Individual differences in the forms of personality trait trajectories.

Changes in personality are often modeled linearly or curvilinearly. It is a simplifying-yet untested-assumption that the chosen sample-level model form accurately depicts all person-level trajectories within the sample. Given the complexity of personality development, it seems unlikely that imposing a single model form across all individuals is appropriate. Although typical growth models can estimate individual trajectories that deviate from the average via random effects, they do not explicitly test whether people differ in the forms of their trajectories. This heterogeneity is valuable to uncover, though, as it may imply that different processes are driving change. The present study uses data from four longitudinal data sets (N = 26,469; Mage = 47.55) to empirically test the degree that people vary in best-fitting model forms for their Big Five personality development. Across data sets, there was substantial heterogeneity in best-fitting forms. Moreover, the type of form someone had was directly associated with their net and total amount of change across time, and these changes were substantially misquantified when a worse-fitting form was used. Variables such as gender, age, trait levels, and number of waves were also associated with people's types of forms. Lastly, comparisons of best-fitting forms from individual- and sample-level models indicated that consequential discrepancies arise from different levels of analysis (i.e., individual vs. sample) and alternative modeling choices (e.g., choice of time metric). Our findings highlight the importance of these individual differences for understanding personality change processes and suggest that a flexible, person-level approach to understanding personality development is necessary. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

High-frequency assessment of mood, personality, and cognition in healthy younger, healthy older and adults with cognitive impairment.

Increased variability in cognitive scores, mood or personality traits can be indicative of underlying neurological disorders. Whether variability in cognition is due to changes in mood or personality is unknown. A total of 66 younger adults, 51 healthy older adults and 38 participants with cognitive impairment completed 21 daily sessions of attention, working memory, mood, and personality assessment. Group differences in mean performance and variability were examined using Bayesian mixed effects location scale models. Variability in attention decreased from younger to older adults and then increased again in cognitive impairment. Younger adults were more variable in agreeableness, openness and conscientiousness compared to older adults. The clinically impaired group differed from the healthy older adults in terms of variability on attention, openness, and conscientiousness. Healthy aging results in greater stability in personality traits over short intervals yet this stability is not redundant with increased stability in cognitive scores.

Examining the Most Important Risk Factors for Predicting Youth Persistent and Distressing Psychotic-Like Experiences.

BACKGROUND: Persistence and distress distinguish more clinically significant psychotic-like experiences (PLEs) from those that are less likely to be associated with impairment and/or need for care. Identifying risk factors that identify clinically relevant PLEs early in development is important for improving our understanding of the etiopathogenesis of these experiences. Machine learning analyses were used to examine the most important baseline factors distinguishing persistent distressing PLEs. METHODS: Using Adolescent Brain Cognitive Development (ABCD) Study data on PLEs from 3 time points (ages 9-13 years), we created the following groups: individuals with persistent distressing PLEs (n = 305), individuals with transient distressing PLEs (n = 374), and individuals with low-level PLEs demographically matched to either the persistent distressing PLEs group (n = 305) or the transient distressing PLEs group (n = 374). Random forest classification models were trained to distinguish persistent distressing PLEs from low-level PLEs, transient distressing PLEs from low-level PLEs, and persistent distressing PLEs from transient distressing PLEs. Models were trained using identified baseline predictors as input features (i.e., cognitive, neural [cortical thickness, resting-state functional connectivity], developmental milestone delays, internalizing symptoms, adverse childhood experiences). RESULTS: The model distinguishing persistent distressing PLEs from low-level PLEs showed the highest accuracy (test sample accuracy = 69.33%; 95% CI, 61.29%-76.59%). The most important predictors included internalizing symptoms, adverse childhood experiences, and cognitive functioning. Models for distinguishing persistent PLEs from transient distressing PLEs generally performed poorly. CONCLUSIONS: Model performance metrics indicated that while most important factors overlapped across models (e.g., internalizing symptoms), adverse childhood experiences were especially important for predicting persistent distressing PLEs. Machine learning analyses proved useful for distinguishing the most clinically relevant group from the least clinically relevant group but showed limited ability to distinguish among clinically relevant groups that differed in PLE persistence.

Replication and reliability of Parkinson's disease clinical subtypes.

