Analyzing Multiple Multivariate Time Series Data Using Multilevel Dynamic Factor Models

REFERENCES

• Asparouhov , T. and Muthén , B. 2007 . Computationally efficient estimation of multilevel high-dimensional latent variable models. , UT : Proceedings of the 2007 JSM meeting in Salt Lake City . Section on Statistics in Epidemiology
   Google Scholar
• Asparouhov , T. and Muthén , B. 2012 . General random effect latent variable modeling: Random subjects, items, contexts, and parameters. , Manuscript submitted for publication . Retrieved from http://www.statmodel.com/download/HighDimension.pdf
   Google Scholar
• Boker , S. M. and Ghisletta , P. 2001 . Random coefficients models for control parameters in dynamical systems . Multilevel Modelling Newsletter , 13 ( 1 ) : 10 – 17 .
   Google Scholar
• Boker , S. M. , Neale , M. C. and Rausch , J. 2004 . “ Latent differential equation modeling with multivariate multi-occasion indicators ” . In Recent developments in structural equation modelling: Theory and applications , Edited by: van Montfort , K. , Oud , J. and Satorra , A. 151 – 174 . Amsterdam , The Netherlands : Kluwer .
   Google Scholar
• Browne , M. W. and Nesselroade , J. R. 2005 . “ Representing psychological processes with dynamic factor models: Some promising uses and extensions of ARMA time series models ” . In Psychometrics: A Festschrift to Roderick P. McDonald , Edited by: Maydeu-Olivares , A. and McArdle , J. J. 415 – 452 . Mahwah , NJ : Erlbaum .
   Google Scholar
• Butner , J. , Amazeen , P. and Mulvey , G. 2005 . Multilevel modeling of two cyclical processes: Extending differential structural equation modeling to coupled systems . Psychological Methods , 10 : 159 – 177 .
   PubMed Web of Science ®Google Scholar
• Cattell , R. B. 1966 . “ Patterns of change: Measurement in relation to state-dimension, trait change, liability, and process concepts ” . In Handbook of multivariate experimental psychology , Edited by: Cattell , R. B. 355 – 402 . Chicago , IL : Rand McNally and Company .
   Google Scholar
• Chow , S.-M. , Ferrer , E. and Nesselroade , J. R. 2007 . An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models . Multivariate Behavioral Research , 42 : 283 – 321 .
   PubMed Web of Science ®Google Scholar
• Chow , S.-M. , Hamaker , E. , Fujita , F. and Boker , S. M. 2009 . Representing time-varying cyclic dynamics using multiple-subject state-space models . British Journal of Mathematical and Statistical Psychology , 62 : 683 – 712 .
   PubMed Web of Science ®Google Scholar
• Chow , S.-M. , Ho , M.-H. , Hamaker , E. L. and Dolan , C. V. 2010 . Equivalence and differences between structural equation modeling and state-space modeling techniques . Structural Equation Modeling , 17 : 303 – 332 .
   Web of Science ®Google Scholar
• Chow , S.-M. and Nesselroade, J. R., Shifren, K., & McArdle, J. J. 2004 . Dynamic structure of emotions among individuals with Parkinson's disease . Structural Equation Modeling , 11 : 560 – 582 .
   Web of Science ®Google Scholar
• Chow , S.-M. , Tang , N. , Yuan , Y. , Song , X. and Zhu , H. 2011 . Bayesian estimation of semiparametric dynamic latent variable models using the Dirichlet process prior . British Journal of Mathematical and Statistical Psychology , 64 ( 1 ) : 69 – 106 .
   PubMed Web of Science ®Google Scholar
• Chow , S.-M. , Zu , J. , Shifren , K. and Zhang , G. 2011 . Dynamic factor analysis models with time-varying parameters . Multivariate Behavioral Research , 46 : 303 – 339 .
