A query theory account of a discrete choice experiment under oath

2019 ◽  
Vol 47 (3) ◽  
pp. 1133-1172
Author(s):  
Nathan P Kemper ◽  
Jennie S Popp ◽  
Rodolfo M Nayga
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Choice Experiments ◽  
Hypothetical Bias ◽  
Discrete Choice Experiments ◽  
Thought Processes ◽  
Stated Preference Methods ◽  
Ex Ante

Abstract One limitation of stated-preference methods is the formation of hypothetical bias. To address this, the honesty oath has been used as an ex ante technique to reduce hypothetical bias. Our study provides a query account of the honesty oath in a discrete-choice experiment setting by using Query Theory to examine the mechanism behind the effectiveness of the honesty oath. Our results show that the honesty oath can change the content and order of queries; potentially reducing hypothetical bias in discrete choice experiments. The study suggests the potential usefulness of Query Theory in examining thought processes of respondents in valuation studies.

State of the Art of Contingent Valuation

2020 ◽  
Author(s):  
Tim Haab ◽  
Lynne Lewis ◽  
John Whitehead
Keyword(s):  
Contingent Valuation ◽  
Discrete Choice ◽  
Stated Preference ◽  
Revealed Preference ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Actual Behavior ◽  
Stated Preference Methods ◽  
Valuation Methods ◽  
Hypothetical Scenario

The contingent valuation method (CVM) is a stated preference approach to the valuation of non-market goods. It has a 50+-year history beginning with a clever suggestion to simply ask people for their consumer surplus. The first study was conducted in the 1960s and over 10,000 studies have been conducted to date. The CVM is used to estimate the use and non-use values of changes in the environment. It is one of the more flexible valuation methods, having been applied in a large number of contexts and policies. The CVM requires construction of a hypothetical scenario that makes clear what will be received in exchange for payment. The scenario must be realistic and consequential. Economists prefer revealed preference methods for environmental valuation due to their reliance on actual behavior data. In unguarded moments, economists are quick to condemn stated preference methods due to their reliance on hypothetical behavior data. Stated preference methods should be seen as approaches to providing estimates of the value of certain changes in the allocation of environmental and natural resources for which no other method can be used. The CVM has a tortured history, having suffered slings and arrows from industry-funded critics following the Exxon Valdez and British Petroleum (BP)–Deepwater Horizon oil spills. The critics have harped on studies that fail certain tests of hypothetical bias and scope, among others. Nonetheless, CVM proponents have found that it produces similar value estimates to those estimated from revealed preference methods such as the travel cost and hedonic methods. The CVM has produced willingness to pay (WTP) estimates that exhibit internal validity. CVM research teams must have a range of capabilities. A CVM study involves survey design so that the elicited WTP estimates have face validity. Questionnaire development and data collection are skills that must be mastered. Welfare economic theory is used to guide empirical tests of theory such as the scope test. Limited dependent variable econometric methods are often used with panel data to test value models and develop estimates of WTP. The popularity of the CVM is on the wane; indeed, another name for this article could be “the rise and fall of CVM,” not because the CVM is any less useful than other valuation methods. It is because the best practice in the CVM is merging with discrete choice experiments, and researchers seem to prefer to call their approach discrete choice experiments. Nevertheless, the problems that plague discrete choice experiments are the same as those that plague contingent valuation. Discrete choice experiment–contingent valuation–stated preference researchers should continue down the same familiar path of methods development.

Design of Discrete Choice Experiments

2019 ◽  
Author(s):  
Deborah J. Street ◽  
Rosalie Viney
Keyword(s):  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Choice Experiments ◽  
Individual Characteristics ◽  
Discrete Choice Experiments ◽  
Random Component ◽  
Choice Sets ◽  
Attribute Levels ◽  
Choice Set

