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Taxometric Analysis as a General Strategy for Distinguishing Categorical from Dimensional Latent Structure
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McGrath, Robert E.; Walters, Glenn D. – Psychological Methods, 2012
Statistical analyses investigating latent structure can be divided into those that estimate structural model parameters and those that detect the structural model type. The most basic distinction among structure types is between categorical (discrete) and dimensional (continuous) models. It is a common, and potentially misleading, practice to…
Descriptors: Factor Structure, Factor Analysis, Monte Carlo Methods, Computation
Growth Modeling with Nonignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial
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Muthen, Bengt; Asparouhov, Tihomir; Hunter, Aimee M.; Leuchter, Andrew F. – Psychological Methods, 2011
This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent…
Descriptors: Structural Equation Models, Depression (Psychology), Models, Trend Analysis
A Model for Integrating Fixed-, Random-, and Mixed-Effects Meta-Analyses into Structural Equation Modeling
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Cheung, Mike W.-L. – Psychological Methods, 2008
Meta-analysis and structural equation modeling (SEM) are two important statistical methods in the behavioral, social, and medical sciences. They are generally treated as two unrelated topics in the literature. The present article proposes a model to integrate fixed-, random-, and mixed-effects meta-analyses into the SEM framework. By applying an…
Descriptors: Structural Equation Models, Effect Size, Meta Analysis, Models
A Double-Structure Structural Equation Model for Three-Mode Data
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Gonzalez, Jorge; De Boeck, Paul; Tuerlinckx, Francis – Psychological Methods, 2008
Structural equation models are commonly used to analyze 2-mode data sets, in which a set of objects is measured on a set of variables. The underlying structure within the object mode is evaluated using latent variables, which are measured by indicators coming from the variable mode. Additionally, when the objects are measured under different…
Descriptors: Structural Equation Models, Data Analysis, Evaluation Methods, Models
Structural Equation Modeling with Interchangeable Dyads
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Olsen, Joseph A.; Kenny, David A. – Psychological Methods, 2006
Structural equation modeling (SEM) can be adapted in a relatively straightforward fashion to analyze data from interchangeable dyads (i.e., dyads in which the 2 members cannot be differentiated). The authors describe a general strategy for SEM model estimation, comparison, and fit assessment that can be used with either dyad-level or pairwise…
Descriptors: Structural Equation Models, Data Analysis, Models, Factor Analysis
Multilevel Modeling of Two Cyclical Processes: Extending Differential Structural Equation Modeling to Nonlinear Coupled Systems
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Butner, Jonathan; Amazeen, Polemnia G.; Mulvey, Genna M. – Psychological Methods, 2005
The authors present a dynamical multilevel model that captures changes over time in the bidirectional, potentially asymmetric influence of 2 cyclical processes. S. M. Boker and J. Graham's (1998) differential structural equation modeling approach was expanded to the case of a nonlinear coupled oscillator that is common in bimanual coordination…
Descriptors: Structural Equation Models, Social Systems, Models, Change