Dominion Resources Inc. (NYSE: D), commonly referred to as Dominion, is a power and energy company headquartered in Richmond, Virginia that supplies electricity in parts of Virginia and North Carolina and supplies natural gas to parts of West Virginia, Ohio, Pennsylvania, and eastern North Carolina. Dominion also has generation facilities in Wisconsin, Indiana, Illinois, Connecticut and Massachusetts.

nt has been since used widely in quantitative marketing research. It has been hailed as the most innovative way of determining consumers’ true preference of products (Green and Wind, 1973, Green and Srinivasan, 1978; Louviere, 1991). (See also our article in the June 2001 issue of Quirk’s for a description of a conjoint study.) However, several limitations of conjoint pose questions and concerns among marketing researchers. First, in a conjoint study, all attributes are presumed to be the same across the products. In other words, we create profiles in which the levels of attributes for each product are the same. The levels of the attribute “price” for drug X and for drug Y, for instance, are the same. So are the levels for efficacy and side effects. We know, in reality, the prices for generic and prescription drugs vary greatly, and this is also true among brand-name products. Secondly, when we are conducting a conjoint study, we are mainly concerned about the main effects of attributes. We evaluate the differences between or among attribute levels, and are ignoring how the change of levels of one attribute may have differential impact on levels of other attributes. For instance, different brands may have different price sensitivities. In a conjoint study, we would assume that all brands have the same price sensitivity. We need to estimate, in such a case, not only the main effects but also interaction effects between brand and price, for which a conjoint study is not adequate. Another problem with the conjoint design is that, upon seeing each profile, the respondent has to give a preference rating, since “none” is not an option among the alternatives. This may cause inaccuracies in estimating utilities when respondents don’t like any of the products they see and are forced to give their preference rating. Finally, a seemingly obvious and also important one is that, when the respondents give a preference rating, it doesn’t necessarily mean he or she is going to prescribe the product. We only assume that respondents’ preference ratings can be translated into their behavior. However, there is a gap between a respondent’s indication of preference of a product and his or her actual behavior. In pharmaceutical marketing research, the respondents may be payers, physicians, patients or caregivers.

The differences between a conjoint study and a discrete choice study
A discrete choice study was thus developed to overcome these limitations manifested in a conjoint study. Discrete choice allows for the interaction effects among the levels of attributes, which is particularly useful in the estimation of price elasticity such as the interaction of brand by price. It doesn’t require that the levels be the same across the attributes. One product may have dosings (e.g., QD, BID) that are different from other products’ dosings (e.g., weekly, bi-weekly). Furthermore, a discrete choice experiment doesn’t force physicians to prescribe a product upon seeing profiles of products. Respondents can choose a “none of these” option if they don’t want to prescribe any of the products presented. More importantly, a discrete choice study asks respondents to make a choice among the alternatives presented to them, which is one step closer to reality than the preference ratings in a conjoint experiment. In a pharmaceutical marketing research study, physicians evaluate a set of drugs varied in the levels of attributes presented on the screen or on paper and indicate which drug they would prescribe. The task mimics what physicians would do virtually on a daily basis. Most marketing researchers would agree that, to understand respondents’ behaviors, we should study their behavioral intentions, not their preferences.

Technically, there is also a difference between conjoint and discrete choice modeling. Discrete choice uses the multinomial logit model, which applies the nonlinear model to estimate utilities at an aggregate level, whereas conjoint analysis applies a linear model to estimate utilities at an individual level. More about this later.

What does a discrete choice analysis do?
As in a conjoint study, the process of conducting a discrete choice study usually includes two parts: experimental design and data analysis.

A. Design
The design of a discrete choice study involves three steps: determine the number of attributes and attribute levels, select the number of choice sets and the number of respondents, and present the choice sets.

1) Attributes and attribute levels

In a discrete choice task, the respondent is presented with several choices and is asked to select one of them. The factors that influence the choice possibilities are called attributes. Each product has several attributes and each attribute has several levels. A combination of attribute levels is called a product profile. Each set of alternative profiles is called a choice set.

The attributes of a drug may include things such as price, efficacy, dosage, formulation and side effects, to name only a few. If the purpose of the study is to assess the factors that may influence physicians’ prescribing behavior of drugs, attributes are these identified factors that may exercise such influence. We may find, for instance, the high level of side effects of a drug will negatively influence physicians’ prescribing behavior of the drug. By the same token, the high efficacy of a drug may drive up the physicians’ prescribing behavior. Each attribute should consist of at least two levels. An attribute of price, for example, may have two: $10.00 and $15.00. An attribute of efficacy could have two levels: “high” and “low” or three levels as “high” “medium” and “low.” For example, if we have five attributes with two two-level attributes (drug delivery form and side effects) and three three-level attributes (efficacy, dosing, managed care plan formulary), the total number of combinations of the attribute levels is 108 (22 x 33 = 108). The number of 108 is called the total number of profiles in the full-profile factorial design. If we have three drugs with 108 profiles each, we then have total of 324 (108 x 3) profiles.

