Integrating state dynamics and trait change: A tutorial using the example of stress reactivity and change in well-being (original) (raw)
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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
Multi-level models of stress and well-being
Stress and Health, 2010
tional stress researchers may investigate individual factors related to stress and well-being (e.g. personality, work-life balance, emotions, coping skills, etc.); job and organizational factors (e.g., work hours and schedules, role overload/underload/ambiguity, emotional labour, job insecurity, organizational climate); and even societal and national factors (e.g. cultural values; social, economic and environmental indicators). Yet, all too often, these variables are all measured at a single level, usually at the individual level. Thus, while we acknowledge that individual, group, organizational and even cultural variables are theoretically necessary to understand and predict stress outcomes, we rarely measure these variables at their appropriate level.
European Journal of Personality, 2007
A conceptual and analytic framework for understanding relationships among traits, states, situations, and behaviours is presented. The framework assumes that such relationships can be understood in terms of four questions. (1) What are the relationships between trait and state level constructs, which include psychological states, the situations people experience and behaviour? (2) What are the relationships between psychological states, between states and situations and between states and behaviours? (3) How do such state level relationships vary as a function of trait level individual differences? (4) How do the relationships that are the focus of questions 1, 2, and 3 change across time? This article describes how to use multilevel random coefficient modelling (MRCM) to examine such relationships. The framework can accommodate different definitions of traits and dispositions (Allportian, processing styles, profiles, etc.) and different ways of conceptualising relationships between...
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.
Social Indicators Research, 2009
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The Journal of Positive Psychology, 2020
Compliance with Ethical Standards Ethical approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the American Psychological Association's ethical standards. The study was reviewed and approved by the Wake Forest IRB prior to data collection (IRB # IRB00021398). No study involved research on animals. Conflict of interest: The authors have no known conflicts of interest to disclose. Availability of data and materials: All data and materials are available from the first author upon request. Code availability: All code utilized in the analyses are available from the first author upon request.
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Various theoretical accounts suggest that within-person effects relating to everyday experiences (assessed, e.g., via experience sampling studies or daily diary studies) are a central element for understanding between-person differences in future outcomes. In this regard, it is often assumed that the within-person effect of a time-varying predictor X on a time-varying mediator M contributes to the long-term development in an outcome variable Y. In the present work, we demonstrate that traditional multilevel mediation approaches fall short in capturing the proposed mechanism, however. We suggest that a model in which between-person differences in the strength of within-person effects predict the outcome Y mediated via mean levels in M more adequately aligns with the presumed theoretical account that within-person effects shape between-person differences. Using simulated data, we show that the central parameters of this multilevel structural equation model can be recovered well in mos...
People are variables too: multilevel structural equations modeling
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The article uses confirmatory factor analysis (CFA) as a template to explain didactically multilevel structural equation models (ML-SEM) and to demonstrate the equivalence of general mixed-effects models and ML-SEM. An intuitively appealing graphical representation of complex ML-SEMs is introduced that succinctly describes the underlying model and its assumptions. The use of definition variables (i.e., observed variables used to fix model parameters to individual specific data values) is extended to the case of ML-SEMs for clustered data with random slopes. Empirical examples of multilevel CFA and ML-SEM with random slopes are provided along with scripts for fitting such models in SAS Proc Mixed, Mplus, and Mx. Methodological issues regarding estimation of complex ML-SEMs and the evaluation of model fit are discussed. Further potential applications of ML-SEMs are explored.
Child Psychiatry and Human Development, 2008
Today's society puts constant demands on the time and resources of all individuals, with the resulting stress promoting a decline in psychological adjustment. Emerging adults are not exempt from this experience, with an alarming number reporting excessive levels of stress and stress-related problems. As a result, the present study addresses the need for a comprehensive model of emerging adult adjustment in the context of stress and coping variables and highlights the importance of accounting for differences between males and females in research concerning stress, social support, coping, and adjustment. Participants for this study are 239 college students (122 males and 117 females), the majority of whom are Caucasian. Results of structural equation modeling suggest that stress, social support, coping, and adjustment show unique patterns of relationships for males versus females. For both males and females, stress and social support show similar relationships to adjustment. In contrast, social support is related only to coping behaviors in females. Finally, social support appears to be a more important variable for female adjustment, whereas other coping behaviors appear to be more pertinent to male adjustment. Limitations and suggestions for future research will be discussed.
A multivariate hierarchical model for studying psychological change within married couples
Journal of Family Psychology, 1995
This article illustrates new statistical methods for the study of psychological change in married couples. The design involves time-series data on each partner. The analysis combines longitudinal methods for studies of individual change with cross-sectional methods for the study of matched pairs. Each person is viewed as changing over time as a function of an individual growth curve or change function. As in previous studies of individual change, a person's trajectory depends on time-invariant personal background characteristics and time-varying changes in the environment. However, unlike typical studies of individual change, a person's changing psychological profile depends, in part, on the influence of that person's partner. These methods apply directly to other types of longitudinal studies on families (e.g., studies that use teacher and parent reports of a child's social behavior). The methodology is flexible in allowing randomly missing data, varying spacing of time points, unbalanced designs, and time-varying and time-invariant covariates.