OUTLINE FOR CHAPTER
3
Bivariate Correlation
- The Key Concept Behind Correlation: "Relationship"
- The need for data on two variables
- What's the basic question being addressed in discussions about
correlation?
- Three possible answers to the basic correlational question:
- High-high, low-low
- High-low, low-high
- Little systematic tendency one way or the other
- Scatter Diagrams
- What they look like
- How to interpret one
- The notions of "strong," "moderate," and "low" relationships
- The Correlation Coefficient
- How it's symbolized
- It's numerical range
- How to label different points on and sections of the continuum
of possible values
- The Correlation Matrix
- Determining the number of bivariate correlations among k variables
- The purpose and general appearance of a scatter diagram
- Saving space when showing one correlation matrix (or two correlation
matrixes)
- Different Kinds of Correlational Procedures
- Quantitative vs. qualitative variables
- Dichotomous, nominal, ordinal, and "raw-score" variables
- True and artificial dichotomies
- Specific correlational procedures:
- For two raw-score variables: Pearson's product-moment correlation
- For two sets of ranks: Spearman's rho and Kendall's tau
- For one raw-score variable & one dichotomous variable:
biserial r & point-biserial r
- For two dichotomous variables: phi and tetrachoric r
- For two nominal variables: Cramer's V
- Warnings About Correlation
- Correlation and cause-and-effect
- The coefficient of determination and explained variability
- Outliers and their influence on correlation coefficients
- Linear and curvilinear relationships
- Correlation and independence
- The subjectivity nature of adjectives used to denote relationship
strength
- A Summary of the Abbreviations & Symbols Associated
with this Chapter
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