Research Methods in Psychology
Correlational Research
What Is Correlational Research?
Correlational research is a type of non-experimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one, meaning that one variable is responsible for creating a change in a second variable. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.
The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.
Consider a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.
8.1 Data Collection in Correlational Research
Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured, but it should be noted that the data be quantitative (i.e., expressed as a number) rather than qualitative (expressed in categories or words). A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data.
Naturalistic observation is an
approach to data collection that involves observing people’s behavior in the
environment in which it typically occurs. Thus naturalistic observation is a
type of field research (as opposed to a type of laboratory research). It could
involve observing shoppers in a grocery store, children on a school playground,
or students a classroom. Researchers engaged in naturalistic
observation usually make their observations as unobtrusively as possible so
that participants are often not aware that they are being studied. For example, Dr. Gold and I have discussed looking at the correlation between the number of apps a student has open in their I-pad during class lecture and how well they perform on a quiz related to that lecture. We hypothesize that the more apps students have open during the lecture, the lower their quiz score will be. Note that both variables are quantifiable (number of apps, numerical grades on quizzes), we have not manipulated an independent variable, there is no random assignment, and we are not attempting to control other variables. Thus, this correlaional study would be using a non-experimental design.
Another approach that can be used to determine whether two variables are related is to use archival data, which are data that have already been collected for some other purpose. This might involve looking at existing national data bases such as the Uniform Crime Report maintained by the FBI, Census Bureau data maintained by the U.S. government or newspapers and other public records. For example, if we wanted to know whether there is a correlation between years of education and income, we could simply pull that data from an archive, perform some simple statistics and determine whether the two are related. If we wanted to know whether hot weather is related to crime, we could review a county's public crime records and check old newspapers for temperature infomation.
In conclusion, correlation is a good place to start to see whether two variables are related to one another. But they cannot show that the change in one variable CAUSED a change in the another. This is due to two problems. The first is called the directionality problem, which refers to being unsure about which varible is affecting which. In the example above, it is hard to know whether living in a low income area leads to less interest in school (thus higher dropout rates and less education) or whether less education is leading to low income jobs (one cannot get a good job without sufficient education). The second problem is called the third variable problem, which refers to the possibility that there is some third variable at play that is causing both of our variables of interest to go up or down. Back to the example, perhaps income and education are NOT related, but some other variable (like health problems) is affecting them both. That is, health problems may keep kids out of school, but they also may make it difficult for a person to hold down a good job. It is important to note that because of these problems, correlational research cannot show cause and effect relations between variables...only experiments can do that. Experiments will be the subject of the remainder of the semester.
Key Takeaways
· Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
· Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.
References from Chapter 8
Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4, 1–39.