Description
Book SynopsisCorrelations, 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.
Table of ContentsCh 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