   PubMed Web of Science ®Google Scholar
• Dolan , C. V. 2005 . MKFM6: Multi-group, multi-subject stationary time series modeling based on the Kalman filter. Retrieved from http://users.fmg.uva.nl/cdolan/
   Google Scholar
• Ferrer , E. and Nesselroade , J. R. 2003 . Modeling affective processes in dyadic relations via dynamic factor analysis . Emotion , 3 : 344 – 360 .
   PubMed Web of Science ®Google Scholar
• Ferrer , E. and Widaman , K. F. 2008 . “ Dynamic factor analysis of dyadic affective processes with inter-group differences ” . In Modeling dyadic and interdependent data in the developmental and behavioral sciences , Edited by: Card , N. A. , Selig , J. P. and Little , T. D. 107 – 137 . Hillsdale, NJ: Psychology Press .
   Google Scholar
• Ferrer , E. and Zhang , G. 2009 . “ Time series models for examining psychological processes: Applications and new developments ” . In Handbook of quantitative methods in psychology , Edited by: Millsap , R. E. and Maydeu-Olivares , A. 637 – 657 . London, UK: Sage Publications .
   Google Scholar
• Hamaker , E. L. , Nesselroade , J. R. and Molenaar , P. C. M. 2007 . The integrated trait-state model . Journal of Research in Personality , 41 : 295 – 315 .
   Web of Science ®Google Scholar
• Hamilton , J. D. 1994 . Time series analysis. , Princeton , NJ : Princeton University Press .
   Google Scholar
• Jones , C. J. and Nesselroade , J. R. 1990 . Multivariate, replicated, single-subject designs and P-technique factor analysis: A selective review of intraindividual change studies . Experimental Aging Research , 16 : 171 – 183 .
   PubMed Web of Science ®Google Scholar
• McArdle , J. J. 1982 . Structural equation modeling of an individual system: Preliminary results from “A Case Study of Alcoholism , Unpublished manuscript, Psychology Department, University of Denver .
   Google Scholar
• Molenaar , P. C. M. 1985 . A dynamic factor model for the analysis of multivariate time series . Psychometrika , 50 : 181 – 202 .
   Web of Science ®Google Scholar
• Molenaar , P. C. M. 1987 . Dynamic assessment and adaptive optimization of the psychotherapeutic process . Behavioral Assessment , 9 : 389 – 416 .
   Google Scholar
• Molenaar , P. C. M. 2004 . A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever . Measurement , 2 : 201 – 218 .
   Google Scholar
• Molenaar , P. C. M. and Nesselroade , J. R. 1998 . A comparison of pseudo-maximum likelihood and asymptotically distribution-free dynamic factor analysis parameter estimation in fitting covariance-structure models to block-Toeplitz matrices representing single-subject multivariate time series . Multivariate Behavioral Research , 33 : 313 – 342 .
   PubMed Web of Science ®Google Scholar
• Muthén , B. 1994 . Multilevel covariance structure analysis . Sociological Methods & Research , 22 : 376 – 398 .
   Web of Science ®Google Scholar
• Muthén , B. and Asparouhov , T. 2009 . “ Growth mixture modeling: Analysis with non-Gaussian random effects ” . In Longitudinal data analysis , Edited by: Fitzmaurice , G. , Davidian , M. , Verbeke , G. and Molenberghs , G. 143 – 165 . Boca Raton , FL : Chapman & Hall/CRC Press .
   Google Scholar
• Muthén , B. and Asparouhov , T. 2012 . Bayesian SEM: A more flexible representation of substantive theory . Psychological Methods , 17 : 313 – 335 .
   PubMed Web of Science ®Google Scholar
• Nesselroade , J. R. and Ford , D. H. 1985 . P-technique comes of age: Multivariate, replicated, single-subject designs for research on older adults . Research on Aging , 7 : 46 – 80 .
   PubMed Web of Science ®Google Scholar
• Nesselroade , J. R. , McArdle , J. J. , Aggen , S. H. and Meyers , J. 2002 . “ Alternative dynamic factor models for multivariate time series analyses ” . In Modeling intraindividual variability with repeated measures data: Advances and techniques , Edited by: Moscowitz , D. M. and Hershberger , S. L. 235 – 265 . Mahwah , NJ : Erlbaum .
