OVERVIEW

Descriptive statistics and descriptive graphs are what they sound like – they are tools that describe variables. These tools are valuable, because they can summarize a tremendous amount of information in a succinct fashion. In this chapter we discuss some of the most commonly used descriptive statistics and graphs, how we should interpret them, how we should use them, and their limitations.

KNOW YOUR DATA

In Chapter 5 we discussed the measurement of variables. A lot of thought and effort goes into the measurement of individual variables. Once measurement has been conducted, it is important for the researcher to get a good idea of the types of values that the individual variables take on before moving to testing for causal connections between two or more variables. What do “typical” values for a variable look like? How tightly clustered (or widely dispersed) are the these values?

Before proceeding to test for theorized relationships between two or more variables, it is essential understand the properties and characteristics of each variable. To put it differently, we want to learn something about what the values of each variable “look like.” How do we accomplish this? One possibility is to list all of the observed values of a measured variable.