2) Selection of the number of choice sets

When there are a large number of attributes and attribute levels, it becomes unrealistic to include all possible combinations of attributes and attribute levels in a choice task. The fatigue produced by a long list of attributes and complexity of levels will lead to low quality of responses and inaccuracies of estimation. It is generally perceived that the total number of attributes in a choice set should be no more than six (Sawtooth, CBC User’s Manual, 2000) and the total number of choice sets should be no more than 30 for each respondent, since the human cognitive processing capability is limited (Miller, 1956). In most discrete choice experiments, like in conjoint, a fractional factorial design with a small number of the profiles is used. In this example, a fractional factorial design consisting of only 18 profiles out of the 108 might be used.

The question is, how do you decide the number of the profiles that are needed in a choice study? In general, there is no single rule to follow. These are the considerations frequently cited in the literature: the number of parameters to be estimated, orthogonality and balance. The orthogonality refers to a design where the effect of each attribute can be estimated independently. The balanced design refers to a design in which the levels of attributes are equally represented, so that the effects of attributes can be estimated efficiently. The number of parameters to be estimated is determined by the number of products, the number of attributes and levels. In the example we cited earlier, we have two two-level and three three-level attributes and the smallest integer that can be divided by 2, 3, 2 x 2, 2 x3 and 3 x 3 is 36. That is, we achieve a perfect orthogonality if we have a total of 36 profiles in the study. However, we know that 36 profiles are too many for respondents to complete and we have to reduce it, say, to 18. The number 18 can be divided by each of the above-mentioned numbers except 2 x 2. Here, we compromise the number of profiles in the study by having imperfect orthogonality. The balance here refers to the frequency of attribute levels appearing in the total number of profiles. In other words, ideally we need to have an equal number of attribute levels for each attribute included in the selected profiles. This is hard to achieve when we have to reduce the number of profiles in a fractional factorial design.
 
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Dominion Resources Inc. (NYSE: D), commonly referred to as Dominion, is a power and energy company headquartered in Richmond, Virginia that supplies electricity in parts of Virginia and North Carolina and supplies natural gas to parts of West Virginia, Ohio, Pennsylvania, and eastern North Carolina. Dominion also has generation facilities in Wisconsin, Indiana, Illinois, Connecticut and Massachusetts.

nt has been since used widely in quantitative marketing research. It has been hailed as the most innovative way of determining consumers’ true preference of products (Green and Wind, 1973, Green and Srinivasan, 1978; Louviere, 1991). (See also our article in the June 2001 issue of Quirk’s for a description of a conjoint study.) However, several limitations of conjoint pose questions and concerns among marketing researchers. First, in a conjoint study, all attributes are presumed to be the same across the products. In other words, we create profiles in which the levels of attributes for each product are the same. The levels of the attribute “price” for drug X and for drug Y, for instance, are the same. So are the levels for efficacy and side effects. We know, in reality, the prices for generic and prescription drugs vary greatly, and this is also true among brand-name products. Secondly, when we are conducting a conjoint study, we are mainly concerned about the main effects of attributes. We evaluate the differences between or among attribute levels, and are ignoring how the change of levels of one attribute may have differential impact on levels of other attributes. For instance, different brands may have different price sensitivities. In a conjoint study, we would assume that all brands have the same price sensitivity. We need to estimate, in such a case, not only the main effects but also interaction effects between brand and price, for which a conjoint study is not adequate. Another problem with the conjoint design is that, upon seeing each profile, the respondent has to give a preference rating, since “none” is not an option among the alternatives. This may cause inaccuracies in estimating utilities when respondents don’t like any of the products they see and are forced to give their preference rating. Finally, a seemingly obvious and also important one is that, when the respondents give a preference rating, it doesn’t necessarily mean he or she is going to prescribe the product. We only assume that respondents’ preference ratings can be translated into their behavior. However, there is a gap between a respondent’s indication of preference of a product and his or her actual behavior. In pharmaceutical marketing research, the respondents may be payers, physicians, patients or caregivers.

