Analysis of Paired Comparison Data Using Monte Carlo EM Algorithms

Abstract

Representations of Thurstonian models cannot be uniquely identified because of the discrete nature and the difference structure of the paired comparison data. In this thesis, the identifiability of model parameters are investigated by studying empirically equivalent Thurstonian models. Equivalence relations and their implications on interpretation are presented in detail for a number of covariance structures. Wherever possible, specific conditions are given to allow researchers to examine the identifiability of the model parameters.