000 03758nam a22005415i 4500
001 978-3-031-56318-8
003 DE-He213
005 20251030113237.0
007 cr nn 008mamaa
008 241021s2024 sz | s |||| 0|eng d
020 _a9783031563188
_9978-3-031-56318-8
024 7 _a10.1007/978-3-031-56318-8
_2doi
050 4 _aHA1-4737
072 7 _aJHBC
_2bicssc
072 7 _aSOC027000
_2bisacsh
072 7 _aJHBC
_2thema
245 1 0 _aDependent Data in Social Sciences Research
_h[electronic resource] :
_bForms, Issues, and Methods of Analysis /
_cedited by Mark Stemmler, Wolfgang Wiedermann, Francis L. Huang.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXXII, 786 p. 70 illus., 64 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 _aGrowth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.
520 _aThis second edition presents a variety of up-to-date statistical issues with regard to dependent or longitudinal data such as continuous time modeling, growth curve modeling, dynamic modeling, network analysis, Bayesian network analysis, directional dependence, multilevel analysis, item response modeling (IRT), estimation of missing data of longitudinal data and other methods for the analysis of dependent data (e.g., configural frequency analysis, ecological momentary assessment, and unobserved within-group individual differences). It presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. In addition, R-scripts to recapture the presented content are provided. Researchers and graduate students in the social and behavioral sciences, education, econometrics, mathematics, biology, physics and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.
650 0 _aSocial sciences
_xStatistical methods.
_975236
650 0 _aStatistics.
_965982
650 0 _aPsychometrics.
_963358
650 1 4 _aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
_975237
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aPsychometrics.
_963358
700 1 _aStemmler, Mark.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986333
700 1 _aWiedermann, Wolfgang.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986334
700 1 _aHuang, Francis L.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_986335
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031563171
776 0 8 _iPrinted edition:
_z9783031563195
776 0 8 _iPrinted edition:
_z9783031563201
856 4 0 _uhttps://doi.org/10.1007/978-3-031-56318-8
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c64351
_d64351