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Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests, Fourth Edition 4th edition [Hardback]

3.43/5 (14 ratings by Goodreads)
(Griffith University, Australia), , (Professor Emeritus, Illinois Institute of Technology, USA)
  • Formāts: Hardback, 230 pages, height x width: 229x152 mm, weight: 476 g, 19 Tables, black and white; 10 Line drawings, black and white; 2 Halftones, black and white; 12 Illustrations, black and white
  • Izdošanas datums: 30-May-2014
  • Izdevniecība: Routledge
  • ISBN-10: 1848725876
  • ISBN-13: 9781848725874
Citas grāmatas par šo tēmu:
Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests, Fourth Edition 4th editionPalielināt
  • Formāts: Hardback, 230 pages, height x width: 229x152 mm, weight: 476 g, 19 Tables, black and white; 10 Line drawings, black and white; 2 Halftones, black and white; 12 Illustrations, black and white
  • Izdošanas datums: 30-May-2014
  • Izdevniecība: Routledge
  • ISBN-10: 1848725876
  • ISBN-13: 9781848725874
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781848725874.html
Keywords:

Noted for its accessible approach, this text applies the latest approaches of power analysis to both null hypothesis and minimum-effect testing using the same basic unified model. Through the use of a few simple procedures and examples, the authors show readers with little expertise in statistical analysis how to 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 choose the appropriate criterion for defining statistically significant outcomes are sprinkled throughout. The book presents a simple and general model for statistical power analysis based on the F statistic and 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; and sensible criteria for statistical significance. The book helps readers design studies, diagnose existing studies, and understand why hypothesis tests come out out the way they do.

The fourth edition features:

-New Boxed Material sections provide examples of power analysis in action and discuss unique issues that arise as a result of applying power analyses in different designs.

-Many more worked examples help readers apply the concepts presented.

-Expanded coverage of power analysis for multifactor analysis of variance (ANOVA) to show readers how to analyze up to four factors with repeated measures on any or all of the factors.

-Re-designed and expanded web based One Stop F Calculator software and data sets that allow users to perform all of the book's analyses and conduct significance tests, power analyses, and assessments of N and alpha needed for traditional and minimum-effects tests.

-Easy to apply formulas for approximating the number of subjects required to reach adequate levels of power in a wide range of studies.

Intended as a supplement for graduate/advanced undergraduate courses in research methods or experimental design, intermediate, advanced, or multivariate statistics, statistics II, or psychometrics, taught in psychology, education, business, and other social and health sciences, researchers also appreciate the book‘s applied approach.

Recenzijas

"The detailed coverage of conducting a full power analysis, as well as the simple estimates ... allows students to not only understand the benefit of power analysis, but also gives them a good guideline for adequate sample sizes to ensure that they at least come close to the adequate sample. ... Faculty members who are teaching quantitative research courses should expose their students to this book. It is written in a way that is approachable to graduate level students." Brigitte Vittrup, Texas Womens University, USA

"The explanation of how power can be estimated using a unified method of F transformations ... helps readers understand the workings behind the mysterious tables that are available elsewhere and ... behind power calculators. ... I use this book in my research design course. I intend to continue using this book. The sections on determining sensitivity and decision criteria are important, well written and frequently missed in other texts. ... The authors write clearly. ... The boxed examples are one of the best features." Patricia Newcomb, University of Texas at Arlington, USA

"I have always liked the critical discussion of the traditional null hypothesis, and the minimum effect size alternative central to this book. ... The [ new edition] ... benefits from adding discussion about various designs such as multifactorial ANOVA [ which] offer ideas [ on] how to plan more complex experiments, and expand the applicability of power analysis." Andres Kriete, Drexel University, USA

"This concise yet detailed text fills a vital niche for graduate students and seasoned researchers alike, providing an understanding of the basic tenets of statistical power, and its application to a variety of hypothesis testing scenarios." Chris Stride, University of Sheffield, UK "The detailed coverage of conducting a full power analysis, as well as the simple estimates ... allows students to not only understand the benefit of power analysis, but also gives them a good guideline for adequate sample sizes to ensure that they at least come close to the adequate sample. ... Faculty members should expose their students ... to this book. It is written in a way that is approachable to graduate level students." Brigitte Vittrup, Texas Womens University, USA

