Multiple imputation (MI) is one of the most popular methods for handling missing data in psychological research. However, many imputation approaches are poorly equipped to handle a large number of variables, which are a common sight in studies that employ questionnaires to assess psychological constructs. In such a case, conventional imputation approaches often become unstable and require that the imputation model be simplified, for example, by removing variables or combining them into composite scores. In this article, we propose an alternative method that extends the fully conditional specification approach to MI with dimension reduction techniques such as partial least squares. To evaluate this approach, we conducted a series of simulation studies, in which we compared it with other approaches that were based on variable selection, composite scores, or dimension reduction through principal components analysis. Our findings indicate that this novel approach can provide accurate results even in challenging scenarios, where other approaches fail to do so. Finally, we also illustrate the use of this method in real data and discuss the implications of our findings for practice. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

---

具有许多变量的大型研究中缺失数据的多重插补：使用偏最小二乘的完全条件规范方法。


多重插补（MI）是心理学研究中处理缺失数据最流行的方法之一。然而，许多插补方法不足以处理大量变量，这在使用问卷评估心理结构的研究中很常见。在这种情况下，传统的插补方法常常变得不稳定，需要简化插补模型，例如通过删除变量或将它们组合成综合分数。在本文中，我们提出了一种替代方法，通过偏最小二乘等降维技术将完全条件指定方法扩展到 MI。为了评估这种方法，我们进行了一系列模拟研究，将其与基于变量选择、综合评分或通过主成分分析降维的其他方法进行了比较。我们的研究结果表明，即使在其他方法无法做到的具有挑战性的情况下，这种新颖的方法也可以提供准确的结果。最后，我们还说明了该方法在实际数据中的使用，并讨论了我们的研究结果对实践的影响。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。