ITEM RESPONSE THEORY AND THE MOS SF-36

在一个成本控制和不断变化的卫生保健提供系统的时代，对价值购买的需求极大地增加了在临床和政策决策中使用自我报告的健康状况和生活质量信息。虽然有许多测量这些结构的工具可用，但测量水平及其对科学推理和分数解释的影响通常被忽视。测量模型，如项目反应理论（IRT），已经成功地应用于教育心理学、社会学和政治学等领域，健康状况研究者开始提倡将其应用于健康状况评估。然而，仅对其在健康状况测量中的适用性进行了有限的实证调查。本研究将通过将这些模型应用于来自特定条件（哮喘、终末期肾病）和规范化（一般人群）研究的独立样本，调查IRT模型在产生精确健康估计方面的有用性。本研究将重点关注广泛使用的健康状况测量的身体功能量表，医学结果研究短表36 （SF-36）。主要IRT假设的单维性将通过因子分析和项目参数稳定性检验来检验。通过拟合优度检验和残差分析，还将检验两参数和多分体IRT模型的贡献。不同测量模型（总结性、IRT）对总得分的影响将通过测量误差、测试信息、组差异和随时间变化的分数的幅度和可变性以及预测效度的比较来检验。本研究将为项目反应模型评估健康状况的有效性提供经验证据。此外，研究结果可以用来指导研究人员选择最有用的模型来评分他们的措施，并从他们的数据中做出有效的科学推断和解释。实际上，通过减少测量误差，更高的测量精度可以转化为更低的研究成本。最终，更好的测量特性将使更精确的理论问题得到检验，并做出更准确的临床和政策预测。

The demand for value purchasing in an era of cost-containment and changing health care delivery systems has dramatically increased the use of self-reported health status and quality of life information in clinical and policy decision-making. Although numerous instruments for measuring these constructs are available, measurement level and its influence on scientific inference and score interpretation have generally been overlooked. Measurement models, such a item response theory (IRT), have been used successfully in fields such as education psychology, sociology and political science, and health status researchers are beginning to advocate their use in health status assessment. However, only limited empirical investigation of their applicability in health status measurement have been performed. This study will investigate the usefulness of IRT models in generating precise estimates of health by applying these models on independent samples from condition-specific (asthma, end-stage renal disease) and norming (general population) studies. This study will focus on the physical functioning scale of the widely used health status measure, the Medical Outcomes Study Short-Form 36 (SF-36). The major IRT assumption of unidimensionality will be tested through factor analyses and an examination of item parameter stability. The contribution of two parameter and polytomous IRT models will also be examined through goodness-of-fit tests an analyses of residuals. The influence of different measurement models (summative, IRT) on aggregate scores will be examined through comparisons of errors of measurement, test information, magnitude and variability of scores for group differences and change over time, and predictive validity. This study will generate empirical evidence of the usefulness of item response models estimating health status. In addition, study results may be used to guide researchers selecting the most useful model for scoring their measures and in making valid scientific inferences and interpretations from their data. Practically, greater measurement precision could translate into lower costs of conducting research, by reducing measurement error. Ultimately, better measurement properties will enable more precise theoretical questions to be tested and more accurate clinical and policy predictions to be made.