Detecting Examinees With Item Preknowledge on Real Data.

IF 1 4区 心理学 Q4 PSYCHOLOGY, MATHEMATICAL Applied Psychological Measurement Pub Date : 2022-06-01 DOI:10.1177/01466216221084202
Dmitry I Belov, Sarah L Toton
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引用次数: 2

Abstract

Recently, Belov & Wollack (2021) developed a method for detecting groups of colluding examinees as cliques in a graph. The objective of this article is to study how the performance of their method on real data with item preknowledge (IP) depends on the mechanism of edge formation governed by a response similarity index (RSI). This study resulted in the development of three new RSIs and demonstrated a remarkable advantage of combining responses and response times for detecting examinees with IP. Possible extensions of this study and recommendations for practitioners were formulated.

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基于真实数据的项目预知检测。
最近,Belov & Wollack(2021)开发了一种方法,用于在图中检测串谋的考生群体。本文的目的是研究他们的方法在具有项目预知(IP)的真实数据上的性能如何依赖于响应相似指数(RSI)控制的边缘形成机制。该研究开发了三种新的rsi,并证明了结合反应和反应时间来检测IP的考生的显着优势。本研究的可能扩展和对从业者的建议被制定。
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来源期刊
CiteScore
2.30
自引率
8.30%
发文量
50
期刊介绍: Applied Psychological Measurement publishes empirical research on the application of techniques of psychological measurement to substantive problems in all areas of psychology and related disciplines.
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