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: ''See also: Conjoint analysis (in marketing), Conjoint analysis (in healthcare), IDDEA, Rule Developing Experimentation, Value based pricing.'' Conjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique that originated in mathematical psychology. It is used in surveys developed in applied sciences, often on behalf of marketing, product management, and operations research. It is not to be confused with the theory of conjoint measurement. Conjoint analysis is a particular application of regression analysis. There is no precise statistical definition of it. Usually two or three of the following properties are applicable: * data are collected among multiple individuals (respondents) whereas there are multiple data points for each individual, which makes it a layered model * the dependent variable reflects a choice or trade-off situation * the independent variables are categorical, thus coded as binary numbers (0,1) ==Method== Conjoint analysis requires research participants to make a series of trade-offs. Analysis of these trade-offs will reveal the relative importance of component attributes. To improve the predictive ability of this analysis, research participants should be grouped into similar segments based on objectives, values and/or other factors. The exercise can be administered to survey respondents in a number of different ways. Traditionally it is administered as a ranking exercise and sometimes as a rating exercise (where the respondent awards each trade-off scenario a score indicating appeal). In more recent years it has become common practice to present the trade-offs as a choice exercise (where the respondent simply chooses the most preferred alternative from a selection of competing alternatives - particularly common when simulating consumer choices) or as a constant sum allocation exercise (particularly common in pharmaceutical market research, where physicians indicate likely shares of prescribing, and each alternative in the trade-off is the description of a real or hypothetical therapy). Analysis is traditionally carried out with some form of multiple regression, but more recently the use of hierarchical Bayesian analysis has become widespread, enabling fairly robust statistical models of individual respondent decision behaviour to be developed. When there are many attributes, experiments with Conjoint Analysis include problems of information overload that affect the validity of such experiments. The impact of these problems can be avoided or reduced by using Hierarchical Information Integration. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Conjoint analysis」の詳細全文を読む スポンサード リンク
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