Scatterplots and correlation coefficients are measures of association between two quantitative variables. For instance, researchers may want to know whether an increased amount of time spent on homework is associated with higher scores on a standardized test. Scatterplots provide descriptive information on the direction, form, and strength of the relationship between the two variables by representing individuals as points on a two-dimensional graph. These points may aggregate to describe a linear relationship, curvilinear relationship, or no relationship. Scatterplots may also indicate whether there is positive or negative association between variables, and suggest the strength of their relationship. A positive association means that high values in one variable are associated with high values in the other variable, whereas a negative association shows that high values in one variable are associated with low values in the other variable (e.g. the relationship between poverty and student achievement).
The Pearson product moment correlation coefficient can be calculated to quantify a linear relationship between two quantitative variables. These coefficients take values between -1 and 1, where values closer to 0 indicate weak relationships, and values closer to 1 or -1 indicate stronger relationships. Positive values show a positive association, whereas negative values show a negative association of the two variables. The narrated presentation provides more details on the interpretation of scatterplots and correlation coefficients. The software tutorials demonstrate how to generate scatterplots and to compute the Pearson product moment coefficient in SPSS.
- Diana Mindrila, Ph.D.
- Phoebe Balentyne, M.Ed.
- Notes: Scatterplots and Pearson Correlation Coefficients (SBSS)
- External Video: The Big Idea My Brother Inspired