Includes bibliographical references.

Characteristics of a Relationship -- Correlation and Causation -- Correlation and Correlational Methods -- Choice of Correlation Indexes -- The Pearson Product-Moment Correlation -- Interpretation of Pearson's r -- Assumptions of Pearson's r in Inferential Statistics -- Sampling Distributions of Pearson's r -- Properties of the Sampling Distribution of Pearson's r -- Null Hypothesis Tests of [rho] = 0 -- Null Hypothesis Tests of [rho] = [rho subscript phi] -- Confidence Intervals of [rho] -- Null Hypothesis Test of [rho subscript 1] = [rho subscript 2] -- Null Hypothesis Test for the Difference Among More Than Two Independent [rho]s -- Null Hypothesis Test for the Difference Between Two Dependent Correlations -- Special Cases of Pearson's r -- Point-Biserial Correlation, r[subscript pb] -- Phi Coefficient, [phi] -- Spearman Rank-Order Correlation, r[subscript rank] -- True Versus Artificially Converted Scores -- Biserial Coefficient, r[subscript bis] -- Tetrachoric Coefficient, r[subscript tet] -- Eta Coefficient, [eta] -- Other Special Cases of Pearson's r -- Applications of Pearson's r -- Effect Size -- Power Analysis -- Meta-Analysis -- Utility Analysis -- Reliability Estimates -- Validation -- Factors Affecting the Size and Interpretation of Pearson's r -- Shapes of Distributions -- Sample Size -- Outliers -- Restriction of Range -- Nonlinearity -- Aggregate Samples -- Ecological Inference -- Measurement Error -- Third Variables -- Other Useful Nonparametric Correlations -- C and Cramer's V Coefficients -- Kendall's [tau] Coefficient -- Kendall's [tau subscript b] and Stuart's [tau subscript c] Coefficients -- Goodman and Kruskal's [gamma] Coefficient -- Kendall's Partial Rank-Order Correlation, [tau subscript XY.Z].

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, you will be able to compare and distinguish the concepts of similarity and relationship, identify the distinction between correlation and causation, and interpret correlations correctly.