University of Texas at Austin, Austin, TX, USA.
J Sch Psychol. 2018 Jun;68:99-112. doi: 10.1016/j.jsp.2018.02.003. Epub 2018 Mar 14.
Methods for meta-analyzing single-case designs (SCDs) are needed to inform evidence-based practice in clinical and school settings and to draw broader and more defensible generalizations in areas where SCDs comprise a large part of the research base. The most widely used outcomes in single-case research are measures of behavior collected using systematic direct observation, which typically take the form of rates or proportions. For studies that use such measures, one simple and intuitive way to quantify effect sizes is in terms of proportionate change from baseline, using an effect size known as the log response ratio. This paper describes methods for estimating log response ratios and combining the estimates using meta-analysis. The methods are based on a simple model for comparing two phases, where the level of the outcome is stable within each phase and the repeated outcome measurements are independent. Although auto-correlation will lead to biased estimates of the sampling variance of the effect size, meta-analysis of response ratios can be conducted with robust variance estimation procedures that remain valid even when sampling variance estimates are biased. The methods are demonstrated using data from a recent meta-analysis on group contingency interventions for student problem behavior.
需要用于元分析单案例设计 (SCD) 的方法，以便为临床和学校环境中的循证实践提供信息，并在 SCD 构成研究基础的大部分的领域中得出更广泛和更有说服力的概括。单案例研究中最广泛使用的结果是使用系统直接观察收集的行为测量，这些测量通常采用比率或比例的形式。对于使用此类测量的研究，一种简单直观的量化效应大小的方法是根据从基线开始的比例变化，使用一种称为对数响应比的效应大小。本文介绍了使用元分析估计对数响应比并组合估计值的方法。这些方法基于一种简单的模型，用于比较两个阶段，其中每个阶段的结果水平都是稳定的，并且重复的结果测量是独立的。尽管自相关会导致效应大小的抽样方差产生偏差估计，但即使在抽样方差估计存在偏差的情况下，仍可以使用稳健的方差估计程序进行响应比的元分析。该方法使用最近关于学生问题行为团体应急干预的元分析中的数据进行了演示。
