Hans Friedrich Köhn

My current research concerns three lines of work:

(1) Combinatorial data analysis of individual differences based on multiple proximity matrices observed from different data sources (e.g., subjects, experimental conditions, time points);

(2) Large-scale nonmodel-based clustering, with particular focus on the p-median model;

(3) Cognitively Diagnostic Modeling

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Selected Publications:

Chiu, C.-Y., & Köhn, H. F. (2019). Consistency theory for the General NonParametric Classification Method. Psychometrika, 84, 830–845.

Köhn, H. F., & Chiu, C.-Y. (2019). Attribute hierarchy models in cognitive diagnosis: Identifiability of the latent attribute space and conditions for completeness of the Q-matrix. Journal of Classification, 36, 541–565

Köhn, H. F., & Chiu, C.-Y. (2018). How to build a complete Q-matrix for a cognitively diagnostic test. Journal of Classification, 35, 273–299.

Köhn, H. F. (2017). Citation classics commentary on Greenhouse and Geisser (1959): On methods in the analysis of profile data. Psychometrika, 82, 1209–1211.

Köhn, H. F., & Chiu, C.-Y. (2017). A procedure for assessing the completeness of the Q-matrices of cognitively diagnostic tests. Psychometrika, 82, 112–132.

Köhn, H. F., & Chiu, C.-Y. (2016). A proof of the duality of the DINA model and the DINO model. Journal of Classification, 33, 171-184.

Chiu, C.-Y., & Köhn, H. F. (2016). The Reduced RUM as a logit model: Parameterization and constraints. Psychometrika, 81, 350-370.

Chiu, C.-Y., & Köhn, H. F. (2016). Consistency of cluster analysis for cognitive diagnosis: The Reduced Reparameterized Unified Model and the General Diagnostic Model. Psychometrika, 81, 585-610.

Köhn, H. F., & Hubert, L. J. (2015). Hierarchical cluster analysis. Wiley StatsRef: Statistics Reference Online (WSR).

Köhn, H. F., Chiu, C.-Y., & Brusco, M. J. (2015). Heuristic cognitive diagnosis when the Q-matrix is unknown. British Journal of Mathematical and Statistical Psychology, 68, 268-291.

Köhn, H. F. (2011). A review of multiobjective programming and its application in quantitative psychology. Journal of Mathematical Psychology, 55, 386-396.

Köhn, H. F. (2010). Representation of individual differences in rectangular proximity data through anti-Q matrix decomposition. Computational Statistics and Data Analysis, 54, 2343-2357.

Köhn, H. F., Steinley, D., & Brusco, M. J. (2010). The p-median model as a tool for clustering psychological data. Psychological Methods, 15, 87-95.

Brusco, M. J., & Köhn, H. F. (2009). Clustering qualitative data based on binary equivalence relations: a variable neighborhood search procedure for the clique partitioning problem. Psychometrika, 74, 685-703.

Brusco, M. J., & Köhn, H. F. (2009). Exemplar-based clustering via simulated annealing: a comparison to affinity propagation and vertex substitution. Psychometrika, 74, 457-475.

Brusco, M. J., & Köhn, H. F. (2008). Optimal partitioning of a data set based on the p-median model. Psychometrika, 73, 89-105.

Brusco, M. J., & Köhn, H. F. (2008). Comment on “Clustering by passing messages between data points”. Science, 319, 726c.

Brusco, M. J., Köhn, H. F., & Stahl, S. (2008). Heuristic implementation of dynamic programming for matrix permutation problems in combinatorial data analysis. Psychometrika, 73, 503-522.