Statistical power analysis for the behavioural sciences

Summary:This is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The second edition includes: a chapter covering power analysis in set correlation and multivariate methods; a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and; expanded power and sample size tables for multiple regression/correlation

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What is statistics for behavioral sciences?
What does a statistical power analysis do?
What is power statistics psychology?
What is statistical power in educational statistics?

Print Book, English, 1988

Publisher:L. Erlbaum Associates, Hillsdale, N.J., 1988

Article citationsMore>>

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

has been cited by the following article:

TITLE: Effectiveness of 4Ps Creativity Teaching for College Students: A Systematic Review and Meta-Analysis
AUTHORS: Hsing-Yuan Liu, Chia-Chen Chang
KEYWORDS: Creativity Teaching, College Students, Systematic Review, Meta-Analysis
JOURNAL NAME: Creative Education, Vol.8 No.6, May 24, 2017
ABSTRACT: Although the creativity teaching claims benefit college students by increasing their problem-solving capacities and enhancing professional competencies. There are also the current academic gap between the teaching constructs and efficacy. This study has compared how these and other teaching strategies have evaluated the efficacy of creativity derived from the 4Ps model (person, process, press, and product). In a systematic search, we identified eleven articles published from 2000-2011. Moreover, this study classified the creativity teaching experiences and analyzed the effect size of its efficacy. The weighted mean effect size (ES) of above studies was 0.95, with a standard deviation of 1.59. The ES of personality on technology students was 1.18 (95% confidence interval [CI95] = 0.39 - 1.42), which was greater than that for education and medical students. Studies with more than 56 subjects were seen to have the highest efficacy. The ES of process on professional courses was 1.18 (CI95 = 0.47 - 1.89), and for press in the classroom base the ES was 1.0 (CI95 = 0.61 - 1.38). The ES for the product combined with the creativity survey was 1.22 (CI95 = -0.70 - 3.14).

Eric Neebo Wiah, Albert Buabeng, Kofi Agyarko
Emerson Abraham Jackson, Edmund Tamuke, Abdulai Sillah
Anatole A. Klyosov
Nicolas Emanuel Monti, Roberto Mariano Garcia
A. K. M. Raquibul Bashar, Chris P. Tsokos

Preface to the Revised Edition
Preface to the Original Edition
Chapter 1. The Concepts of Power Analysis
1.1. General Introduction
1.2. Significance Criterion
1.3. Reliability of Sample Results and Sample Size
1.4. The Effect Size
1.5. Types of Power Analysis
1.6. Significance Testing
1.7. Plan of Chapters 2-9
Chapter 2. The t Test for Means
2.1. Introduction and Use
2.2. The Effect Size Index: d

2.3. Power Tables
2.4. Sample Size Tables

2.5. The Use of the Tables for Significance Testing
Chapter 3. The Significance of a Product Moment rs
3.1. Introduction and Use
3.2. The Effect Size: r
3.3. Power Tables
3.4. Sample Size Tables
3.5. The Use of the Tables for Significance Testing of r
Chapter 4. Differences between Correlation Coefficients
4.1. Introduction and Use
4.2. The Effect Size Index: q
4.3. Power Tables
4.4. Sample Size Tables
4.5. The Use of the Tables for Significance Testing
Chapter 5. The Test that a Proportion is .50 and the Sign Test
5.1. Introduction and Use
5.2. The Effect Size Index: g
5.3. Power Tables

5.4. Sample Size Tables
5.5. The Use of the Tables for Significance Testing
Chapter 6. Differences between Proportions
6.1. Introduction and Use
6.2. The Arcsine Transformation and the Effect Size Index: h
6.3. Power Tables
6.4. Sample Size Tables
6.5. The Use of the Tables for Significance Testing
Chapter 7. Chi-Square Tests for Goodness of Fit and Contingency Tables
7.1. Introduction and Use
7.2. The Effect Size Index: w
7.3. Power Tables
7.4. Sample Size Tables
Chapter 8. F Tests on Means in the Analysis of Variance and Covariance
8.1. Introduction and Use
8.2. The Effect Size Index: f
8.3. Power Tables

8.4. Sample Size Tables
8.5. The Use of the Tables for Significance Testing
Chapter 9. F Tests of Variance Proportions in Multiple Regression/Correlation Analysis
9.1. Introduction and Use
9.2. The Effect Size Index: i
9.3. Power Tables
9.4. L Tables and the Determination of Sample Size
Chapter 10. Technical Appendix : Computational Procedures
10.1. Introduction
10.2. t Test for Means
10.3. The Significance of a Product Moment r
10.4. Differences between Correlation Coefficients
10.5. The Test that a Proportion is .50 and the Sign Test
10.6. Differences between Proportions
10.7. Chi-Square Tests for Goodness of Fit and Contingency Tables
10.8. F Test on Means and the Analysis of Variance and Covariance
10.9. F Test of Variance Proportions in Multiple Regression/Correlation Analysis
References
Index

What is statistics for behavioral sciences?

PsycLearn: Statistics for the Behavioral Sciences was designed to help instructors support their students by presenting statistics using everyday language. It makes a field that can often be daunting for students more relatable, manageable, and even exciting.

What does a statistical power analysis do?

A power analysis is the calculation used to estimate the smallest sample size needed for an experiment, given a required significance level, statistical power, and effect size. It helps to determine if a result from an experiment or survey is due to chance, or if it is genuine and significant.

What is power statistics psychology?

Statistical power is the likelihood that a test will be able to to detect an effect (during a research study) when one truly exists. When conducting a study, researchers are essentially trying to find out if their hypothesis is correct.

What is statistical power in educational statistics?

In statistics, power refers to the likelihood of a hypothesis test detecting a true effect if there is one. A statistically powerful test is more likely to reject a false negative (a Type II error).