Analysis of Intensive Categorical Longitudinal Data

  • Alexander von Eye
  • G. Anne Bogat


Intensive longitudinal data are defined as data that come from more than the usual three or four observation points in time yet from fewer than the 100 or more required for time series analysis (Walls & Schafer, 2006). Consider, for example, a clinical design with 20 repeated observations. Data from this design are hard to analyze. Unless the sample is very large, 20 observations are too many for structural modeling. For repeated measures ANOVA with polynomial decomposition, polynomials of up to the 19th order would have to be estimated (which is the easy part) and interpreted (which is the hard part). This applies accordingly to hierarchical linear models of this design. For longitudinal, P-technique factor analysis, 100 observation are needed. In brief, data that are intensive in the sense that more observations are made over time than usual pose specific analytic problems.


Posttraumatic Stress Posttraumatic Stress Symptom Violence Status Categorical Data Analysis Discrimination Type 
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  1. Agresti, A. (2002). Categorical data analysis (2nd ed.). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  2. Beck, A. T., Ward, C. H., Mendelson, M., Mock, J., & Erbaugh, J. (1961). An inventory for measuring depression. Archives of General Psychiatry, 4, 561–571.PubMedCrossRefGoogle Scholar
  3. Bergman, L. R., & Magnusson, D. (1997). A person-oriented approach in research on developmental psychopathology. Development and Psychopathology, 9, 291–319.PubMedCrossRefGoogle Scholar
  4. Bogat, G. A., Levendosky, A. A., & von Eye, A. (2005). The future of research on intimate partner violence: Person-oriented and vairable-oriented perspectives. American Journal of Community Psychology, 36, 49–70.PubMedCrossRefGoogle Scholar
  5. Bortz, J., Lienert, G. A., & Boehnke, K. (1990). Verteilungsfreie Methoden in der Biostatistik. Berlin: Springer.Google Scholar
  6. Jöreskog, K. G., & Sörbom, D. (2004). Lisrel 8.7 for Windows [computer software]. Lincolnwood, IL: Scientific Software International, Inc.Google Scholar
  7. Lawal, B. (2003). Categorical data analysis with SAS and SPSS applications. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  8. Lienert, G. A. (1969). Die Konfigurationsfrequenzanalyse als Klassifikationsmittel in der klinischen Psychologie. In M. Irle (Ed.), Bericht über den 26. Kongreß der Deutschen Gesellschaft für Psychologie 1968 in Tübingen (pp. 244–255). Göttingen: Hogrefe.Google Scholar
  9. Lunneborg, C. E. (2005). Runs test. In B. S. Everitt & D. C. Howell (Eds.). Encyclopedia of statistics in behavioral science (p. 1771). Chichester, UK: Wiley.Google Scholar
  10. Marshall, L. L. (1992). Severity of the violence against women scales. Journal of Family Violence, 7, 103–121.CrossRefGoogle Scholar
  11. Saunders, D. G. (1994). Posttraumatic stress symptom profiles of battered women: A comparison of survivors in two settings. Violence and Victims, 9, 31–44.PubMedGoogle Scholar
  12. Siegel, S. (1956). Nonparametric statistics for the behavioral sciences. New York: McGraw-Hill.Google Scholar
  13. Stevens, W. L. (1939). Distribution of groups in a sequence of alternatives. Annals of Eugenics, 9, 10–17.CrossRefGoogle Scholar
  14. Swed, F. S., & Eisenhart, C. (1943). Tables for testing randomness of grouping in a sequence of alternatives. Annals of Mathematical Statistics, 14, 66–87.CrossRefGoogle Scholar
  15. von Eye, A. (2002). Configural frequency analysis—Methods, models, and applications. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  16. 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
  17. von Eye, A., & Bogat, G. A. (2005). Logistic regression and prediction configural frequency analysis—A comparison. Psychology Science, 47, 407–414.Google Scholar
  18. von Eye, A., & Gutiérrez Peña, E. (2004). Configural frequency analysis—The search for extreme cells. Journal of Applied Statistics, 31, 981–997.CrossRefGoogle Scholar
  19. von Eye, A., Mair, P., & Bogat, G. A. (2005). Prediction models for configural frequency analysis. Psychology Science, 47, 342–355.Google Scholar
  20. von Eye, A., & Mun, E.-Y. (2007). A note on the analysis of difference patterns—Structural zeros by design. Psychology Science, 49, 14–25.Google Scholar
  21. Wald, A., & Wolfowitz, J. (1940). On a test whether two alternatives are from the same population. Annals of Mathematical Statistics, 11, 147–162.CrossRefGoogle Scholar
  22. Wallis, W. A., & Moore, G. H. (1941). A significance test for time series analysis. Journal of the American Statistical Association, 20, 257–267.Google Scholar
  23. Walls, T. A., & Schafer, J. L. (2006). Models for intensive longitudinal data. New York: Oxford University Press.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of PsychologyMichigan State UniversityMichiganUSA

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