Conjoint Analysis

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Conjoint Analysis

Conjoint Analysis is used to answer the following question

To what extent does each component (factor) contribute to the total utility of a product?

The Independent variables are Object attributes, and the dependent variable is preferences of the interviewed person for the fictive products. It is however important that choice of factors are

Relevant, Interfere with the objective of survey, Independent, Realizable, Compensatory relationships of the various factor values, Should not constitute exclusion criteria.

Example: (Source: IBM SPSS Manual)

Suppose that you want to book an airline flight. You have the choice of sitting in a cramped seat or a spacious seat. If this were the only consideration, your choice would be clear. You would probably prefer a spacious seat. Or suppose you have a choice of ticket prices: $225 or $800. On price alone, taking nothing else into consideration, the lower price would be preferable. Finally, suppose you can take either a direct flight, which takes two hours, or a flight with one layover, which takes five hours. Most people would choose the direct flight

Using conjoint analysis, you can determine both the relative importance of each attribute as well as which levels of each attribute are most preferred. If the most preferable product is not feasible for some reason, such as cost, you would know the next most preferred alternative. If you have other information on the respondents, such as background demographics, you might be able to identify market segments for which distinct products can be packaged.


1. Can break large sets of attributes into smaller bundles for analysis

2. Calculates utilities at the individual respondent level.

3. Straightforward experimental designs.


1. With too many options, respondents resort to simplification strategies

2. Poorly designed studies may over-value emotional/preference variables and undervalue concrete variables