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) 建模可用于在检验等式研究中获取更详细的信息，并通过考虑协变量来提高等式的准确性。本研究中，贝叶斯非参数模型下的等式包含两个协变量，一个是连续的，另一个是离散的。研究中一个小组的单组设计获得了与该模型相同的分数。将模型获得的等式分数与经典测试理论中的均值和线性等式方法进行比较。考虑到从三种不同方法获得的等式分数，发现使用 BNP 模型获得的等式分数产生的分布更接近目标测试。当使用小样本时，即使是经典方法也会以最小的误差给出良好的结果，这使得等同研究很有价值。在经典检验等同过程中将协变量包含在模型中是基于一些假设，并且尤其是使用小组时无法实现。不管这个限制如何，BNP 模型将比使用频率论方法更有利。有关小册子和变量的信息可以从经销商处获得，并可以通过 BNP 模型获得相应的分数。在这种情况下，可以比较子类别。这可以表示为指示差异项目功能（DIF）的存在。因此，BNP模型可以积极用于检验等同研究，并同时提供了检验个体参与者特征的机会。因此，即使在小样本中，它也允许测试相等，并提供了达到更接近目标测试分数的值的机会。