Statistical power and optimal design in experiments in which samples of participants respond to samples of stimuli.

By J. Westfall, David A. Kenny, C. Judd. Journal of experimental psychology. General, 2014

731 citations recorded by the source corpus.

Subfields: Developmental and Educational Psychology, Food Science, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8c4284f32d87e48235b8abe13d9cb91e34bbe1fc

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In Developmental and Educational Psychology

In Food Science

In Management Science and Operations Research

Citations in the corpus

Power Analysis and Effect Size in Mixed Effects Models: A Tutorial (2018, methodology): “First, Westfall et al. (2014) showed how you can calculate the effect size (measured as d) for a design with random participants and random items.”
But You Don’t Look Like A Scientist!: Women Scientists with Feminine Appearance are Deemed Less Likely to be Scientists (2016, positive): “An approximate power analysis based on the calculations given by Westfall et al. (2014), assuming a counterbalanced design and using their default variance partitioning coefficients, suggests that an experiment with 80 stimuli and 50 participants should have 80 % power to detect an effect size as sm”
The cave of Shadows. Addressing the human factor with generalized additive mixed models (2015, background): “Publication bias (Francis, 2012), non-random selection of stimuli and subjects (Forster, 2000; Sander and Sanders, 2006; Francis, 2013), societal changes (Ramscar et al., 2015) and especially lack of power (Button et al., 2013; Westfall et al., 2014) are major concerns.”
An Introduction to Mixed Models for Experimental Psychology (2019, positive): “Random e ﬀ ects with a low number of levels also have an extremely detrimental e ﬀ ect on statistical power (Westfall et al., 2014).”
Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R (2020, methodology): “There are various ways to conduct a power analysis for mixed models, with Westfall et al. (2014) and Brysbaert and Stevens (2018) constituting important introductory works in this domain.”
With Little Power Comes Great Responsibility (2020, background): “Because most NLP tasks do not take the form of standard experiments in other sciences (Kraemer and Blasey, 2015; Westfall et al., 2014), it is nontrivial to run power analyses for many tasks of interest.”
Sample Size Justification (2021, positive): “Statistical power can be computed to determine the number of participants, or the number of items (Westfall, Kenny, & Judd, 2014) but can also be performed for single case studies (Ferron & Onghena, 1996; McIntosh & Rittmo, 2020) Although it is common to set the Type I error rate to 5% and aim for…”
Choosing prediction over explanation in psychology: Lessons from machine learning (2017, background): “To maximize statistical power and support more generalizable conclusions (Judd, Westfall, & Kenny, 2016; Westfall, Kenny, & Judd, 2014), the authors opted to dynamically generate every stimulus at presentation time by combining constituent elements into a never-before-seen composite.”