What is correlational research and how is it conducted?
Correlational research attempts to investigate whether a relationship exists between two or more variables as well as the nature (direction and magnitude) of the relationship (if it exists). The tool for achieving this is the correlation coefficient, a mathematical expression of the extent of association between any two variables. The values of the coefficient range from –1 to +1.
There are several kinds of correlation coefficients but the most widely used are Pearson’s Product Moment Correlation Coefficient (r), and Spearman Rank Order Correlation Coefficient (rho or ℓ). Others include the phi coefficient (four-fold correlation), tetrachoric correlation coefficient, Kendal’s tau correlation coefficients, and Kendall’s coefficient of concordance, the bi-serial coefficient, and the point bi-serial coefficient, among others. The use of any coefficient is dependent on the nature of the variables/data under scrutiny.
In a nutshell, the main objective of correlational research is to probe and clarify the degree to which changes in one factor or variable influence or induce changes in one or more factors or variables. Note that correlation is not causation. That a relationship exists between A and B does not necessarily mean that A causes B or vice-versa.
Types of correlation
There are three possible outcomes in correlational research – positive correlation, negative correlation, and zero correlation
- Positive correlation
In positive correlation, a decrease or increase of one variable creates the same change in the other. It is therefore a case of statistically corresponding variables. Based on the values of the correlation coefficient above, if each variation in X induces exactly the same variation in Y, the relationship is said to be perfectly positive and the correlation coefficient will be +1.00. If Y decreases in exactly the same proportion as a decrease in X, then the relationship is perfectly negative and the coefficient will be –1.00. If the relationship is not perfectly positive, the value will be between 0 and +1.00.
- Negative correlation
This is a case of statistically contradictory variables. An increase in one variable leads to a decrease in the other variable. A relationship is negative or inverse if the correlation coefficient is less than zero or between 0 and -1.
- Zero correlation
Zero correlation occurs when there is no statistical relationship between variables. Hence, changes or variations occurring in one variable do not trigger any change whatsoever in the other variable. The value of the correlation coefficient in this case is zero.
Methods of collecting data for correlational research
- Survey method
This is a popular and flexible method of correlational research. Variables of interest can be selected randomly or participants can be surveyed with questionnaires. It can lead to the generation of a large number of data within a short time. Notable challenges here include asking biased questions or survey response bias on the part of subjects. A small sample can also invalidate the conclusions.
- Naturalistic Observation
In this method, the researcher observes certain characteristics of his subjects for some time in their natural geographic locations without their consent. It allows the researcher to observe the natural behavior of his subjects which they can alter if they realize they are being observed. However, the researcher must be careful so as not to arouse the suspicion of those he is observing.
- Archival data
The data collected in this method comes of already existing sources or archives. Hence, archival data is previously collected data. Some of the sources of this method include past research or the historical records of the variables of interest. It is simpler process than the other methods since the data is already available.
Some ways correlation research can be applied
Like other forms of research, correlational research has several real-life applications. It can be used to investigate debatable notions or hypotheses. For instance, a researcher may want to investigate the authenticity of the impression that those who are uneducated are likely to die younger than educated people. To determine this, a researcher can decide to examine the relationship between illiteracy and high mortality rate.
Advantages of correlational research
Provides more knowledge on the link between variables
Correlational research can make it possible to establish new relationships between variables or phenomena that were previously thought to have no such relationship (and vice-versa). Such possibilities can help create a better understanding of relationships than other types of research.
Helps to establish the direction and magnitude of relationships
it is a type of research that helps to determine the direction and magnitude of relationships among variables. Once this is established, experimental research can then be used to find cause and effect.
Ease of measurement
Unlike some other concepts where measurement can be tedious and time-consuming, the correlation coefficient is a relatively simple calculation. It is to easy to read when variables are positively or negatively related or when no relationship exists between them.
Variables are not manipulated
The variables in correlational research are not subject to manipulation but are only measured for changes. This is unlike in experiments where the researcher manipulates an independent variable and measures its impact on one or more dependent variables.
Disadvantages of correlational research
Only measures causation.
Correlational research measures just the relationships between variables but does not offer strong explanations about why these relationships exist or cause and effect relationships. While it is scientifically advantageous to know the links between variables, the likes of Clive Granger have shown that causation is equally no less important.
No control over variables
Sometimes, it may be necessary to control variables so as to make some conclusions. Unlike experimental research, it is not possible for the researcher to control his variables in correlational research.