Correlational Research
Any scientific process begins with description, based on observation, of an event or events, from which theories may later be developed to explain the observations. In psychology, techniques used to describe behaviour include case studies, surveys, naturalistic observation, interviews, and psychological tests.
Case studies.
A case study is a method of obtaining information from the detailed observation of an individual or individuals. Much information about behaviour and mental processes has been obtained through such studies of individual clinical cases. (Sigmund Freud, for example, formulated psychoanalytic theory after many years of treating and studying patients with emotional problems.) Although valuable information about certain types of problems may be obtained by this method, the procedure is time consuming, and it is difficult to obtain data from a broad sampling of people.
Surveys.
In a survey, people from a wide sample are asked questions about the topic of concern. The Kinsey survey on sexual behaviour is a well‐known example. Surveys can supply useful information, but they have their problems and limitations. For example, the people who respond may not be representative of the population in general, or those polled may be reluctant to respond to questionnaires or to answer them accurately.
Naturalistic observation.
In another approach to gathering information, naturalistic observation, people or animals are observed in their everyday behaviours, and their behaviours of interest are documented. For example, valuable information on wild animals, such as lions, has come from studying them in their natural habitats as opposed to observing them in a zoo because their zoo behaviour may be quite different from their natural behaviour. Similarly, the behaviour of a human in a home environment may differ considerably from that in a laboratory.
Psychological testing.
Many standardized procedures (tests) have been developed to measure specific behaviours or characteristics of organisms. Most of us have been subjected to such tests—for example, the intelligence, aptitude, and achievement tests used to predict behaviours. To be useful, tests must be both reliable and valid.
Correlation.
Correlation, a statistical measure of a relationship between two or more variables, gives an indication of how one variable may predict another. The descriptive techniques discussed above permit a statement, in the form of correlations, about that relationship. However, correlation does not imply causation; that is, simply because two events are in some way correlated (related) does not mean that one necessarily causes the other. For example, some test data indicate that boys receive higher math‐aptitude scores on college entrance exams than girls, indicating a correlation of gender with mathematical ability. But before concluding that gender determines mathematics aptitude, one must demonstrate that both the boys and the girls in the study have had the same mathematics background. Some studies have shown that girls are discouraged from taking or at least not encouraged to take more than the minimum mathematics requirements. Such discrepancies in mathematical accomplishment may also arise in the home—for example, from a parental belief that a girl does not need much mathematical training to be a good wife and mother.
The Importance of Correlational Studies
Correlation does not necessarily imply causation, as you know if you read scientific research. Two variables may be associated without having a causal relationship. However, just because a correlation has limited value as a causative inference doesn’t mean that correlation studies are not important to science. The idea that correlation does not necessarily imply causation has led many to de-value correlation studies. However, used appropriately, correlation studies are important to science.
“First, many scientific hypotheses are stated in terms of correlation or lack of correlation, so that such studies are directly relevant to these hypotheses…”
“Second, although correlation does not imply causation, causation does imply correlation. That is, although a correlational study cannot definitely prove a causal hypothesis, it may rule one out."
Third, correlational studies are more useful than they may seem, because some of the recently developed complex correlation designs allow for some very limited causal inferences.
…some variables simply cannot be manipulated for ethical reasons (for instance, human malnutrition or physical disabilities). Other variables, such as birth order, sex, and age are inherently correlational because they cannot be manipulated, and, therefore, the scientific knowledge concerning them must be based on correlation evidence.”
Once correlation is known it can be used to make predictions. When we know a score on one measure we can make a more accurate prediction of another measure that is highly related to it. The stronger the relationship between/among variables the more accurate the prediction. When practical, evidence from correlation studies can lead to testing that evidence under controlled experimental conditions. While it is true that correlation does not necessarily imply causation, causation does imply correlation. Correlational studies are a stepping-stone to the more powerful experimental method, and with the use of complex correlational designs (path analysis and cross-lagged panel designs), allow for very limited causal inferences.
Notes:
There are two major problems when attempting to infer causation from a simple correlation:
- directionality problem- before concluding that a correlation between variable 1 and 2 is due to changes in 1 causing changes in 2, it is important to realize the direction of causation may be the opposite, thus, from 2 to 1
- third-variable problem- the correlation in variables may occur because both variables are related to a third variable