重新审视量表评估中 Alpha 的使用:量表长度和样本量的影响

IF 2.7 4区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH
Leifeng Xiao, Kit-Tai Hau, Melissa Dan Wang
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引用次数: 0

摘要

对于参与者来说,短量表节省时间,在研究中也具有成本效益。然而,研究人员往往错误地认为短量表与长量表具有相同的信度,而没有考虑量表长度的影响。我们认为,采用一个通用的阿尔法基准是有问题的,因为低质量项目对短量表的影响更大。在本研究中,我们提出了在量表构建过程中使用 "如果α-项目-删除 "程序来减少项目的简单指导原则。如果 alpha 的增减小于 0.02,就可以删除一个项目,尤其是短量表。反之,如果删除一个项目后 alpha 下降超过 0.04,则应保留该项目。在大多数情况下,0.80 是相对安全的信度基准,但对于较长的量表和较小的样本量,建议采用更高的信度基准。补充分析,包括项目内容、表面效度和内容覆盖率,对于确保量表质量至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Revisiting the Usage of Alpha in Scale Evaluation: Effects of Scale Length and Sample Size

Revisiting the Usage of Alpha in Scale Evaluation: Effects of Scale Length and Sample Size

Short scales are time-efficient for participants and cost-effective in research. However, researchers often mistakenly expect short scales to have the same reliability as long ones without considering the effect of scale length. We argue that applying a universal benchmark for alpha is problematic as the impact of low-quality items is greater on shorter scales. In this study, we proposed simple guidelines for item reduction using the “alpha-if-item-deleted” procedure in scale construction. An item can be removed if alpha increases or decreases by less than .02, especially for short scales. Conversely, an item should be retained if alpha decreases by more than .04 upon its removal. For reliability benchmarks, .80 is relatively safe in most conditions, but higher benchmarks are recommended for longer scales and smaller sample sizes. Supplementary analyses, including item content, face validity, and content coverage, are critical to ensure scale quality.

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来源期刊
CiteScore
3.90
自引率
15.00%
发文量
47
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