Share
Correlation
Parametric and Nonparametric Measures
First Edition
- Peter Y Chen - Auburn University, USA, Colorado State University, USA
- Paula M Popovich - Ohio University, USA
Volume:
139
July 2002 | 104 pages | SAGE Publications, Inc
How can correlation be more effectively used so that one does not misinterpret the data? This book reveals how to do this by examining Pearson r from its conceptual meaning, to assumptions, special cases of the Pearson r, the biserial coefficient and tetrachoric coefficient estimates of the Pearson r, its uses in research (including effect size, power analysis, meta-analysis, utility analysis, reliability estimates and validation), factors that affect the Pearson r, and finally to additional nonparametric correlation indexes.
After reading this book, the reader will be able to compare and distinguish the concepts of similarity and relationship, identify the distinction between correlation and causation and to interpret correlations correctly.
Ch 1. Introduction
Characteristics of a Relationship
Correlation and Causation
Correlation and Causation
Correlation and Correlational Methods
Choice of Correlation Indexes
Ch 2. The Pearson Product-Moment Correlation
Interpretation of Pearson r
Assumptions of Pearson r in Inferential Statistics
Sampling Distributions of the Pearson r
Properties of the Sampling Distribution of the Pearson
Null Hypothesis Tests of r = 0
Null Hypothesis Tests of r = rø
Confidence Intervals of r
Null Hypothesis Test of r1 = r2
Null Hypothesis Test for the Difference Among More Than Two Independent r's
Null Hypothesis Test for the Difference Between Two Dependent Correlations
Chapter 3: Special Cases of The Pearson r
Point-Biserial Correlation, rpb
Phi Coefficient, f
Spearman Rank-Order Correlation, rrank
True vs. Artificially Converted Scores
Biserial Coefficient,
Tetrachoric Coefficient,
Eta Coefficient,
Other Special Cases of the Pearson r
Chapter 4: Applications of the Pearson r
Application I: Effect Size
Application II: Power Analysis
Application III: Meta-Analysis
Application IV: Utility Analysis
Application V: Reliability Estimates
Application VI: Validation
Chapter 5: Factors Affecting the Size and Interpretation of the Pearson r
Shapes of Distributions
Sample Size
Outliers
Restriction of Range
Nonlinearity
Aggregate Samples
Ecological Inference
Measurement Error
Third Variables
Chapter 6: Other Useful Nonparametric Correlations
C and Cramér's V Coefficients
Kendall's t Coefficient
Kendall's tb and Stuart's tc Coefficients
Goodman-Kruskal's g Coefficient
Kendall's Partial Rank-Order Correlation,
References
Lists of Tables
Lists of Figures
List of Appendixes
About the Authors
I need to see more examples... I understand the book needs to be brief...
School Of Management, Walden University
April 17, 2013
