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Introduction to Power Analysis
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Introduction to Power Analysis
Two-Group Studies

Volume: 176
February 2018 | 160 pages | SAGE Publications, Inc
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Introduction to Power Analysis: Two-Group Studies provides readers with the background, examples, and explanation they need to read technical papers and materials that include complex power analyses.  This clear and accessible guide explains the components of test statistics and their sampling distributions, and author Eric Hedberg walks the reader through the simple and complex considerations of this research question. Filled with graphics and examples, the reader is taken on a tour of power analyses from covariates to clusters, seeing how the complicated task of comparing two groups, and the power analysis, can be made easy.

 
Chapter 1: The what, why, and when of power analysis
What is statistical power?

 
Why should power be a consideration when planning studies?

 
When should you perform a power analysis?

 
Significance and Effect 8

 
What do you need to know to perform a power analysis?

 
The structure of the volume

 
 
Chapter 2: Statistical distributions
Normally distributed random variables

 
The x^2 distribution

 
The t distribution

 
The F distribution

 
F to t

 
 
Chapter 3: General topics in hypothesis testing and power analysis when the population standard deviation is known: the case of two group means
The difference in means as a normally distributed random variable when the population standard deviation is known

 
Hypothesis testing with the difference between two group means when the population standard deviation is known

 
Power analysis for testing the difference between two group means when the population standard deviation is known

 
Scale-free parameters

 
Balance or unbalanced?

 
Types of power analyses

 
Power tables

 
 
Chapter 4: The difference between two groups in simple random samples where the population standard deviation must be estimated
Data generating process

 
Testing the difference between group means with samples

 
Power analysis for samples without covariates

 
 
Chapter 5: Using covariates when testing the difference in sample group means for balanced designs
Example analysis

 
Tests employing a covariate (ANCOVA) with balanced samples

 
Power analysis with a covariate correlated with the treatment indicator

 
Power analysis with a covariate uncorrelated to the treatment indicator

 
 
Chapter 6: Multilevel Models I: Testing the difference in group means in two-level cluster randomized trials
Example data

 
Understanding the single level test as an ANOVA

 
The hierarchical mixed model for cluster randomized trials

 
Power parameters for cluster randomized trials

 
Example analysis of a cluster randomized trial

 
Power analyses for cluster randomized trials

 
 
Chapter 7: Multilevel Models II: Testing the difference in group means in two-level multisite randomized trials
Power parameters for multisite randomized trials

 
Example analysis of a multisite randomized trial

 
Power analyses for multisite randomized trails

 
 
Chapter 8: Reasonable assumptions
Power analyses are arguments

 
Strategies for using the literature to make reasonable assumptions

 
 
Chapter 9: Writing about power
What to include

 
Examples

 
 
Chapter 10: Conclusions, further reading, and regression
The case study of comparing two groups

 
Further reading

 
Observational regression

 

Supplements

Student Study Site
The open-access Student Study Site includes example power analyses using the major software packages (SPSS, Stata, and R), complete with code and output.

“Introduction to Power Analysis provides detailed coverage of the topic in a succinct and concise way. Graduate students and others (including faculty who are also researchers) can benefit from this resource as it outlines the steps to conduct and evaluate power analysis to produce rigorous quantitative research in the social sciences, as well as why power analysis and effects are important to understand and apply in research.”

Stephanie Jones
Texas Tech University

“Although there are a number of software programs available for power analysis, this volume teaches the reader how to employ power analysis using a popular software program (R) that can also be used to perform the desired statistical analyses on the data.”

Leslie Echols
Missouri State University

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