在多样化的群体中提高测量有效性:评估差异性项目功能差异的现代方法
1Department of Psychology and Neuroscience, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
概括
不同项目功能 (DIF) 分析确保心理测量措施准确评估个人. 新的方法可以评估跨多个交叉特征的偏差,提高得分有效性.
科学领域:
- 心理测量 心理测量 心理测量
- 教育测量教育的测量
- 心理评估 心理评估
背景情况:
- 准确的心理测量测量对于评估个体差异至关重要.
- 项目偏差,即响应反映了无关的特征 (例如种族,性别),可以使得分数比较无效.
- 传统的差异性项目功能 (DIF) 分析通常集中在二进制组比较上.
研究的目的:
- 审查和整合基于模型的现代方法来评估差异性项目功能 (DIF).
- 针对复杂的交叉身份概念化,解决传统DIF方法的局限性.
- 澄清应用这些先进的DIF建模技术的机遇和挑战.
主要方法:
- 对DIF分析现有的基于模型的方法进行比较和整合性审查.
- 专注于能够同时评估多个背景变量 (连续和分类) 的方法.
- 考虑可以模拟背景变量之间的相互作用的方法.
主要成果:
- 存在先进的DIF建模技术,可以容纳多维和交叉标识变量.
- 与传统方法相比,这些新方法在检测项目偏差方面提供了更大的灵活性和准确性.
- 成功的应用需要仔细考虑机遇和固有的挑战.
结论:
- 新的DIF建模方法为改善心理测量测量评估提供了显著的机会.
- 研究人员必须应对与应用这些先进方法相关的挑战.
- 这项工作阐明了当代DIF技术对于可靠评估的实用性.
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