TY  - BOOK
AU  - Van der Elst,Wim
ED  - SpringerLink (Online service)
TI  - Regression-Based Normative Data for Psychological Assessment: A Hands-On Approach Using R 
SN  - 9783031509513
AV  - BF1-990 
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Psychology
KW  - Psychological tests
KW  - Methodology
KW  - Social sciences
KW  - Statistical methods
KW  - Behavioral Sciences and Psychology
KW  - Psychological Assessment
KW  - Psychological Testing
KW  - Psychological Methods
KW  - Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
N1  - General introduction.-The R programming language -- Normative data accounting for a binary independent variable -- Assumption of the normal error regression model -- Normative data accounting for a non-binary qualitative independent variable -- Normative data accounting for a quantitative independent variable -- Normative data accounting for multiple qualitative and/or quantitative independent variables -- Quantifying uncertainty in regression-based norms
N2  - 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.
UR  - https://doi.org/10.1007/978-3-031-50951-3
ER  -