How Many Subjects?: Statistical Power Analysis in Research
Autor Helena Chmura Kraemer, Christine M. Blaseyen Limba Engleză Paperback – 6 apr 2015
New to this edition:
- Power computations are now placed in the proper context as one small but crucial step in applying the scientific method.
- The number of tests to which the methods can be applied has been extended.
- The book now incorporates the authors’ experience where errors in design and interpretation of statistical hypothesis testing occur.
- Recent emphasis on effect sizes rather than p-values is endorsed and emphasized throughout.
- Recent developments in consideration of moderators and mediators are acknowledged.
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Specificații
ISBN-13: 9781483319544
ISBN-10: 1483319547
Pagini: 160
Dimensiuni: 152 x 229 x 8 mm
Greutate: 0.26 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
ISBN-10: 1483319547
Pagini: 160
Dimensiuni: 152 x 229 x 8 mm
Greutate: 0.26 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
Recenzii
"Kraemer and Blasey provide an authoritative and readable introduction into applied statistical power analysis and its application with many common statistical procedures."
“This is a necessary text for anyone conducting research in the real world. Nowhere else will you find a better answer to the question, 'How Many Subjects?'”
“This is a necessary text for anyone conducting research in the real world. Nowhere else will you find a better answer to the question, 'How Many Subjects?'”
Cuprins
PREFACE
1. The Rules of the Game
Exploratory Studies
Hypothesis Formulation
Null Hypothesis
Design
The Statistical Test
Effect Sizes: Critical, True, and Estimated
Power
2. General Concepts
Introduction to the Power Table
Statistical Considerations
3. The Pivotal Case: Interclass Correlation
The Intraclass Correlation Test
The ANOVA Approach to Intraclass Correlation Test
Normal Approximation to the Intraclass Theory
Non-Central t
Variance Ratios
Conclusion
4. Equality of Means: Z- and T-Test, Balanced ANOVA
Single-Sample Test, Variance Known: z-test
Single-Sample t-test
Two Sample t-test
An Exercise in Planning
Balanced Analysis of Variance (ANOVA)
5. Correlation Coefficients
Intraclass Correlation Coefficient
Product-Moment Correlation Coefficient
Rank Correlation Coefficients
You Study What You Measure!
6. Linear Regression Analysis
Simple Linear Regression
Experimental Design: Choosing the X-Values
Simple Linear Moderation Example
Problems: Collinearity and Interactions
Multiple Linear Regression
7. Homogeneity of Variance Tests
Two Independent Samples
Matched Samples
8. Binomial Tests
Single-Sample Binomial Tests
Two-Sample Binomial Tests
9. Contingency Table Analysis
Introduction
The I X J x^2-test
An Example of a 3 X 2 Contingency Table Analysis
10. Wrap-Up
1. The Rules of the Game
Exploratory Studies
Hypothesis Formulation
Null Hypothesis
Design
The Statistical Test
Effect Sizes: Critical, True, and Estimated
Power
2. General Concepts
Introduction to the Power Table
Statistical Considerations
3. The Pivotal Case: Interclass Correlation
The Intraclass Correlation Test
The ANOVA Approach to Intraclass Correlation Test
Normal Approximation to the Intraclass Theory
Non-Central t
Variance Ratios
Conclusion
4. Equality of Means: Z- and T-Test, Balanced ANOVA
Single-Sample Test, Variance Known: z-test
Single-Sample t-test
Two Sample t-test
An Exercise in Planning
Balanced Analysis of Variance (ANOVA)
5. Correlation Coefficients
Intraclass Correlation Coefficient
Product-Moment Correlation Coefficient
Rank Correlation Coefficients
You Study What You Measure!
6. Linear Regression Analysis
Simple Linear Regression
Experimental Design: Choosing the X-Values
Simple Linear Moderation Example
Problems: Collinearity and Interactions
Multiple Linear Regression
7. Homogeneity of Variance Tests
Two Independent Samples
Matched Samples
8. Binomial Tests
Single-Sample Binomial Tests
Two-Sample Binomial Tests
9. Contingency Table Analysis
Introduction
The I X J x^2-test
An Example of a 3 X 2 Contingency Table Analysis
10. Wrap-Up
Notă biografică
Descriere
Addressing a common question posed by researchers, this book introduces readers to power analysis and sample size determination and clearly illustrates why sample sizes need to be sufficiently large to give good power properties and low error rates.