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Bivariate Analysis: Interrelationship of Variables

Bivariate analysis involves analyzing the interrelationship of two variables through statistical measures such as correlation. This helps determine the existence, form, and strength of the relationship between the variables.

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Bivariate Analysis: Interrelationship of Variables

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  1. The basic task of most research = Bivariate Analysis What does that involve? Analyzing the interrelationship of 2 variables Null hypothesis = independence (unrelatedness) Two analytical perspectives: Analysis of differences:  Select Independent Variable and Dependent variable  Compare Dependent Var. across values of Indep. Var. Analysis of associations: Covariation or Correspondence of variables Predictability of one variable from the other Agreement between two variables

  2. “Bivariate Analysis” If both variables = categorical?(either nominal or ordinal) Use cross-tabulations (contingency tables) to show the relationship If both variables = numerical? Then cross-tabs are no longer manageable and interpretable We can graph their relationship  scatter plot Need a statistical measure to index the inter-relationship between 2 numeric variables (which is called their “correlation”)

  3. “Bivariate Analysis” Note: several relevant questions about the relationship between variables Does a relationship exist or are they independent? (significance test) What is the form of the inter-relationship? Linear or non-linear (for numerical variables) Monotonic or non-monotonic (for ordinal variables) Positive or negative (for ordered variables) What is the strength or magnitude of the relationship? (coefficient of association) What is the meaning of the correlation? (Note: this is not a statistical but a substantive question)

  4. I. Correlation A quantitative measure of the degree of association between 2 numeric variables The analytical model? Agreement Predictability Covariance

  5. I. Correlation The analytical model: Key concept = covariance of two variables This reflects: How strongly variables are related to or predictable from each other Yi = a + bXi + ei The direction of this relation: positive vs. negative It presumes that the relationship is “linear” Covariance reflects how closely points of the bivariate distribution are bunched around a straight line

  6. Formula for Covariance?

  7. Correlation (continued) Scatter Plot #1 (of moderate correlation):

  8. Correlation (continued) Scatter Plot #2 (of negative correlation):

  9. Correlation (continued) Scatter Plot #3 (of high correlation)

  10. Correlation (continued) Scatter Plot #4 (of very low correlation)

  11. Correlation (continued) How to compute a correlation coefficient? By hand: Definitional formula (the familiar one) Computational formula (different but equivalent) By SPSS: Analyze  Correlate  Bivariate

  12. Correlation Coefficient (r): Definitional Formula Correlation Coefficient (r): Computational Formula

  13. Correlation (continued) How to test correlation for significance? Test Null Hypothesis that: r = 0 Use t-test:

  14. Correlation (continued) What are assumptions/requirements of correlation Numeric variables (interval or ratio level) Linear relationship between variables Random sampling (for significance test) Normal distribution of data (for significance test) What to do if the assumptions do not hold May be able to transform variables May use ranks instead of scores Pearson Correlation Coefficient (scores) Spearman Correlation Coefficient (ranks)

  15. Correlation (continued) How to interpret correlations Sign of coefficient? Magnitude of coefficient ( -1 < r < +1) Usual Scale: (slightly different from textbook) +1.00 perfect correlation +.75  strong correlation +.50  moderately strong correlation +.25  moderate correlation +.10  weak correlation .00  no correlation (unrelated) -.10  weak negative correlation (and so on for negative correlations)

  16. Correlation (continued) How to interpret correlations (continued)  NOTE: Zero correlation may indicate that relationShip is nonlinear (rather than no association between variables) Important to check shape of distribution  linearity; lopsidedness; weird “outliers” Scatterplots = usual method Line graphs (if scatter plot is hard to read) May need to transform or edit the data: Transforms to make variable more “linear” Exclusion or recoding of “outliers”

  17. Correlation (continued) Scatterplots vs. Line graphs (example)

  18. Correlation (continued) crc319 crc383 dth177 pvs500 pfh493 crc319: Violent Crime rate ----- .614 -.048 .268 .034 crc383: Property Crime rate .614 ----- .265 .224 .042 dth177: Suicide rate -.048 .265 ----- .178 .304 pvs500: Poverty rate .268 .224 .178 ----- -.191 pfh493: Alcohol Consumption .034 .042 .304 -.191 ----- How to report correlational results? Single correlations (r and significance - in text) Multiple correlations (matrix of coefficients in a separate table) Note the triangular-mirrored nature of the matrix

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