Avoiding multicollinearity through conjoint analysis

The premier research companies have helped to make conjoint analysis and its sequel, discrete choice analysis, into popular survey-design research methods. These methods ask respondents to evaluate a series of products or, in special applications where the attributes center on positioning rather than product features, a set of brands.

Each product is generally presented as a bundle of listed attributes, e.g., a series of notebook computers, each with a different list of memory size, processor speed, screen size, price, and brand. In conjoint analysis, respondents are asked to rate or rank each of the products (not the attributes, but the overall products) on some desired outcome variable, such as likelihood to purchase. In discrete choice analysis, respondents are generally asked to choose (rather than rate) one product each from several sets of competing products.

The analysis of the survey’s results reveals how much the addition or subtraction of a particular attribute would affect the preference for the overall product. After calibration with historical results and inclusion of cost factors, researchers can then use these results to predict the changes in market share or profits that would occur as the result of changing the product’s attributes (or the brand’s messaging).

Research clients like such methods because they seem very “real world” compared to prior methods. Prior methods, still very much in use today, asked respondents to evaluate the importance of each attribute to their purchase decisions rather than asking respondents to choose or rate the overall product as they tend to when preparing “short lists” or shopping.

Nevertheless, from an analytical point of view, the greatest benefit of conjoint/discrete-based survey design is that they allow the researcher to avoid the problem of multicollinearity. If the objective of the research project is to figure out how important each factor (or product attribute) is to a market segment’s perception of a product or brand, it’s frustrating to learn “because the factors have multicollinearity, i.e., generally appear in the company of each other or change levels simultaneously (e.g., people who say quality is important are less likely to say that price is important), we can’t estimate the independent impact of each factor.” Conjoint and discrete analysis almost entirely avoid this problem by asking respondents to evaluate products that are as likely as unlikely to share the same pairs of product or brand attribute levels. For example, to figure out the independent impacts of price and quality, respondents can be shown products that have both high quality and high price, high quality but low price, and low quality but high price. It is this capacity of conjoint and discrete to isolate the impacts of each attribute that makes them such powerful aids to decision making–of the businesses marketing the products, not the respondents taking the survey.