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Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests, Third Edition 3rd New edition [Mīkstie vāki]

3.43/5 (15 ratings by Goodreads)
(Landy Litigation and Colorado State University, USA), (Griffith University, Australia), (Professor Emeritus, Illinois Institute of Technology, USA)
  • Formāts: Paperback / softback, 224 pages, height x width: 229x152 mm, weight: 366 g
  • Izdošanas datums: 05-Dec-2008
  • Izdevniecība: Routledge
  • ISBN-10: 1841697745
  • ISBN-13: 9781841697741
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Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests, Third Edition 3rd New editionPalielināt
  • Formāts: Paperback / softback, 224 pages, height x width: 229x152 mm, weight: 366 g
  • Izdošanas datums: 05-Dec-2008
  • Izdevniecība: Routledge
  • ISBN-10: 1841697745
  • ISBN-13: 9781841697741
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781841697741.html
Keywords:
Noted for its accessible approach, this bestseller applies power analysis to both null hypothesis and minimum-effect testing using the same basic model. Through the use of a few relatively simple procedures and examples from the behavioral and social sciences, the authors show readers with little expertise in statistical analysis how to quickly obtain the values needed to carry out the power analysis for their research. Illustrations of how these analyses work and how they can be used to understand problems of study design, to evaluate research, and to choose the appropriate criterion for defining "statistically significant" outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis that is based on the F statistic.



Statistical Power Analysis reviews how to determine:







The sample size needed to achieve desired levels of power The level of power needed in a study The size of effect that can be reliably detected by a study Sensible criteria for statistical significance.



The third edition features:







Re-designed, user-friendly software at www.psypress.com/statistical-power-analysis that allows users to perform all of the book's analyses on a wider range of tests and conduct significance tests, power analyses, and assessments of N and alpha A new chapter on Complex ANOVA Designs that demonstrates the use of power analysis in split-plot and randomized block factorial designs New boxed sections that provide examples of power analysis in action and unique issues that arise when applying power analyses Expanded coverage of minimum-effect tests, the fundamentals of power analysis and the application of these concepts to correlational studies.



Ideal for students and researchers in the social, behavioral, and health sciences, business, and education, this valuable resource helps readers apply methods of power analysis to their research. PV and F tables serve as a quick reference.



More details - plus a link to download the One Stop F Calculator - can be found at http://www.psypress.com/statistical-power-analysis/ .

Recenzijas

"The change to the software is a substantial improvement and could go a long way to making power analysis more accessible. ... I often field ... questions along the lines of, I have ten subjects per variable in my study is that enough? It would be refreshing to direct the questioner to a text that is as clear and usable as this one." - Stephen Brand, University of Rhode Island



"I see much need for a guide on power analysis among the graduate students and I think many students will benefit from reading this book. I especially like the boxed sections. ... They greatly help readers understand basic concepts." - Jaihyun Park, Baruch College



"The ... addition of worked examples for each type of analysis ... will ... make the book more useful. ... The boxed examples present difficult concepts in student-friendly language. ... I have used this book in the past ... in my graduate-level Experimental Design class. ... I would consider adopting it for my course." - Corinne Zimmerman, Illinois State University

Preface ix
1 The Power of Statistical Tests 1
The Structure of Statistical Tests
2
The Mechanics of Power Analysis
9
Statistical Power of Research in the Social and Behavioral Sciences
17
Using Power Analysis
19
Hypothesis Tests Versus Confidence Intervals
23
Summary
24
2 A Simple and General Model for Power Analysis 25
The General Linear Model, the F Statistic, and Effect Size
27
The F Distribution and Power
29
Using the Noncentral F Distribution to Assess Power
32
Translating Common Statistics and ES Measures Into F
33
Defining Large, Medium, and Small Effects
38
Nonparametric and Robust Statistics
39
From F to Power Analysis
40
Analytic and Tabular Methods of Power Analysis
41
Using the One-Stop F Table
42
The One-Stop F Calculator
45
Summary
47
3 Power Analyses for Minimum-Effect Tests 49
Implications of Believing That the Null Hypothesis Is Almost Always Wrong
53
Minimum-Effect Tests as Alternatives to Traditional Null Hypothesis Tests
56
Testing the Hypothesis That Treatment Effects Are Negligible
59
Using the One-Stop Tables to Assess Power to Test Minimum-Effect Hypotheses
64
Using the One-Stop F Calculator for Minimum-Effect Tests
67
Summary
68
4 Using Power Analyses 71
Estimating the Effect Size
72
Four Applications of Statistical Power Analysis
77
Calculating Power
78
Determining Sample Sizes
79
Determining the Sensitivity of Studies
81
Determining Appropriate Decision Criteria
82
Summary
87
5 Correlation and Regression 89
The Perils of Working With Large Samples
90
Multiple Regression
92
Power in Testing for Moderators
96
Why Are Most Moderator Effects Small?
97
Implications of Low Power in Tests for Moderators
99
Summary
100
6 t-Tests and the Analysis of Variance 101
The t-Test
101
Independent Groups t-Test
103
Traditional Versus Minimum-Effect Tests
105
One-Tailed Versus Two-Tailed Tests
107
Repeated Measures or Dependent t-Test
108
The Analysis of Variance
110
Which Means Differ?
113
Summary
116
7 Multifactor ANOVA Designs 117
The Factorial Analysis of Variance
118
Factorial ANOVA Example
124
Fixed, Mixed, and Random Models
126
Randomized Block ANOVA: An Introduction to Repeated-Measures Designs
128
Independent Groups Versus Repeated Measures
129
Complexities in Estimating Power in Repeated-Measures Designs
134
Summary
135
8 Split-Plot Factorial and Multivariate Analyses 137
Split-Plot Factorial ANOVA
137
Power for Within-Subject Versus Between-Subject Factors
140
Split-Plot Designs With Multiple Repeated-Measures Factors
141
The Multivariate Analysis of Variance
141
Summary
144
9 The Implications of Power Analyses 145
Tests of the Traditional Null Hypothesis
146
Tests of Minimum-Effect Hypotheses
147
Power Analysis: Benefits, Costs, and Implications for Hypothesis Testing
151
Direct Benefits of Power Analysis
151
Indirect Benefits of Power Analysis
153
Costs Associated With Power Analysis
154
Implications of Power Analysis: Can Power Be Too High?
155
Summary
157
References 159
Appendices 163
Author Index 209
Subject Index 211
Pennsylvania State University, University Park, USA Griffith University, Nathan, Australia Illinois Institute of Technology