Incremental validity refers to the additional benefit a particular predictor variable has above other predictors.
Incremental Validity in Practice
An example of incremental validity:
Let’s say a medical practitioner is more likely to correctly diagnose a kidney infection if a urine test is ordered, rather than relying on a physical examination and discussion of symptoms alone. We can say that the urine test has incremental validity.
If running a blood test in addition to the urine test gives the medical practitioner a further advantage, the blood test would also have incremental validity. But if the combination of blood test, urine test, and physical exam/interview has no higher predictive power than just the urine test and physical exam/interview, we would say that the blood test has no incremental validity in that situation.
Note that this type of validity depends not only on the variable in question, but also on the predictors which make up the base set. Both the situation and the predictors in the base set must be well defined for incremental validity to be a meaningful concept.
Estimating Incremental Validity
Hierarchical multiple regression is the technique most used to asses the amount of variability a predictor explains. This is often done by fitting a model to the data without the variable of interest, and then afterward adding in the focal variable and fitting a new model. The two models are compared (by calculating the R-squares statistic); and a significant change is understood to mean that the new variable does indeed have significant incremental validity, or additional predictive power.
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