A change-point analysis procedure based on weighted residuals to detect back random responding.

概化理论 计算机科学 I类和II类错误 残余物 心理信息 统计能力 统计 实证研究 过程(计算) 数据挖掘 点(几何) 统计过程控制 功率(物理) 数学 人工智能 统计假设检验 算法 物理 操作系统 政治学 几何学 法学 梅德林 量子力学
作者
Xiaofeng Yu,Ying Cheng
出处
期刊:Psychological Methods [American Psychological Association]
卷期号:24 (5): 658-674 被引量:31
标识
DOI:10.1037/met0000212
摘要

Careless or inattentive responding is frequently observed in questionnaire or survey data, which jeopardizes test validity and the generalizability of research findings. It is therefore very important to detect such response behavior. The most frequently encountered type of careless response behavior is back random responding (BRR). Literature suggests that BRR is challenging to detect, with reported power of detection around .5 or lower. Change point analysis (CPA), which is a widely used statistical process control method, can be applied to item response data to detect if aberrant behavior exists in a response pattern. Existing CPA methods, however, may not be suitable for detecting BRR, because the change may not be directional. In this article we propose a weighted-residual-based CPA procedure to detect BRR behavior. The performance of the proposed method was evaluated in a comprehensive simulation study and compared against 3 existing CPA methods. Results indicated that the proposed residual-based CPA procedure can detect BRR behavior with high power for tests of 20 items or longer, while keeping the Type-I error rate well under control. Compared with the 3 existing CPA methods, it leads to comparable empirical Type-I error but a gain in power of 17%-42%. An empirical study further illustrated the utility of the proposed method to detect BRR with a real dataset. Implications of the proposed method, its limitations, as well as future directions are provided at the end. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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