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如何在不损失可预测性的情况下减少评定量表项目的数量?

How to reduce the number of rating scale items without predictability loss?

作者信息

Koczkodaj W W, Kakiashvili T, Szymańska A, Montero-Marin J, Araya R, Garcia-Campayo J, Rutkowski K, Strzałka D

机构信息

Computer Science, Laurentian University, 935 Ramsey Lake Rd., Sudbury, ON P3E 2C6 Canada.

Sudbury Therapy, Sudbury, ON Canada.

出版信息

Scientometrics. 2017;111(2):581-593. doi: 10.1007/s11192-017-2283-4. Epub 2017 Feb 16.

Abstract

Rating scales are used to elicit data about qualitative entities (e.g., research collaboration). This study presents an innovative method for reducing the number of rating scale items without the predictability loss. The "area under the receiver operator curve method" (AUC ROC) is used. The presented method has reduced the number of rating scale items (variables) to 28.57% (from 21 to 6) making over 70% of collected data unnecessary. Results have been verified by two methods of analysis: Graded Response Model (GRM) and Confirmatory Factor Analysis (CFA). GRM revealed that the new method differentiates observations of high and middle scores. CFA proved that the reliability of the rating scale has not deteriorated by the scale item reduction. Both statistical analysis evidenced usefulness of the AUC ROC reduction method.

摘要

评分量表用于获取有关定性实体(如研究合作)的数据。本研究提出了一种创新方法,可在不损失可预测性的情况下减少评分量表项目的数量。使用了“受试者工作特征曲线下面积法”(AUC ROC)。所提出的方法已将评分量表项目(变量)的数量减少到28.57%(从21个减少到6个),使得超过70%的收集数据不再必要。结果已通过两种分析方法进行验证:分级反应模型(GRM)和验证性因素分析(CFA)。GRM表明新方法能够区分高分和中分的观察结果。CFA证明评分量表的可靠性不会因量表项目的减少而恶化。两种统计分析都证明了AUC ROC减少法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7e9e/5400800/f04430d4a8ec/11192_2017_2283_Fig1_HTML.jpg

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