Conjoint analysis

From DDL Wiki

Conjoint analysis, also known as multiattribute compositional models.

Contents 1 Basic Process 2 Data Collection Methods 3 Simulation Analysis 4 Example 5 External links 6 Reference

Basic Process

The basic process for a conjoint analysis is as follows:

• The survey-taker is given numerous different sets of is given a set of stimulus profiles (constructed along factorial design principles in the full profile case). In the two factor approach, pairs of factors are presented, each appearing approximately an equal number of times.
• 2. The respondents rank or rate the stimuli according to some overall criterion, such as preference, acceptability, or likelihood of purchase.
• 3. In the analysis of the data, part-worths are identified for the factor levels such that each specific combination of part-worths equals the total utility of any given profile. A set of part-worths are derived for each respondent.
• 4. The goodness-of-fit criterion relates the derived ranking or rating of stimulus profiles to the original ranking or rating data.
• 5. A set of objects are defined for the choice simulator. Based on previously determined part-worths for each respondent, each simulator computes an utility value for each of the objects defined as part of the simulation.
• 6. Choice simulator models are invoked which rely on decision rules (first choice model, average probability model, logit model) to estimate the respondent's object of choice. Overall choice shares are computed for the sample.

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 fractional-factorial results. These

Simulation Analysis

The most common simulator models include the first choice model, the average choice (Bradley-Terry-Luce) model, and the Logit model. The First choice model identifies the product with the highest utility as the product of choice. This product is selected and receives a value of 1. Ties receive a .5 value. After the process is repeated for each respondent's utility set, the cumulative "votes" for each product are evaluated as a proportion of the votes or respondents in the sample. The Bradley-Terry-Luce model estimates choice probability in a different fashion. The choice probability for a given product is based on the utility for that product divided by the sum of all products in the simulated market. The logit model uses an assigned choice probability that is proportional to an increasing monotonic function of the alternative's utility. The choice probabilities are computed by dividing the logit value for one product by the sum for all other products in the simulation. These individual choice probabilities are averaged across respondents. In summary, while the literature shows the maximum utility (first choice model) to provide the best overall validation, choice behavior has a strong probabilistic component. We have not measured this component adequately, but instead attributed lack of validity to "noise", our inability to model information search and overload effects, and measurement error.

Example

External links

• What is conjoint analysis? A useful conjoint software site that explains conjoint analysis in detail.

• SAS Software Software for designing full-factorial and fractional factorial surveys.

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.

• Green, P. E. and V. Srinivasan (1978), "Conjoint Analysis in Consumer Research" Issues and Outlook", Journal of Consumer Research, Vol. 5, (September), pp 103-123.

• Luce, R. D. and J. W. Tukey. "Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement," Journal of Mathematical Psychology, 1 (February 1964), pp 1-27.