A Computationally Efficient State Space Approach to Estimating Multilevel Regression Models and Multilevel Confirmatory Factor Models

Multivariate Behavioral Research

Volumn 49, Issue 2, 2014, Pages 119-129

  Gu, Fei ^a   Preacher, Kristopher J ^b   Wu, Wei ^c   Yung, Yiu Fai ^d  

^a MCGILL UNIVERSITY   (Canada)

^b VANDERBILT UNIVERSITY   (United States)

^c UNIVERSITY OF KANSAS   (United States)

^d SAS INSTITUTE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898751577     PISSN: 00273171     EISSN: None     Source Type: Journal    
DOI: 10.1080/00273171.2013.866537     Document Type: Article

References (22)

1. Bauer, D. J. 2003. Estimating multilevel linear models as structural equation models. Journal of Educational and Behavioral Statistics, 28: 135 - 167.
2. Bollen, K. A. 1989. Structural equations with latent variables, New York, NY: Wiley.
3. Chow, S.-M., Ho, M. -H. R., 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.
4. Curran, P. J. 2003. Have multilevel models been structural equation models all along?. Multivariate Behavioral Research, 38: 529 - 569.
5. 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/
6. Durbin, J. and Koopman, S. J. 2001. Time series analysis by state space methods, New York, NY: Oxford University Press.
7. Goldstein, H. 1986. Multilevel mixed linear model analysis using iterative generalized least squares. Biometrika, 73: 43 - 56.
8. Goldstein, H. 1995. Multilevel statistical models (2nd ed.), London, UK: Edward Arnold.
9. Harvey, A. C. 1989. Forecasting, structural time series models and the Kalman filter, Princeton, NJ: Princeton University Press.
10. Ho, M. -H. R., Shumway, R. and Ombao, H. 2006. " State-space approach to modeling dynamic processes: Applications in biological and social sciences ". In Models for intensive longitudinal data, Edited by: Walls, T. A. and Schafer, J. L. 148 - 170. New York, NY: Oxford University Press.
11. Hox, J. J. 2002. Multilevel analysis: Techniques and applications, Mahwah, NJ: Erlbaum.
12. Icaza, G. and Jones, R. H. 1999. A state-space EM algorithm for longitudinal data. Journal of Time Series Analysis, 20: 537 - 550.
13. Jones, R. H. 1993. Longitudinal data with serial correlation: A state-space approach, London, UK: Chapman & Hall.
14. Kalman, R. E. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering (Series D), 82: 35 - 45.
15. Kamata, A., Bauer, D. J. and Miyazaki, Y. 2008. " Multilevel measurement modeling ". In Multilevel modeling of educational data, Edited by: O'Connell, A. A. and McCoach, D. B. 345 - 388. Charlotte, NC: Information Age.
16. Laird, N. M. and Ware, J. H. 1982. Random-effects models for longitudinal data. Biometrics, 38: 963 - 974.
17. Mehta, P. D. and Neale, M. C. 2005. People are variables too: Multilevel structural equations modeling. Psychological Methods, 10: 259 - 284.
18. Schweppe, F. 1965. Evaluation of likelihood functions for Gaussian signals. IEEE Transactions on Information Theory, 11: 61 - 70.
19. Snijders, T. A. B. and Bosker, R. J. 2012. Multilevel analysis: An introduction to basic and advanced multilevel modeling, Thousand Oaks, CA: Sage.
20. 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.
21. Wang, T. and Tobias, R. All the cows in Canada: Massive mixed modeling with the HPMIXED procedure in SAS 9.2. Paper presented at SAS Global Forum 2009, Washington, DC,
22. 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.