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001			23371975
003			OSt
005			20241105112621.0
008			231102s2024 flua b 001 0 eng
010			_a 2023045806
020			_a9781032363967 _q(hardback)
020			_a9781032363943 _q(paperback)
020			_z9781003331711 _q(ebook)
040			_aDLC _beng _erda _cDLC _dDLC
042			_apcc
050	0	0	_aHA31.35 _b.F56 2024
082	0	0	_a005.5/5 _223/eng/20231204
100	1		_aFinch, W. Holmes _q(William Holmes), _eauthor.
245	1	0	_aMultilevel modeling using R / _cW. Holmes Finch, Jocelyn E. Bolin.
250			_aThird edition.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2024.
300			_axi, 325 pages : _billustrations ; _c24 cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aChapman & Hall/CRC statistics in the social & behavioral sciences series
504			_aIncludes bibliographical references (pages 318-321) and index.
520			_a"Like its bestselling predecessor, Multilevel Modeling Using R, Third Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The third edition of the book includes several new topics that were not present in the second edition. Specifically, a new chapter has been included, focussing on fitting multilevel latent variable modeling in the R environment. With R, it is possible to fit a variety of latent variable models in the multilevel context, including factor analysis, structural models, item response theory, and latent class models. The third edition also includes new sections in chapter 11 describing two useful alternatives to standard multilevel models, fixed effects models and generalized estimating equations. These approaches are particularly useful with small samples and when the researcher is interested in modeling the correlation structure within higher level units (e.g., schools). The third edition also includes a new section on mediation modeling in the multilevel context, in chapter 11. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research"-- _cProvided by publisher.
650		0	_aSocial sciences _xStatistical methods.
650		0	_aMultivariate analysis.
650		0	_aR (Computer program language)
776	0	8	_iOnline version: _aFinch, W. Holmes (William Holmes). _tMultilevel modeling using R _bThird edition. _dBoca Raton : CRC Press, Taylor & Francis Group, 2024 _z9781003331711 _w(DLC) 2023045807
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cBK _n0
999			_c27756 _d27756