Hostname: page-component-745bb68f8f-s22k5 Total loading time: 0 Render date: 2025-01-08T11:15:06.281Z Has data issue: false hasContentIssue false
  • English
  • Français

Developing the person-oriented approach: Theory and methods of analysis

Published online by Cambridge University Press:  28 April 2010

Alexander von Eye
Alexander von Eye*
Affiliation:
Michigan State University
*
Address correspondence and reprint requests to: Alexander von Eye, Department of Psychology, Michigan State University, 316 Psychology Building, East Lansing, MI 48824-1116; E-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

The development of paradigms, or perspectives of research takes place at the level of theory, in the domain of methodology, and in the context of existing paradigms and perspectives. The development of the person-oriented approach has made considerable progress at the level of theory. In addition, the approach has found a large number of applications. Sterba and Bauer's Keynote Article has closed a gap by discussing methodological implications of the person-oriented approach. In particular, the authors have discussed whether and, if yes, how the tenets of the person-oriented approach can be tested using tools of applied statistics popular in current empirical psychological research. Continuing this discussion, this article focuses on recent developments in all three areas. First, the importance and the implications of the concept of dimensional identity are discussed. It is argued that dimensional identity needs to be established across time and individuals for comparisons to be valid, both in person-oriented and in variable-oriented research. Second, methods not covered in Sterba and Bauer's Keynote are discussed and their application is exemplified. One focus of this discussion is on configural frequency analysis, which allows researchers to make statements about particular cells or groups of cells in cross-classifications of categorical variables. Third, person-oriented research is compared to differential psychology. It is argued that the concept of dimensional identity represents the next step in the development of a psychological subdiscipline that allows one to consider that individuals differ and develop in unique ways. These differences not only manifest in means but in any parameter, including covariance structures, and they can also manifest in the differential meaningfulness of variables for the description of individuals.

