Neuropsychologists administer neuropsychological tests to decide whether a patient is cognitively impaired. This clinical decision is made by comparing a patient’s scores to those of healthy participants in a normative sample. In a multivariate normative comparison, a patient’s entire profile of scores is compared to scores in a normative sample. Such a multivariate comparison has been shown to improve clinical decision making. However, it requires a multivariate normative data set, which often is unavailable. To obtain such a multivariate normative data set, the authors propose to aggregate healthy control group data from existing neuropsychological studies. As not all studies administered the same tests, this aggregated database will contain substantial amounts of missing data. The authors therefore propose two solutions: multiple imputation and factor modeling. Simulation studies show that factor modeling is preferred over multiple imputation, provided that the factor model is adequately specified. This factor modeling approach will therefore allow routine use of multivariate normative comparisons, enabling more accurate clinical decision making.