Latent Variable Growth Modeling with Multilevel Data
01 Jan 1997-pp 149-161
TL;DR: Growth modeling of multilevel data is presented within a latent variable framework that allows analysis with conventional structural equation modeling software and a mean structure is imposed in addition to the covariance structure.
Abstract: Growth modeling of multilevel data is presented within a latent variable framework that allows analysis with conventional structural equation modeling software. Latent variable modeling of growth considers a vector of observations over time for an individual, reducing the two-level problem to a one-level problem Analogous to this, three-level data on students, time points, and schools can be modeled by a two-level growth model. An interesting feature of this two-level model is that contrary to recent applications of multilevel latent variable modeling, a mean structure is imposed in addition to the covariance structure. An example using educational achievement data illustrates the methodology.
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19 Jun 2002TL;DR: In this article, the Multivariate Normal Distribution, Multivariate Normality, and Covariance Structure were used for one-and two-sample tests to compare the performance of vector and matrix algebra.
Abstract: Introduction.- Vector and Matrix Algebra.- The Multivariate Normal Distribution, Multivariate Normality, and Covariance Structure.- One- and Two-Sample Tests.- Multivariate Analysis of Variance.- Discriminant Analysis.- Canonical Correlation.- Principal Component Analysis.- Factor Analysis.- Structural Equations.
651 citations
TL;DR: An overview is given of modeling of longitudinal and multilevel data using a latent variable framework and particular emphasis is placed on growth modeling.
Abstract: An overview is given of modeling of longitudinal and multilevel data using a latent variable framework. Particular emphasis is placed on growth modeling. A latent variable model is presented for three-level data, where the modeling of the longitudinal part of the data imposes both a covariance and a mean structure. Examples are discussed where repeated observations are made on students sampled within classrooms and schools.
333 citations
Cites methods from "Latent Variable Growth Modeling wit..."
...estimators (see, e.g., Muthen, 1990, 1994a, 1994b )....
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...data are given in Muthen (1994b) , also giving suggestions for analysis strategies....
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TL;DR: This intervention had beneficial effects on diverse aspects of quality of life after treatment for breast cancer, which appear linked to a specific stress management skill taught in the intervention.
Abstract: The range of effects of psychosocial interventions on quality of life among women with breast cancer remains uncertain. Furthermore, it is unclear which components of multimodal interventions account for such effects. To address these issues, the authors tested a 10-week group cognitive-behavioral stress management intervention among 199 women newly treated for nonmetastatic breast cancer, following them for 1 year after recruitment. The intervention reduced reports of social disruption and increased emotional well-being, positive states of mind, benefit finding, positive lifestyle change, and positive affect for up to 12 months (indeed, some effects strengthened over time). With respect to mechanisms tested, the intervention increased confidence in being able to relax at will. There was also evidence that effects of the intervention on the various outcomes examined were mediated by change in confidence about being able to relax. Thus, this intervention had beneficial effects on diverse aspects of quality of life after treatment for breast cancer, which appear linked to a specific stress management skill taught in the intervention.
311 citations
Additional excerpts
...Intervention effects were tested by latent growth-curve modeling (LGM; Duncan, Duncan, Strycker, Li, & Alpert, 1999; Llabre, Spitzer, Saab, & Schneiderman, 2001; B. Muthén, 1997), a form of structural equation modeling....
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TL;DR: A pedagogical introduction to the structural equation modeling based latent trajectory model, or LTM, is presented and several issues that might be particularly salient for research in psychopathology are highlighted.
Abstract: 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.
261 citations
Cites methods from "Latent Variable Growth Modeling wit..."
...Approaches for these types of models have been explored in LTM (e.g., Khoo & Muthén, 2000; McArdle & Hamagami, 1996; Muthén, 1997a, 1997b), but the HLM framework may be currently best suited for these higher order nested structures....
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TL;DR: Structured, group-based cognitive behavior stress management may ameliorate cancer-related anxiety during active medical treatment for breast cancer and for 1 year following treatment.
Abstract: Objective: After surgery for breast cancer, many women experience anxiety relating to the cancer that can adversely affect quality of life and emotional functioning during the year postsurgery. Symptoms such as intrusive thoughts may be ameliorated during this period with a structured, group-based cognitive behavior intervention. Method: A 10-week group cognitive behavior stress management intervention that included anxiety reduction (relaxation training), cognitive restructuring, and coping skills training was tested among 199 women newly treated for stage 0-III breast cancer. They were then followed for 1 year after recruitment. Results: The intervention reduced reports of thought intrusion, interviewer ratings of anxiety, and emotional distress across 1 year significantly more than was seen with the control condition. The beneficial effects were maintained well past the completion of adjuvant therapy. Conclusions: Structured, group-based cognitive behavior stress management may ameliorate cancer-relate...
244 citations
References
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03 Mar 1992TL;DR: The Logic of Hierarchical Linear Models (LMLM) as discussed by the authors is a general framework for estimating and hypothesis testing for hierarchical linear models, and it has been used in many applications.
Abstract: Introduction The Logic of Hierarchical Linear Models Principles of Estimation and Hypothesis Testing for Hierarchical Linear Models An Illustration Applications in Organizational Research Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known Three-Level Models Assessing the Adequacy of Hierarchical Models Technical Appendix
23,126 citations
TL;DR: This chapter discusses Hierarchical Linear Models in Applications, Applications in Organizational Research, and Applications in the Study of Individual Change Applications in Meta-Analysis and Other Cases Where Level-1 Variances are Known.
19,282 citations
TL;DR: In this article, a unified approach to fitting two-stage random-effects models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed.
Abstract: Models for the analysis of longitudinal data must recognize the relationship between serial observations on the same unit. Multivariate models with general covariance structure are often difficult to apply to highly unbalanced data, whereas two-stage random-effects models can be used easily. In two-stage models, the probability distributions for the response vectors of different individuals belong to a single family, but some random-effects parameters vary across individuals, with a distribution specified at the second stage. A general family of models is discussed, which includes both growth models and repeated-measures models as special cases. A unified approach to fitting these models, based on a combination of empirical Bayes and maximum likelihood estimation of model parameters and using the EM algorithm, is discussed. Two examples are taken from a current epidemiological study of the health effects of air pollution.
8,410 citations
2,682 citations
TL;DR: In this paper, structural equation modeling analysis is used for the analysis of large-scale surveys using complex sample designs, where the authors identify several recent methodological lines of inquiry which taken together provide a powerful and general statistical basis for a complex sample.
Abstract: Large-scale surveys using complex sample designs are frequently carried out by government agencies. The statistical analysis technology available for such data is, however, limited in scope. This study investigates and further develops statistical methods that could be used in software for the analysis of data collected under complex sample designs. First, it identifies several recent methodological lines of inquiry which taken together provide a powerful and general statistical basis for a complex sample, structural equation modeling analysis. Second, it extends some of this research to new situations of interest. A Monte Carlo study that empirically evaluates these techniques on simulated data comparable to those in largescale complex surveys demonstrates that they work well in practice. Due to the generality of the approaches, the methods cover not only continuous normal variables but also continuous nonnormal variables and dichotomous variables. Two methods designed to take into account the complex sample structure were
1,407 citations