Comparison of Preferences and Data Quality between Discrete Choice Experiments Conducted in Online and Face-to-Face...

Discrete choice experiments (DCE) are increasingly being conducted using online panels. However, the comparability of such DCE-based preferences to traditional modes of data collection (e.g., in-person) is not well established. In this study, supervised, face-to-face DCE was compared with its unsupervised, online facsimile on face validity, respondent behavior, and modeled preferences.

Data from face-to-face and online EQ-5D-5L health state valuation studies were compared, in which each used the same experimental design and quota sampling procedure. Respondents completed 7 binary DCE tasks comparing 2 EQ-5D-5L health states presented side by side (health states A and B). Data face validity was assessed by comparing preference patterns as a function of the severity difference between 2 health states within a task. The prevalence of potentially suspicious choice patterns (i.e., all As, all Bs, and alternating As/Bs) was compared between studies. Preference data were modeled using multinomial logit regression and compared based on dimensional contribution to overall scale and importance ranking of dimension-levels.

One thousand five Online respondents and 1,099 face-to-face screened (F2F_S) respondents were included in the main comparison of DCE tasks. Online respondents reported more problems on all EQ-5D dimensions except for Mobility. The face validity of the data was similar between comparators. Online respondents had a greater prevalence of potentially suspicious DCE choice patterns ([Online]: 5.3% [F2F_S] 2.9%, P = 0.005). When modeled, the relative contribution of each EQ-5D dimension differed between modes of administration. Online respondents weighed Mobility more importantly and Anxiety/Depression less importantly.

Although assessments of face validity were similar between Online and F2F_S, modeled preferences differed. Future analyses are needed to clarify whether differences are attributable to preference or data quality variation between modes of data collection.