudinal Measurement Invariance

In order to ensure that the same psychological construct (e.g.,Neuroticism) operates in the same way at different time points andthat the measure of that construct has equivalent measurementproperties, one has to establish longitudinal measurement invari-ance (MI; cf. Bontempo & Hofer, 2006; Horn & McArdle, 1992;Meredith, 1993; Meredith & Horn, 2001). Briefly, MI entails thedegree to which a measure behaves equivalently over testingoccasions and/or across different groups such as age groups. Onemight distinguish four forms of longitudinal MI (cf. Meredith,1993): (a) Configural invariance entails that the number of factorsand according salient and nonsalient loadings are equal over time,which ensures that the dimensionality of the measured constructsis longitudinally equivalent. (b) Weak MI requires that patternmatrices be fully invariant across measurement occasions. Thisform of MI ensures that the same indicators (manifest variables) ondifferent measurement occasions do relate to constructs (latentvariables) in the same way. (c) Strong MI requires that patternmatrices and intercepts of the manifest indicators be invariant overtime. Establishing this form of MI allows for meaningfully com-paring means, covariances, and variances across measurementoccasions. (d) Strict MI requires that pattern matrices, intercepts,and unique variances be invariant over time. This strictest form ofinvariance implies that all of the differences in means, covariances,and variances of the observed indicators across measurement oc-casions arise from differences in latent variables or factors. Ex-amining different degrees of MI is accomplished by employingconfirmatory factor models with increasingly severe across-groupand across-time restrictions on parameters (cf. Allemand et al.,2007, 2008; Martin & Zimprich, 2005; Zimprich, Allemand, &Hornung, 2006).

The Present Study

In the present study, we examined long-term changes in person-ality from the early 60s into the mid-70s by examining structuralstability, differential stability, mean-level changes, and changes ininterindividual differences in personality traits. Furthermore, weinvestigated correlated change in personality to determine whetherchange in one personality trait over time is related to changes inother traits. Specifically, we focused on the following researchquestions: (a) Can strict MI of the manifest indicators be estab-lished in the measurement of the Big Five personality factors overtime? The affirmative answer to this question represents a prereq-uisite for addressing the following issues. (b) Does structural

1 We thank Christopher Hertzog for suggesting the intercorrelationsstationarity model.

546 ALLEMAND, ZIMPRICH, AND MARTIN

stability in the Big Five personality traits hold over time? (c) Whatis the level of differential stability in the Big Five personality traitsover time? (d) Does the amount of interindividual differences inthe Big Five personality traits change over time? (e) Are theremean-level changes in the Big Five personality factors over time?(f) Are there correlated changes in the Big Five personality factorsover time in individuals? Finally, (g) are the intercorrelationsamong personality changes equal to the cross-sectional correla-tions among personality factors at T1?

Method

Sample

We used data from the Interdisciplinary Study on Adult Devel-opment (ILSE; Martin, Grunendahl, & Martin, 2001). In ILSE,participants come from two cohorts, one comprising individualsborn before World War II and the other including individuals bornshortly after the war (i.e., 1930–1932 vs. 1950–1952, respec-tively). The ILSE started in 1994 (T1), followed by reassessmentsin 1998 (Time 2; T2) and in 2006 (Time 3; T3). So far, onlyparticipants from the older age cohort (1930–1932) were reas-sessed at T3. Because the focus of the present study was onlong-term changes in personality, we selected persons who partic-ipated at the initial, the second, and the third measurement occa-sion, but we concentrated on the T1-T3 changes only. The data onpersonality changes between T1 and T2 have been reported else-where (Allemand et al., 2007). Of those 314 participants fromoriginally 500 participants at T1 who returned at T3, 300 hadcomplete data records for the variables of interest (the Big Fivepersonality traits). Participants were paid €50 (�US$68) for par-ticipation at T3. Reasons for attrition before T3 were categorizedas follows: 32% of the nonreturning participants had passed away,20% stayed away due to health reasons, while 18% did notmention a concrete reason. In addition, 7% had moved to anotherregion, 6% considered the reimbursement for participation asinsufficient, 6% regarded participation as being too involved, 4%lost their interest in participation, 4% had no time because theywere caregivers for a family member, and 3% (e.g., a relative)suffered from an unspecified type of cognitive impairment accord-ing to an informant.

Due to the fact that attrition may have an effect on the magni-tude and types of change, we tested whether attrition was infor-mative regarding (a) demographic variables and (b) personalitytraits. First, we conducted attrition analyses by comparing demo-graphic variables of the individuals included in this study withthose participants who dropped out (n � 200). The average age ofparticipants at T1 (1994) was 62.46 (SD � 0.86, range: 60–64). Incomparison to those participants who were not included in thisreport (M � 62.57, SD � 0.95, range � 60–65), there was nostatistically significant difference in age at T1, t(484) � 1.30, p �.10, Cohen’s d � .12 (see Cohen, 1988, p. 20). The gender balancewas equal, with 50.7% of the sample being female, whereas 44%of those who dropped out were female. This difference was notstatistically significant, �2(1) � 2.14, p � .10, Cohen’s w � 0.07(see Cohen, 1988, p. 216). Years of education were, on average,10.31 (SD � 2.76) for those who attended both measurementoccasions and 10.03 (SD � 2.76) for those who dropped out. Thisdifference was not statistically significant at T1, t(484) � 1.27,

p � .30, d � 0.10. There was, however, a group difference withrespect to the general knowledge subtest of the Wechsler AdultIntelligence Scale—Revised (Wechsler, 1981), with those partic-ipants who dropped out showing a lower knowledge score (M �14.68, SD � 4.90) than those included in this report (M � 16.23,SD � 4.65), t(484) � 3.56, p � .001, d � 0.32. Albeit beingstatistically significant, this difference reflects a small effect size.On a 5-point Likert-type scale ranging from 1 ( poor) to 5 (verygood), average subjective health ratings were 3.44 (SD � 1.43) forthose who remained in the study and 3.34 (SD � 1.43) for thosewho did not return. No significant difference was found, t(484) �0.78, p � .10, d � 0.07.

