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Three‐mode models for multivariate longitudinal data
Author(s) -
Oort Frans J.
Publication year - 2001
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1348/000711001159429
Subject(s) - multivariate statistics , autoregressive model , latent variable , latent growth modeling , mode (computer interface) , longitudinal field , econometrics , longitudinal data , mathematics , statistics , latent variable model , multivariate analysis , field (mathematics) , computer science , data mining , physics , quantum mechanics , magnetic field , pure mathematics , operating system
Multivariate longitudinal data are characterized by three modes: variables, occasions and subjects. Three‐mode models are described as special cases of a linear latent variable model. The assumption of measurement invariance across occasions yields three‐mode models that are suited for the analysis of multivariate longitudinal data. These so‐called longitudinal three‐mode models include autoregressive models and latent curve models as special cases. Empirical data from the field of industrial psychology are used in an example of how to test substantive hypotheses with the longitudinal, autoregressive and latent curve three‐mode models.