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首页> 外文期刊>Frontiers in Psychology >The Impact of Test and Sample Characteristics on Model Selection and Classification Accuracy in the Multilevel Mixture IRT Model
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The Impact of Test and Sample Characteristics on Model Selection and Classification Accuracy in the Multilevel Mixture IRT Model

机译：试验特征对多级混合IRT模型模型选择和分类精度的影响

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The standard item response theory (IRT) model assumption of a single homogenous population may be violated in real data. Mixture extensions of IRT models have been proposed to account for latent heterogeneous populations, but these models are not designed to handle multilevel data structures. Ignoring the multilevel structure is problematic as it results in lower-level units aggregated with higher-level units and yields less accurate results, because of dependencies in the data. Multilevel data structures cause such dependencies between levels but can be modeled in a straightforward way in multilevel mixture IRT models. An important step in the use of multilevel mixture IRT models is the fit of the model to the data. This fit is often determined based on relative fit indices. Previous research on mixture IRT models has shown that performances of these indices and classification accuracy of these models can be affected by several factors including percentage of class-variant items, number of items, magnitude and size of clusters, and mixing proportions of latent classes. As yet, no studies appear to have been reported examining these issues for multilevel extensions of mixture IRT models. The current study aims to investigate the effects of several features of the data on the accuracy of model selection and parameter recovery. Results are reported on a simulation study designed to examine the following features of the data: percentages of class-variant items (30, 60, and 90%), numbers of latent classes in the data (with from 1 to 3 latent classes at level 1 and 1 and 2 latent classes at level 2), numbers of items (10, 30, and 50), numbers of clusters (50 and 100), cluster size (10 and 50), and mixing proportions [equal (0.5 and 0.5) vs. non-equal (0.25 and 0.75)]. Simulation results indicated that multilevel mixture IRT models resulted in less accurate estimates when the number of clusters and the cluster size were small. In addition, mean Root mean square error (RMSE) values increased as the percentage of class-variant items increased and parameters were recovered more accurately under the 30% class-variant item conditions. Mixing proportion type (i.e., equal vs. unequal latent class sizes) and numbers of items (10, 30, and 50), however, did not show any clear pattern. Sample size dependent fit indices BIC, CAIC, and SABIC performed poorly for the smaller level-1 sample size. For the remaining conditions, the SABIC index performed better than other fit indices.
机译：标准项目响应理论（IRT）模型的单一均匀群体的假设可以在实际数据中违反。已经提出了混合IRT模型的扩展以考虑潜在的异构群体，但这些模型不设计用于处理多级数据结构。忽略多级结构是有问题的，因为它导致较低级别的单位与更高级别的单位聚合并产生较少的准确结果，因为数据中的依赖性。多级数据结构导致级别之间的这种依赖性，但可以在多级混合IRT模型中以直接的方式建模。使用多级混合物IRT模型的一个重要步骤是模型对数据的拟合。这种拟合通常基于相对拟合指标确定。以前关于混合IRT模型的研究表明，这些模型的这些指标和分类准确性的性能可能受到几个因素的影响，包括类 - 变体项目的百分比，集群的项目数，幅度和大小，以及潜在的比例。目前，似乎没有研究过关于混合IRT模型的多级扩展的这些问题。目前的研究旨在调查数据对模型选择和参数恢复准确性的若干特征的影响。结果报告了一个仿真研究，旨在检查数据的以下功能：类 - 变体项目（30,60和90％）的百分比，数据中的潜在类数（在级别为1到3个潜在类） 1和1和2级潜在2），项目数（10,30和50），簇（50和100），簇大小（10和50）的数量，并混合比例[相等（0.5和0.5 ）与非等于（0.25和0.75）]。仿真结果表明，当簇数和簇尺寸小时，多级混合物IRT模型导致较低的准确估计。此外，随着在30％类 - 变体的项目条件下更准确地回收了阶级 - 变体项目的百分比而增加的平均均方误差（RMSE）值增加。然而，混合比例类型（即，等于不等潜在课程尺寸）和物品数量（10,30和50）没有显示任何明确的模式。样品尺寸依赖性折合指数BIC，CAIC和SABIC对于较小的1级样品大小表现不佳。对于剩余条件，SABIC指数比其他拟合指数更好。

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