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J Pers Soc Psychol. Author manuscript; available in PMC 2012 Mar ane.

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PMCID: PMC3058678

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Genetic and environmental influences on personality trait stability and growth during the transition to machismo: A three wave longitudinal study

Christopher J. Hopwood

Michigan State Academy

M. Brent Donnellan

Michigan State University

Daniel M. Blonigen

VA Palo Alto Wellness Intendance Organization and Stanford University School of Medicine

Robert F. Krueger

University of Minnesota

Matt McGue

University of Minnesota

William M. Iacono

University of Minnesota

Southward. Alexandra Burt

Michigan State University

Abstruse

During the transition to machismo individuals typically settle into developed roles in dearest and piece of work. This transition also involves pregnant changes in personality traits that are more often than not in the direction of greater maturity and increased stability. Competing hypotheses have been offered to business relationship for these personality changes: the intrinsic maturation hypothesis suggests that modify trajectories are endogenous, whereas the life-form hypothesis suggests that these changes occur because of transactions with the social surroundings. This written report investigated the patterns and origins of personality trait changes from ages 17 to 29 using 3 waves of Multidimensional Personality Questionnaire data provided by twins. Results suggest that a) trait changes were more profound in the first relative to the 2d half of the transition to adulthood; b) traits tend to become more stable during the 2nd half of this transition, with all the traits yielding retest correlations betwixt .74 and .78; c) negative affectivity declined over time and constraint increased over fourth dimension; minimal change was observed on agentic or communal aspects of positive affectivity; and d) both genetic and not-shared ecology factors accounted for personality changes. Overall, these genetically-informed results back up a life-course perspective on personality evolution during the transition to adulthood.

The transition to adulthood between the ages of 18 and xxx involves significant psychological development with regard to intimacy, identity, work, and parenthood (run into Arnett, 2000, 2007). These changes are also accompanied by both stability and change in personality traits (e.g., Blonigen, Carlson, Hicks, Krueger, & Iacono, 2008; Donnellan, Conger, & Burzette, 2007; Roberts, Caspi, & Moffitt, 2001; Robins, Fraley, Roberts, & Trzesniewski, 2001). Still, debates exist regarding whether exogenous or endogenous factors are more responsible for personality evolution during this period of the lifespan (McCrae & Costa, 2006; Roberts, Walton, & Viechtbauer, 2006a). Appropriately, the goal of this study was to evaluate genetic and environmental influences on personality stability and change during the transition to adulthood using 3 waves of personality trait data. Specifically, we examined the etiological influences on stability and modify in the higher gild personality traits of the Multidimensional Personality Questionnaire (MPQ; Tellegen & Waller, 2008) using a sample of twins assessed in late boyhood (approximately age 17), emerging machismo (approximately age 24), and immature adulthood (approximately age 29).

Characterizing the Transition to Adulthood

Sociologists (Furstenberg, Kennedy, McLoyd, Rumbaut, & Settersten, 2004; Shanahan, 2000), demographers (Rindfuss 1991), and psychologists (Arnett, 2000; 2007) have identified several themes that narrate the transition to adulthood. Commencement, this transition takes time as individuals negotiate aspects of identity evolution and gradually assume adult roles and responsibilities (Arnett, 2004). In light of this fact, Arnett (2000) has proposed that the flow of development from the belatedly teens through the end of the 20s is a time of "emerging adulthood". Indeed, he described the menses of the early 20s as the "volitional years of life" (p. 469) because individuals seem to spend considerable resource exploring bug of identity and intimacy. Second, on average, more demographic transitions occur during the years between 18 and xxx than any other in the life course (Rindfuss, 1991). Thus, past the age of effectually 30, most individuals have assumed at least one of the major roles of adulthood and therefore the 30s seem to mark the outset of machismo proper. Third, there appears to be a considerable corporeality of variability among individuals in terms of the sequencing of the life transitions betwixt boyhood and adulthood (Rindfuss, 1991).

The salient developmental challenges and diversity of experiences people encounter in this stage of the life course suggest that the transition to adulthood represents an of import catamenia for inquiry on personality development. Existing research suggests that many of the psychological changes that occur during this flow tend to be in the direction of increased psychological maturity (Caspi, Roberts, & Shiner, 2005). Indicators of psychological well-beingness such every bit self-esteem appear to increase (Donnellan, Trzesniewski, & Robins, 2006; Galambos, Barker, & Krahn, 2006) whereas attributes such as depression, anger, and externalizing problems appear to decline (Arnett, 2000; 2007 Galambos et al. 2006). Consistent with these trends, personality trait change is generally positive: traits become more stable in full general (Roberts & Del Vecchio, 2000) and individuals tend to decline in negative affectivity and show increases in behavioral constraint during the transition to adulthood (Blonigen et al., 2008; Donnellan et al. 2007; Roberts et al., 2001). Moreover, young adulthood appears to be the fourth dimension in the life span when the majority of normative personality changes occur (Roberts, Walton, & Viechtbauer, 2006b). In light of these trends, Caspi et al. (2005) noted that "the causes of normative personality alter are likely to be identified past narrowing research attention to the study of young adulthood" (p. 468).

