Himelfarb Igor, Marcoulides Katerina M, Fang Guoliang, Shotts Bruce L
National Board of Chiropractic Examiners, Greeley, CO, USA.
University of Minnesota, Minneapolis, MN, USA.
Educ Psychol Meas. 2020 Apr;80(2):293-311. doi: 10.1177/0013164419871597. Epub 2019 Sep 3.
The chiropractic clinical competency examination uses groups of items that are integrated by a common case vignette. The nature of the vignette items violates the assumption of local independence for items nested within a vignette. This study examines via simulation a new algorithmic approach for addressing the local independence violation problem using a two-level alternating directions testlet model. Parameter values for item difficulty, discrimination, test-taker ability, and test-taker secondary abilities associated with a particular testlet are generated and parameter recovery through Markov Chain Monte Carlo Bayesian methods and generalized maximum likelihood estimation methods are compared. To aid with the complex computational efforts, the novel so-called TensorFlow platform is used. Both estimation methods provided satisfactory parameter recovery, although the Bayesian methods were found to be somewhat superior in recovering item discrimination parameters. The practical significance of the results are discussed in relation to obtaining accurate estimates of item, test, ability parameters, and measurement reliability information.
整脊临床能力考试使用由一个共同病例 vignette 整合的项目组。vignette 项目的性质违反了 vignette 中嵌套项目的局部独立性假设。本研究通过模拟检验一种新的算法方法，该方法使用两级交替方向测试题模型来解决局部独立性违反问题。生成与特定测试题相关的项目难度、区分度、考生能力和考生次要能力的参数值，并比较通过马尔可夫链蒙特卡罗贝叶斯方法和广义最大似然估计方法进行的参数恢复。为了辅助复杂的计算工作，使用了新颖的所谓 TensorFlow 平台。两种估计方法都提供了令人满意的参数恢复，尽管发现贝叶斯方法在恢复项目区分度参数方面略胜一筹。结合获得项目、测试、能力参数的准确估计以及测量可靠性信息，讨论了结果的实际意义。
