TY - JOUR

T1 - Response time in online stated choice experiments

T2 - the non-triviality of identifying fast and slow respondents

AU - Campbell, Danny

AU - Mørkbak, Morten Raun

AU - Olsen, Søren Bøye

PY - 2017

Y1 - 2017

N2 - In this paper, we use paradata relating to the length of time respondents required in a self-administered online stated preference surveys. Although this issue has been previously explored, there is little guidance on how to identify and deal with ‘fast’ and ‘slow’ respondents. In this paper, we use scale-adjusted latent class models to address preference and variance heterogeneity and explore how class membership varies with response latency. To test our methodology, we use stated choice data collected via an online survey to establish German anglers’ preferences for fishing site attributes in Denmark. Results from our analysis corroborate that response latency has a bearing on the estimates of utility coefficients and the error variance. Although the results highlight the non-triviality of identifying fast and slow respondents, they signal the need to estimate a large number of candidate models to identify the most appropriate ‘fast’ and ‘slow’ thresholds. Not doing so is likely to lead to an inferior model and has repercussions for marginal willingness to pay estimates and choice predictions.

AB - In this paper, we use paradata relating to the length of time respondents required in a self-administered online stated preference surveys. Although this issue has been previously explored, there is little guidance on how to identify and deal with ‘fast’ and ‘slow’ respondents. In this paper, we use scale-adjusted latent class models to address preference and variance heterogeneity and explore how class membership varies with response latency. To test our methodology, we use stated choice data collected via an online survey to establish German anglers’ preferences for fishing site attributes in Denmark. Results from our analysis corroborate that response latency has a bearing on the estimates of utility coefficients and the error variance. Although the results highlight the non-triviality of identifying fast and slow respondents, they signal the need to estimate a large number of candidate models to identify the most appropriate ‘fast’ and ‘slow’ thresholds. Not doing so is likely to lead to an inferior model and has repercussions for marginal willingness to pay estimates and choice predictions.

U2 - 10.1080/21606544.2016.1167632

DO - 10.1080/21606544.2016.1167632

M3 - Journal article

VL - 6

SP - 17

EP - 35

JO - Journal of Environmental Economics and Policy

JF - Journal of Environmental Economics and Policy

SN - 2160-6544

IS - 1

ER -