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Experimental Design for Discrete Choice Experiments

Chris Skedgel Research Health Economist Atlantic Clinical Cancer Research Unit, Capital Health. Experimental Design for Discrete Choice Experiments. Outline. Discrete choice experiments (DCE) Full factorial designs Fractional factorial designs Blocking the design

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Experimental Design for Discrete Choice Experiments

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  1. Chris Skedgel Research Health Economist Atlantic Clinical Cancer Research Unit, Capital Health Experimental Design for Discrete Choice Experiments

  2. Outline • Discrete choice experiments (DCE) • Full factorial designs • Fractional factorial designs • Blocking the design • Briefly, analysis of choice data in SAS

  3. Discrete choice experiments • Choice-based form of stated preference elicitation • Based on idea that even if people can’t provide a direct measure of value, they can usually indicate which scenario they prefer

  4. DCE example

  5. Experimental design • DCE elicitations rely on an effective experimental design -- the combination of attributes and attribute levels presented to respondents • Degrees of freedom (d.f.)

  6. Full factorial (FF) designs • Number of attribute-level combinations in a simple full factorial design = LA • L: number of levels • A: number of attributes • Each possible attribute-level combination appears once; no correlations • %MktEx(3**6) → 729 combinations

  7. Fractional factorial (FrF) designs • Unless you have few attributes/levels, FF designs likely to be impractical • Fractional factorial (FrF) designs use a subset of the FF design • Orthogonal FrF designs • Optimized FrF designs

  8. Orthogonal FrF designs • Orthogonal FrF designs emphasize statistical independence no correlations • %MktRuns(3**6) ←main effects only

  9. Optimal FrF designs • Optimal FrF designs emphasize statistical efficiency at expense of independence • Maximum information from respondents for a given survey design • Usually some correlation between attributes • Requires model to be pre-specified in order to ensure sufficient d.f.’s and minimal correlation between effects

  10. %MktRuns to identify designs main effects and all 2-way interactions • %MktRuns(3**6, interact=@2) ←Saturated = 73Full Factorial = 729 Some Reasonable Cannot Be Design Sizes Violations Divided By 81 0 162 0 108 15 81 135 15 81 90 35 27 81 99 35 27 81 117 35 27 81 126 35 27 81 144 35 27 81 153 35 27 81 73 S 56 3 9 27 81 S - Saturated Design - The smallest design that can be made. Note that the saturated design is not one of the recommended designs for this problem. It is shown to provide some context for the recommended sizes.

  11. Optimal FrF with %ChoiceEff • %ChoiceEff optimizes FF design subject to specified constraints: • Candidate design (usually FF) • Number of runs (# of choice tasks) • Model to be estimated (main effects, interactions) • Expected β’s (huh?)

  12. Optimal FrF with %ChoiceEff • %ChoiceEffoptimizes design subject to specified constraints: • Candidate design (usually FF) • Number of runs (# of choice tasks) • Model to be estimated (main effects, interactions) • Expected β’s (huh?) • 4 principles of efficient design: • Orthogonality, level balance, minimal overlap, utility balance

  13. %ChoiceEff syntax • %ChoiceEff(data=FF_Logical, /* candidate design */model=class(x1-x6), /* model to be estimated */nsets=&runs, /* total choice sets */flags=f1-f2, /* alternatives per set */maxiter=100, /* optimization iterations */seed=201109,converge=1e-12, options=nodups relative,beta=0); /* expected betas, H0=0 */

  14. Attribute correlations

  15. Blocking the design • Optimized FrF design has 18 choice sets; probably still too many to present to each respondent • Solution is to block the design: %MktBlock(data=best, /* default D-efficient design */nalts=2, /* alternatives per set */nblocks=2, /* number of blocks*/ factors=x1-x6, /* attributes in each alt */seed=201109,out=library.lineardesign2B2A18R_D);

  16. Final D-efficient design

  17. Analyzing DCE responses • Briefly, SAS doesn’t do it well • SAS can estimate McFadden’s conditional choice model (multinomial logistic) using PROC PHREG • Doesn’t account for repeated choices by respondents • DCEs increasingly modelled using multinomial probit or mixed logistic models  Stata, R • Allow for correlated choices within respondents

  18. SAS design macros • Warren Kuhfeld’s Marketing Research Methods in SAS: http://support.sas.com/resources/papers/tnote/tnote_marketresearch.html • Bonus! Comprehensive (1165 pages) manual covering many aspects of stated preference design

  19. Thank you • Questions?cds.accru@gmail.com

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