Following this recommendation, nosotros evaluated etiological influences on personality trait stability and alter during the transition from boyhood to machismo. Specifically, nosotros assessed iv wide personality traits assessed by the MPQ. Although arguments regarding the verbal number of traits that best draw the broadest level of personality persist, diverse personality attributes can exist organized around a relatively pocket-sized set of higher guild traits (Markon, Krueger, & Watson, 2005). It has been shown that candidate college society trait models can exist integrated such that they are not and so much direct competitors as alternative means of organizing personality dimensions within an integrated bureaucracy (Church & Burke, 1994; Markon et al., 2005). The iv factors that appear in more than or less all structural models of personality involve negative emotionality or neuroticism (NEM), agentic positive emotionality or extraversion (PEM-A), communal positive emotionality or conjuration (PEM-C), and constraint (CON) (Caspi et al., 2005; Clark & Watson, 2008).

NEM involves a susceptibility to negative emotions as opposed to a tendency to be emotionally placid and adaptable. This domain appears to map onto neurobiological systems governing withdrawal behavior in the context of environmental threats (Watson et al., 1999) and represents a risk factor for psychopathology and interpersonal difficulties beyond the lifespan (Krueger, Caspi, & Moffitt, 2000; Watson, Hubbard, & Wiese, 2000). PEM generally involves the propensity for positive emotions such as happiness besides as behavioral surgency and vigor. This trait domain is associated with enhanced reward sensitivity (Lucas et al., 2000) and extraverted, prosocial beliefs (Ashton et al., 2002). PEM has also been linked to neurobiological systems governing approach motivation (Depue & Collins, 1999). Agentic manifestations of this trait (i.east., PEM-A) involve social potency as well as ambition in achievement-related domains, whereas communal manifestations (i.e., PEM-C) involve closeness to others, amalgamation, and well-being (Humbad, Donnellan, Iacono, & Burt, in printing; Tellegen & Waller, 2010; Wiggins, 1991). CON involves the capacity to control or attune one's behavioral reactions to internal states or external stimuli, to nourish carefully and plan responsibly, and to be oriented toward long term goals. Reduced CON is a adventure cistron for substance apply and externalizing issues (Krueger et al., 2000) as well every bit general wellness risks across the life span (Bogg & Roberts, 2004). This dimension of personality has been linked to neurobiological markers of attentional focus and other frontal lobe functions (Nigg, 2000).

Characterizing Personality Stability and Change during the Transition to Adulthood

In addition to questions regarding which traits to assess, researchers studying personality development must also consider multiple kinds of personality stability and modify (Caspi et al., 2005; Donnellan & Robins, 2009; Roberts, Woods, & Caspi, 2008). Each of these types of stability involves a different analytic method and interpretive meaning. The two near commonly studied types are absolute or hateful-level stability and differential or rank-club stability.

Absolute (mean-level) stability refers to changes in group averages over time on a given trait. Accented stability can exist indexed directly with repeated-measures ANOVA models or growth bend frameworks (e.grand., Vaidya et al., 2008). During the transition to machismo, age differences in absolute levels of personality traits seem to exist well described by the maturity principle of personality development (Caspi et al. 2005) or the generalization that traits linked with cocky-command (i.east., CON) seem to increase whereas traits linked with negative affectivity (NEM) seem to subtract during the transition to adulthood (Donnellan & Robins, 2009). That is, as individuals mature they go more able to command their impulses and less prone to negative emotions. These kinds of personality changes are concordant with the fulfillment of adult roles and converge well with existing developmental research concerning trends in well-beingness (Galambos et al., 2006).

Across these hateful-level trends there may also be significant variability between individuals in their degree of alter (i.east., individual-level change). Chiefly, growth curve frameworks permit for the label of absolute stability and change in a way that as well draws attending to intra-individual differences in change (Vaidya et al., 2008). For example in a linear growth model, the fixed effect for the gradient describes normative changes in terms of the average increase or decrease for the sample as a whole whereas the random effect indicates the amount of variability around that average trend. Statistically significant variability effectually the slope, or alternatively a statistically insignificant random effect for the slope, indicates the presence of meaningful private differences in change (see Mroczek, Spiro, & Griffin, 2006). Such variability points to the fact that some individuals increase in absolute trait levels whereas others decrease in absolute trait levels.

Differential (rank-lodge) stability refers to the caste of consistency in rank-ordering of individuals over time on a given trait. This type of stability is virtually oftentimes indexed by retest correlations. Notably, such retest coefficients tend to increase across development before eventually reaching a plateau in center to afterward life (ages 50–70) (Roberts & DelVecchio, 2000). In other words, stability coefficients tend to be lower for adolescent samples than adult samples, a design that has been referred to as the cumulative continuity principle of personality evolution (Caspi et al., 2005). Moreover, personality maturity is temporally linked with increases in differential stability such that individuals who are lower in negative affectivity and college in constraint in adolescence tend to show more differential stability during the transition to adulthood (Roberts et al, 2001), and individuals with borderline personality disorder show less differential stability over time (Hopwood et al., 2009).

Despite notable consistencies in the full general patterns of absolute and differential stability among personality traits during the transition to adulthood, there are also controversies (come across Costa & McCrae, 2006; McCrae & Costa, 2008). The most active area of disagreement involves the explanation of personality maturation (i.e., change). One perspective argues that biologically-based intrinsic processes underlie changes in personality in young machismo (e.1000., Costa & McCrae, 2006; McCrae & Costa, 2003, 2008), whereas another perspective argues that personality maturation is at to the lowest degree partially tied to participation in the social roles of adulthood (due east.g., Roberts et al., 2006b; Roberts, Wood, & Smith, 2005). Consistent with this life-grade perspective, some evidence links adult personality changes with contextual conditions such as work and romantic relationships (Neyer & Lehnart, 2007; Roberts, Caspi, & Moffitt, 2003; Robins, Caspi, & Moffitt, 2002; Roberts & Wood, 2006; although see McCrae & Costa, 2008, p. 168). A genetically-informed arroyo is well suited to informing this broad contend because the intrinsic maturation perspective would seem to predict that near trait changes during the transition to adulthood are driven predominantly by genetic factors, whereas the life-grade perspective would seem to predict that personality changes during this period are likewise tied to environmental factors (Bleidorn, Kandler, Riemann, Angleitner, & Spinath, 2009).

