2018
DOI: 10.1044/2017_ajslp-16-0197
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Tau-U: A Quantitative Approach for Analysis of Single-Case Experimental Data in Aphasia

Abstract: 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.

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Cited by 33 publications
(13 citation statements)
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“…There are, however, some quantitative approaches for analysing SSED data that can complement visual analysis and offer significance testing. One such approach, the Tau‐U, has shown promise for SSED research within the SLT field (Lee and Cherney 2018). It was not possible to accurately derive all the original data from the included studies to undertake Tau‐U calculations for this review; however, this approach to analysis should be considered for use in future SLT SSED studies.…”
Section: Resultsmentioning
confidence: 99%
“…There are, however, some quantitative approaches for analysing SSED data that can complement visual analysis and offer significance testing. One such approach, the Tau‐U, has shown promise for SSED research within the SLT field (Lee and Cherney 2018). It was not possible to accurately derive all the original data from the included studies to undertake Tau‐U calculations for this review; however, this approach to analysis should be considered for use in future SLT SSED studies.…”
Section: Resultsmentioning
confidence: 99%
“…The present research also addresses a growing concern within single‐case design: supplemental statistics for visual analysis of graphed time series data (Bulté & Onghena, ; Lee & Cherney, ; Parker, Cryer, & Byrns, ; Shadish, ; Shadish, Hedges, Horner, & Odom, ). A lack of universal decision rules and unreliability across raters illustrate the limitations of visual analysis (Brossart, Parker, Olson, & Mahadevan, ; Campbell & Herzinger, ).…”
Section: Discussionmentioning
confidence: 99%
“…Statistics focused on the standardized mean difference within each individual, an effect size calculation developed by Busk and Serlin (1992; for discussion, see Beeson & Robey, 2006). Standardized mean difference is problematic for a variety of reasons, including that it does not provide significance values and that effect size estimates can be inflated if there is little variability in the baseline probes (for discussion see Lee & Cherney, 2018). With this caveat in mind, all 5 PWA showed strong effect sizes for trained items in the shorter lag condition, whereas 2 PWA showed strong effect sizes and 2 PWA showed moderate effect sizes for trained items in the longer lag condition.…”
Section: Distributed Practice Effects In Naming Treatment Of Aphasiamentioning
confidence: 99%