G*Power 3.1 Tutorial: Overview (Episode 1)

Summary

In this tutorial, the presenter introduces G*Power, a free, open-source tool for conducting power analysis in statistical research. Version 3.1 of G*Power is explored, demonstrating its various test families such as t-tests, F-tests, chi-squared tests, and more. The tutorial covers key features like selecting test families, inputting parameters (e.g., alpha level, effect size, power), and understanding the output (sample size, power, etc.). The presenter emphasizes the importance of power analysis for research design and explains how G*Power can help calculate the necessary sample size or power for meaningful results. Future videos will dive deeper into specific tests and practical applications.

Takeaways

• 😀 G*Power 3.1 is a free and open-source software for performing power analysis in statistical tests.
• 😀 The tutorial introduces the user interface and key features of G*Power 3.1, including test families and power analysis types.
• 😀 G*Power supports various statistical tests, such as T-tests, F-tests, chi-squared tests, and Z-tests.
• 😀 Power analysis in G*Power helps researchers calculate sample sizes, effect sizes, and test power for experiments.
• 😀 The software offers different types of power analysis, including a priori (sample size calculation), post hoc (power calculation after data collection), and sensitivity analysis.
• 😀 Users can input parameters like alpha, power, sample size, and effect size, and G*Power will output critical values like t-values and degrees of freedom.
• 😀 The program includes an effect size calculation tool, which allows for determining values like Pearson’s r based on previous research or observed data.
• 😀 G*Power allows users to choose between one-tailed or two-tailed tests, depending on the statistical model used.
• 😀 The software includes graphing and table options to visualize results from power analysis, but detailed analysis of these features will be covered in future tutorials.
• 😀 Results from G*Power can be exported and used for pre-registration protocols, helping with transparency in research, especially in open science practices.
• 😀 The tutorial series will continue with more detailed instructions on how to use G*Power for various statistical tests and power analyses.

Q & A

What is G*Power and why is it useful?

G*Power is a free and open-source program designed for power analysis. It helps researchers determine the required sample size, the power of a statistical test, or the effect size in different statistical tests, which is crucial for ensuring that a study has sufficient power to detect meaningful effects.

What version of G*Power is being discussed in the tutorial?

The tutorial focuses on G*Power version 3.1, which is the current available version of the software.

What kind of statistical tests can G*Power handle?

G*Power can handle a wide range of statistical tests, including t-tests, F-tests, chi-squared tests, z-tests, and regression analyses. The tutorial primarily covers t-tests but briefly mentions other available tests.

What are the two main views in G*Power's interface?

G*Power provides two main views: central and non-central distributions. These views represent different statistical models and help users in their power analysis based on the type of distribution.

What is the significance of 'Test Family' in G*Power?

The 'Test Family' in G*Power refers to the type of statistical test you are conducting, such as t-tests, F-tests, or chi-squared tests. Choosing the correct test family determines the options available for conducting power analysis in the software.

What are the different types of power analysis available in G*Power?

G*Power offers several types of power analysis: a priori (calculating the required sample size), post hoc (calculating achieved power), sensitivity (determining effect size), and criterion (calculating the alpha level needed for a given power and effect size).

How does effect size factor into power analysis in G*Power?

Effect size is a critical parameter in power analysis as it represents the magnitude of the relationship or difference in the data. In G*Power, effect size can be inputted manually or calculated based on prior research or coefficients of determination (like r-squared).

What is the typical power level used in many social and behavioral sciences?

The typical power level used in social and behavioral sciences is 0.80, meaning there is an 80% chance of correctly rejecting the null hypothesis if it is false.

Can you graph or table results in G*Power?

Yes, G*Power allows users to graph or table the results by inputting relevant statistical parameters, making it easier to visualize and interpret the output.

What is the purpose of pre-registration in open science, and how does G*Power facilitate this?

Pre-registration is a practice in open science where researchers publicly declare their planned study design and analysis methods before collecting data. G*Power supports this by allowing users to generate a protocol for their power analysis that can be copied and uploaded to platforms like the Open Science Framework, ensuring transparency in research.