Discrete choice experiments are a popular stated preference tool in health economics and have been used to address policy questions, establish consumer preferences for health and healthcare, and value health states, among other applications. They are particularly useful when revealed preference data are not available. Most commonly in choice experiments respondents are presented with a situation in which a choice must be made and with a a set of possible options. The options are described by a number of attributes, each of which takes a particular level for each option. The set of possible options is called a “choice set,” and a set of choice sets comprises the choice experiment. The attributes and levels are chosen by the analyst to allow modeling of the underlying preferences of respondents. Respondents are assumed to make utility-maximizing decisions, and the goal of the choice experiment is to estimate how the attribute levels affect the utility of the individual. Utility is assumed to have a systematic component (related to the attributes and levels) and a random component (which may relate to unobserved determinants of utility, individual characteristics or random variation in choices), and an assumption must be made about the distribution of the random component. The structure of the set of choice sets, from the universe of possible choice sets represented by the attributes and levels, that is shown to respondents determines which models can be fitted to the observed choice data and how accurately the effect of the attribute levels can be estimated. Important structural issues include the number of options in each choice set and whether or not options in the same choice set have common attribute levels. Two broad approaches to constructing the set of choice sets that make up a DCE exist—theoretical and algorithmic—and no consensus exists about which approach consistently delivers better designs, although simulation studies and in-field comparisons of designs constructed by both approaches exist.

Präferenzen von Patienten, pflegenden Angehörigen, Leistungserbringern, Kostenträgern und politischen Entscheidungsträgern bei der integrierten Versorgung in Deutschland: Ein Discrete Choice Experiment

2021 ◽  
Author(s):  
Verena Struckmann ◽  
Verena Vogt ◽  
Julia Köppen ◽  
Theresa Meier ◽  
Maaike Hoedemakers ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Pflegende Angehörige ◽  
Triple Aim ◽  
Körperliche Funktionsfähigkeit

Zusammenfassung Ziel Ziel dieser Studie ist Präferenzen zu erheben, die multimorbide Patienten, pflegende Angehörige, Leistungserbringer, Kostenträger oder politische Entscheidungsträger verschiedenen Endpunkten von integrierten Versorgungsprogrammen (IV-Programmen) in Deutschland beimessen und diese zu vergleichen. Methodik Mit Hilfe eines Discrete Choice Experiments (DCE) wurden die Präferenzen der Befragten für die Endpunkte von zwei IV-Programmen ermittelt. Jedes IV-Programm wurde anhand von Attributen, bzw.Endpunkten präsentiert, die das „Triple Aim“ abbilden. Sie waren in die Endpunkte Wohlbefinden, Erfahrung mit Versorgung und Kosten unterteilt, mit insgesamt acht Attributen und jeweils drei Ausprägungen. Ergebnisse Die Ergebnisse von 676 Fragebögen zeigen, dass die Attribute „Lebensfreude“ und „Kontinuität der Versorgung“ interessengruppenübergreifend die höchsten Bewertungen erhalten. Am geringsten blieben die relativen Bewertungen für alle Interessengruppen bei dem Attribut „Kosten“. Die Präferenzen der Leistungserbringer und pflegenden Angehörigen unterschieden sich am deutlichsten von denen der Patienten. Diese Unterschiede betrafen meist die „körperliche Funktionsfähigkeit“, die von Patienten am höchsten bewertet wurde, die „Personenzentrierung“ und „Kontinuität der Versorgung“, die die höchsten Bewertungen von den Leistungserbringern erhielten. Schlussfolgerung Die identifizierten Präferenzheterogenitäten in Bezug auf die Endpunkte von IV-Programmen zwischen den Interessengruppen verdeutlichen, wie wichtig es für eine optimale Ausgestaltung von IV-Programmen ist, Vertreter der Praxis und politische Entscheidungsträger über die unterschiedlichen Perspektiven zu informieren. Die Ergebnisse unterstreichen zudem die Relevanz von gemeinsamen Entscheidungsfindungs- und Abstimmungsprozessen zwischen Leistungserbringern, pflegenden Angehörigen und Patienten.

scholarly journals The impact of health vs. non-health goals on individuals’ lifestyle program choices: a discrete choice experiment approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Tim M. Benning ◽  
Benedict G. C. Dellaert ◽  
Theo A. Arentze
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Choice Experiments ◽  
Discrete Choice Experiments ◽  
Experimental Conditions ◽  
Health Goals ◽  
Health Goal ◽  
Lifestyle Program ◽  
The Impact

