Bayesian Estimation of Categorical Dynamic Factor Models

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 under...

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Bibliographic Details
Published inMultivariate behavioral research Vol. 42; no. 4; pp. 729 - 756
Main Authors Zhang, Zhiyong, Nesselroade, John R.
Format Journal Article
LanguageEnglish
Published Mahwah Taylor & Francis Group 28.12.2007
Psychology Press
Taylor & Francis Ltd
Subjects
Affective Behavior
Bayesian analysis
Bayesian Statistics
Behavior
Behavior Change
Behavioral Science Research
Computation
Emotional Response
Emotions
Evaluation Methods
Goodness of Fit
Multivariate analysis
Psychological Patterns
Simulation
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ISSN0027-3171
1532-7906
DOI10.1080/00273170701715998

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Summary: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 a categorical time series data set from an emotion study. To estimate the categorical dynamic factor models, a Bayesian method via Gibbs sampling is used. The results show that today's affect directly influences tomorrow's affect. The results are then validated by means of simulation studies. Differences between continuous and categorical dynamic factor models are examined.
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ISSN:0027-3171
1532-7906
DOI:10.1080/00273170701715998