By Jordan J. Louviere
This quantity introduces the speculation, procedure, and purposes of 1 kind of conjoint research method. those options are used to review person judgement and choice approaches. established upon details Integration concept, metric conjoint research allows assessment of multi-attribute choices in response to period point information. The version, which justifies use of metric conjoint equipment and the statistical recommendations drawn from it, are the middle 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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Extra resources for Analyzing Decision Making: Metric Conjoint Analysis (Quantitative Applications in the Social Sciences)
Let Uj be a single dimensional array of dimension J × 1 representing a consumer's overall values or utilities for the j-th brand. Let p(j | A) represent a single dimensional array of dimension J × 1 that refers to the probability of selecting the j-th brand from a choice set A of (j = 1, 2, . . , J) brands, of which j is a member; p(j | A) is defined over all elements of A (brands in A). These formal definitions allow us to outline the relationships that we assume to operate in complex decision making.
67 author : Louviere, Jordan J. publisher : Sage Publications, Inc. 8/342 subject : Consumer behavior--Mathematical models, Decision making--Mathematical models, Conjoint analysis (Marketing) Page i Analyzing Decision Making Metric Conjoint Analysis Page ii SAGE UNIVERSITY PAPERS Series: Quantitative Applications in the Social Sciences Series Editor: Michael S. Lewis-Beck, University of Iowa Editorial Consultants Richard A. Berk, Sociology, University of California, Los Angeles William D. Berry, Political Science, Florida State University Kenneth A.
Chapter 1 probably will interest academic researchers most, although the material in Chapter 1 is important to understand compromises required in applied work. Chapter 2 discusses the design of factorial and fractional factorial experiments to implement conjoint research studies. Chapter 3 focuses on using the theory in practical research settings, primarily based on application experiences with IIT in marketing, transportation planning, and related areas. Chapter 4 discusses approaches to forecasting consumer choice behavior, including an introduction to the design and analysis of discrete choice experiments based on IIT, discrete choice theory in econometrics, and discrete multivariate analysis in statistics.