BACKGROUND: We recently identified three distinct Parkinson's disease subtypes: "motor only" (predominant motor deficits with intact cognition and psychiatric function); "psychiatric & motor" (prominent psychiatric symptoms and moderate motor deficits); "cognitive & motor" (cognitive and motor deficits). OBJECTIVE: We used an independent cohort to replicate and assess reliability of these Parkinson's disease subtypes. METHODS: We tested our original subtype classification with an independent cohort (N = 100) of Parkinson's disease participants without dementia and the same comprehensive evaluations assessing motor, cognitive, and psychiatric function. Next, we combined the original (N = 162) and replication (N = 100) datasets to test the classification model with the full combined dataset (N = 262). We also generated 10 random split-half samples of the combined dataset to establish the reliability of the subtype classifications. Latent class analyses were applied to the replication, combined, and split-half samples to determine subtype classification. RESULTS: First, LCA supported the three-class solution - Motor Only, Psychiatric & Motor, and Cognitive & Motor- in the replication sample. Next, using the larger, combined sample, LCA again supported the three subtype groups, with the emergence of a potential fourth group defined by more severe motor deficits. Finally, split-half analyses showed that the three-class model also had the best fit in 13/20 (65%) split-half samples; two-class and four-class solutions provided the best model fit in five (25%) and two (10%) split-half replications, respectively. CONCLUSIONS: These results support the reproducibility and reliability of the Parkinson's disease behavioral subtypes of motor only, psychiatric & motor, and cognitive & motor groups.

Longitudinal within-person variability around personality trajectories.

Decades of research have identified average patterns of normative personality development across the lifespan. However, it is unclear how well these correspond to trajectories of individual development. Past work beyond general personality development might suggest these average patterns are oversimplifications, necessitating novel examinations of how personality develops and consideration of new individual difference metrics. This study uses five longitudinal data sets from Germany, Australia, the Netherlands, and the United States (N = 128,345; Mage = 45.42; 53% female) to examine personality development using mixed-effects location scale models. These models quantify individual differences in within-person residual variability, or sigma, around trajectories-thereby testing if models that assume sigma is homogeneous, unsystematic noise are appropriate. We investigate if there are individual differences in longitudinal within-person variability for Big Five trajectories, if there are variables associated with this heterogeneity, and if person-level sigma values can uniquely predict an outcome. Results indicated that, across all models, there was meaningful heterogeneity in sigma-the magnitude of which was comparable to and often even greater than that of intercepts and slopes. Individual differences in sigma were further associated with covariates central to personality development and had robust predictive utility for health status, an outcome with long-established personality associations. Collectively, these findings underscore the presence, degree, validity, and potential utility of heterogeneity in longitudinal within-person variability and indicate the typical linear model does not adequately depict individual development. We suggest it should become the default to consider this individual difference metric in personality development research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Alcohol milestones and internalizing, externalizing, and executive function: longitudinal and polygenic score associations.

BACKGROUND: Although the link between alcohol involvement and behavioral phenotypes (e.g. impulsivity, negative affect, executive function [EF]) is well-established, the directionality of these associations, specificity to stages of alcohol involvement, and extent of shared genetic liability remain unclear. We estimate longitudinal associations between transitions among alcohol milestones, behavioral phenotypes, and indices of genetic risk. METHODS: Data came from the Collaborative Study on the Genetics of Alcoholism (n = 3681; ages 11-36). Alcohol transitions (first: drink, intoxication, alcohol use disorder [AUD] symptom, AUD diagnosis), internalizing, and externalizing phenotypes came from the Semi-Structured Assessment for the Genetics of Alcoholism. EF was measured with the Tower of London and Visual Span Tasks. Polygenic scores (PGS) were computed for alcohol-related and behavioral phenotypes. Cox models estimated associations among PGS, behavior, and alcohol milestones. RESULTS: Externalizing phenotypes (e.g. conduct disorder symptoms) were associated with future initiation and drinking problems (hazard ratio (HR)⩾1.16). Internalizing (e.g. social anxiety) was associated with hazards for progression from first drink to severe AUD (HR⩾1.55). Initiation and AUD were associated with increased hazards for later depressive symptoms and suicidal ideation (HR⩾1.38), and initiation was associated with increased hazards for future conduct symptoms (HR = 1.60). EF was not associated with alcohol transitions. Drinks per week PGS was linked with increased hazards for alcohol transitions (HR⩾1.06). Problematic alcohol use PGS increased hazards for suicidal ideation (HR = 1.20). CONCLUSIONS: Behavioral markers of addiction vulnerability precede and follow alcohol transitions, highlighting dynamic, bidirectional relationships between behavior and emerging addiction.

Antecedents of mind wandering states in healthy aging and mild cognitive impairment.

