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Closing the gap between person-oriented theory and methods

Published online by Cambridge University Press:  28 April 2010

Eun Young Mun ,
Marsha E. Bates  and
Evgeny Vaschillo
Eun Young Mun*
Affiliation:
Rutgers, The State University of New Jersey
Marsha E. Bates
Affiliation:
Rutgers, The State University of New Jersey
Evgeny Vaschillo
Affiliation:
Rutgers, The State University of New Jersey
*
Address correspondence and reprint requests to: Eun Young Mun, Center of Alcohol Studies, Rutgers, The State University of New Jersey, 607 Allison Road, Piscataway, NJ 08854; E-mail: eymun@rci.rutgers.edu.
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Abstract

Sterba and Bauer's Keynote Article discusses the blurred distinction between theoretical principles and analytical methods in the person-oriented approach as problematic and review which of the person-oriented principles are testable under the four types of latent variable models for longitudinal data. Although the issue is important, some arbitrariness exists in determining whether a given principle can be tested within each analytic approach. To close the gap between person-oriented theory and methods and to extend the person-oriented approach more generally, it is necessary to embrace both variable-oriented and person-oriented methods because it is not the individual analytic methods but how studies are implemented as a whole that defines the person-oriented approach. Three areas in developmental psychopathology are discussed in which variable-oriented and person-oriented methods can be complementary. The need to better understand the target system using an appropriate person-specific tool is graphically illustrated. Several concepts of dynamic systems such as attractors, phase transitions, and control parameters are illustrated using experimentally perturbed cardiac rhythms (heart rate variability) as an example in the context of translational alcohol research.

Type
Special Section Commentary
Information
Development and Psychopathology , Volume 22 , Issue 2 , May 2010 , pp. 261 - 271
Copyright
Copyright © Cambridge University Press 2010

