Analysis of paired comparison data based on experimental design: Expression using structural equation modeling

Hideki Toyoda, Hiroto Murohashi, Kouken Ozaki, Mayomi Haga

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Paired comparison is a useful method for assessing ranks among several objects, and it enables us to obtain more reliable data than assessing objects one by one. But paired comparison principally provides information only about the ranks of the objects. On the other hand, experimental design provides a framework for elucidating causal associations. If we could analyze paired comparison data by the experimental design framework, it would be a very effective method. But experimental design, in its original form, is not readily applicable to paired comparison data. However, if we adopt the perspective of structural equation modeling (SEM), we can deal with paired comparison and experimental design in a unified way, because they are both submodels of SEM. The purpose of this study is to provide a new method to analyze causal connection of paired comparison data by using SEM. Here, two actual numerical examples are shown, one of which is obtained by within-subject design and the other is obtained by between-subject design.

Original languageEnglish
Pages (from-to)33-40
Number of pages8
JournalShinrigaku Kenkyu
Volume75
Issue number1
Publication statusPublished - 2004 Apr

Fingerprint

Matched-Pair Analysis
Research Design

Keywords

  • Covariance structure
  • Eexperimental design
  • Paired comparison
  • Structural equation modeling

ASJC Scopus subject areas

  • Psychology(all)

Cite this

Analysis of paired comparison data based on experimental design : Expression using structural equation modeling. / Toyoda, Hideki; Murohashi, Hiroto; Ozaki, Kouken; Haga, Mayomi.

In: Shinrigaku Kenkyu, Vol. 75, No. 1, 04.2004, p. 33-40.

Research output: Contribution to journalArticle

Toyoda, Hideki ; Murohashi, Hiroto ; Ozaki, Kouken ; Haga, Mayomi. / Analysis of paired comparison data based on experimental design : Expression using structural equation modeling. In: Shinrigaku Kenkyu. 2004 ; Vol. 75, No. 1. pp. 33-40.
@article{aa167b4e0c9f476991f6bc5b3e577d04,
title = "Analysis of paired comparison data based on experimental design: Expression using structural equation modeling",
abstract = "Paired comparison is a useful method for assessing ranks among several objects, and it enables us to obtain more reliable data than assessing objects one by one. But paired comparison principally provides information only about the ranks of the objects. On the other hand, experimental design provides a framework for elucidating causal associations. If we could analyze paired comparison data by the experimental design framework, it would be a very effective method. But experimental design, in its original form, is not readily applicable to paired comparison data. However, if we adopt the perspective of structural equation modeling (SEM), we can deal with paired comparison and experimental design in a unified way, because they are both submodels of SEM. The purpose of this study is to provide a new method to analyze causal connection of paired comparison data by using SEM. Here, two actual numerical examples are shown, one of which is obtained by within-subject design and the other is obtained by between-subject design.",
keywords = "Covariance structure, Eexperimental design, Paired comparison, Structural equation modeling",
author = "Hideki Toyoda and Hiroto Murohashi and Kouken Ozaki and Mayomi Haga",
year = "2004",
month = "4",
language = "English",
volume = "75",
pages = "33--40",
journal = "Shinrigaku Kenkyu",
issn = "0021-5236",
publisher = "Japanese Psychological Association",
number = "1",

}

TY - JOUR

T1 - Analysis of paired comparison data based on experimental design

T2 - Expression using structural equation modeling

AU - Toyoda, Hideki

AU - Murohashi, Hiroto

AU - Ozaki, Kouken

AU - Haga, Mayomi

PY - 2004/4

Y1 - 2004/4

N2 - Paired comparison is a useful method for assessing ranks among several objects, and it enables us to obtain more reliable data than assessing objects one by one. But paired comparison principally provides information only about the ranks of the objects. On the other hand, experimental design provides a framework for elucidating causal associations. If we could analyze paired comparison data by the experimental design framework, it would be a very effective method. But experimental design, in its original form, is not readily applicable to paired comparison data. However, if we adopt the perspective of structural equation modeling (SEM), we can deal with paired comparison and experimental design in a unified way, because they are both submodels of SEM. The purpose of this study is to provide a new method to analyze causal connection of paired comparison data by using SEM. Here, two actual numerical examples are shown, one of which is obtained by within-subject design and the other is obtained by between-subject design.

AB - Paired comparison is a useful method for assessing ranks among several objects, and it enables us to obtain more reliable data than assessing objects one by one. But paired comparison principally provides information only about the ranks of the objects. On the other hand, experimental design provides a framework for elucidating causal associations. If we could analyze paired comparison data by the experimental design framework, it would be a very effective method. But experimental design, in its original form, is not readily applicable to paired comparison data. However, if we adopt the perspective of structural equation modeling (SEM), we can deal with paired comparison and experimental design in a unified way, because they are both submodels of SEM. The purpose of this study is to provide a new method to analyze causal connection of paired comparison data by using SEM. Here, two actual numerical examples are shown, one of which is obtained by within-subject design and the other is obtained by between-subject design.

KW - Covariance structure

KW - Eexperimental design

KW - Paired comparison

KW - Structural equation modeling

UR - http://www.scopus.com/inward/record.url?scp=15744402719&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=15744402719&partnerID=8YFLogxK

M3 - Article

C2 - 15724512

AN - SCOPUS:15744402719

VL - 75

SP - 33

EP - 40

JO - Shinrigaku Kenkyu

JF - Shinrigaku Kenkyu

SN - 0021-5236

IS - 1

ER -