ISSN 1671-3710
CN 11-4766/R
主办:中国科学院心理研究所
出版:科学出版社

• •    

密集追踪调查中不认真作答的识别和处理

李辉, 刘红云   

  1. 中国政法大学社会学院, 北京 100088 中国
    北京师范大学心理学部, 北京 100875 中国
  • 收稿日期:2026-08-21 修回日期:2026-09-20 接受日期:2026-09-22
  • 基金资助:
    在线密集追踪调查中不认真作答的影响、识别和处理(32500994)

Identification and Handling of Careless Responding in Intensive Longitudinal Studies

Li Hui, Liu Hongyun   

  1. , 100088, China
    , 100875, China
  • Received:2026-08-21 Revised:2026-09-20 Accepted:2026-09-22
  • Supported by:
    The impact, identification, and handling of careless responding in online intensive longitudinal study(32500994)

摘要: 近年来,密集追踪调查在心理学研究中应用广泛,为研究者捕捉个体内部心理状态的变化,并揭示心理过程随时间发展的动态规律提供了有益工具。然而,其高频、多次测量的特点会加重被试负担,易导致被试不认真作答(careless responding, CR),进而威胁数据质量和研究结论的可靠性。本研究聚焦密集追踪调查中的CR问题,探究CR对数据分析结果和结论的影响,探索如何利用机器学习技术与作答行为日志数据相结合的方式精准识别CR,设计并对比多种处理CR的策略,并在此基础上开发用户友好的识别和处理CR的工具,以方便研究者高效清理密集追踪调查数据。

关键词: 密集追踪, 作答质量, 不认真作答, 不努力作答, 数据清理

Abstract: Intensive longitudinal studies have become increasingly popular in psychological research because they allow researchers to capture within-person variability and examine the temporal dynamics of psychological processes. However, the intensive nature of repeated assessments may place additional burden on participants and increase the likelihood of careless responding (CR), which can undermine data quality and lead to biased research conclusions. The present study focuses on CR in intensive longitudinal study and aims to systematically examine its consequences for data analysis and statistical inference, develop effective approaches for identifying CR by integrating machine learning techniques with behavioral log data recorded during the response process, develop and evaluate strategies for handling CR, and develop a user-friendly tool for CR detection and handling to facilitate data screening and preprocessing in intensive longitudinal studies.

Key words: intensive longitudinal study, data quality, careless responding, insufficient effort responding, data preprocessing