OBJECTIVE: Mind wandering refers to periods of internally directed attention and comprises up to 30% or more of our waking thoughts. Frequent mind wandering can be detrimental to ongoing task performance. We aim to determine whether rates of mind wandering change in healthy aging and mild cognitive impairment and how differences in mind wandering contribute to differences in attention and working memory. METHOD: We administered a standard behavioral task, the Sustained Attention to Response Test, to measure mind wandering in healthy younger adults (N = 66), healthy older adults (N = 51), and adults with cognitive impairment (N = 38), that was completed daily for 3 weeks. The N-back test was also administered at a reduced frequency as a measure of working memory performance. RESULTS: Generally speaking, averaged across 3 weeks of testing, relative to healthy older adults, mind wandering was higher in younger adults and in cognitive impairment, although the specific patterns varied across mind wandering states. Multiple states of mind wandering also predicted working memory performance; however, reaction time variability tended to be the best predictor based on model comparisons. Each state was also modestly associated with different dispositional factors including mood and Agreeableness. CONCLUSIONS: Patterns of mind wandering change across healthy aging and cognitive impairment and are related to individual differences in multiple dispositional factors and also working memory performance. These results suggest that different states of mind wandering should be measured and accounted for when modeling cognitive change in healthy and pathological aging. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Relationships between hourly cognitive variability and risk of Alzheimer's disease revealed with mixed-effects location scale models.

OBJECTIVE: Observational studies on aging and Alzheimer's disease (AD) typically focus on mean-level changes in cognitive performance over relatively long periods of time (years or decades). Additionally, some studies have examined how trial-level fluctuations in speeded reaction time are related to both age and AD. The aim of the current project was to describe patterns of variability across repeated days of testing as a function of AD risk in cognitively normal older adults. METHOD: The current project examined the performance of the Ambulatory Research in Cognition (ARC) smartphone application, a high-frequency remote cognitive assessment paradigm, that administers brief tests of episodic memory, spatial working memory, and processing speed. Bayesian mixed-effects location scale models were used to explore differences in mean cognitive performance and intraindividual variability across 28 repeated sessions over a 1-week assessment interval as function of age and genetic risk of AD, specifically the presence of at least one apolipoprotein E (APOE) ε4 allele. RESULTS: Mean performance on processing speed and working memory was negatively related to age and APOE status. More importantly, e4 carriers exhibited increased session-level variability on a test of processing speed compared to noncarriers. Age and education did not consistently relate to cognitive variability, contrary to expectations. CONCLUSION: Preclinical AD risk, defined as possessing at least one APOE ε4 allele, is not only associated with mean-level performance differences, but also with increases in variability across repeated testing occasions particularly on a test of processing speed. Thus, cognitive variability may serve as an additional and important indicator of AD risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Behavioral and psychiatric correlates of brain responses to social feedback.

Maladaptive responses to peer acceptance and rejection arise in numerous psychiatric disorders in adolescence; yet, homogeneity and heterogeneity across disorders suggest common and unique mechanisms of impaired social function. We tested the hypothesis that social feedback is processed similarly to other forms of feedback (e.g., monetary) by examining the correspondence between the brain's response to social acceptance and rejection and behavioral performance on a separate reward and loss task. We also examined the relationship between these brain responses and depression and social anxiety severity. The sample consisted of one hundred and thirteen 16-21-year olds who received virtual peer acceptance/rejection feedback in an event-related potential (ERP) task. We used temporospatial principal component analysis and identified a component consistent with the reward positivity (RewP) or feedback negativity (FN). RewP to social acceptance was not significantly related to reward bias or the FN to social rejection related to loss avoidance. The relationship between RewP and depression severity, while nonsignificant, was of a similar magnitude to prior studies. Exploratory analyses yielded a significant relationship between lower socioeconomic status (SES) and blunted RewP and between lower SES and heightened loss avoidance and blunted reward bias. These findings build on prior work to improve our understanding of the function of the brain's response to social feedback, while also suggesting a pathway for further study, whereby poverty leads to depression via social and reward learning mechanisms.

Do changes in personality predict life outcomes?

