04047nam a22005535i 4500 978-3-031-50951-3 DE-He213 20251030112255.0 cr nn 008mamaa 240603s2024 sz | s |||| 0|eng d 9783031509513 978-3-031-50951-3 10.1007/978-3-031-50951-3 doi BF1-990 JM bicssc PSY000000 bisacsh JM thema Van der Elst, Wim. author. aut http://id.loc.gov/vocabulary/relators/aut 78148 Regression-Based Normative Data for Psychological Assessment [electronic resource] : A Hands-On Approach Using R / by Wim Van der Elst. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XI, 482 p. 164 illus., 75 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. . Psychology. 63758 Psychological tests. 78149 Psychology Methodology. 77306 Social sciences Statistical methods. 75236 Behavioral Sciences and Psychology. 68075 Psychological Assessment. 78150 Psychological Testing. 78151 Psychological Methods. 77307 Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 75237 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031509506 Printed edition: 9783031509520 Printed edition: 9783031509537 https://doi.org/10.1007/978-3-031-50951-3 ZDB-2-BSP ZDB-2-SXBP 60470 60470