Analyzing and Modeling Rank Data
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€210.80
A01=John I Marden
Author_John I Marden
Balanced Incomplete Block Design
BTM
Category=PBT
Category=PBWH
Complete Rankings
Contingency Table
contrast
distance
Distance Model
Draft Lottery
E Sm
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
exponential
Exponential Family
Exponential Family Model
family
graduate statistics textbook
Incomplete Rankings
likelihood estimation techniques
Luce's Choice Axiom
Marginals Model
MLE
modal
Modal Ranking
Negative Semi-definite
ordinal data modeling
orthogonal
Orthogonal Contrast
Paired Comparisons
Post-materialist Goals
preference data analysis
Rank Data
Rank Vector
ranking
ranking data analysis in research
saturated
social science statistics
Spectral Decomposition
statistical ranking methods
thurstonian
Thurstonian Model
Unfolding Models
vector
Vice Versa
Watch Tv Show
Product details
- ISBN 9780412995217
- Weight: 750g
- Dimensions: 152 x 229mm
- Publication Date: 01 Aug 1996
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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This book is the first single source volume to fully address this prevalent practice in both its analytical and modeling aspects. The information discussed presents the use of data consisting of rankings in such diverse fields as psychology, animal science, educational testing, sociology, economics, and biology. This book systematically presents the basic models and methods for analyzing data in the form of ranks. Integrating material from a wide range of fields, this book applies graphical, numerical, and modeling techniques to data sets, uncovering fascinating structures in the rank data.
Providing the most extensive coverage of the subject found in statistical literature, this book will be a welcomed reference to statisticians. In addition, this volume is also accessible to people in all areas of quantitative research. Researchers in psychology and consumer preference will discover a valuable resource; and sociologists, biologists, political and animal scientists will also benefit. As a text, it will be ideal for graduate students in courses on statistics and other quantitative disciplines.
John I Marden University of Illinois, Champaign, USA
