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010   $a9783031509513$b(electronic bk.)
010   $z9783031509506
024 7 $a10.1007/978-3-031-50951-3
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035   $a(Au-PeEL)EBL31364750
035   $a(CKB)32227786700041
035   $a(DE-He213)978-3-031-50951-3
035   $a(OCoLC)1438668415
035   $a(EXLCZ)9932227786700041
100   $a20240603d2024      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aRegression-Based Normative Data for Psychological Assessment $eA Hands-On Approach Using R /$fby Wim Van der Elst
205   $a1st ed. 2024.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024.
215   $a1 online resource (485 pages)
311 08$aPrint version: Van der Elst, Wim Regression-Based Normative Data for Psychological Assessment Cham : Springer,c2024 9783031509506 
327   $aGeneral 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.
330   $aOver 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. .
606   $aPsychology
606   $aPsychological tests
606   $aPsychology$xMethodology
606   $aSocial sciences$xStatistical methods
606   $aBehavioral Sciences and Psychology
606   $aPsychological Assessment
606   $aPsychological Testing
606   $aPsychological Methods
606   $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy
615  0$aPsychology.
615  0$aPsychological tests.
615  0$aPsychology$xMethodology.
615  0$aSocial sciences$xStatistical methods.
615 14$aBehavioral Sciences and Psychology.
615 24$aPsychological Assessment.
615 24$aPsychological Testing.
615 24$aPsychological Methods.
615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
676   $a153.930285
700   $aVan der Elst$b Wim$01742745
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
912   $a9910865244003321
996   $aRegression-Based Normative Data for Psychological Assessment$94169433
997   $aUNINA