Conjoint analysis
From DDL Wiki
(Difference between revisions)
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*Choice (e.g. four options, which do you choose?) | *Choice (e.g. four options, which do you choose?) | ||
- | + | The conjoint analysis can be done with different types of data, which include stated choice and revealed preference. The differences between the two are outlined below: | |
- | + | ||
+ | *Stated choice - Using data from a survey | ||
+ | Pros: - controlled experiment | ||
+ | Cons: - survey item choices are not always the same as what is desired or already in the marketplace | ||
+ | |||
+ | *Revealed Preference - Using data collected from actual results in the marketplace | ||
+ | Pros: | ||
+ | |||
+ | Surveys can be generated by using SAS to get full-factorial and partial-factorial results. | ||
==Data Analysis== | ==Data Analysis== | ||
logit and probit | logit and probit | ||
+ | |||
+ | ==Example== | ||
Revision as of 18:57, 28 November 2006
Conjoint analysis, also known as multiattribute compositional models.
Contents |
Basic Process
The basic process for a conjoint analysis is as follows:
Data Collection Methods
- Rating (e.g. rating with a scale from 1-10)
- Ranking (e.g. rank best as 1st, 2nd, 3rd, etc.)
- Choice (e.g. four options, which do you choose?)
The conjoint analysis can be done with different types of data, which include stated choice and revealed preference. The differences between the two are outlined below:
- Stated choice - Using data from a survey
Pros: - controlled experiment Cons: - survey item choices are not always the same as what is desired or already in the marketplace
- Revealed Preference - Using data collected from actual results in the marketplace
Pros:
Surveys can be generated by using SAS to get full-factorial and partial-factorial results.
Data Analysis
logit and probit
Example
External links
- DONLP2 is an open-sourced code of implementing SQP algorithm. The website offers packages in Fortran 77, F90 and ANSI-C versions.
Reference
- G.N. Vanderplaats, Numerical Optimization techniques for Engineering Design with Applications, 1984, McGraw-Hill Inc.
- P.Y. Papalambros and D.J. Wilde, Principles of Optimal Design: Modeling and Computation, 1988, Cambridge University Press.
- S.S. Rao, Engineering Optimization: Theory and Practice, 1996, 3rd Ed., John Wiley & Sons, Inc.