It is possible that those persons who were assessed on T1 and T3manifested distinct developmental patterns in personality traitsthan those who dropped out. Previous research on personality traitdevelopment has shown that attrition apparently has little effect onestimates of differential stability (Roberts & DelVecchio, 2000)and mean-level changes in personality traits (Roberts et al., 2006).We examined whether there are group difference with respect tothe Big Five personality traits at T1. Those persons who werereassessed at T3 were statistically significantly less neurotic at T1(M � 18.05, SD � 6.69) than those who dropped out (M � 19.39,SD � 6.79), t(484) � 2.18, p � .05, d � 0.20, and they were moreopen to experience (M � 26.05, SD � 4.78) compared to thosedropped out (M � 25.17, SD � 4.61), t(484) � 2.04, p � .05, d �0.19—effect sizes were small, however. Thus, there was evidencethat attrition was informative for at least two personality traitsin the sense that those dropping out during the study were differentat the beginning of the study. We also compared change in per-sonality between T1 and T2 for those who remained in the studyuntil T3 (n � 300) and those who dropped out after T2 (n � 106)using latent change models. Results showed that those whodropped out after T2 decreased in Agreeableness between T1 andT2, whereas those who returned at T3 increased in Agreeablenessbetween T1 and T2. For the correlations among changes anintriguing pattern emerged: For those who dropped out after T2,changes in Neuroticism between T1 and T2 were stronglyrelated to changes in Extraversion (r � –.71) but not to changesin the other personality traits. By contrast, for those whoreturned at T3, changes in Neuroticism between T1 and T2 wererelated to changes in Extraversion (r � –.47), Openness (r �–.49), Agreeableness (r � –.45), and most strongly to changesin Conscientiousness (r � –.75).

Together, these analyses point to a number of differences be-tween those who did and those who did not return for a thirdassessment in the sense that attrition was selective and, hence,informative. This does not compromise the results reported below,but it narrows their generalizability, because it appears unwar-ranted to consider the data as being missing at random (R. J. A.Little & Rubin, 1987) once one assumes that one might extrapolatefrom findings regarding changes between T1 and T2 to changesbetween T1 and T3.

Measures

We measured the Big Five personality traits using the GermanRevised NEO Five-Factor Inventory (NEO-FFI; Borkenau & Os-tendorf, 1993; Costa & McCrae, 1992). The NEO-FFI contains 60statements that participants were asked to respond on a 5-point

547LONG-TERM CORRELATED CHANGE IN PERSONALITY TRAITS

Likert scale ranging from 0 (strongly disagree) to 4 (stronglyagree). The NEO-FFI yields scores for the following personalityconstructs: Neuroticism, Extraversion, Openness to Experience,Agreeableness, and Conscientiousness. Each scale consists of 12items, which were all scaled in a way so that higher scores indicatehigher values in the direction consistent with the construct label.Estimates of internal consistency (Cronbach’s �) based on thesample of 300 participants were as follows: Neuroticism � � .78(T1) and .82 (T3), Extraversion � � .73 (T1) and .76 (T3),Openness to Experience � � .57 (T1) and .59 (T3), Agreeableness� � .65 (T1) and .74 (T3), and Conscientiousness � � .75 (T1)and .82 (T3).

Analytic Procedures

To investigate our research questions, we utilized multiple-groups confirmatory factor analyses by means of structural equa-tion modeling. We assessed MI over time and then performeddirect statistical comparisons of the similarities and differences inthe factor means, variances, and covariances among the constructs.Statistical modeling proceeded in a sequence of nine steps: (a) atest of an unconstrained measurement model that longitudinallyspecified the relationship between manifest indicators (e.g., theNEO-FFI items) and the latent constructs (e.g., the Big Fivepersonality factors), (b) a test of a model of weak MI, (c) a test ofa model of strong MI, (d) a test of a model of strict MI, (e) a testof a model of equal covariances of the latent constructs across time(longitudinal structural stability), (f) a test of a model of equalvariances of the latent constructs across time (change in interindi-vidual differences), (g) a test of a model of equal means of thelatent constructs across time (mean-level change), (h) a testof latent change models to investigate correlated change among thelatent constructs, and (i) a test of an intercorrelations stationaritymodel, that is, equal factor and latent change factor correlations.

In the measurement model there were five latent constructs:Neuroticism, Extraversion, Openness to Experience, Agreeable-ness, and Conscientiousness. For each of the five latent variables,we created domain-representative parcels to form three manifestindicators. Parceling is a technique commonly used with estab-lished measures (cf. Bandalos & Finney, 2001; T. D. Little, Cun-ningham, Shahar, & Widaman, 2002). A parcel is an aggregate-level indicator comprising the sum (or average) of several singleitems. To create parcels, we used the item-to-construct balancingtechnique (T. D. Little et al., 2002, p. 166). Briefly, the three itemswith the highest loadings were selected to anchor the three parcelsof each personality factor. Subsequently, the three items with thenext highest item-to-construct loadings were added to the anchorsin an inverted order. This procedure was repeated until all itemshad been assigned to a parcel. As a result, three parcels consistingof the sum of four single items each were built for each of the fivepersonality factors.

In order to identify and scale the models, instead of usingtraditional procedures such as setting the loading of one manifestreference variable to unity and the intercept of this referencevariable to zero (Meredith & Horn, 2001), we utilized an alterna-tive parameterization to identification and scale setting: Commonfactors were scaled by fixing their variances to unity at T1, and allloadings were estimated freely. Furthermore, we set the factormeans to zero and estimated intercepts of all manifest indicators

instead. These identification constraints were relaxed in conjunc-tion with more restrictive models of MI.

