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Observations on the use of growth mixture models in psychological research
 Multivariate Behavioral Research
, 2007
"... Psychologists are applying growth mixture models at an increasing rate. This article argues that most of these applications are unlikely to reproduce the underlying taxonic structure of the population. At a more fundamental level, in many cases there is probably no taxonic structure to be found. La ..."
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Psychologists are applying growth mixture models at an increasing rate. This article argues that most of these applications are unlikely to reproduce the underlying taxonic structure of the population. At a more fundamental level, in many cases there is probably no taxonic structure to be found. Latent growth classes then categorically approximate the true continuum of individual differences in change. This approximation, although in some cases potentially useful, can also be problematic. The utility of growth mixture models for psychological science thus remains in doubt. Some ways in which these models might be more profitably used are suggested. Growth mixture models (GMMs) are designed to separate a general population of individuals into subgroups characterized by qualitatively distinct patterns of change over time. In this article, I offer a few observations on the application of these models in psychological science. Like many, I was initially excited about the potential of GMMs. After several years of evaluating these models and reviewing applications, however, I am now skeptical that they will meaningfully advance our understanding of psychosocial development. In what follows, I outline key methodological and theoretical concerns that I have with current applications of GMMs. Correspondence concerning this article should be addressed to Daniel Bauer, Department of
New Approaches to Studying Problem Behaviors: A Comparison of Methods for Modeling Longitudinal, Categorical Adolescent Drinking Data
"... Analyzing problembehavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normaltheory statistical models. In this article, the authors present several currently available modeling options, all of which make ..."
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Analyzing problembehavior trajectories can be difficult. The data are generally categorical and often quite skewed, violating distributional assumptions of standard normaltheory statistical models. In this article, the authors present several currently available modeling options, all of which make appropriate distributional assumptions for the observed categorical data. Three are based on the generalized linear model: a hierarchical generalized linear model, a growth mixture model, and a latent class growth analysis. They also describe a longitudinal latent class analysis, which requires fewer assumptions than the first 3. Finally, they illustrate all of the models using actual longitudinal adolescent alcoholuse data. They guide the reader through the modelselection process, comparing the results in terms of convergence properties, fit and residuals, parsimony, and interpretability. Advances in computing and statistical software have made the tools for these types of analyses readily accessible to most researchers. Using appropriate models for categorical data will lead to more accurate and reliable results, and their application in real data settings could contribute to substantive advancements in the field of development and the science of prevention.
An Overview of Structural Equation Models and Recent Extensions
"... analysis is perhaps the first work that originated many of the key characteristics that still appear in contemporary SEMs. But the current SEMs have evolved, becoming more inclusive and general than even Wright probably ever imagined. SEMs represent a synthesis of knowledge about multivariate analys ..."
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analysis is perhaps the first work that originated many of the key characteristics that still appear in contemporary SEMs. But the current SEMs have evolved, becoming more inclusive and general than even Wright probably ever imagined. SEMs represent a synthesis of knowledge about multivariate analysis from econometrics, psychometrics, sociometrics (“quantitative sociology”), biostatistics, and statistics, though its development over the last 30 years has occurred mostly in the social and behavioral sciences. Indeed, it is only relatively recently that biostatistics and statistics have become interested in SEMs. Blalock (1964) and Duncan (1966) were early influential works that stimulated research in path analysis and SEM related procedures in sociology and the other social sciences. Two edited books that represent the early takeoff period of SEMs in the social sciences are Blalock (1971) and Goldberger and Duncan (1973). The LISREL software program (Jöreskog & Sörbom, 1978) was another major turning point that made sophisticated maximum
British Journal of Mathematical and Statistical Psychology (2014) © 2014 The British Psychological Society
"... www.wileyonlinelibrary.com A generalized linear factor model approach to the hierarchical framework for responses and response times ..."
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www.wileyonlinelibrary.com A generalized linear factor model approach to the hierarchical framework for responses and response times
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, 2013
"... doi: 10.3389/fnhum.2013.00424 Conical expansion of the outer subventricular zone and the role of neocortical folding in evolution and development ..."
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doi: 10.3389/fnhum.2013.00424 Conical expansion of the outer subventricular zone and the role of neocortical folding in evolution and development
Understanding Linkages Among Mixture Models
"... The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear ..."
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The methodological literature on mixture modeling has rapidly expanded in the past 15 years, and mixture models are increasingly applied in practice. Nonetheless, this literature has historically been diffuse, with different notations, motivations, and parameterizations making mixture models appear disconnected. This pedagogical review facilitates an integrative understanding of mixture models. First, 5 prototypic mixture models are presented in a unified format with incremental complexity while highlighting their mutual reliance on familiar probability laws, common assumptions, and shared aspects of interpretation. Second, 2 recent extensions— hybrid mixtures and parallelprocess mixtures—are discussed. Both relax a key assumption of classic mixture models but do so in different ways. Similarities in construction and interpretation among hybrid mixtures and among parallelprocess mixtures are emphasized. Third, the combination of both extensions is motivated and illustrated by means of an example on oppositional defiant and depressive symptoms. By clarifying how existing mixture models relate and can be combined, this article bridges past and current developments and provides a foundation for understanding new developments. Over the past 15 years, the number of finite mixture modeling applications has dramatically increased in the social sciences (Collins & Lanza, 2010; Muthén & Muthén, 2000; Nagin & Odgers, 2010). Mixture models are now more commonly used for accommodating discrete population heterogeneity than are nonmodelbased classification methods such as cluster analysis and taxometrics
A Semiparametric Approach
, 2009
"... This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan or sublicensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express ..."
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This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan or sublicensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.