Selecting the proper Tau-U measure for single-case experimental designs: Development and application of a decision flowchart
Abstract
A variety of measures have been developed to quantify intervention effects for single-case experimental design studies. Within the family of non-overlap indices, the Tau-U measure is one of the most popular indices. There are several Tau-U variants, each one calculated differently. The appropriateness of each Tau-U variant depends upon the data characteristics present within the study (e.g. number of measurement occasions, the within-case variability, and baseline trend). However, inconsistent terminology is used to refer to the Tau-U variants, and researchers can overlook the attributes of the different Tau-U variants. As a result, the Tau-U variants can be applied inappropriately, and this can result in invalid conclusions of intervention effectiveness. This paper proposes a Tau-U flowchart that can assist the decision-making process when using Tau-U with single-case experimental designs that incorporate baseline-intervention (AB) comparisons (e.g. multiple-baseline designs, withdrawal/reversal designs, etc.). The flowchart can help researchers select the appropriate Tau-U variant to use based on their data and research questions. The flowchart is applied to two single-case experimental studies to demonstrate its use.
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Within-participant analyses of sleep diary data were conducted using Tau-U, and TauUadj scores were calculated consistent with recommendations.
“Within-participant analyses of sleep diary data were conducted across the study phases (A -E), using a measure of non-overlap for single case data, the Tau-U (Parker et al ., 2011). Consistent with the recommendations of Fingerhut et al . (2021), we calculated TauUadj scores.”
From ACT-i, an insomnia intervention for autistic adults: A pilot study (2023) · cites this paper as Fingerhut et al., 2021
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“An initial step in this direction is the development of a decision-making flowchart for selected ES metrics as demonstrated by Fingerhut et al. (2021).”
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“For a nonparametric measure such as Tau (Parker, Vannest, Davis, & Sauber, 2011, see also Tarlow, 2017, for an alternative), Fingerhut et al. (2021) provide indications about how to choose one of the possible versions.”
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“Consistent with the recommendations of Fingerhut et al .”
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“This flowchart along with an empirical demonstration can be seen in Fingerhut et al. (2021), within this same special issue.”
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“Meanwhile, many sophisticated ways of analyzing the results of single-case studies have been established (for an overview see Fingerhut et al., 2021a; Shadish, 2014), complementing traditional visual analysis.”
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“SCED users may not realize that non-overlap quantification techniques cannot represent the magnitude of change/size of the effect (Fingerhut et al., 2021a,b).”
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“3 The percentage of intervals with indices happiness and indices of unhappiness during baseline and presession pairing ◂ several variants that are calculated differently, which can make it difficult for researchers to select the appropriate one based on data characteristics (Fingerhut et al., 2021).”
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“59 A second baseline-corrected Tau-U 62 removes the baseline trend with Theil-Sen robust regression.”
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“Decisions about which Tau-U measure to use were based on recommendations from Fingerhut et al. (2021).”
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“Each of the three Tau-U variants have strengths and weaknesses and should be used in different circumstances (Fingerhut et al., 2021a, 2021b).”
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“As there were ≤ 5 measurement occasions in the baseline for the IES, baseline trend for the IES was tested and corrected, when necessary, using Tau-U trend A (Fingerhut et al., 2021; Parker et al., 2011).”
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“Visual analysis has been criticized due to limited inter-rater agreement, the possibility of Type II errors, and lack of ability to generalize to other studies (Fingerhut et al., 2021).”
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“Given that the goal of this study is to document participant improvement across phases Tau-U comparing the A versus B phase was selected (Fingerhut et al., 2021) Tau-U was originally proposed as a method that could control for baseline trend, in addition to a measure of effect.”
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Wendt, O., & Rindskopf, D. (2023) · Evidence Based Communication Assessment and Intervention · doi:10.1080/17489539.2023.2272906 · Cited by 2
“The authors are following up on these important findings with a second paper offering concrete recommendations for applied researchers when deciding which Tau-U variant to use with two different SCED scenarios (Fingerhut et al., 2021b).”
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“It is also a conservative measure to control for Type I error in AB designs (Fingerhut et al., 2021), as differently from other non-overlap indices, such as NAP or IRD, it allows researchers to account for baseline trends.”
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“Fingerhut et al. (2021) provide a useful tutorial for selecting the proper Tau-U measure for single-case experimental designs.”
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“Due to risks of Type II errors and the limited generalisability about intervention effectiveness (Fingerhut et al ., 2021) using visual analysis, additional Tau-U analysis was conducted using the Tau-U online calculator (Vannest et al ., 2016).”
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“Additionally, it was appropriate for this study because there were fewer than five data points in the baseline on most measures (Fingerhut et al., 2021); and because the mid-test and post-test phases were not substantially longer than the baseline (Tarlow, 2017).”
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“The base-line phase had only three measurement points, which was less than the minimum of five required (Fingerhut et al., 2021).”
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“A variety of tools are available to help choose, justify, and appropriate use quantification techniques (e.g., Fingerhut & Moeyaert, 2022; Fingerhut et al., 2021b; Manolov et al., 2022; Manolov & Moeyaert, 2017).”
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“Many variants of the Tau exist and hence recommendations from Fingerhut et al. (2021) were followed in selecting the proper Tau for the current study.”
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“…that the authors’ interpretation of the magnitude of change via Tau-U may be inaccurate and vary based on the context and which version of the index used (Pustejovsky, 2016) and researchers may consider a decision-making flowchart to determine the most appropriate variant (Fingerhut et al., 2021).”
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“In terms of non-overlap approaches, based on recommendations outlined by Fingerhut and colleagues [45], Tau-U is used if structured visual analysis shows no significant baseline trend, whereas the baseline corrected Tau-adj [46]. is used if significant baseline trend is present.”
- Authors
- Fingerhut, J., Xu, X., & Moeyaert, M.
- Affiliations
- University at Albany, State University of New York (US)
- Corresponding author
- Fingerhut, Joelle
- Venue
- Evidence-Based Communication Assessment and Intervention
- Publisher
- Informa UK Limited
- Published
- 2021-07-03
- Volume
- 15
- Issue
- 3
- Pages
- 99-114
- ISSN
- 1748-9539 (ISSN-L)
- 1748-9547
- OpenAlex
- W3194924411
- Cited by
- 43
- References
- 0
- Citations by year
- 2026: 11 · 2025: 7 · 2024: 10 · 2023: 7 · 2022: 6 · 2021: 2
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evidence-Based Communication Assessment and Intervention (publishedVersion)
- Funders
- Institute of Education Sciences, U.S. Department of Education (R305D190022)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Behavioral and Psychological Studies
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Education › Educational Research and Methods
- Keywords
- single-case experimental design, decision flowchart, multiple baseline design, intervention effectiveness
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