To examine correlated change in the Big Five personality fac-tors, we modeled interindividual differences in intraindividualchange in the five personality factors by using latent changemodels, which involve a reparameterization of the structural partof the longitudinal factor model (McArdle & Nesselroade, 1994).In latent change models, the level of a latent construct and thechange of this latent construct over time are estimated. Moreprecisely, if the indicators at T1 and T2 load on one latent variableand the unstandardized factor loadings of the indicators are invari-ant over time, and a second latent variable with equal factorloadings is introduced for the indicators at T2, the variance of thissecond latent variable captures interindividual differences in latentvariable change over time. Thus, the second latent variable may becalled a latent change factor. It follows that if the variance of thesecond latent variable is significantly different from 0, there areinterindividual differences in intraindividual development (cf.Nesselroade, 1991).

All analyses were conducted using Mx (Neale, Boker, Xie, &Maes, 2003). The absolute goodness-of-fit of models was evalu-ated using the chi-square test and two additional criteria, thecomparative fit index (CFI) and the root-mean-square error ofapproximation (RMSEA). Values of the CFI above .90 are con-sidered to be adequate, whereas for the RMSEA values less than.08 indicate an acceptable model fit (cf. Browne & Cudeck, 1993;Hu & Bentler, 1999). In comparing the relative fit of nestedmodels, we used the chi-square difference test. Due to its depen-dency on sample size, we complemented the chi-square differenceby calculating 90% RMSEA confidence intervals (CIs) for the modelsestimated (MacCallum, Browne, & Sugawara, 1996). Since the RM-SEA is virtually independent of sample size, the comparison ofRMSEA CIs provides an effective, alternative method of assessingrelative model fit of nested models. As a measure of effect size formean differences, we report Cohen’s d (Cohen, 1988, p. 20). Todetermine whether parameters of personality traits at T1 weresignificantly different from those at T2 on the 5% level, wecalculated 95% inferential CIs (Tyron, 2001).

Results

Raw data were checked for departures from both univariate andmultivariate normality and, apart from the first and the third parcelof Conscientiousness at T3, the skewness and kurtosis estimates ofthe personality parcels did not exceed 1 or –1 (average skewness �–0.32; average kurtosis � 0.52). The distribution of the first andthird parcel of Conscientiousness in 2006 was negatively skewed(g1 � –1.5, –1.2, respectively) and both exhibited inflated kurtosis(g2 � 4.5, 4.0, respectively). This was mainly due to 5 individualparticipants who showed comparatively low scores in Conscien-tiousness, and not to a distribution deviating in general fromnormality. The normalized estimate of Mardia’s coefficient ofmultivariate kurtosis was 0.92. Hence, with the limitation that thedistribution of two Conscientiousness parcels was inconsistentwith univariate normality, the multivariate distributional propertiesof the 15 manifest personality trait variables warranted maximumlikelihood parameter estimation.

548 ALLEMAND, ZIMPRICH, AND MARTIN

Longitudinal MI

To examine MI of the NEO-FFI over time, we imposed differentdegrees of MI by constraining parameters to be equal acrossmeasurement occasions. The confirmatory factor analysis startedwith an unconstrained model (Model LCM1) that specified the fivefactors of personality without any constraints across measurementoccasions. In order to scale the latent variables, we fixed factorvariances to 1 and factor means to 0. As can be seen from Table 1,Model LCM1 did achieve an acceptable fit as judged by the CFIand RMSEA. Accordingly, configural invariance of the five-factormodel of personality appears to hold across the two measurementoccasions regarding 15 NEO-FFI item parcels. Next, for ModelLCM2, factor loadings were constrained to be equal across mea-surement occasions, thus imposing weak MI. LCM2 also evincedan acceptable fit, as can be seen from Table 1. With respect torelative fit and the CFI and RMSEA, Model LCM2 represented thedata as well as the former model, while at the same time beingmore parsimonious. Therefore, one might conclude that weak MIholds across measurement occasions with respect to the five per-sonality traits. Subsequently, in Model LCM3, the additional con-straint of equal intercepts of the manifest indicators, implyingstrong MI, was tested. Model LCM3 also achieved an acceptablefit and in comparison to the preceding model, the chi-squaredifference was not significant. The CFI and the RMSEA, however,had improved. Hence, one might conjecture that strong MI holds.In a final model (LCM4), strict MI was examined by constrainingresidual variances of the item parcels to be equal across measure-ment occasions. The resulting model still yielded an acceptable fit.Further, compared to Model LCM3, Model LCM4 did not exhibita significant reduction in relative model fit. From this one mightconclude that the assumption of strict MI was tenable. Conse-quently, we selected Model LCM4 (i.e., the model of strict MI inpersonality traits) as adequately describing the associations amongthe Big Five personality traits at both testing occasions. All sub-sequent analyses were based on this reference model. Parameterestimates based on the model of strict MI (Model LCM4) areshown in Table 2.

To summarize, the tests of different degrees of MI revealed thatthe measurement properties of the NEO-FFI parcels appear to belongitudinally stable in the sense that the NEO-FFI measures the

same construct over time. Results showed that the criteria for strictMI were met, implying that unique item variances of the manifestindicators were constant across measurement occasions. Variancechanges are, thus, changes in true scores.

