1. Statistics in Applied Science and Technology Chapter 13, Correlation and Regression Part I, Correlation (Measure of Association)

2. Key Concepts in This Chapter Associations between two variables measured at Interval- ratio level Scatter diagram, Pearson’s correlation coefficient Positive correlation, negative correlation, curvilinear relationships Coefficient of Determination Associations between two variables measured at Ordinal level Associations between two variables measured at Nominal level Associations between one quantitative variable and one nominal or ordinal variable

3. Association Between Two Variables Measured at Interval- ration level Positive Correlation Negative Correlation No Association Curvilinear Relationship

4. Insofar as relatively low values are paired with relatively low values, and relatively high values are paired with relatively high values, the relationship is positive. Example: time studied for test and test scores Positive Correlation

5. Negative Correlation Insofar as relatively low values are paired with relatively high values, and relatively high values are paired with relatively low values, the relationship is negative. Example: life span in years and history of heavy smoking in years

6. No Association No regularity is apparent among the pairs of observations - no association. Example: IQ score and shoe size

7. Curvilinear Relationship A relationship that can be described best with a curved line. Example: Physical strength and years of age.

8. Preview a relationship - Scatterplots Definition of scatterplots - a graph containing a cluster of dots that represent all pairs of observations. Construction of scatterplots- scale each of the two variables along the horizontal (X) and vertical (Y) axes and use each pair of observations to locate a dot within the scatterplot.

9. Is there a Association?

10. Plot out the data The Scattergram

11. The Scattergram

12. Anxiety & Skill Performance Curvilinear Relationship

13. Measurement of strength of correlation - correlation coefficient r Intuitively: the more closely the dot cluster approximate a straight line, the stronger the relationship will be. A correlation coefficient is a number between -1 and 1 that describes the relationship between variables. The sign of r indicated the types of linear relationship, whether positive or negative.

14. The value of r without regard to sign, indicates the strength of the linear relationship. Types of correlation coefficient: –Pearson’s correlation coefficient r (appropriate when both variables are normal in distribution) –Spearman’s correlation coefficient r s (appropriate when one or both variables are not normal in distribution) Measurement of strength of correlation - correlation coefficient r

15. Calculation of Pearson’s r In which: x, y - pair of observations x, y - mean of x and y

16. Coefficient of Determination r 2 r 2 : simply square the r value What percentage of the variance in each variable is explained by knowledge of the variance of the other variable –what percentage of the variance within Y is predicted by the variance within X?

17. Notes about r 2 Coefficient of determination explains explained variance tTherefore, 1-r 2 is unexplained variance r for example, r= 0.70 gives about 50% explained variance (why???) aAlways calculate r 2 to evaluate extent of the correlation

18. Not Causation Only Association Thomas Sowell opinion piece

19. Smoking & LBP Smoking Low Back Pain r = 0.45

20. Smoking & LBP Smoking Low Back Pain r = 0.45 ? Low Back Pain Smoking

21. Smoking & LBP Smoking Low Back Pain r = 0.45 Lifestyle factors ( ie strength) ?

22. Smoking & LBP Smoking Low Back Pain r =.45 Lifestyle choices

23. How Would I Measure Association Between Variables Measured at the Ordinal Level? Like associations between two variables measured at Interval-ratio level, associations between two variables measured at the ordinal level have three types: –positive association –Negative association –No association

24. Positive association: Cases tend to be ranked in the same order on both variables. For example, if case A is ranked above case B on one variable, it would also be ranked above case B on the second variable. If “burnout” among elementary school teachers are positively associated with the number of years of service, teacher A who had more years of service than teacher B would be ranked higher than teacher B on “burnout”.

25. Negative association The order of cases would be ranked reversed between two variables. If case A ranked above case B on one variable, it would tend to rank below case B on the second variable. If “burnout” among elementary school teachers are negatively associated with the number of years of service, teacher A who had more years of service than teacher B would be ranked lower than teacher B on “burnout”.

26. No Association No regularity is apparent among two variables - no association.

27. Preview a Association - Conditional Distribution of Y Table 1. Burnout by Length of Service

28. Preview a Association - Conditional Distribution of Y Table 1 displays the association between burnout and length of service. Each column shows the pattern of scores on the dependent variable for each score on the independent variable. For example, the first column indicates that, of the 38 teacher whose length of service is low, 20 (52.6%) were low on burnout, 10 (26.3%) were moderate on burnout, and 8 (21.1%) were high on burnout. Second column shows that, of the 32 teacher whose length of service is moderate, 6 (18.8%) were low on burnout, 15 (46.9%) were moderate on burnout, and 11 (34.3%) were high on burnout.

