Identifying subtypes in persons, situations and person-situation interactions: Categorical latent state-trait modelling approaches.

The latent state-trait theory posits that a psychological construct may reflect stable influences specific to a person (i.e., trait), ephemeral influences from situations (i.e., state), and interactions between them (i.e., state-trait interactions). Researchers conventionally apply mixture modelling to explore heterogeneity in variables by identifying homogenous classes with respect to the measured variable, yet rarely distinguishing between person- and situation-specific classes. The current study introduces novel categorical latent state-trait models to identify subgroups in states and traits, quantifying the effects of person-specific classes, situation-specific classes, and person-situation interactions. The proposed models are applied to an empirical dataset. We discuss statistical inference, effect size measures, and model visualization for the proposed models. Based on realistic parameter values from the empirical dataset, preliminary simulation studies were conducted to investigate models' performances. Bayesian estimation in the proposed models allows flexible testing of a wide range of hypotheses related to state, trait, and interaction effects. We discuss limitations and future directions.

The Predictive Validity of Item Effect Variables in the Satisfaction With Life Scale for Psychological and Physical Health.

Although the Satisfaction with Life Scale strives to capture a single dimension, describing respondents' satisfaction with life as a whole, individual items might also capture unique aspects of life satisfaction leading to some form of multidimensionality. Such systematic item-specific variance can be viewed as a content-laden secondary trait. Information on the nomological net and predictive validity can be useful to aid the interpretation of these item-specific effects. Therefore, the present study on N = 2,543 Dutch respondents adopts revised latent state-trait theory to disentangle common construct variance, random measurement error, and person-specific item effects in the Satisfaction with Life Scale across three measurement occasions. The reported analyses not only demonstrate how to examine item-specific multidimensionality in longitudinal data but also emphasize how different identification constraints for the latent variable lead to different interpretations. Moreover, the predictive validity of item effect variables for the prediction of psychological and physical health is examined. A cross-validation with the same sample at a later measurement period and robustness checks with incomplete data, support our findings on the substantive value of a multidimensional specification of the Satisfaction with Life Scale for substantive analyses. Finally, the contributions of person-specific item effects for psychological assessments are discussed.

A Latent Variable Mixed-Effects Location Scale Model with an Application to Daily Diary Data.

A mixed-effects location scale model allows researchers to study within- and between-person variation in repeated measures. Key components of the model include separate variance models to study predictors of the within-person variance, as well as predictors of the between-person variance of a random effect, such as a random intercept. In this paper, a latent variable mixed-effects location scale model is developed that combines a longitudinal common factor model and a mixed-effects location scale model to characterize within- and between-person variation in a common factor. The model is illustrated using daily reports of positive affect and daily stressors for a large sample of adult women.

A latent state-trait model for analyzing states, traits, situations, method effects, and their interactions.

OBJECTIVE: Studies on situation and Person × Situation interaction effects often use only one method of measurement, so that the extent to which the effects may be method-specific cannot be determined. We introduce a new multimethod latent state-trait model for random and fixed situations (MM-LST-RF), which allows examining person, situation, and Person × Situation interaction effects in the context of multimethod measurement designs (e.g., studies with multiple reporters), and discuss potential areas of application for the new model in personality research. METHOD: The new model allows analyzing novel features of multimethod, multi-situation data, including (a) the convergent validity and method specificity of trait (person) effects, situation effects, and Person × Situation interaction effects; (b) the degree of situation specificity of method effects; and (c) potential Method × Situation interactions. An application to smoker's affect (N = 235; 57% female; 93% Caucasian) before and after quitting smoking is presented with positively and negatively worded items as methods. RESULTS: The MM-LST-RF model fit the smoking data well. Method specificity of many effects was high. CONCLUSIONS: The MM-LST-RF model provides researchers with a new framework for testing method specificity of person, situation, and interaction effects.

Variance in saccadic eye movements reflects stable traits.

Saccadic tasks are widely used to study cognitive processes, effects of pharmacological treatments, and mechanisms underlying psychiatric disorders. In genetic studies, it is assumed that saccadic endophenotypes are traits. While internal consistency and temporal stability of saccadic performance is high for most of the measures, the magnitude of underlying trait components has not been estimated, and influences of situational aspects and person by situation interactions have not been investigated. To do so, 68 healthy participants performed prosaccades, antisaccades, and memory-guided saccades on three occasions at weekly intervals at the same time of day. Latent state-trait modeling was applied to estimate the proportions of variance reflecting stable trait components, situational influences, and Person × Situation interaction effects. Mean variables for all saccadic tasks showed high to excellent reliabilities. Intraindividual standard deviations were found to be slightly less reliable. Importantly, an average of 60% of variance of a single measurement was explained by trans-situationally stable person effects, while situation aspects and interactions between person and situation were found to play a negligible role. We conclude that saccadic variables, in standard laboratory settings, represent highly reliable measures that are largely unaffected by situational influences. Extending previous reliability studies, these findings clearly demonstrate the trait-like nature of these measures and support their role as endophenotypes.

On the psychometric properties of the aggressiveness-IAT for children and adolescents.

In research on aggression, implicit association tests (IATs) have been constructed to elucidate automatic processes involved in aggressiveness. Despite an increasing number of applications of the "Aggressiveness-IAT" in basic and applied research, the psychometric properties of this method for measuring an automatic aggressive self-concept have not been comprehensively investigated. Although the Aggressiveness-IAT has been used both as a cross-situationally consistent trait measure and as a measure to indicate situational changes, prior studies have not tested to what extent it reliably captures a stable trait vs. an occasion-specific aggressive self-concept. The present research scrutinizes the psychometric properties of the Aggressiveness-IAT by addressing two issues. First, we tested the reliability, consistency, and occasion specificity of the Aggressiveness-IAT in a longitudinal panel study with four waves and 574 Austrian school children/adolescents by applying latent-state trait (LST) theory. Second, we validated latent trait scores of the IAT vis-à-vis other measures either clearly related to aggression or not. Results demonstrate that 20-30% of the variance in children's and adolescents' IAT scores is situation-unspecific (i.e., "stable"), whereas 36-50% are situation-specific. Regarding its construct validity, the Aggressiveness-IAT is correlated with explicit measures of aggression and related constructs, but it is not associated with discriminant variables (e.g., school achievement). Implications for using the Aggressiveness-IAT are discussed in the light of these findings. Aggr. Behav. 41:84-95 2015. © 2014 Wiley Periodicals, Inc.

First Versus Second Order Latent Growth Curve Models: Some Insights From Latent State-Trait Theory.

First order latent growth curve models (FGMs) estimate change based on a single observed variable and are widely used in longitudinal research. Despite significant advantages, second order latent growth curve models (SGMs), which use multiple indicators, are rarely used in practice, and not all aspects of these models are widely understood. In this article, our goal is to contribute to a deeper understanding of theoretical and practical differences between FGMs and SGMs. We define the latent variables in FGMs and SGMs explicitly on the basis of latent state-trait (LST) theory and discuss insights that arise from this approach. We show that FGMs imply a strict trait-like conception of the construct under study, whereas SGMs allow for both trait and state components. Based on a simulation study and empirical applications to the CES-D depression scale (Radloff, 1977) we illustrate that, as an important practical consequence, FGMs yield biased reliability estimates whenever constructs contain state components, whereas reliability estimates based on SGMs were found to be accurate. Implications of the state-trait distinction for the measurement of change via latent growth curve models are discussed.