Over the last 20 years, so-called regression-based normative methods have become increasingly popular. In this approach, regression models for the mean and the residual variance structure are used to derive the normative data.

The regression-based normative approach has some important advantages over the traditional normative approach, e. g.

, it allows for deriving more fine-grained norms and typically requires a substantially smaller sample size to derive accurate norms. This book focuses on regression-based methods to derive normative data.

The target audience are psychologists and other researchers in the behavioral sciences who are interested in deriving normative data for psychological tests (e. g.

, cognitive tests, questionnaires, rating scales, etc. ).

The book provides the essential theoretical background that is needed to understand the methodology, with a strong emphasis on the practical/real-life application of the methodology. To this end, the book is also accompanied by an open-source software package (the R library NormData) that is used to exemplify how normative data can be derived in several case studies.

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