Bao Yu, Shen Yawei, Wang Shiyu, Bradshaw Laine
University of Georgia, Athens, USA.
Appl Psychol Meas. 2021 Jan;45(1):3-21. doi: 10.1177/0146621620965730. Epub 2020 Nov 6.
The Scaling Individuals and Classifying Misconceptions (SICM) model is an advanced psychometric model that can provide feedback to examinees' misconceptions and a general ability simultaneously. These two types of feedback are represented by a discrete and a continuous latent variable, respectively, in the SICM model. The complex structure of the SICM model brings difficulties in estimating both misconception profile and ability efficiently in a linear test. To overcome this challenge, this study proposes a flexible computerized adaptive test (FCAT) design as a new test delivery method to increase test efficiency by administering an individualized test to examinees. We propose three item selection methods and two transition criteria to determine adaptive steps based on the needs of estimating one or two latent variables. Through two simulation studies, we demonstrate how to select an appropriate item selection method for an adaptive step and what transition criterion should be used between two adaptive steps. Results reveal the combination of the item selection method and the transition criterion could improve the estimation accuracy of a specific latent variable to a different extent and thus provide further guidance in designing an FCAT.
个体缩放与误解分类（SICM）模型是一种先进的心理测量模型，它可以同时为考生的误解和一般能力提供反馈。在SICM模型中，这两种反馈分别由一个离散的和一个连续的潜在变量表示。SICM模型的复杂结构给在线性测试中有效估计误解概况和能力带来了困难。为了克服这一挑战，本研究提出了一种灵活的计算机自适应测试（FCAT）设计，作为一种新的测试实施方法，通过对考生进行个性化测试来提高测试效率。我们提出了三种项目选择方法和两种转换标准，以根据估计一个或两个潜在变量的需要来确定自适应步骤。通过两项模拟研究，我们展示了如何为自适应步骤选择合适的项目选择方法，以及在两个自适应步骤之间应使用何种转换标准。结果表明，项目选择方法和转换标准的组合可以在不同程度上提高特定潜在变量的估计精度，从而为设计FCAT提供进一步的指导。
