Chien Tsair-Wei, Shao Yang, Kuo Shu-Chun
Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan.
Department of Hospital and Health Care Administration, Chia-Nan University of Pharmacy and Science, Tainan, Taiwan.
BMC Med Res Methodol. 2017 Jan 10;17(1):4. doi: 10.1186/s12874-016-0276-2.
Many continuous item responses (CIRs) are encountered in healthcare settings, but no one uses item response theory's (IRT) probabilistic modeling to present graphical presentations for interpreting CIR results. A computer module that is programmed to deal with CIRs is required. To present a computer module, validate it, and verify its usefulness in dealing with CIR data, and then to apply the model to real healthcare data in order to show how the CIR that can be applied to healthcare settings with an example regarding a safety attitude survey.
Using Microsoft Excel VBA (Visual Basic for Applications), we designed a computer module that minimizes the residuals and calculates model's expected scores according to person responses across items. Rasch models based on a Wright map and on KIDMAP were demonstrated to interpret results of the safety attitude survey.
The author-made CIR module yielded OUTFIT mean square (MNSQ) and person measures equivalent to those yielded by professional Rasch Winsteps software. The probabilistic modeling of the CIR module provides messages that are much more valuable to users and show the CIR advantage over classic test theory.
Because of advances in computer technology, healthcare users who are familiar to MS Excel can easily apply the study CIR module to deal with continuous variables to benefit comparisons of data with a logistic distribution and model fit statistics.
在医疗环境中会遇到许多连续项目反应（CIR），但没有人使用项目反应理论（IRT）的概率模型来呈现图形展示以解释CIR结果。需要一个编程用于处理CIR的计算机模块。呈现一个计算机模块，对其进行验证，并验证其在处理CIR数据方面的有用性，然后将该模型应用于实际医疗数据，以便通过一个关于安全态度调查的例子展示CIR如何应用于医疗环境。
使用微软Excel VBA（应用程序可视化Basic），我们设计了一个计算机模块，该模块可使残差最小化，并根据项目间的个人反应计算模型的预期分数。基于赖特图和KIDMAP的拉施模型被用于解释安全态度调查的结果。
作者制作的CIR模块产生的拟合均方（MNSQ）和个人测量值与专业的拉施Winsteps软件产生的结果相当。CIR模块的概率模型为用户提供了更有价值的信息，并显示了CIR相对于经典测试理论的优势。
由于计算机技术的进步，熟悉MS Excel的医疗用户可以轻松应用本研究的CIR模块来处理连续变量，以利于对具有逻辑分布和模型拟合统计的数据进行比较。
