1 / 23

Bivariate Correlation

Bivariate Correlation. Lesson 10. Measuring Relationships. Correlation degree relationship b/n 2 variables linear predictive relationship Covariance If X changes, does Y change also? e.g., height ( X ) and weight ( Y ) ~. Covariance. Variance

brody
Download Presentation

Bivariate Correlation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. BivariateCorrelation Lesson 10

  2. Measuring Relationships • Correlation • degree relationship b/n 2 variables • linear predictive relationship • Covariance • If X changes, does Y change also? • e.g., height (X) and weight (Y) ~

  3. Covariance • Variance • How much do scores (Xi) vary from mean? • (standard deviation)2 • Covariance • How much do scores (Xi, Yi) from their means

  4. Covariance: Problem • How to interpret size • Different scales of measurement • Standardization • like in z scores • Divide by standard deviation • Gets rid of units • Correlation coefficient (r)

  5. Pearson Correlation Coefficient • Both variables quantitative (interval/ratio) • Values of r • between -1 and +1 • 0 = no relationship • Parameter = ρ (rho) • Types of correlations • Positive: change in same direction • X then Y; or X then Y • Negative: change in opposite direction • X then Y; or X then Y ~

  6. Correlation & Graphs • Scatter Diagrams • Also called scatter plots • 1 variable: Y axis; other X axis • plot point at intersection of values • look for trends • e.g., height vs shoe size ~

  7. 6 7 8 9 10 11 12 Scatter Diagrams 84 78 Height 72 66 60 Shoe size

  8. Slope & value of r • Determines sign • positive or negative • From lower left to upper right • positive ~

  9. Slope & value of r • From upper left to lower right • negative ~

  10. Width & value of r • Magnitude of r • draw imaginary ellipse around most points • Narrow: r near -1 or +1 • strong relationship between variables • straight line: perfect relationship (1 or -1) • Wide: r near 0 • weak relationship between variables ~

  11. Weak relationship Strong negative relationship r near 0 r near -1 300 300 250 250 Weight Weight 200 200 150 150 3 3 6 6 9 9 12 12 15 15 18 18 21 21 100 100 Chin ups Chin ups Width & value of r

  12. Strength of Correlation • R2 • Coefficient of Determination • Proportion of variance in X explained by relationship with Y • Example: IQ and gray matter volume • r = .25 (statisically significant) • R2 = .0625 • Approximately 6% of differences in IQ explained by relationship to gray matter volume ~

  13. Factors that affect size of r • Nonlinear relationships • Pearson’s r does not detect more complex relationships • r near 0 ~ Y X

  14. Factors that affect size of r • Range restriction • eliminate values from 1 or both variable • r is reduced • e.g. eliminate people under 72 inches ~

  15. Hypothesis Test for r • H0: ρ = 0 rho = parameter H1: ρ≠ 0 • ρCV • df = n – 2 • Table: Critical values of ρ • PASW output gives sig. • Example: n = 30; df=28; nondirectional • ρCV = + .335 • decision: r = .285 ? r = -.38 ? ~

  16. Using Pearson r • Reliability • Inter-rater reliability • Validity of a measure • ACT scores and college success? • Also GPA, dean’s list, graduation rate, dropout rate • Effect size • Alternative to Cohen’s d ~

  17. Evaluating Effect Size • Pearson’s r • r= ±.1 • r = ±.2 • r = ±.5 ~ • Cohen’s d • Small: d = 0.2 • Medium: d = 0.5 • High: d = 0.8 Note: Why no zero before decimal for r ?

  18. Correlation and Causation • Causation requires correlation, but... • Correlation does not imply causation! • The 3d variable problem • Some unkown variable affects both • e.g. # of household appliances negatively correlated with family size • Direction of causality • Like psychology  get good grades • Or vice versa ~

  19. Point-biserial Correlation • One variable dichotomous • Only two values • e.g., Sex: male & female • PASW/SPSS • Same as for Pearson’s r ~

  20. Correlation: NonParametric • Spearman’s rs • Ordinal • Non-normal interval/ratio • Kendall’s Tau • Large # tied ranks • Or small data sets • Maybe better choice than Spearman’s ~

  21. Correlation: PASW • Data entry • 1 column per variable • Menus • Analyze  Correlate  Bivariate • Dialog box • Select variables • Choose correlation type • 1- or 2-tailed test of significance ~

  22. Correlation: PASW Output Figure 6.1 – Pearson’s Correlation Output

  23. Reporting Correlation Coefficients • Guidelines • No zero before decimal point • Round to 2 decimal places • significance: 1- or 2-tailed test • Use correct symbol for correlation type • Report significance level • There was a significant relationship between the number of commercials watch and the amount of candy purchased, r = +.87, p (one-tailed) < .05. • Creativity was negatively correlated with how well people did in the World’s Biggest Liar Contest, rS = -.37, p (two-tailed) = .001.

More Related