Composite scores are an exceptionally important psychometric tool for behavioral science research applications. A prototypical example occurs with self-report data, where researchers routinely use questionnaires with multiple items that tap into different features of a target construct. Item-level missing data are endemic to composite score applications. Many studies have investigated this issue, and the near-universal theme is that item-level missing data treatment is superior because it maximizes precision and power. However, item-level missing data handling can be challenging because missing data models become very complex and suffer from the same "curse of dimensionality" problem that plagues the estimation of psychometric models. A good deal of recent missing data literature has focused on advancing factored regression specifications that use a sequence of regression models to represent the multivariate distribution of a set of incomplete variables. The purpose of this paper is to describe and evaluate a factored specification for composite scores with incomplete item responses. We used a series of computer simulations to compare the proposed approach to gold standard multiple imputation and latent variable modeling approaches. Overall, the simulation results suggest that this new approach can be very effective, even under extreme conditions where the number of items is very large (or even exceeds) the sample size. A real data analysis illustrates the application of the method using software available on the internet. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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具有项目级缺失数据的综合分数的因式回归模型。

综合分数是行为科学研究应用中非常重要的心理测量工具。一个典型的例子是自我报告数据，研究人员通常使用包含多个项目的问卷调查，这些项目可以挖掘目标结构的不同特征。项目级缺失数据是综合评分应用程序特有的。许多研究已经调查了这个问题，并且几乎普遍的主题是项目级缺失数据处理是优越的，因为它最大限度地提高了精度和功率。然而，项目级缺失数据处理可能具有挑战性，因为缺失数据模型变得非常复杂，并且遭受同样困扰心理测量模型估计的“维数灾难”问题。大量最近的缺失数据文献侧重于推进因子回归规范，这些规范使用一系列回归模型来表示一组不完整变量的多元分布。本文的目的是描述和评估具有不完整项目响应的综合分数的因式规范。我们使用一系列计算机模拟来比较所提出的方法与黄金标准多重插补和潜在变量建模方法。总的来说，模拟结果表明这种新方法非常有效，即使在项目数量非常大（甚至超过）样本量的极端条件下也是如此。真实的数据分析说明了使用互联网上可用软件的方法的应用。（PsycInfo 数据库记录 (c) 2023 APA，