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13
The Pearson Correlation Coefficient
LEARNING OBJECTIVES
Differentiate difference tests from relationship tests.
Define and describe a relationship.
Compute a Pearson r.
Interpret the results of a Pearson r.
Take into account the effects of a confounding variable.
CHAPTER OVERVIEW
Previous chapters on hypothesis tests focused on two types of tests—t tests and ANOVAs. With these tests, cases are assigned to or classified in groups on the basis of an explanatory variable, and then the outcomes of the groups are compared. These tests are called “difference” tests because they determine whether there is a difference in the mean of the dependent variable between the groups. Classic experiments in which an experimental group is compared to a control group to see which has a better mean outcome are examples of difference tests.
In this chapter, we’re going to learn about another type of test, what is called a “relationship” test. With relationship tests, there is one group of cases. Each case in the group is measured on two variables to determine if a relationship, or an association, exists between the two variables. For example, we could measure a group of college students to determine if there is a relationship between how extroverted they are and how often they date.
13.1 Introduction to the Pearson Correlation Coefficient
13.2 Calculating the Pearson Correlation Coefficient
13.3 Interpreting the Pearson Correlation Coefficient
13.4 Calculating a Partial Correlation