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Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D. – Psychometrika, 2011
Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…
Descriptors: Structural Equation Models, Simulation, Behavioral Sciences, Social Sciences
Deboeck, Pascal R.; Boker, Steven M. – Psychometrika, 2010
Complex intraindividual variability observed in psychology may be well described using differential equations. It is difficult, however, to apply differential equation models in psychological contexts, as time series are frequently short, poorly sampled, and have large proportions of measurement and dynamic error. Furthermore, current methods for…
Descriptors: Psychometrics, Models, Statistical Analysis, Measurement
Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S. – Psychometrika, 2012
We propose a new method of structural equation modeling (SEM) for longitudinal and time series data, named Dynamic GSCA (Generalized Structured Component Analysis). The proposed method extends the original GSCA by incorporating a multivariate autoregressive model to account for the dynamic nature of data taken over time. Dynamic GSCA also…
Descriptors: Structural Equation Models, Longitudinal Studies, Data Analysis, Reliability
Peer reviewed
Molenaar, Peter C. M.; Nesselroade, John R. – Psychometrika, 2001
Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…
Descriptors: Factor Structure, Multivariate Analysis
Ansari, Asim; Iyengar, Raghuram – Psychometrika, 2006
We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…
Descriptors: Markov Processes, Monte Carlo Methods, Computation, Bayesian Statistics