LEADER 04673nam 22007335 450 001 9910300120503321 005 20200702145613.0 010 $a3-319-73471-7 024 7 $a10.1007/978-3-319-73471-2 035 $a(CKB)4100000004243561 035 $a(DE-He213)978-3-319-73471-2 035 $a(MiAaPQ)EBC5396652 035 $a(PPN)227404157 035 $a(EXLCZ)994100000004243561 100 $a20180516d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Multidimensional Scaling and Unfolding /$fby Ingwer Borg, Patrick J.F. Groenen, Patrick Mair 205 $a2nd ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (IX, 122 p. 65 illus.) 225 1 $aSpringerBriefs in Statistics,$x2191-544X 311 $a3-319-73470-9 327 $a1 First steps -- 2 The purpose of MDS and Unfolding -- 3 The fit of MDS and Unfolding solutions -- 4 Proximities -- 5 Variants of MDS models -- 6 Confirmatory MDS -- 7 Typical mistakes in MDS -- 8 Unfolding -- 9 MDS algorithms -- 10 MDS Software -- Subject Index. 330 $aThis book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.). This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis). 410 0$aSpringerBriefs in Statistics,$x2191-544X 606 $aStatistics 606 $aPsychometrics 606 $aMathematics 606 $aVisualization 606 $aSocial sciences?Data processing 606 $aSocial sciences?Computer programs 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 606 $aPsychometrics$3https://scigraph.springernature.com/ontologies/product-market-codes/Y43000 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030 606 $aVisualization$3https://scigraph.springernature.com/ontologies/product-market-codes/M14034 606 $aComputational Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X34000 615 0$aStatistics. 615 0$aPsychometrics. 615 0$aMathematics. 615 0$aVisualization. 615 0$aSocial sciences?Data processing. 615 0$aSocial sciences?Computer programs. 615 14$aStatistics and Computing/Statistics Programs. 615 24$aPsychometrics. 615 24$aStatistics for Social Sciences, Humanities, Law. 615 24$aStatistics for Life Sciences, Medicine, Health Sciences. 615 24$aVisualization. 615 24$aComputational Social Sciences. 676 $a001.4226 700 $aBorg$b Ingwer$4aut$4http://id.loc.gov/vocabulary/relators/aut$0103137 702 $aGroenen$b Patrick J.F$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aMair$b Patrick$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910300120503321 996 $aApplied Multidimensional Scaling and Unfolding$92102304 997 $aUNINA