Genetically informed cross-sectional studies accept made important contributions to personality psychology by demonstrating that higher order traits are substantially and similarly heritable but also influenced by non-shared environmental factors (those environmental factors that make siblings inside the same family different, east.yard., Loehlin, 2001). However, longitudinal behavior genetic designs are needed to assess the genetic and environmental underpinnings of personality stability and change. In one such study, McGue et al. (1993) found that genetic factors were largely responsible for differential stability whereas modify was primarily influenced by the non-shared environmental factors amid twins assessed twice around the ages twenty and xxx. Blonigen and colleagues (2008) used the first two waves of information analyzed in the electric current report to evaluate genetic and environmental contributions to personality development between the ages 17 and 24. Bleidorn et al. (2009) used genetically informed growth modeling as applied to a mixed-age sample of German twins assessed at multiple waves. Both of these studies reported results consistent with the maturity hypothesis and found that genetic and environmental factors accounted for personality changes.

These studies also provide consistency of results across ages, samples, and methods in back up of the maturity hypothesis. However, this emerging behavioral genetic literature on personality development is limited in several ways. Almost notably, although it is widely recognized that multiple moving ridge studies provide the opportunity to employ more sophisticated methodological approaches (e.thousand., Biesanz et al., 2003) nigh genetically informed longitudinal personality inquiry has used only two measurement waves. In fact, most phenotypic studies of personality development during the transition to adulthood accept relied on 2-wave studies (but come across Vaidya et al., 2008 for an exception). However, the application of growth bend modeling techniques to datasets with three or more than assessments addresses important questions about personality evolution (Mroczek & Spiro, 2003). For example, such approaches can exist used to test the capability of linear models of absolute growth and identify individual differences in accented change. Twin studies that use this analytic approach are particularly important as they tin be used to decompose the origins of individual differences in change into genetic and environmental components.

In low-cal of these advantages, the work of Bleidorn and colleagues (2009) represents a seminal contribution as the outset multi-wave beliefs genetic written report of personality trait stability. All the same, this study assessed a relatively small sample of individuals who varied widely in age at first assessment. Thus, this work was limited in its power to specifically characterize personality stability and change during the transition to adulthood. By comparison, the Minnesota Twin Family Report (MTFS) data, which sampled twins during this period, are well-suited to address such issues and tin potentially resolve contrasting views regarding on the origins of personality stability and change.. Specifically, given varying links of NEM, PEM-A, PEM-C, and CON to biological and environmental processes and their varying phenotypic patterns of personality development in previous inquiry (e.yard., Blonigen et al., 2008; Donnellan et al., 2007; Roberts et al., 2001; Vaidya et al., 2007), the degree to which the intrinsic or life-course perspective is supported may also vary by trait.

The Present Study

The present written report was designed to address these gaps in current cognition about the influences on personality change during the transition to adulthood. The specific goal of this project was to extend inquiry on personality development during the transition to machismo using three waves of information from a community sample of twins who were assessed in boyhood (mean age 17), emerging machismo (24) and young machismo (29) with the MPQ (Tellegen & Waller, 2008). This study offers an important extension of the previous study on personality stability and change from this sample (Blonigen et al., 2008) in its test of an additional wave of information that was non yet available at the time of the Blonigen et al. report. This additional moving ridge allows tests regarding the linearity of personality change during the transition to adulthood, such as whether more changes occur during the peak of emerging adulthood (due east.thousand., 17 to 24) as opposed to the period between 24 and 29 when participants are more likely to have settled into machismo. Moreover, this additional wave allows the awarding of analytic techniques used past Bleidorn et al. (2009) to understand genetic and environmental contributions to personality changes. Dissimilar Bleidorn et al. (2009), nevertheless, our data allow u.s. to focus on the critical menses of the lifespan when individuals are transitioning to adulthood. Accordingly, the results can more specifically adjudicate among intrinsic and lifespan perspectives on the underlying impetus for personality maturation during the transition to adulthood.

Method

Participants

Participants were same-sex activity male and female monozygotic (MZ) and dizygotic (DZ) twins from the Minnesota Twin Family Study (MTFS), which is a population-based sample of reared-together twins (Iacono & McGue, 2002). Zygosity was determined past parent and MTFS staff reports on physical resemblance and an algorithm which uses ponderal and cephalic indices and fingerprint ridge counts to appraise similarity. When these estimates did not agree a serological analysis was performed to confirm twin condition. This study used MPQ information from a cohort born between 1972 and 1979 who were identified in Minnesota public nascency records and recruited to participate at approximately 17 years of age (range = 16–eighteen years). Exclusion criteria included living more than a one-twenty-four hour period drive from the information collection site or serious cognitive or physical disabilities which would preclude participation. Of those recruited, 83% agreed to participate and no meaning differences were observed betwixt participating and not-participating families or the Minnesota population more than generally in terms of cocky-reported psychopathology or SES (Iacono et al., 1999; Holdcraft & Iacono, 2004). Even so, MZ twins are over-represented relative to DZ twins in terms of population incidence (Hur, McGue, & Iacono, 1995), and in that location was a slightly higher rate of agreement to participate among MZ families. At baseline in that location were 626 consummate pairs of twins (Women n_MZ = 223, n_DZ = 114; Men northward_MZ = 188, n_DZ = 101). Twins were reassessed at the average ages of 24 and 29.

