SFP - Correlational Research Lesson

Correlational Research

Correlational research detects the relationships between variables. It can be used to analyze the data gathered by descriptive methods and is valuable because it allows researchers to identify factors that merit further study and to also make meaningful predictions. The number one thing you should remember about correlational research is that CORRELATION DOES NOT EQUAL CAUSATION.

Again, correlation shows relationships and is measured using a correlation coefficient. The correlation coefficient is a numerical indication of the strength of a relationship between two factors. There are two parts to a correlation coefficient: the number, which indicates the strength of the relationship, and the sign, which indicates the direction of the relationship. A correlation coefficient always falls between -1.00 and +1.00. The closer you are to one the stronger the relationship. This is true whether the number is + or -. So, for example, a -.91 indicates a stronger relationship than +.30.

A scatterplot is used to illustrate correlation. A positive correlation tells us that two factors are increasing together and moving in the same direction. A negative correlation tells us that two factors move in opposite directions. Remember the terms positive and negative describe the direction of the relationship, not whether something is good or bad. For example, the more time I spend brushing and flossing will lead to fewer cavities. This is a good thing but would illustrate a negative correlation.

The image illustrates what a perfect positive correlation, a perfect negative correlation, and no correlation would look like on a scatterplot. Perfect positives or perfect negatives are rare, but the direction of the correlation will be the same. An example of uncorrelated variables or no correlation would be the relationship between your shoe size and your grades.

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