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Wang, Lijuan; Grimm, Kevin J. – Multivariate Behavioral Research, 2012
Reliabilities of the two most widely used intraindividual variability indicators, "ISD[superscript 2]" and "ISD", are derived analytically. Both are functions of the sizes of the first and second moments of true intraindividual variability, the size of the measurement error variance, and the number of assessments within a burst. For comparison,…
Descriptors: Reliability, Statistical Analysis, Measurement, Models
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Zhang, Guangjian; Browne, Michael W. – Multivariate Behavioral Research, 2010
Dynamic factor analysis summarizes changes in scores on a battery of manifest variables over repeated measurements in terms of a time series in a substantially smaller number of latent factors. Algebraic formulae for standard errors of parameter estimates are more difficult to obtain than in the usual intersubject factor analysis because of the…
Descriptors: Statistical Inference, Error of Measurement, Factor Analysis, Simulation
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Molenaar, Peter C. M.; Nesselroade, John R. – Multivariate Behavioral Research, 2009
It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…
Descriptors: Factor Analysis, Multivariate Analysis, Simulation, Affective Behavior
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Zhang, Zhiyong; Nesselroade, John R. – Multivariate Behavioral Research, 2007
Dynamic factor models have been used to analyze continuous time series behavioral data. We extend 2 main dynamic factor model variations--the direct autoregressive factor score (DAFS) model and the white noise factor score (WNFS) model--to categorical DAFS and WNFS models in the framework of the underlying variable method and illustrate them with…
Descriptors: Bayesian Statistics, Computation, Simulation, Behavioral Science Research