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Introducing Person-Centered Methods

  • Mark Stemmler
Chapter
  • 946 Downloads
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Abstract

This chapter explains the term person-centered methods and how Configural Frequency Analysis (CFA) works. Instead of analyzing means, variances and covariances of scale scores as in the common variable-centered approach, the person-centered approach analyzes persons or objects grouped according to their characteristic configurations in contingency tables. CFA is a statistical method that looks for over- and under-frequented cells or patterns. Over-frequented means, that the observations in this cell or configuration are observed more often than expected, under-frequented means that this configurations is observed less often than expected. In CFA a pattern or configuration that contains more observed cases than expected is called a type; similarly, configurations that are less observed than expected are called an antitype. In addition, Meehl’s paradox (Meehl, J Consult Psychol 14:165–171, 1950) is explained. Meehl’s paradox postulates that it is possible to have a bivariate relationship with a zero association or correlation but also a higher order association or correlation. Meehl argued for investigating higher order interactions (beyond bivariate interactions), which can be detected with CFA.

Keywords

Affective Disturbance Loglinear Modeling Configural Frequency Analysis Bonferroni Alpha Adjustment Multinomial Sampling 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.CrossRefGoogle Scholar
  2. Bergman, L. R., von Eye, A., & Magnusson, D. (2006). Person-oriented research strategies in developmental psychopathology. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology (2nd ed., pp. 850–888). London: Wiley.Google Scholar
  3. Fienberg, S. E. (1987). The analysis of cross-classified categorical data. 5th printing.Google Scholar
  4. Krauth, J., & Lienert, G. A. (1973). Die Konfigurationsfrequenzanalyse und ihre Anwendung in Psychologie und Medizin [Configural frequency analysis and its application in psychology and medicine]. Freiburg, Germany: Alber.Google Scholar
  5. Lautsch, E., & von Weber, S. (1995). Methoden und Anwendung der Konfigurationsfrequenzanalyse (KFA) [Methods and application of configural frequency analysis (CFA)]. Weinheim, Germany: Beltz, Psychologie-Verlags-Union.Google Scholar
  6. Lehmacher, W. (2000). Die Konfigurationsfrequenzanalyse als Komplement des log-linearen Modells [Configural frequency analysis as a complimentary tool to log-linear modeling]. Psychology Science, 42(3), 418–427.Google Scholar
  7. Lienert, G. A., & Krauth, J. (1975). Configural frequency analysis as a statistical tool for defining types. Educational Psychology and Measurement, 35, 231–238.CrossRefGoogle Scholar
  8. Meehl, P. E. (1950). Configural scoring. Journal of Consulting Psychology, 14, 165–171.CrossRefGoogle Scholar
  9. Melcher, A., Lautsch E. & Schmutzler, S. (2012). Non-parametric methods – Tree and P-CFA – For the ecological evaluation and assessment of suitable aquatic habitats: A contribution to fish psychology. Psychological Tests and Assessment Modeling, 54(3), 293–306.Google Scholar
  10. Reinecke, J. & Tarnai, C. (Eds.). (2008). Klassifikationsanalysen in Theorie und Praxis [Analysis of classifications in theory and practice]. Münster, Germany: Waxmann Verlag.Google Scholar
  11. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasiexperimental designs for generalized causal inference. Boston: Houghton-Mifflin.Google Scholar
  12. Stemmler, M., Lautsch, E., & Martinke, D. (Eds.). (2008). Configural frequency analysis and other non-parametrical methods: A Gustav A. Lienert memorial issue. Lengerich, Germany: Pabst Publishing.Google Scholar
  13. Stemmler, M., & von Eye, A. (Eds.) (2012). Configural frequency analysis (CFA) and other non-parametrical statistical methods (special issue) – Part I and II. Psychological Tests and Assessment Modeling, 54(2 and 3).Google Scholar
  14. Victor, N. (1989). An alternative approach to configural frequency analysis. Methodika, 3, 61–73.Google Scholar
  15. von Eye, A. (1990). Introduction to configural frequency analysis: The search for types and 102 antitypes in cross-classifications. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  16. von Eye, A. (2002). Configural frequency analysis: Methods, models and applications. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  17. von Eye, A., & Gutiérrez-Penã, E. (2004). Configural frequency analysis: The search for extreme cells. Journal of Applied Statistics, 31, 981–997.CrossRefzbMATHMathSciNetGoogle Scholar
  18. Wickens, T. (1989). Multiway contingency tables analysis for the social sciences. Hillsdale, NJ: Lawrence Erlbaum.zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mark Stemmler
    • 1
  1. 1.Institute of PsychologyFriedrich-Alexander University of Erlangen-Nuremberg (FAU)ErlangenGermany

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