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Related Experiment Video

Updated: Jun 2, 2026

In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening

In Vitro Assay for Studying the Aggregation of Tau Protein and Drug Screening

Published on: November 20, 2018

Combining nonoverlap and trend for single-case research: Tau-U.

Richard I Parker¹, Kimberly J Vannest, John L Davis

¹Texas A&M University, College Station, TX 77843, USA. rparker@tamu.edu

Behavior Therapy

|April 19, 2011

Summary

A new Tau-U index for single-case research data analysis combines nonoverlap and trend, offering a robust alternative to existing methods. This novel approach shows promise for improving the analysis of single-case experimental designs.

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Area of Science:

Behavioral Sciences
Psychology
Research Methodology

Background:

Single-case research designs are crucial for evaluating interventions in various fields.
Existing nonoverlap indices for single-case data analysis have limitations, including artificial ceilings and sensitivity to autocorrelation.
There is a need for a more comprehensive and robust index to analyze single-case data effectively.

Purpose of the Study:

To introduce and validate a new index, Tau-U, for the analysis of single-case research data.
To demonstrate how Tau-U integrates nonoverlap and trend analysis within single-case designs.
To evaluate the performance of Tau-U, including its ability to control for undesirable Phase A trends and its behavior with autocorrelated data.

Main Methods:

The derivation of Tau-U from Kendall's Rank Correlation and the Mann-Whitney U test is explained.
The equivalence of trend and nonoverlap measures is demonstrated.
Tau-U calculations are exemplified for AB and ABA designs and field-tested on 382 published data series.

Main Results:

Controlling for undesirable Phase A trend had a modest impact on Tau-U.
Including Phase B trend provided more modest results compared to simple nonoverlap.
The Tau-U score distribution avoided the artificial ceiling effect observed in other nonoverlap techniques and performed well with autocorrelated data.

Conclusions:

Tau-U is a promising new index for single-case data analysis, offering a more comprehensive approach by combining nonoverlap and trend.
The index demonstrates robustness, particularly in avoiding artificial ceilings and handling autocorrelated data.
Further research is recommended to fully explore the potential and applications of Tau-U in single-case research.