Longitudinal Structural Stability

To assess structural stability of the Big Five personality traitsover time, we constrained factor covariances to be equal acrossmeasurement occasions. The resulting model (Model LCM5) stillyielded an acceptable fit (see Table 1). Compared to Model LCM4,this model represented a statistically significant decrement asjudged from the chi-square difference. This implies that equalfactor covariances could not be assumed over the 12-year periodfor older adults. To localize changes in structural stability, wedepict factor covariances between the five personality traits withrespect to both measurement occasions in Figure 1. Figure 1 is tobe read as follows: If the 95% inferential CI of a factor covariancebetween, for example, Neuroticism and Extraversion, at T2 over-laps with the 95% inferential CI of T1, factor covariances are notsignificantly different at the 5% level. In turn, if the 95% inferen-tial CI of a factor covariance at T3 does not overlap with the 95%inferential CI of the factor covariance at T1, factor covariancesshould be considered as being significantly different at the 5%level. As can be seen from Figure 1, three significant differencesin factor covariances emerged. The factor covariance betweenExtraversion and Conscientiousness at T3 was significantly higheras compared to the covariance at T1 (0.520 vs. 0.776). In addition,the covariance between Openness and Conscientiousness (0.291vs. 0.681) and between Agreeableness and Conscientiousness(0.383 vs. 0.679) increased significantly. To cross-check this find-ing, we reestimated Model LCM5 with the three factor covariancesbetween Conscientiousness and Extraversion, Openness, andAgreeableness at T3 being freely estimated. For this relaxedmodel, �2(387) � 701.67, p � .05, CFI � 0.928, RMSEA �0.052. Compared to the reference Model (LCM4), fit was statisti-cally indistinguishable, ��2(7) � 10.62, p � .15.

Taken together, the findings revealed that covariances among atleast four of the Big Five were not similar across measurementoccasions, implying variant covariation patterns of Conscientious-ness with Extraversion, Openness, and Agreeableness over time.

Table 1Fit Indices for Latent Change Models

Model �2 df CFI RMSEA RMSEA 90% CI ��2 �df �LCM

LCM1 644.45� 345 0.931 0.054 0.046, 0.060LCM2 661.83� 355 0.929 0.054 0.047, 0.060 17.38� 10 2–1LCM3 668.06� 365 0.930 0.053 0.044, 0.059 6.23� 10 3–2LCM4 691.05� 380 0.929 0.052 0.046, 0.058 22.99� 15 4–3LCM5 718.25� 390 0.924 0.053 0.047, 0.059 27.20� 10 5–4LCM6 705.13� 385 0.926 0.053 0.046, 0.059 16.61� 5 6–4LCM7 746.82� 385 0.917 0.056 0.050, 0.062 55.77� 5 7–4LCM8 733.64� 390 0.921 0.054 0.048, 0.060 42.59� 10 8–4

Note. N � 300. CFI � comparative fit index; RMSEA � root-mean-square error of approximation; CI � confidence interval; �LCM � comparison oflatent change models; LCM1 � unconstrained model; LCM2 � model of weak measurement invariance (MI); LCM3 � model of strong MI; LCM4 � modelof strict MI; LCM5 � model of strict MI and equal factor variances across time; LCM6 � model of strict MI and equal factor covariances across time;LCM7 � model of strict MI and equal factor means across time; LCM8 � stationarity model (i.e., equal latent level factor and latent change factorcorrelations).� p � .01.

549LONG-TERM CORRELATED CHANGE IN PERSONALITY TRAITS

Differential Stability

To assess differential stability of the Big Five personality traits,we estimated factor test–retest correlations based on Model LCM4.Neuroticism (.762), Extraversion (.830), and Openness to Experi-

ence (.686) showed rather strong differential stability, whereasConscientiousness (.612) and, especially, Agreeableness (.506)appeared to be less stable over a 12-year period in older adults. Themean differential stability index across all personality traits wascalculated using the Fisher’s r-to-z transformation approach, re-sulting in r � .696. These findings imply that individual differ-ences in change of personality traits exist, because differentialstability was far less than perfect. In order to test for stability morerigorously, we constrained the respective factor stability coeffi-cients to unity. For this more constrained model, �2(385) �1239.34, p � .05, CFI � 0.803, RMSEA � 0.086. Compared tothe reference Model (LCM4), fit had decreased significantly,��2(5) � 548.29, p � .05. In sum, then, there were some pro-nounced shifts of rank-order in the five personality factors.

Change in Interindividual Differences

To examine change of divergence, we constrained factor vari-ances to be equal across measurement occasions. Although the fitof the tested model (Model LCM6) was acceptable, compared tothe reference model (Model LCM4), the model yielded a signifi-cant loss in relative and absolute fit (see Table 1), implying changeof divergence across the 12-year period in older adults. To locatethe longitudinal differences more precisely, we present factorvariances and the 95% inferential CIs for both measurement oc-casions in Figure 2. As can be seen from Figure 2, the 95%inferential CI of the Openness to Experience variance at T2 did notoverlap with the 95% inferential CI of the variance at T1. Thisimplies that older adults became more heterogeneous with respect

Table 2Parameter Estimates of Model LCM4 (Strict MeasurementInvariance)

ParcelFactorloading

Latentintercept R2 Time 1 R2 Time 2

NEURO1 2.052 10.42 0.550 0.551NEURO2 1.860 9.94 0.497 0.498NEURO3 2.079 9.59 0.627 0.627EXTRA1 1.304 12.08 0.336 0.479EXTRA2 1.654 12.74 0.510 0.557EXTRA3 2.101 13.51 0.631 0.674OPEN1 1.913 12.39 0.553 0.623OPEN2 1.935 13.90 0.588 0.656OPEN3 1.621 14.11 0.496 0.568AGRE1 1.829 14.31 0.665 0.668AGRE2 1.667 13.24 0.574 0.578AGRE3 1.731 15.36 0.677 0.681CONS1 1.588 15.38 0.418 0.504CONS2 1.674 14.61 0.448 0.535CONS3 1.520 16.39 0.582 0.663

Note. Factor loadings are unstandardized. NEURO1 to NEURO3 �parcels of neuroticism; EXTRA1 to EXTRA3 � parcels of extraversion;OPEN1 to OPEN3 � parcels of openness; AGRE1 to AGRE3 � parcels ofagreeableness; CONS1 to CONS3 � parcels of conscientiousness.

