Inhaltspezifische Aktionen

Prof. Dr. Christof Schuster


Fachbereich 06 Psychologie und Sportwissenschaft
Justus-Liebig-Universität Giessen
Otto-Behaghel-Str. 10
D-35394 Giessen

Tel: +49(0)641 99-26 120
Tel: +49(0)641 99-26 121, Sek.
Fax: +49(0)641 99-26 139

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Jahrgang 1963; 1992 Diplom im Fach Psychologie an der Technischen Universität Berlin; 1993-1995 Biometriker am Institut für Toxikologie der Freien Universität Berlin; 1995-1997 wissenschaftlicher Mitarbeiter am Institut für Psychologie der Friedrich-Schiller-Universität Jena; 1997 Promotion zum Dr. phil. an der Technischen Universität Berlin; 1997-1999 Research Investigator (post-doc) am Survey Research Center des Institute for Social Research der University of Michigan in Ann Arbor, Michigan; 1999-2004 Assistant Professor im Department of Psychology der University of Notre Dame in South Bend, Indiana; seit 2004 Inhaber der Professur für Psychologische Methodenlehre am Fachbereich Psychologie und Sportwissenschaft der Justus-Liebig-Universität Giessen.


His research interests include item response theory, multivariate analysis, statistical models for response styles, and rater agreement.

Selected Publications

  • Lubbe, D. & Schuster, C. (2020). A Scaled Threshold Model for Measuring Extreme Response Style. Journal of Educational and Behavioral Statistics, 45(1), 86-107.
  • Schuster, C. & Lubbe, D. (2020). A note on residual M-distances for identifying aberrant response patterns. British Journal of Mathematical and Statistical Psychology, 73(164-169).
  • Lubbe, D. & Schuster, C. (2019). A graded response model framework for questionnaires with uniform response formats. Applied Psychological Measurement, 43(4), 290-302.
  • Lubbe, D. & Schuster, C. (2017). The graded response differential discrimination model accounting for extreme response style. Multivariate Behavioral Research, 52(5), 616-629.
  • Lubbe, D. & Schuster, C. (2016). Consistent differential discrimination model estimation. Multivariate Behavioral Research, 51(4), 581-587.
  • Schuster, C. & Lubbe, D. (2015). MANOVA versus mixed models: Comparing approaches to modeling within-subject dependence. In Stemmler, M., von Eye, A. & Wiedermann, W. (Eds.), Dependent Data in Social Sciences Research, pp. 369-385. Heidelberg: Springer.
  • Yuan, K.-H. & Schuster, C. (2013). Overview of Statistical Estimation Methods. In Little, T. D. (Ed.), The Oxford Handbook of Quantitative Methods, Vol. 1 (pp. 361-387). Oxford: Oxford University Press.
  • Schuster, C. & Yuan, K.-H. (2011). Robust estimation of latent ability in item response models. Journal of Educational and Behavioral Statistics, 36(6), 720-735.
  • Bortz, J. & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7. Aufl.). Heidelberg: Springer.
  • Wenger, M. J. & Schuster, C. (2007). Statistical and process models for cognitive neuroscience and aging. Mahwah, NJ: Erlbaum.
  • Schuster, C. & Smith, D. A. (2006). Estimating with a latent class model the reliability of nominal judgments upon which two raters agree. Educational and Psychological Measurement, 66(5), 739-747.
  • Schuster, C. & Smith, D. A. (2005). Dispersion weighted Kappa: An integrative framework for metric and nominal scale agreement coefficients. Psychometrika, 70(1), 135-146.
  • Schuster, C. (2004). A note on the interpretation of weighted kappa and its relations to other rater agreement statistics for metric scales. Educational and Psychological Measurement, 64(2), 243-253.
  • Schuster, C. (2002). A mixture model approach to indexing rater agreement. British Journal of Mathematical and Statistical Psychology, 55(2), 289-303.
  • Schuster, C. & Smith, D. A. (2002). Indexing systematic rater agreement with a latent class model. Psychological Methods, 7(3), 384-395. 
  • Schuster, C. (2001). Kappa as a parameter of a symmetry model for rater agreement. Journal of Educational and Behavioral Statistics, 26(3), 331-342. 
  • Schuster, C. & von Eye, A. (2001). Modeling ordinal agreement data. Biometrical Journal, 43(7), 795-808.
  • von Eye, A. & Schuster, C. (1998). Regression analysis for social sciences. San Diego, CA: Academic Press.