   Google Scholar
• Nesselroade , J. R. and Molenaar , P. C. M. 1999 . “ Pooling lagged covariance structures based on short, multivariate time series for dynamic factor analysis ” . In Statistical strategies for small sample research , Edited by: Hoyle , R. H. 223 – 250 . Newbury Park , CA : Sage .
   Google Scholar
• Preacher , K. , Zyphur , M. and Zhang , Z. 2010 . A general multilevel SEM framework for assessing multilevel mediation . Psychological Methods , 15 : 209 – 233 .
   PubMed Web of Science ®Google Scholar
• Ram , N. , Brose , A. and Molenaar , P. 2013 . “ Dynamic factor analysis: Modeling person-specific process ” . In The Oxford handbook of quantitative methods , Edited by: Little , T. D. New York , NY : Oxford University Press .
   Google Scholar
• Roesch , S. C. , Aldridge , A. A. , Stocking , S. N. , Villodas , F. , Leung , Q. , Bartley , C. E. and Black , L. J. 2010 . Multilevel factor analysis and structural equation modeling of daily diary coping data: Modeling trait and state variation . Multivariate Behavioral Research , 45 : 767 – 789 .
   PubMed Web of Science ®Google Scholar
• Shifren , K. , Wood , P. , Hooker , K. and Nesselroade , J. R. 1997 . Structure and variation of mood in individuals with Parkinson's disease: A dynamic factor analysis . Psychology and Aging , 12 : 328 – 339 .
   PubMed Web of Science ®Google Scholar
• Song , H. and Ferrer , E. 2009 . State-space modeling of dynamic psychological processes via the Kalman smoother algorithm: Rationale, finite sample properties, and applications . Structural Equation Modeling , 16 : 338 – 363 .
   Web of Science ®Google Scholar
• Song , H. and Ferrer , E. 2012 . Bayesian analysis of random coefficient dynamic factor models . Multivariate Behavioral Research , 47 : 26 – 60 .
   Web of Science ®Google Scholar
• Timmerman , M. E. , Ceulemans , E. , Lichtwarck-Aschoff , A. and Vansteelandt , K. 2009 . “ Multilevel component analysis for studying intra-individual variability and inter-individual differences ” . In Dynamic process methodology in the social and developmental sciences , Edited by: Valsiner , J. 291 – 318 . New York, NY: Springer . P. C. M. Molenaar, M. C. D. P. Lyra, & N. Chaudhary
   Google Scholar
• Voelkle , M. C. and Oud , J. H. L. 2013 . Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes . British Journal of Mathematical and Statistical Psychology , : 103 – 126 .
   PubMed Web of Science ®Google Scholar
• Voelkle , M. C. , Oud , J. H. L. , von Oertzen , T. and Lindenberger , U. 2012 . Maximum likelihood dynamic factor modeling for arbitrary N and T using SEM . Structural Equation Modeling , 19 : 329 – 350 .
   Web of Science ®Google Scholar
• Wood , P. and Brown , D. 1994 . The study of intraindividual differences by means of dynamic factor models: Rationale, implementation, and interpretation . Psychological Bulletin , 116 : 166 – 186 .
   Web of Science ®Google Scholar
• Zhang , Z. , Hamaker , E. L. and Nesselroade , J. R. 2007 . Bayesian estimation of categorical dynamic factor models . Multivariate Behavioral Research , 42 : 729 – 756 .
   Web of Science ®Google Scholar
• Zhang , Z. , Hamaker , E. L. and Nesselroade , J. R. 2008 . Comparisons of four methods for estimating a dynamic factor model . Structural Equation Modeling , 15 : 377 – 402 .
   Web of Science ®Google Scholar
• Zhang , Z. and Song , H. 2013 . Comparing time delay embedding and state space methods for estimating dynamic factor models .
   Google Scholar