Power Analysis

Example 1: Power and Sample Size Calculation for the Independent Sample t-Test
Selecting an analysis type
Example 2: Analyzing Power, Sample Size, and Effect Size in 1-Way ANOVA
Example 3: Sample Size Calculation in Factor Analysis
Example 4: Power and Sample Size in Complex Factorial ANOVA
Example 5: Power Calculation in a 1-Way Repeated Measures ANOVA
In a 1-way repeated measures ANOVA, you have the choice of analyzing the data with a univariate or multivariate approach. In this example, we show how to calculate power for the univariate F-test. Suppose that a group of N = 10 subjects are observed performing a task on p = 6 different occasions. In this subjects by trials design, the degrees of freedom are p -1 and (N - 1)(p - 1). However, the noncentrality parameter δ must be adjusted, because the repeated measures are generally correlated. Estimation, a priori, of this noncentrality parameter requires that you specify a value for the RMSSE, assume a common correlation between the various trial occasions as well as a common variance, and provide an estimate of this common correlation, ρ.
Example 6: Hypotheses About the Noncentrality Parameter of the F Distribution
Example 7: Constructing a Confidence Interval on the Noncentrality Parameter
Example 8: Power of Nonstandard Significance Tests in the Analysis of Variance
Traditionally, major hypothesis tests in the analysis of variance have been performed to assess whether main effects, interactions, or simple main effects exist at all. The traditional null hypothesis F-test is equivalent to a test that the RMSSE is equal to zero.
Example 9: Exact Tests and Confidence Intervals for the Correlation Coefficient
Example 10: Confidence Intervals and Special Tests on the Multiple Correlation

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Power Analysis