An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau

Tarlow, Kevin R.

doi:10.1177/0145445516676750

Cited by 231 publications

(267 citation statements)

References 49 publications

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“…Theil-Sen regression has been recommended for single-case data analysis (Tarlow, 2017; and has been used in other fields to analyze autocorrelated, non-normal, monotonically-trended time-series data (e.g., Yue et al, 2002).…”

Section: Stage I: Parameter Estimationmentioning

confidence: 99%

A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

Tarlow¹,

Brossart²

2018

School Psychology Quarterly

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Single-case experimental methods are used across a range of educational and psychological research. Single-case data are analyzed with a variety of methods, but no statistic has demonstrated clear superiority over other methods. The time-series nature of single-case designs requires special consideration for baseline trend and autocorrelation when estimating intervention effect size. However, standard correction methods are limited because they assume precise statistical estimation of trend and autocorrelation. Unlike standard correction methods, Monte Carlo simulation methods can address the poor precision of single-case effect size indices. This paper presents the rationale for a new simulation method, Interrupted Time-Series Simulation (ITSSIM). A small field test was also conducted, and ITSSIM performed similarly to sophisticated multilevel methods for single-case research. ITSSIM is accessible as a free software application that requires no prior knowledge of statistical computing or syntax. ITSSIM may be used to estimate the effect size of a single interrupted time-series (AB design), and multiple ITSSIM effect size estimates may be combined via meta-analysis.A new method of single-case data analysis, ITSSIM, uses computer simulation to test the effectiveness of school/psychological interventions at the individual subject level. ITSSIM gave results similar to sophisticated multilevel modeling methods when tested on a small example data set, suggesting ITSSIM is a viable tool for single-case research. This new method of analysis is easy for all investigators to use because ITSSIM software is free to download and requires no previous statistical computing experience.

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Section: Stage I: Parameter Estimationmentioning

confidence: 99%

A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

Tarlow¹,

Brossart²

2018

School Psychology Quarterly

Self Cite

View full text Add to dashboard Cite

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“…Two other nonoverlap indices also entail baseline trend control. According to the Badditional output^calculated at http://ktarlow.com/stats/tau/, the baseline-corrected Tau (Tarlow, 2017) removes baseline trend from the data using…”

Section: Nonoverap Indicesmentioning

confidence: 99%

“…Aim of the review It has already been stated (Parker et al, 2011) and illustrated (Tarlow, 2017) that baseline trend extrapolation can lead to impossible forecasts for the subsequent intervention-phase data. Accordingly, the research question we chose was the percentage of studies in which extrapolating the baseline trend of the data set (across several different techniques for fitting the trend line) leads to values that are below the lower bound or above the upper bound of the outcome variable.…”

Section: A Review Of Research Published In 2015mentioning

confidence: 99%

“…We consider that either of these descriptive approaches is likely to be more reasonable than testing the statistical significance of the baseline trend before deciding whether or not to take it into account, because such a statistical test might lack power for short baselines (Tarlow, 2017). Using Kendall's tau as a measure of the percentage of improving data points (Vannest, Parker, Davis, Soares, & Smith, 2012) would not inform one about whether a clear linear trend were present, because it refers more generally to a monotonic trend.…”

Section: Avoiding the Issuesmentioning

confidence: 99%

“…When Parker et al (2011) discussed the issue that certain procedures to control for baseline trend could lead to projecting trend beyond rational limits, they proposed the conservative trend correction procedure implemented in Tau-U. This procedure could be useful for statistically controlling baseline trend, although the evidence provided by Tarlow (2017) suggests that the trend control incorporated in Tau-U is insufficient (i.e., leads to false positive results), especially as compared to other procedures, including MPD. An additional limitation of this trend correction procedure is that it cannot be used when extrapolating baseline trend.…”

Section: Fourth Issue: Out-of-bounds Forecastsmentioning

confidence: 99%

See 2 more Smart Citations

Extrapolating baseline trend in single-case data: Problems and tentative solutions

2018

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Single-case data often contain trends. Accordingly, to account for baseline trend, several data-analytical techniques extrapolate it into the subsequent intervention phase. Such extrapolation led to forecasts that were smaller than the minimal possible value in 40% of the studies published in 2015 that we reviewed. To avoid impossible predicted values, we propose extrapolating a damping trend, when necessary. Furthermore, we propose a criterion for determining whether extrapolation is warranted and, if so, how far out it is justified to extrapolate a baseline trend. This criterion is based on the baseline phase length and the goodness of fit of the trend line to the data. These proposals were implemented in a modified version of an analytical technique called Mean phase difference. We used both real and generated data to illustrate how unjustified extrapolations may lead to inappropriate quantifications of effect, whereas our proposals help avoid these issues. The new techniques are implemented in a user-friendly website via the Shiny application, offering both graphical and numerical information. Finally, we point to an alternative not requiring either trend line fitting or extrapolation.

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Blending as a treatment for feeding disorders: A review of the literature

King

Burch

2020

Behavioral Interventions

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Research indicates feeding disorders are relatively common in children. Described as combining two or more food items in a way that prevents separation, blending is recommended when the child avoids novel textures or flavors. As a nonrestrictive approach to feeding disorders, blending may appeal to consumers. The present study reviewed experimental research published from 1998 to 2018 involving the use blending to address feeding disorders. In addition to study quality, methods, and effects, this review targeted procedures used to progressively introduce novel textures. Identified studies (n ¼ 10) evaluated blending using single-case designs. Studies included children (n ¼ 14) generally under the age of four with diagnoses of disabilities or severe feeding disorders. Although the studies provided insight into practical considerations, the research did not exhibit sufficient rigor to support the use of blending. Special considerations may nonetheless justify the use of blending in treatment. Implications for practice follow a description of findings.

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An Improved Rank Correlation Effect Size Statistic for Single-Case Designs: Baseline Corrected Tau

Cited by 231 publications

References 49 publications

A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

A comprehensive method of single-case data analysis: Interrupted Time-Series Simulation (ITSSIM).

Extrapolating baseline trend in single-case data: Problems and tentative solutions

Blending as a treatment for feeding disorders: A review of the literature

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