DSC 410/510 - Multivariate Statistical Methods

Chapter 7

Suggested Solutions

4. WHAT ARE THE PRACTICAL LIMITS OF CONJOINT ANALYSIS IN TERMS OF VARIABLES OR TYPES OF VALUES FOR EACH VARIABLE? WHAT TYPE OF CHOICE PROBLEMS ARE BEST SUITED TO ANALYSIS WITH CONJOINT ANALYSIS? WHICH ARE LEAST WELL SERVED BY THE USE OF CONJOINT ANALYSIS?
   
   
   1. Conjoint analysis is limited in terms of both the type and number of attributes that can be used to describe the choice objects. Perhaps more limiting is the fact that only tangible and easily communicated attributes are feasible, since other attributes are not easily accommodated in either of the presentation methods. Moreover, the number of attributes is usually limited to less than ten, such that a choice object must be characterized on a small number of dimensions.
   2. Conjoint analysis is best suited to examining the choice of hypothetical objects which have easily quantifiable characteristics. Moreover, the product must be viewed as comprised of separate attributes and not really valued by "the whole is greater than the sum of its parts" axiom.
      It is ill-suited to examine existing objects that are hard to describe in simple terms and objects which have intangible attributes (e.g., sensory-based attributes or "images" which convey an emotional appeal).
   
   
5. HOW WOULD YOU ADVISE A MARKET RESEARCHER TO CHOOSE AMONG THE THREE TYPES OF CONJOINT METHODOLOGIES? WHAT ARE THE MOST IMPORTANT ISSUES TO CONSIDER, ALONG WITH EACH METHODOLOGY'S STRENGTHS AND WEAKNESSES?
   
   The choice of a conjoint methodology revolves around three basic characteristics of the proposed research: (1) the number of attributes, (2) level of analysis and (3) the permitted model form. Traditional conjoint analysis is characterized by a simple additive model containing up to nine factors for each individual. The adaptive conjoint method, also an additive model, can accommodate up to 30 factors for each individual. A choice-based conjoint method employs a unique form of presenting stimuli in sets rather than one-by-one. It also differs in that it directly includes interaction and must be estimated at the aggregate level. Choice of a method should be made based on the number of factors and the need to represent interaction effects.