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Applied Power Analysis for the Behavioral Sciences [Mīkstie vāki]

2.75/5 (8 ratings by Goodreads)
  • Formāts: Paperback / softback, 272 pages, height x width: 229x152 mm, weight: 363 g
  • Izdošanas datums: 09-Feb-2010
  • Izdevniecība: Routledge Academic
  • ISBN-10: 1848728352
  • ISBN-13: 9781848728356
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Applied Power Analysis for the Behavioral SciencesPalielināt
  • Formāts: Paperback / softback, 272 pages, height x width: 229x152 mm, weight: 363 g
  • Izdošanas datums: 09-Feb-2010
  • Izdevniecība: Routledge Academic
  • ISBN-10: 1848728352
  • ISBN-13: 9781848728356
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781848728356.html
Keywords:
This practical guide on conducting power analyses using IBM SPSS was written for students and researchers with limited quantitative backgrounds. Readers will appreciate the coverage of topics that are not well described in competing books such as estimating effect sizes, power analyses for complex designs, detailed coverage of popular multiple regression and multi-factor ANOVA approaches, and power for multiple comparisons and simple effects. Practical issues such as how to increase power without increasing sample size, how to report findings, how to derive effect size expectations, and how to support null hypotheses, are also addressed. Unlike other texts, this book focuses on the statistical and methodological aspects of the analyses.









Performing analyses using software applications rather than via complex hand calculations is demonstrated throughout. Ready-to-use IBM SPSS syntax for conducting analyses are included to perform calculations and power analyses at http://www.psypress.com/applied-power-analysis . Detailed annotations for each syntax protocol review the minor modifications necessary for researchers to adapt the syntax to their own analyses. As such, the text reviews both power analysis techniques and provides tools for conducting analyses. Numerous examples enhance accessibility by demonstrating specific issues that must be addressed at all stages of the power analysis and providing detailed interpretations of IBM SPSS output. Several examples address techniques for estimation of power and hand calculations as well. Chapter summaries and key statistics sections also aid in understanding the material.









Chapter 1 reviews significance testing and introduces power. Chapters 2 through 9 cover power analysis strategies for a variety of common designs. Precision analysis for confidence intervals around mean difference, correlations, and effect sizes is the focus of chapter 10. The book concludes with a review of how to report power analyses, a review of freeware and commercial software for power analyses, and how to increase power without increasing sample size. Chapters focusing on simpler analyses such as t-tests present detailed formulae and calculation examples. Chapters focusing on more complex topics such as mixed model ANOVA/MANOVA present primarily computer-based analyses.









Intended as a supplementary text for graduate-level research methods, experimental design, quasi-experimental methods, psychometrics, statistics, and/or advanced/multivariate statistics taught in the behavioral, social, biological, and medical sciences, researchers in these fields also appreciate this books practical emphasis. A prerequisite of introductory statistics is recommended.

Recenzijas

"This book presents concepts in a more accessible manner than the other books out there. The step-by-step explanations should make it accessible to a wide range of readers, even advanced undergraduatesthe inclusion of SPSS syntaxmakes the material such that more advanced readers are still interested and engagedI would consider using this book for a third course in statisticsI would also consider purchasing a copy for my own use." Allen I. Huffcutt, Bradley University, USA









" The book provides users with the means to compute power accurately for many situations where no other methods are readily available The SPSS syntax provides a framework that allows the user to see a range of possible outcomesinformation that can help the user gain a better feel for the costs and benefits of various sample sizes[ it] provides methods for dealing with complex data with greater accuracy appropriate for a short course called statistical power[ or] as a supplement to any multivariate course." Dale Berger, Claremont Graduate University, USA









"An important addition to every applied workers tool chestThis book allows the author to make a very important contribution to the science of research methodologythis would be a nice complement to our ANOVA/ANOCOVA course, MANOVA/MANCOVA course." - Shlomo Sawilowsky, Wayne State University, USA









"This book will meet a significant need in the market. I would consider adopting it and recommending it to colleagues. In fact, I would expect that it would become a required reading in our statistics sequence here at Claremont. I would definitely purchase a copy..." - Stewart Donaldson, Claremont Graduate School, USA



"The book will be highly relevant to upper-division undergraduate courses and (particularly) graduate-level courses in psychology and related fields. I would likely include the book as a recommended source for my graduate-level course in statistics. In addition to my teaching, the book would also be of use to me professionally." - P. Wesley Schultz, California State University - San Marcos, USA

