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一个修改后的隐藏马尔科夫模型,用于检测在问卷中的不够努力答案
Hangqi Xu1, Jiawei Xiong2, Feiming Li3
1Zhejiang Philosophy and Social Science Laboratory for the Mental Health and Crisis Intervention of Children and Adolescents, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China.
Behavior research methods
|November 19, 2025
概括
本研究引入了一种修改后的隐藏马尔科夫模型 (M-HMM),用于检测在问卷中的不足力度响应 (IER). 通过使用响应和响应时间数据,M-HMM通过动态识别各种IER类型来提高数据质量.
科学领域:
- 心理测量 心理测量 心理测量
- 数据质量评估数据质量评估
- 行为数据分析 行为数据分析
背景情况:
- 努力回应不足 (IER) 显著降低了问卷数据的质量,并影响了研究的有效性.
- 现有的IER检测方法往往缺乏捕获多种IER类型或考虑参与者状态变化的能力.
研究的目的:
- 开发和验证一种先进的统计方法,用于在问卷调查中动态检测不足的努力响应 (IER).
- 通过整合响应模式和响应时间,提高IER识别的准确性和全面性.
主要方法:
- 在隐藏的马尔科夫模型 (HMM) 框架内重建响应和响应时间 (RT) 模型.
- 开发一个修改后的隐藏马尔科夫模型 (M-HMM),专门用于识别IER特征.
- 在各种条件下进行模拟研究,以评估M-HMM的参数恢复和检测灵敏度.
主要成果:
- 在模拟研究中,M-HMM证明了有效的参数恢复.
- 检测灵敏度受到IER流行,RT分布差异和IER异质性的影响.
- 使用M-HMM对经验数据的分析为IER事件提供了更深入的见解.
结论:
- M-HMM提供了一种可靠的方法,可以动态检测各种类型的不足力度响应 (IER).
- 这种方法提高了项目质量和问卷数据完整性的评估.
- 结果为研究人员和从业人员提供了有价值的工具,旨在提高数据有效性.
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