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用于评分、排序和配对比较的视觉模拟量表(VAS-RRP):一种新的心理测量技术。

The Visual Analogue Scale for Rating, Ranking and Paired-Comparison (VAS-RRP): A new technique for psychological measurement.

机构信息

Department of Educational Psychology and Counseling/Chinese Language and Technology Center, National Taiwan Normal University, Taipei, Taiwan.

Research Center for Psychological and Educational Testing, National Taiwan Normal University, New Taipei City, Taiwan.

出版信息

Behav Res Methods. 2018 Aug;50(4):1694-1715. doi: 10.3758/s13428-018-1041-8.


DOI:10.3758/s13428-018-1041-8
PMID:29667082
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6096654/
Abstract

Traditionally, the visual analogue scale (VAS) has been proposed to overcome the limitations of ordinal measures from Likert-type scales. However, the function of VASs to overcome the limitations of response styles to Likert-type scales has not yet been addressed. Previous research using ranking and paired comparisons to compensate for the response styles of Likert-type scales has suffered from limitations, such as that the total score of ipsative measures is a constant that cannot be analyzed by means of many common statistical techniques. In this study we propose a new scale, called the Visual Analogue Scale for Rating, Ranking, and Paired-Comparison (VAS-RRP), which can be used to collect rating, ranking, and paired-comparison data simultaneously, while avoiding the limitations of each of these data collection methods. The characteristics, use, and analytic method of VAS-RRPs, as well as how they overcome the disadvantages of Likert-type scales, ranking, and VASs, are discussed. On the basis of analyses of simulated and empirical data, this study showed that VAS-RRPs improved reliability, response style bias, and parameter recovery. Finally, we have also designed a VAS-RRP Generator for researchers' construction and administration of their own VAS-RRPs.

摘要

传统上,视觉模拟量表(VAS)被提议用来克服来自李克特量表的有序测量的局限性。然而,VAS 克服对李克特量表的反应模式的局限性的功能尚未得到解决。以前使用排序和配对比较来弥补李克特量表的反应模式的研究受到了限制,例如,特质得分的总分是一个常数,不能用许多常见的统计技术进行分析。在这项研究中,我们提出了一种新的量表,称为用于评分、排序和配对比较的视觉模拟量表(VAS-RRP),它可以同时用于收集评分、排序和配对比较数据,同时避免了这些数据收集方法的局限性。讨论了 VAS-RRPs 的特点、使用和分析方法,以及它们如何克服李克特量表、排序和 VAS 的缺点。基于对模拟和实证数据的分析,本研究表明 VAS-RRPs 提高了可靠性、反应模式偏差和参数恢复。最后,我们还设计了一个 VAS-RRP 生成器,供研究人员构建和管理他们自己的 VAS-RRP。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/02aa9977e1c1/13428_2018_1041_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/27eea052c575/13428_2018_1041_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/5c36da334b12/13428_2018_1041_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/728e2b681634/13428_2018_1041_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/859203633191/13428_2018_1041_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/f53bab2e2e27/13428_2018_1041_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/4f957956eab1/13428_2018_1041_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/02aa9977e1c1/13428_2018_1041_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/27eea052c575/13428_2018_1041_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/5c36da334b12/13428_2018_1041_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/728e2b681634/13428_2018_1041_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/859203633191/13428_2018_1041_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/f53bab2e2e27/13428_2018_1041_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/4f957956eab1/13428_2018_1041_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73f2/6096654/02aa9977e1c1/13428_2018_1041_Fig7_HTML.jpg

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Behav Res Methods. 2017-12

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