000			03129cam a2200361 i 4500
001			22444577
003			Ost
005			20260410175043.0
008			260106s2022 nyuad frb 001 0 eng
010			_a 2022009851
020			_a9781462549863
040			_aDLC _beng _erda _cDLC _dDLC _dclrauoh
042			_apcc
082	0	0	_a300.15195
100	1		_aEnders, Craig K., _eauthor.
245	1	0	_aApplied missing data analysis / _cCraig K. Enders.
250			_aSecond edition.
260			_aNew York : _bThe Guilford Press, _c2022
300			_axiv, 546 pages : _billustrations.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aMethodology in the social sciences
500			_aRevised edition of the author's Applied missing data analysis, 2010.
504			_aIncludes bibliographical references (pages 493-517) and index.
505			_aIntroduction to missing data -- Maximum likelihood estimation -- Maximum likelihood estimation with missing data -- Bayesian estimation -- Bayesian estimation with missing data -- Bayesian estimation for categorical variables -- Multiple imputation -- Multilevel missing data -- Missing not at random processes -- Special topics and applications -- Wrap-up.
520			_a"The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website includes datasets and analysis examples from the book, up-to-date software information, and other resources. Subject areas/Key words: advanced quantitative methods, management, survey, longitudinal, structural equation modeling, handling, how to handle, incomplete, multivariate, social research, behavioral sciences, statistical techniques, textbooks, seminars, doctoral courses, multiple imputation, models, MCAR, MNAR, Bayesian Audience: Researchers and graduate students in psychology, education, management, family studies, public health, sociology, and political science"-- _cProvided by publisher.
650		0	_aSocial sciences _xStatistical methods.
650		0	_aMissing observations (Statistics)
650		0	_aSocial sciences _xResearch _xMethodology.
856			_uhttps://itmsp.museknowledge.com/UOH?qurl=https://libros-uoh.uoh.cl/ebook_2/Applied-missing-data-analysis _yVER LIBRO DIGITAL
942			_2ddc _cBOOK _n0
999			_c6873 _d6873