Division of Measurement and Evaluation in Education, Department of Educational Sciences, Inönü University, Malatya, Turkey.
Division of Measurement and Evaluation in Education, Department of Educational Sciences, Hacettepe University, Ankara, Turkey.
Sci Prog. 2021 Jul-Sep;104(3):368504211028371. doi: 10.1177/00368504211028371.
Bayesian Nonparametric (BNP) modelling can be used to obtain more detailed information in test equating studies and to increase the accuracy of equating by accounting for covariates. In this study, two covariates are included in the equating under the Bayes nonparametric model, one is continuous, and the other is discrete. Scores equated with this model were obtained for a single group design for a small group in the study. The equated scores obtained with the model were compared with the mean and linear equating methods in the Classical Test Theory. Considering the equated scores obtained from three different methods, it was found that the equated scores obtained with the BNP model produced a distribution closer to the target test. Even the classical methods will give a good result with the smallest error when using a small sample, making equating studies valuable. The inclusion of the covariates in the model in the classical test equating process is based on some assumptions and cannot be achieved especially using small groups. The BNP model will be more beneficial than using frequentist methods, regardless of this limitation. Information about booklets and variables can be obtained from the distributors and equated scores that obtained with the BNP model. In this case, it makes it possible to compare sub-categories. This can be expressed as indicating the presence of differential item functioning (DIF). Therefore, the BNP model can be used actively in test equating studies, and it provides an opportunity to examine the characteristics of the individual participants at the same time. Thus, it allows test equating even in a small sample and offers the opportunity to reach a value closer to the scores in the target test.
贝叶斯非参数（BNP）建模可用于在测试等效研究中获得更详细的信息，并通过考虑协变量来提高等效的准确性。在这项研究中，贝叶斯非参数模型中包含了两个协变量，一个是连续的，另一个是离散的。该模型为研究中的一个小组设计的单个组获得了等效分数。与经典测试理论中的均值和线性等效方法相比，该模型获得的等效分数。考虑到三种不同方法获得的等效分数，发现与目标测试相比，BNP 模型获得的等效分数产生的分布更接近。即使使用小样本，经典方法也会以最小的误差给出很好的结果，因此等效研究是有价值的。在经典测试等效过程中，协变量包含在模型中是基于一些假设的，特别是使用小样本时无法实现。无论存在这种局限性，BNP 模型都将比使用频率论方法更有益。有关测验和变量的信息可以从分销商处获得，并且可以使用 BNP 模型获得等效分数。在这种情况下，可以进行子类别比较。这可以表示为存在差异项目功能（DIF）。因此，BNP 模型可以在测试等效研究中积极使用，同时为检查个体参与者的特征提供机会。因此，即使在小样本中也可以进行测试等效，并提供更接近目标测试分数的机会。
