Discrete choice experiments (DCEs) have applications in many areas such as social sciences, economics, transportation research, health systems, and clinical decisions to mention a few. Usually discrete choice models (DCMs) focus on predicting the product choice; however, these models do not provide information about what attributes of the products are impacting consumers’ choices the most. Today, it is common to record the best and worst features of a product (or profile), also called attribute levels, and the goal is to investigate and build models for estimation of attribute and attribute-level impacts on consumer behavior. Attribute-level best-worst DCEs provide information into what consumers find the most important when considering different products. The design of attribute-level best-worst DCEs and the associated theory are discussed by Street and Knox (2012). Attribute-level best-worst discrete choice models can help to market products to the consumers and are often used in health economics research. These experiments help companies to best target consumers with their products or services. The latter can better advertise their products by highlighting and/or downplaying certain key attributes (or attribute-levels) to best earn the interests and business of consumers. We propose a time dependent model that can adapt to changes that occur in areas such as public opinion. A time dependent model accounts for the impact of time in as consumer’s perception of a product and adjusts the utility to reflect that. These models are Markov processes and are often found under dynamic programming. Rust (1994), provides time dependent models for the usual DCEs. We extend this time dependent model to the attribute-level best-worst DCEs. Two example studies are presented to examine the dynamic versus static performance of transition matrices for estimation and inference of attributes and attribute level effects with regards to the expected utilities.
","abstract_html":"<p>Discrete choice experiments (DCEs) have applications in many areas such as social sciences, economics, transportation research, health systems, and clinical decisions to mention a few. Usually discrete choice models (DCMs) focus on predicting the product choice; however, these models do not provide information about what attributes of the products are impacting consumers’ choices the most. Today, it is common to record the best and worst features of a product (or profile), also called attribute levels, and the goal is to investigate and build models for estimation of attribute and attribute-level impacts on consumer behavior. Attribute-level best-worst DCEs provide information into what consumers find the most important when considering different products. The design of attribute-level best-worst DCEs and the associated theory are discussed by Street and Knox (2012). Attribute-level best-worst discrete choice models can help to market products to the consumers and are often used in health economics research. These experiments help companies to best target consumers with their products or services. The latter can better advertise their products by highlighting and/or downplaying certain key attributes (or attribute-levels) to best earn the interests and business of consumers. We propose a time dependent model that can adapt to changes that occur in areas such as public opinion. A time dependent model accounts for the impact of time in as consumer’s perception of a product and adjusts the utility to reflect that. These models are Markov processes and are often found under dynamic programming. Rust (1994), provides time dependent models for the usual DCEs. We extend this time dependent model to the attribute-level best-worst DCEs. Two example studies are presented to examine the dynamic versus static performance of transition matrices for estimation and inference of attributes and attribute level effects with regards to the expected utilities.</p>","abstract_has_math":false,"creators":["Working, Amanda Faye"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Mathematics & Statistics","degree_department":null,"school":null,"contributors":["Norou Diawara","N. Rao Chaganty","Lucia Tabacu","Cynthia Jones"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-10-01T07:00:00Z","date_published":"2017-10-01T07:00:00Z","updated_at":"2026-07-24T03:34:02Z","subjects":["Discrete choice models","markov processes","Time dependent","Statistics and Probability"],"languages":[],"rights":["In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
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"]},{"key":"dc:title","label":"Title","values":["Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments"]}]}],"canonical_facts":{"dc:contributor":["Norou Diawara","N. Rao Chaganty","Lucia Tabacu","Cynthia Jones"],"dc:creator":["Working, Amanda Faye"],"dc:date.available":["2019-02-01T08:00:00Z"],"dc:description.abstract":["Discrete choice experiments (DCEs) have applications in many areas such as social sciences, economics, transportation research, health systems, and clinical decisions to mention a few. Usually discrete choice models (DCMs) focus on predicting the product choice; however, these models do not provide information about what attributes of the products are impacting consumers’ choices the most. Today, it is common to record the best and worst features of a product (or profile), also called attribute levels, and the goal is to investigate and build models for estimation of attribute and attribute-level impacts on consumer behavior. Attribute-level best-worst DCEs provide information into what consumers find the most important when considering different products. The design of attribute-level best-worst DCEs and the associated theory are discussed by Street and Knox (2012). Attribute-level best-worst discrete choice models can help to market products to the consumers and are often used in health economics research. These experiments help companies to best target consumers with their products or services. The latter can better advertise their products by highlighting and/or downplaying certain key attributes (or attribute-levels) to best earn the interests and business of consumers. We propose a time dependent model that can adapt to changes that occur in areas such as public opinion. A time dependent model accounts for the impact of time in as consumer’s perception of a product and adjusts the utility to reflect that. These models are Markov processes and are often found under dynamic programming. Rust (1994), provides time dependent models for the usual DCEs. We extend this time dependent model to the attribute-level best-worst DCEs. Two example studies are presented to examine the dynamic versus static performance of transition matrices for estimation and inference of attributes and attribute level effects with regards to the expected utilities.
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"],"dc:subject":["Discrete choice models","markov processes","Time dependent","Statistics and Probability"],"dc:title":["Methods for Analyzing Attribute-Level Best-Worst Discrete Choice Experiments"],"thesis:degree_discipline":["Mathematics & Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:34:02Z"}