makes use of quasi-experiments. A quasi-experiment compares naturally occurring groups of people. We could compare whether first-years, sophomores, juniors, or seniors have higher GPAs and higher levels of college satisfaction. Here, year-in-school is a quasi-independent variable in the sense that people tend to fall into one of these four types of students. Realize, as with correlational methods, we cannot conclude that being a senior causes students to do better or worse in school (or anything else) than being a first-year. However, such data could still be of interest. For instance, suppose we find the somewhat counterintuitive result that first-years earn higher GPAs and are more satisfied with college than are sophomores. College faculty and administrators would likely want to do more research to understand this relationship (and perhaps do something to facilitate the sophomore experience on their campuses).
Prediction is more powerful than description. Although prediction is never perfect (just think about weather forecasts), it does provide insights into the world around us. Chapters 12 and 13 will provide us with tools that allow us to make predictions from our data.
Predictive research: makes forecasts about future events.
Correlational method: examines how and the extent to which two variables are related to each other.
Positive correlation: increases (or decreases) in one variable tend to be accompanied by increases (or decreases) in a second variable. In other words, the two variables tend to relate in the same direction.
Negative correlation: increases in one variable tend to be accompanied by decreases in a second variable. In other words, the two variables tend to relate in the opposite direction.
Zero correlation: no relationship exists between two variables.
Quasi-experiment: compares naturally existing groups, such as socioeconomic groups.
Goal: To Explain
Explanatory research takes descriptive and predictive research one step further; that is, explanatory research (also called experimental research) allows researchers to draw cause-and-effect conclusions between phenomena of interest. The researcher uses “control” to establish cause-and-effect conclusions. By “control” in the context of explanatory research, a researcher must manipulate (i.e., control) some aspect of behavior. The behavior that is controlled is called the independent variable. In this example, the independent variable is level of aerobic exercise. It is “independent” because the researchers can decide, within ethical boundaries, what to expose participants to in the experiment. It is called “variable” because some participants engaged in aerobic exercise, and others did not. Had all participants engaged in aerobic exercise, there would be nothing that varied. There must be at least two groups created by manipulating (controlling) an independent variable. Without at least two groups, you would have no way to make a comparison on how people’s behavior was affected.5
Of course, we want to know an outcome of the independent variable. That outcome is called the dependent variable. That is, are there differences in academic performance based on whether people engaged in aerobic exercise? Such potential differences “depend” on the independent variable.
You might well be wondering at this point how we can draw cause-and-effect conclusions from the independent variable’s effect on the dependent variable. Researchers use random assignment of participants in the sample either to engage in aerobic exercise or not to engage in aerobic exercise. Think about the many ways people differ from one another. For instance, I grew up during the relative economic boom years of the 1980s in the North Dallas suburbs, raised by parents from the northeastern United States. Such an upbringing may well differ from yours, likely in more than one way. And that’s just a couple of ways we might differ from each other. Not that such differences are unimportant, but in this context, they are not of interest to the researchers. Therefore, we want to control for their influence on how people in the sample behave, so that we can isolate the effect of the independent variable. Through the process of random assignment, we can minimize the influences of variables (e.g., where people grew up, when people grew up, and socioeconomic status) other than the independent variable. In doing so, any effects we find can be linked to the independent variable.
Suppose we find that students who engaged in aerobic exercise throughout the semester had higher grades at the end of that semester. By using experimentation, which involves manipulating at least one variable, and by using random assignment of participants to groups created by that manipulation, we can draw cause-and-effect conclusions between behaviors. We will explore statistical tools that are often used with experimental data in Chapters 7 through 11.
Explanatory research: draws cause-and-effect relationships between variables.
Independent variable: variable that a researcher manipulates (changes) to create experimental groups (conditions). It should affect subsequent behavior or mental processes.
Dependent variable: behavior that results from the independent variable.
Random assignment: uses a random process, such as flipping a coin, to put members of a sample in one of the groups (conditions) in an experiment. Its purpose is to minimize preexisting differences among members of the sample so that researchers can be confident of the effects of the independent variable.
Goal: To Apply
Finally, applied research does not have specific research methods associated with it. Rather, it makes use of the findings from the methods described previously and uses them in specific contexts. For instance, when you listen to the weather forecast (a prediction) for the next day, you will apply that information by dressing accordingly. Likewise, we already know that the manner in which information is presented influences how people respond to that information (called the framing effect). The makers of Ruffles potato chips were clearly aware of the framing effect when they labeled the cooking method of “baked” on those bags of chips, but they did not label the cooking method of the “fried” chips.
To continue our example of health-related behaviors in college students, many colleges and universities are keenly interested in promoting physical and psychological well-being in their student populations. Like all organizations, these schools face budgetary constraints. Therefore, they want to maximize the desired outcomes (well-being in their student populations) within those financial limitations. To do so, many schools rely on the sorts of research studies we have described in this part of the chapter. For instance, one college wanted to update its aerobic exercise machines. It first conducted a survey of its students and faculty by asking these people to complete a questionnaire. Based on the results of this survey, the college invested a portion of available funds and updated some of the aerobic exercise equipment. Then, after this new equipment was available for use, it conducted naturalistic observation to learn if in fact the equipment was being used as indicated it would be used on the questionnaire. When those naturalistic observations suggested heavy use of the new equipment, the college immediately invested the remaining portion of its funds for this purpose.
Applied research: uses descriptive, predictive, and explanatory research methods to answer specific research questions in specific contexts.
