2014
DOI: 10.1186/1472-6963-14-235
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Developing attributes and attribute-levels for a discrete choice experiment on micro health insurance in rural Malawi

Abstract: BackgroundDiscrete choice experiments (DCEs) are attribute-driven experimental techniques used to elicit stakeholders’ preferences to support the design and implementation of policy interventions. The validity of a DCE, therefore, depends on the appropriate specification of the attributes and their levels. There have been recent calls for greater rigor in implementing and reporting on the processes of developing attributes and attribute-levels for discrete choice experiments (DCEs). This paper responds to such… Show more

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Cited by 84 publications
(111 citation statements)
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“…A well-designed DCE has been described as 'one that has sufficiently rich set of attributes and choice contexts, together with enough variation in the factor levels necessary to produce meaningful behavioural responses'. 27 Abiiro et al 85 advise employing quantitative methods, such as ranking exercises, to support the process of selecting factors for inclusion to a manageable number. In the first instance, the expert multidisciplinary panel in the current study (consisting of three stroke physicians who undertake clinical research, a trainee stroke physician, two patient representatives, two health psychologists, two health economists and an expert in shared decision-making) screened the list of factors in terms of whether they would be feasible or meaningful to include in a DCE, to be further scrutinised using a structured prioritisation exercise (SPE).…”
Section: Stage 2: Expert Panel Discussion -Inclusion and Exclusion Cmentioning
confidence: 99%
“…A well-designed DCE has been described as 'one that has sufficiently rich set of attributes and choice contexts, together with enough variation in the factor levels necessary to produce meaningful behavioural responses'. 27 Abiiro et al 85 advise employing quantitative methods, such as ranking exercises, to support the process of selecting factors for inclusion to a manageable number. In the first instance, the expert multidisciplinary panel in the current study (consisting of three stroke physicians who undertake clinical research, a trainee stroke physician, two patient representatives, two health psychologists, two health economists and an expert in shared decision-making) screened the list of factors in terms of whether they would be feasible or meaningful to include in a DCE, to be further scrutinised using a structured prioritisation exercise (SPE).…”
Section: Stage 2: Expert Panel Discussion -Inclusion and Exclusion Cmentioning
confidence: 99%
“…This study employed a multi‐stage attribute development methodology incorporating a literature review, semi‐structured interviews and a review panel. A flow‐chart outlining this process is presented in Figure .…”
Section: Methodsmentioning
confidence: 99%
“…In a DCE, the time taken to choose each preferred scenario and the cognitive burden placed on participants is a product of the number of attributes and their levels. Alongside such design considerations, the review panel sought to limit attributes and their levels to the smallest valid number to reduce the participant cognitive burden . They also ensured that the terminology used to describe the attributes and attribute levels were realistic, policy‐relevant and understandable (that is, written in simple language consistent with that used by the participants).…”
Section: Methodsmentioning
confidence: 99%
“…In the fourth step, we defined the final list of attributes. We followed the recommendation of Abiiro et al (2014), i.e. a relatively low number of attributes keeps the number of choice cards manageable for respondents.…”
Section: Developing Attributes and Attribute Levelsmentioning
confidence: 99%
“…The validity of a choice experiment depends on the appropriate specification of the attributes and their levels (Abiiro et al 2014). Therefore, a systematic process of developing and selecting the final short-list of attributes about RAS adoption was designed.…”
Section: Data Issuesmentioning
confidence: 99%