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Analyzing Decision Making: Metric Conjoint Analysis by Jordan J. Louviere

By Jordan J. Louviere

This quantity introduces the idea, procedure, and functions of 1 kind of conjoint research procedure. those strategies are used to check person judgement and choice strategies. dependent upon info Integration concept, metric conjoint research enables review of multi-attribute choices in line with period point info. The version, which justifies use of metric conjoint tools and the statistical thoughts drawn from it, are the middle of this monograph. additionally defined are functions of the version in advertising and marketing, 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 info for Analyzing Decision Making: Metric Conjoint Analysis (Quantitative Applications in the Social Sciences)

Sample text

The multilinear form allows a general approach to studying decision processes, because it accommodates completely multiplicative processes (all attributes are complements); distributive processes (one attribute complements the other two, which are jointly independent); and dual-distributive processes (one attribute is independent of two others that are complements). These models can be specified as follows: where all terms have been defined previously. 17 are subsets of the multilinear form in which one or more C terms are equal to zero.

The rank-orderings can be analyzed to derive estimates of the part-worth utilities for each level of each attribute, assuming that certain kinds of simple polynomial conjoint models are correct. , 1972; Barron, 1977). , additive model forms). In addition, it also can be demonstrated that if the axioms are satisfied, part-worth utilities can be measured on scales that are asymptotically interval in measurement level. Asymptotically interval means that if there is a sufficiently dense spacing of the levels of the attributes and the axioms are satisfied, the derived part-worth utilities become closer to interval-level measures as the spacing density increases.

To avoid mistaking spurious interactions for real, it is wise to (a) examine the magnitude of the estimated parameter if multiple linear regression is used to make the test and/or (b) plot the data to see if the response curves are parallel. Let us now turn our attention to estimating the part-worth utilitiesthe V(S1p), V(S2q) and V(S3r). ) refer to the effects of averaging. ) of a consumer's response data are equal to the unknown part-worth utilities (V(Sk)'s) up to a linear transformation. 2).

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