1. UNDERSTANDING DESCRIPTION AND CORRELATION

2. CORRELATION COEFFICIENTS: DESCRIBING THE STRENGTH OF RELATIONSHIPS Pearson r Correlation Coefficient Strength of relationship Direction of relationship Values of r range from 0.00 to ±1.00 Scatterplots

3. CORRELATION COEFFICIENT OF ±1.00

4. SCATTERPLOTS

5. IMPORTANT CONSIDERATIONS Restriction of Range Curvilinear Relationship

6. EFFECT SIZE General Term that Refers to the Strength of Association Between Variables Pearson r Correlation Coefficient is One Indicator of Effect Size Advantage of Reporting Effect Size is that it Provides a Scale of Values that is Consistent Across All Types of Studies

7. EFFECT SIZE Differences in effect sizes Small effects near r =.15 Medium effects near r =.30 Large effects above r =.40 Squared value of the coefficient r² - transforms the value of r to a percentage Percent of shared variance between the two variables

8. REGRESSION EQUATIONS Calculations used to predict a person’s score on one variable when that person’s score on another variable is already known General Form: Y=a + bX Y = Score we wish to predict X = Score that is known a = constant b = weighing adjustment

9. MULTIPLE CORRELATION Used to combine a number of predictor variables to increase the accuracy of prediction of a given criterion or outcome variable Symbolized R Y=a+b 1 X 1 + b 2 X 2 +…+b n X n a=constant, b=weights, X=predictor R 2

10. PARTIAL CORRELATION AND THE THIRD-VARIABLE PROBLEM Provides a Way of Statistically Controlling Third Variables

11. STRUCTURAL MODELS Expected Pattern of Relationships Among a Set of Variables Path analysis