Mixture Distribution Latent State–Trait Analysis: Basic Ideas and Applications (original) (raw)

Analyzing the convergent and discriminant validity of states and traits: Development and applications of multimethod latent state-trait models

Psychological Assessment, 2008

The analysis of convergent and discriminant validity is an integral part of the construct validation process. Models for analyzing the convergent and discriminant validity have typically been developed for cross-sectional data. There exist, however, only a few approaches for longitudinal data that can be applied for analyzing the construct validity of fluctuating states. In this article, the authors show how models of latent state-trait theory can be combined with models of multitrait-multimethod analysis to develop a model that allows for analyzing convergent and discriminant validity in time: the multimethod latent state-trait model. The model allows for identifying different sources of variance (trait consistency, trait-method specificity, occasion-specific consistency, occasion-specific method specificity, and unreliability). It is applied to the repeated measurement of depression and anxiety in children, which was assessed by self and teacher reports (N ϭ 375). The application shows that the proposed models fit the data well and allow a deeper understanding of method effects in clinical assessment.

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models

Frontiers in Psychology, 2013

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models. Keywords: latent state-trait analysis, multiple-indicator latent growth curve models, multilevel structural equation models, individually-varying and unequally-spaced time points, mixed-effects models, ecological momentary assessment data, intensive longitudinal data www.frontiersin.org December 2013 | Volume 4 | Article 975 | 1 Geiser et al. Multilevel state-trait and growth analysis Frontiers in Psychology | Quantitative Psychology and Measurement

Integrating state dynamics and trait change: A tutorial using the example of stress reactivity and change in well-being

European Journal of Personality, 2021

Recent theoretical accounts on the causes of trait change emphasize the potential relevance of states. In the same vein, reactions to daily stress have been shown to prospectively predict change in well-being, speaking for the proposition that state dynamics can be a precursor to long-term change in more stable individual-differences characteristics. A common analysis approach towards linking state dynamics such as stress reactivity and change in some more stable individual differences characteristic has been a two-step approach, modeling state dynamics and trait change separately. In this paper, we elaborate on one-step procedures to simultaneously model state dynamics and trait change, realized in the multilevel structural equation modeling framework. We highlight three distinct advantages over the two-step approach which pre-exists in the methodological literature, and we disseminate these advantages to a larger audience. We target a readership of substantive researchers interest...

Positive Affect Over Time and Emotion Regulation Strategies: Exploring Trajectories With Latent Growth Mixture Model Analysis

Frontiers in Psychology, 2020

The influence of Positive Affect (PA) on people's well-being and happiness and the related positive consequences on everyday life have been extensively described by positive psychology in the past decades. This study shows an application of Latent Growth Mixture Modeling (LGMM) to explore the existence of different trajectories of variation of PA over time, corresponding to different groups of people, and to observe the effect of emotion regulation strategies on these trajectories. We involved 108 undergraduates in a 1-week daily on-line survey, assessing their PA. We also measured their emotion regulation strategies before the survey. We identified three trajectories of PA over time: a constantly high PA profile, an increasing PA profile, and a decreasing PA profile. Considering emotion regulation strategies as covariates, reappraisal showed an effect on trajectories and class membership, whereas suppression regulation strategy did not.

Longitudinal Models for Studying Multivariate Changes and Dynamics

Annals of Nutrition and Metabolism, 2014

terindividual differences in intraindividual changes, interrelationships in behavioral changes, causes (determinants) of intraindividual changes and causes (determinants) of interindividual differences in intraindividual changes. Although expressed in the context of psychological processes and development, these principles have a broader scope and are as timely today as when first outlined. In fact, the last two objectives-related to causes, antecedents and sequences-still underlie most fundamental questions in longitudinal research. Moreover, of the many techniques developed over the years, few, if any, can fully achieve these last two objectives. In this paper, we present a modeling approach centered on changes and dynamics. This modeling approach, based on latent change scores (LCS) [2-4] , can be used to examine questions related to Baltes and Nesselroade's [1] last objectives of longitudinal methodology. We first describe this approach, highlighting desirable features for identifying dynamics among multiple processes, then provide a few examples from the extant literature, illustrate the model with an empirical example and conclude with some potential steps for advancing the modeling possibilities.

Latent Markov and growth mixture models for ordinal individual responses with covariates: A comparison

Statistical Analysis and Data Mining: The ASA Data Science Journal

We propose a short review between two alternative ways of modeling stability and change of longitudinal data when time-fixed and time-varying covariates referred to the observed individuals are available. They both build on the foundation of the finite mixture models and are commonly applied in many fields. They look at the data by a different perspective and in the literature they have not been compared when the ordinal nature of the response variable is of interest. The latent Markov model is based on time-varying latent variables to explain the observable behavior of the individuals. The model is proposed in a semi-parametric formulation as the latent Markov process has a discrete distribution and it is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of normally distributed random variable to account for the variability of growth rates. To illustrate the main differences among them we refer to a real data example on the self reported health status.

The Use of Latent Trajectory Models in Psychopathology Research

Journal of Abnormal Psychology, 2003

Despite the recent surge in the development of powerful modeling strategies to test questions about individual differences in stability and change over time, these methods are not currently widely used in psychopathology research. In an attempt to further the dissemination of these new methods, the authors present a pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM. They review several different types of LTMs, discuss matching an optimal LTM to a given question of interest, and highlight several issues that might be particularly salient for research in psychopathology. The authors augment each section with a review of published applications of these methods in psychopathology-related research to demonstrate the implementation and interpretation of LTMs in practice.

Latent variable models for multivariate longitudinal ordinal responses

British Journal of Mathematical and Statistical Psychology, 2009

The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for dependencies among items within time and between time. One model fits item-specific random effects which account for the between time points correlations and the second model uses a common factor. The relationships between the time-dependent latent variables are modelled with a non-stationary autoregressive model. The proposed models are fitted to a real data set.