Analysis of paired comparison data using Mx

Rung-Ching Tsai, Tsung Lin Wu

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

By postulating that the random utilities associated with the choice options follow a multivariate normal distribution, Thurstonian models (Thurstone, 1927) provide a straightforward representation of paired comparison data. The use of Monte Carlo Expectation-Maximization (MCEM) algorithms and limited information approaches have been proposed to overcome the estimation intractability in analyzing data with a large number of choice items. However, these approaches have not yet been implemented into standard statistical software. For paired comparison data with a medium number of items (≤6), it is possible to use the free software program Mx to obtain parameter estimates. This article shows how Mx can be used to obtain parameter estimates for Thurstonian paired comparison models. A number of simulations are conducted to assess its validity in obtaining the estimates in comparison to MCEM. In addition, 2 datasets are analyzed to demonstrate the use of MX in real applications.

Original languageEnglish
Pages (from-to)73-91
Number of pages19
JournalStructural Equation Modeling
Volume11
Issue number1
DOIs
Publication statusPublished - 2004 Jan 1

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Paired Comparisons
Normal distribution
Estimate
model comparison
Statistical Software
Multivariate Normal Distribution
Expectation Maximization
Monte Carlo Algorithm
Expectation-maximization Algorithm
simulation
Software
Model
Demonstrate
Paired comparisons
Simulation
software

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Modelling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

Cite this

Analysis of paired comparison data using Mx. / Tsai, Rung-Ching; Wu, Tsung Lin.

In: Structural Equation Modeling, Vol. 11, No. 1, 01.01.2004, p. 73-91.

Research output: Contribution to journalArticle

Tsai, Rung-Ching ; Wu, Tsung Lin. / Analysis of paired comparison data using Mx. In: Structural Equation Modeling. 2004 ; Vol. 11, No. 1. pp. 73-91.
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