Examining creative performance over time using hierarchical linear modeling: An illustration using film directors

Longitudinal performance of 73 film directors are examined using hierarchical linear modeling (HLM). The HLM analyses model intraindividual performance trajectories (i.e., performance change over time) and interindividual differences in the trajectory parameters (i.e., initial status and rate of change in performance). Results reveal that as a group, directors' performance over careers, measured by critics' ratings, can be described using a quadratic trajectory with an increase in ratings following the initial film, followed by an eventual decline in ratings in later films. However, at the intraindividual level, directors differ in their initial status as well as rate of linear change, and some directors follow an accelerating or decelerating performance pattern. These interindividual differences in initial status, direction of change, and rate of change are related to the mean number of films per year directed by the individual. Implications and future research challenges for modeling longitudinal creative performance with HLM are discussed.     

Genetic and environmental influences on academic achievement trajectories during adolescence

Most studies have considered the effects of particular characteristics on academic achievement individually, which means that little is known about how they function together. Using the population-based Minnesota Twin Family Study, the authors investigated the effects of child academic engagement (interest, involvement, effort), IQ, depression, externalizing behavior, and family environmental risk on academic achievement (reported school grades) from ages 11 through 17. Hierarchical linear growth curve modeling showed main effects on initial reported Grades for all variables, and IQ mitigated the deleterious effects of family risk and externalizing. Only engagement affected change in Grades through adolescence. Influences on initial Grades were strongly genetically influenced, associated primarily with IQ, engagement, and externalizing behavior. Shared environmental influences on initial Grades linked engagement, IQ, and family risk. Genetic influences on change in Grades were substantial, but they were not associated with the academic, family risk, and mental health covarying factors. These results indicate that age 11 achievement and change in achievement through adolescence show systematic patterns and document the existence of individual differences in the commonly shared developmental experience of adapting to the school environment.     

Measuring change for a multidimensional test using a generalized explanatory longitudinal item response model

Even though many educational and psychological tests are known to be multidimensional, little research has been done to address how to measure individual differences in change within an item response theory framework. In this paper, we suggest a generalized explanatory longitudinal item response model to measure individual differences in change. New longitudinal models for multidimensional tests and existing models for unidimensional tests are presented within this framework and implemented with software developed for generalized linear models. In addition to the measurement of change, the longitudinal models we present can also be used to explain individual differences in change scores for person groups (e.g., learning disabled students versus non‐learning disabled students) and to model differences in item difficulties across item groups (e.g., number operation, measurement, and representation item groups in a mathematics test). An empirical example illustrates the use of the various models for measuring individual differences in change when there are person groups and multiple skill domains which lead to multidimensionality at a time point.     

A meta-analytic approach to growth curve analysis

A meta-analytic approach to growth curve analysis is described and illustrated by applying it to the evaluation of the Arizona Pilot Project, an experimental project for financing the treatment of the severely mentally ill. In this approach to longitudinal data analysis, each individual subject for which repeated measures are obtained is initially treated as a separate case study for analysis. This approach has at least two distinct advantages. First, it does not assume a balanced design (equal numbers of repeated observations) across all subjects; to accommodate a variable number of observations for each subject, individual growth curve parameters are differentially weighted by the number of repeated measures on which they are based. Second, it does not assume homogeneity of treatment effects (equal slopes) across all subjects. Individual differences in growth curve parameters representing potentially unequal developmental rates through time are explicitly modeled. A meta-analytic approach to growth curve analysis may be the optimal analytical strategy for longitudinal studies where either (1) a balanced design is not feasible or (2) an assumption of homogeneity of treatment effects across all individuals is theoretically indefensible. In our evaluation of the Arizona Pilot Project, individual growth curve parameters were obtained for each of the 13 rationally derived subscales of the New York Functional Assessment Survey, over time, by linear regression analysis. The slopes, intercepts, and residuals obtained for each individual were then subjected to meta-analytic causal modeling. Using factor analytic models and then general linear models for the latent constructs, the growth curve parameters of all individuals were systematically related to each other via common factors and predicted based on hypothesized exogenous causal factors. The same two highly correlated common factors were found for all three growth curve parameters analyzed, a general psychological factor and a general functional factor. The factor patterns were found to be nearly identical across the separate analyses of individual intercepts, slopes, and residuals. Direct effects on the unique factors of each subscale of the New York Functional Assessment Survey were tested for each growth curve parameter by including the common factors as hierarchically prior predictors in the structural model for each of the indicator variables, thus statistically controlling for any indirect effect produced on the indicator through the common factors. The exogenous predictors modeled were theoretically specified orthogonal contrasts for Method of Payment (comparing Arizona Pilot Project treatment or "capitation" to traditional or "fee-for-service" care as a control), Treatment Administration Site (comparing various locations within treatment or control groups), Pretreatment Assessment (comparing general functional level at intake as assigned by an Outside Assessment Team), and various interactions among these main effects. The intercepts, representing the initial status of individual subjects on both the two common factors and the 13 unique factors of the subscales of the New York Functional Assessment Survey, were found to vary significantly across many of the various different treatment conditions, treatment administration sites, and pretreatment functional levels. This indicated a severe threat to the validity of the originally intended design of the Arizona Pilot Project as a randomized experiment. When the systematic variations were statistically controlled by including intercepts as hierarchically prior predictors in the structural models for slopes, recasting the experiment as a nonequivalent groups design, the effects of the intercepts on the slopes were found to be both statistically significant and substantial in magnitude. (ABSTRACT TRUNCATED)     