Abstract Background Goals play an important role in the choices that individuals make. Yet, there is no clear approach of how to incorporate goals in discrete choice experiments. In this paper, we present such an approach and illustrate it in the context of lifestyle programs. Furthermore, we investigate how non-health vs. health goals affect individuals’ choices via non-goal attributes. Methods We used an unlabeled discrete choice experiment about lifestyle programs based on two experimental conditions in which either a non-health goal (i.e., looking better) or a health goal (i.e., increasing life expectancy) was presented to respondents as a fixed attribute level for the goal attribute. Respondents were randomly distributed over the experimental conditions. Eventually, we used data from 407 Dutch adults who reported to be overweight (n = 212 for the non-health goal, and n = 195 for the health goal). Results Random parameter logit model estimates show that the type of goal significantly (p < 0.05) moderates the effect that the attribute diet has on lifestyle program choice, but that this is not the case for the attributes exercise per week and expected weight loss. Conclusions A flexible diet is more important for individuals with a non-health goal than for individuals with a health goal. Therefore, we advise policy makers to use information on goal interactions for developing new policies and communication strategies to target population segments that have different goals. Furthermore, we recommend researchers to consider the impact of goals when designing discrete choice experiments.

Nutzung von Conjoint-Analysen zur Messung von Therapiezielpräferenzen aus Patientenperspektive in der Behandlung psychischer Störungen

2015 ◽  
Vol 44 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Frederik Haarig ◽  
Stephan Mühlig
Keyword(s):  
Conjoint Analysis ◽  
Discrete Choice ◽  
Stated Preference ◽  
Choice Experiments ◽  
Chronische Schmerzen ◽  
Discrete Choice Experiments ◽  
Depressive Störungen ◽  
Bipolare Störungen ◽  
Stated Preference Methods

Hintergrund: Im Zuge der wachsenden Bedeutung von Ansätzen zur Patientenorientierung und -partizipation in der Gesundheitsversorgung gewinnt die Bestimmung subjektiver Therapiezielpräferenzen unterschiedlicher Akteure (Patienten, Behandler, Angehörige) zunehmend an Forschungsinteresse. Stated-Preference-Methods ermöglichen die systematische Untersuchung speziell patientenorientierter Fragestellungen. Ziele der Studie: Identifikation und Beschreibung (nach formalen, methodischen und inhaltlichen Merkmalen) von Studien mit Stated-Preference-Methods (Conjoint Measurements, Conjoint Analysis, Discrete Choice Experiments) in der Versorgung von Patienten mit psychischen Störungen mit dem Ziel, eine Bewertung zur Anwendbarkeit der Methode (Potential, Nutzen, Grenzen) in zukünftiger patientenorienterter Forschung abzuleiten. Methode: Systematische Literaturrecherche mit folgenden Studieneinschlusskriterien: Participants: Interventionen zur Behandlung von Patienten mit psychischer Störung; Intervention: psychotherapeutische, psychiatrische, hausärztliche Behandlungen (stationär, teil-stationär, ambulant); Comparison: Studien mit keiner (Ein-Gruppen-Design) oder mindestens einer Kontrollgruppe; Outcomes: conjoint-spezifische Angaben zu Nutzenwerten. Ergebnisse: Conjoint-Analysen werden in unterschiedlichen Forschungsdesigns und unter heterogenen Rahmenbedingungen (Stichprobe, Störungsbild, Setting, Intervention, Zieldimension) zur Messung von Therapiezielpräferenzen eingesetzt. Die Erstellung des Conjoint-Designs erfolgt in der Regel reduziert (orthogonal), mithilfe von Softwarepaketen, die Erhebung als Fragebogen. Schlussfolgerungen: Conjoint-Analysen ermöglichen differenzierte Aussagen über Therapiepräferenzstrukturen auf Basis relationaler Beurteilungsszenarien und stellen damit eine fundiertere Basis zur Verbesserung der Patientenorientierung in der Gesundheitsversorgung zur Verfügung. Die Befundlage belegt, dass sich die Methode zur Untersuchung patientenorientierter Fragestellungen (mehrheitlich zu Pharmakotherapie und Kombinationsbehandlung) in der Versorgung psychischer Störungen (depressive Störungen, ADHS, Schizophrenie, bipolare Störungen, Tabak- und Alkoholabhängigkeit und chronische Schmerzen) eignet. Allerdings ist der erfolgreiche Einsatz der Methodik an einige Voraussetzungen geknüpft (u. a. Unabhängigkeit der betrachteten Therapiezielaspekte, Designkomplexität). Forschungsbedarf besteht u. a. im Hinblick auf bisher nicht untersuchte Störungsbilder (u. a. somatoforme, Angst-, Ess-, Persönlichkeitsstörungen) und Interventionen (u. a. reine Psychotherapie, störungsspezifische Behandlungen).

