Impact of within-case variability on Tau-U indices and the hierarchical linear modeling approach for multiple-baseline design data: A Monte Carlo simulation study

ABSTRACT

This study focuses on two different measures to quantify intervention effectiveness for multiple-baseline design data. The influence of 216 design conditions on the estimate of the Tau-U index (with three different variants) and the regression-based effect size is empirically investigated through a Monte Carlo simulation study. Results demonstrate that the magnitude of the within-case variance influences the Tau-U indices the most when the true intervention effect is small relative to large within-case variance. The within-case variance does not systematically influence the regression-based effect size measure. The Critical Tau-U is introduced to help determine if Tau-U estimates indicate evidence in support of an intervention effect and address underlying issues with currently used benchmarks.

KEYWORDS:

• Hierarchical linear modeling
• multiple-baseline across participants design
• single-case experimental design
• Tau-U

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was supported by the Institute of Education Sciences, U.S. Department of Education, through grant [R305D190022]. The content is solely the responsibility of the author and does not necessarily represent the official views of the Institute of Education Sciences, or the U.S. Department of Education.