29. Preview a Association - Conditional Distribution of Y Like Table 1, these “within-column” frequency distributions are called the conditional distribution of Y, since they display the distribution of scores on the dependent variable for each condition (score) of the independent variable. By observing the conditional distribution of Y in Table 1, a positive association exists between burn out and length of service (Why?)

30. Measurement of strength of association between two variables measured at Ordinal Level- Gamma (G) Gamma (G) can be calculated by (SPSS will be used) Interpreting G: similarly to r 2, G indicates that the percentage errors can be reduced if we predict the order of pairs of cases on one variable from the order of pairs of cases on the other - as opposed to predicting order while ignoring the other variable Where: Ns - the number of pairs of cases ranked the same as both variable. Nd - the number of pairs of cases ranked differently on the two variables.

31. How Would I Measure Association Between Variables Measured at the Nominal Level? Associations between two variables measured at the nominal level have no direction, therefore only two types: –Existence of association –No association

32. Preview a Association - Conditional Distribution of Y Table 2. Height by Gender

33. Preview a Association - Conditional Distribution of Y Table 2 displays the association between height and gender. Each column shows the pattern of scores on the dependent variable for each score on the independent variable. For example, the first column indicates that, of the 50 people who are male, 44 (88%) were tall, and 6 (12%) were short. Second column shows that, of the 50 people who are female, 8 (16%) were tall, and 42 (84%) were short. Calculating percentages as in Table 2 will help identifying the pattern of association. Males tend to be tall and females tend to be short.

34. Measurement of strength of association between two variables measured at Nominal Level- Phi (  ) and Cramer’s V Phi (  ) is appropriate for 2  2 table. It is defined as: For tables large than 2  2, a more general form of Cramer’s V should be used. It is defined as: Where:  2 - a test statistics based on expected frequency and observed frequency. N - total number of observation Where:  2, N are defined as above. Min of r-1, c-1 = minimum value of r-1 (# of rows minus 1) or c-1(# of column minus 1).

35. Phi (  ) and Cramer’s V Calculations of these two parameter will be conducted using SPSS (You don’t need to know about the calculation). Values of  and Cramer’s V are both between 0.00 and 1.00. A value close to 1 indicate a strong association, and a value close to 0.00 indicate a very weak association.

36. A limitation of Phi (  ) and Cramer’s V A limitation of Phi and Cramer’s V is the absence of a direct and meaningful interpretation for values between the extremes of 0.00 and 1.00 In another word, the values between 0.00 and 1.00 can not be interpreted as anything other than an index of the relative strength of association.

37. Another measurement of strength of association between two variables measured at Nominal Level- Lambda ( ) Lambda is calculated based on E 1 (prediction errors made while ignoring and independent variable) and E 2 (prediction errors made while taking independent variables into account).

38. Interpretation of Lambda ( ) The value of lambda ranges from 0.00 to 1.00 When multiplied by 100, the value of lambda indicates the percentage reduction in error if we take independent variable into account - as opposed to ignoring the independent variable, therefore, the strength of the association.

39. Lambda ( ) is asymmetric. This means that the value of will vary depending on which variable is taken as independent When one of the row totals is much larger than the others, lambda can take on a value of 0.00 even when other measure of association would not be 0.00, and calculating percentages for the table indicates some association between variables. Notes about Lambda ( )

40. Another Popular Way Measuring the Strength of Association between two nominal variables (Chap 12.9) Relative risk is defined as the ratio of the incidence rate for persons exposed to a risk factor to the incidence rate for those not exposed to the risk factor: incidence rate among exposed Relative Risk (RR) = incidence rate among unexposed

41. A generalized 2  2 table (Table 3)

42. Relative Risk and Odds Ratio Relative Risk is also called risk ratio and it can be computed using frequencies listed in Table 3. Another commonly used measure of strength of association is the odds ratio (OR), and is defined as

43. How to measure strength of association between one quantitative variable and one nominal (or ordinal) variable? Measure of strength of association begin with summary statistics such as mean, median, percentage, etc. The measure of strength of association is created by comparing these summary statistics, such as subtract the summary statistics. The bigger the difference, the stronger the association.

44. An Example When exploring the relationship between benzene and white blood cell count, we might use the mean white blood cell counts for the exposed and non-exposed workers and then subtract these two means. The bigger the difference, the stronger the association.