By Jordan J. Louviere
This quantity introduces the idea, strategy, and functions of 1 form of conjoint research process. those innovations are used to check person judgement and determination methods. dependent upon info Integration conception, metric conjoint research permits assessment of multi-attribute choices in accordance with period point facts. The version, which justifies use of metric conjoint equipment and the statistical thoughts drawn from it, are the center 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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Additional resources for Analyzing decision making: metric conjoint analysis
We adhere to the previous notation established in Chapter 1, recalling that interest centers on responses made by consumers on category-rating scales. The problem, therefore, is to diagnose (if we lack an a priori hypothesis) or test (if we have an a priori hypothesis) an appropriate decision model for a consumer who evaluates combinations of levels of two determinant attributes on a category-rating scale. Let us consider three possible models for this problem (although there are other possibilities): Page 29 where all terms are as defined in Chapter 1.
Berk, Sociology, University of California, Los Angeles William D. Berry, Political Science, Florida State University Kenneth A. Bollen, Sociology, University of North Carolina, Chapel Hill Linda B. Bourque, Public Health, University of California, Los Angeles Jacques A. Hagenaars, Social Sciences, Tilburg University Sally Jackson, Communications, University of Arizona Richard M. Jaeger, Education, University of North Carolina, Greensboro Gary King, Department of Government, Harvard University Roger E.
Responses to levels of one attribute are independent of responses to levels of other attributes, a property that generalizes to any number of attributes. Thus, if the decision process revealed by a consumer's rating behavior is additive, we expect to retain the null hypothesis of no interaction effects in an analysis of variance or multiple linear regression analysis. Hence, the additive decision process hypothesis can be falsified by using analysis of variance or multiple linear regression to test the main and interaction effects of consumers' ratings data.
Analyzing decision making: metric conjoint analysis by Jordan J. Louviere