Measure

Participants completed a 198-detail version of the Multidimensional Personality Questionnaire (MPQ) and analyses focused on the 4 higher order MPQ traits, NEM (baseline α = .92), PEM-A (α = .89), PEM-C (α = .91), CON (α = .88). At baseline, complete MPQ data were available for 1111 participants (N_women = 614; N_men = 497), at age 24 at that place were 943 (North_women = 553; Northward_men = 390), and at age 29 in that location were 956 (N_women = 505; N_men = 451). Attrition analyses suggested that the baseline trait scores betwixt those who continued versus those who dropped out at each follow-upwards were less than .10 standard deviations unlike on each of the four traits examined, suggesting that individuals who provided data at follow-ups were generally representative of the baseline sample. Equally in Blonigen et al. (2008), MPQ trait data were standardized with a T-calibration metric using wave ane data.

Analyses

Phenotypic Analyses

Test-retest coefficients were used to bespeak the degree of differential stability of personality traits over fourth dimension. Growth curve analyses were used to guess the degree of mean-level modify in traits across the three waves, equally well every bit individual variability in rates of alter following procedures outlined in Kashy, Donnellan, Burt, and McGue (2008) for working with twin data. These models were fit using maximum likelihood estimation in AMOS 17.0. Slope paths were fixed to 0 for the baseline assessment, 1 for the third assessment, and were estimated from the information for the 2nd assessment (i.e., the same coefficient was estimated for both twins). This allows the model to estimate the extent to which change occurred during the start or 2d intervals given that an empirical value shut to .58 (seven years/12 years = .58) would indicate linear growth over this time (see Preacher, Wichman, MacCallum, & Briggs, 2008, p. 52–55). Intercept paths were fixed to 1 for each measurement occasion so that the intercept represents scores at the first assessment. Residuals were freely estimated at each wave (but fixed to the same value for each twin) and we specified a twin correlation betwixt these residuals within measurement occasions. Twins were constrained to have equal intercept and gradient parameters.

Biometric Analyses

Structural equation modeling of twin data is based on the difference in the proportion of genes shared betwixt MZ twins, who share 100% of their genetic material, and DZ twins, who share an boilerplate of fifty% of their segregating genetic material. MZ and DZ twin correlations are compared to estimate the relative contributions of additive genetic furnishings (a²), shared environmental effects (c^two), and non-shared environmental furnishings plus measurement mistake (e²) to the variance within observed behaviors or characteristics (i.e., phenotypes). Crucial to this methodology is the Equal Environments Assumption (EEA), which assumes that MZ pairs are no more than likely to share the environmental factors that are etiologically relevant to the phenotype under study than are DZ pairs (Kendler, Neale, Kessler, Heath, & Eaves, 1993). Any differences in the MZ and DZ correlations are thus causeless to be due to differences in the genetic similarity of the corresponding twins.

To evaluate the origins of differential stability, nosotros fitted a Cholesky decomposition model. Within a triangular, or Cholesky decomposition, model (see Effigy 1), the variance inside and the covariance betwixt personality traits across each assessment were decomposed into their genetic and ecology components. In this model, the genetic, shared, and not-shared ecology covariances can exist standardized on their corresponding variances to produce genetic, shared environmental, and non-shared environmental correlations. These statistics reveal the extent to which a specific effect (east.k., the genetic effect) at one assessment is correlated with the aforementioned effect at another cess. A genetic correlation of 1.0 would betoken that all genetic influences persist across assessments, whereas a correlation of nil would betoken no genetic overlap. This model thus enabled us to explicitly guess the extent to which genetic and environmental influences contribute to the differential or rank-guild stability of personality over time.

Path diagram of Cholesky decomposition model

Note. The variance in liability to personality at each assessment is parsed into that which is due to additive genetic effects (A1, A2, and A3), shared environmental effects, and not-shared environmental effects (E1, E2, and E3). Though used in the model, shared environmental effects (C) are not represented herein for ease of presentation. Similarly, this path diagram represents only one twin in a pair (results are identical for the co-twin). Paths, which are squared to estimate the proportion of variance deemed for, are represented by lowercase letters followed by two numerals (e.grand., a11). Genetic and environmental correlations are indicated by a lowercase r, followed by data regarding the specific correlation in question (e.g., rA_17→24 Indicates the genetic correlation between ages 17 and 24).

Biometric latent growth curve modeling was used to evaluate the origins of accented stability and change (Neale & McArdle, 2000). The full biometric growth model is depicted in Figure ii. In this model, the variance in personality at whatever given assessment was decomposed into iii portions, all of which were then further decomposed into their additive genetic, shared environmental, and non-shared environmental components. We showtime examined genetic and environmental contributions to the latent intercept (i.due east., a_i, c_i, e_i), which captures individual differences at the commencement assessment. Nosotros next examined genetic and environmental contributions to variability in absolute-level changes in personality over time. The gene loadings identified in the phenotypic growth curve analyses (as described previously) comprised the slope's factor loadings at ages 17, 24, and 29 (i.e., 0, .75, and 1.0, respectively, for Constraint; 0, .84, and i.0, respectively, for Negative Emotionality). We finally examined the genetic and environmental contributions to the variance remaining at each assessment after accounting for the furnishings of the intercept and slope factors (i.eastward., a_i, c₁, e_one at time 1, a₂, c_two, due east₂ at time 2, a₃, c₃, due east₃ at time 3, respectively).

