通过潜在类分析验证性能标准

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED Journal of Educational Measurement Pub Date : 2022-05-05 DOI:10.1111/jedm.12325
Salih Binici, Ismail Cuhadar
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引用次数: 1

摘要

绩效标准的有效性是标准制定结果的可辩护性的关键因素,验证绩效标准需要在标准制定过程的每一步收集多个证据。本研究采用统计程序潜类分析来设定性能标准,并通过修正angoff方法将潜类分析结果与先前建立的性能标准进行交叉验证。该研究的背景是在美国南部的一个州进行的一项可操作的大规模科学评估。结果表明,在潜在类分析中出现的类的数量与现有性能水平的数量一致。此外,在将学生划分为相同的成绩水平方面,潜在类分析结果与修正angoff方法之间存在相当程度的一致性。总体而言,研究结果为通过改进的angoff方法确定的绩效标准的有效性提供了证据。讨论了研究结果的实际意义。
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Validating Performance Standards via Latent Class Analysis

Validity of performance standards is a key element for the defensibility of standard setting results, and validating performance standards requires collecting multiple pieces of evidence at every step during the standard setting process. This study employs a statistical procedure, latent class analysis, to set performance standards and compares latent class analysis results with previously established performance standards via the modified-Angoff method for cross-validation. The context of the study is an operational large-scale science assessment administered in one of the southern states in the United States. Results show that the number of classes that emerged in the latent class analysis concurs with the number of existing performance levels. In addition, there is a substantial level of agreement between latent class analysis results and modified-Angoff method in terms of classifying students into the same performance levels. Overall, the findings establish evidence for the validity of the performance standards identified via the modified-Angoff method. Practical implications of the study findings are discussed.

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来源期刊
CiteScore
2.30
自引率
7.70%
发文量
46
期刊介绍: The Journal of Educational Measurement (JEM) publishes original measurement research, provides reviews of measurement publications, and reports on innovative measurement applications. The topics addressed will interest those concerned with the practice of measurement in field settings, as well as be of interest to measurement theorists. In addition to presenting new contributions to measurement theory and practice, JEM also serves as a vehicle for improving educational measurement applications in a variety of settings.
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