Power Analysis and Effect Size in Mixed Effects Models: A Tutorial
By Marc Brysbaert, Michaël A. Stevens. Journal of Cognition, 2018
1,141 citations recorded by the source corpus.
In psychology, attempts to replicate published findings are less successful than expected. For properly powered studies replication rate should be around 80%, whereas in practice less than 40% of the studies selected from different areas of psychology can be replicated. Researchers in cognitive psychology are hindered in estimating the power of their studies, because the designs they use present a sample of stimulus materials to a sample of participants, a situation not covered by most power formulas. To remedy the situation, we review the literature related to the topic and introduce recent software packages, which we apply to the data of two masked priming studies with high power. We checked how we could estimate the power of each study and how much they could be reduced to remain powerful enough. On the basis of this analysis, we recommend that a properly powered reaction time experiment with repeated measures has at least 1,600 word observations per condition (e.g., 40 participants, 40 stimuli). This is considerably more than current practice. We also show that researchers must include the number of observations in meta-analyses because the effect sizes currently reported depend on the number of stimuli presented to the participants. Our analyses can easily be applied to new datasets gathered.
Subfields: Applied Psychology, Developmental and Educational Psychology, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/1c5e5e50a442850776568078022a8de27f7aba46
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In Applied Psychology
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Citations in the corpus
- How Many Participants Do We Have to Include in Properly Powered Experiments? A Tutorial of Power Analysis with Reference Tables (2019, methodology): “Brysbaert and Stevens (2018) recommended 40 participants and 40 stimuli per condition as good practice in reaction time studies with two repeated-measures conditions.”
- Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R (2020, methodology): “Instead, we wish to highlight an approach introduced in Brysbaert and Stevens (2018).”
- Cognitive state dependent enhancement of cognitive control with transcranial magnetic stimulation (2026, unknown): “…the behavioural state at the time of stimulation, our sample size was determined via power analysis of (generalised) linear mixed effects models 41,42 to achieve 80% statistical power and targeting a moderate effect size ( p2 = 0.08) based on DLPFC-neuromodulation studies in cognition and…”