Abstract
BackgroundExperimental designs constitute a vital component of all Stated Choice (aka discrete choice experiment) studies. However, there exists limited empirical evaluation of the statistical benefits of Stated Choice (SC) experimental designs that employ non-zero prior estimates in constructing non-orthogonal constrained designs. This paper statistically compares the performance of contrasting SC experimental designs. In so doing, the effect of respondent literacy on patterns of Attribute non-Attendance (ANA) across fractional factorial orthogonal and efficient designs is also evaluated. The study uses a ‘real’ SC design to model consumer choice of primary health care providers in rural north India. A total of 623 respondents were sampled across four villages in Uttar Pradesh, India.MethodsComparison of orthogonal and efficient SC experimental designs is based on several measures. Appropriate comparison of each design’s respective efficiency measure is made using D-error results. Standardised Akaike Information Criteria are compared between designs and across recall periods. Comparisons control for stated and inferred ANA. Coefficient and standard error estimates are also compared.ResultsThe added complexity of the efficient SC design, theorised elsewhere, is reflected in higher estimated amounts of ANA among illiterate respondents. However, controlling for ANA using stated and inferred methods consistently shows that the efficient design performs statistically better. Modelling SC data from the orthogonal and efficient design shows that model-fit of the efficient design outperform the orthogonal design when using a 14-day recall period. The performance of the orthogonal design, with respect to standardised AIC model-fit, is better when longer recall periods of 30-days, 6-months and 12-months are used.ConclusionsThe effect of the efficient design’s cognitive demand is apparent among literate and illiterate respondents, although, more pronounced among illiterate respondents.This study empirically confirms that relaxing the orthogonality constraint of SC experimental designs increases the information collected in choice tasks, subject to the accuracy of the non-zero priors in the design and the correct specification of a ‘real’ SC recall period.
Highlights
IntroductionExperimental designs constitute a vital component of all Stated Choice (aka discrete choice experiment) studies
Experimental designs constitute a vital component of all Stated Choice studies
A multinomial logit (MNL) model is used to identify the determinants of consumer choice of health providers in rural north India
Summary
Experimental designs constitute a vital component of all Stated Choice (aka discrete choice experiment) studies. There exists limited empirical evaluation of the statistical benefits of Stated Choice (SC) experimental designs that employ non-zero prior estimates in constructing non-orthogonal constrained designs. The research focuses of SC studies in this literature are diverse covering a range of perspectives These include: patient preferences for non-market medical interventions, health professional preferences towards prescribing medicines and treatments, health care priority setting and consumer preferences. Different statistical properties and constraints governing the mixing of attribute levels are used in many SC studies across the broad field of applied economics [7,8]. An alternative group of designs are referred to as efficient These designs, assuming non-zero prior parameter estimates, mix attribute levels so as to reduce elements of the Asymptotic Variance-Covariance (AVC) matrix
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