Creating graphs and computing for correlation coefficients are frequenly done in such research projects.
The scattergram or scatter plot (or simply scatter) is the graph that is used in correlational studies.
Click the provided links to review how to interpret scatter plots.
- scatter plots and correlation
- correlation demo
- MyMath's lesson on scatter graphs
Please note that NOT all relationships are linear. Click here for scattergrams illustrating nonlinear relationship.
The most popular correlation coefficient is the Pearson’s r.
Please take note that one of the assumptions of Pearson’s r is linearity and there are variables that may have nonlinear relationship.
Another assumption is level of measurement.
But sometimes the only information is binary in nature (e.g., right or wrong answer, passed or failed the test, etc) Such data are definitely lower than interval level. We can use point biserial correlation coefficient or phi coefficient, both “descendants” of the Pearson’s r.
Click here to review the alternatives to Pearson's r when its assumptions could not be satisfied. Additional Links
- Using SPSS to compute for Pearson's r, create a scattergram
- Using MS-Excel to create a scattergram, compute for Pearson's r
- Computing for Pearson's r, point-biserial r, phi coefficient online
- Describing Bivariate Data from Lane's (2005) online stat book
- Introduction to Linear Correlation and Regression from Lowry's (2006) online stat book
- Scattergram that changes when the value of r is changed
- Guess the approximate value of r by studying the scattergram
- Type a target value of r and specified number of random points will be generated in the scatter
- Interpretation of Correlation by Kimbrough Oller (2007)
- Prediction from Lane's (2005) online stat book