MARC Record from marc_columbia

LEADER: 04355cam a2200649Ma 4500
001 15092573
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006 m o d
007 cr |||||||||||
008 130725s2008 fluad ob 001 0 eng d
035 $a(OCoLC)ocn857368669
035 $a(NNC)15092573
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019 $a994985340$a995765813$a1086513638$a1140432024
020 $a9781584889618$q(e-book ;$qPDF)
020 $a1584889616$q(e-book ;$qPDF)
020 $z9781584889601
020 $z1584889608
020 $a9781439889787$q(e-book)
020 $a1439889783
020 $a9781138464544$q(hardback)
020 $a1138464546
035 $a(OCoLC)857368669$z(OCoLC)994985340$z(OCoLC)995765813$z(OCoLC)1086513638$z(OCoLC)1140432024
050 4 $aHA29$b.A5824 2008
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084 $aCM 2500$2rvk
084 $aQH 234$2rvk
084 $a62P25$a62-01$a62H25$2msc
049 $aZCUA
245 00 $aAnalysis of multivariate social science data /$cDavid J. Bartholomew [and others].
250 $a2nd ed.
260 $aBoca Raton :$bCRC Press,$c2008.
300 $a1 online resource (xi, 371 pages) :$billustrations.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aChapman & Hall/CRC statistics in the social and behavioral sciences series
500 $aRevised edition of: The analysis and interpretation of multivariate data for social scientists. c2002.
504 $aIncludes bibliographical references (pages 357-362) and index.
505 0 $a1. Setting the scence -- 2. Cluster analysis -- 3. Multidimensional scaling -- 4. Correspondence analysis -- 5. Principal components analysis -- 6. Regression analysis -- 7. Factor analysis -- 8. Factor analysis for binary data -- 9. Factor analysis for ordered categorical variables -- 10. Latent class analysis for binary data -- 11. Confirmatory factor analysis and structural equation models -- 12. Multilevel modelling.
520 8 $aDrawing on the authors' varied experiences working and teaching in the field, Analysis of Multivariate Social Science Data, Second Editionenables a basic understanding of how to use key multivariate methods in the social sciences. With updates in every chapter, this edition expands its topics to include regression analysis, confirmatory factor analysis, structural equation models, and multilevel models. After emphasizing the summarization of data in the first several chapters, the authors focus on regression analysis. This chapter provides a link between the two halves of the book, signaling the move from descriptive to inferential methods and from interdependence to dependence. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data. Relying heavily on numerical examples, the authors provide insight into the purpose and working of the methods as well as the interpretation of data. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional exercises, encouraging readers to explore new ground in social science research. Requiring minimal mathematical and statistical knowledge, this book shows how various multivariate methods reveal different aspects of data and thus help answer substantive research questions.
650 0 $aSocial sciences$xStatistical methods.
650 0 $aMultivariate analysis.
650 2 $aMultivariate Analysis
650 6 $aSciences sociales$xMéthodes statistiques.
650 6 $aAnalyse multivariée.
650 7 $aMultivariate analysis.$2fast$0(OCoLC)fst01029105
650 7 $aSocial sciences$xStatistical methods.$2fast$0(OCoLC)fst01122983
650 7 $aMultivariate Analyse$2gnd
655 0 $aElectronic books.
655 4 $aEinführung.
655 7 $aEinführung.$2swd
700 1 $aBartholomew, David J.
776 1 $z9781584889601
830 0 $aStatistics in the social and behavioral sciences series.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15092573$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS