Fitting Ordinal Factor Analysis Models With Missing Data: A Comparison Between Pairwise Deletion and Multiple Imputation
Fitting Ordinal Factor Analysis Models With Missing Data: A Comparison Between Pairwise Deletion and Multiple Imputation
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DOI：
10.1177/0013164419845039
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发表时间：
2020-02
影响因子：
2.7
通讯作者：
Dexin Shi;Taehun Lee;Amanda J. Fairchild;Albert Maydeu-Olivares
中科院分区：
文献类型：
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作者：
Dexin Shi;Taehun Lee;Amanda J. Fairchild;Albert Maydeu-Olivares
本研究比较了有序因子分析模型中的两种缺失数据处理方法：成对删除（PD; Mplus中的默认设置）和多重插补（MI）。我们研究的过程表明，参数估计和模型拟合指数更接近完整的数据。PD和MI的性能进行了比较，在广泛的条件下，包括响应类别的数量，样本量，缺失的百分比，模型失配的程度。结果表明，PD和MI的产量参数估计值类似于在数据完全随机缺失（MCAR）的条件下对完整数据进行分析的结果。当数据随机缺失（MAR）时，PD参数估计值在研究中的参数组合中存在严重偏倚。当缺失百分比小于50%时，MI产生的参数估计值与完整数据的结果相似。然而，拟合指数（即，χ2、RMSEA和WRMR）产量估计值表明拟合度比完整数据中观察到的结果更差。我们建议应用研究人员在拟合有缺失数据的有序因子模型时使用MI。我们还建议根据TLI和CFI增量拟合指数解释模型拟合。
This study compares two missing data procedures in the context of ordinal factor analysis models: pairwise deletion (PD; the default setting in Mplus) and multiple imputation (MI). We examine which procedure demonstrates parameter estimates and model fit indices closer to those of complete data. The performance of PD and MI are compared under a wide range of conditions, including number of response categories, sample size, percent of missingness, and degree of model misfit. Results indicate that both PD and MI yield parameter estimates similar to those from analysis of complete data under conditions where the data are missing completely at random (MCAR). When the data are missing at random (MAR), PD parameter estimates are shown to be severely biased across parameter combinations in the study. When the percentage of missingness is less than 50%, MI yields parameter estimates that are similar to results from complete data. However, the fit indices (i.e., χ2, RMSEA, and WRMR) yield estimates that suggested a worse fit than results observed in complete data. We recommend that applied researchers use MI when fitting ordinal factor models with missing data. We further recommend interpreting model fit based on the TLI and CFI incremental fit indices.