Choice certainty, consistency, and monotonicity in discrete choice experiments

Mattmann, Matteo; Logar, Ivana; Brouwer, Roy

doi:10.1080/21606544.2018.1515118

Cited by 24 publications

(18 citation statements)

References 47 publications

(66 reference statements)

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“…Differences in hypothetical and real choices are only found for 16% of all choice occasions. As such, choice consistency between the hypothetical and real experiment is higher than the choice consistency rates generally found in hypothetical choice experiments where the same choice task is repeated in a choice sequence to test preference stability and choice consistency (e.g., Brouwer, Dekker, Rolfe, & Windle, 2010;Mattmann, Logar, & Brouwer, 2019). This raises the question how conscious respondents were of the fact that they were asked to make the same choices again in the real experiment compared to the previous hypothetical choices.…”

Section: Discussionmentioning

confidence: 91%

Testing hypothetical bias in a framed field experiment

Brouwer

Tarfasa

2020

Canadian J Agri Economics

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Hypothetical bias is tested based on inter‐ and intra‐respondent comparisons of choice behavior, applying a hypothetical and real choice experiment. The inter‐respondent comparison commonly applied in the environmental and agricultural economics literature consists of a control group of buyers who are asked to hypothetically choose between conventional and organic beans and an experimental group of buyers who are endowed to purchase the same beans using an identical experimental design. Hypothetical bias is tested by comparing inter‐ and intra‐respondents’ (i) hypothetical and real choices, (ii) preference parameters of the estimated choice models related to hypothetical and real choices, and (iii) hypothetical and real willingness to pay (WTP). Choices in the experimental group are highly consistent when switching from hypothetical to real choices for this study's homegrown goods. However, after being endowed, the price sensitivity of lower income households drops, suggesting a house money effect. WTP derived from actual purchases is higher than WTP based on hypothetical choices, indicating a negative hypothetical bias, but differences are only significant in the case of the inter‐respondent comparison. Actual prices paid by respondents in the field experiment appear to be considerably lower than the estimated WTP values and yield a mixed picture of hypothetical bias.

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Section: Discussionmentioning

confidence: 91%

Testing hypothetical bias in a framed field experiment

Brouwer

Tarfasa

2020

Canadian J Agri Economics

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“…These reports must therefore be reliable. Experimental control of factors that might modulate responses to and stated choice certainty for each item should, therefore, be considered in the experimental design ( Mattmann et al, 2019 ). This would allow examination of, for example, whether knowledge of having to state choice certainty influences item responses, how stable item responses are with and without this requirement across testing (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This would allow examination of, for example, whether knowledge of having to state choice certainty influences item responses, how stable item responses are with and without this requirement across testing (e.g. test-retest) and different surveys, depending on what information the subject is asked, and how this requirement influences consistency of responses across the items of a survey ( Mattmann et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Knowledge extraction from pointer movements and its application to detect uncertainty

Cepeda

Dias

Rindlisbacher

et al. 2021

Heliyon

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“…There appear to be three primary methods to measure or assess participant understanding in the health DCE literature: qualitative methods in pretesting, rationality tests within the DCE, and self-reporting within the DCE. We did not find any studies including econometric approaches, such as the analysis of attribute non-attendance [ 28 ] or controlling for inconsistency and fatigue by allowing for unobserved preferences or scale heterogeneity [ 17 , 117 ]. Regardless of the method used to assess understanding, the reporting of these methods, their results, and the implications for analysis and interpretation are not consistent.…”

Section: Discussionmentioning

confidence: 99%

Respondent Understanding in Discrete Choice Experiments: A Scoping Review

Pearce

Harrison

Watson

et al. 2020

Patient

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Introduction Despite the recognised importance of participant understanding for valid and reliable discrete choice experiment (DCE) results, there has been limited assessment of whether, and how, people understand DCEs, and how 'understanding' is conceptualised in DCEs applied to a health context. Objectives Our aim was to identify how participant understanding is conceptualised in the DCE literature in a health context. Our research questions addressed how participant understanding is defined, measured, and used. Methods Searches were conducted (June 2019) in the MEDLINE, EMBASE, PsychINFO and Econlit databases, as well as hand searching. Search terms were based on previous DCE systematic reviews, with additional understanding keywords used in a proximity-based search strategy. Eligible studies were peer-reviewed journal articles in the field of health, related to DCE or best-worst scaling type 3 (BWS3) studies, and reporting some consideration or assessment of participant understanding. A descriptive analytical approach was used to chart relevant data from each study, including publication year, country, clinical area, subject group, sample size, study design, numbers of attributes, levels and choice sets, definition of understanding, how understanding was tested, results of the understanding tests, and how the information about understanding was used. Each study was categorised based on how understanding was conceptualised and used within the study. Results Of 306 potentially eligible articles identified, 31 were excluded based on titles and abstracts, and 200 were excluded on full-text review, resulting in 75 included studies. Three categories of study were identified: applied DCEs (n = 52), pretesting studies (n = 7) and studies of understanding (n = 16). Typically, understanding was defined in relation to either the choice context, such as attribute terminology, or the concept of choosing. Very few studies considered respondents' engagement as a component of understanding. Understanding was measured primarily through qualitative pretesting, rationality or validity tests included in the survey, and participant self-report, however reporting and use of the results of these methods was inconsistent. Conclusions Those conducting or using health DCEs should carefully select, justify, and report the measurement and potential impact of participant understanding in their specific choice context. There remains scope for research into the different components of participant understanding, particularly related to engagement, the impact of participant understanding on DCE validity and reliability, the best measures of understanding, and methods to maximise participant understanding.

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Choice certainty, consistency, and monotonicity in discrete choice experiments

Cited by 24 publications

References 47 publications

Testing hypothetical bias in a framed field experiment

Testing hypothetical bias in a framed field experiment

Knowledge extraction from pointer movements and its application to detect uncertainty

Respondent Understanding in Discrete Choice Experiments: A Scoping Review

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