Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random
Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random
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DOI：
10.1111/bmsp.12243
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发表时间：
2021-04-15
影响因子：
2.6
通讯作者：
Jamil, Haziq
中科院分区：
文献类型：
--
作者：
Katsikatsou, Myrsini;Moustaki, Irini;Jamil, Haziq
提出了在态度量表和大规模评估中，在成对似然（PL）估计框架下和随机缺失（MAR）机制下处理项目无应答的方法。在完全信息似然估计框架和MAR下，缺失数据机制的可重复性不会导致有偏估计。然而，对于伪似然方法（诸如PL），情况并非如此。我们开发并研究了PL框架下，完整对（CP），可用的情况下（AC）和双重稳健（DR）的方法，将缺失值纳入验证性因素分析的三种策略的性能。CP和AC只需要一个模型的观测数据和标准误差很容易计算。PL估计的双重稳健版本需要针对给定观测响应的缺失响应的预测模型，并且在计算上比AC和CP要求更高。仿真研究是用来比较所提出的方法。建议的方法被用来分析英国的数据，作为经合组织成人技能调查的一部分，收集的算术和识字。
Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.