Kirby Megan S, Spencer Trina D, Ferron John
College of Behavioral and Community Sciences, University of South Florida, 13301 Bruce B. Downs Blvd. MHC 1702, Tampa, FL 33612 USA.
College of Education, University of South Florida, Tampa, FL USA.
Perspect Behav Sci. 2021 Aug 2;44(2-3):389-416. doi: 10.1007/s40614-021-00301-2. eCollection 2021 Sep.
The Repeated Acquisition Design (RAD) is a type of single-case research design (SCRD) that involves repeated and rapid measurement of irreversible discrete skills or behaviors through pre-and postintervention probes across different sets of stimuli. Researchers interested in the study of learning in animals and humans have used the RAD because of its sensitivity to detect immediate changes in rate or accuracy. Despite its strengths, critics of the RAD have cautioned against its use due to reasonable threats to internal validity like pretest effects, history, and maturation. Furthermore, many methodologists and researchers have neglected the RAD in their SCRD standards (e.g., What Works Clearinghouse [WWC], 2020; Horner et al., 2005). Unless given guidance to address threats to internal validity, researchers may avoid the design altogether or continue to use a weak version of the RAD. Therefore, we propose a set of 15 quality RAD indicators, comprising foundational elements that should be present in all RAD studies and additional features that enhance causal inference and external validity. We review contemporary RAD use and describe how the additional features strengthen the rigor of RAD studies. We end the article with suggested guidelines for interpreting effects and the strength of the evidence generated by RAD studies. We invite researchers to use these initial guidelines as a jumping off point for a more RAD future.
重复习得设计(RAD)是一种单案例研究设计(SCRD),它通过对不同刺激集进行干预前和干预后的探测,对不可逆的离散技能或行为进行重复且快速的测量。对动物和人类学习研究感兴趣的研究人员使用了RAD,因为它对检测速率或准确性的即时变化很敏感。尽管RAD有其优点,但由于存在诸如预测试效应、历史和成熟度等对内部效度的合理威胁,RAD的批评者已告诫要谨慎使用它。此外,许多方法学家和研究人员在他们的SCRD标准中忽略了RAD(例如,有效信息中心[WWC],2020;霍纳等人,2005)。除非得到解决内部效度威胁的指导,研究人员可能会完全避开这种设计,或者继续使用较弱版本的RAD。因此,我们提出了一组15个高质量的RAD指标,包括所有RAD研究都应具备的基础要素以及增强因果推断和外部效度的附加特征。我们回顾了当代RAD的使用情况,并描述了这些附加特征如何加强RAD研究的严谨性。我们在文章结尾给出了解释效应和RAD研究所产生证据强度的建议指南。我们邀请研究人员将这些初步指南作为迈向更完善的RAD未来的起点。