Evidence map›Paper›PMID 31976425›Full record

ArticlePerspectives on behavior science2019

Tutorial: Small-N Power Analysis.

Elizabeth G E Kyonka

Abstract read
In one paragraph

Article in Perspectives on behavior science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Editorial: Behavior Science for the Public Welfare.Perspectives on behavior science · 2019
    Article
  10. Article
  11. Randomization tests as alternative analysis methods for behavior-analytic data.Journal of the experimental analysis of behavior · 2019
    Article
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Elizabeth G E KyonkaPsychology, University of New England, Armidale, NSW 2351 Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

A priori power analysisEffect sizeExperimental designG*PowerHypothesis testingSample sizeTests of statistical significance

Identifiers

PMID31976425
PMCPMC6701714

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.