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References

Appelhans, B. M., & Luecken, L. J. (2006). Heart rate variability as an index of regulated emotional responding. Review of General Psychology, 10, 229–240.CrossRefGoogle Scholar
Bauer, D. J., & Curran, P. J. (2003). Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods, 8, 338–363.CrossRefGoogle ScholarPubMed
Bauer, D. J., & Curran, P. J. (2004). The integration of continuous and discrete latent variable models: Potential problems and promising opportunities. Psychological Methods, 9, 3–29.CrossRefGoogle ScholarPubMed
Bauer, D. J., Preacher, K. J., & Gil, K. M. (2006). Conceptualizing and testing random indirect effects and moderated mediation in multilevel models: New procedures and recommendations. Psychological Methods, 11, 142–163.CrossRefGoogle ScholarPubMed
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.Google Scholar
Chow, S.-M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5, 208–225.CrossRefGoogle ScholarPubMed
Cicchetti, D. (2006). Development and psychopathology. In Cicchetti, D. & Cohen, D. (Eds.), Developmental psychopathology (2nd ed., Vol. 1, pp. 1–23). New York: Wiley.Google Scholar
Cicchetti, D., & Toth, S. L. (2009). The past achievements and future promises of developmental psychopathology: The coming of age of a discipline. Journal of Child Psychology and Psychiatry, 50, 16–25.Google Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed).Hillsdale, NJ: Erlbaum.Google Scholar
Cudeck, R., & Henly, S. J. (2003). A realistic perspective on pattern representation in growth data: Comment on Bauer and Curran (2003). Psychological Methods, 8, 378–383.Google Scholar
Feldman, B. J., Masyn, K. E., & Conger, R. D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45, 652–676.CrossRefGoogle ScholarPubMed
Foster, E. M., Dodge, K. A., & Jones, D. (2003). Issues in the economic evaluation of prevention programs. Applied Developmental Science, 7, 76–86.Google Scholar
Granic, I., & Hollenstein, T. (2003). Dynamic systems methods for models of developmental psychopathology. Development and Psychopathology, 15, 641–669.Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: Vanilla or resting baseline. Psychophysiology, 29, 742–750.CrossRefGoogle ScholarPubMed
King, N. J., Tonge, B. J., Heyne, D., Pritchard, M., Rollings, S., Young, D., et al. (1998). Cognitive-behavioral treatment of school-refusing children: A controlled evaluation. Journal of American Academy of Child & Adolescent Psychiatry, 37, 395–403.Google Scholar
Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1999). International Affective Picture System (IAPS): Instruction manual and affective ratings. Technical Report A-4. Miami: University of Florida, Center for Research in Psychophysiology.Google Scholar
Lehrer, P. M., Vaschillo, E., Vaschillo, B., Lu, S.-E., Eckberg, D. L., Edelberg, E., et al. (2003). Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow. Psychosomatic Medicine, 65, 796–805.CrossRefGoogle ScholarPubMed
MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York: Erlbaum.Google Scholar
Magnusson, D. (1998). The logic and implications of a person-oriented approach. In Cairns, R. B., Bergman, L. R., & Kagan, J. (Eds.), Methods and models for studying the individual (pp. 33–62). Thousand Oaks, CA: Sage.Google Scholar
McCartney, K., & Rosenthal, R. (2000). Effect size, practical importance, and social policy for children. Child Development, 71, 173–180.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.Google Scholar
Molenaar, P. C. M. (2010). Testing all six person-oriented principles in dynamic factor analysis. Development and Psychopathology, 22, 255–259.CrossRefGoogle ScholarPubMed
Mun, E. Y., von Eye, A., Bates, M. E., & Vaschillo, E. G. (2008). Finding groups using model-based cluster analysis: Heterogeneous emotional self-regulatory processes and heavy alcohol use risk. Developmental Psychology, 44, 481–495.Google Scholar
Mun, E. Y., Windle, M., & Schainker, L. M. (2008). A model-based cluster analysis approach to adolescent problem behaviors and young adult outcomes. Development and Psychopathology, 20, 291–318.Google Scholar
Muthén, B. (2001). Second-generation structural equation modeling with a combination of categorical and continuous latent variables. In Collins, L. M. & Sayer, A. G. (Eds.), New methods for the analysis of change (pp. 289–322). Washington, DC: American Psychological Association.Google Scholar
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40, 879–891.Google Scholar
Richters, J. E. (1997). The Hubble hypothesis and the developmentalist's dilemma. Development and Psychopathology, 9, 193–229.CrossRefGoogle ScholarPubMed
Rosenthal, R., & Rubin, D. B. (1982). A simple, general purpose display of magnitude of experimental effect. Journal of Educational Psychology, 74, 166–169.CrossRefGoogle Scholar
Sampson, R. J., & Laub, J. H. (2005). Seductions of method: Rejoinder to Nagin and Tremblay's “Developmental trajectory groups: Fact or a useful statistical fiction?” Criminology, 43, 905–913.Google Scholar
Shockley, K. (2005). Cross recurrence quantification of interpersonal postural activity. In Riley, M. A. & Van Orden, G. C. (Eds.), Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 143–177). Washington, DC: NSF. Retrieved April 16, 2009, from http://www.nsf.gov/sbe/bcs/nmbs/nmbs.jspGoogle Scholar
Sterba, S. K., & Bauer, D. J. (2010). Matching method with theory in person-oriented developmental psychopathology research. Development and Psychopathology, 22, 239–254.CrossRefGoogle ScholarPubMed
Stritzke, W. G. K., Patrick, C. J., & Lang, A. R. (1995). Alcohol and human emotion: A multidimensional analysis incorporating startle-probe methodology. Journal of Abnormal Psychology, 104, 114–122.Google Scholar
Thelen, E., & Smith, L. B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press.Google Scholar
Tomarken, A. J., & Waller, N. G. (2003). Potential problems with well fitting models. Journal of Abnormal Psychology, 112, 578–598.Google Scholar
Vaschillo, E. G., Vaschillo, B., & Lehrer, P. M. (2006). Characteristics of resonance in heart rate variability stimulated by biofeedback. Applied Psychophysiology and Biofeedback, 31, 129–142.Google Scholar
von Eye, A. (2010). Developing the person-oriented approach: Theory and methods of analysis. Development and Psychopathology, 22, 277–285.Google Scholar
von Eye, A., & Bergman, L. R. (2003). Research strategies in developmental psychopathology: Dimensional identity and the person-oriented approach. Development and Psychopathology, 15, 553–580.Google Scholar
Webber, C. L. Jr., & Zbilut, J. P. (2005). Recurrence quantification analysis of nonlinear dynamical systems. In Riley, M. A. & Van Orden, G. C. (Eds.), Tutorials in contemporary nonlinear methods for the behavioral sciences (pp. 26–83). Washington, DC: NSF. Retrieved April 16, 2009, from http://www.nsf.gov/sbe/bcs/nmbs/nmbs.jspGoogle Scholar