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.

中文翻译：

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具有分类变量和随机缺失数据的验证性因子分析模型的成对似然估计

提出了在成对似然（PL）估计框架和随机缺失（MAR）机制下的态度尺度和大规模评估中项目不响应的处理方法。在完整的信息似然估计框架和 MAR 下，缺失数据机制的可忽略性不会导致估计有偏差。但是，对于 PL 等伪似然方法，情况并非如此。我们开发并研究了在 PL 框架下将缺失值纳入验证性因子分析的三种策略的性能，即完整对 (CP)、可用案例 (AC) 和双重稳健 (DR) 方法。CP 和 AC 只需要观测数据的模型，标准误差很容易计算。PL 估计的双重稳健版本需要一个预测模型来预测给定观察到的缺失响应，并且在计算上比 AC 和 CP 要求更高。模拟研究用于比较所提出的方法。所提出的方法用于分析作为经合组织成人技能调查的一部分收集的英国算术和识字数据。