Specify Attributes and Levels

The base CBC software is limited to 30 attributes with at most 15 levels of an attribute, though we encourage you to develop attributes that have about 5 or fewer levels whenever possible. (The advanced design module extension of CBC allows up to 100 attribute, each with 254 levels).
(Hint: you can select a list of attributes or levels from Word or Excel and paste into SSI Web using the Paste list member(s) from the clipboard icon. This can save a great deal of time.)

An attribute is a characteristic of a product (e.g. color) which can take on various levels (e.g., red, yellow, blue). Every attribute must have at least two levels. The underlying theory of conjoint analysis holds that a buyer places a certain part-worth (or utility value) on each attribute level, and that the overall utility of any product is obtained by summing up the part-worth values of its specific attribute levels.

In conjoint experiments, we show respondents product concepts described using different combinations of attribute levels and ask them to somehow express their preferences for those concepts. One way of thinking about conjoint analysis is that we are conducting a designed experiment for each respondent to see how preferences vary as we change attribute levels.

After observing how respondents evaluate products in response to changes in the underlying attribute levels, we can estimate the impact (part-worth) each attribute level has upon overall product preference. Once we learn respondents' preferences for the various attribute levels, we can predict how buyers might respond to any potential combination of levels in our study, whether or not that actual product was ever displayed during the interview.

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Conditional Relationships…

(Not available within ACA studies.)

The conditional relationships button lets you associate conditional prices or graphics with one or more attributes in your study.

Conditional Graphics: Sometimes, you may want to associate a graphic with an attribute or multiple attributes. For example, if a combination of brand and form factor make up a graphic, one can specify the two attributes separately and associating a conditional graphic with their combined levels. This permits estimation of separate effects for brand and form factor.

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Guidelines

In generating attributes and levels, consider the following guidelines:

1. Attributes should be independent. It is therefore important to economize; including attributes with overlapping meanings is wasteful and can lead to biased part-worth utilities.

Furthermore, levels for related attributes often cannot combine naturally and freely with one another. Though it can lead to more realistic interviews, it is often detrimental (and sometimes fatal) to prohibit levels from occurring with others.

2. Levels within each attribute should be mutually exclusive. This point becomes clear when you specify products using the market simulator (during the analysis phase) and are forced to choose only a single level from each attribute.

Consider the following attributes for a study on optional automobile features:

Optional Features:

Sunroof

GPS (Global Positioning System)

Extended warranty

This formulation doesn't permit simulating preference for a car that has both a Sunroof and a GPS. Similarly, we could not simulate preference for an automobile that had none of these features. There are two ways to resolve this quandary:

a) Create an attribute with levels representing all potential combinations of these features. This results in an eight-level attribute, if you include the option that none of these features is available.

b) Formulate three separate attributes each with two levels: (No Sunroof, Sunroof), (No GPS, GPS), (No Warranty, Warranty).

Option (a) adds seven parameters to the model (after dummy-coding) and forces the measurement of an explicit three-way interaction. With the more complex model definition, we can investigate whether there are diminishing returns by bundling the features. By splitting the options into three distinct binary attributes (Option b), only three parameters are added to the model; however, interactions are not automatically measured.

3. Attribute levels should cover the full range of possibilities for existing products as well as products that may not yet exist, but that you want to investigate. Although the market simulator allows you to extrapolate and interpolate, only linear interpolation and extrapolation are possible. Although interpolation is likely to produce acceptable results, extrapolation is prone to error and should be avoided. One way to ensure that you are including the appropriate levels and ranges is to ask your client to specify ahead of time the market simulations to be run during the analysis phase of your study. That exercise can often reveal weaknesses in your attribute specifications.

4. Prohibitions, if at all possible, should be avoided. Specifying unnecessary or excessive prohibitions is one of the most common mistakes. The problem usually begins when either the analyst (or the analyst's client) notices that some product combinations displayed during the interview are not realistic, given what currently exists in the market. Sometimes a product is shown with all the best features at the lowest price; or two attribute levels that would not naturally occur in the real world are paired together. The inclination is simply to prohibit such combinations. We urge you to exercise restraint when considering prohibiting levels from occurring with one another.

Too many prohibitions can lead to confounded effects and the complete inability to calculate stable utilities. It is better to prompt respondents that they will see combinations during the interview that are not yet available in the market or that seem unlikely. You can urge respondents to answer as if these products were actually available today.

There are other strategies for dealing with prohibitions. Consider the example below with brands of soda and package types:

Brand:

Sawtooth Spritz

Kong Kola

Splut

Package Type:

2-liter bottle

6-pack of 12-oz cans

Suppose that Splut was only available in 6-packs of cans. Furthermore, you are displaying actual pictures of the products, and thus can only display actual (not potential) products. Rather than define a prohibition between Splut and the 2-liter bottle, it would make more sense to combine these two attributes as a single attribute with five levels:

Sawtooth Spritz in a 2-liter bottle

Sawtooth Spritz in a 6-pack of 12-oz cans

Kong Kola in a 2-liter bottle

Kong Kola in a 6-pack of 12-oz cans

Splut in a 6-pack of 12-oz cans

Under this strategy, no prohibitions are required.

5. The number of levels you choose to define an attribute can have a significant bearing on the results. The first concern has been called the "Number-of-Levels Effect." All else equal, attributes defined on more levels tend to get more importance.

The second concern is that you limit the number of levels on which quantitative attributes are described. We suggest not including more than about five levels to describe attributes such as price or speed. It's usually better to have more data at each price point than to have thinner measurements at more price points. Measuring too many points along a quantitative function can result in troublesome reversals. If you cover the entire range of interest with fewer levels, you can interpolate between levels within the market simulator to get finer granularity if needed.

6. Attributes that cannot be adequately described in words should be represented in multimedia. But if attributes do not require multimedia to adequately communicate their properties, it would probably be a mistake to make them multimedia. Though the interview might appear more attractive, it might bias the results in favor of multimedia attributes.