Assessing predictive validity involves establishing that the scores from a measurement procedure (., a test or survey) make accurate predictions about the construct they represent (., constructs like intelligence, achievement, burnout, depression, etc.). Such predictions must be made in accordance with theory ; that is, theories should tell us how scores from a measurement procedure predict the construct in question. In order to be able to test for predictive validity, the new measurement procedure must be taken after the well-established measurement procedure. By after , we typically would expect there to be quite some time between the two measurements (., weeks, if not months or years). Take the following example:
There are many techniques to calculate the correlation coefficient, but in correlation in SPSS there are four methods to calculate the correlation coefficient. For continuous variables in correlation in SPSS, there is an option in the analysis menu, bivariate analysis with Pearson correlation. If data is in rank order, then we can use Spearman rank correlation. This option is also available in SPSS in analyses menu with the name of Spearman correlation. If data is Nominal then Phi, contingency coefficient and Cramer’s V are the suitable test for correlation. We can calculate this value by requesting SPSS in cross tabulation. Phi coefficient is suitable for 2×2 table. Contingency coefficient C is suitable for any type of table.