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An Alternative Way to Model Population Ability Distributions in Large-Scale Educational Surveys

机译：大规模教育调查中人口能力分布建模的另一种方法

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In large-scale educational surveys, a latent regression model is used to compensate for the shortage of cognitive information. Conventionally, the covariates in the latent regression model are principal components extracted from background data. This operational method has several important disadvantages, such as the handling of missing data and the high model complexity. The approach introduced here to identify multiple groups that can account for the variation among students is to conduct a latent class analysis (LCA). In the LCA, one or more latent nominal variables are identified that can be used to classify respondents with respect to their background characteristics. These classifications are then introduced as predictors in the latent regression. The primary goal of this study was to explore whether this approach yields similar estimates of group means and standard deviations compared with the operational procedure. The alternative approaches based on LCA differed regarding the number of classes, the items used for the LCA, and whether manifest class membership information or class membership probabilities were used as independent variables in the latent regression. Overall, recovery of the operational approach's group means and standard deviations was very satisfactory for all LCA approaches. Furthermore, the posterior means and standard deviations used to generate plausible values derived from the operational approach and the LCA approaches correlated highly. Thus, incorporating independent variables based on an LCA of background data into the latent regression model appears to be a viable alternative to the operational approach.
机译：在大规模的教育调查中，潜在的回归模型用于补偿认知信息的不足。按照惯例，潜在回归模型中的协变量是从背景数据中提取的主要成分。这种操作方法有几个重要的缺点，例如丢失数据的处理和较高的模型复杂性。此处介绍的识别多个可以解释学生差异的群体的方法是进行潜伏类分析（LCA）。在LCA中，确定了一个或多个潜在名义变量，这些变量可用于根据背景特征对受访者进行分类。然后将这些分类作为潜在回归中的预测变量引入。这项研究的主要目的是探索与操作程序相比，这种方法是否能得出类似的组均值和标准差估计值。基于LCA的替代方法在类数，用于LCA的项以及清单类成员信息或类成员概率是否用作潜在回归中的自变量方面有所不同。总体而言，对于所有LCA方法，操作方法的组均值和标准差的恢复都非常令人满意。此外，用于产生从操作方法和LCA方法得出的合理值的后均值和标准偏差具有高度相关性。因此，将基于背景数据的LCA的自变量合并到潜在回归模型中似乎是一种可行的方法。

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