| Record ID | marc_columbia/Columbia-extract-20221130-034.mrc:114659807:3341 |
| Source | marc_columbia |
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LEADER: 03341cam a22006018i 4500
001 16905180
005 20221112230609.0
006 m o d
007 cr |||||||||||
008 221006s2023 enk ob 001 0 eng
010 $a 2022030375
035 $a(OCoLC)on1346988871
035 $a(NNC)16905180
040 $aDLC$beng$erda$cDLC$dTYFRS
020 $a9781003279693$q(ebook)
020 $a1003279694
020 $a9781000810554$q(electronic bk. : PDF)
020 $a1000810550$q(electronic bk. : PDF)
020 $a9781000810592$q(electronic bk. : EPUB)
020 $a1000810593$q(electronic bk. : EPUB)
020 $z9781032246680$q(hardback)
020 $z9781032246697$q(paperback)
024 7 $a10.4324/9781003279693$2doi
035 $a(OCoLC)1346988871
037 $a9781003279693$bTaylor & Francis
050 00 $aHA32
072 7 $aPSY$x030000$2bisacsh
072 7 $aSOC$x024000$2bisacsh
072 7 $aKCHS$2bicssc
082 00 $a519.5/4028553$223/eng/20221006
049 $aZCUA
100 1 $aGarson, G. David,$eauthor.
245 10 $aFactor analysis and dimension reduction in R :$ba social scientist's toolkit /$cG. David Garson.
263 $a2212
264 1 $aAbingdon, Oxon ;$aNew York, NY:$bRoutledge,$c2023.
300 $a1 online resource
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
504 $aIncludes bibliographical references and index.
520 $a"Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods. Presented in colour with R code and introduction to R and RStudio, this book will be suitable for graduate level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses"--$cProvided by publisher.
588 $aDescription based on print version record and CIP data provided by publisher; resource not viewed.
545 0 $aG. David Garson is Professor Emeritus in the School of Public and International Affairs, NCSU, specializing in advanced research methodology. His most recent works are Data Analytics for the Social Sciences: Applications in R (Routledge, 2022) and Multilevel Modeling: Applications in STATA, IBM, SPSS, SAS, R & HLM (Sage, 2020).
650 0 $aSocial sciences$xStatistical methods.
650 0 $aFactor analysis.
650 0 $aSocial sciences$xMathematical models.
650 0 $aSocial sciences$xData processing.
650 0 $aR (Computer program language)
650 7 $aPSYCHOLOGY / Research & Methodology$2bisacsh
650 7 $aSOCIAL SCIENCE / Research$2bisacsh
776 08 $iPrint version:$aGarson, G. David.$tFactor analysis and dimension reduction in R$dAbingdon, Oxon ; New York, NY: Routledge, 2023$z9781032246680$w(DLC) 2022030374
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16905180$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS