## How Many Subjects?: Statistical Power Analysis in ResearchHow Many Subjects? is a practical guide to sample size calculations and general principles of cost-effective research. It introduces a simple technique of statistical power analysis which allows researchers to compute approximate sample sizes and power for a wide variety of research designs. Because the same technique is used with only slight modifications for different statistical tests, researchers can easily compare the sample sizes required by different designs and tests to make cost-effective decisions in planning a study. These comparisons, emphasized throughout the book, demonstrate important principles of design, measurement and analysis that are rarely discussed in courses or textbooks. |

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### Contents

Intraclass Correlation | 31 |

z and tTests | 37 |

Homogeneity of Variance Tests | 67 |

Contingency Table Analysis | 85 |

Master Table | 105 |

About the Authors | 120 |

### Other editions - View all

How Many Subjects?: Statistical Power Analysis in Research Helena Chmura Kraemer,Christine Blasey No preview available - 2015 |

How Many Subjects?: Statistical Power Analysis in Research Helena Chmura Kraemer,Christine Blasey No preview available - 2015 |

### Common terms and phrases

abstainers alternative hypothesis ANOVA approximately arcsin assumptions binomial test bivariate normal CHAPTER CMI scores coffee consumption coffee drinkers coffee-drinking compute confirmatory data analysis Contingency Table control group critical effect size Critical Effect Sizes cups per day decaffeinated coffee design and measurement dichotomized distributed with mean drinking coffee endpoint design equal number estimate example factors Glass's effect group sizes hypothesis is true increase intraclass correlation coefficient Kendall's tau Kraemer level with 90 Linear Regression Master Table matched matched-pairs t-test McNemar's test mean CMI median test necessary sample nonparametric normally distributed null hypothesis number of subjects obtain one-tailed test pair t-test power calculations predictor variables preliminary evidence proportion rank correlation regular coffee repeated measures design research hypothesis response result sample size necessary significance level specific standard deviation statistical power statistical test stratify theory tion total sample trial two-sample t-test two-tailed validity variance a2 variance ratio X2-test