1 / 14

Measures of Association

Chapter 18. Measures of Association. Deepak Khazanchi. Bivariate Correlation vs. Nonparametric Measures of Association. Parametric correlation requires two continuous variables measured on an interval or ratio scale

stacy
Download Presentation

Measures of Association

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. Chapter 18 Measures of Association Deepak Khazanchi

  2. Bivariate Correlation vs. Nonparametric Measures of Association • Parametric correlation requires two continuous variables measured on an interval or ratio scale • The coefficient does not distinguish between independent and dependent variables

  3. Bivariate Correlation Analysis • Pearson correlation coefficient • r symbolized the coefficient's estimate of linear association based on sampling data • Correlation coefficients reveal the magnitude and direction of relationships • Coefficient’s sign (+ or -) signifies the direction of the relationship • Assumptions of r • Linearity • Bivariate normal distribution

  4. Bivariate Correlation Analysis • Scatterplots • Provide a means for visual inspection of data • the direction of a relationship • the shape of a relationship • the magnitude of a relationship • (with practice)

  5. Interpretation of Coefficients • Relationship does not imply causation • Statistical significance does not imply a relationship is practically meaningful

  6. Interpretation of Coefficients • Suggests alternate explanations for correlation results • X causes Y. . . or • Y causes X . . . or • X & Y are activated by one or more other variables . . . or • X & Y influence each other reciprocally

  7. Interpretation of Coefficients • Artifact Correlations • Goodness of fit • F test • Coefficient of determination • Correlation matrix • used to display coefficients for more than two variables

  8. Bivariate Linear Regression • Used to make simple and multiple predictions • Regression coefficients • Slope • Intercept • Error term • Method of least squares

  9. Interpreting Linear Regression • Residuals • what remains after the line is fit or (Yi-Yi) • Prediction and confidence bands ^

  10. Interpreting Linear Regression • Goodness of fit • Zero slope • Y completely unrelated to X and no systematic pattern is evident • constant values of Y for every value of X • data are related, but represented by a nonlinear function

  11. Nonparametric Measures of Association • Measures for nominal data • When there is no relationship at all, coefficient is 0 • When there is complete dependency, the coefficient displays unity or 1

  12. Characteristics of Ordinal Data • Concordant- subject who ranks higher on one variable also ranks higher on the other variable • Discordant- subject who ranks higher on one variable ranks lower on the other variable

  13. Measures for Ordinal Data • No assumption of bivariate normal distribution • Most based on concordant/discordant pairs • Values range from +1.0 to -1.0

  14. Measures for Ordinal Data • Tests • Gamma • Somer’s d • Spearman’s rho • Kendall’s tau b • Kendall’s tau c

More Related