INTRODUCTION TO POWER ANALYSIS : Two-Group Studies

Title:

Personal Author:

Hedberg, E. C., author

Series:

Quantitative Applications in the Social Sciences ; 176

Physical Description:

xvii, 138 pages ; 22 cm

ISBN:

9781506343129

Subject Term:

Statistical power analysis

Mathematical statistics

Multivariate analysis

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010371739	QA277 H43 2018	Open Access Book	Book	Searching... Unknown

Introduction to Power Analysis: Two-Group Studiesprovides 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.

Series Editor's Introduction	p. xi
Preface	p. xiii
About the Author	p. xv
Acknowledgments	p. xvii
1 The What, Why, and When of Power Analysis	p. 1
What Is Statistical Power?	p. 1
Why Should Power Be a Consideration When Planning Studies?	p. 3
When Should You Perform a Power Analysis?	p. 6
Significance and Effect	p. 7
What Do You Need to Know to Perform a Power Analysis?	p. 8
The Structure of the Volume	p. 9
Summary	p. 9
2 Statistical Distributions	p. 10
Normally Distributed Random Variables	p. 10
The X 2 Distribution	p. 12
The t Distribution	p. 15
The F Distribution	p. 15
F to t	p. 16
Summary	p. 17
3 General Topics in Hypothesis Testing and Power Analysis When the Population Standard Deviation Is Known: The Case of Two Group Means	p. 18
The Difference in Means as a Normally Distributed Random Variable When the Population Standard Deviation Is Known	p. 18
Hypothesis Testing With the Difference Between Two Group Means When the Population Standard Deviation Is Known	p. 19
Power Analysis for Testing the Difference Between Two Group Means When the Population Standard Deviation Is Known	p. 24
Scale-Free Parameters	p. 28
Balanced or Unbalanced?	p. 29
Types of Power Analyses	p. 30
Power Tables	p. 34
Summary	p. 35
4 The Difference Between Two Groups in Simple Random Samples Where the Population Standard Deviation Must Be Estimated	p. 36
Data-Generating Process	p. 37
Testing the Difference Between Group Means With Samples	p. 38
Power Analysis for Samples Without Covariates	p. 46
Summary	p. 52
5 Using Covariates When Testing the Difference in Sample Group Means for Balanced Designs	p. 54
Example Analysis	p. 55
Tests Employing a Covariate (ANCOVA) With Balanced Samples	p. 56
Power Analysis With a Covariate Correlated With the Treatment Indicator	p. 61
Power Analysis With a Covariate Uncorrelated to the Treatment Indicator	p. 67
Summary	p. 70
6 Multilevel Models I: Testing the Difference in Group Means in Two-Level Cluster Randomized Trials	p. 71
Example Data	p. 71
Understanding the Single Level Test as an ANOVA	p. 72
The Hierarchical Mixed Model for Cluster Randomized Trials	p. 76
Power Parameters for Cluster Randomized Trials	p. 80
Example Analysis of a Cluster Randomized Trial	p. 82
Power Analyses for Cluster Randomized Trials	p. 85
Summary	p. 88
7 Multilevel Models II: Testing the Difference in Group Means in Two-Level Multisite Randomized Trials	p. 89
Power Parameters for Multisite Randomized Trials	p. 92
Example Analysis of a Multisite Randomized Trial	p. 94
Power Analyses for Multisite Randomized Trails	p. 95
Summary	p. 98
8 Reasonable Assumptions	p. 99
Power Analyses Are Arguments	p. 99
Strategies for Using the Literature to Make Reasonable Assumptions	p. 102
Summary	p. 108
9 Writing About Power	p. 109
What to Include	p. 109
Examples	p. 110
Summary	p. 114
10 Conclusions, Further Reading, and Regression	p. 115
The Case Study of Comparing Two Groups	p. 115
Further Reading	p. 116
Observational Regression	p. 118
Summary	p. 122
Appendix	p. 123
References	p. 127
Index	p. 131

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