Power to the People: A Beginner’s Tutorial to Power Analysis using jamovi

Abstract

Authors have highlighted for decades that sample size justification through power analysis is the exception rather than the rule. Even when authors do report a power analysis, there is often no justification for the smallest effect size of interest, or they do not provide enough information for the analysis to be reproducible. We argue one potential reason for these omissions is the lack of a truly accessible introduction to the key concepts and decisions behind power analysis. In this tutorial, we demonstrate a priori and sensitivity power analysis using jamovi for two independent samples and two dependent samples. Respectively, these power analyses allow you to ask the questions: “How many participants do I need to detect a given effect size?”, and “What effect sizes can I detect with a given sample size?”. We emphasise how power analysis is most effective as a reflective process during the planning phase of research to balance your inferential goals with your available resources. By the end of the tutorial, you will be able to understand the fundamental concepts behind power analysis and extend them to more advanced statistical models.

Psychological distress among Afghan refugees in Norway as a function of their integration

“…Based on our sample size, we did a sensitivity power analysis (G*Power 3.1.9.6) for multiple regression with 8 predictors to detect a small effect for the selected scales (t-tests for linear multiple regression). A sample of 114 participants would be sensitive to effects of above Cohen’s d = 0.07 with 80% power (alpha = 0.05, two-tailed) ( Faul et al, 2009 ; Bartlett, 2022 ).…”

Using preregistration as a tool for transparent fNIRS study design

“…Importantly, the relationship between the targeted effect size and its corresponding sample size and statistical power is curve shaped. 86 Thus, for instance, even small deviations between targeted versus observed effect sizes may result in a substantial loss of statistical power. Traditionally, effect size estimates are drawn from previous, comparable research.…”

Power Analysis and Exploratory Research

“…Sensitivity power analysis, also known as sensitivity analysis, is conducted when the researcher has no flexibility to increase the sample due to unavoidable constraints or if they are working on a specific population of known size and must rely on that fixed available sample size (Bartlett, 2022;Perugini et al, 2018). Sensitivity power analysis aims to determine the minimum effect size that can be detected reliably, given a fixed sample size, a particular alpha level, and a desired level of power.…”

“…Each of these is conducted at different time points in the research studies and has different purposes. Detailed instructions on performing various types of power analysis can be found elsewhere (Bartlett, 2022;Bulus, 2023;Kang, 2021).…”