東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Dependent data in social sciences re...

Stemmler, Mark.

FindBook

Google Book

Amazon

博客來

Dependent data in social sciences research = forms, issues, and methods of analysis /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dependent data in social sciences research/ edited by Mark Stemmler, Alexander von Eye, Wolfgang Wiedermann.
其他題名:	forms, issues, and methods of analysis /
其他作者:	Stemmler, Mark.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	xiii, 385 p. :ill., digital ;24 cm.
內容註:	Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.
Contained By:	Springer eBooks
標題:	Social sciences - Research -
電子資源:	http://dx.doi.org/10.1007/978-3-319-20585-4
ISBN:	9783319205854

Dependent data in social sciences research = forms, issues, and methods of analysis /
Dependent data in social sciences researchforms, issues, and methods of analysis /[electronic resource] :edited by Mark Stemmler, Alexander von Eye, Wolfgang Wiedermann. - Cham :Springer International Publishing :2015. - xiii, 385 p. :ill., digital ;24 cm. - Springer proceedings in mathematics & statistics,v.1452194-1009 ;. - Springer proceedings in mathematics & statistics ;v.19..

Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.

This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models) Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.

ISBN: 9783319205854

Standard No.: 10.1007/978-3-319-20585-4doiSubjects--Topical Terms:

524282
Social sciences
--Research

LC Class. No.: H62.A5

Dewey Class. No.: 300.72

Dependent data in social sciences research = forms, issues, and methods of analysis /
LDR:02494nmm a2200325 a 4500 001 2013507
003 DE-He213
005 20160413150949.0
006 m d
007 cr nn 008maaau
008 160518s2015 gw s 0 eng d
020 $a 9783319205854 $q (electronic bk.)
020 $a 9783319205847 $q (paper)
024 7 $a 10.1007/978-3-319-20585-4 $2 doi
035 $a 978-3-319-20585-4
040 $a GP $c GP
041 0 $a eng
050 4 $a H62.A5
072 7 $a JHBC $2 bicssc
072 7 $a SOC027000 $2 bisacsh
082 0 4 $a 300.72 $2 23
090 $a H62.A5 $b D419 2015
245 0 0 $a Dependent data in social sciences research $h [electronic resource] : $b forms, issues, and methods of analysis / $c edited by Mark Stemmler, Alexander von Eye, Wolfgang Wiedermann.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xiii, 385 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer proceedings in mathematics & statistics, $x 2194-1009 ; $v v.145
505 0 $a Growth Curve Modeling -- Directional Dependence -- Dydatic Data Modeling -- Item Response Modeling -- Other Methods for the Analyses of Dependent Data.
520 $a This volume presents contributions on handling data in which the postulate of independence in the data matrix is violated. When this postulate is violated and when the methods assuming independence are still applied, the estimated parameters are likely to be biased, and statistical decisions are very likely to be incorrect. Problems associated with dependence in data have been known for a long time, and led to the development of tailored methods for the analysis of dependent data in various areas of statistical analysis. These methods include, for example, methods for the analysis of longitudinal data, corrections for dependency, and corrections for degrees of freedom. This volume contains the following five sections: growth curve modeling, directional dependence, dyadic data modeling, item response modeling (IRT), and other methods for the analysis of dependent data (e.g., approaches for modeling cross-section dependence, multidimensional scaling techniques, and mixed models) Researchers and graduate students in the social and behavioral sciences, education, econometrics, and medicine will find this up-to-date overview of modern statistical approaches for dealing with problems related to dependent data particularly useful.
650 0 $a Social sciences $x Research $x Methodology. $3 524282
650 0 $a Social sciences $x Statistical methods. $3 529420
650 1 4 $a Statistics. $3 517247
650 2 4 $a Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law. $3 894294
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Psychometrics. $3 520603
700 1 $a Stemmler, Mark. $3 2068598
700 1 $a Eye, Alexander von. $3 525952
700 1 $a Wiedermann, Wolfgang. $3 2162957
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer proceedings in mathematics & statistics ; $v v.19. $3 1619928
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-20585-4
950 $a Mathematics and Statistics (Springer-11649)