Regression-Based Normative Data for Psychological Assessment: A Hands-On Approach Using R - Hardcover

Van Der Elst, Wim

Hardcover

ISBN 10: 3031509501 ISBN 13: 9783031509506

Publisher: Springer, 2024

Synopsis

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.

About this title

About the Author

Wim 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.

From the Back Cover

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.

• Provides a solid introduction in regression-based normative methods without being overly technical;
• Comes with a comprehensive open-source software package to help efficiently derive regression-based normative data;
• Focuses strongly on the practical application of the methodology using various real-life case studies. 

Edition details

• Publisher: Springer
• Publication date: 2024
• Language: English
• ISBN 10: 3031509501
• ISBN 13: 9783031509506
• Binding: Hardcover
• Number of pages: 493