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Conducting Power Analyses to Determine Sample Sizes in Quantitative Research: A Primer for Technology Education Researchers Using Common Statistical Tests Cover

Conducting Power Analyses to Determine Sample Sizes in Quantitative Research: A Primer for Technology Education Researchers Using Common Statistical Tests

By:   
Open Access
|Jun 2024

Figures & Tables

Table 1

Type I and Type II errors.

Reality
Ho is true: no difference between ACJ and traditional peer sharing and feedbackHo is false: there is a difference between ACJ and traditional peer sharing and feedback
Result of statistical test within an empirical study of a sampleEvidence to reject HoFalse positive Type I error Probability = αCorrect decision True positive Probability = 1 – β
No evidence to reject HoCorrect decision True negative Probability = 1 – αFalse negative Type II error Probability = β
Figure 1

Results of power analysis for d = 0, 0.2, and 0.3 with n per group = 100, 150, and 200.

Figure 2

Results of power analysis for r = 0, 0.2, and 0.3 with n = 50, 70, and 90.

Figure 3

G*Power interface for conducting a power analysis for an independent samples t-test.

Figure 4

Sample size × effect size simulations.

Language: English
Page range: 81 - 109
Submitted on: Jan 5, 2024
Accepted on: Apr 5, 2024
Published on: Jun 25, 2024
Published by: Virginia Tech
In partnership with: Paradigm Publishing Services

© 2024 Jeffery Buckley, published by Virginia Tech
This work is licensed under the Creative Commons Attribution 4.0 License.