Figure 1. Factor covariances among personality factors at both measurement occasions. Estimates are basedon Model LCM4 (strict measurement invariance). Error bars reflect the 95% inferential confidence interval (CIs).N � Neuroticism; E � Extraversion; O � Openness to Experience; A � Agreeableness; C � Conscientiousness.

550 ALLEMAND, ZIMPRICH, AND MARTIN

to Openness. Similarly, the amount of interindividual differencesin Conscientiousness increased over time. The remaining Big Fivepersonality traits (i.e., Neuroticism, Extraversion, and Agreeable-ness) did not show significant changes in terms of factor variances.In order to cross-check this finding, we again reestimated ModelLCM6 with the Openness and Conscientiousness factor variancesat the second testing occasion being free parameters, �2(383) �693.87, p � .05, CFI � 0.928, RMSEA � 0.052. Compared to thereference Model (LCM4), there was no statistically significantdifference in fit, ��2(3) � 2.84, p � .41.

To summarize, the findings revealed significant change of di-versity with respect to Openness to Experience and Conscientious-ness. In other words, compared to T1, the sample showed asignificantly higher amount of interindividual differences in thetwo personality traits 12 years after the T1.

Mean-Level Change

To test for mean-level change in the Big Five personality traitsover time, we constrained factor means to be equal across themeasurement occasions. Although Model LCM7 still yielded anacceptable fit (see Table 1), it exhibited a statistically and substan-tively significant decrement in fit as compared to the referencemodel. This implies that the assumption of equal factor meansacross time was not tenable for older adults. Figure 3 shows themean-level changes in factor means for each of the Big Fivepersonality. As can be seen from Figure 3, participants became, onaverage, significantly less neurotic and less extraverted as theypassed from later midlife into old age. These age-related mean-

level changes reflected small (Neuroticism) to medium (Extraver-sion) effects sizes. To cross-check this result, we estimated ModelLCM7 again, this time with the factor means of Neuroticism andExtraversion at T3 being freely estimated, �2(383) � 695.95, p �.05, CFI � 0.928, RMSEA � 0.052. Importantly, the difference infit compared to Model LCM4 was no longer statistically signifi-cant, ��2(3) � 4.90, p � .17. Thus, we may conclude that acrossthe 12-year period, Neuroticism, on average, decreased slightly,while Extraversion, on average, decreased more substantially.

Correlated Change

To examine specific versus general changes in the Big Fivepersonality traits, we utilized latent change models. The analysisstarted with a latent change model that specified the latent initiallevel and latent change factors over the 12-year period for each ofthe NEO-FFI personality traits. All latent initial and change factorswere allowed to covary. The overall fit of the model exactlymirrored the fit of Model LCM4 (see Table 1). The statisticallysignificant latent change variances (�Var) of the Big Five person-ality for older adults were as follows: Neuroticism: �Var � .478,Extraversion: �Var � .383, Openness to Experience: �Var �.751, Agreeableness: �Var � .996, and Conscientiousness:�Var � .959. This implies, that in Agreeableness, Conscientious-ness, and Openness to Experience interindividual differences inintraindividual change were most pronounced.

Subsequently, covariances among the latent change-scores ofthe NEO-FFI were estimated. Table 3 reports three kinds of latentcorrelations. First, the correlations between the initial levels of the

Figure 2. Variances of personality factors at both measurement occasions. Factor variances at T1 are scaledto unity. Estimates are based on Model LCM4 (strict measurement invariance). Error bars reflect the 95%inferential confidence interval (CIs).

551LONG-TERM CORRELATED CHANGE IN PERSONALITY TRAITS

Big Five factors are shown in the upper-left partition of thecorrelation matrix. The findings revealed that Neuroticism wasnegatively related to all other personality traits, with the highestcorrelation emerging between Neuroticism and Extraversion (r �–.458), and effect sizes being in the medium to large range (rs; cf.Cohen, 1988). Thus, participants who were less neurotic were, onaverage, more extraverted, more open to experience, more agree-able, and more conscientious. Extraversion was also significantlyrelated to all other personality traits ranging from r � .520 withConscientiousness to r � .419 with Agreeableness, thus represent-ing large effect sizes. Accordingly, participants who were moreextraverted were less neurotic and more open to experience, agree-able, and conscientious.

Second, the correlations between initial levels and changes forthe Big Five personality factors are summarized in the lower-leftpartition (see Table 3)2 and show that, with few exceptions,level-change correlations were negative. These correlations implythat participants with higher initial scores, for example in Extra-version, tended to show less pronounced changes over time. Effectsizes were in the small to large range. Additionally, several across-domain level-change correlations were found (see Table 3). InitialNeuroticism was significantly related to change in Agreeableness,indicating that participants with higher baseline scores in Neurot-icism were more likely to decrease in Agreeableness. Extraversionat T1 was correlated with changes in Openness to Experience andAgreeableness. Moreover, participants with higher baseline scoresin Agreeableness tended to show a slightly less pronounced changein Extraversion, Openness to Experience, and Conscientiousness.

Finally, a cross-domain correlation was found for Conscientious-ness and change in Agreeableness.

