General Introduction

Psychological tests and questionnaires are often used to assess a person’s cognitive, emotional, and behavioral functioning. The large majority of psychological tests are norm-referenced. This means that normative data are needed to allow for a meaningful interpretation of the test scores. For example, knowing that a person answered 20 out of 50 items correctly on a test of abstract reasoning is not informative by itself. To give this test score a meaningful interpretation, the relative position of the score in a broader reference group (i.e., a normative sample) should be known. Traditional normative data consist of subgroup-specific summary statistics of the test scores in a normative sample. This normative approach is straightforward, but it has some fundamental limitations. For example, it is difficult to derive fine-grained norms that account for the impact of several independent variables (such as Age, Gender, and Level of Education). Indeed, the subgroups become small when multiple independent variables have to be accounted for, which results in imprecise norms. The regression-based normative approach provides a statistically principled alternative to the traditional normative method that is substantially less hampered by such issues.