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Statistical Power Analysis for the Behavioral Sciences
A comprehensive handbook for behavioral scientists that explains the concept of statistical power and provides practical methods and tables to calculate it for various statistical tests, enabling more rational research planning and interpretation of results.
A profile of this book is on the way.
What it’s about
Most behavioral science research is plagued by underpowered studies, where researchers have a low probability of detecting real effects, leading to a literature filled with ambiguous 'not significant' findings. 'Statistical Power Analysis for the Behavioral Sciences' is the definitive guide to overcoming this problem, providing researchers with the conceptual framework and practical tools to rationally plan their studies. Jacob Cohen demystifies the four key parameters of statistical inference—significance level (α), power (1-β), sample size (n), and effect size (ES)—and provides extensive tables to calculate power for a given sample size or determine the sample size needed to achieve a desired power for a wide array of common statistical tests. By emphasizing the crucial concept of 'effect size,' the book shifts the focus from mere statistical significance to the magnitude of the phenomenon under study, empowering researchers to design more sensitive experiments, avoid wasted effort on studies doomed to fail, and more meaningfully interpret their results.
The through-line
- Who it’s for
- A behavioral or social science researcher who wants to conduct meaningful research that yields clear, publishable results and contributes to their field.
- The problem
- Conducting studies that frequently yield statistically non-significant results, making it difficult to publish or draw firm conclusions, and struggling to determine the appropriate sample size for studies. Feeling frustrated, uncertain, and discouraged when hard work results in ambiguous findings, and worrying about wasting time and resources on studies that are destined to fail from the start.
- The plan
- Understand the fundamental concepts of power analysis: the interplay of alpha, beta, sample size, and effect size.
- For your specific statistical test, learn the appropriate effect size index (e.g., d, r, f) from the relevant chapter.
- Use the provided tables to either determine the power of your study or calculate the required sample size to achieve your desired power.
- The payoff
- Design studies with a rational basis for their sample size. · Achieve a high, predetermined probability of detecting the effects you are looking for. · Waste less time and resources on inconclusive research.
See our guide
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Additional reading
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences · Cohen, J., & Cohen, P.
The book recommends this text for its excellent, accessible narrative discussions on regression, particularly for explanatory purposes in the social sciences.
- Classical and Modern Regression with Applications · Myers, R.
Cited as an excellent resource for its modern approach to regression analysis, especially its strong treatment of regression diagnostics for checking assumptions and identifying influential data points.
- Multivariate Statistical Methods in Behavioral Research · Bock, R. D.
This text is frequently cited by the author for more advanced or technical explanations of concepts in MANOVA, repeated measures, and step-down analysis.
- The Analysis of Covariance and Alternatives · Huitema, B.
Recommended as a very comprehensive and thorough text for readers wishing to gain a deeper understanding of Analysis of Covariance (ANCOVA).
- Structural Equations with Latent Variables · Bollen, K. A.
The guest-authored chapter on SEM heavily references this book as a key source for understanding fundamental concepts like model identification.
- Hierarchical Linear Models: Applications and Data Analysis Methods · Raudenbush, S., & Bryk, A.
The guest-authored chapter on Hierarchical Linear Modeling (HLM) cites this as the seminal text on the topic and the basis for the HLM software.
- Applied Discriminant Analysis · Huberty, C.
The chapter on Discriminant Analysis introduces this book as an excellent, current, and very thorough resource on the topic.
- Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan · John Kruschke
Recommended for reading 'During this book' to get additional information and a different perspective on Bayesian statistics and modeling.
- Regression and Other Stories · Andrew Gelman, Jennifer Hill, & Aki Vehtari
Recommended for reading 'During this book' as a supplementary text for a broader understanding of regression and Bayesian modeling.
- Statistical Rethinking: A Bayesian Course with Examples in R and Stan · Richard McElreath
Recommended for reading 'After this book' as a next step for readers who have mastered the concepts and wish to deepen their understanding of Bayesian modeling.