Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses

By Franz Faul, Edgar Erdfelder, Axel Buchner, Albert-Georg Lang. Behavior Research Methods, 2009

31,664 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a7c3e3fda30aec943f34c4862ce7e51bf5e5a453

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Citations in the corpus

Activity flow over resting-state networks shapes cognitive task activations (2016, result): “Overall these results support the conclusion that the choice of whether or not to use GSR does not substantially affect the outcome of activity flow mapping.”
Antecedents and Outcomes of Resident Empowerment through Tourism (2021, positive): “3 software (Faul et al. 2009) was used to check whether the sample size (N = 415) reflected an adequate statistical power for the model, following recommendations of Lu et al.”
Sample Size Guideline for Correlation Analysis (2016, neutral): “Generally, correlation coefficient of 0.3 is considered sizeable (Cohen, 1992).”
Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting (2017, methodology): “If another effect size or statistical power level is desired, power analysis can be conducted for example with the help of the G∗Power 3 (Faul et al., 2007, 2009) or the “pwr” package (Champely, 2016) available for the R statistical package, which facilitates the calculation of required sample size.”
Using Bayes to get the most out of non-significant results (2014, methodology): “Power can be easily calculated with the free software Gpower (Faul et al., 2009): To calculate a priori power (i.e., in advance of collecting data, which is when it should be calculated), one enters the effect size predicted in advance, a number of participants, the significance level to be used, an”
School environment and mastery experience as predictors of teachers’ self-efficacy beliefs towards inclusive teaching (2020, methodology): “This sample size is based on Tabachnick and Fidell’s (2007, 2013) participant calculation relevant to multiple linear regression analyses (suggested sample size of 130) and a priori power analysis carried out using G* Power 3.1 (suggested sample size of 118) (Faul et al. 2007; Faul et al. 2009).”
Behavioural Brain Research (2016, neutral): “Thereafter, the training of the two Cohorts was matched”