DIFFERENCE AND SIMILARITY BETWEEN MONANOVA AND OLS IN CONJOINT ANALYSIS

Hiromu Kono, Hiroaki Ishii and Shogo Shiode
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Hiromu Kono: Graduate School of Information Science and Technology, Osaka University, 2-1 Yamada-oka, Suita 565–0871, Japan
Hiroaki Ishii: Graduate School of Information Science and Technology, Osaka University, 2-1 Yamada-oka, Suita 565–0871, Japan
Shogo Shiode: Graduate School of Information Science and Technology, Osaka University, 2-1 Yamada-oka, Suita 565–0871, Japan

Chapter 4 in Recent Advances in Stochastic Operations Research II, 2009, pp 41-54 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractMONANOVA is a traditional method of conjoint analysis used for measuring the part worth value of factors in the total evaluation, exclusively using when evaluations is non-metrical data. The part worth values obtained by MONANOVA give an approximate comparison of each factor's contribution to the total evaluation, but it is impossible to utilize their contributions for statistical use since they are usually obtained by numerical solution. Moreover, they are not necessarily unique. In this paper, we first show the problems of MONANOVA and then propose a method to obtain its definite solution. With this, we also show the difference and similarity between MONANOVA and OLS which is typical method for measuring metric data.

Keywords: Operations Research; Uncertainty; Applied Probability; Stochastic Process; Optimization; Decision Science (search for similar items in EconPapers)
Date: 2009
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