Dependability of personality, life satisfaction, and affect in short-term longitudinal data

The consistency of individual differences across time has implications for theory building and clinical applications. Indeed, personality psychologists have long worked to place constructs on the continuum of consistency of more trait-like to more state-like constructs. Recently, Chmielewski and Watson () highlighted the importance of dependability coefficients for interpreting the results of stability studies. These coefficients provide an estimate of how strongly short-term transient error affects retest correlations for a given measure. In this article, we use a modified version of Kenny and Zautra's (, ) STARTS model to estimate dependability of personality, life satisfaction, and affect in a 2-month longitudinal study of 8 waves. Results from 226 undergraduate students indicated that personality ratings were least influenced by transient state factors, whereas affect was most influenced. We discuss these findings in terms of their implications for the continuum of consistency and for the practical issue of selecting retest intervals for dependability analyses.     

纵向数据的调节效应分析

目前调节效应检验主要是基于截面数据, 本文讨论纵向(追踪)数据的调节效应分析。如果自变量X和因变量Y有纵向数据, 调节效应可分为三类：调节变量Z不随时间变化、Z随时间变化、调节变量从自变量或因变量中产生。评介了基于多层模型、多层结构方程模型、交叉滞后模型和潜变量增长模型的纵向数据的多种调节效应分析方法。调节效应的分解和潜调节结构方程法的使用是纵向数据的调节效应分析的两大特点。对基于四类模型的调节效应分析方法进行综合比较后, 总结出一个纵向数据的调节效应分析流程。随后用实际例子演示如何进行纵向数据的调节效应分析, 并给出相应的Mplus程序。随后展望了纵向数据的调节效应分析的拓展方向, 例如基于动态结构方程模型的密集追踪数据的调节效应分析。     

Developing probability limits for longitudinal physiological data

The objective of this study was to determine what amount of change in heart electrophysiology is necessary to yield a significant difference between a baseline condition and an experimental treatment condition. In a single-subject longitudinal design, a modification of at test was applied to baseline data, and the performance levels for both .05 and .01 probability levels were obtained. It was concluded that it is both ethically sound and physiologically safe to permit a subject in this type of experiment to physiologically respond at levels that would reach both the .05 and the .01 levels of significance.     

Ordinal state-trait regression for intensive longitudinal data

In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).     

An irt-based model for dichotomous longitudinal data

The LLRA (linear logistic model with relaxed assumptions; Fischer, 1974, 1977a, 1977b, 1983a) was developed, within the framework of generalized Rasch models, for assessing change in dichotomous item score matrices between two points in time; it allows to quantify change on latent trait dimensions and to explain change in terms of treatment effects, treatment interactions, and a trend effect. A remarkable feature of the model is that unidimensionality of the item set is not required. The present paper extends this model to designs with any number of time points and even with different sets of items presented on different occasions, provided that one unidimensional subscale is available per latent trait. Thus unidimensionality assumptions within subscales are combined with multidimensionality of the item set. Conditional maximum likelihood methods for parameter estimation and hypothesis testing are developed, and a necessary and sufficient condition for unique identification of the model, given the data, is derived. Finally, a sample application is presented.To my friend Josef Roppert who has taught me how to apply statistical reasoning to substantive problems.This research was supported in part by Österreichische Forschungsgemeinschaft under grant No. 01/0054. The author wishes to thank B. Wild for the numerical computation of the sample application in section 5.     

Dual imputation model for incomplete longitudinal data

Missing values are a practical issue in the analysis of longitudinal data. Multiple imputation (MI) is a well‐known likelihood‐based method that has optimal properties in terms of efficiency and consistency if the imputation model is correctly specified. Doubly robust (DR) weighing‐based methods protect against misspecification bias if one of the models, but not necessarily both, for the data or the mechanism leading to missing data is correct. We propose a new imputation method that captures the simplicity of MI and protection from the DR method. This method integrates MI and DR to protect against misspecification of the imputation model under a missing at random assumption. Our method avoids analytical complications of missing data particularly in multivariate settings, and is easy to implement in standard statistical packages. Moreover, the proposed method works very well with an intermittent pattern of missingness when other DR methods can not be used. Simulation experiments show that the proposed approach achieves improved performance when one of the models is correct. The method is applied to data from the fireworks disaster study, a randomized clinical trial comparing therapies in disaster‐exposed children. We conclude that the new method increases the robustness of imputations.     

The longitudinal relationship of personality traits and disorders

Personality disorders are presumed to be stable because of underlying stable and maladaptive personality traits, but while previous research has demonstrated a link between personality traits and personality disorders cross-sectionally, personality disorders and personality traits have not been linked longitudinally. This study explores the extent to which relevant personality traits are stable in individuals diagnosed with 4 personality disorders (schizotypal, borderline, avoidant, and obsessive-compulsive personality disorders) and examines the assumption that these personality disorders are stable by virtue of stable personality traits. This assumption was tested via the estimation of a series of latent longitudinal models that evaluated whether changes in relevant personality traits lead to subsequent changes in personality disorders. In addition to offering large consistency estimates for personality traits and personality disorders, the results demonstrate significant cross-lagged relationships between trait change and later disorder change for 3 of the 4 personality disorders studied.     

Person-Environment Fit and its implications for personality development: a longitudinal study

Continuity and change in Person-Environment Fit (PE Fit) and its relation to personality development was studied in a 4-year longitudinal study of college students (N=305). PE Fit demonstrated moderate rank-order stability and small increases in mean-levels over time. Antecedents to PE Fit included gender (being male), high academic ability, low agreeableness, and low neuroticism. Outcomes associated with PE Fit included greater personality consistency and changes in personality in the direction of higher self-esteem and lower agreeableness and neuroticism. The implications of the findings for personality development are discussed.     