"This concise yet detailed text fills a vital niche for graduate students and seasoned researchers alike, providing an understanding of the basic tenets of statistical power, and its application to a variety of hypothesis testing scenarios."- Chris Stride, University of Sheffield, UK

"The explanation of how power can be estimated using a unified method of F transformations ... helps readers understand the workings behind the mysterious tables that are available elsewhere. ... I use this book in my research design course. ... The sections on determining sensitivity and decision criteria are... frequently missed in other texts. ... The authors write clearly." Patricia Newcomb, University of Texas at Arlington, USA

"I have always liked the critical discussion of the traditional null hypothesis, and the minimum effect size alternative central to this book. ... The [ new edition] ... benefits from adding discussion about various designs such as multifactorial ANOVA [ which] offer ideas [ on] how to plan more complex experiments and expand the applicability of power analysis." Andres Kriete, Drexel University, USA

Preface ix
About the Authors xiii
1 The Power of Statistical Tests
1(27)
The Structure of Statistical Tests
2(7)
Null Hypotheses vs. Nil Hypotheses
4(2)
Understanding Conditional Probability
6(3)
The Mechanics of Power Analysis
9(9)
Understanding Sampling Distributions
10(6)
d vs. delta vs. g
16(2)
Statistical Power of Research in the Social and Behavioral Sciences
18(2)
Using Power Analysis
20(3)
The Meaning of Statistical Significance
20(3)
Hypothesis Tests vs. Confidence Intervals
23(2)
Accuracy in Parameter Estimation
24(1)
What Can We Learn From a Null Hypothesis Test?
25(3)
Summary
26(2)
2 A Simple and General Model for Power Analysis
28(25)
The General Linear Model, F Statistic, and Effect Size
30(3)
Effect Size
30(2)
Understanding Linear Models
32(1)
The F Distribution and Power
33(3)
Using the Noncentral F Distribution to Assess Power
36(1)
Translating Common Statistics and ES Measures Into F
37(5)
Worked Example---Hierarchical Regression
38(1)
Worked Examples Using the D Statistic
39(3)
Defining Large, Medium, and Small Effects
42(1)
Nonparametric and Robust Statistics
43(1)
From F to Power Analysis
43(1)
Analytic and Tabular Methods of Power Analysis
44(1)
Using the One-Stop F Table
45(3)
Worked Example---Interpolating Between Values for Power of .50 and .80
47(1)
The One-Stop F Calculator
48(5)
Summary
51(2)
3 Power Analyses for Minimum-Effect Tests
53(22)
Nil Hypothesis Testing
53(4)
Is the Nil Hypothesis Almost Always Wrong?
55(2)
Implications of Believing That the Nil Hypothesis Is Almost Always Wrong
57(3)
Polar Bear Traps: Why Type I Error Control Is a Bad Investment
58(1)
The Nil May Not Be True, but It Is Often Fairly Accurate
59(1)
Minimum-Effect Tests as Alternatives to Traditional Null Hypothesis Tests
60(4)
How Do You Know the Effect Size?
63(1)
Testing the Hypothesis That Treatment Effects Are Negligible
64(5)
Using the One-Stop F Tables to Assess Power for Minimum-Effect Tests
69(2)
A Worked Example of Minimum-Effect Testing
70(1)
Using the One-Stop F Calculator for Minimum-Effect Tests
71(4)
Does Another Perspective on Type I and Type II Errors Solve the Problem That the Traditional Nil Hypothesis Is False in Most Cases?
73(1)
Summary
74(1)
4 Using Power Analyses
75(19)
Estimating the Effect Size
76(5)
Using the One-Stop F Calculator to Perform Power Analysis
77(2)
Worked Example: Calculating F-equivalents and Power
