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Parametric and Nonparametric Measures

First Edition

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


Restriction of Range


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,

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...

Dr Christos Makrigeorgis
School Of Management, Walden University
April 17, 2013

For instructors

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