Surveys and Correlational Designs

Surveys are very popular, and loved by some but hated by others. It’s no wonder why: surveys are extremely flexible, but sometimes that flexibility gets applied in disastrous ways. Surveys are technically both a full-fledged design and a method of data collection that can be used in other designs, so we’ll look at a few different ways that surveys can be used and how to use them for good and not for evil.

Students taking a research methods class often feel that surveys are self-explanatory and that there is no need to dedicate any class time to learning about it. This feeling is understandable, as surveys are part of our everyday lives. We’ve probably all taken one, heard about their results in the news, and perhaps we’ve even administered one ourselves. Students quickly learn that there is more to constructing a good survey than meets the eye. Survey design takes a great deal of thoughtful planning and often many rounds of revision, but it is worth the effort. As we’ll learn in this chapter, there are many benefits to choosing survey research as your data collection method. We’ll discuss what a survey is, their potential benefits and drawbacks, how to construct a survey, and what to do with survey data.

Survey research is a quantitative method in which a researcher poses a set of predetermined questions to an entire group, or sample, of individuals. Survey research is an especially useful approach when a researcher aims to describe or explain features of a very large group or multiple groups. This method may also be used to quickly gain general details about the population of interest to help prepare for a more focused, in-depth study using time-intensive methods. In this case, a survey may help a researcher identify specific individuals or locations from which to collect additional data.

As is true of all methods of data collection, survey research is better suited to answering some kinds of research questions more than others. In addition, as you’ll recall from Chapter 9, operationalization works differently with different research methods. If your interest is in political activism, for example, you likely operationalize that concept differently in a survey than you would for an experimental study of the same topic.

Correlational designs are often used in conjunction with surveys, and are aimed at finding relationships between variables but not necessarily cause and effect. Connect this back to the previous page, where we talk about correlation vs. causation?