Individual Pooling for Group-Based Modeling Under the Assumption of Ergodicity

REFERENCES

• Anderson, T.W. (1963). The use of factor analysis in the statistical analysis of multiple time series. Psychometrika, 28, 1–25.
   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. (2005). DyFA: Dynamic factor analysis of lagged correlation matrices. Retrieved from http://faculty.psy.ohio-state.edu/browne/software.php
   Google Scholar
• Castro-Schilo, L., & Ferrer, E. (2013). Comparison of nomothetic versus idiographic-oriented methods for making predictions about human behavior. Multivariate Behavior Research, 48, 175–207.
   PubMed Web of Science ®Google Scholar
• Cattell, R.B., Cattell, A.K. S., & Rhymer, R.M. (1947). P-technique demonstrated in determining psycho-physiological source traits in a normal individual. Psychometrika, 12, 267–288.
   PubMed Web of Science ®Google Scholar
• Chen, F., Curran, P.J., Bollen, K.A., Kirby, J., & Paxton, P. (2008). An empirical evaluation of the use of fixed cutoff points in RMSEA test statistic in structural equation models. Sociological Methods & Research, 36, 462–494. doi:10.1177/0049124108314720
   PubMed Web of Science ®Google Scholar
• Chow, S., Nesselroade, J.R., Shifren, K., & McArdle, J.J. (2004). Dynamic structure of emotions among individuals with Parkinson's disease. Structural Equation Modeling: A Multidisciplinary Journal, 11, 560–582. doi:10.1207/s15328007sem1104_4
   Web of Science ®Google Scholar
• Curran, P.J., & Wirth, R.J. (2004). Interindividual differences in intraindividual variation: Balancing internal and external validity. Measurement: Interdisciplinary Research and Perspectives, 2, 219–247.
   PubMedGoogle Scholar
• Ferrer, E., & Song, H. (2012). Longitudinal structural models for examining dynamics in dyadic interactions. In R.H. Hoyle (Ed.), Handbook of structural equation modeling (pp. 601–616). New York, NY: Guilford Press.
   Google Scholar
• Ferrer, E., & Steele, J. (2012). Dynamic systems analysis of affective processes in dyadic interactions using differential equations. In G.R. Hancock & J.R. Harring (Eds.), Advances in longitudinal methods in the social and behavioral sciences (pp. 111–134). Charlotte, NC: Information Age.
   Google Scholar
• Ferrer, E., Steele, J.S., & Hsieh, F. (2012). Analyzing the dynamics of affective dyadic interactions using patterns of intra- and interindividual variability. Multivariate Behavioral Research, 47, 136–171. doi:10.1080/00273171.2012.640605
   Web of Science ®Google Scholar
• Ferrer, E., & Widaman, K.F. (2008). Dynamic factor analysis of dyadic affective processes with inter-group differences. In N.A. Card, J.P. Selig., & T.D. Little (Eds.), Modeling dyadic and interdependent data in the developmental and behavioral sciences (pp. 107–137). Hillsdale, NJ: Psychology Press.
   Google Scholar
• Ferrer, E., & Zhang, G. (2009). Time series models for examining psychological processes: Applications and new developments. In R.E. Millsap & A. Maydeu-Olivares (Eds.), Handbook of quantitative methods in psychology (pp. 637–657). London, UK: Sage.
   Google Scholar
• Gates, K.M., & Molenaar, P.C. M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. Neuroimage, 63, 310–319. doi:10.1016/j.neuroimage.2012.06.026
   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.
   PubMed Web of Science ®Google Scholar
• Hooker, K., Nesselroade, D.W., Nesselroade, J.R., & Lerner, R.M. (1987). The structure of intraindividual temperament in the context of mother-child dyads: P-technique factor analyses of short-term change. Developmental Psychology, 23(3), 332–346.
   Web of Science ®Google Scholar
• Hu, L., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6, 1–55.
   Web of Science ®Google Scholar
• Institute for Developmental & Health Research Methodology. (2004). DFA: Tools for dynamic factor analysis and the pooling of lagged covariance structures. . Retrieved from http://www.psychstat.org/us/upload/DFANotes3.pdf
   Google Scholar
• MacCallum, R.C., Widaman, K.F., Zhang, S., & Hong, S. (1999). Sample size in factor analysis. Psychological Methods, 4(1), 84–99.
   Web of Science ®Google Scholar
• Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58, 525–543.
   Web of Science ®Google Scholar
• Meredith, W., & Horn, J.L. (2001). The role of factorial invariance in modeling growth and change. In L.M. Collins & A.G. Sayer (Eds.), New methods for the analysis of change (pp. 203–240). Washington, DC: American Psychological Association.
   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. (2004). A manifesto of psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research and Perspectives, 2, 201–218.
   Google Scholar
• Molenaar, P.C. M. (2008). On the implications of the classical ergodic theorems: Analysis of developmental processes has to focus on intra-individual variation. Developmental Psychobiology, 50, 60–69. doi:10.1002/dev.20262
   PubMed 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.
   PubMed Web of Science ®Google Scholar
• Molenaar, P.C. M., & Nesselroade, J.R. (2009). The recoverability of P-technique factor analysis. Multivariate Behavioral Research, 44, 130–141. doi:10.1080/00273170802620204
   PubMed Web of Science ®Google Scholar
• Molenaar, P.C. M., & Nesselroade, J.R. (2012). Merging the idiographic filter with dynamic factor analysis to model processes. Applied Developmental Science, 16, 210–219. doi:10.1080/10888691.2012.722884
   Web of Science ®Google Scholar
• Muthén, L.K., & Muthén, B.O. (1998–2007). Mplus user's guide . (5th ed.). Los Angeles, CA: Author.
   Google Scholar
• Nesselroade, J.R. (2004). Yes, it is time: Commentary on Molenaar's manifesto. Measurement: Interdisciplinary Research and Perspectives, 2, 227–230.
   Google Scholar
• Nesselroade, J.R., & 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., & Molenaar, P.C. M. (1999). Pooling lagged covariance structures based on short, multivariate time series for dynamic factor analysis. In R.H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 223–250). Thousand Oaks, CA: Sage.
   Google Scholar
• Nesselroade, J.R., & Molenaar, P.C. M. (2003). Quantitative models for developmental processes. In J. Valsine & K. Connolly (Eds.), Handbook of developmental psychology (pp. 622–639). London, UK: Sage.
   Google Scholar
• Rogosa, D. (2004). Some history of modeling the processes that generate the data. Measurement: Interdisciplinary Research and Perspectives, 2, 231–234.
   Google Scholar
• Runyan, W.M. (1983). Idiographic goals and methods in the study of lives. Journal of Personality, 51, 413–437.
   PubMed Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Song, H., & Ferrer, E. (2009). State-space modeling of dynamic psychological processes via the Kalman smoother algorithm: Rationale, finite sample properties, and applications. Structural Equation Modeling: A Multidisciplinary Journal, 16, 338–363.
   Web of Science ®Google Scholar
• 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.
   PubMed Web of Science ®Google Scholar
• Wood, P., & 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, 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/software.php
   Google Scholar
• Zhang, Z. (2005). How to use DFA. . Retrieved from http://www.psychstat.org/us/article.php/8.htm
   Google Scholar