By Jordan J. Louviere
This quantity introduces the speculation, process, and functions of 1 kind of conjoint research method. those concepts are used to review person judgement and selection techniques. dependent upon info Integration idea, metric conjoint research makes it possible for evaluate of multi-attribute possible choices in accordance with period point facts. The version, which justifies use of metric conjoint equipment and the statistical concepts drawn from it, are the center of this monograph. additionally defined are functions 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.
Following the discussion of design prin- Page 28 ciples, we explain how to analyze conjoint data with analysis of variance and / or multiple linear regression. The chapter concludes with the analysis of individual differences. 1 The Design of Factorial Experiments As explained in the previous chapter, factorial designs involve combinations of levels of decision attributes. " Experimental factors and their levels are completely under the control of the experimenter; and one is free to choose whatever factors and levels are of interest.
2) If a consumer's decision process can be approximated by a multilinear conjoint model, one can obtain interval scale estimates of the unknown "part-worth utilities" by calculating the marginal means of the respondent's data, or the corresponding regression coefficients. Page 25 (3) One can relate the part-worth estimates (V(Sk)) to the corresponding Sk's (the levels of the attributes used in the experiment). 2 for each consumer. 1. However, if the attributes are not physical variables, one must independently study the relationship between the positioning measures (beliefs) and the corresponding physical variables.
Analyzing decision making: metric conjoint analysis by Jordan J. Louviere