A time-varying effect model for intensive longitudinal data

Understanding temporal change in human behavior and psychological processes is a central issue in the behavioral sciences. With technological advances, intensive longitudinal data (ILD) are increasingly generated by studies of human behavior that repeatedly administer assessments over time. ILD offer unique opportunities to describe temporal behavioral changes in detail and identify related environmental and psychosocial antecedents and consequences. Traditional analytical approaches impose strong parametric assumptions about the nature of change in the relationship between time-varying covariates and outcomes of interest. This article introduces time-varying effect models (TVEMs) that explicitly model changes in the association between ILD covariates and ILD outcomes over time in a flexible manner. In this article, we describe unique research questions that the TVEM addresses, outline the model-estimation procedure, share a SAS macro for implementing the model, demonstrate model utility with a simulated example, and illustrate model applications in ILD collected as part of a smoking-cessation study to explore the relationship between smoking urges and self-efficacy during the course of the pre- and postcessation period.     

A longitudinal study of personality change in young adulthood

The present research examined personality continuity and change in a sample of young men and women assessed at the beginning and end of college. Two-hundred seventy students completed measures of the Big Five personality traits when they first entered college and then 4 years later. Analyses indicate small- to medium-sized normative (i.e., mean-level) changes, large rank-order stability correlations, high levels of stability in personality structure, and moderate levels of ipsative (i.e. profile) stability. Overall, the findings are consistent with the perspective that personality traits exhibit considerable continuity over time, yet can change in systematic ways.     

Multiple personality and the concept of a person

The phenomenology and psychosocial conditions of the multiple personality syndrome are examined, and the problem that this syndrome seems to raise for the idea of a single self-conscious psychological subject is explored. Tracing the development of the disorder in a disturbed, emotionally repressive, and often violent family background, an explanation for this process is sought in terms of the cognitive effort involved in the achievement of self-identity. It is contended that, far from undermining a strong principle of the self-conscious psychological unity of the individual, this disorder provides a key to the understanding of that unity and the influences to which it is subject.     

The personality bases of ideology: a one-year longitudinal study

The cross-lagged effects of the Big-Five personality dimensions on Social Dominance Orientation (SDO) and Right-Wing Authoritarianism (RWA) were examined over 1 year (N = 112). Consistent with the Dual Process Cognitive-Motivational Model, SDO and RWA exhibited markedly different personality bases. Low Agreeableness and unexpectedly high Extraversion predicted change in the motivational goal for group-based dominance and superiority (SDO), whereas Openness to Experience predicted change in the motivational goal for social cohesion and collective security (RWA). Neuroticism and Conscientiousness did not predict change in SDO or RWA over time. These findings extend previous cross-sectional (correlational) research and indicate that key dimensions of personality (primarily Agreeableness and Openness to Experience) are an important temporal antecedent of the group-based motivational goals underlying individual differences in prejudice.     

A longitudinal analysis of personality structure and adolescent substance use

Within the context of a prospective longitudinal research design, the structure of personality and initial levels of drug use were used to predict, multivariately, later drug use among male and female adolescents. The findings indicated that personality and belief measures of nonconventionality provide a significant increment in the prediction of later drug use beyond a level achieved by knowing initial usage rates. In addition, other personality and belief measures yield further increments in the prediction of subsequent drug use. Developmentally, the results revealed a differentiation of drug use patterns across the year's time, with one pattern primarily reflecting alcohol use, and a second pattern primarily reflecting use of marijuana and ‘hard’ drugs. While both patterns were related to measures of nonconventionality, certain personality features distinguished between them. Emergent use of marijuana and ‘hard’ drugs was related to depression, mistrust and attractiveness, while emergent alcohol use was related to generosity, ambition, agility and cheerfulness. These results were cross-validated in two samples of subjects. Implications of the findings for adolescent psychosocial development are discussed.     

The Seattle longitudinal study: relationship between personality and cognition

This article reviews the history, measures and principal findings of the Seattle Longitudinal Study. This study began in 1956 focusing upon age differences and age changes in cognitive abilities. Its sampling frame is a large HMO in the Pacific Northwest. The study has been expanded to investigate various influences on cognitive aging including, cognitive styles, personality traits, life styles, and family environment. Current interest is also in the early detection of risk for dementia. In addition, this article reports original analyses of the relation of personality dimensions to cognitive abilities (both concurrent and longitudinal). While personality remains relatively stable over the adult life span, modest proportions of variance are shared between various personality traits and the cognitive abilities.     

Factor analytic approaches to personality item-level data

Factor analysis models have played a central role in formulating conceptual models in personality and personality assessment, as well as in empirical examinations of personality measurement instruments. Yet, the use of item-level data presents special problems for factor analysis, applications. In this article, we review recent developments in factor analysis that are appropriate for the type of item-level data often collected in personality. Included in this review are discussions of how these developments have been addressed in the context of two different (but formally related) statistical models item response theory (IRT: Hambleton, Swaminathan, & Rogers, 1991) and structural, equation modeling (Bollen 1989) for item-level data. We also discuss the relevance of item scaling in the context of these models. Using the restandardization data for the Minnesota Multiphasic Personality Inventory-2 Scale (cf. Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989), we show brief examples of the utility of these approaches to address basic questions about responses to personality scale items regarding: (a) scale, dimensionality and general item properties, (b) the "appropriateness" of the observed responses, and (c) differential item functioning across subsamples. implications for analyses of personality item-level data in the IRT and factor analytic traditions are discussed.