The differences between a conjoint study and a discrete choice study
A discrete choice study was thus developed to overcome these limitations manifested in a conjoint study. Discrete choice allows for the interaction effects among the levels of attributes, which is particularly useful in the estimation of price elasticity such as the interaction of brand by price. It doesn’t require that the levels be the same across the attributes. One product may have dosings (e.g., QD, BID) that are different from other products’ dosings (e.g., weekly, bi-weekly). Furthermore, a discrete choice experiment doesn’t force physicians to prescribe a product upon seeing profiles of products. Respondents can choose a “none of these” option if they don’t want to prescribe any of the products presented. More importantly, a discrete choice study asks respondents to make a choice among the alternatives presented to them, which is one step closer to reality than the preference ratings in a conjoint experiment. In a pharmaceutical marketing research study, physicians evaluate a set of drugs varied in the levels of attributes presented on the screen or on paper and indicate which drug they would prescribe. The task mimics what physicians would do virtually on a daily basis. Most marketing researchers would agree that, to understand respondents’ behaviors, we should study their behavioral intentions, not their preferences.

Technically, there is also a difference between conjoint and discrete choice modeling. Discrete choice uses the multinomial logit model, which applies the nonlinear model to estimate utilities at an aggregate level, whereas conjoint analysis applies a linear model to estimate utilities at an individual level. More about this later.

What does a discrete choice analysis do?
As in a conjoint study, the process of conducting a discrete choice study usually includes two parts: experimental design and data analysis.

A. Design
The design of a discrete choice study involves three steps: determine the number of attributes and attribute levels, select the number of choice sets and the number of respondents, and present the choice sets.

1) Attributes and attribute levels

In a discrete choice task, the respondent is presented with several choices and is asked to select one of them. The factors that influence the choice possibilities are called attributes. Each product has several attributes and each attribute has several levels. A combination of attribute levels is called a product profile. Each set of alternative profiles is called a choice set.

The attributes of a drug may include things such as price, efficacy, dosage, formulation and side effects, to name only a few. If the purpose of the study is to assess the factors that may influence physicians’ prescribing behavior of drugs, attributes are these identified factors that may exercise such influence. We may find, for instance, the high level of side effects of a drug will negatively influence physicians’ prescribing behavior of the drug. By the same token, the high efficacy of a drug may drive up the physicians’ prescribing behavior. Each attribute should consist of at least two levels. An attribute of price, for example, may have two: $10.00 and $15.00. An attribute of efficacy could have two levels: “high” and “low” or three levels as “high” “medium” and “low.” For example, if we have five attributes with two two-level attributes (drug delivery form and side effects) and three three-level attributes (efficacy, dosing, managed care plan formulary), the total number of combinations of the attribute levels is 108 (22 x 33 = 108). The number of 108 is called the total number of profiles in the full-profile factorial design. If we have three drugs with 108 profiles each, we then have total of 324 (108 x 3) profiles.

2) Selection of the number of choice sets

When there are a large number of attributes and attribute levels, it becomes unrealistic to include all possible combinations of attributes and attribute levels in a choice task. The fatigue produced by a long list of attributes and complexity of levels will lead to low quality of responses and inaccuracies of estimation. It is generally perceived that the total number of attributes in a choice set should be no more than six (Sawtooth, CBC User’s Manual, 2000) and the total number of choice sets should be no more than 30 for each respondent, since the human cognitive processing capability is limited (Miller, 1956). In most discrete choice experiments, like in conjoint, a fractional factorial design with a small number of the profiles is used. In this example, a fractional factorial design consisting of only 18 profiles out of the 108 might be used.

The question is, how do you decide the number of the profiles that are needed in a choice study? In general, there is no single rule to follow. These are the considerations frequently cited in the literature: the number of parameters to be estimated, orthogonality and balance. The orthogonality refers to a design where the effect of each attribute can be estimated independently. The balanced design refers to a design in which the levels of attributes are equally represented, so that the effects of attributes can be estimated efficiently. The number of parameters to be estimated is determined by the number of products, the number of attributes and levels. In the example we cited earlier, we have two two-level and three three-level attributes and the smallest integer that can be divided by 2, 3, 2 x 2, 2 x3 and 3 x 3 is 36. That is, we achieve a perfect orthogonality if we have a total of 36 profiles in the study. However, we know that 36 profiles are too many for respondents to complete and we have to reduce it, say, to 18. The number 18 can be divided by each of the above-mentioned numbers except 2 x 2. Here, we compromise the number of profiles in the study by having imperfect orthogonality. The balance here refers to the frequency of attribute levels appearing in the total number of profiles. In other words, ideally we need to have an equal number of attribute levels for each attribute included in the selected profiles. This is hard to achieve when we have to reduce the number of profiles in a fractional factorial design.

Hey netra, great information on Dominion Resources Inc and i would like to thank you for your research work. Well, after reading your document i thought i should also add some more information on Dominion Resources Inc so uploading a document which would be useful.
 

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