Path Diagram of Biometric Latent Growth Curve Model

Annotation. For ease of presentation, this path diagram represents simply 1 twin in a pair (results are identical for the co-twin). Variances in the intercept and linear gradient factors are parsed into that which is due to additive genetic effects (A), shared environmental effects (C), and non-shared environmental effects (East). Paths are represented by lowercase messages followed by sub-scripted letters respective to their respective factor (east.one thousand., a_i, a_s). Genetic and ecology correlations betwixt the factors are presented at the acme of the diagram (i.e., r_A, r_C, r_E). The assessment-specific rest paths loads directly onto personality at each assessment, and are indicated by a lowercase letter followed by a single sub-scripted numeral (i.e., a₁). Factor loadings for the intercept are stock-still prior to assay (x is determined past the phenotypic growth bend modeling results; .75 for Constraint and .84 for Negative Emotionality.

To address incidental missing data, we made utilise of Full-Information Maximum-Likelihood (FIML) estimation fit to raw data for both the Cholesky and the latent growth bend model, which produce less biased and more than efficient and consistent estimates than techniques similar pairwise or listwise deletion for missing data (Little & Rubin, 1987). Mx (Neale, 1997) was used to fit the models to the raw data. When plumbing fixtures models to raw data, variances, covariances, and ways of those data are freely estimated by minimizing minus twice the log-likelihood (−2lnL). The −2lnL nether this unrestricted baseline model is and so compared with −2lnL under more than restrictive biometric models. This comparison provides a likelihood-ratio chi-square exam of goodness of fit for the model, which is and so converted to the Akaike'due south information criterion (Akaike, 1987); AIC=χ²-2df), the traditional fit alphabetize of behavioral genetics research. The AIC measures model fit relative to parsimony. Better fitting models have more than negative values.

Results

Phenotypic Results

Differential Stability and Change

Stability coefficients are reported in Table ane for the three intervals under investigation (17 to 24, 24 to 29, and 17 to 29). These results were consequent with previous studies in suggesting substantial differential stability in personality traits in general, with 12-year correlations ranging from .49 to .57. More change occurred during the commencement wave of the study (range = .52–62) relative to the 2d (.74–.78), consistent with the cumulative continuity principle of personality development. Overall, the magnitude of differential stability was similar across traits.

Table 1

Stability and Change in Negative Emotionality, Agentic and Communal Positive Emotionality, and Constraint during the Transition to Adulthood.

	Differential Correlations			Accented
				Mean (S.D.)			Cohen's d			F
Trait	17–24	24–29	17–29	17	24	29	17–24	24–29	17–29
NEM	.53	.74	.49	l.00 (ten.00)	42.thirty (x.23)	41.01 (10.05)	−.77	−.13	−.xc	550.79*
PEM-A	.59	.77	.58	50.00 (10.00)	50.75 (ix.58)	50.43 (9.77)	.09	.03	.12	16.91*
PEM-C	.52	.74	.52	50.00 (10.00)	48.97 (9.91)	48.31 (9.68)	−.11	−.07	−.17	11.96*
CON	.62	.78	.56	50.00 (10.00)	54.95 (9.67)	56.62 (9.59)	.52	.17	.69	322.02*

Absolute Stability and Change

Observed changes in the absolute levels of traits (Table ane) were generally consistent with the maturity principle of personality evolution. Figure iii plots changes in all 4 traits over the course of the study, with trait values standardized at the baseline assessment. NEM declined substantially in the first interval and more than modestly in the second. CON tended to increment with changes again existence more dramatic in the first than 2nd interval. Agentic and Communal Aspects of PEM increased slightly but the trajectories of these aspects of PEM were somewhat different: whereas Agentic PEM increased very modestly across both intervals, Communal PEM increased during the offset interval and decreased very modestly during the 2d. The overall results, however, advise that much more developmental change occurs for NEM and CON relative to PEM-linked traits.

Absolute Changes in Personality Traits during the Transition to Adulthood

Note. Trait scores were standardized at the kickoff wave using a T-score metric.

Greater individual-level change besides occurred on NEM and CON. Individual-level variability tin exist conceptualized every bit the number of individuals who reliably change on a given variable over time (Jacobsen & Truax, 1991). Using the short-term retest coefficients provided by Tellegen and Waller (2008; i.e., .89 for all traits), fifty% of the sample reliably (i.eastward., > two standard errors) changed on NEM over the course of the written report, 43% inverse on CON, 34% changed on PEM-A, and 36% changed on PEM-C. All the same, these results as well suggest that, while most of the changes on NEM and CON were in a similar direction (46 out of 50% of those who changed on NEM showed decreases; 38 out of 43% showed increases on CON), change in PEM was the event of both individuals who increased and those that decreased on PEM-A (18% increased and 16% decreased) and PEM-C (xiii% increased and 23% decreased). Thus, the direction of private alter was more compatible for NEM and CON.

