1. Correlation & Regression Chapter 15

2. Correlation It is a statistical technique that is used to measure and describe a relationship between two variables. Usually the two variables are simply observed as they exist – there is no attempt to manipulate or control them in any way. To study relationship between age and delinquency a research will look at the ages of children and how many delinquent acts they have committed – she is not manipulating she is just observing. This gives us two scores, one for age (X) and one for delinquency (Y), and if we plot it on a scatter plot, we can see the relationship between the two.

3. The same set of n = 6 pairs of scores (X and Y values) is shown in a table and in a scatterplot. Note that the scatterplot allows you to see the relationship between X and Y.

4. The Characteristics of a Relationship Correlation measures three characteristics of a relationship between X and Y: 1.The Direction of the relationship: two types either positive or negative. In positive correlation as X increases Y increases, or as X decreases Y decreases (identified by the sign +) In negative correlation as X increases Y decreases and vice versa (identified by the sign - )

5. The Characteristics of a Relationship Correlation measures three characteristics of a relationship between X and Y: 1.The Form of the Relationship: Some have linear forms – there are other forms as well. 1.The Degree of the Relationship: A correlation also measures how well the data fit the form being considered. So in a linear relationship, we like to see how well the data fit into a straight line. A perfect Correlation is identified by either +1 (positive) or -1 (negative). Correlation ranges from 0 to +/- 1, O is when there is no fit at all.

6. Examples of positive and negative relationships. Beer sales (gallons) are positively related to temperature, and coffee sales (gallons) are negatively related to temperature.

7. Examples of different values for linear correlations: (a) a strong positive relationship, approximately + 0.90 (b) a relatively weak negative correlation, approximately  0.40 (c) a perfect negative correlation,  1.00 (d) no linear trend, 0.00

8. Pearson Correlation Measures the degree and direction of the linear relationship between the two variables. Pearson Correlation = r = degree to which X and Y vary together degree to which X and Y vary separately Easy to do on SPSS, go to Analyze/Correlate/Bivariate – put variables of interest into the box on the right and check mark against Pearson, Click OK. SPSS gives you the output with the value of r and also the significance.

9. Pearson Correlation – Uses and interpretation 1.Prediction: if two variables are known to correlate in a systematic way then you can use one to make predictions about the other. Example: SAT scores used to predict college performance. 1.Validity: To check if certain tests or measures are measuring what they are supposed to be measuring. Eg. Is a new IQ test really measuring IQ, if it is it should have a strong positive correlation with scores on other IQ tests. 1.Reliability: Do certain measurement procedures provide consistent results. Eg. The same individuals will produce same (or similar) scores on an IQ test when repeated at different times, so you could correlate scores at time one with scores at Time2 on the same test to see if there is a positive correlation indicating reliability of the measure. 1.Theory Verification: to test if certain theories are true. Relationship between brain size and IQ.

10. Pearson Correlation – Uses and interpretation 1. Correlation simply describes a relationship between two variables and does not tell us why they are related. Does not tell us about Cause and Effect. 2. The value of a correlation can be greatly affected by the range of scores represented in the data. 3.One or two extreme data points (outliers) can have a dramatic effect on the value of a correlation. 4. The numbers do not directly relate to the size of the effect. 0.5 does not mean that you can predict with 50% accuracy, you have to use r 2 (coefficient of determination) for that, so a correlation of 0.5 produces a r 2 of (0.5 x 0.5).25 or 25%.

11. Example: Causation Issues Hypothetical data showing the logical relationship between the number of churches and the number of serious crimes for a sample of US cities (large and small).

12. (a) In this example, the full range of X and Y values shows a strong, positive correlation, but the restricted range of scores produces a correlation near zero. (b) Here the full range of X and Y values shows a correlation near zero but the scores in the restricted range produce a strong positive correlation.

13. A demonstration of how one extreme data point (an outlier) can influence the value of a correlation.

14. Three sets of data showing three different degrees of linear relationship. r = 0 (and r2=0) r = 0.60 r2 =.36 or 36% r = 1.00 r2 = 1 or 100%

15. The main purpose of the hypothesis test using Correlation is to see if the nonzero correlation is simply due to chance or sampling error The Hypothesis Test with Pearson’s Correlation Scatter plot of a population of X and Y values with a near- zero correlation. However, a small sample of n = 3 data points from this population shows a relatively strong, positive correlation. Data points in the sample are circled.

16. Hypothesis Testing using Pearson’s Correlation State Null and alternate Hypothesis: (the common term for population correlation is represented by the Greek symbol ρ (rho) H0: ρ = 0 (no population correlation) H1: ρ ≠ 0 (There is real correlation) Degrees of Freedom for the correlation test is always n-2 Look up table for Pearson Correlation to identify the critical value based on your df and alpha value (Or just do it in SPSS and be happy!) Accept or reject your null hypothesis. Report results in APA format (next slide)

17. Reporting Correlations The correlation coefficient revealed that amount of education and annual income were significantly related, r = +.65, n=30, p<.01, two tails. If you have looked at a number of variables in your study, you can present results in a correlation matrix: TABLE 1 Correlation Matrix for income, amount of education, age and intelligence EducationAgeIQ Income+.65*+.41**+.27 Education+.11+.38** Age-.02 n = 30 *p<.01, two tails **p<.05, two tails

18. The Spearman Correlation Pearson measures degree of linear relationships between two variable used for data from an interval or ratio level of measurement. Spearman is used when data has been measured using an ordinal scale (when X and Y values are in ranks), also used when there could be relationships that are not linear. also when you have outliers in your interval level data that could be affecting the r value drastically, Spearman converts those raw scores to ranks and does the calculations. As for calculations leave it to SPSS!

19. Example of a non-linear relationship Hypothetical data showing the relationship between practice and performance. Although this relationship is not linear, there is a consistent positive relationship. An increase in performance tends to accompany an increase in practice.

20. How to do it SPSS SPSS commands are: Analyze, Correlate, Bivariate – put two variables of interest in the variable box. Check both Pearson’s and Spearman’s correlation – click OK. Interpret the output and report results as shown in previous slides. To get a scatter plot in SPSS, Graphs, Scatter, select ‘Simple’ click ‘Define’ – Put variables on X and Y axes. Add titles etc if you want by clicking on ‘Titles’. Click OK. You are interested in this cell Or this one

21. How to do it SPSS SPSS commands are: Analyze, Correlate, Bivariate – put two variables of interest in the variable box. Check both Pearson’s and Spearman’s correlation – click OK. Interpret the output and report results as shown in previous slides. To get a scatter plot in SPSS, Graphs, Scatter, select ‘Simple’ click ‘Define’ – Put variables on X and Y axes. Add titles etc if you want by clicking on ‘Titles’. Click OK. You are interested in this cell Or this one