Analyzing categorical data from split-plot and other multi-stratum experiments
AbstractMany factorial experiments yield categorical response data. Moreover, the experiments are often run under a restricted randomization for logistical reasons and/or because of time and cost constraints. The combination of categorical data and restricted randomization necessitates the use of generalized linear mixed models. In this paper, we demonstrate the use of Hasse diagrams for laying out the randomization structure of a complex factorial design involving seven two-level factors, four three-level factors and a five-level factor, and three repeated observations for each experimental unit. The Hasse diagrams form the basis of the mixed model analysis of the ordered categorical data produced by the experiment. We also discuss the added value of categorical data over binary data and difficulties with the estimation of variance components and, consequently, with the statistical inference. Finally, we show how to deal with repeats in the presence of categorical data, and describe a general strategy for building a suitable generalized linear mixed model.
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Bibliographic InfoPaper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2010021.
Length: 37 pages
Date of creation: Sep 2010
Date of revision:
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Postal: Prinsstraat 13, B-2000 Antwerpen
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/
More information through EDIRC
Binary data; Cumulative logit regression; Generalized linear mixed model; Hasse diagram; Ordered categorical data; Split-plot analysis;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-11-13 (All new papers)
- NEP-ECM-2010-11-13 (Econometrics)
- NEP-EXP-2010-11-13 (Experimental Economics)
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