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A Dynamic Factor Model for the Analysis of Multivariate Time Series
Abstract
As a method to ascertain the structure of intra-individual variation, P-technique has met difficulties in the handling of a lagged covariance structure. A new statistical technique, coined dynamic factor analysis, is proposed, which accounts for the entire lagged covariance function of an arbitrary second order stationary time series. Moreover, dynamic factor analysis is shown to be applicable to a relatively short stretch of observations and therefore is considered worthwhile for psychological research. At several places the argumentation is clarified through the use of examples.
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Citations
Cites
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“Using ecological momentary assessment (EMA) data in the form of thrice-daily diary entries, these authors demonstrated that both dynamic factor models (Molenaar, 1985) and spectral analyses of within-individual time series could be used to quantify the structure of anxious experience in time.”
BackgroundA Primer on Two-Level Dynamic Structural Equation Models for Intensive Longitudinal Data
McNeish, D. (2018) · doi:10.31234/osf.io/j56bm · Cited by 12
Melsen, K. A. C., Kunst, J. F., Crossa, J., Krause, M. R., van Eeuwijk, F. A., Kruijer, W., & Peeters, C. F. W. (2025) · Biometrical Journal · doi:10.1002/bimj.70081 · Cited by 2
Metadata
- Authors
- Molenaar, P. C. M.
- Venue
- Psychometrika
- Publisher
- Cambridge University Press (CUP)
- Published
- 1985-06
- Volume
- 50
- Issue
- 2
- Pages
- 181-202
- ISSN
- 0033-3123 (ISSN-L)
- 1860-0980
- OpenAlex
- W1978894823
- Cited by
- 466
- References
- 1
- FWCI
- 6.42
- Type
- journal article
- Language
- en
- Open access
- closed
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