Type
Special Section Commentary
Information
Development and Psychopathology , Volume 22 , Issue 2 , May 2010 , pp. 277 - 285
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Achenbach, T. M. (1991). Manual for the Youth Self-Report and 1991 profile. Burlington, VT: University of Vermont, Department of Psychiatry.Google Scholar
Bergman, L. R. (2001). A person approach in research on adolescence: Some methodological challenges. Journal of Adolescent Research, 16, 28–53.CrossRefGoogle Scholar
Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.CrossRefGoogle ScholarPubMed
Bergman, L. R., Magnusson, D., & El-Khouri, B. M. (2003). Studying individual development in an interindividual context. Mahwah, NJ: Erlbaum.CrossRefGoogle Scholar
Bergman, L. R., von Eye, A., & Magnusson, D. (2006). Person-oriented research strategies in developmental psychopathology. In Cicchetti, D. & Cohen, D. J. (Eds.), Developmental psychopathology (2nd ed., pp. 850–888). London: Wiley.Google Scholar
Billard, L., & Diday, E. (2006). Symbolic data analysis. Conceptual statistics and data mining. Chichester: Wiley.CrossRefGoogle Scholar
Block, J. (1978). The Q-sort method in personality assessment and psychiatric research. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Bogat, G. A. (2009). Is it necessary to discuss person-oriented research in community Psychology? American Journal of Community Psychology, 43, 22–34.CrossRefGoogle Scholar
Caramani, D. (2009). Introduction to the comparative method with Boolean algebra. Los Angeles: Sage.CrossRefGoogle Scholar
Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53, 134–140.CrossRefGoogle ScholarPubMed
Glas, C. A. W., & Meijer, R. R. (2003). A Bayesian approach to person fit analysis in item response theory models. Applied Psychological Measurement, 27, 217–233.CrossRefGoogle Scholar
Goodman, L. A. (1984). The analysis of cross-classified data having ordered categories. Cambridge, MA: Harvard University Press.Google Scholar
Hayden, E. C. (2009). Personalized cancer therapy gets closer. Nature, 458, 131–132.CrossRefGoogle ScholarPubMed
Heilmann, W.-R., Lienert, G. A., & Maly, V. (1979). Prediction models in configural frequency analysis. Biometrical Journal, 21, 79–86.CrossRefGoogle Scholar
Heubeck, B. G. (2000). Cross-cultural generalizability of CBCL syndromes across three continents: From the USA and Holland to Australia. Journal of Abnormal Child Psychology, 28, 439–450.CrossRefGoogle ScholarPubMed
Holland, B. S., & Copenhaver, M. D. (1987). An improved sequentially rejective Bonferroni procedure. Biometrics, 43, 417–423.CrossRefGoogle Scholar
Hsieh, C., & von Eye, A. (2009a). The best of two worlds: A joint modeling approach for the assessment of change across repeated measurements. International Journal of Psychological Research.Google Scholar
Hsieh, C., & von Eye, A. (2009b). A unified model for the analysis of change: Using a multivariate multilevel polytomous item response theory model to study the parallel process of change. Manuscript in preparation.Google Scholar
Lambert, M. C., Essau, C. A., Schmitt, N., & Samms-Vaughan, M. E. (2007). Dimensionality and psychometric invariance of the Youth Self-Report form of the Child Behavior Checklist in cross-national settings. Assessment, 14, 231–245.CrossRefGoogle ScholarPubMed
Levendosky, A. A., Bogat, G. A., Davidson, W. S. II, & von Eye, A. Intergenerational transmission of violence (CDC 9/1/00–8/31/2004). Unpublished manuscript.Google Scholar
Lienert, G. A., & Krauth, J. (1975). Configural frequency analysis as a statistical tool for defining types. Educational and Psychological Measurement, 35, 231–238.CrossRefGoogle Scholar
Loevinger, J. (1965). Person and population as psychometric concepts. Psychological Review, 72, 143–155.CrossRefGoogle ScholarPubMed
Martinez-Torteya, C., Bogat, G. A., von Eye, A., & Levendosky, A. A. (2009). Resilience among children exposed to domestic violence: The role of protective and vulnerability factors. Child Development, 80, 562–577.CrossRefGoogle Scholar
Molenaar, P. C. M. (2004). A manifesto on psychology as ideographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218.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
Molenaar, P. C. M., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18, 112–117.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Nesselroade, J. R. (2002). Elaborating the differential in differential psychology. Multivariate Behavioral Research, 37, 543–561.CrossRefGoogle ScholarPubMed
Nesselroade, J. R., Gerstorf, D., Hardy, S. A., & Ram, N. (2007). Idiographic filters for psychological constructs. Measurement, 5, 217–235.Google Scholar
Noble, C. E. (1952). An analysis of meaning. Psychological Review, 59, 421–430.CrossRefGoogle ScholarPubMed
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danmarks Paedagogiske Institut.Google Scholar
Rost, J. (1991). A logistic mixture distribution model for polytomous item responses. British Journal of Mathematical and Statistical Psychology, 44, 75–92.CrossRefGoogle Scholar
Rost, J. (1996). Testtheorie, Testkonstruktion [Test theory, test construction]. Bern: Huber.Google Scholar
Saunders, D. G. (1994). Posttraumatic stress symptom profiles of battered women: A comparison of survivors in two settings. Violence and Victims, 9, 31–44.CrossRefGoogle ScholarPubMed
Schmidt, H.-D. (1977). Methodologische Probleme der entwicklungspsychologischen Forschung [Methodological problems of developmental research]. Probleme und Ergebnisse der Psychologie, 62, 5–27.Google 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
Stern, W. (1911). Die differentielle Psychologie in ihren methodischen Grundlagen [Differential psychology in its methodological foundations]. Leipzig: Barth.Google Scholar
von Davier, M., & Rost, J. (1995). Polytomous mixed Rasch models. In Fischer, G. H. & Molenaar, I. W. (Eds.), Rasch models—Foundations, recent developments, and applications (pp. 371–379). New York: Springer.Google Scholar
von Eye, A. (2002). Configural frequency analysis—Methods, models, and applications. Mahwah, NJ: Erlbaum.Google Scholar
von Eye, A. (2004a). The treasures of Pandora's box. Measurement: Interdisciplinary Research and Perspectives, 2, 244–247.Google Scholar
von Eye, A. (2004b). Base models for configural frequency analysis. Psychology Science, 46, 150–170.Google Scholar
von Eye, A. (2009). Universals and individuals—Is this the end of the discussion? Measurement: Interdisciplinary Research & Perspective, 7, 3–7.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.CrossRefGoogle ScholarPubMed
von Eye, A., & Bergman, L. R. (2009). Person-orientation in person-situation research. Journal of Research in Personality, 43, 276–277.CrossRefGoogle Scholar
von Eye, A., & Bogat, G. A. (2006). Person orientation—Concepts, results, and development. Merrill–Palmer Quarterly, 52, 390–420.Google Scholar
von Eye, A., & Bogat, G. A. (2007). Methods of data analysis in person-oriented research. The sample case of ANOVA. In Ittel, A., Stecher, L., Merkens, H., & Zinnecker, J. (Eds.), Jahrbuch Jugendforschung 2006 (pp. 161–182). Wiesbaden: Verlag für Sozialwissenschaften.Google Scholar
von Eye, A., Bogat, G. A., & Rhodes, J. E. (2006). Variable-oriented and person-oriented perspectives of analysis: The example of alcohol consumption in adolescence. Journal of Adolescent Research, 29, 981–1004.CrossRefGoogle ScholarPubMed
von Eye, A., & Gutiérrez Peña, E. (2004). Configural frequency analysis—The search for extreme cells. Journal of Applied Statistics, 31, 981–997.CrossRefGoogle Scholar
von Eye, A., & Mair, P. (2008a). Functional configural frequency analysis: Explaining types and antitypes. Bulletin de la Société des Sciences Médicales, Luxembourg, 144, 35–52.Google Scholar
von Eye, A., & Mair, P. (2008b). A functional approach to configural frequency analysis. Austrian Journal of Statistics, 37, 161–173.Google Scholar
von Eye, A., Mair, P., & Bogat, G. A. (2005). Prediction models for configural frequency analysis. Psychology Science, 47, 342–355.Google Scholar
von Eye, A., Mair, P., & Mun, E. Y. (in press). Configural frequency analysis—New models and methods for categorical data analysis. New York: Guilford Press.Google Scholar
von Eye, A., Mun, E. Y., & Bogat, G. A. (2010). Temporal patterns of variable relationships in person-oriented research—Prediction and auto-association models of configural frequency analysis. Applied Developmental Science, 13, 172–187.Google Scholar
Walls, T. A., & Schafer, J. L. (Eds.). (2006). Models for intensive longitudinal data. New York: Oxford University Press.CrossRefGoogle Scholar
Webb, C. P., & Cherba, D. M. (2009). Systems Biology of personalized Medicine. In Krawetz, S. (Ed.), Bioinformatics for systems biology (pp. 615–630). New York: Humana Press.CrossRefGoogle Scholar