Growth curve modeling allows more specific inferences regarding absolute change in these traits. Models for PEM-A or PEM-C were non interpreted because of negative variances (i.e., 'Heywood cases'). Given that absolute alter was not impressive in a descriptive sense, no farther efforts were fabricated to modify these models to obtain an admissible solution, and the origins of change were non pursued for these personality domains. The CON and NEM models were saturated after correcting the fit statistics for twin information (see Kashy et al., 2008; Kenny & Olson, 2006) and thus no fit statistics are reported. Parameters from these models are given in Table 2, and text explaining what these and other key parameters signify can be plant in the Table two notation equally well as the notes for the other tables. The paths from the gradient factor to the second measurement occasion were substantially greater than .58, consistent with descriptive results in suggesting that almost of the change in NEM and CON occurred between adolescence and emerging machismo, and that the rate of change declined from emerging to immature machismo. Both slope and intercept means and variances were statistically significant, again pointing to the beingness of meaningful inter-individual variability in trait levels and modify trajectories for both of these traits. The correlations between slopes and intercepts were negative for both traits, and merely significant for CON.

Table 2

Univariate Growth Curve Parameters for Models Depicting Accented Stability and Change in Negative Emotionality and Constraint during the Transition to Machismo.

Trait	Intercept		Slope		Time 2 Gradient path coefficient	Gradient – intercept correlation
	Mean	Variance	Mean	Variance
Negative Emotionality	l.11*	70.97*	−9.18*	51.43*	.84*	−.34*
Constraint	49.94*	75.13*	6.78*	47.88*	.72*	−.35*

Biometric Results

Fit statistics for the biometric Cholesky (to exam influences on differential stability and change) and latent growth curve (to test influences on absolute stability and modify) models are presented in Tabular array 3. We initially estimated variances, covariances, and ways for the raw data to become a baseline alphabetize of fit for each trait. The Cholesky and latent growth curve biometric models were them compared to the baseline model to yield a χ² goodness of fit examination, which is then converted to AIC. All models fit their respective data well.

Table three

Fit Statistics for Cholesky (to appraise influences on differential stability and alter) and Growth Curve (to assess influences on accented stability and change) biometric models.

Personality Trait	Model	−2lnL	df	10² (Δdf)	AIC
Negative Emotionality	Baseline	24273.26	3117	---	---
	Cholesky	24310.44	3150	37.18 (33)	−28.82
	Growth Curve	24310.48	3147	37.22 (30)	−22.78
Constraint	Baseline	24854.86	3117	---	---
	Cholesky	24881.21	3150	26.35 (33)	−39.65
	Growth Curve	24880.09	3147	25.23 (30)	−34.77
Positive Emotionality - Agentic	Baseline	24326.69	3146	---	---
	Cholesky	24372.97	3179	46.28 (33)	−19.72
	Growth Curve	---	--	---	---
Positive Emotionality - Communion	Baseline	24549.54	3145	---	---
	Cholesky	24590.67	3178	41.13 (33)	−24.87
	Growth Curve	---	--	---	---

Differential Stability and Modify

Parameter estimates for the Cholesky models are presented in Table 4, separately for each trait. There was prove of significant genetic contributions to all four traits (accounting for 33–56% of the variance), as well as significant not-shared environmental influences (bookkeeping for 42–61% of the variance). There was no evidence of significant shared environmental influence across any trait. These proportions of variance were essentially invariant across age, with little to no differences observed beyond the three assessments. These results advise that personality is as heritable in belatedly boyhood as it is in young adulthood.

Table 4

Standardized parameter estimates from the Biometric Cholesky Decomposition Model.

Trait	Component of variance	% Age 17	% Age 24	% Age 29	r17→24	r17→29	r24→29
Negative Emotionality	A	.34*	.33*	.33*	.75* (.32, 1.0)	.86* (.47, ane.0)	.99* (.77, ane.0)
	C	.05	.09	.10	--	--	--
	E	.61*	.58*	.57*	.36* (26, .44)	.32* (23, .40)	.60* (54, .66)
Constraint	A	.53*	.56*	.49*	.81* (67, .98)	72* (58, .88)	.96* (86, 1.0)
	C	.02	.01	.01	--	--	--
	E	.44*	.42*	.l*	.44* (35, .51)	.38* (28, .46)	65* (58, .70)
Positive Emotionality - Agentic	A	.fifty*	.50*	.53*	79* (67, .93)	.73* (62, .88)	.96* (91, 1.0)
	C	.00	.00	.00	--	--	--
	Eastward	.50*	.50*	.47*	39* (thirty, .48)	.42* (34, .fifty)	.58* (51, .65)
Positive Emotionality - Communion	A	.38*	.46*	.42*	66* (29, .85)	.69* (32, .91)	.95* (82, one.0)
	C	.04	.02	.06	--	--	--
	East	.58*	.51*	.52*	.37* (.27, .46)	.35* (.26, .43)	.56* (.48, .63)

Equally indicated by the generally non-overlapping 95% confidence intervals for the genetic and non-shared environmental correlations (run across Table 5), genetic influences appeared to be more stable over time than were non-shared environmental influences beyond all personality factors. More than importantly, however, the non-shared environmental correlations by and large appeared to increase with age. In particular, the non-shared ecology correlations from ages 17 to 24 were significantly smaller (as evidenced by non-overlapping conviction intervals) than were those from ages 24 to 29, suggesting increased stability in the environmental furnishings associated with personality stability following emerging adulthood. The same general pattern of increasing etiological stability with age was as well present for genetic effects, all the same, these differences were less pronounced (perhaps reflecting the rather high levels of genetic stability in general). In any case, such findings serve both to highlight strong genetic contributions to the differential stability of personality from late-boyhood through young adulthood and propose that these influences become specially stable post-obit emerging adulthood.

Tabular array 5

Biometric Latent Growth Curve Model results.

