Conjoint Analysis Spss

    conjoint analysis

  • Conjoint analysis, also called multi-attribute compositional models or stated preference analysis, is a statistical technique that originated in mathematical psychology.
  • Pharmaceutical manufacturers need deeper and deeper market information they can rely on to make the right decisions and to identify the most promising market opportunities[1][6] .
  • Conjoint analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service.


  • SPSS is a computer program used for statistical analysis. Between 2009 and 2010 the premier software for SPSS was called PASW (Predictive Analytics SoftWare) Statistics. The company announced July 28, 2009 that it was being acquired by IBM for US$1.2 billion.
  • Supplementary Pertussis Surveillance System
  • A software program that facilitates quantitative analysis.

conjoint analysis spss

conjoint analysis spss – Applied Multivariate

Applied Multivariate Statistical Analysis
Applied Multivariate Statistical Analysis
With a wealth of examples and exercises, this is a brand new edition of a classic work on multivariate data analysis. A key advantage of the work is its accessibility. This is because, in its focus on applications, the book presents the tools and concepts of multivariate data analysis in a way that is understandable for non-mathematicians and practitioners who need to analyze statistical data. In this second edition a wider scope of methods and applications of multivariate statistical analysis is introduced. All quantlets have been translated into the R and Matlab language and are made available online.
Groots reclamebord getrokken van uit de auto op de autostrade rond Delft…SPSS is everywhere…help!


Luke discussing conjoint analysis

conjoint analysis spss

Choice-Based Conjoint Analysis: Models and Designs
Conjoint analysis (CA) and discrete choice experimentation (DCE) are tools used in marketing, economics, transportation, health, tourism, and other areas to develop and modify products, services, policies, and programs, specifically ones that can be described in terms of attributes. A specific combination of attributes is called a concept profile. Building on the authors’ significant work in the field, Choice-Based Conjoint Analysis: Models and Designs explores the design of experiment (DOE) issues that occur when constructing concept profiles and shows how to modify commonly used designs for solving DCE and CA problems. The authors provide historical and statistical background and discuss the concepts and inference.
The book covers designs appropriate for four classes of DOE problems: (1) attributes in CA and DCE studies are often ordered; (2) studies increasingly are computer-assisted; (3) choice is often influenced by competition; and (4) constraints may exist on attribute levels. Discussion begins with commonly used “generic” designs. The text then presents designs that avoid “dominated” or “dominating” profiles that may occur with ordered attributes and explores the use of orthogonal polynomials to describe relationships between ordered attribute levels and preference. Computer administration entails limited “screen real estate” for presenting concept profiles. The book covers approaches for subsetting attributes and/or levels to “fit” profiles into available “screen real estate.” It then discusses strategies for sequential experimentation. Choice also is influenced by the availability of competing alternatives. The book uses availability and cross-effects designs to illustrate the design and analysis of portfolios and shows the relationship between availability effects and interaction effects in analysis of variance models. The last chapter highlights approaches to experimental design in which constraints are imposed on the levels of attributes. These designs provide the means to untangle the pricing and formulation problems in CA and DCE.


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