Statistical Graphics for Visualizing Multivariate Data Quantitative Applications in the Social Sciences, 120

Jacoby, William G.:

ISBN 10: 0761908994 / ISBN 13: 9780761908999

Published by SAGE Publications, Inc, 1998

About this Item

103 Seiten; Das hier angebotene Buch stammt aus einer teilaufgelösten Bibliothek und kann die entsprechenden Kennzeichnungen aufweisen (Rückenschild, Instituts-Stempel.); der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 150.

Seller Inventory # 2192153

Bibliographic Details

• Title: Statistical Graphics for Visualizing Multivariate Data Quantitative Applications in the Social Sciences, 120
• Publisher: SAGE Publications, Inc
• Publication Date: 1998
• Language: English
• ISBN 10: 0761908994
• ISBN 13: 9780761908999
• Binding: Broschiert
• Condition: Gut
• Weight: 150 grams
• Seller catalogs:
  • 90 Varia

About this title

Synopsis

Author William G. Jacoby explores a variety of graphical displays that are useful for visualizing multivariate data. The basic problem involves representing information that varies along several dimensions when the display medium (a computer screen or printed page) is inherently two-dimensional. In order to address this problem, Jacoby introduces the concepts of a "data space." He then explains several methods for coding information directly into the plotting symbols used to represent the observations. He next describes pictorial representations of three-dimensional space followed by a discussion of the scatterplot matrix as a way of "flattening out" the multiple dimensions of a multivariate data space. In addition, he examines conditioning plots (which are strategies for "looking into subregions" of the multidimensional data space), and presents the biplot as a technique for showing observations and variables together in a single display. He concludes with a discussion of some general ideas about data visualization. Statistical Graphics for Visualizing Multivariate Data will enable researchers to better explore the contents of a dataset, find the structure in their data, check the underlying assumptions of the statistical model they used, and better communicate the results of their analysis.

About the Author

William G. Jacoby is a Professor in the Department of Political Science at Michigan State University. He is also a Research Scientist at the University of Michigan, where he serves as Director of the Inter-University Consortium for Political and Social Research (ICPSR) Summer Training Program in Quantitative Methods of Social Research.
Professor Jacoby joined the MSU faculty in 2003. Previously, he held positions at the University of South Carolina, Ohio State University, and the University of Missouri. He received his Ph.D. from the University of North Carolina, Chapel Hill in 1983.
Professor Jacoby′s main professional interests are mass political behavior (public opinion, political attitudes, voting behavior) and quantitative methodology (measurement theory, scaling methods, statistical graphics, modern regression). His current research focuses on citizen ideology and belief system organization, value choices and their implications for subsequent political orientations, measuring policy priorities in the American states, the implications of measurement assumptions for statistical models, and graphical strategies for data analysis.
Recently, Professor Jacoby has taught courses on public opinion, regression analysis, scaling methods, and statistical graphics.