Tutorial: Small-N Power Analysis
Run a quick G*Power check so your small-N study is big enough to detect the smallest change that would help your client.
01Research in Context
What this study did
Kyonka (2019) wrote a how-to guide on power for small-N studies.
The paper shows step-by-step clicks in the free G*Power program.
It uses “just-noticeable difference” as the effect size you care about.
What they found
You can plan a single-case study the same way you plan a group study.
Enter the smallest change that would matter in your client’s life.
G*Power tells you how many sessions or participants you need.
How this fits with other research
Paff et al. (2019) built the EBP-COM to watch teachers in real classrooms.
Their tool needs fewer visits when you first run Kyonka’s power steps.
Mandell et al. (2016) proved the 8-item AMSE keeps sensitivity at a large share.
A priori power explains why their tiny sample still caught true cases.
Mulder et al. (2020) trimmed the PSI-SF to 21 items for ASD parents.
Kyonka’s method shows how small that scale can go before it breaks.
Why it matters
Stop guessing how many baseline points you need.
Plug the smallest meaningful change into G*Power before you start.
You will write stronger grant proposals and waste fewer client hours.
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02At a glance
03Original abstract
Power analysis is an overlooked and underreported aspect of study design. A priori power analysis involves estimating the sample size required for a study based on predetermined maximum tolerable Type I and II error rates and the minimum effect size that would be clinically, practically, or theoretically meaningful. Power is more often discussed within the context of large-N group designs, but power analyses can be used in small-N research and within-subjects designs to maximize the probative value of the research. In this tutorial, case studies illustrate how power analysis can be used by behavior analysts to compare two independent groups, behavior in baseline and intervention conditions, and response characteristics across multiple within-subject treatments. After reading this tutorial, the reader will be able to estimate just noticeable differences using means and standard deviations, convert them to standardized effect sizes, and use G*Power to determine the sample size needed to detect an effect with desired power.
Perspectives on Behavior Science, 2019 · doi:10.1007/s40614-018-0167-4