Most Item Response Theory (IRT) models for dichotomous responses are based on probit or logit link functions which assume a symmetric relationship between the probability of a correct response and the latent traits of individuals submitted to a test. This assumption restricts the use of those models to the case in which all items have a symmetric behaviour. On the other hand, asymmetric models proposed in the literature impose that all the items in a test have an asymmetric behaviour. This assumption is inappropriate for great part of the tests which are, in general, composed by both symmetric and asymmetric items. Furthermore, a straightforward extension of the existing models in the literature would require a prior selection of the items' symmetry/asymmetry status. This paper proposes a Bayesian IRT model that accounts for symmetric and asymmetric items in a flexible though parsimonious way. That is achieved by assigning a finite mixture prior to the skewness parameter, with one of the mixture components being a point-mass at zero. This allows for analyses under both model selection and model averaging approaches. Asymmetric item curves are designed through the centred skew normal distribution, which has a particularly appealing parameterisation in terms of parameter interpretation and computational efficiency. An efficient MCMC algorithm is proposed to perform Bayesian inference and its performance is investigated in some simulated examples. Finally, the proposed methodology is applied to a data set from a large scale educational exam in Brazil.
翻译:大部分项反应的二分反应理论( IRT) 模型基于 probit 或 logit 链接功能, 假设正确反应的可能性和提交测试的个人的潜质之间的对称关系。 这个假设将这些模型的使用限于所有项目具有对称行为的情况。 另一方面, 文献中提议的不对称模型规定, 测试中的所有项目都具有不对称行为。 这个假设不适合大部分测试, 一般而言, 由对称和不对称项目组成。 此外, 文献中现有模型的直截了当的扩展将需要事先选择项目对称/ 对称状态。 这个假设将限制这些模型的使用到所有项目都具有对称行为对称行为的情况。 另一方面, 文献中提议的不对称模型将测试中的所有项目都分为一定的混合物, 其中一个混合物是零点。 这允许在模型选择和模型平均分析方法下分析现有模型的较大范围扩展需要事先选择项目对称的对称/ 对称/ 对称状态。 本文建议Bayesian IRT 模型是正常数据运算法, 在正常运算中, 在正常运算中, 其测算中, 其测算的测算是正常运算中, 测算中, 测算的测算是正常运算中, 。