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重新审视对多基线设计中内部效度威胁的分析

Revisiting an Analysis of Threats to Internal Validity in Multiple Baseline Designs.

作者信息

Slocum Timothy A, Joslyn P Raymond, Nichols Beverly, Pinkelman Sarah E

机构信息

Utah State University, 2865 Old Main Hill, Logan, UT 84322 USA.

出版信息

Perspect Behav Sci. 2022 Jul 26;45(3):681-694. doi: 10.1007/s40614-022-00351-0. eCollection 2022 Sep.

Abstract

In our previous article on threats to internal validity of multiple baseline design variations (Slocum et al., 2022), we argued that nonconcurrent multiple baseline designs (NCMB) are capable of rigorously demonstrating experimental control and should be considered equivalent to concurrent multiple baselines (CMB) in terms of internal validity. We were fortunate to receive five excellent commentaries on our article from experts in single-subject research design-four of whom endorsed the conclusion that NCMBs should be considered strong experimental designs capable of demonstrating experimental control. In the current article, we address the most salient points made in the five commentaries by further elaborating and clarifying the logic described in our original article. We address arguments related to classic threats including maturation, testing and session experience, and coincidental events (history). We rebut the notion that although NCMBs are strong, CMBs provide an increment of additional control and discuss the application of probability-based analysis of the likelihood of threats to internal validity. We conclude by emphasizing our agreement with many of the commentaries that selection of single-case experimental designs should be based on the myriad subtleties of research priorities and contextual factors rather than on a decontextualized hierarchy of designs.

摘要

在我们之前关于多重基线设计变体内部效度威胁的文章中(斯洛克姆等人,2022年),我们认为非同时多重基线设计(NCMB)能够严格证明实验控制,并且就内部效度而言,应被视为等同于同时多重基线(CMB)。我们很幸运收到了来自单被试研究设计领域专家对我们文章的五篇精彩评论——其中四位赞同以下结论:NCMB应被视为能够证明实验控制的强大实验设计。在本文中,我们通过进一步阐述和澄清我们原文章中描述的逻辑,来回应五篇评论中提出的最突出要点。我们回应了与经典威胁相关的论点,包括成熟、测试和阶段经验以及偶发事件(历史)。我们反驳了以下观点:尽管NCMB很强大,但CMB能提供额外的控制增量,并讨论了基于概率分析内部效度威胁可能性的应用。我们在结论中强调,我们同意许多评论的观点,即单案例实验设计的选择应基于研究优先级和背景因素的诸多细微差别,而不是基于脱离背景的设计等级制度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f9cd/9458797/1417f530e2df/40614_2022_351_Fig1_HTML.jpg

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