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 targeted at complete beginners, 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 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.

Exploring writer vs content focused reference in written student feedback: mixed evidence from quantitative and content analysis results

“…Due to the lack of previous studies, we could not perform an a priori power analysis. Instead, we conducted a sensitivity power analysis (Bartlett & Charles, 2022), suggesting our final sample size of 106 would be sensitive to detect effects of Cohen's d = 0.32 for any pairwise comparison comparing two conditions and r = .31 for any correlation (two-tailed, 90% power).…”

Development of a Video Game as a Therapeutic Tool to Improve Cognitive and Motor Skills in Persons with Down Syndrome.

“…Subsequently, a video game was developed using the Unity engine, based on the SUM methodology, which was applied to the experimental group for ve weeks, while the control group was kept without the intervention of the video game. Data analysis was performed using Jamovi 2.4.11 software, which according to Bartlett et al (2022) is a free and open source graphical user interface (GUI). Thus, statistical tests such as ShapiroWilk and Mann-Whitney U, which applied a combined approach of descriptive and inferential analysis, correspondingly performed to examine the hypotheses related to each indicator.…”

Multiverse Meta-Analysis: Proposing an Exploratory Framework

“…Many researchers have already uncovered the low statistical power of psychological findings (Bartlett & Charles, 2022;Chambers, 2017). This means that studies are searching for smaller effect sizes than the ability of their design (Bartlett & Charles, 2022). In other words, by underpowered studies, true effects cannot be found often (Nuijten, 2019), and as it is not the case in the literature, we can see how lowpowered studies can affect the reliability of science (Chambers, 2017).…”

“…Although most aforementioned approaches were more focused on changing the alpha value and Bartoš & Maier (2022) also argue it is more efficient to change the alpha level to change the statistical power, we cannot overlook the problems caused by low statistical power. Many researchers have already uncovered the low statistical power of psychological findings (Bartlett & Charles, 2022;Chambers, 2017). This means that studies are searching for smaller effect sizes than the ability of their design (Bartlett & Charles, 2022).…”

“…One way to mitigate this problem is to recruit enough participants for the study; this process is called "power analysis" (Bartlett & Charles, 2022). This approach is not the only way to justify the sample size (see Lakens, 2022), but it is a way to avoid low statistical power.…”