ACA and CVA projects allow up to 30 attributes, with up to 15 levels per attribute.
(Hint: you can select a list of attributes or levels from Word and paste into the project 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 that are described by 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 his/her preferences vary as we change attribute levels.
By 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.
For each attribute, you can specify whether it has known (a priori) order. If you know ahead of time that respondents prefer low prices to high prices (all else equal) or fast speeds to slow speeds, you can avoid having to ask what might seem to be an obvious question. For CVA studies, that means avoiding showing products that are clearly superior on all aspects to another.
We strongly encourage you to specify an order for attributes that have known rational order, like price and speed. When you specify a ranking order "worst to best" or "best to worst," you must be certain that the rational respondent would agree with you, and that you have specified the correct order.
For example, the levels:
Level 1: $5
Level 2: $10
Level 3: $15
are arranged from "Best to Worst," meaning, the best level is first and the worst level is last.
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 incorrect answers.
Furthermore, levels for related attributes may not combine naturally with one another. Though it can lead to more realistic interviews, it is usually detrimental (and sometimes fatal) to prohibit levels from occurring with others. (There are some intriguing exceptions to this that we will not discuss in this documentation.)
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:
GPS (Global Positioning System)
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 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 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 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 pairs.
Too many prohibitions can lead to imprecise utility estimation and unresolvable (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:
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.
CBC (full-profile) and CVA are even more sensitive to too many attribute prohibitions than ACA. ACA tends to be more robust in the face of attribute prohibitions given that a significant portion of the information used to estimate part-worth utilities comes from the priors section, which is not affected by attribute level prohibitions. And, partial-profile designs generally are less affected by prohibitions than full-profile designs.
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 Number-of-Levels Effect is less problematic in ACA than full-profile conjoint methods. Even so, we suggest you at least approximately balance the number of levels across attributes.
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.
7. In our experience, respondents have a difficult time dealing with more than about six or eight attributes in full-profile conjoint methods like CBC or CVA. When faced with too much information, respondents may resort to simplification strategies to deal with the difficulty of the task. Unless respondents employ the same sort of simplification strategies when making real-world decisions, CVA results may place too much emphasis on the few most important features.
Traditional CBC or CVA will work well for relatively small conjoint designs, and we generally suggest other methods (such as Adaptive CBC or ACA) for larger problems.