验证网络测试平台英语词汇知识评估的两种方法:简要评估

IF 1.1 2区 文学 0 LANGUAGE & LINGUISTICS Linguistics Vanguard Pub Date : 2023-09-13 DOI:10.1515/lingvan-2022-0116
Lee Drown, Nikole Giovannone, David B. Pisoni, Rachel M. Theodore
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引用次数: 0

摘要

词汇量测试(VST)和词汇熟悉度测试(WordFAM)这两种评估英语词汇知识的方法最近在网络管理中得到了验证。对这些评估的心理测量特性的分析显示出高度的内部一致性,表明稳定的评估可以用较少的测试项目来实现。由于研究人员可以将这些评估与其他实验任务结合使用,因此如果评估持续时间较短,其效用可能会得到增强。为此,开发了VST和WordFAM的两个“简短”版本并提交验证测试。每个版本都包含了完整评估中大约一半的项目,在每个简短的版本中都有新的项目。参与者(n = 85)在第一阶段完成了VST和WordFAM的一个简短版本,随后在第二阶段完成了每个评估的另一个简短版本。结果显示VST (r = 0.68)和WordFAM (r = 0.82)的重测信度均较高。评估也显示中等的收敛效度(r = 0.38 ~ 0.59),表明评估的效度。这项工作提供了开源的英语词汇知识评估和规范性数据,研究人员可以使用这些数据在基于网络的环境中促进高质量的数据收集。
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Validation of two measures for assessing English vocabulary knowledge on web-based testing platforms: brief assessments
Abstract Two measures for assessing English vocabulary knowledge, the Vocabulary Size Test (VST) and the Word Familiarity Test (WordFAM), were recently validated for web-based administration. An analysis of the psychometric properties of these assessments revealed high internal consistency, suggesting that stable assessment could be achieved with fewer test items. Because researchers may use these assessments in conjunction with other experimental tasks, the utility may be enhanced if they are shorter in duration. To this end, two “brief” versions of the VST and the WordFAM were developed and submitted to validation testing. Each version consisted of approximately half of the items from the full assessment, with novel items across each brief version. Participants ( n = 85) completed one brief version of both the VST and the WordFAM at session one, followed by the other brief version of each assessment at session two. The results showed high test-retest reliability for both the VST ( r = 0.68) and the WordFAM ( r = 0.82). The assessments also showed moderate convergent validity (ranging from r = 0.38 to 0.59), indicative of assessment validity. This work provides open-source English vocabulary knowledge assessments with normative data that researchers can use to foster high quality data collection in web-based environments.
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来源期刊
CiteScore
2.00
自引率
18.20%
发文量
105
期刊介绍: Linguistics Vanguard is a new channel for high quality articles and innovative approaches in all major fields of linguistics. This multimodal journal is published solely online and provides an accessible platform supporting both traditional and new kinds of publications. Linguistics Vanguard seeks to publish concise and up-to-date reports on the state of the art in linguistics as well as cutting-edge research papers. With its topical breadth of coverage and anticipated quick rate of production, it is one of the leading platforms for scientific exchange in linguistics. Its broad theoretical range, international scope, and diversity of article formats engage students and scholars alike. All topics within linguistics are welcome. The journal especially encourages submissions taking advantage of its new multimodal platform designed to integrate interactive content, including audio and video, images, maps, software code, raw data, and any other media that enhances the traditional written word. The novel platform and concise article format allows for rapid turnaround of submissions. Full peer review assures quality and enables authors to receive appropriate credit for their work. The journal publishes general submissions as well as special collections. Ideas for special collections may be submitted to the editors for consideration.
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