	A	C	E	Total Variance	Factors %
Negative Emotionality
Factors
Intercept (east.1000., ai)	.455*	.013	.532*	121.56	--
Gradient (due east.thou., asouth)	.122	.086	.792*	85.01	--
Genetic/environmental        correlations (eastward.g., rA)	−.xi	--	−.46	−29.81	--
Residuals
Age 17 (eastward.yard., a1)	.082	.000	.246*	--	67%
Age 24 (e.g., a2)	.029	.000	.222*	--	75%
Age 29 (eastward.k., a3)	.000	.017	.155*	--	83%
Constraint
Factors
Intercept (e.grand., ai)	.677*	.000	.323*	198.43	--
Gradient (due east.g., asouth)	.504*	.000	.496*	120.49	--
Genetic/environmental        correlations (eastward.g., rA)	−.40	--	−.32	−55.63	--
Residuals
Age 17 (due east.g., ai)	.048	.000	.197*	--	75%
Age 24 (due east.g., atwo)	.049	.027	.124*	--	eighty%
Age 29 (eastward.yard., athree)	.000	.000	.121*	--	88%

Absolute Stability and Alter

Results from the biometric latent growth curve models are presented in Table 5. Equally seen in that location, the intercept factor for NEM was significantly influenced by both genetic and non-shared ecology forces. The shared surround contribution was not significantly different from nothing. The slope factor for NEM, by contrast, was influenced primarily by non-shared environmental influences. Moreover, these influences appeared to differ from those contained in the gradient, as evidenced by the rather small non-shared environmental correlation between the two factors that were non-significant. The contributions of genetic and shared ecology influences to the slope were minor and were not statistically pregnant. Finally, the residuals were relatively minor and were solely non-shared ecology in origin. Equally measurement mistake will also be contained within the not-shared environmental residuals, such findings may or may not imply that in that location are cess-specific non-shared ecology influences that meaningfully contribute to changes in personality over fourth dimension. This cautious estimation is augmented past the rather large amount of phenotypic variance accounted for past the latent intercept and gradient factors (67–83% of the variance in NEM). All in all, information technology appears that there are unique environmental experiences that differ across the twin siblings and which meaningfully influence accented changes in NEM over time.

The pattern of stability and change in CON was somewhat different. The intercept cistron was primarily genetic in origin (68%), although non-shared ecology influences also contributed (32%). Moreover, the gradient factor was both genetic and non-shared ecology in origin. Such findings suggest that genetic influences play an important function in explaining absolute changes in CON that are associated with age. As earlier, these interpretations are augmented by the prominent corporeality of phenotypic variance in CON that is collectively accounted for by the latent intercept and slope factors (75–88% of the variance at each age).

Word

This study extends research on personality development past exploring genetic and environmental contributions to differential and absolute stability and change in personality traits assessed at the offset, middle, and terminate of the transition to adulthood. The results for the differential stability of phenotypic traits corroborate previous reports suggesting that differential stability tends to increase with age form virtually personality traits (Roberts & Del Vecchio, 2000). These trends represent an example of the cumulative continuity principle of personality development – differential (or rank-order) stability tends to increase with age. In particular, differential stability is lower in the period from 17 to 24 than in the period from 24 to 29. This suggests that some important developmental periods tin exist characterized, in part, by dissimilar rates of personality stability. Arnett (2000) originally suggested that the period of emerging adulthood extended from the late teens to the mid 20s and these stability coefficients are consistent with the idea that there is more personality instability during this menstruation of the life bridge as compared with the interval between 24 and 29.

Findings with respect to absolute stability and change varied more beyond the traits only they generally supported the maturity principle of developed personality development (e.g., Caspi et al., 2005) (see Figure 3). NEM showed overall decreases with historic period, only the decreases tend to exist strongest in the early part of the transition to adulthood, and so started to level off somewhat during the mid-20s. CON showed overall increases with historic period, merely once again change was more profound during the first interval than the second interval. Agentic and Communal PEM (PEM-A, PEM-C, respectively) tended to evidence less systematic change than NEM or CON. Greater relative changes in NEM and CON relative to PEM-A and PEM-C during the transition to adulthood may be due to the fact that these former traits have more straightforward and more than empirically consistent links to adaptation. Although both PEM-related traits increased modestly overall, patterns of alter were also somewhat unlike beyond PEM-A and PEM-C. Whereas increasing levels of PEM-A remained fairly constant during the transition to adulthood, PEM-C increased initially, but and so decreased somewhat. This may reflect the fact that relational patterns ofttimes tend to resolve somewhat before the attainments of careers or other bureau-related achievement. For example, in Erikson'southward (1950) classic model, the effect of intimacy versus isolation is posited to exist typical of immature adulthood, whereas generativity versus stagnation is more salient during adulthood.

Beyond characterizing personality evolution during this period at a phenotypic level, this written report provides insights into the genetic and environmental origins of stability and change, and is therefore particularly relevant to deciding between competing intrinsic maturation versus lifespan perspectives for the origins of adult personality development (meet also Bleidorn et al. 2009). The intrinsic maturation perspective (McCrae & Costa, 2003, 2008) suggests that both stability and change are driven primarily past genetic influences. In contrast, the lifespan perspective (due east.chiliad., Roberts et al., 2006a; Roberts, Wood, & Smith, 2005) posits that personality changes occur partly as a consequence of interactions with and efforts to adapt to the social surround.

