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Caveats for the Spatial Arrangement Method: Comment on Hout, Goldinger, and Ferguson (2013)

机译:空间布置方法的注意事项:评论Hout,Goldinger和Ferguson(2013)

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The gold standard among proximity data collection methods for multidimensional scaling is the (dis)similarity rating of pairwise presented stimuli. A drawback of the pairwise method is its lengthy duration, which may cause participants to change their strategy over time, become fatigued, or disengage altogether. Hout, Goldinger, and Ferguson (2013) recently made a case for the Spatial Arrangement Method (SpAM) as an alternative to the pairwise method, arguing that it is faster and more engaging. SpAM invites participants to directly arrange stimuli on a computer screen such that the interstimuli distances are proportional to psychological proximity. Based on a reanalysis of the Hout et al. (2013), data we identify three caveats for SpAM. An investigation of the distributional characteristics of the SpAM proximity data reveals that the spatial nature of SpAM imposes structure on the data, invoking a bias against featural representations. Individual-differences scaling of the SpAM proximity data reveals that the two-dimensional nature of SpAM allows individuals to only communicate two dimensions of variation among stimuli properly, invoking a bias against high-dimensional scaling representations. Monte Carlo simulations indicate that in order to obtain reliable estimates of the group average, SpAM requires more individuals to be tested. We conclude with an overview of considerations that can inform the choice between SpAM and the pairwise method and offer suggestions on how to overcome their respective limitations.
机译:多维缩放的邻近数据收集方法中的金标准是成对呈现的刺激的(不相似)评级。成对方法的一个缺点是持续时间长,这可能会导致参与者随时间改变其策略,变得疲劳或完全脱离接触。 Hout,Goldinger和Ferguson(2013)最近提出了一种空间排列方法(SpAM)作为成对方法的替代方法的理由,认为这种方法更快且更具吸引力。 SpAM邀请参与者直接在计算机屏幕上安排刺激,使刺激之间的距离与心理接近度成正比。根据对Hout等人的重新分析。 (2013),数据确定了SpAM的三个警告。对SpAM邻近数据的分布特征的调查表明,SpAM的空间性质在数据上强加了结构,从而引起了对特征表示的偏见。 SpAM接近度数据的个体差异缩放显示,SpAM的二维性质允许个体仅正确传达刺激之间变化的两个维度,从而引起对高维度缩放表示的偏见。蒙特卡洛模拟表明,为了获得可靠的群体平均值估计,SpAM需要测试更多的个体。我们以考虑因素的概述作为结束,这些因素可以指导SpAM和成对方法之间的选择,并提供有关如何克服它们各自局限性的建议。

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