Analyzing decision making: metric conjoint analysis - download pdf or read online

By Jordan J. Louviere

ISBN-10: 0803927576

ISBN-13: 9780803927575

This quantity introduces the idea, technique, and purposes of 1 kind of conjoint research procedure. those options are used to review person judgement and determination techniques. established upon info Integration conception, metric conjoint research enables assessment of multi-attribute possible choices in keeping with period point facts. The version, which justifies use of metric conjoint tools and the statistical strategies drawn from it, are the middle of this monograph. additionally defined are purposes of the version in advertising, psychology, economics, sociology, making plans, and different disciplines, all of which relate to forecasting the decision-making habit of people.

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One of the following formats can be adapted (depending on the style manual used): (1) LOUVIERE, JORDAN J. (1988) Analyzing Decision Making: Metric Conjoint Analysis. Sage University Paper Series on Quantitative Applications in the Social Sciences, 07-067. Newbury Park, CA: Sage. OR (2) Louviere, Jordan J. (1991). Analyzing decision making: Metric conjoint analysis (Sage University Paper series on Quantitative Applications in the Social Sciences, series no. 07-067). Newbury Park, CA: Sage. Page 3 Contents Acknowledgments 4 Series Editor's Introduction 5 Preface 7 1.

This conclusion can be proved by deducing what happens if we subtract the response to level 2 of attribute 1 from the response to level 1 of attribute 1, holding other attributes constant: All terms were defined previously. 11 indicates that the difference in the part-worth utilities of levels 1 and 2 of attribute 1 is a constant; hence, curves of response data for each level of attribute 1 graphed against levels of attribute 2 (or 3) must be parallel. Responses to levels of one attribute are independent of responses to levels of other attributes, a property that generalizes to any number of attributes.

The numerical data of interest consist of individual or group category-rating responses to combinations of different decision vari- Page 15 ables (attributes). Complex decision making involves searching for, acquiring, and processing information; hence one can use IIT to study information processing revealed by consumers' responses to multi-attribute options. 3). These latter two paradigms also allow one to develop conjoint models of individual decision processes, however their theoretical and methodological bases differ from one another and from IIT.

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