Testing Process Factor Analysis Models Using the Parametric Bootstrap

References

• Allport, G. W. (1937). Personality: A psychological interpretation. New York: Holt, Rinehart, & Winston.
   Google Scholar
• Anderson, T. W. (1963). The use of factor analysis in the statistical analysis of multiple time series. Psychometrika, 28, 1–25. doi: 10.1007/bf02289543
   Web of Science ®Google Scholar
• Bollen, K. A., & Stine, R. (1993). Boostrapping goodness of fit measures in structural equation models. In K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 111–135). Newbury Park, CA: Sage.
   Google Scholar
• Borkenau, P., & Ostendorf, F. (1998). The big five as states: How useful is the five factor model to describe intraindividual variations over time? Journal of Research in Personality, 32, 202–221. doi: 10.1006/jrpe.1997.2206
   Web of Science ®Google Scholar
• Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71, 791–799. doi: 10.1080/01621459.1976.10480949
   Web of Science ®Google Scholar
• Brockwell, P. J., & Davis, R. A. (1991). Time series: Theory and methods (2nd ed.). New York: Springer-Verlag. doi: 10.1007/978-1-4419-0320-4
   Google Scholar
• Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures. British Journal of Mathematical and Statistical Psychology, 37, 62–83. doi: 10.1111/j.2044-8317.1984.tb00789.x
   PubMed Web of Science ®Google Scholar
• Browne, M. W., & Nesselroade, J. R. (2005). Representing psychological processes with dynamic factor models: some promising uses and extensions of ARMA time series models. In A. Maydeu-Olivares, & J. J. McArdle (Eds.), Advances in psychometrics: A festschrift for Roderick P. McDonald (pp. 415–452). Mahwah, NJ: Erlbaum.
   Google Scholar
• Browne, M. W., & Zhang, G. (2007). Developments in the factor analysis of individual time series. In R. Cudeck, & R. C. MacCallum (Eds.), Factor analysis at 100: Historical developments and future directions (pp. 265–291). Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Browne, M. W., & Zhang, G. (2010). DyFA 3.00 user guide. Retrieved from http://quantrm2.psy.ohiostate.edu/browne/.
   Google Scholar
• Cattell, R. B., Cattell, A. K. S., & Rhymer, R. M. (1947). P-technique demonstrated in determining psychophysiological source traits in a normal individual. Psychometrika, 12, 267–288. doi: 10.1007/bf02288941
   PubMed Web of Science ®Google Scholar
• Chow, S.-M., Nesselroade, J., Shifren, K., & McArdle, J. J. (2004). Dynamic structure of emotions among individuals with Parkinson's disease. Structural Equation Modeling, 11, 560–582. doi: 10.1207/s15328007sem1104\_4
   Web of Science ®Google Scholar
• Dolan, C. V. (2010). MKF: provisional documentation. Retrieved from http://www.fgb.vu.nl/en/about-the-faculty/all-employees/staff-c-e/Dolan-c-v/index.aspx.
   Google Scholar
• Du Toit, S., & Browne, M. W. (2007). Structural equation modeling of multivariate time series. Multivariate Behavioral Research, 42, 67–101. doi: 10.1080/00273170701340953
   PubMed Web of Science ®Google Scholar
• Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall. doi: 10.1007/978-1-4899-4541-9
   Google Scholar
• Eizenman, D. R., Nesselroade, J. R., Fetherman, D. L., & Rowe, J. W. (1997). Intraindividual variability in perceived control in an older sample: The MacArthur successful aging studies. Psychology and Aging, 12, 489–502. doi: 10.1037//0882-7974.12.3.489
   PubMed Web of Science ®Google Scholar
• 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. doi: 10.1080/01621459.1981.10477720
   Web of Science ®Google Scholar
• Ferrer, E., & Nesselroade, J. R. (2003). Modeling affective process in dyadic relations via dynamic factor analysis. Emotion, 3, 344–360. doi: 10.1037/1528-3542.3.4.344
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2005). Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis. Multivariate Behavioral Research, 40, 207–233. doi: 10.1207/s15327906mbr4002\_3
   PubMed Web of Science ®Google Scholar
• Hamaker, E. L., Nesselroad, J. R., & Molenaar, P. C. M. (2007). The integrated trait-state model. Journal of Research in Personality, 41, 295–315. doi: 10.1016/j.jrp.2006.04.003
   Web of Science ®Google Scholar
• Hershberger, S. L., Corneal, S. E., & Molenaar, P. C. M. (1995). Dynamic factor analysis: An application to emotional response patterns underlying daughter/father and stepdaughter/stepfather relationships. Structural Equation Modeling, 2, 31–52. doi: 10.1080/10705519509539993
   Web of Science ®Google Scholar
• Holmes, E., Ward, E., & Wills, K. (2013). Multivariate autoregressive state-space modeling, version 3.9. Seattle, USA: NOAA. Retrieved from https://cran.r-project.org/web/packages/MARSS/MARSS.pdf (version 3.9)
   Google Scholar
• Lahiri, S. N. (2003). Resampling methods for dependent data. New York: Springer-Verlag.
   Google Scholar
• Molenaar, P. C. M. (1985). A dynamic factor analysis model for the analysis of multivariate time series. Psychometrika, 50, 181–202. doi: 10.1007/bf02294246
   Web of Science ®Google Scholar
• Molenaar, P. C. M., & 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. doi: 10.1207/s15327906mbr3303\_1
   PubMed Web of Science ®Google Scholar
• Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Dynamic factor analysis models for representing process in multivariate time-series. In D. Moskowitz, & S. L. Hershberger (Eds.), Modeling intraindividual variability with repeated measures data: Methods and applications (pp. 235–265). Mahwah, NJ: Erlbaum.
   Google Scholar
• Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48, 28–56. doi: 10.1080/00273171.2012.710386
   PubMed Web of Science ®Google Scholar
• Sbarra, D. A., & Ferrer, E. (2006). The structure and process of emotional experience following nonmarital relationship dissolution: Dynamic factor analyses of love, anger, and sadness. Emotion, 6, 224–238. doi: 10.1037/1528-3542.6.2.224
   PubMed Web of Science ®Google Scholar
• Zhang, G., & Browne, M. W. (2010a). Bootstrap standard error estimates in dynamic factor analysis. Multivariate Behavioral Research, 45, 453–482. doi: 10.1080/00273171.2010.483375
   PubMed Web of Science ®Google Scholar
• Zhang, G., & Browne, M. W. (2010b). DyFA bootstrap: Dynamic factor analysis with bootstrap standard errors and goodness of fit test. Retrieved from http://faculty.psy.ohio-state.edu/browne/.
   Google Scholar
• Zhang, Z., Hamaker, E. L., & Nesselroade, J. R. (2008). Comparisons of four methods for estimating a dynamic factor model. Structural Equation Modeling, 15, 377–402. doi: 10.1080/10705510802154281
   Web of Science ®Google Scholar