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Purpose: Traditionally, researchers have relied on eliciting preferences through face-to-face interviews. Recently, there has been a shift towards using internet-based methods. Different methods of data collection may be a source of variation in the results. In this study, we compare the preferences for the Adult Social Care Outcomes Toolkit (ASCOT) service user measure elicited using best-worst scaling (BWS) via a face-to-face interview and an online survey.\ud Methods: Data were collected from a representative sample of the general population in England. The respondents (face-to-face: n=500; online: n=1,001) completed a survey, which included the BWS experiment involving the ASCOT measure. Each respondent received eight best-worst scenarios and made four choices (best, second best, worst, second worst) in each scenario. Multinomial logit regressions were undertaken to analyse the data taking into account differences in the characteristics of the two samples and the repeated nature of the data.\ud Results: We initially found a number of small significant differences in preferences between the two methods across all ASCOT domains. These differences were substantially reduced – from 15 to five out of 30 coefficients being different at the 5% level – and remained small in value after controlling for differences in observable and unobservable characteristics of the two samples.\ud Conclusions: This comparison demonstrates that face-to-face and internet surveys may lead to fairly similar preferences for social care-related quality of life when differences in sample characteristics are controlled for. With or without a constant sampling frame, studies should carefully design the BWS exercise and provide similar levels of clarification to participants in each survey to minimise the amount of error variance in the choice process.

Aging societies are confronted by a growing need for adequate and reliable long-term care (LTC). Against the background of limited financial resources, measurement of LTC outcomes has attracted increasing interest. This chapter discusses how conventional and behavioral economic approaches contribute to our understanding of LTC outcomes and how LTC outcomes can be measured. A production-centered approach is used to identify final LTC outcomes that indicate the impact of LTC on care recipients’ and carers’ quality of life (QoL). Conventional economic approaches assume rational, utility-maximizing individuals as well as perfect markets and suggest that it is possible to measure LTC outcomes indirectly, using revealed-preference approaches, with preferences inferred from care recipients’ choices. However, behavioral economics holds that revealed preferences are not a reliable starting point for measuring QoL gains from LTC services. At the same time, behavioral economists also caution against taking self-assessments of QoL outcomes and stated-preference approaches in economic evaluation at face value. Whichever approach is being followed to measure LTC outcomes, at least three issues need to be addressed: specifying LTC outcome domains, eliciting preferences for these domains, and measuring the impact of LTC on QoL.