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Robust confirmatory factor analysis based on the forward search algorithm

机译:基于前向搜索算法的稳健确认因子分析

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摘要

A key concept of the forward search algorithm in confirmatory factor analysis is ordering of the data on the basis of observational residuals. These residuals are computed under the proposed model and measure the discrepancy between the observed and predicted response for each unit of the sample. Regression-type factor scores are used to estimatemodel predictions. Informative forward plots are created for indexing influential observations and to showthe dynamics of the estimates throughout the search. The detailed influence of each observation on the model parameters and fit indices is analyzed and a robust model inference is achieved. Real and simulated data sets with known contamination schemes are used to demonstrate the performance of the forward search algorithm.
机译:在确定性因素分析中,正向搜索算法的关键概念是根据观测残差对数据进行排序。这些残差是在提出的模型下计算的,并测量了样本中每个单位的观测响应与预测响应之间的差异。回归类型因子得分用于估计模型预测。创建信息性的前向图以索引有影响力的观察结果,并显示整个搜索过程中估计的动态。分析了每个观测值对模型参数和拟合指标的详细影响,并获得了可靠的模型推断。具有已知污染方案的真实和模拟数据集用于证明正向搜索算法的性能。

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