Understanding Correlated Data Analysis

1. Analysing correlated data Maureen Meadows Senior Lecturer in Management, Open University Business School

2. Aims of today’s session • Discuss different forms of dependency and correlation that we might find in our datasets • Explore why it might sometimes be problematic to get a good measure of correlation • Look at examples of data analysis where correlation is an important issue • Discuss why it is important to deal with correlations, and not to forget about them!

3. What is dependence? • Dependence is any statistical relationship between two random variables, or two sets of data • In other words, they are not independent of each other • E.g. the relationship between the height of parents and the height of children, or the relationship between the demand for a product and its price

4. What is correlation? • Two variables are said to be correlated if changes in one variable are associated with changes in the other variable • So, if we know how one variable is changing, we have a good idea how the other variable is likely to be changing too • Hence it is widely used in many forms of forecasting etc.

5. What is the correlation coefficient? • Pearson’s product moment correlation coefficient is also known as r, R, or Pearson's r • It is a measure of the strength and direction of the association - in particular, the linear relationship- between two (metric) variables • It is defined as the covariance of the variables divided by the product of their standard deviations

6. Pearson’s correlation coefficient • It is a measure of the linear dependence or correlation between the two variables • The sign (+ or -) indicates the direction of the relationship • The value can range from -1 to +1, with +1 indicating a perfect positive relationship, 0 indicating no relationship and -1 indicating a perfect negative or reverse relationship

7. Other correlation coefficients • Arank correlation is any of several statistics that measure the relationship between rankings of different ordinal variables or different rankings of the same variable, where a "ranking" is the assignment of the labels "first", "second", "third", etc. to different observations of a particular variable • Arank correlation coefficient measures the degree of similarity between two rankings, and can be used to assess the significance of the relation between them

8. Rank correlation coefficients • Spearman’s rank correlation coefficient is a measure of how well the relationship between two variables can be described by a monotonic function • Kendall tau rank correlation coefficient is a measure of the portion of ranks that match between two data sets • Goodman and Kruskal'sgamma is a measure of the strength of association of the cross tabulated data when both variables are measured at the ordinal level

9. Correlation and sample size • Is a correlation coefficient significantly different from zero or not? • Example: three different correlation coefficients: 0.50, 0.35, and 0.17. • Assume that we want to test whether there is no significant relationship between the two variables at hand. The null hypothesis (H0) to be tested is that these r values are not statistically different from zero (rho = 0).

10. Sample size example (continued) • For rho = 0, H0 can be tested using a two tailed t-test at a given confidence level, usually at a 95% level • If tcalculated ≥ ttable, H0 is rejected • If tcalculated < ttable H0 is not rejected and there is no significant correlation between variables • Here tcalculated is computed as r/SEr = r*SQRT[((n – 2)/(1 – r2))] while ttablevalues are obtained from the literature 

11. Sample size example (continued) • For n = 14, all three r values (0.50, 0.35, and 0.17) are not statistically different from zero • For n = 30, r = 0.50 is statistically different from zero while r = 0.35 and r = 0.17 are not • Conversely, r = 0.50 is not statistically different from zero when n is equal to or less than 14 while r = 0.35 is not different from zero when n is equal to or less than 30 • Finally, r = 0.17 is not statistically different from zero at any of the sample sizes tested

12. What is a correlation matrix? • It is a table showing the correlations between a set of variables • Often inspected before multivariate methods are applied, e.g. regression analysis or factor analysis • Examples: Kim et al (2011), Mohammed (2013)

13. The correlation coefficient and least squares regression analysis • The square of the correlation coefficient, typically denoted r2, is called the coefficient of determination • It estimates the fraction of the variance in Y that is explained by X in a simple linear regression • Example: McDaniel (1981)

14. What is collinearity? • An expression of the relationship between two variables (collinearity), or more than two variables (multicollinearity) • Two variables exhibit complete collinearity if their correlation coefficient is 1, and complete lack of collinearity if their correlation coefficient is 0 • Multicollinearity occurs when a variable is highly correlated with a set of other variables

15. When can multicollinearity be a problem? • Multicollinearity is the extent to which a variable can be ‘explained’ by the other variables in the analysis • As multicollinearity increases, it complicates the interpretation of the variate(the linear combination of variables formed in a technique such as regression) • It becomes more difficult to ascertain the effect of any single variables, because of their inter-relationships

16. The Variance Inflation Factor (VIF) • An indicator of the effect that the other independent variables have on the standard error of a regression coefficient • Large VIF values indicate a high degree of collinearity or multicollinearity among the independent variables • The VIF is directly related to the tolerance value (VIF = 1/tol) • Example: Zhou et al (2013)

17. Tolerance • Another commonly used measure of collinearity and multicollinearity • The tolerance of a variable is 1- r2, where r2is the coefficient of determination for the prediction of that variable by the other independent variables • As the tolerance grows smaller, the variable is more highly predicted by the other independent variables (collinearity)

18. Correlations and cluster analysis • Variables that are multicollinear are implictly weighted more heavily • E.g. if we cluster on 10 equally weighted variables, and they form two dimensions (one of 8 variables and the other of the remaining 2), then the first dimension will have four times as many chances to affect the similarity measure

19. Correlations and factor analysis • Some degree of multicollinearity is desirable, because the objective is to identify sets of variables that are interrelated • Examples: Kim et al (2011), Mohammed (2013)

20. References • Hair, Anderson, Tatham and Black, Multivariate Data Analysis, 7th edition (Prentice Hall, 2009) • Kim, JY, Shim, JP and Ahn, KM (2011) ‘Social Networking Service: Motivation, Pleasure, and Behavioral Intention to Use’, Journal of Computer Information Systems, Summer, 92-101. • McDaniel, SW (1981) ‘Multicollinearity in advertising-related data’, Journal of Advertising Research, 21:3, 59-63. • Mohammed, S. (2013) ‘Factors Affecting E-Banking Usage in India: an Empirical Analysis’, Economic Insights – Trends and Challenges, Vol. II (LXV) No. 1, 17-25. • Zhou, X, Han, Y and Wang, R (2013) ‘An Empirical Investigation on Firms’ Proactive and Passive Motivation for Bribery in China’, Journal of Business Ethics, 118:461-472.