Using linear regression techniques to accurately predict a score on a selected distribution of scores is tremendously helpful to K-12 educators, especially central office personnel.
Assessment data, while abundant, is often not connected to future predictions, nor is subject matter performance correlated with other variables. For example, performance in one subject should be able to predict performance in another, using linear regression and prediction.
It is important to note, however, that for many compared variables, correlation does not indicate causation. “The researcher must still interpret the statistics in the content of the situation” (Hinkle, Wiersma, & Jurs, 2003, p. 122), and therefore, for the educational practitioner, making inferences about data can be challenging, if not erroneous.
Correlation vs. causation. The rain didn’t cause the kid to put on rain boots. It’s correlated to the rain. In fact, his mom put the rain boots on, and the rain had nothing to do with it…
For example, a practitioner might notice a correlation between scores on the ACT and scores on the SAT exams between students. It might then be tempting to make predictions on success in college based on the positive correlation between the two distributions (just because both are used for college entrance purposes) and make the claim that high scores on the SAT yields a high graduation rate from college.
Likely, no such comparison can be made. The prediction in this case is not related to the two distributions under study, and therefore the research cannot claim correlation or prediction. Many fall into this trap, and it would be best if practitioners better understood that correlation between X and Y “does not imply” (Hinkle et al., 2003, p. 122) causation.
Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied Statistics for the Behavioral Sciences. 5th Ed. Boston: Houghton Mifflin.