Using a stated preference discrete choice experiment to assess societal value from the perspective of decision-makers in Europe. Does it work for rare diseases?

Health Policy ◽  
2019 ◽  
Vol 123 (2) ◽  
pp. 152-158 ◽  
Author(s):  
J. López-Bastida ◽  
J.M. Ramos-Goñi ◽  
I. Aranda-Reneo ◽  
M. Trapero-Bertran ◽  
P. Kanavos ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Rare Diseases ◽  
Choice Experiment ◽  
Stated Preference ◽  
Decision Makers ◽  
Societal Value

scholarly journals Understanding uptake of digital health products: Methodology tutorial for a Discrete Choice Experiment using a Bayesian efficient design (Preprint)

2021 ◽  
Author(s):  
Dorothy Szinay ◽  
Rory Cameron ◽  
Felix Naughton ◽  
Jennifer A. Whitty ◽  
Jamie Brown ◽  
...  
Keyword(s):  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Stated Preference ◽  
Digital Health ◽  
Revealed Preferences ◽  
Quantitative Methodology ◽  
Efficient Design ◽  
Trade Offs ◽  
Health Products

UNSTRUCTURED Understanding the preferences of potential users of digital health products is beneficial for digital health policy and planning. Stated preference methods could help elicit individuals’ preferences in the absence of observational data. A discrete choice experiment (DCE) is a commonly used stated preference method; a quantitative methodology that argues that individuals make trade-offs when engaging in a decision by choosing an alternative of a product or service that offers the greatest utility, or benefit. This methodology is widely used in health economics in situations where revealed preferences are difficult to collect but is much less used in the field of digital health. This article outlines the stages involved in developing a discrete choice experiment. As a case study, it uses the application of a DCE for revealing preferences in targeting the uptake of smoking cessation apps. It describes the establishment of attributes, the construction of choice tasks of two or more alternatives, and the development of the experimental design. This tutorial offers a guide for researchers with no prior knowledge of this research technique.

scholarly journals Understanding Midwives’ Preferences for Providing Information About Newborn Bloodspot Screening

2018 ◽  
Vol 3 (1) ◽  
pp. 238146831774617 ◽  
Author(s):  
Stuart James Wright ◽  
Fiona Ulph ◽  
Tina Lavender ◽  
Nimarta Dharni ◽  
Katherine Payne
Keyword(s):  
Conjoint Analysis ◽  
Discrete Choice Experiment ◽  
Discrete Choice ◽  
Choice Experiment ◽  
Screening Program ◽  
Stated Preference ◽  
Information Provision ◽  
Stated Preference Survey ◽  
Newborn Bloodspot Screening ◽  
Types Of Information

Background: Understanding preferences for information provision in the context of health care service provision is challenging because of the number of potential attributes that may influence preferences. This study aimed to identify midwives’ preferences for the process and outcomes of information provision in an expanded national newborn bloodspot screening program. Design: A sample of practicing midwives completed a hybrid-stated preference survey including a conjoint analysis (CA) and discrete choice experiment to quantify preferences for the types of, and way in which, information should be provided in a newborn bloodspot screening program. Six conjoint analysis questions captured the impact of different types of information on parents’ ability to make a decision, and 10 discrete choice experiment questions identified preferences for four process attributes (including parents’ ability to make a decision). Results: Midwives employed by the UK National Health Service (n = 134) completed the survey. All types of information content were perceived to improve parents’ ability to make a decision except for the possibility of false-positive results. Late pregnancy was seen to be the best time to provide information, followed by day 3 postbirth. Information before 20 weeks of pregnancy was viewed as reducing parents’ ability to make a decision. Midwives preferred information to be provided by an individual discussion and did not think parents should receive information on the Internet. Conclusion: A hybrid stated preference survey design identified that a wide variety of information should be provided to maximize parents’ ability to make a decision ideally provided late in pregnancy or on day 3 postbirth.

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