Question

Describe the difference between simple and multiple correlation and give examples of each. Discuss the benefits/drawbacks of using each

Answer 1

Simple correlation is a measure used to determine the strength and the direction of the relationship between two variables, X and Y. A simple correlation coefficient can range from –1 to 1. However, maximum (or minimum) values of some simple correlations cannot reach unity
(i.e., 1 or –1). Examples of this generalized simple correlation include Kendall's tau coefficient, Spearman correlation coefficient, and Pearson correlation coefficient. A simple correlation between variables X and Y does not imply that either variable is an independent variable or a dependent variable. Whereas, multiple correlation is a measure of how well a given variable can be predicted using a linear function of a set of other variables. It is the correlation between the variable's values and the best predictions that can be computed linearly from the predictive variables. Forexample, crimes in a city may be influenced by illiteracy, increased population and unemployment in the city, etc.
Advantages- multiple correlation provides better prediction about a variable as compared to simple correlation because it is based on three or more variables. this also helps in making better decisions.
Another benefit of simple correlation is that it opens up a great deal of further research to other scholars. It allows researchers to determine the strength and direction of a relationship so that later studies can narrow the findings down and, if possible, determine causation experimentally.