G*Power 3

G*Power is a tool to compute statistical power analyses for many different t tests, F tests, χ2 tests, z tests and some exact tests. G*Power can also be used to compute effect sizes and to display graphically the results of power analyses.


References in zbMATH (referenced in 24 articles )

Showing results 1 to 20 of 24.
Sorted by year (citations)

1 2 next

  1. Tyler Morgan-Wall, George Khoury: Optimal Design Generation and Power Evaluation in R: The skpr Package (2021) not zbMATH
  2. Zha, Ruochen; Harel, Ofer: Power calculation in multiply imputed data (2021)
  3. Fleiß, Jürgen; Ackermann, Kurt A.; Fleiß, Eva; Murphy, Ryan O.; Posch, Alfred: Social and environmental preferences: measuring how people make tradeoffs among themselves, others, and collective goods (2020)
  4. Komaroff, Eugene: Relationships between (p)-values and Pearson correlation coefficients, type 1 errors and effect size errors, under a true null hypothesis (2020)
  5. Schnuerch, Martin; Erdfelder, Edgar; Heck, Daniel W.: Sequential hypothesis tests for multinomial processing tree models (2020)
  6. Verma, J. P.; Verma, Priyam: Determining sample size and power in research studies. A manual for researchers (2020)
  7. Harrar, Solomon W.; Ronchi, Fabrizio; Salmaso, Luigi: A comparison of recent nonparametric methods for testing effects in two-by-two factorial designs (2019)
  8. Molho, Catherine; Balliet, Daniel; Wu, Junhui: Hierarchy, power, and strategies to promote cooperation in social dilemmas (2019)
  9. Ly, Cheng; Marsat, Gary: Variable synaptic strengths controls the firing rate distribution in feedforward neural networks (2018)
  10. Zhao, Kun; Kashima, Yoshihisa; Smillie, Luke D.: From windfall sharing to property ownership: prosocial personality traits in giving and taking dictator games (2018)
  11. Füllbrunn, Sascha C.; Luhan, Wolfgang J.: Decision making for others: the case of loss aversion (2017)
  12. Noohi, Ehsan; Žefran, Miloš: Estimating human intention during a human-robot cooperative task based on the internal force model (2017)
  13. Barbieri, Christina; Booth, Julie L.: Support for struggling students in algebra: contributions of incorrect worked examples (2016) MathEduc
  14. Chen, Cheng-Huan; Chiu, Chiung-Hui: Employing intergroup competition in multitouch design-based learning to foster student engagement, learning achievement, and creativity (2016) MathEduc
  15. Goodman, Sara G.; Seymour, Travis L.; Anderson, Barrett R.: Achieving the performance benefits of hands-on experience when using digital devices: a representational approach (2016) MathEduc
  16. Herbst, Patricio; Chazan, Daniel; Kosko, Karl W.; Dimmel, Justin; Erickson, Ander: Using multimedia questionnaires to study influences on the decisions mathematics teachers make in instructional situations (2016) MathEduc
  17. Mansouri, S. Afshin; Aktas, Emel; Besikci, Umut: Green scheduling of a two-machine flowshop: trade-off between makespan and energy consumption (2016)
  18. Glöckner, Andreas; Hilbig, Benjamin E.; Jekel, Marc: What is adaptive about adaptive decision making? A parallel constraint satisfaction account (2014) MathEduc
  19. Janczyk, Markus; Pfister, Roland: Understanding inference statistics. From A for significance test to Z for confidence interval (2013)
  20. Li, Yuelin; Baron, Jonathan: Behavioral research data analysis with R (2012)

1 2 next


Further publications can be found at: http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/literature