Third, correlations between the latent change scores of the fivepersonality factors, which refer to the aspect of specific versusgeneral change, are summarized in the lower-right partition of thecorrelation matrix (see Table 3). For example, changes in Extra-version were significantly and positively related to Openness toExperience, Agreeableness, and Conscientiousness, with effectsizes being in the large range. The findings imply that participantswho exhibited higher latent change scores in Extraversion tendedto become more open, agreeable, and conscientious. Furthermore,participants produced substantial latent change scores correlationsbetween Conscientiousness and Extraversion, Openness to Expe-rience, and Agreeableness. The effect sizes for the change corre-lations were in the large range. Interestingly, changes in Neuroti-cism were unrelated to the other Big Five personality changescores (see Table 3), indicating that participants with an increase inNeuroticism did not show related changes in other personalitytraits.

To summarize, the present data provide evidence for interindi-vidual differences in intraindividual change in all Big Five per-sonality traits in older adults across a 12-year time period. Withrespect to the personality traits examined, interindividual differ-

2 It should be noted that estimating correlations between level andchange scores is difficult in a study with two measurement occasions (cf.Raudenbush & Bryk, 2002, p. 166).

Figure 3. Means of personality factors at both measurement occasions. The initial measurement occasion wasused as a reference, having factor means of zero, that is, factor means at the second measurement occasion reflectdeviations from the reference. Factor means were scaled as Cohen’s ds. Estimates are based on Model LCM4

(strict measurement invariance). Error bars reflect the 95% inferential confidence interval (CIs).

552 ALLEMAND, ZIMPRICH, AND MARTIN

ences in initial level were negatively correlated with the amount ofindividual change. Furthermore, several across-domain level-change correlations were found. Finally, a number of statisticallysignificant latent change correlations among personality traitsemerged, except for Neuroticism. Together, these findings indicatethat personality trait change across the 12-year time period seemsto occur in a concerted manner.3

To test for the equality of level factor and change factor corre-lations (intercorrelations stationarity), we constrained level corre-lations in Model LCM8 to equal their according slope correlations(e.g., the correlation between Neuroticism [N] and Extraversion[E] equals the correlation between �N and �E). As can be seenfrom Table 1, Model LCM8 did achieve an adequate model fit,which, however, represented a statistically significant loss in fitcompared to Model LCM4, indicating that not all level intercor-relations were equal to their respective change correlations. Accord-ing to Figure 4, there were four statistically significant differencesbetween level factor and change factor correlations: Neuroticism andExtraversion, Neuroticism and Openness, Neuroticism and Agree-ableness, and Agreeableness and Conscientiousness. While withrespect to Neuroticism, the change factor correlations were smallerthan their level factor counterparts; for Agreeableness and Con-scientiousness, the change factor correlation was stronger thantheir level factor correlation. Taking this significant difference andthe general picture of correlations into account, it appears as ifNeuroticism disassociates from the other four personality factors,while these move together.4 In order to cross-check the finding offour statistically level factor and change factor correlations, wereestimated Model LCM8 with the Neuroticism and Extraver-sion, Neuroticism and Openness, Neuroticism and Agreeable-ness, and Agreeableness and Conscientiousness correlationsbeing unconstrained, �2(386) � 700.88, p � .05, CFI � 0.928,RMSEA � 0.052, which compared to the reference model(LCM4) did not represent a statistically significant difference,��2(6) � 9.83, p � .13. Thus, we may conclude that apart fromthe aforementioned correlations, there was stationarity regard-ing level and slope correlations.

Discussion

The aim of the present article was to extend previous researchon personality trait development in old age by investigating struc-

tural stability, differential stability, and mean-level change over anapproximately 12-year time period. In addition, change in interin-dividual differences and intraindividual correlated change wereexamined.

We started our analyses with an emphasis on the measurementproperties of the NEO-FFI parcels, which were subjected to aseries of increasingly rigorous tests of the comparability of theirscores over time. We found that the criteria for strict MI were metacross the 12-year follow-up period (cf. Meredith, 1993). Hence,comparisons of factor (co)variances and means were deemed in-terpretable as reflecting quantitative shifts in invariant measures.Our inferences about MI are tempered, however, by the fact thatwe were not able to evaluate invariance across intact personalityfacet scales (i.e., the NEO Personality Inventory—Revised; Costa& McCrae, 1992), which are nonexistent in the NEO-FFI (but seeChapman, 2007; Saucier, 1998). By using parcels as an alternativeof item-level modeling, we specified a less complex measurementmodel due to fact that the number of manifest variables enteringthe analyses was reduced considerably. This fact probably contrib-uted to the feasibility of finding strict MI. At the same time, thedistributional properties of the parcel warranted the use of maxi-

3 Upon the suggestion of an anonymous reviewer, we reestimatedchange correlations using linear latent growth models and data from allthree measurement occasions, that is, including T2. Results regardingchange correlations were virtually the same, which, in light of the fact thatthe T2 data receive only one ninth of the weighting of that of the T3 datawith respect to slope variances and covariances, is what one would haveexpected.

4 An anonymous reviewer suggested that the correlational patternsamong personality traits at T1 and, in particular, among the personality traitchanges across 12 years might suggest a second-order factor model. Wetested such a model with Extraversion, Openness, Agreeableness, andConscientiousness at T1 loading on one common factor and Extraversion,Openness, Agreeableness, and Conscientiousness changes loading on asecond common factor. This model did achieve an almost adequate fit asjudged by the standalone fit indices, �2(411) � 834.58, CFI � .903,RMSEA � 0.059, 90% CI � 0.053, 0.064. However, compared to LCM4,it represented a pronounced loss in fit, ��2(31) � 143.53, p � .01. Uponinspection, the loss in model fit was mainly due to the fact that second-order factors did not adequately capture the T1 change correlations of thepersonality traits.

