Correlational research is not experimental. It is descriptive in nature. Correlation does not always mean causation. Variables can be related but lack a causal relationship. Although a correlation might not have a significant value in causal inference, it does not imply that correlational studies are not essential. In some cases, individuals consider correlational studies as invaluable because they do not indicate causation. However, these studies are vital in multiple ways hence should not be taken lightly or ignored.
Most scientific or research hypotheses are stated as either having or lacking a correlation. Therefore, correlation is used to make predictions to be tested using other study designs. Also, although correlation does not mean that causation exists, causation indicates correlation. Therefore, although correlation might not prove that causality exists, it can rule out causality. Correlational studies are vital because once a correlation is established, predictions can be made. When a score on a specific measure is determined, it is possible to make a more precise prediction of the other measure associated or related to it. A strong relationship between variables indicates a more precise prediction. Besides, in some practical cases, evidence provided by correlational studies can be useful in conducting controlled experiments.
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The aim of a correlational study is to provide a description of the relationship that exists between variables. They help determine if, to some level, a change in one variable causes a change on the other variable. Also, such studies determine the strength of an association. Correlation describes the direction of a relationship. Results obtained from correlational studies are used in identifying the prevalence and interrelationships between variables, thus helpful in predicting events using the available data or information. However, caution should be exercised when using the design and conducting data analysis. Researchers who conduct correlational studies single out vital issues and discuss them to reduce the likelihood of making mistakes when analyzing data.
