City officials were planning to improve their city’s public transportation system, and they wanted to identify the characteristics of the system that potential users would find attractive. They were concerned with the relative desirability of the three main system attributes of fare; frequency of services; and comfort (identified as the presence or absence of air conditioning and recorded music). Each of these attributes could be offered in three or four different ways or levels. For example, city officials were considering three fare levels (75 ¢, $1.00, $1.25); three levels of frequency of service (every 10 minutes, every 15 minutes, every 20 minutes); and four different comfort features (both air conditioning and recorded music, air conditioning only, recorded music only, neither air conditioning nor recorded music).
City researchers selected a representative sample of 500 adults interested in using the improved public transportation if it were available. These adults were shown all possible combination of fare levels, service frequencies and comfort features and they were asked to identify the combination that was their first preference, the combination that was their second preference, the combination that was their third preference and so on. The city researchers then addressed the issue of how to analyze these data in order to identify the most desirable combination of fare level, service frequency and comfort features to be offered in the improved transportation system.
In the third example, city officials are trying to identify the best combination of fare level, frequency of service, and comfort features to include in their improved public transportation system. Most people’s preferred combination of features would be the most frequent service with the most comfort features at the lowest possible price. Since that combination is probably uneconomical from the city’s point of view, it is necessary to carefully analyze each person’s second, third, fourth, and so on, preference to see if there is any combination of service frequency, comfort features and fare level that is both acceptable to potential users and economically feasible for the city. In order to be successful, a product or service must represent an acceptable blend of desired features and reasonable price from the buyer’s viewpoint. Identifying the best combination of desired features and acceptable price is a frequently encountered problem in marketing, and conjoint analysis is probably the most widely used method of analysis in such situations.
These Methods Identify Interdependence among variables:
Although readers should consider a continuation of the data analysis topic already discussed, the methods discussed in this article differ in one important way, Cross tabulation, regression, LDA and AID all attempt to explain the variation observed in a dependent variable through the use of one or more independent variables. That does not hold true for cluster analysis, factor analysis, or conjoint analysis. These methods are different in the sense that they do not treat some variables as independent and some as dependent. Instead, these methods try to identify interdependencies among a number of variables without treating any of them as dependent or independent.
Four questions Can Help you understand these methods
To help the reader have a better understanding of when cluster analysis, factor analysis, and conjoint should be used instead of cross tabulation, regression, LDA, or AID, the discussions here will follow the same four questions used in the last chapter (i.e. What is the typical problem that can be studied with the method? What does the method do? What types of variables can be analyzed? Does the method identify interdependencies among a number of variables without treating any of them as dependent or independent?)