Table 3Personality Factor and Change Factor Correlations

Variable 1 2 3 4 5 6 7 8 9 10

1. Neuroticism —2. Extraversion �.458 —3. Openness �.262 �.482 —4. Agreeableness �.263 �.419 �.240 —5. Conscientiousness �.299 �.520 �.291 �.383 —6. �Neuroticsim �.343 �.064 �.097 �.018 �.050 —7. �Extraversion �.019 �.144 �.002 �.198 �.112 �.133 —8. �Openness �.001 �.134 �.240 �.154 �.126 �.030 �.554 —9. �Agreeableness �.137 �.168 �.051 �.491 �.189 �.001 �.435 �.410 —

10. �Conscientiousness �.041 �.075 �.089 �.190 �.278 �.141 �.658 �.486 �.686 —

Note. N � 300. Correlations in italics are not statistically significant at p � .05. Correlation estimates are based on Model LCM4 (strict measurementinvariance).

553LONG-TERM CORRELATED CHANGE IN PERSONALITY TRAITS

mum likelihood parameter estimation, whereas directly factoringindividual items would have required multiple-groups factor anal-ysis of ordered-categorical data (Yun-Tein & Millsap, 2004).

Next, structural stability was investigated. Our data suggestedthat the relations among the five personality traits are subject tochange over time. Specifically, the pattern of covariation betweenConscientiousness and three other traits (i.e., Extraversion, Open-ness to Experience, and Agreeableness) showed an increase at T2,indicating that the relative significance of Conscientiousness withrespect to these three other personality traits seemed to becomestronger over time. This finding contrasts with other studies, wherethe interrelations among the five personality traits across age havebeen reported to be highly stable both cross-sectionally and lon-gitudinally (e.g., Allemand et al., 2007; Costa & McCrae, 1997;Small et al., 2003; Srivastava et al., 2003). However, in themajority of studies investigating structural stability in personalitytraits in old age, the longitudinal time span was shorter than in ourstudy, where it was long enough to capture structural changes ofpersonality.

The present finding of structural change implies that personalitymight become less differentiated or, in turn, more dedifferentiatedover time in old age. A similar finding concerning the developmentof the structure of traits in adolescents, yet in the opposite direc-tion, has been reported by Allik, Laidra, Realo, and Pullmann(2004). In a large cross-sectional sample of 12- to 18-year-olds,they found that self-reported personality trait structure matures andbecomes sufficiently differentiated around age 14–15 and grows tobe practically indistinguishable from adult personality by the ageof 15. The most striking age difference was found for the corre-

lation between Agreeableness and Conscientiousness, which de-creased with age from .49 to .18. Together with our results, thissuggests a lifespan pattern of differentiation of personality intoadulthood, followed by a dedifferentiation into old age (Baltes etal., 1980).

Regarding the associations within factors, but across occasions,mean differential stability of .70 over 12 years in old age appearshigh; however, it leaves room for individual change. Generally, thepresent data closely correspond to the longitudinal stability coef-ficients reported in previous longitudinal aging studies (Mroczek& Spiro, 2003; Roberts & DelVechio, 2000; Small et al., 2003).Recently, Terracciano, Costa, and McCrae (2006) reported differ-ential stability of the Big Five personality traits for adults olderthan 65 across an average time interval of approximately 10 years.In our study, Openness to Experience, Agreeableness, and Con-scientiousness had lower stabilities than reported by Terraccianoand colleagues (2006). These attenuated stability coefficients mayhave three potential sources: First, personality traits appear to beless stable when assessed with the shorter and less precise NEO-FFI test form (Costa & McCrae, 1992). Terracciano et al. (2006)and other researchers (e.g., Costa, Herbst, McCrae, & Siegler,2000), for example, reported lower stability coefficients with re-spect to specific traits or facets of the five factors. Second, ourparticipants were slightly younger and more homogeneous withrespect to age (60–64 years at T1) than Terracciano et al.’s (2006)oldest age group (66–89 at T1), which might have attenuatedstabilities. Eventually, the relatively lower differential stability ofthe personality traits in our study might be explained, in part, bythe transition phase from young–old age into old–old age occur-

Figure 4. Level and change factor correlations. Estimates are based on Model LCM4 (strict measurementinvariance). Error bars reflect the 95% inferential confidence interval (CIs). N � Neuroticism; E � Extraversion;O � Openness to Experience; A � Agreeableness; C � Conscientiousness.

554 ALLEMAND, ZIMPRICH, AND MARTIN

ring during the longitudinal follow-up. That is, one might expectthat transitional phases in life and the way different individualsdeal with them should decrease the stability estimates of person-ality.

An oftentimes neglected aspect of change is change of diver-gence, which refers to increasing or decreasing individual differ-ences over time. Although change of divergence is preferablyexamined longitudinally, sample selectivity could systematicallyaffect variances, which may be one reason for a neglect of inves-tigating variance changes in developmental personality research.Another reason probably is that existent theories on personalitydevelopment do not touch the issue of variance changes directly,which makes it difficult to deduce hypotheses. Our results indicateincreases in individual differences in Openness to Experience andConscientiousness revealed a statistically significant increase,amounting to about 29% and 34%, respectively. Hence, 12 yearsafter the T1 the sample had become more heterogeneous withrespect to these two personality traits.

For individuals, this necessarily implies that they develop dif-ferentially, giving rise to the so-called fan-spread phenomenon(e.g., McArdle, 1988). Elsewhere, developmental psychologistshave argued that age-graded influences including biological andenvironmental aspects (e.g., developmental tasks) that may shapedevelopment in relatively normative ways should lead to relativelyhomogeneous trajectories. By contrast, nonnormative events thatimpact only some individuals may result in increased heterogene-ity (Baltes et al., 1980). In light of such a view of development,one might interpret the increasing variances in Openness andConscientiousness as reflecting the impact of nonnormative eventsmore strongly than the other traits. The increasing Openness andConscientiousness variances across time also show that chrono-logical age becomes an increasingly inaccurate indicator of thesepersonality traits.

