Chen Ping, Wang Chun
National Innovation Center for Assessment of Basic Education Quality, Beijing Normal University, No. 19, Xin Jie Kou Wai Street, Hai Dian District, Beijing, 100875 , China.
Department of Psychology, University of Minnesota, Minneapolis, MN, USA.
Psychometrika. 2016 Sep;81(3):674-701. doi: 10.1007/s11336-015-9482-9. Epub 2015 Nov 25.
Multidimensional-Method A (M-Method A) has been proposed as an efficient and effective online calibration method for multidimensional computerized adaptive testing (MCAT) (Chen & Xin, Paper presented at the 78th Meeting of the Psychometric Society, Arnhem, The Netherlands, 2013). However, a key assumption of M-Method A is that it treats person parameter estimates as their true values, thus this method might yield erroneous item calibration when person parameter estimates contain non-ignorable measurement errors. To improve the performance of M-Method A, this paper proposes a new MCAT online calibration method, namely, the full functional MLE-M-Method A (FFMLE-M-Method A). This new method combines the full functional MLE (Jones & Jin in Psychometrika 59:59-75, 1994; Stefanski & Carroll in Annals of Statistics 13:1335-1351, 1985) with the original M-Method A in an effort to correct for the estimation error of ability vector that might otherwise adversely affect the precision of item calibration. Two correction schemes are also proposed when implementing the new method. A simulation study was conducted to show that the new method generated more accurate item parameter estimation than the original M-Method A in almost all conditions.
多维方法A（M-方法A）已被提出作为多维计算机自适应测试（MCAT）的一种高效在线校准方法（陈&辛，在荷兰阿纳姆举行的心理测量学会第78届会议上发表的论文，2013年）。然而，M-方法A的一个关键假设是它将人员参数估计值视为其真实值，因此当人员参数估计值包含不可忽略的测量误差时，该方法可能会产生错误的项目校准。为了提高M-方法A的性能，本文提出了一种新的MCAT在线校准方法，即全功能极大似然估计-M-方法A（FFMLE-M-方法A）。这种新方法将全功能极大似然估计（琼斯和金，《心理测量学》59:59 - 75，1994；斯特凡斯基和卡罗尔，《统计学年鉴》13:1335 - 1351，1985）与原始的M-方法A相结合，以纠正能力向量的估计误差，否则可能会对项目校准的精度产生不利影响。在实施新方法时还提出了两种校正方案。一项模拟研究表明，在几乎所有条件下，新方法都比原始的M-方法A产生更准确的项目参数估计。