The Big Five personality traits predict many important life outcomes. These traits, although relatively stable, are also open to change across time. However, whether these changes likewise predict a wide range of life outcomes has yet to be rigorously tested. This has implications for the types of processes linking trait levels and changes with future outcomes: distal, cumulative processes versus more immediate, proximal processes, respectively. The present study used seven longitudinal data sets (N = 81,980) to comprehensively examine the unique relationship that changes in the Big Five traits have with static levels and changes in numerous outcomes in the domains of health, education, career, finance, relationships, and civic engagement. Meta-analytic estimates were calculated and study-level variables were examined as potential moderators of these pooled effects. Results indicated that changes in personality traits are sometimes prospectively related to static outcomes-such as health status, degree attainment, unemployment, and volunteering-above and beyond associations due to static trait levels. Moreover, changes in personality more frequently predicted changes in these outcomes, with associations for new outcomes emerging as well (e.g., marriage, divorce). Across all meta-analytic models, the magnitude of effects for changes in traits was never larger than that of static levels and there were fewer change associations. Study-level moderators (e.g., average age, number of Big Five waves, internal consistency estimates) were rarely associated with effects. Our study suggests personality change can play a valuable role in one's development and highlights that both cumulative and proximal processes matter for some trait-outcome associations. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Are some people more consistent? Examining the stability and underlying processes of personality profile consistency.

Personality traits are relatively consistent across time, as indicated by test-retest correlations. However, ipsative consistency approaches suggest there are individual differences in this consistency. Despite this, it is unknown whether these differences are due to person-level characteristics (i.e., some people are just more consistent) or exogenous forces (i.e., lack of consistency is due to environmental changes). Moreover, it is unclear whether the processes promoting long-term consistency are the same across people. We examine these two questions using item-level profile correlations across four to nine waves of data with four data sets (N = 21,616) with multilevel asymptotic growth models. Results indicated that there were, on average, high levels of profile consistency. However, there were notable individual differences in initial profile correlation values as well as in changes in levels of consistency across time, indicating that some people are more stably consistent than others. Moreover, the directions of people's trajectories across increasing time intervals suggest that the mechanisms responsible for reinforcing personality consistency vary across people. These effects were typically moderated by age at 30 years old, maturity-related traits, and education level. Overall, findings indicate some people are more consistent than others, such that this stable level of (in)consistency is a dispositional factor. Additionally, individual differences in profile consistency are shaped by different levels of three processes. On average, stochastic factors are not impactful for most individuals, and transactional processes have an important role in increasing consistency for a sizable amount of people-nuances not previously revealed when focusing on rank-order stability. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The associations between life events and person-centered personality consistency.

OBJECTIVE: Few environments reliably influence mean-level and rank-order changes in personality-perhaps because personality development needs to be examined through an individualized, person-centered lens. METHODS: The current study used Bayesian multilevel linear models to examine the association between 16 life events and changes in person-centered, Big Five personality consistency across 4 to 10 waves of data using four datasets (N = 24,491). RESULTS: Selection effects were found for events such as marriage, (un)employment, retirement, and volunteering, whereas between-person effects for slopes were found for events such as beginning formal education, employment, and retirement. Within-person changes were often small and emerged inconsistently across datasets but, when present, were brief and negative in direction, suggesting life events can serve as a short-term disruption to the personality system. However, there were many individual differences around event-related trajectories. CONCLUSION: Our results highlight that the effects of life events depend on how personality and its changes are quantified-with these findings underscoring the utility of a person-centered approach as it can capture the full range of these idiosyncrasies. Overall, these findings suggest that life events are associated with a range of idiosyncratic effects and can serve as a short-term, destabilizing shock to one's personality system.

Working memory capacity preferentially enhances implementation of proactive control.

Previous research has linked working memory capacity (WMC) with enhanced proactive control. However, it remains unclear the extent to which this relationship reflects the influence of WMC on the tendency to engage proactive control, or rather, the ability to implement it. The current study sought to clarify this ambiguity by leveraging the Dual Mechanisms of Cognitive Control (DMCC) version of the AX-CPT task, in which the mode of cognitive control is experimentally manipulated across distinct testing sessions. To adjudicate between competing hypotheses, Bayesian mixed modeling was used to conduct sequential analyses involving two separate data sets. Posterior parameter estimates obtained from the initial analysis were entered as informed priors during the replication analysis to evaluate the influence of new data on previous estimates. Results yielded strong evidence demonstrating that the influence of WMC on proactive control is most robust under experimentally controlled conditions, during which use of proactive control is standardized across participants via explicit training and instruction. Critically, the observed pattern of findings suggests that the relationship between WMC and proactive control may be better characterized as individual differences in the ability to implement proactive control, rather than a more generalized tendency to engage it. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Social motivation in infancy is associated with familial recurrence of ASD.

