Incremental validity is a predictor’s ability to explain an outcome above and beyond other predictors included in a given model. For example, assume predictor A accounts for 25% of the variance in an outcome of interest and, when entered separately, predictor B also accounts for 25% of the variance. Because the information contained in predictor A and predictor B is likely at least partially redundant, it is also important to understand the amount of variance each predictor explains when considered in conjunction with the other. One scenario is that predictor A and predictor B account for much of the same variance, so predictor B can be said to have low incremental validity because it contributes little unique explanatory power to the prediction equation. Another scenario ...

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