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塔乌:分析失语症单病例实验数据的定量方法。

Tau-U: A Quantitative Approach for Analysis of Single-Case Experimental Data in Aphasia.

机构信息

James Madison University, Harrisonburg, VA.

Shirley Ryan AbilityLab (formerly the Rehabilitation Institute of Chicago), IL.

出版信息

Am J Speech Lang Pathol. 2018 Mar 1;27(1S):495-503. doi: 10.1044/2017_AJSLP-16-0197.

Abstract

PURPOSE

Tau-U is a quantitative approach for analyzing single-case experimental design (SCED) data. It combines nonoverlap between phases with intervention phase trend and can correct for a baseline trend (Parker, Vannest, & Davis, 2011). We demonstrate the utility of Tau-U by comparing it with the standardized mean difference approach (Busk & Serlin, 1992) that is widely reported within the aphasia SCED literature.

METHOD

Repeated writing measures from 3 participants with chronic aphasia who received computer-based writing treatment are analyzed visually and quantitatively using both Tau-U and the standardized mean difference approach.

RESULTS

Visual analysis alone was insufficient for determining an effect between the intervention and writing improvement. The standardized mean difference yielded effect sizes ranging from 4.18 to 26.72 for trained items and 1.25 to 3.20 for untrained items. Tau-U yielded significant (p < .05) effect sizes for 2 of 3 participants for trained probes and 1 of 3 participants for untrained probes. A baseline trend correction was applied to data from 2 of 3 participants.

CONCLUSIONS

Tau-U has the unique advantage of allowing for the correction of an undesirable baseline trend. Although further study is needed, Tau-U shows promise as a quantitative approach to augment visual analysis of SCED data in aphasia.

摘要

目的

Tau-U 是一种分析单案例实验设计(SCED)数据的定量方法。它结合了阶段之间的非重叠与干预阶段趋势,并可以纠正基线趋势(Parker、Vannest 和 Davis,2011)。我们通过将 Tau-U 与广泛报道于失语症 SCED 文献中的标准化均数差方法(Busk 和 Serlin,1992)进行比较,展示了 Tau-U 的实用性。

方法

对 3 名患有慢性失语症并接受基于计算机的写作治疗的参与者的重复写作测量结果进行视觉和定量分析,同时使用 Tau-U 和标准化均数差方法。

结果

仅进行视觉分析不足以确定干预和写作改善之间的效果。标准化均数差方法产生的效应大小范围为训练项目的 4.18 至 26.72,未训练项目的 1.25 至 3.20。Tau-U 对 3 名参与者中的 2 名产生了训练探针的显著(p<.05)效应大小,对 3 名参与者中的 1 名产生了未训练探针的显著效应大小。对 3 名参与者中的 2 名的数据应用了基线趋势校正。

结论

Tau-U 具有独特的优势,允许纠正不良的基线趋势。尽管还需要进一步的研究,但 Tau-U 显示出作为一种增强失语症 SCED 数据视觉分析的定量方法的潜力。

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