Regarding mean-level changes across time, we found statisti-cally significant decreases in Neuroticism and Extraversion, im-plying that older adults become, on average, less neurotic and lessextraverted as they move from young–old age into old–old age. Interms of effect size, these effects were small (Neuroticism) andmedium (Extraversion). Other researchers have reported similarfindings (e.g., Mroczek & Spiro, 2003; Roberts, Robins, Caspi, &Trzesniewski, 2003, Roberts et al., 2006; Small et al., 2003;Terracciano et al., 2005). Of particular interest regarding Extra-version is that it has been suggested that people disengage orwithdraw from society as they grow older (Achenbaum & Bengt-son, 1994; Cumming & Henry, 1961). Further, Openness to Ex-perience tended to decrease in old age, which is consistent withmost other findings (e.g., Field & Millsap, 1991; Roberts et al.,2006; Small et al., 2003; Terracciano et al., 2005). This mightreflect, in part, the increasing influence of social or interpersonalfactors, such as the more constricted life space or greater socio-emotional selectivity (cf. Carstensen, Mikels, & Mather, 2006).

Our findings regarding Agreeableness and Conscientiousnessappear to be in contrast with meta-analytic findings. Roberts et al.(2006) reported continuing longitudinal increases in Agreeable-ness and Conscientiousness in adulthood, but there is little infor-mation on the developmental pattern of those personality traits inold samples. A cross-sectional study of patients aged 65 to 100found evidence for higher levels of Agreeableness among olderindividuals (Weiss et al., 2005), and a longitudinal study reported

increases in Agreeableness in old age (Terracciano et al., 2005).By contrast, in the study by Small et al. (2003), neither initial levelof Agreeableness or Conscientiousness nor change across 6 yearswas related to age. This discrepancy between others’ results andour results are not easily explained but might, in part, be due to thefact that effect sizes of age differences in personality and agechanges in personality are typically in the small to medium range,which might lead to more fluctuations in terms of significant ornonsignificant mean changes from study to study. Another possi-ble explanation is that there may be cultural differences betweenour German sample and the North American samples used in otherstudies (cf. McCrae & Costa, 2006).

Long-Term Correlated Change in Personality Traits

Up to the present, personality trait development researchershave neglected the aspect of whether changes in the Big Fivepersonality factors are correlated. By utilizing latent change mod-els (e.g., Hertzog & Nesselroade, 2003; McArdle & Nesselroade,1994), we found, first, substantial initial factor intercorrelations forthe personality traits. Second, the within-domain level-changerelations indicate that initial levels of personality traits are nega-tively related to change in personality traits. This implies thatpeople with high T1 scores, especially on Neuroticism and Agree-ableness, tend to show less pronounced changes over time. Moreover,we observed some small across-domain level-change correlations,which suggest that people with high initial level on Agreeablenesstend to show less pronounced changes over time with respect toExtraversion, Openness, and Conscientiousness. Finally, a numberof statistically significant and large latent change correlationsemerged among Extraversion, Openness to Experience, Agree-ableness, and Conscientiousness, reflecting the fact that individualchange in one personality trait was accompanied by a tendency ofproportional individual changes in other personality traits. Inter-estingly, change in Neuroticism, which reflects an individual’semotional reactivity, tendency to worry, and also susceptibility tonegative mood, was not significantly related to change in theremaining four Big Five traits. This finding might imply thatNeuroticism did not codevelop with the other traits from young–old age into old–old age.

In an attempt to rigorously compare cross-sectional and longi-tudinal correlations among personality factors, we estimated anintercorrelations stationarity model. From this model we con-cluded that changes in Neuroticism were significantly less stronglyrelated to Extraversion, Openness, and Agreeableness than theircross-sectional counterparts. This result, again, indicated that thedevelopment of Neuroticism appeared to uncouple from the men-tioned traits. In turn, Agreeableness and Conscientiousness seemedto coalesce longitudinally. We think that this result could be takenas an indication that, longitudinally, personality might be regardedas a fabric of dynamically interwoven traits.

The overall pattern suggests that four personality traits appear todedifferentiate somewhat. By contrast, we found some indicationsof a differentiation of Neuroticism from the other personalityfactors. This pattern suggests multiple causes for personalitychange in old age. Causes could be homogeneous with respect tothe latter four personality traits, such as similar environmentalinfluences or similar reactivity to environmental contingencies,and be heterogeneous with respect to Neuroticism, such as Neu-

555LONG-TERM CORRELATED CHANGE IN PERSONALITY TRAITS

roticism being influenced by changes in individual health status(e.g., Caspi & Roberts, 2001; Smith & Spiro, 2002). This findingmay also reflect the result of a survival effect, with those havinghigher Neuroticism being less likely to survive into older age, thusuncoupling the relation between Neuroticism and the remainingBig Five personality traits. Although this remains speculative atthis point, it discloses the need of revisiting the correlated person-ality trait development in old age from both an empirical and atheoretical perspective. Not only is the question whether person-ality remains stable or change as people age—our results confirmthat personality traits are, in fact, plastic—but how and whystability and changes in personality trait development are related.

To conclude, we have shown that individual differences inpersonality may become more pronounced with age. Furthermore,we have demonstrated that, regarding their commonality, person-ality changes operate on an intermediate level: They are neithercompletely specific or isolated, nor are they totally general orshared. Traditional conceptualizations of personality tended toemphasize the stability of personality traits after the age of 30 (e.g.,Costa & McCrae, 1994), which may explain why theoreticalaccounts of personality are only beginning to emerge (e.g., Baltes,Lindenberger, & Staudinger, 2006; Mroczek & Little, 2006; Rob-erts, Wood, & Caspi, in press). From our perspective, the resultspresented herein exemplify the need for a theoretical understand-ing of the dynamics of personality change in old age.

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Received October 10, 2007Revision received June 23, 2008

Accepted June 24, 2008 �

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