Erasmus Choice Modelling Centre, Erasmus University Rotterdam, The Netherlands; Erasmus School of Health Policy & Management, Erasmus University Rotterdam, The Netherlands.
Erasmus Choice Modelling Centre, Erasmus University Rotterdam, The Netherlands; Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands.
Value Health. 2018 Jul;21(7):767-771. doi: 10.1016/j.jval.2017.10.002. Epub 2017 Nov 17.
The aim of this study was to test the hypothesis that level overlap and color coding can mitigate or even preclude the occurrence of attribute nonattendance in discrete choice experiments.
A randomized controlled experiment with five experimental study arms was designed to investigate the independent and combined impact of level overlap and color coding on respondents' attribute nonattendance. The systematic differences between the study arms allowed for a direct comparison of observed dropout rates and estimates of the average number of attributes attended to by respondents, which were obtained by using augmented mixed logit models that explicitly incorporated attribute non-attendance.
In the base-case study arm without level overlap or color coding, the observed dropout rate was 14%, and respondents attended, on average, only two out of five attributes. The independent introduction of both level overlap and color coding reduced the dropout rate to 10% and increased attribute attendance to three attributes. The combination of level overlap and color coding, however, was most effective: it reduced the dropout rate to 8% and improved attribute attendance to four out of five attributes. The latter essentially removes the need to explicitly accommodate for attribute non-attendance when analyzing the choice data.
On the basis of the presented results, the use of level overlap and color coding are recommendable strategies to reduce the dropout rate and improve attribute attendance in discrete choice experiments.
本研究旨在验证以下假设，即水平重叠和颜色编码可以减轻甚至消除离散选择实验中属性未被关注的情况。
设计了一项随机对照实验，包含五个实验研究组，旨在研究水平重叠和颜色编码对受访者属性未被关注的独立和综合影响。通过使用明确纳入属性未被关注的增强混合对数模型，对研究组之间的系统差异进行直接比较，获得观察到的辍学率和受访者关注的平均属性数量的估计值。
在没有水平重叠或颜色编码的基础研究组中，观察到的辍学率为 14%，受访者平均只关注五个属性中的两个。独立引入水平重叠和颜色编码将辍学率降低到 10%，并将属性关注度提高到三个属性。然而，水平重叠和颜色编码的组合最为有效：它将辍学率降低到 8%，并将五个属性中的四个属性的关注度提高到四个属性。后者在分析选择数据时基本上消除了明确适应属性未被关注的需要。
根据所呈现的结果，在离散选择实验中，使用水平重叠和颜色编码是减少辍学率和提高属性关注度的推荐策略。
