This guide contains all of the ASC's statistics resources. If you do not see a topic, suggest it through the suggestion box on the Statistics home page.

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- Learn about the different input parameters needed to determine the minimum sample size for common correlation analyses using G*Power. Practice conducting each type of a priori (before data collection) analysis.

The correlation analysis is used to measure the direction and relationship between two variables. It's important to note that *correlation does not equal causation*. That means that while a relationship may be observed, it's impossible to say that one variable caused or affected the other variable. The relationship observed may be due to other variables not accounted for in the model.

**Research Question & Hypotheses Examples**

RQ: What is the relationship between height and age?

- H0: There is no relationship between height and age.
- Ha: There is a relationship between height and age.

RQ: Is there a significant linear relationship between hours of exercise and weight?

- H0: There is not a significant linear relationship between hours of exercise and weight.
- Ha: There is a significant linear relationship between hours of exercise and weight.

**Direction**

The direction of the relationship can be assessed by looking at the sign of the correlation coefficient.

**Strength**

The strength of the relationship can be assessed by evaluating the numerical value of the correlation coefficient. Correlation values can range from -1 to +1.

**Effect Size**

The measure of effect size used for correlation analyses is called the coefficient of determination or R-Squared. This value can be found by simply squaring the value of the correlation coefficient (r). For example, if *r* = .3, then the effect size is .09. This is interpreted as saying that 9% (.09 x 100 = 9%) of the variability in one variable is explained by the other variable.

See the Correlation page for additional information

- Last Updated: Mar 12, 2023 6:02 PM
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