Correlational research tests for statistical relationships between variables. The researcher begins with the idea that there might be a relationship between two variables. She or he then measures both variables for each of a large number of cases and checks to see if they are in fact related. The relationship of interest could be either a D relationship or an R relationship, so this might involve making a bar graph and computing D or making a line graph or scatter plot and computing R. It probably also involves null hypothesis testing to see if the observed relationship is statistically significant.
EXAMPLES OF CORRELATIONAL RESEARCH
1.Imagine that a health psychologist is interested in testing the claim that people with more friends tend to be healthier. She surveys 500 people in her community, asking them how many friends they have and getting some measure of their overall health. Then she makes a scatter plot and sees that there is a positive correlation between these variables. Specifically, she finds that r = +.3, concluding that there is a moderate tendency for people with more friends to be healthier.
2. An electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling; however, statistical dependence is not sufficient to demonstrate the presence of such a causal relationship
Base on the text that I have read, “A correlational research is just a test for statistical relationship” which means a one idea can brought up to two or more ideas that complement the particular idea or it can be called as relationship because the particular idea also affects the other ideas and it falls out that they are connected to each other.
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