University at Albany, Albany NY, USA.
University of Barcelona, Spain.
Behav Modif. 2020 Mar;44(2):265-295. doi: 10.1177/0145445518806867. Epub 2018 Oct 26.
Multilevel modeling is an approach that can be used to summarize single-case experimental design (SCED) data. Multilevel models were developed to analyze hierarchical structured data with units at a lower level nested within higher level units. SCEDs use time series data collected from multiple cases (or subjects) within a study that allow researchers to investigate intervention effectiveness at the individual level and also to investigate how these individual intervention effects change over time. There is an increased interest in the field regarding how SCEDs can be used to establish an evidence base for interventions by synthesizing data from a series of intervention studies. Although using multilevel models to meta-analyze SCED studies is promising, application is often hampered by being potentially excessively technical. First, this article provides an accessible description and overview of the potential of multilevel meta-analysis to combine SCED data. Second, a summary of the methodological evidence on the performance of multilevel models for meta-analysis is provided, which is useful given that such evidence is currently scattered over multiple technical articles in the literature. Third, the actual steps to perform a multilevel meta-analysis are outlined in a brief practical guide. Fourth, a suggestion for integrating the quantitative results with a visual representation is provided.
多水平模型是一种可以用来总结单案例实验设计 (SCED) 数据的方法。多水平模型是为分析具有嵌套在较高层次单元中的较低层次单元的层次结构数据而开发的。SCED 使用从研究中多个案例（或主体）收集的时间序列数据，允许研究人员在个体水平上研究干预效果，还可以研究这些个体干预效果随时间的变化。人们越来越关注如何通过综合一系列干预研究的数据，利用 SCED 来为干预措施建立证据基础。尽管使用多水平模型对 SCED 研究进行荟萃分析很有前途，但由于其潜在的技术复杂性，应用往往受到阻碍。首先，本文提供了对多水平荟萃分析结合 SCED 数据的潜力的易理解的描述和概述。其次，提供了对用于荟萃分析的多水平模型的性能的方法学证据的总结，鉴于此类证据目前在文献中的多个技术文章中分散，因此该总结很有用。第三，在简要的实用指南中概述了执行多水平荟萃分析的实际步骤。第四，提供了一种将定量结果与可视化表示集成的建议。
