Chapter 6: Data Collection Strategies

# 6.2.2 Correlational Research

** Correlational research **is a type of non-experimental research in which the researcher is interested in the relationship between variables; however, the researcher does not attempt to influence the variables (in contrast to experimental research where the researcher manipulates the variables) (Siegle, 2015). Relationships between variables can be visualized with the aid of a graph known as a

**diagram.**

*scatterplot*Scatterplots provide information on two dimensions. The first dimension demonstrates the direction of relationship: linear, curvilinear, or no relationship. Linear relationships can be positive or negative. A positive relationship or correlation is demonstrated through a rise from left to right, while a negative correlation falls from left to right (Palys & Atchison, 2014). Here is a short video that effectively demonstrates positive relationships and no relationship:

The second dimension related to scatterplots is that they can provide an indication of the magnitude or strength of the relationship. The strongest of relationships are evidenced when all points in a scatterplot graph fall along the same straight line (known as the regression line). The next strongest of relationships are evidenced by a little bit of dispersion around the line; however, if one were to draw an oval close to the line all points would be captured within the oval. The more dispersed the points (i.e., the points do not adhere as closely to the line), the weaker the relationship (Palys & Atchison, 2014).

Near the beginning of the 20th century, Karl Pearson developed a method to statistically measure the strength of relationships between variables. This method, known as the ** Pearson Product-moment Correlation Coefficient (Pearson’s r)**, was developed to measure the strength of linear relationships only. There are two aspects to Pearson’s r: The first is the direction, represented by a sign (+ or -). A plus sign (+) indicates a positive or a directional relationship, while a negative sign (-) indicates a negative or an inverse relationship. The second aspect is a number, where a zero represents no linear relationship, and a 1.0 represents a perfect linear relationship. A 1.0 is represented on a scatterplot whenever the point lies on the same straight line. For these purposes, we will not delve further into how to compute a correlational coefficient; however, there are many online and library statistical resources if you wish to seek more information on this measure.