EVALUATING DATA QUALITY IN DISCRETE CHOICE EXPERIMENTS
2019-12-03T01:08:48Z (GMT) by
Although data collection through discrete choice experimentsconducted using surveys are commonly used in research, aimingto improve data quality is still serviceable and necessary. Three distinct experiments were conducted with the objectives of improving data quality by better tailoring experiments to market conditionsas well as decreasing complexity and fatigue. First, consumer willingness-to-pay(WTP) for yogurt attributes was estimatedusing a survey targeted to be nationally representative of the US.A novel approach was used to allow for self-selection into the choice experiment for commonly purchased types of yogurt.On average, respondentswere willing-to-paya positive amount for requiring pasture access and not permitting dehorning/disbudding for both traditional and Greek yogurt. Respondents had positive WTPfor Greek yogurt labeled free of high fructose corn syrup, and were willing-to-pay morefor low-fat yogurt when compared to nonfat for both yogurt types.
Second, anew WTP data collection method, employing component discrete choice experiments in place of traditional larger experimental designs,was proposedand compared to the traditional method to elicit yogurt consumer’s WTP for attributes in yogurt. The new WTP data collection method was designed with the objective of decreasing complexity by having respondents participate in fewer choice scenarios. Incidences of attribute non-attendance (ANA), a potential simplifying heuristic that results from complexity, occurred less frequently for all attributes in the new WTP data collection method with one exception. Exhibiting ANA for any attribute was negatively correlated with the time respondents took to complete the choice experiment.
Finally, through the use of a newbest-worst scaling(BWS)data collection method,consumer preferences for fluid dairy milk attributes were elicited and results as well as measures of data quality were compared to the traditional method of BWS. Nine attributes of fluid milk were included in this study: container material, rbST-free, price, container size, fat content, humane handling of cattle, brand, required pasture access for cattle, and cattle fed an organic diet. The top (price) and bottom (container material) attributes in terms of relative ranking did not change between the new BWS data collection method and the traditional BWS method. The new BWS data collection method resulted in fewer incidences of ANA for all attributes except one. There was not a statistical difference in the number of transitivity (an axiom of consumer theory) violators,between the new and traditional BWS methods.