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Title:Regression Models for Paired Comparisons
Author(s):Verkuilen, John V.
Doctoral Committee Chair(s):Budescu, David V.
Department / Program:Psychology
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Psychology, Psychometrics
Abstract:Paired comparisons are among the most widely used experimental methods in psychology and related fields. First employed in the middle of the 19th Century, many models have been developed over the years to analyze data from such experiments, for instance the Thurstone and Bradley-Terry-Luce models. This dissertation proposes an integrative framework in which the vast majority of extant scaling procedures for paired comparisons can be cast. The framework is based on the Generalized Linear Mixed Model (GLMM) and it characterizes paired comparison models according to four characteristics, taken in order: (1) the response format, (2) the link function, (3) the error distribution, and (4) the mixing distribution. It expresses the scaling problem as a regression fitting in the GLMM, and shows how particular aspects of paired comparisons restrict the choices that can be made among these four model components. All known response formats---binary, discrete ordinal, continuous constant sum and continuous constant product---fit as examples in this framework. Given this, it is easy to consider other models based on the four characteristics. In addition to the framework, models designed to analyze data for graded response formats where subjects are heterogeneous in their response styles are proposed and examined. Finally a simulation study illustrates the robustness of the classic "Case V" specification.
Issue Date:2007
Description:91 p.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2007.
Other Identifier(s):(MiAaPQ)AAI3290414
Date Available in IDEALS:2015-09-25
Date Deposited:2007

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