netrashetty
Netra Shetty
Domtar Corporation (TSX: UFS, NYSE: UFS) is the largest integrated producer of uncoated freesheet paper in North America and the second largest in the world based on production capacity, and is also a manufacturer of papergrade pulp.
Domtar designs, manufactures, markets and distributes a wide range of business, commercial printing, publication as well as technical and specialty papers with recognized brands such as Cougar, Lynx Opaque Ultra, Husky Opaque Offset, First Choice and Domtar EarthChoice Office Paper, part of a family of environmentally and socially responsible papers.
ources and acquisition of data
The sources of data are more centered on secondary research from wherein information especially on the literature review has been acquired in JSTOR journals, Questia library, emerald insight and Google scholar documents. The resources are acquired through research undertakings, collating relevant data and information upon understanding patterns of research in such ideal types.
VI Method of data analysis
The method of analysing data could be in the process of case study analysis upon which social science method examples are being linked as well as and interpreted from several perspectives/views pointing to Max Weber’s Ideal Type. To integrate a response scaling system of which there has to be recognition of agreements and disagreements of responses from sample respondents found in the social science research arena. Data will be collected and analysed through using qualitative techniques such as pointing towards document analysis, interviews and surveys. The primary data is to be collected from the respondents in case situations, secondary data is to comprise of reference concerning research subject, the using of existing information’ on such levels into the study to be realized upon. The data collection will involve oral narrative inquiry interviews Clandinin and Connelly (1999), noted in narrative inquiry that certain scholars have argued as the process is into the linguistic form as it will be uniquely suited for displaying research existence as situated into ideal type ways”.
VII Form of presentation
Research presentation will be in word document format, produced in soft and hard copy for recording, evaluation and printing purposes. Thus, such tables and diagrams are to be used in order to give in a precise picture of the research study and to allow the material as a useful study reference in the future.
The second part of research design involves laying out a plan to collect the information within the research method selected. To gather research marketers have three choices:
acquire pre-existing research
undertake new research themselves
out-source the task of new research to a third-party, such as a market research company
The first option is associated with Secondary Research, which involves accessing information that was previously collected. The last two options are associated with conducting Primary Research, which involves the collection of original data generally for one’s own use.
As we will see, the data collection approach used depends on what the researcher determined in the Steps 1-3 of the research plan. That is, the optimal data collection technique is selected only after the researcher has determined the purpose, the information sought and the basic research design method. In many instances the researcher uses both secondary and primary data collection as part of the same research project.
An extensive discussion of Secondary Research can be found in two tutorials: Data Collection: Low-Cost Secondary Research and Data Collection: High-Cost Secondary Research. While detailed coverage of Primary Research can be found in the tutorial Data Collection: Primary Research Methods.
nce 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 ne
Domtar designs, manufactures, markets and distributes a wide range of business, commercial printing, publication as well as technical and specialty papers with recognized brands such as Cougar, Lynx Opaque Ultra, Husky Opaque Offset, First Choice and Domtar EarthChoice Office Paper, part of a family of environmentally and socially responsible papers.
ources and acquisition of data
The sources of data are more centered on secondary research from wherein information especially on the literature review has been acquired in JSTOR journals, Questia library, emerald insight and Google scholar documents. The resources are acquired through research undertakings, collating relevant data and information upon understanding patterns of research in such ideal types.
VI Method of data analysis
The method of analysing data could be in the process of case study analysis upon which social science method examples are being linked as well as and interpreted from several perspectives/views pointing to Max Weber’s Ideal Type. To integrate a response scaling system of which there has to be recognition of agreements and disagreements of responses from sample respondents found in the social science research arena. Data will be collected and analysed through using qualitative techniques such as pointing towards document analysis, interviews and surveys. The primary data is to be collected from the respondents in case situations, secondary data is to comprise of reference concerning research subject, the using of existing information’ on such levels into the study to be realized upon. The data collection will involve oral narrative inquiry interviews Clandinin and Connelly (1999), noted in narrative inquiry that certain scholars have argued as the process is into the linguistic form as it will be uniquely suited for displaying research existence as situated into ideal type ways”.
VII Form of presentation
Research presentation will be in word document format, produced in soft and hard copy for recording, evaluation and printing purposes. Thus, such tables and diagrams are to be used in order to give in a precise picture of the research study and to allow the material as a useful study reference in the future.
The second part of research design involves laying out a plan to collect the information within the research method selected. To gather research marketers have three choices:
acquire pre-existing research
undertake new research themselves
out-source the task of new research to a third-party, such as a market research company
The first option is associated with Secondary Research, which involves accessing information that was previously collected. The last two options are associated with conducting Primary Research, which involves the collection of original data generally for one’s own use.
As we will see, the data collection approach used depends on what the researcher determined in the Steps 1-3 of the research plan. That is, the optimal data collection technique is selected only after the researcher has determined the purpose, the information sought and the basic research design method. In many instances the researcher uses both secondary and primary data collection as part of the same research project.
An extensive discussion of Secondary Research can be found in two tutorials: Data Collection: Low-Cost Secondary Research and Data Collection: High-Cost Secondary Research. While detailed coverage of Primary Research can be found in the tutorial Data Collection: Primary Research Methods.
nce 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 ne
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