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OverviewOver 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. Full Product DetailsAuthor: Wim Van der ElstPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: 2024 ed. ISBN: 9783031509506ISBN 10: 3031509501 Pages: 482 Publication Date: 04 June 2024 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: Manufactured on demand We will order this item for you from a manufactured on demand supplier. Table of ContentsReviewsAuthor InformationWim Van der Elst (PhD) has a background in psychology and statistics, and is currently employed as a statistician in the pharmaceutical industry. He has published extensively on regression-based normative data, psychometrics, psychological assessment, and the statistical evaluation of biomarkers. He is also the lead programmer of several R libraries. Tab Content 6Author Website:Countries AvailableAll regions |