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How Many Subjects?: Statistical Power Analysis in Research 2nd Revised edition


How Many Subjects?: Statistical Power Analysis in Research 2nd Revised edition

Paperback by Kraemer, Helena Chmura; Blasey, Christine M.

How Many Subjects?: Statistical Power Analysis in Research

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ISBN:
9781483319544
Publication Date:
7 Apr 2015
Edition/language:
2nd Revised edition / English
Publisher:
SAGE Publications Inc
Pages:
160 pages
Format:
Paperback
For delivery:
Estimated despatch 28 May - 2 Jun 2024
How Many Subjects?: Statistical Power Analysis in Research

Description

With increased emphasis on helping readers understand the context in which power calculations are done, this Second Edition introduces a simple technique of statistical power analysis that allows researchers to compute approximate sample sizes and power for a wide range of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can then easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons demonstrate important principles of design, measurement, and analysis that are rarely discussed in courses or textbooks, making this book a valuable instructional resource as well as a must-have guide for frequent reference.

Contents

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

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