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Testing all six person-oriented principles in dynamic factor analysis

Published online by Cambridge University Press:  28 April 2010

Peter C. M. Molenaar*
Affiliation:
Pennsylvania State University
*
Address correspondence and reprint requests to: Peter C. M. Molenaar, Department of Human Development and Family Studies, College of Health and Human Development, 113 Henderson Building, Pennsylvania State University, University Park, PA 16802-6504; E-mail: [email protected].

Abstract

All six person-oriented principles identified by Sterba and Bauer's Keynote Article can be tested by means of dynamic factor analysis in its current form. In particular, it is shown how complex interactions and interindividual differences/intraindividual change can be tested in this way. In addition, the necessity to use single-subject methods in the analysis of developmental processes is emphasized, and attention is drawn to the possibility to optimally treat developmental psychopathology by means of new computational techniques that can be integrated with dynamic factor analysis.

Type
Special Section Commentary
Copyright
Copyright © Cambridge University Press 2010

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References

Barlow, D. H., Nock, M. K., & Hersen, M. (2009). Single case experimental designs: Strategies for studying behavior change (3rd ed.). Boston: Allyn & Bacon.Google Scholar
Borsboom, D. (2005). Measuring the mind: Conceptual issues in contemporary psychometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Carver, C. S., & Scheier, M. F. (1998). On the self-regulation of behavior. New York: Cambridge University Press.CrossRefGoogle 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, 207233.CrossRefGoogle ScholarPubMed
Hyland, M. E. (1987). Control theory of psychological mechanisms of depression. Psychological Bulletin, 102, 109121.CrossRefGoogle ScholarPubMed
Johnson, R. E., Chang, C. H., & Lord, R. G. (2006). Moving from cognition to behavior: What the research says. Psychological Bulletin, 132, 381415.CrossRefGoogle ScholarPubMed
Lord, F. M., & Novick, M. R. (1968). Statistical theories of mental test scores. Reading, MA: Addison–Wesley.Google Scholar
Lütkepohl, H. (2005). New introduction to multiple time series analysis. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Molenaar, P. C. M. (1985). A dynamic factor model for the analysis of multivariate time series. Psychometrika, 50, 181202.CrossRefGoogle Scholar
Molenaar, P. C. M. (1994). Dynamic latent variable models in developmental psychology. In: Von Eye, A. & Clogg, C. C. (Eds.), Latent variables analysis: Applications for developmental research. London: Sage.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, 201218.Google Scholar
Molenaar, P. C. M. (2007). On the implications of the classical ergodic theorems: Analysis of developmental processes has to focus on intra-individual variation. Developmental Psychobiology, 50, 6069.CrossRefGoogle Scholar
Molenaar, P. C. M. (2008). Consequences of the ergodic theorems for classical test theory, factor analysis, and the analysis of developmental processes. In Hofer, S. M. & Alwin, D. F. (Eds.), Handbook of cognitive aging (pp. 90104). Thousand Oaks, CA: Sage.Google Scholar
Molenaar, P. C. M. (in press). Note on the optimization of psychotherapeutic processes. Journal of Mathematical Psychology.Google Scholar
Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychology, 18, 112117.CrossRefGoogle Scholar
Molenaar, P. C. M., de Gooijer, J. G., & Schmitz, B. (1992). Dynamic factor analysis of nonstationary multivariate time series. Psychometrika, 57, 333349.CrossRefGoogle Scholar
Molenaar, P. C. M., & Newell, K. M. (2003). Direct fit of a theoretical model of phase transition in oscillatory finger motions. British Journal of Mathematical and Statistical Psychology, 56, 199214.CrossRefGoogle ScholarPubMed
Molenaar, P. C. M., Sinclair, K. O., Rovine, M. J., Ram, N., & Corneal, S. E. (2009). Analyzing developmental processes on an individual level using nonstationary time series modeling. Developmental Psychology, 45, 260271.CrossRefGoogle Scholar
Molenaar, P. C. M., Ulbrecht, J., Gold, C., Rovine, M. J., Wang, Q., & Zhou, J. (2009). State-space modeling of continuously monitored blood glucose time series of type 1 diabetic patients: A new inductive approach to prediction and dynamic regression on insulin dose and meal intake. Manuscript submitted for publication.Google Scholar
Nesselroade, J. R., Gerstorf, D., Hardy, S. A., & Ram, N. (2007). Idiographic filters for psychological constructs. Measurement, 5, 217235.Google Scholar
Priestley, M. B. (1988). Nonlinear and non-stationary time series. London: Academic Press.Google Scholar