Expanding the Lognormal Response Time Model Using Profile Similarity Metrics to Improve the Detection of Anomalous Testing Behavior

IF 1.4 4区 心理学 Q3 PSYCHOLOGY, APPLIED
Gregory M. Hurtz, Regi Mucino
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引用次数: 0

Abstract

The Lognormal Response Time (LNRT) model measures the speed of test-takers relative to the normative time demands of items on a test. The resulting speed parameters and model residuals are often analyzed for evidence of anomalous test-taking behavior associated with fast and poorly fitting response time patterns. Extending this model, we demonstrate the connection between the existing LNRT model parameters and the “level” component of profile similarity, and we define two new parameters for the LNRT model representing profile “dispersion” and “shape.” We show that while the LNRT model measures level (speed), profile dispersion and shape are conflated in model residuals, and that distinguishing them provides meaningful and useful parameters for identifying anomalous testing behavior. Results from data in a situation where many test-takers gained preknowledge of test items revealed that profile shape, not currently measured in the LNRT model, was the most sensitive response time index to the abnormal test-taking behavior patterns. Results strongly support expanding the LNRT model to measure not only each test-taker's level of speed, but also the dispersion and shape of their response time profiles.

利用配置文件相似度指标扩展对数正态响应时间模型,改进异常测试行为的检测
对数正态响应时间(LNRT)模型测量的是应试者相对于测验项目正常时间要求的速度。通常会对由此得出的速度参数和模型残差进行分析,以寻找与快速和不太吻合的反应时间模式相关的异常应试行为的证据。通过扩展该模型,我们证明了现有 LNRT 模型参数与特征相似性的 "水平 "部分之间的联系,并为 LNRT 模型定义了两个新参数,分别代表特征 "分散 "和 "形状"。我们表明,虽然 LNRT 模型测量的是水平(速度),但在模型残差中,剖面离散度和形状是混在一起的,将它们区分开来可为识别异常测试行为提供有意义且有用的参数。在许多应试者预先知道测试项目的情况下,数据结果显示,目前 LNRT 模型没有测量的轮廓形状是对异常应试行为模式最敏感的反应时间指标。研究结果强烈支持扩展 LNRT 模型,使其不仅能测量每个应试者的速度水平,还能测量其反应时间曲线的分散性和形状。
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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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