79(2)
Four Applications, of Statistical Power Analysis
81(1)
Calculating Power
82(1)
Determining Sample Sizes
83(3)
A Few Simple Approximations for Determining Sample Size Needed
85(1)
Determining the Sensitivity of Studies
86(2)
Using the One-Stop F Calculator to Evaluate Sensitivity
87(1)
Determining Appropriate Decision Criteria
88(6)
Finding a Sensible Alpha
91(1)
Summary
92(2)
5 Correlation and Regression
94(13)
The Perils of Working With Large Samples
94(2)
Multiple Regression
96(5)
Testing Minimum-Effect Hypotheses in Multiple Regression
97(4)
Sample Size Estimation
101(1)
Power in Testing for Moderators
101(1)
Why Are Most Moderator Effects Small?
102(1)
Power Analysis for Moderators
103(1)
Implications of Low Power in Tests for Moderators
103(2)
If You Understand Regression, You Will Understand (Almost) Everything
105(2)
Summary
105(2)
6 t-Tests and the One-Way Analysis of Variance
107(17)
The T Test
107(2)
The T Distribution vs. the Normal Distribution
109(1)
Independent Groups T Test
109(4)
Determining an Appropriate Sample Size
110(3)
One- vs. Two-Tailed Tests
113(2)
Reanalysis of Smoking Reduction Treatments: One-Tailed Tests
114(1)
Repeated Measures or Dependent T Test
115(1)
The Analysis of Variance
116(3)
Retrieving Effect Size Information From F Ratios
119(1)
Which Means Differ?
119(3)
Designing a One-Way ANOVA Study
122(2)
Summary
123(1)
7 Multifactor ANOVA Designs
124(15)
The Factorial Analysis of Variance
125(6)
Calculating PV From F and df in Multifactor ANOVA: Worked Example
129(2)
Factorial ANOVA Example
131(3)
Eta Squared vs. Partial Eta Squared
133(1)
General Design Principles for Multifactor ANOVA
134(2)
Fixed, Mixed, and Random Models
136(3)
Summary
137(2)
8 Studies With Multiple Observations for Each Subject: Repeated Measures and Multivariate Analyses
139(15)
Randomized Block ANOVA: An Introduction to Repeated-Measures Designs
139(2)
Independent Groups vs. Repeated Measures
141(5)
Complexities in Estimating Power in Repeated-Measure Designs
146(1)
Split-Plot Factorial ANOVA
147(3)
Estimating Power for a Split-Plot Factorial ANOVA
149(1)
Power for Within-Subject vs. Between-Subject Factors
150(1)
Split-Plot Designs With Multiple Repeated-Measures Factors
150(2)
The Multivariate Analysis of Variance
152(2)
Summary
153(1)
9 The Implications of Power Analyses
154(15)
Tests of the Traditional Null Hypothesis
154(2)
Tests of Minimum-Effect Hypotheses
156(4)
Power Analysis: Benefits, Costs, and Implications for Hypothesis Testing
160(1)
Direct Benefits of Power Analysis
160(2)
Indirect Benefits of Power Analysis
162(1)
Costs Associated With Power Analysis
163(1)
Implications of Power Analysis: Can Power Be Too High?
164(5)
Summary
166(3)
Appendix A Translating Common Statistics into F-Equivalent and PV Values 169(2)
Appendix B One-Stop F Table 171(18)
Appendix C One-Stop PV Table 189(18)
Appendix D Working With the Noncentral F Distribution 207(4)
Appendix E The dfErr Needed for Power = .80 (Alpha = 0.05) in Tests of Traditional Null Hypothesis 211(4)
Appendix F The dfErr Needed for Power = .80 (Alpha = 0.05) in Tests of the Hypothesis That Treatments Account for 1% or Less in the Variance of Outcomes 215(4)
References 219(6)
Author Index 225(2)
Subject Index 227
Kevin R. Murphy is an Affiliate Faculty member at Colorado State University.



Brett Myors is an Adjunct Professor of Psychology at Griffith University in Australia.



Allen Wolach is a retired Professor of Psychology from Illinois Institute of Psychology.