Department of Psychology.
Department of Special Education.
Neuropsychology. 2018 Feb;32(2):176-189. doi: 10.1037/neu0000427.
Executive function (EF) is a commonly used but difficult to operationalize construct. In this study, we considered EF and related components as they are commonly presented in the neuropsychological literature, as well as the literatures of developmental, educational, and cognitive psychology. These components have not previously been examined simultaneously, particularly with this level of comprehensiveness, and/or at this age range or with this sample size. We expected that the EF components would be separate but related, and that a bifactor model would best represent the data relative to alternative models.
We assessed EF with 27 measures in a large sample (N = 846) of late elementary school-age children, many of whom were struggling in reading, and who were demographically diverse. We tested structural models of EF, from unitary models to methodological models, utilizing model-comparison factor analytic techniques. We examined both a common factor as well as a bifactor structure.
Initial models showed strong overlap among several latent EF variables. The final model was a bifactor model with a common EF, and five specific EF factors (working memory-span/manipulation and planning; working memory-updating; generative fluency, self-regulated learning; metacognition).
Results speak to the commonality and potential separability of EF. These results are discussed in light of prevailing models of EF and how EF might be used for structure/description, prediction, and for identifying its mechanism for relevant outcomes. (PsycINFO Database Record
执行功能（EF）是一种常用但难以操作的结构。在这项研究中，我们考虑了 EF 及其相关组成部分，这些组成部分在神经心理学文献、发展心理学、教育心理学和认知心理学文献中都有出现。这些组成部分以前没有被同时检查过，特别是在这个综合性的水平上，或者在这个年龄范围或这个样本大小上。我们预计 EF 组成部分将是独立的，但又是相关的，双因素模型将相对于其他模型更好地代表数据。
我们在一个由 846 名年龄较大的小学生组成的大样本中使用 27 个指标来评估 EF，其中许多人在阅读方面存在困难，且在人口统计学上存在多样性。我们利用模型比较因素分析技术，测试了 EF 的结构模型，从单一模型到方法学模型。我们既考察了共同因素，也考察了双因素结构。
初始模型显示了几个潜在的 EF 变量之间的强烈重叠。最终模型是一个双因素模型，有一个共同的 EF 和五个特定的 EF 因素（工作记忆广度/操作和计划；工作记忆更新；生成流畅性、自我调节学习；元认知）。
结果表明 EF 的共性和潜在可分离性。这些结果是根据 EF 的流行模型以及 EF 如何用于结构/描述、预测以及识别其对相关结果的机制来讨论的。
