Hypothetical bias mitigation in representative and convenience samples

Abstract

This is a case study comparing outcomes for a probability-based representative sample versus a nonprobability convenience sample for the valuation of beach condition information among Gulf of Mexico residents. We test the efficacy of several techniques used to adjust for hypothetical bias and sample weighting to reduce hypothetical willingness to pay (WTP). Weighting makes the WTP between the two samples similar, but model equivalence with respect to the significance of explanatory variables is rejected. The results support the use of certainty follow-ups, which consistently reduced WTP, regardless of the sampling approach or weighting.

Impact of despotic leadership and workplace incivility on innovative work behavior of employees: Application of mediation-moderation model

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Integration of Service-Learning Theory and Social Capital Theory in Volunteering Work for Sustainable Development: A Study of the Role of Education Curricula in Saudi Arabia

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Battling bots: Experiences and strategies to mitigate fraudulent responses in online surveys

“…Similarly, many researchers have taken advantage of conducting online surveys by purchasing responses through various marketing panels or platforms, for example, Qualtrics, Amazon's Mechanical Turk (MTurk), or Google Surveys. However, online surveys more acutely face issues affecting data quality, such as inattention or carelessness of participants (Cheng et al, 2022;Gao et al, 2016;Malone & Lusk, 2018a), nonrepresentative samples (Penn et al, 2023;Sandstrom et al, 2023;Whitehead et al, 2023) and fraudulent responses (Belliveau & Yakovenko, 2022;Chmielewski & Kucker, 2020;Griffin et al, 2021;Kramer et al, 2014;Rommel et al, 2022;Storozuk et al, 2020;Teitcher et al, 2015).…”