Covariates have been used in mixture IRT models to help explain why examinees are classed into different latent classes. Previous research has considered manifest variables as covariates in a mixture Rasch analysis for prediction of group membership. Latent covariates, however, are more likely to have higher correlations with the latent class variable. This study investigated effects of including latent variables as covariates in a mixture Rasch model, in presence of and in absence of interactions between the covariates. Results indicated the latent and manifest covariates influenced latent class membership but did not have much influence on class ability means or class proportions. The influence was relatively higher for latent covariates compared to manifest covariates. The effects of the covariates on class membership and on item parameters were class specific. Substantial effects of covariates on item parameters yielded smaller standard errors for item parameter estimates. A significant interaction term also had an effect on the coefficients for predicting and explaining latent class membership.

中文翻译：

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具有协变量对潜在类别成员的主要影响和交互作用的混合Rasch模型

协变量已用于混合IRT模型中，以帮助解释为什么应试者被分类为不同的潜在类别。先前的研究已将清单变量视为混合Rasch分析中的协变量，以预测组成员身份。但是，潜在协变量与潜在类变量的相关性更高。这项研究调查了在混合变量Rasch模型中存在和不存在互变量之间的相互作用时，将潜在变量作为协变量包括在内的影响。结果表明，潜在和明显的协变量影响潜在的班级成员，但对班级能力平均值或班级比例没有太大影响。与明显协变量相比，潜在协变量的影响相对较高。协变量对类成员资格和项目参数的影响是特定于类的。协变量对项目参数的实质性影响产生了较小的项目参数估计标准误。一个重要的相互作用项也对预测和解释潜在类成员的系数产生影响。