Predicting individual change in personality disorder features by simultaneous individual change in personality dimensions linked to neurobehavioral systems: the longitudinal study of personality disorders

Personality disorders (PDs), long thought to be immutable over time, show considerable evidence of individual change and malleability in modern prospective longitudinal studies. The factors responsible for the evident individual change in PDs over time, however, remain essentially unknown. A neurobehavioral model that posits negative emotion (NEM), nonaffective constraint (CON), communal positive emotion (PEM-C), and agentic positive emotion (PEM-A) as important systems underlying PD provides a theoretical basis for investigating predictors of change in PD features over time. Thus, in this study, the authors investigated how individual change in NEM, CON, PEM-C, and PEM-A over time predicted individual change in PD features over time, using longitudinal data on PD assessed by the International Personality Disorders Examination (A. W. Loranger, 1999), as well as data on normal personality features gathered within a 4-year prospective multiwave longitudinal study (N = 250). The authors used the method of latent growth modeling to conduct their analyses. Lower initial levels of PEM-C predicted initial levels of the growth trajectories for those with elevated Cluster A PD features. Elevated NEM, lower CON, and elevated PEM-A initial levels were found to characterize the initial levels of growth trajectories for those with increased Cluster B PD features. Interestingly, subjects with higher initial levels of PEM-A revealed a more rapid rate of change (declining) in Cluster B PD features over time. Elevated NEM and decreased PEM-C initial levels were found to characterize the growth trajectories for subjects with increased Cluster C PD features. The substantive meaning of these results is discussed, and the methodological advantages offered by this statistical approach are also highlighted.     

A multidimensional latent trait model for measuring learning and change

A latent trait model is presented for the repeated measurement of ability based on a multidimensional conceptualization of the change process. A simplex structure is postulated to link item performance under a given measurement condition or occasion to initial ability and to one or more modifiabilities that represent individual differences in change. Since item discriminations are constrained to be equal within a measurement condition, the model belongs to the family of multidimensional Rasch models. Maximum likelihood estimators of the item parameters and abilities are derived, and an example provided that shows good recovery of both item and ability parameters. Properties of the model are explored, particularly for several classical issues in measuring change.     

Predicting consistent psychological test item responses: A comparison of models

The efficacy of four models for predicting the stability of a given individual's test item responses on a structured inventory was examined. Two models were based on item characteristics alone and predicted that an individual would be most likely to change responses to items with moderate endorsement probabilities, or with moderate social desirability scale values. Two other prediction models incorporated individual differences in the perception of item characteristics by predicting that unstable items would have relatively long response latencies for an individual, or would be near an individual's threshold for responding desirably to items. Results from two studies yielded support for the following conclusions: (a) a person's test item responses are relatively stable over short time intervals; (b) items to which a person will show response changes on retest can be identified to a statistically significant degree; (c) the models based on response latencies constituted in both studies a significantly better predictor than the other models examined. The implications of these results for the threshold model were discussed as were the practical and theoretical applications of the response latency-item stability relationship at the level of an individual's test protocol.     

Self-regulation and recall: growth curve modeling of intervention outcomes for older adults

Memory training has often been supported as a potential means to improve performance for older adults. Less often studied are the characteristics of trainees that benefit most from training. Using a self-regulatory perspective, the current project examined a latent growth curve model to predict training-related gains for middle-aged and older adult trainees from individual differences (e.g., education), information processing skills (strategy use) and self-regulatory factors such as self-efficacy, control, and active engagement in training. For name recall, a model including strategy usage and strategy change as predictors of memory gain, along with self-efficacy and self-efficacy change, showed comparable fit to a more parsimonious model including only self-efficacy variables as predictors. The best fit to the text recall data was a model focusing on self-efficacy change as the main predictor of memory change, and that model showed significantly better fit than a model also including strategy usage variables as predictors. In these models, overall performance was significantly predicted by age and memory self-efficacy, and subsequent training-related gains in performance were best predicted directly by change in self-efficacy (text recall), or indirectly through the impact of active engagement and self-efficacy on gains (name recall). These results underscore the benefits of targeting self-regulatory factors in intervention programs designed to improve memory skills.     

