Is ACA the Appropriate Technique?

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Many methods are available for collecting and analyzing conjoint data, and the researcher contemplating a conjoint study must choose among them.  We at Sawtooth Software have had many years of direct experience with these methods, as well as the benefit of many conversations with users of our own and other software.  Based on that experience, we offer the following suggestions:


The Full Profile Method (such as used in CVA) was the first conjoint method introduced to the marketing research community, and it is considered the original standard.  Green and Srinivasan (1990) recommended use of the full profile method when the number of attributes was six or fewer.  Our opinion is that this is a useful guideline, but there isn't a hard limit, and the number of attributes should depend on the complexity of the attribute descriptions and respondent interest/knowledge of the product category.  


During the 1980s and 1990s, we tended to favor ACA for projects involving about six or more attributes.  As choice-based approaches with HB estimation have captivated the industry and shown their worth, we shifted our attention toward CBC and Adaptive CBC.  Adaptive CBC combines some of the best aspects of ACA of CBC and the early evidence suggests that it is a valuable technique.  We have not conducted nor seen research that directly compares ACA to Adaptive CBC, but we expect that ACBC should have important advantages when the number of attributes is between 5 and 12.  When the number of attributes exceeds about 15, and pricing research isn't a goal, we don't know which method would prevail.


Over the last fifteen years, it has become clear to us that the self-explicated importance question is the aspect of ACA that either "makes or breaks" it.  If the importance questions are asked poorly, they can misinform utility estimation and be harmful.  An example of poor execution is to not educate respondents ahead of time regarding the array of attributes, and to ask the importance questions one-by-one on separate screens.  Some ideas for improving the quality of the importance questions are given in the section of this documentation entitled Customizing the Importance Question.


ACA is a "main effects only" model, and assumes there are no interactions among attributes.  Many conjoint practitioners agree that one must remain alert for the possibility of interactions, but that it is usually possible to choose attributes so that interactions will not present severe problems.  Like other conjoint methods, ACA can deal with interactions in a limited way by collapsing two attributes into one.  For example, we could deal with an interaction between car color and body style by cross-classifying the levels:


Red Convertible

Black Convertible

Red Sedan

Black Sedan


However, if the attributes in question have many levels, or if an attribute (such as price, for example) is suspected of having interactions with many others, then collapsing attributes will not be enough.  In that case too many parameters must be estimated to permit analysis at the individual level, and the most common solution is to evaluate interactions by pooling data from many respondents.  


ACA has been shown to have weaknesses in pricing research, where it often underestimates the importance of price.  Since about 1995, some ACA researchers have included price in their ACA studies, but have adjusted its importance based on other information included in the survey.  This was called the "dual conjoint" approach, and there are articles on this in our Technical Papers Library on our website.


When ACA is executed well, with a good importance section implementation, it provides solid results for applications such as product design and segmentation work.  It can work with the smallest of sample sizes, even in the case of revealing the preferences for a few or even one individual.  Even though the bulk of interest lately is in favor of CBC and ACBC, those who have become expert in ACA methodology and ACA study execution will continue to use the method confidently in appropriate contexts for many years to come.


For more information about selecting the appropriate conjoint method, see the article in the technical papers library on our website entitled: "Which Conjoint Method Should I Use?" or use the Interactive Advisor at


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