NYU Steinhardt School of Culture, Education, and Human Development, New York, NY.
J Speech Lang Hear Res. 2019 Apr 15;62(4):783-801. doi: 10.1044/2018_JSLHR-S-18-0075.
Purpose Research in communication sciences and disorders frequently involves the collection of clusters of observations, such as a series of scores for each individual receiving treatment over the course of an intervention study. However, little discipline-specific guidance is currently available on the subject of building and interpreting multilevel models. This article offers a tutorial on multilevel models, using notation from the R statistical software, and discusses their implications for research in communication sciences and disorders. Method This tutorial introduces multilevel models and contrasts them with other methods to analyze repeated measures data, such as repeated measures analysis of variance or standard linear regression. It also provides guidance on interpreting the components of a multilevel model and selecting the best-fitting model. Finally, these models are illustrated through an analysis of real data from a study of speech production training in second-language speakers of English. Conclusions As a flexible method that can increase the rigor of modeling for clustered data, multilevel modeling represents an important tool for research in communication disorders. Given their increasingly prominent role in the analysis of experimental data in communication sciences, it is important for researchers to be familiar with the basics of building and interpreting these models.
目的 沟通科学与障碍研究中经常涉及到观测数据的聚类收集，例如在干预研究过程中对每个接受治疗的个体的一系列分数。然而，目前针对构建和解释多层次模型的问题，相关学科的指导意见很少。本文使用 R 统计软件的符号，提供了关于多层次模型的教程，并讨论了其对沟通科学与障碍研究的意义。
方法 本教程介绍了多层次模型，并将其与其他分析重复测量数据的方法进行了对比，如重复测量方差分析或标准线性回归。它还提供了关于解释多层次模型组成部分和选择最佳拟合模型的指导。最后，通过对英语作为第二语言者的语音产生训练研究中真实数据的分析来说明这些模型。
结论 作为一种可以提高聚类数据建模严谨性的灵活方法，多层次模型是沟通障碍研究的重要工具。鉴于它们在沟通科学中分析实验数据的作用越来越突出，研究人员熟悉构建和解释这些模型的基础知识非常重要。
