Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences

Psychometrika

Volumn 68, Issue 1, 2003, Pages 105-121

  Timmerman, Marieke E ^a   Kiers, Henk A L ^a  

^a UNIVERSITY OF GRONINGEN   (Netherlands)

Author keywords

Exploratory longitudinal analysis; Three way analysis

Indexed keywords

EID: 0037795677     PISSN: 00333123     EISSN: None     Source Type: Journal    
DOI: 10.1007/BF02296656     Document Type: Article

References (32)

1. Andersen, T.W. (1963). The use of factor analysis in the statistical analysis of multiple time series. Psychometrika, 28, 1-25.
2. Bijleveld, C.C.J.H. (1989). Exploratory linear dynamic systems analysis. Leiden, Netherlands: DSWO Press.
3. Bijleveld, C.C.J.H., & Bijleveld, F.D. (1997). A comparison of the applicability of two approaches to linear dynamic systems analyze for N subjects. Kwantitatieve Methoden, 55, 37-57.
4. Bro, R., Andersson, C.A., & Kiers, H.A.L. (1999), PARAFAC2-Part II. Modeling Chromatographic data with retention time shifts. Journal of Chemometrics, 13, 295-309.
5. Bro, R., & Smilde, A.K. (2003). Centering and scaling in component analysis. Journal of Chemometrics, 17, 16-33.
6. Browne, M.W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21, 230-258.
7. Carroll, J.D., & Chang, J.J. (1970). Analysis of individual differences in multidimensional scaling via an N-way generalization of Eckart-Young decomposition. Psychometrika, 35, 283-319.
8. Cattell, R.B. (1952). Factor analysis. New York, NY: Harper.
9. Cattell, R.B. (1963). The structuring of change by P-technique and incremental R-technique. In C.W. Harris (Ed.), Problems in measuring change. Madison, WI: The University of Wisconsin Press.
10. Cliff, N. (1966). Orthogonal rotation to congruence. Psychometrika, 31, 33-42.
11. Engle, R., & Watson, M. (1981). A one-factor multivariate time series model of metropolitan wage rates. Journal of the American Statistical Association, 76, 774-781.
12. Harshman, R.A. (1972). Determination and proof of minimum uniqueness conditions for PARAFAC1. UCLA Working Papers in Phonetics, 22, 111-117.
13. Harshman, R.A., & Lundy, M.E. (1984a). The PARAFAC model for three-way factor analysis and multidimensional scaling. In H.G. Law, C.W. Conrad, Jr., J.A. Hattie & R.P. McDonald (Eds.), Research methods for multimode data analysis (pp. 122-215). New York, NY: Praeger.
14. Harshman, R.A. & Lundy, M.E. (1984b). Data preprocessing and the extended PARAFAC model. In H.G. Law, C.W. Conrad, Jr., J.A. Hattie, & R.P. McDonald (Eds.), Research methods for multimode data analysis (pp. 216-284). New York: Praeger.
15. Hastie, T, Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York, NY: Springer.
16. Immink, W. (1986). Parameter estimation in Markov models and dynamic factor analysis. Unpublished doctoral thesis, University of Utrecht, Utrecht, Netherlands.
17. Jones, C.J., & Nesselroade, J.R. (1990). Multivariate, replicated, single-subject, repeated measures designs and P-technique factor analysis: A review of intraindividual change studies. Experimental Aging Research, 16, 171-183.
18. Kaiser, H.F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200.
19. Kiers, H.A.L., & ten Berge, J.M.F. (1994). Hierarchical relations between methods for simultaneous component analysis and a technique for rotation to a simple simultaneous structure. British Journal of Mathematical and Statistical Psychology, 47, 109-126.
20. Kiers, H.A.L., ten Berge, J.M.F., & Bro, R. (1999). PARAFAC2-Part 1. A direct fitting algorithm for the PARAFAC2 model. Journal of Chemometrics, 13, 275-294.
21. Little, R.J.A., & Rubin, D.B. (1987). Statistical analysis with missing data. New York, NY: Wiley.
22. Louwerse, D.J., Smilde, A.K., & Kiers, H.A.L. (1999). Cross-validation of multiway component models. Journal of Chemometrics, 13, 491-510.
23. Molenaar, P.M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181-202.
24. Nesselroade, J.R., & Molenaar, P.C.M. (1999). Pooling lagged covariances structures based on short, multivariate time series for dynamic factor analysis. In R.H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 223250). Thousand Oaks, CA: Sage Publications.
25. Niesing, J. (1997). Simultaneous component and factor analysis methods for two or more groups: A comparative study. Leiden, Netherlands: DSWO Press.
26. Shifren, K., Hooker, K., Wood, P., & 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.
27. Timmerman, M.E. (2001). Component analysis of multisubject multivariate longitudinal data. Unpublished doctoral thesis, University of Groningen, Groningen, Netherlands.
28. Timmerman, M.E., & Kiers, H.A.L. (2002). Three-way component analysis with smoothness constraints. Computational Statistics and Data Analysis, 3, 447-470.
29. Tucker, L.R. (1951). A method for synthesis of factor analysis studies(Personnel Research sections Rep. No. 984). Washington, DC: Department of the Army.
30. van Buuren, S. (1990). Optimal scaling of time series. Leiden, Netherlands: DSWO Press.
31. Watson, D. (1988). Intraindividual and interindividual analyses of positive and negative affect: Their relation to health complaints, perceived stress, and daily activities. Journal of personality and social psychology, 54, 1020-1030.
32. Watson, D., Clark, L.A., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063-1070.