Examining Interindividual Differences in Cyclicity of Pleasant and Unpleasant Affects Using Spectral Analysis and Item Response Modeling

Weekly cycles in emotion were examined by combining item response modeling and spectral analysis approaches in an analysis of 179 college students' reports of daily emotions experienced over 7 weeks. We addressed the measurement of emotion using an item response model. Spectral analysis and multilevel sinusoidal models were used to identify interindividual differences in intraindividual cyclic change. Simulations and incomplete data designs were used to examine how well this combination of analysis techniques might work when applied to other practical data problems. Empirically, we found systematic individual differences in the extent to which individuals' emotions follow a weekly cycle, and in how such cycles are exhibited. Weekly cycles accounted for very little variance in day to day emotions at the individual level. Analytically, we illustrate how measurement, change, and interindividual difference models from different traditions may be combined in a practical manner to describe some of the complexities of human behavior. The authors gratefully acknowledge the support provided by grant T32 AG20500 from the National Institute on Aging in the preparation of this article. Special thanks to those at the Institute for Developmental and Health Research Methodology at the University of Virginia and to Paul De Boeck and the reviewers for helpful comments on earlier versions of this work.     

Factors Affecting the Adequacy and Preferability of Semiparametric Groups-Based Approximations of Continuous Growth Trajectories

Psychologists have long been interested in characterizing individual differences in change over time. It is often plausible to assume that the distribution of these individual differences is continuous in nature, yet theory is seldom so specific as to designate its parametric form (e.g., normal). Semiparametric groups-based trajectory models (SPGMs) were thus developed to provide a discrete approximation for continuously distributed growth of unknown form. Previous research has demonstrated the adequacy of the approximation provided by SPGM but only under relatively narrow, theoretically optimal conditions. Under alternative conditions, which may be more common in practice (e.g., higher dimension random effects, smaller sample sizes), this study shows that approximation adequacy can suffer. Furthermore, this study also evaluates whether SPGM's discrete approximation is preferable to a parametric trajectory model that assumes normally distributed random effects when in fact the distribution is modestly nonnormal. The answer is shown to depend on distributional characteristics of both repeated measures (binary or continuous) and random effects (bimodal or skewed). Implications for practice are discussed in light of empirical examples on externalizing behavior.     

Global and domain-specific changes in cognition throughout adulthood

Normative adult age-related decrements are well documented for many diverse forms of effortful cognitive processing. However, it is currently unclear whether each of these decrements reflects a distinct and independent developmental phenomenon, or, in part, a more global phenomenon. A number of studies have recently been published that show moderate to large magnitudes of positive relations among individual differences in rates of changes in different cognitive variables during adulthood. This suggests that a small number of common dimensions or even a single common dimension may underlie substantial proportions of individual differences in aging-related cognitive declines. This possibility was directly examined using data from 1,281 adults 18-95 years of age who were followed longitudinally over up to 7 years on 12 different measures of effortful processing. Multivariate growth curve models were applied to examine the dimensionality of individual differences in longitudinal changes. Results supported a hierarchical structure of aging-related changes, with an average of 39% of individual differences in change in a given variable attributable to global (domain-general) developmental processes, 33% attributable to domain-specific developmental processes (abstract reasoning, spatial visualization, episodic memory, and processing speed), and 28% attributable to test-specific developmental processes. Although it is often assumed that systematic and pervasive sources of cognitive decline only emerge in later adulthood, domain-general influences on change were apparent for younger (18-49 years), middle aged (50-69 years), and older (70-95 years) adults.     

Mixture Modeling Methods for the Assessment of Normal and Abnormal Personality,Part II: Longitudinal Models

Studying personality and its pathology as it changes, develops, or remains stable over time offers exciting insight into the nature of individual differences. Researchers interested in examining personal characteristics over time have a number of time-honored analytic approaches at their disposal. In recent years there have also been considerable advances in person-oriented analytic approaches, particularly longitudinal mixture models. In this methodological primer we focus on mixture modeling approaches to the study of normative and individual change in the form of growth mixture models and ipsative change in the form of latent transition analysis. We describe the conceptual underpinnings of each of these models, outline approaches for their implementation, and provide accessible examples for researchers studying personality and its assessment.     

Correlates of Mental Disorders in the Quebec General Population of 6 to 14-Year Olds

Previous epidemiological studies of correlates of child and adolescent mental disorders in the general population have focused more on child/adolescent and socioeconomic/sociodemographic characteristics than on family characteristics. Moreover, there are no generally accepted methods to analyze and interpret correlates. The purpose of the Quebec Child Mental Health Survey in this regard was twofold: (1) to identify correlates of DSM-III-R internalizing and externalizing disorders according to informant (youth, parent, teacher), for three age groups (6–8, 9–11, and 12–14 years), including relevant family characteristics not considered in previous studies; and (2) to interpret the relative importance of risk indicators by ranking correlates according to strength and consistency of association across age groups. Logistic regression models suggest the inconsistency of correlates across informants. The ranking of correlates reveals that individual and family characteristics make a more important contribution than do socioeconomic characteristics, thereby supporting the relevance of proximal variables in the development of psychopathology.     

