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Correlation
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Correlation
Parametric and Nonparametric Measures

First Edition


June 2002 | 104 pages | SAGE Publications, Inc
Correlations, in general, and the Pearson product-moment correlation in particular, can be used for many research purposes, ranging from describing a relationship between two variables as a descriptive statistic to examining a relationship between two variables in a population as an inferential statistic, or to gauge the strength of an effect, or to conduct a meta-analytic study. How can correlation be more effectively used so that one doesn't 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...

Dr Christos Makrigeorgis
School Of Management, Walden University
April 17, 2013
Key features
  • -examines Pearson r from its conceptual meaning and to its assumptions
  • how to use the Pearson r in research (including effect size, power analysis, meta-analysis, utility analysis, reliability estimates and validation)
  • explains the factors that affect the Pearson r

 

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