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020 _a9783031509513
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024 7 _a10.1007/978-3-031-50951-3
_2doi
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_2bicssc
072 7 _aPSY000000
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100 1 _aVan der Elst, Wim.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978148
245 1 0 _aRegression-Based Normative Data for Psychological Assessment
_h[electronic resource] :
_bA Hands-On Approach Using R /
_cby Wim Van der Elst.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXI, 482 p. 164 illus., 75 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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. .
650 0 _aPsychology.
_963758
650 0 _aPsychological tests.
_978149
650 0 _aPsychology
_xMethodology.
_977306
650 0 _aSocial sciences
_xStatistical methods.
_975236
650 1 4 _aBehavioral Sciences and Psychology.
_968075
650 2 4 _aPsychological Assessment.
_978150
650 2 4 _aPsychological Testing.
_978151
650 2 4 _aPsychological Methods.
_977307
650 2 4 _aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
_975237
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031509506
776 0 8 _iPrinted edition:
_z9783031509520
776 0 8 _iPrinted edition:
_z9783031509537
856 4 0 _uhttps://doi.org/10.1007/978-3-031-50951-3
912 _aZDB-2-BSP
912 _aZDB-2-SXBP
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