Preface ix
What is Power? Why is Power Important?
1(16)
Introduction
1(1)
Review of Null Hypothesis Significance Testing
1(1)
Effect Sizes and Their Interpretation
2(1)
What Has an Impact on Power?
3(3)
Central and Noncentral Distributions
6(2)
Misconceptions About Power
8(1)
Empirical Reviews of Power
9(1)
Overview of Approaches to Determining Effect Size for Power Analysis
10(3)
Post Hoc Power
13(2)
How Much Power?
15(1)
Summary
16(1)
Notes
16(1)
Chi Square and Tests for Proportions
17(20)
Introduction
17(1)
Necessary Information
17(1)
Factors Affecting Power
18(1)
Key Statistics
18(11)
Tests for Single Samples and Independent Proportions
29(6)
Summary
35(1)
Notes
36(1)
Independent Samples and Paired t-Tests
37(30)
Introduction
37(1)
Necessary Information
37(1)
Factors Affecting Power
38(1)
Key Statistics
39(2)
A Note About Effect Size for Two-Group Comparisons
41(10)
Dealing With Unequal Variances, Unequal Sample Sizes, and Violation of Assumptions
51(11)
Additional Issues
62(3)
Summary
65(1)
Note
65(2)
Correlations and Differences Between Correlations
67(20)
Introduction
67(1)
Necessary Information
67(1)
Factors Affecting Power
67(1)
Zero-Order Correlation
68(3)
Comparing Two Independent Correlations
71(4)
Comparing Two Dependent Correlations (One Variable in Common)
75(2)
Comparing Two Dependent Correlations (No Variables in Common)
77(6)
Note on Effect Sizes for Comparing Correlations
83(2)
Additional Issues
85(1)
Summary
85(1)
Note
85(2)
Between-Subjects ANOVA (One Factor, Two or More Factors)
87(26)
Introduction
87(1)
Necessary Information
87(1)
Factors Affecting Power
87(1)
Omnibus Versus Contrast Power
88(1)
Key Statistics
88(6)
SPSS Syntax for the One-Factor ANOVA
94(1)
Other Contrast Options for One-Factor ANOVA
95(6)
ANOVA With Two Factors
101(7)
Additional Issues
108(3)
Summary
111(1)
Note
112(1)
Within-Subjects Designs
113(20)
Introduction
113(1)
Necessary Information
113(1)
Factors Affecting Power
113(1)
Key Statistics
114(7)
Multivariate Approach to Repeated Measures
121(3)
Trend Analysis
124(6)
Additional Issues
130(1)
Summary
130(1)
Notes
130(3)
Mixed-Model ANOVA and Multivariate ANOVA
133(18)
Introduction
133(1)
Necessary Information
133(1)
Factors Affecting Power
133(1)
Key Statistics
134(1)
Mixed-Model Designs
134(6)
Multivariate ANOVA
140(8)
Additional Issues
148(1)
Summary
149(2)
Multiple Regression
151(30)
Introduction
151(1)
Necessary Information
151(1)
Factors Affecting Power
152(1)
Key Statistics
153(13)
Power for Detecting Differences Between Two Dependent Coefficients
166(3)
Power for Detecting Differences Between Two Independent Coefficients
169(3)
Power for Comparing Two Independent R2 Values
172(3)
Additional Issues
175(3)
Summary
178(1)
Note
179(2)
Covariate Analyses and Regression Interactions
181(22)
Introduction
181(1)
Analysis of Covariance
181(4)
Moderated Regression Analysis (Regression With Interactions)
185(12)
Multivariate Analysis of Covariance
197(1)
Additional Issues
198(3)
Summary
201(2)
Precision Analysis for Confidence Intervals
203(22)
Introduction
203(1)
Necessary Information
204(1)
Confidence Intervals
204(1)
Types of Confidence Intervals
205(1)
Confidence Limits Around Differences Between Means
206(4)
Determining Levels of Precision
210(1)
Confidence Intervals Around Effect Sizes
210(3)
Precision for a Correlation
213(1)
Precision for R2 Change
214(2)
Precision for the R2 Model
216(1)
Supporting Null Hypotheses
216(5)
Additional Issues
221(2)
Summary
223(2)
Additional Issues and Resources
225(14)
Introduction
225(1)
How to Report Power Analyses
225(1)
Statistical Test Assumptions
226(1)
Effect Size Conversion Formulae
227(1)
Probability of Replication
228(2)
General (Free) Resources for Power and Related Topics
230(1)
Resources for Additional Analyses
231(1)
Comparison of Power Programs
232(2)
SPSS Warnings and How to Deal With Them
234(1)
Improving Power Without Increasing Sample Size or Cost
235(4)
References 239(6)
Author Index 245(4)
Subject Index 249
Christopher L. Aberson is Professor of Psychology at Humboldt State University. He received his Ph.D. in Psychology with a concentration on Social/Quantitative Psychology, from Claremont Graduate University in 1999. Dr. Aberson currently serves as Executive Editor of The Journal of Social Psychology and on the editorial boards of the Journal of Applied Social Psychology, Teaching of Psychology and the Journal of Clinical Psychology. His research interests include prejudice, racism, and attitudes toward affirmative action.