國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Applied multidimensional scaling and...

Borg, Ingwer.

Applied multidimensional scaling and unfolding

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied multidimensional scaling and unfoldingby Ingwer Borg, Patrick J. F. Groenen, Patrick Mair.
作者:	Borg, Ingwer.
其他作者:	Groenen, Patrick J. F.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	ix, 122 p. :digital ;24 cm.
Contained By:	Springer eBooks
標題:	Multidimensional scaling.
電子資源:	http://dx.doi.org/10.1007/978-3-319-73471-2
ISBN:	9783319734712$q(electronic bk.)

Applied multidimensional scaling and unfolding
Borg, Ingwer.

Applied multidimensional scaling and unfolding[electronic resource] /by Ingwer Borg, Patrick J. F. Groenen, Patrick Mair. - 2nd ed. - Cham :Springer International Publishing :2018. - ix, 122 p. :digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

This 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)

ISBN: 9783319734712$q(electronic bk.)

Standard No.: 10.1007/978-3-319-73471-2doiSubjects--Topical Terms:

186306
Multidimensional scaling.

LC Class. No.: BF39.2.M85 / B67 2018

Dewey Class. No.: 150.15195

Applied multidimensional scaling and unfolding
LDR:02437nmm a2200325 a 4500 001 538963
003 DE-He213
005 20181130153623.0
006 m d
007 cr nn 008maaau
008 190122s2018 gw s 0 eng d
020 $a 9783319734712$q(electronic bk.)
020 $a 9783319734705$q(paper)
024 7 $a 10.1007/978-3-319-73471-2 $2 doi
035 $a 978-3-319-73471-2
040 $a GP $c GP
041 0 $a eng
050 4 $a BF39.2.M85 $b B67 2018
072 7 $a UFM $2 bicssc
072 7 $a COM077000 $2 bisacsh
082 0 4 $a 150.15195 $2 23
090 $a BF39.2.M85 $b B732 2018
100 1 $a Borg, Ingwer. $3 186305
245 1 0 $a Applied multidimensional scaling and unfolding $h [electronic resource] / $c by Ingwer Borg, Patrick J. F. Groenen, Patrick Mair.
250 $a 2nd ed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a ix, 122 p. : $b digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-544X
520 $a This 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)
650 0 $a Multidimensional scaling. $3 186306
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Psychometrics. $3 182715
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 274394
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Visualization. $3 182994
650 2 4 $a Computational Social Sciences. $3 773096
700 1 $a Groenen, Patrick J. F. $3 186304
700 1 $a Mair, Patrick. $3 816305
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in statistics. $3 557771
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-73471-2
950 $a Mathematics and Statistics (Springer-11649)