You may find it useful to read about validity first. See:
Validity Coefficient: Definition
Validity tells you how useful your experimental results are; a validity coefficient is a gauge of how strong (or weak) that “usefulness” factor is. For example, let’s say your research shows that a student with a high GPA. should perform well on the SAT and in college. A validity coefficient can tell you more about the strength of that relationship between test results and your criterion variables.
Example (for testing concurrent validity): you want to design an instrument that measures “success in college.” You design a scale called the SUCCESS scale which measures how well students will do in their first year of college based on GPA, social skills, extra-curricular interests and other criteria. The score ranges from 0 to 10, with career counselors grading students on a 5-point item for each set of criteria. As a criterion, you have a second set of college advisers grade the students at the end of their first year. You correlate your SUCCESS rankings with the rankings obtained from the college advisers. This gives you a validity coefficient.
In general, validity coefficients range from zero to .50, where 0 is a weak validity and .50 is moderate validity. The possible range of the validity coefficient is the same as other correlation coefficients (0 to 1) and so, in general, validity coefficients tend not to be that strong; this means that other tests are usually required. It’s not unusual for validity coefficients to max out at around .30. For the above example, this low correlation means that some students with GPAs may not perform well on standardized tests or in college.
How to find Validity Coefficients
The validity coefficient is just another type of correlation coefficient. Therefore, you can use any statistical software to find validity correlation.
You can use the Excel CORREL function to find correlation coefficients:
- Type your data into a worksheet. Your independent variables should be on one column and your dependent variables should be in a second column.
- Click the function button on the toolbar (fx).
- Type “Correl” to find the Correl function. Click on “Correl.”
- Type the cell locations of your independent variables into the array 1 box. For example, A1:A30.
- Type the cell locations of your dependent variables into the array 2 box. For example, B1:B30.
- Click “OK.”
Click one of the links below to see directions for finding validity correlations in different software programs:
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!