Abstract Best-worst scaling (BWS) has become so useful that practitioners feel pressure to include ever more items in their experiments. Researchers wanting more items and enough observations of each item by each respondent to support individual respondent-level utility models may greatly increase the burden on respondents, resulting in respondent fatigue and potentially in lower quality responses. Wirth and Wolfrath (2012) proposed two methods for creating BWS designs that allow for large numbers of items and respondent-level utility estimation, Sparse and Express BWS. This study aims to uncover the recommended approach when the goal is recovering individual respondent-level utilities and intends to do so by comparing the relative ability of Sparse and Express BWS to capture the utilities that would have resulted from a full BWS experiment, one with at least three observations of each item by each respondent. The current study repeats previous comparisons of Sparse and Express BWS using a new empirical data set. It also extends previous findings by collecting enough observations from each respondent for both a full experiment and one of the proposed methods, Express BWS and Sparse BWS. The results replicate and extend previous findings regarding the superior ability of the Sparse BWS methodology, relative to Express, to reproduce “known” utilities or utilities that result from a full BWS design.

中文翻译：

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最坏缩放比例，包含许多项

摘要最差标度（BWS）变得如此有用，以至于从业者感到压力很大，他们需要在实验中包括更多的项目。研究人员希望每个受访者提供更多的项目以及对每个项目的足够观察以支持单个受访者级别的实用程序，这可能会大大增加受访者的负担，从而导致受访者感到疲劳，并可能降低质量。Wirth和Wolfrath（2012）提出了两种用于创建BWS设计的方法，即稀疏和Express BWS，该方法可用于大量项目和响应者级别的效用估算。这项研究的目的是在目标是恢复单个响应者级别的实用程序时发现建议的方法，并打算通过比较稀疏和Express BWS捕获完整的BWS实验所产生的实用程序的相对能力来做到这一点，每个受访者对每个项目的至少三项观察结果。本研究使用新的经验数据集重复了先前对稀疏和快速BWS的比较。它还通过收集来自每个受访者的充分观察结果来扩展先前的发现，以进行完整的实验和提议的方法之一，即Express BWS和Sparse BWS。这些结果重复并扩展了先前的发现，即有关稀疏BWS方法相对于Express的卓越能力，可以再现“已知”实用程序或由完整BWS设计产生的实用程序。它还通过收集来自每个受访者的充分观察结果来扩展先前的发现，以进行完整的实验和提议的方法之一，即Express BWS和Sparse BWS。这些结果重复并扩展了先前的发现，即有关稀疏BWS方法相对于Express的卓越能力，可以再现“已知”实用程序或由完整BWS设计产生的实用程序。它还通过收集来自每个受访者的充分观察结果来扩展先前的发现，以进行完整的实验和提议的方法之一，即Express BWS和Sparse BWS。这些结果重复并扩展了先前的发现，即有关稀疏BWS方法相对于Express的卓越能力，可以再现“已知”实用程序或由完整BWS设计产生的实用程序。