A Mixed Model to Disentangle Variance and Serial Autocorrelation in Affective Instability Using Ecological Momentary Assessment Data

Affective instability, the tendency to experience emotions that fluctuate frequently and intensively over time, is a core feature of several mental disorders including borderline personality disorder. Currently, affect is often measured with Ecological Momentary Assessment protocols, which yield the possibility to quantify the instability of affect over time. A number of linear mixed models are proposed to examine (diagnostic) group differences in affective instability. The models contribute to the existing literature by estimating simultaneously both the variance and serial dependency component of affective instability when observations are unequally spaced in time with the serial autocorrelation (or emotional inertia) declining as a function of the time interval between observations. In addition, the models can eliminate systematic trends, take between subject differences into account and test for (diagnostic) group differences in serial autocorrelation, short-term as well as long-term affective variability. The usefulness of the models is illustrated in a study on diagnostic group differences in affective instability in the domain of eating disorders. Limitations of the model are that they pertain to group (and not individual) differences and do not focus explicitly on circadian rhythms or cycles in affect.     

Individual differences in sensitivity to reward and punishment predict moral judgment

Dual process models of moral judgment propose that such judgments are produced by interacting neural systems: a controlled cognitive system and an automatic affective system. Individual differences in moral judgment may therefore arise from variation in cognitive control ability and/or from variation in affective sensitivity. Previous research indicates that individual differences in cognitive control, indexed by working memory capacity, predict moral judgment (Moore, Clark, & Kane, 2008). Here we replicate group level findings from Moore et al. (2008) and demonstrate that individual differences in sensitivity to reward and punishment are strong predictors of moral judgment. Higher reward sensitivity positively correlates with willingness to sacrifice one life to save multiple others and moderates the impact of self-interest on participants’ judgments. Higher punishment sensitivity negatively correlates with willingness to kill, particularly when negative affective information is present. These results help to revise current dual process models of moral judgment.     

Assessment of individual differences in faking

In recent years, change scores obtained under neutral conditions and under faking-inducing conditions have become one of the main alternatives for operationalizing faking. A pending issue regarding these measures is the relevance of individual differences under similar conditions of pressure. This study proposes a simple approach based on the classic test theory that allows the issue to be rigorously assessed. The approach, from which three indices are derived, is based on a pre-test post-test design with a control group, and models the amount of change as an individual parameter. The proposal is applied to an empirical study in personality, and some interesting initial results are obtained.     

Measuring dynamic bipolarity in positive and negative activation

The dynamic bipolarity of the positive and negative affective activation, measured with the PANAS scales, was studied using a pre-post design with an intervening experiment. The correlations between (a) the initial positive and negative constructs and (b) the respective change scores were estimated, random and systematic error being removed owing to a convenient structural equation modeling technique. Results demonstrated that a moderate perturbation may induce a medium correlation between latent change scores. Both strict dynamic independence and bipolarity were rejected. This result highlights the importance of individual differences in the way people perceive their affective changes. It is concluded that the PANAS two-factor model of affect provides only an approximate view of the structure and dynamics of mood.     

Contextualism and the issue of continuity

In the study of continuity development, two models have predominated in the research literature: organismic vs. contextual model. The first, the organismic, is characterized by the claim that early individual characteristics—what I refer to as traits—have predictive power in relation to subsequent behavior. The contextual model, on the other hand, stresses that predictive power of early individual characteristics is rather weak and that the best predictor of later behavior is the nature of the environment the individual occupies at that point in time. In this paper, both models are presented (including an interactive one), using data from a longitudinal study of attachment. Findings from children 1 to 18 years reveal that 18-year-old models of attachment, as well as the level of psychopathology shown, are best predicted by concurrent family status (whether the mother and father are divorced), rather than early attachment or the interaction between early attachment and family status.     

The theoretical status of latent variables

This article examines the theoretical status of latent variables as used in modern test theory models. First, it is argued that a consistent interpretation of such models requires a realist ontology for latent variables. Second, the relation between latent variables and their indicators is discussed. It is maintained that this relation can be interpreted as a causal one but that in measurement models for interindividual differences the relation does not apply to the level of the individual person. To substantiate intraindividual causal conclusions, one must explicitly represent individual level processes in the measurement model. Several research strategies that may be useful in this respect are discussed, and a typology of constructs is proposed on the basis of this analysis. The need to link individual processes to latent variable models for interindividual differences is emphasized.