Pre-diagnostic deficits in social motivation are hypothesized to contribute to autism spectrum disorder (ASD), a heritable neurodevelopmental condition. We evaluated psychometric properties of a social motivation index (SMI) using parent-report item-level data from 597 participants in a prospective cohort of infant siblings at high and low familial risk for ASD. We tested whether lower SMI scores at 6, 12, and 24 months were associated with a 24-month ASD diagnosis and whether social motivation's course differed relative to familial ASD liability. The SMI displayed good internal consistency and temporal stability. Children diagnosed with ASD displayed lower mean SMI T-scores at all ages and a decrease in mean T-scores across age. Lower group-level 6-month scores corresponded with higher familial ASD liability. Among high-risk infants, strong decline in SMI T-scores was associated with 10-fold odds of diagnosis. Infant social motivation is quantifiable by parental report, differentiates children with versus without later ASD by age 6 months, and tracks with familial ASD liability, consistent with a diagnostic and susceptibility marker of ASD. Early decrements and decline in social motivation indicate increased likelihood of ASD, highlighting social motivation's importance to risk assessment and clarification of the ontogeny of ASD.

Personalized Prediction of Behaviors and Experiences: An Idiographic Person-Situation Test.

A longstanding goal of psychology is to predict the things that people do and feel, but tools to accurately predict future behaviors and experiences remain elusive. In the present study, we used intensive longitudinal data (N = 104 college-age adults at a midwestern university; total assessments = 5,971) and three machine-learning approaches to investigate the degree to which three future behaviors and experiences-loneliness, procrastination, and studying-could be predicted from past psychological (i.e., personality and affective states), situational (i.e., objective situations and psychological situation cues), and time (i.e., trends, diurnal cycles, time of day, and day of the week) phenomena from an idiographic, person-specific perspective. Rather than pitting persons against situations, such an approach allows psychological phenomena, situations, and time to jointly predict future behaviors and experiences. We found (a) a striking degree of prediction accuracy across participants, (b) that a majority of participants' future behaviors are predicted by both person and situation features, and (c) that the most important features vary greatly across people.

Childhood temperament and adulthood personality differentially predict life outcomes.

Debate has long surrounded whether temperament and personality are distinct sets of individual differences or are rather two sides of the same coin. To the extent that there are differences, it could indicate important developmental insights concerning the mechanisms responsible for linking traits with outcomes. One way to test this is to examine the joint and incremental predictive validity of temperament and personality in the same individuals across time. Using a longitudinal sample spanning 3 decades starting at infancy and followed up to 37 years old (N = 7081), we ran a series of Bayesian generalized linear models with measures of childhood temperament and adult-based personality to predict outcomes in several life domains. Results indicated that while each set of individual differences were often related to the same outcomes, there were instances in which temperament provided incremental validity above adult personality, ranging from 2 to 10% additional variance explained. Personality in childhood explained the most variance for outcomes such as cognitive ability and educational attainment whereas personality performed best for outcomes such as health status, substance use, and most internalizing outcomes. These findings indicate childhood and adulthood assessments of personality are not redundant and that a lifespan approach is needed to understand fully understand life outcomes.

Proteinopathy and Longitudinal Cognitive Decline in Parkinson Disease.

BACKGROUND AND OBJECTIVES: People with Parkinson disease (PD) commonly experience cognitive decline, which may relate to increased α-synuclein, tau, and ß-amyloid accumulation. This study examines whether the different proteins predict longitudinal cognitive decline in PD. METHODS: All participants (PD n = 152, controls n = 52) were part of a longitudinal study and completed a lumbar puncture for CSF protein analysis (α-synuclein, total tau [tau], and ß-amyloid42 [ß-amyloid]), a ß-amyloid PET scan, and/or provided a blood sample for APOE genotype (ε4+, ε4-), which is a risk factor for ß-amyloid accumulation. Participants also had comprehensive, longitudinal clinical assessments of overall cognitive function and dementia status, as well as cognitive testing of attention, language, memory, and visuospatial and executive function. We used hierarchical linear growth models to examine whether the different protein metrics predict cognitive change and multivariate Cox proportional hazard models to predict time to dementia conversion. Akaike information criterion was used to compare models for best fit. RESULTS: Baseline measures of CSF ß-amyloid predicted decline for memory (p = 0.04) and overall cognitive function (p = 0.01). APOE genotypes showed a significant group (ε4+, ε4-) effect such that ε4+ individuals declined faster than ε4- individuals in visuospatial function (p = 0.03). Baseline ß-amyloid PET significantly predicted decline in all cognitive measures (all p ≤ 0.004). Neither baseline CSF α-synuclein nor tau predicted cognitive decline. All 3 ß-amyloid--related metrics (CSF, PET, APOE) also predicted time to dementia. Models with ß-amyloid PET as a predictor fit the data the best. DISCUSSION: Presence or risk of ß-amyloid accumulation consistently predicted cognitive decline and time to dementia in PD. This suggests that ß-amyloid has high potential as a prognostic indicator and biomarker for cognitive changes in PD.

