Research methods can be categorized majorly in four ways including “observational research, correlational research, true experiments, and quasi-experiments “. Correlational research examines the relationship that may exist between two or more variables for example a research in the health setting may examine the relationship between those who smoke and the frequency of lung cancer occurrence in such individuals. In that particular study, the nature of correlation is ascertained to evaluate the possibilities of smoking as a predisposing factor for lung cancer disease. The relationship is regarded as covariation. Correlational research can be best perfected by thorough collection of discrete or empirical data. Correlational research is regarded as one of the observational methods of studies simply because there is no manipulation of the subjects( Angst,1999).
The principal investigator commonly referred to as the researcher does nothing on the subjects but assess or studies them in their natural environment and draws inferences from his observations. For example in the study of the relationship between smokers and non smokers as predisposing factors for lung cancer the smokers are not advised to stop smoking and might be aware of may not be aware of the research objectives. The main point to note is that correlational research does not prove causality or the cause of the relationships. Thai is to say that the relationships between cause and variable effects cannot be established through correlational studies. They just act as guiding steps in areas where variables may be compared. This is because another variable often referred to as confounder may modify or affect the results of the study without the researcher’s idea (Kellner, 2002).
Looking at the need for correlational studies, it can be clearly stated that it is used to asses the would be relationship between two variables. In any correlational study, there is a possibility of obtaining three possible results including, negative correlation, positive one and that where there is no correlation often referred to as zero correlation. Correlation strength is a great predictor of correlation and is majorly measured as correlation coefficient that ranges from –1.00 to +1.00. In the assessment of positive correlation, it is clear that both the independent as well as the outcome variables increase or may be observed to decrease with time. If the computed correlation coefficient tends towards +1, then it is regarded as a strong positive correlation. A scatter plot of values is plotted on a Cartesian plane and the nature of relation ascertained. In the graph plotted below, positive correlation is being used to demonstrate the relationship between the number of years spent at school and the total income earned by an individual.
The major disadvantage of correlational studies may be observed from the fact that as much as these particular studies indicate possibilities of the relationships between any variables, the show nothing or causal or effect relationships between the predictor and the outcome variables among the subjects being studied. It is therefore important to appreciate that these particular studies only show the existence as well as the strength of the relationships for example like in the previous chapters the correlational study may indicate that there is a relationship between smoking and the incidences of lung cancer but does not tell whether smoking causes or increases the chances of contracting lung cancer.