Correlational studies are an important tool for understanding the relationship between two or more variables. Correlational studies are used to identify patterns of relationships between variables, and they are used to predict how changes in one variable may influence changes in another. This type of research is often used to inform policy decisions, and it can help explain the causes of certain outcomes. In this article, we will discuss the basics of correlational studies, the types of correlations that can be examined, and the implications of correlational research.
Correlational studies measure the degree of association between two or more variables. Generally, a correlation coefficient is used to quantify the degree of association between two variables. Depending on the type of correlation being examined, different methods may be used to calculate the correlation coefficient. For example, for a Pearson correlation, the correlation coefficient is calculated using the formula r= Cov(X, Y)/SQRT[Var(X)*Var(Y)]. In this formula, Cov(X, Y) is the covariance of X and Y, Var(X) is the variance of X, and Var(Y) is the variance of Y.
There are several types of correlation that can be studied, including positive, negative, and no correlation. Positive correlations indicate that as one variable increases, the other variable also increases. Negative correlations indicate that as one variable increases, the other variable decreases. No correlation indicates that there is no relationship between the two variables.
The implications of correlational research are significant. Correlations can help to inform policy decisions, as they can indicate the relationships between different variables. For example, if there is a positive correlation between a particular policy and an increase in economic growth, this can help inform policy makers to implement that policy. Correlations can also be used to identify the causes of certain outcomes. For example, if there is a positive correlation between a particular political party and an increase in violent crime, this can help inform policy makers to investigate why this is the case.
In conclusion, correlational studies are an important tool for understanding the relationship between two or more variables. They can be used to identify patterns of relationships between variables, and they can be used to predict how changes in one variable may influence changes in another. Correlational research can help inform policy decisions, and it can also be used to identify the causes of certain outcomes.
References
Bressler, E.R. (2017). Correlational research: Definition, purpose, and examples. Retrieved from https://explorable.com/correlational-research
Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Allyn & Bacon.