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Reducing the Number-of-Attribute-Levels Effect in ACA with Optimal Weighting (1998)

This paper has been archived and removed from our list of current Technical Papers. The information it provides may be outdated or irrelevant based on our present understanding of the topic. However, we will continue to publish it here for historical purposes.

The Number-of-Levels (NOL) Effect is troublesome in conjoint research. One can increase the apparent importance of an attribute by adding more levels. Past research has shown that ACA (Equal Weighting) is less susceptible to the NOL effect than traditional conjoint and CBC. There is yet another way to reduce the NOL effect even further with ACA by using the "Optimal Weighting" feature available in ACA Version 4. This paper demonstrates the difference in attribute importances between the Equal Weighting and Optimal Weighting options, and quantifies the reduction in the effect for two data sets.

The author concludes: "The optimal weighting method appears to have been a nice addition to ACA. It probably deserves more credit than it has been given. We . . . have believed for some time that optimal weighting provided modest improvements to ACA utilities . . . We now recognize that (it) plays a significant role in reducing the NOL effect, and recommend that ACA users use optimal weighting, especially when the number of levels varies across attributes in the design."

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