Optimal design for discrete choice experiments

Graßhoff, Ulrike; Großmann, Heiko; Holling, Heinz; Schwabe, Rainer

doi:10.1016/j.jspi.2012.06.019

Cited by 12 publications

(17 citation statements)

References 6 publications

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“…The continuous variable was unrestricted and used as a “manipulating” attribute to offset dominating alternatives or alternatives with a zero probability of being selected in a choice task. This finding, however, was conditional on the type of quantitative variable and was concluded to be unrealistic in the study [ 32 ]. Details of the studies exploring these design characteristics are presented in Tables 1 a and 1b (Appendix).…”

Section: Resultsmentioning

confidence: 99%

“…The number of statistical efficiency measures, scenarios, and design characteristics varied from study to study. Of the outcomes assessed for each scenario, four studies reported relative D-efficiency [ 1 , [30] , [31] , [32] ], two D-error [ 33 , 34 ], three D b -error (a Bayesian variation of D-error) [ 30 , 35 , 36 ], and two percentage changes in D-error [ 34 , 37 ]. Of the design characteristics explored, one study explored the impact of attributes on statistical efficiency [ 1 ], two explored alternatives [ 1 , 30 ], one explored choice tasks [ 1 ], two explored attribute levels [ 1 , 32 ], two explored choice behaviour [ 33 , 37 ], three explored priors [ 30 , 31 , 34 ], and four explored methods to create the design [ 30 , [34] , [35] , [36] ].…”

Section: Resultsmentioning

confidence: 99%

“…size, weight, or speed). The third alternative differed from the two others in the binary attributes [ 32 ]. The continuous variable was unrestricted and used as a “manipulating” attribute to offset dominating alternatives or alternatives with a zero probability of being selected in a choice task.…”

Section: Resultsmentioning

confidence: 99%

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Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey

Vanniyasingam

Daly

Jin

et al. 2018

Contemporary Clinical Trials Communications

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ObjectivesThis study reviews simulation studies of discrete choice experiments to determine (i) how survey design features affect statistical efficiency, (ii) and to appraise their reporting quality.OutcomesStatistical efficiency was measured using relative design (D-) efficiency, D-optimality, or D-error.MethodsFor this systematic survey, we searched Journal Storage (JSTOR), Since Direct, PubMed, and OVID which included a search within EMBASE. Searches were conducted up to year 2016 for simulation studies investigating the impact of DCE design features on statistical efficiency. Studies were screened and data were extracted independently and in duplicate. Results for each included study were summarized by design characteristic. Previously developed criteria for reporting quality of simulation studies were also adapted and applied to each included study.ResultsOf 371 potentially relevant studies, 9 were found to be eligible, with several varying in study objectives. Statistical efficiency improved when increasing the number of choice tasks or alternatives; decreasing the number of attributes, attribute levels; using an unrestricted continuous “manipulator” attribute; using model-based approaches with covariates incorporating response behaviour; using sampling approaches that incorporate previous knowledge of response behaviour; incorporating heterogeneity in a model-based design; correctly specifying Bayesian priors; minimizing parameter prior variances; and using an appropriate method to create the DCE design for the research question. The simulation studies performed well in terms of reporting quality. Improvement is needed in regards to clearly specifying study objectives, number of failures, random number generators, starting seeds, and the software used.ConclusionThese results identify the best approaches to structure a DCE. An investigator can manipulate design characteristics to help reduce response burden and increase statistical efficiency. Since studies varied in their objectives, conclusions were made on several design characteristics, however, the validity of each conclusion was limited. Further research should be conducted to explore all conclusions in various design settings and scenarios. Additional reviews to explore other statistical efficiency outcomes and databases can also be performed to enhance the conclusions identified from this review.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey

Vanniyasingam

Daly

Jin

et al. 2018

Contemporary Clinical Trials Communications

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show abstract

“…Van Der Pol et al 2014presented the systematic components of the utility defined as linear functions, quadratic functions, or as stepwise functions of the attributes. Grasshoff et al (2013) defined the functions as regression functions of the attributes and attribute-levels in the model.…”

Section: Functional Form Of Attribute-level Best-worst Discrete Choicmentioning

confidence: 99%

Dynamic Attribute-Level Best Worst Discrete Choice Experiments

Alqawba¹,

Diawara²

2019

IJMS

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Dynamic modelling of decision maker choice behavior of best and worst in discrete choice experiments (DCEs) has numerous applications. Such models are proposed under utility function of decision maker and are used in many areas including social sciences, health economics, transportation research, and health systems research. After reviewing references on the study of such experiments, we present example in DCE with emphasis on time dependent best-worst choice and discrimination between choice attributes. Numerical examples of the dynamic DCEs are simulated, and the associated expected utilities over time of the choice models are derived using Markov decision processes. The estimates are computationally consistent with decision choices over time.

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“…choice models with larger choice sizes, more attribute levels from discrete as well as continuous attributes [7] should be analyzed.…”

Section: Further Researchmentioning

confidence: 99%

‘The usefulness of Bayesian optimal designs for discrete choice experiments’ by Roselinde Kessels, Bradley Jones, Peter Goos and Martina Vandebroek

Holling¹,

Schwabe

2011

Appl. Stochastic Models Bus. Ind.

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Optimal design for discrete choice experiments

Cited by 12 publications

References 6 publications

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey

Investigating the impact of design characteristics on statistical efficiency within discrete choice experiments: A systematic survey

Dynamic Attribute-Level Best Worst Discrete Choice Experiments

‘The usefulness of Bayesian optimal designs for discrete choice experiments’ by Roselinde Kessels, Bradley Jones, Peter Goos and Martina Vandebroek

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