Although there was show for genetic contributions to stability and modify in personality, these results provide support for the lifespan perspective given that not-shared environmental factors accounted for personality changes over time (see likewise Bleidorn et al., 2009). For both NEM and CON, genetic factors tended to influence trait levels overall, too as the stability of those levels, but the non-shared environment was an of import influence on changes in trait levels over time. This effect appeared to be somewhat stronger for NEM than for CON. CON involves the person's ability to attune their emotional responses to internal and ecology stimuli, and is linked to the forebrain (DeYoung & Gray, 2009), a structure that continues to develop into the transition to adulthood (Giedd et al., 1999; Gogtay et al., 2004). Thus, the finding that genetic effects are stronger predictors of change in CON may reflect the fact that there are genetic influences on developments in the forebrain that go along past adolescence. Dissimilar CON, NEM is linked to more primitive structures in the encephalon which are fairly-well adult past machismo. Negative emotionality may be more than responsive, during this age, to ecology conditions than to genetically-influenced developmental processes. There are besides hints in the literature that changes in NEM are linked to ecology factors such as experiences in romantic relationships (e.g., Robins et al., 2002).

With regard to differential stability, genetic influences were important for explaining the differential stability of those levels over time; however, experiences that were unique to each individual twin (i.eastward., non-shared environs) likewise influenced differential patterns of change and these influences tended to increase during the 2nd wave. This pattern suggests that environment factors that are unique to each twin human action to promote greater stability in personality traits during the transition to adulthood. Moreover, the increasing level of connection between non-shared environmental influences from the second to the third wave suggests that individuals might be more consistently selecting into "stability-promoting" environments starting at around age 24. . Information technology may be the case that the selection into developed roles and relationships enhances personality stability (Roberts & Wood, 2006; Robins et al., 2002). Accordingly, the domains of love and work are likely a fruitful area of exploration for environmental influences on personality stabilization.

In addition to providing valuable data relevant for ongoing debates about the underlying causes of adult personality development, the current findings also shed low-cal on the nature of emerging machismo. Arnett (2000; 2004) described the transition to adulthood in Western society as involving both a processes of exploration and identity consolidation. It is also a period marked by relative liberty from conspicuously defined roles and responsibilities. Individuals at this age are relatively free from external constraints such as the limitations on beliefs imposed by parents during boyhood and the constraints imposed by formal adult roles that occur in machismo (e.g., family and career demands). However, towards the mid 20s, individuals typically begin a process of selecting into environments that place constraints on development and may press for certain kinds of thoughts, feelings, and behaviors – they frequently enter into committed romantic relationships and form their own families, establish permanent residences, and settle into careers. These choices may further reinforce and accentuate personality traits that were linked with those choices in the offset place, as posited past the corresponsive principle of adult personality development (Caspi et al., 2005).

All in all, the electric current report suggests that genetic factors are important for understanding both personality stability and change. Even so, environmental factors thing as well which tends to support the broader transactional perspectives on developed personality development. The transactional or life grade perspective on personality evolution suggests that both intrinsic and extrinsic factors play a part in shaping individual dispositions. Indeed, previous genetically-informed studies also point to environmental influences on personality development in adulthood (Bleidorn et al., 2009; Johnson, McGue, & Krueger, 2005). Collectively, these findings are seemingly incompatible with a pure intrinsic maturation explanation. Nonetheless, future genetically-informed inquiry is needed to definitively resolve this debate.

Despite several methodological strengths such as the utilise of twins and age-targeted sampling, a number of study limitations are also notable. First, controversies exist regarding the structure of personality traits; given that we observed some varying patterns beyond the traits, future research should exam personality development using different trait models. Fortunately, contempo work indicates that most omnibus trait models can be integrated within a hierarchical structure (Markon et al., 2005; Watson et al., 2008) suggesting that unlike trait models are not so much competitors but simply culling ways of organizing personality attributes at different levels of abstraction.

2d, this written report relied on a single self-report measure of personality. Across typical concerns associated with using whatever method in isolation or with self-written report in detail, some testify suggests that non-shared environmental effects may be over-estimated when using self-report measures only (Riemann et al., 1997). Thus, futusre research should employ multiple methods for the assessment of personality traits. Finally, although our sampling finer targeted an important developmental menstruum, futurity research that spans wider historic period ranges, and with assessments that occur more frequently, is needed to more than clearly draw patterns of and influences on personality modify across the life bridge. Related to this point, findings of greater variability in the starting time wave of this study may chronicle to both developmental processes described here as well equally the somewhat longer assessment interval (7 versus 5 years). As such, future enquiry is needed to confirm that this finding indeed reflects greater trait changes in the first stages of the transition to adulthood.

Overall, this study highlights the utility of an integrative and historic period-targeted approach for understanding genetic and environmental contributions to personality development during the transition to adulthood. Consistent with previous research, this study showed that traits tend to show quite like patterns of differential stability but varying patterns of accented stability. The general trend was in the direction of increased personality maturity and information technology appears that the period from 17 to 24 was more than "active" than the flow from 24 to 29. Biometric analyses provided greater insight into the etiology of stability and change and facilitated a straightforward test of different theoretical perspectives on personality changes during evolution. In conclusion, increased maturity coupled with a transactional perspective appears to best characterize personality development during the transition to adulthood.

Acknowledgments

This inquiry was supported by NIH grants R01 DA05147 (Iacono) and R01 AA09367 (McGue).

Contributor Data

Christopher J. Hopwood, Michigan State Academy.

M. Brent Donnellan, Michigan Country University.

Daniel K. Blonigen, VA Palo Alto Health Intendance Organisation and Stanford Academy Schoolhouse of Medicine.

Robert F. Krueger, University of Minnesota.

Matt McGue, Academy of Minnesota.

William G. Iacono, University of Minnesota.

S. Alexandra Burt, Michigan State Academy.

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Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3058678/

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