检验自报健康调查数据中的项目功能差异（DIF）：基于多层建模
Examining Differential Item Functioning (DIF) in Self-Reported Health Survey Data: Via Multilevel Modeling
AI总结：
本文通过多层建模分析自报健康调查数据中的项目功能差异，证明多层模型优于单层模型，并推广其在医疗研究中的应用。
摘要：
很少有健康相关构念或测量工具在测量等价性方面得到严格评估，例如自报健康调查数据。项目功能差异（DIF）分析对于评估自报健康调查中的测量等价性至关重要，这类调查通常具有层级结构。传统的单层DIF方法在此类情况下存在不足，使得多层模型成为更好的替代方案。我们强调了多层建模在DIF分析中的优势，具体做法是将健康调查数据集应用于多层二元逻辑回归（用于分析二元响应数据）和多层多项逻辑回归（用于分析多分类响应数据），并将其与对应的单层模型进行比较。我们的研究结果表明，多层模型拟合更优，且比单层模型解释了更多的方差。本文旨在提高对多层建模的认识，并帮助医疗保健研究人员和实践者理解多层建模在DIF分析中的应用。
英文摘要：
Few health-related constructs or measures have received a critical evaluation in terms of measurement equivalence, such as self-reported health survey data. Differential item functioning (DIF) analysis is crucial for evaluating measurement equivalence in self-reported health surveys, which are often hierarchical in structure. Traditional single-level DIF methods in this case fall short, making multilevel models a better alternative. We highlight the benefits of multilevel modeling for DIF analysis, when applying a health survey data set to multilevel binary logistic regression (for analyzing binary response data) and multilevel multinominal logistic regression (for analyzing polytomous response data), and comparing them with their single-level counterparts. Our findings show that multilevel models fit better and explain more variance than single-level models. This article is expected to raise awareness of multilevel modeling and help healthcare researchers and practitioners understand the use of multilevel modeling for DIF analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