Prospective self- and informant-personality associations with inflammation, health behaviors, and health indicators.

OBJECTIVE: Personality influences many aspects of the health process. It is unclear to what extent self- and informant-reports of the Big Five offer incremental validity for the prediction of inflammatory biomarkers and whether inflammation provides a unique pathway between personality and indicators of physical health, independent of health behaviors. METHOD: Using data from older adults (N = 1,630) enrolled in the St. Louis Personality and Aging Network study, we tested whether self- and informant-reported Big Five traits show unique associations with inflammation (IL-6, CRP, TNF-α). Further, we tested whether inflammation and health behaviors indirectly link personality to health-related quality of life, body mass index, and chronic disease burden using longitudinal mediation in a structural equation modeling framework. RESULTS: Self-reports, informant-reports, and general trait factors of personality predicted future inflammatory biomarker levels (unstandardized regression coefficients ranged -.08 to .07 for self, -.13 to -.10 for informants, and -.16 to -.11 for general). Additionally, all assessment methods of personality were associated with the indicators of physical health through biomarker and health behavior pathways. Effects were primarily found for conscientiousness and neuroticism; IL-6 and CRP were the biomarkers with the most indirect effects; and indirect paths overall emerged more frequently through health behaviors, but this varied by outcome. CONCLUSIONS: Self- and informant-reports provided unique predictive validity of inflammatory biomarkers. Findings highlight the benefits of using of multiple assessments of personality and the importance of examining multiple, distinct pathways by which personality might influence health to understand the mechanisms underlying this relationship more fully. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A mega-analysis of personality prediction: Robustness and boundary conditions.

Decades of studies identify personality traits as prospectively associated with life outcomes. However, previous investigations of personality characteristic-outcome associations have not taken a principled approach to covariate use or other sampling strategies to ensure the robustness of personality-outcome associations. The result is that it is unclear (a) whether personality characteristics are associated with important outcomes after accounting for a range of background variables, (b) for whom and when personality-outcome associations hold, and (c) that background variables are most important to account for. The present study examines the robustness and boundary conditions of personality-outcome associations using prospective Big Five associations with 14 health, social, education/work, and societal outcomes across eight different person- and study-level moderators using individual participant data from 171,395 individuals across 10 longitudinal panel studies in a mega-analytic framework. Robustness and boundary conditions were systematically tested using two approaches: propensity score matching and specification curve analysis. Three findings emerged: First, personality characteristics remain robustly associated with later life outcomes. Second, the effects generalize, as there are few moderators of personality-outcome associations. Third, robustness was differential across covariate choice in nearly half of the tested models, with the inclusion or exclusion of some of these flipping the direction of association. In summary, personality characteristics are robustly associated with later life outcomes with few moderated associations. However, researchers still need to be careful in their choices of covariates. We discuss how these findings can inform studies of personality-outcome associations, as well as recommendations for covariate inclusion. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Detecting Idiographic Personality Change.

Personality changes across the lifespan, but strong evidence regarding the mechanisms responsible for personality change remains elusive. Studies of personality change and life events, for example, suggest that personality is difficult to change. But there are two key issues with assessing personality change. First, most change models optimize population-level, not individual-level, effects, which ignores heterogeneity in patterns of change. Second, optimizing change as mean-levels of self-reports fails to incorporate methods for assessing personality dynamics, such as using changes in variances of and correlations in multivariate time series data that often proceed changes in mean-levels, making variance change detection a promising technique for the study of change. Using a sample of N = 388 participants (total N = 21,790) assessed weekly over 60 weeks, we test a permutation-based approach for detecting individual-level personality changes in multivariate time series and compare the results to event-based methods for assessing change. We find that a non-trivial number of participants show change over the course of the year but that there was little association between these change points and life events they experienced. We conclude by highlighting the importance in idiographic and dynamic investigations of change.