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David K. Hildebrand - Analysis of ordinal data

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Ordinal data can be rank ordered but not assumed to have equal distances between categories. Using support by judges for civil rights measures and bussing as the primary example, this paper indicates how such data can best be analyzed.

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title Analysis of Ordinal Data Sage University Papers Series Quantitative - photo 1

title:Analysis of Ordinal Data Sage University Papers Series. Quantitative Applications in the Social Sciences ; No. 07-008
author:Hildebrand, David K.; Laing, James D.; Rosenthal, Howard
publisher:Sage Publications, Inc.
isbn10 | asin:0803907958
print isbn13:9780803907959
ebook isbn13:9780585216683
language:English
subjectSocial sciences--Statistical methods, Numbers, Ordinal.
publication date:1977
lcc:HA33.H54 1977eb
ddc:300/.01/5195
subject:Social sciences--Statistical methods, Numbers, Ordinal.
Page a
Analysis of Ordinal Data
Page b
SAGE UNIVERSITY PAPERS
Series: Quantitative Applications in the Social Sciences
Series Editor: Michael S. Lewis-Beck, University of Iowa
Editorial Consultants
Richard A. Berk, Sociology, University of California, Los Angeles
William D. Berry, Political Science, Florida State University
Kenneth A. Bollen, Sociology, University of North Carolina, Chapel Hill
Linda B. Bourque, Public Health, University of California, Los Angeles
Jacques A. Hagenaars, Social Sciences, Tilburg University
Sally Jackson, Communications, University of Arizona
Richard M. Jaeger, Education, University of North Carolina, Greensboro
Gary King, Department of Government, Harvard University
Roger E. Kirk, Psychology, Baylor University
Helena Chmura Kraemer, Psychiatry and Behavioral Sciences, Stanford University
Peter Marsden, Sociology, Harvard University
Helmut Norpoth, Political Science, SUNY, Stony Brook
Frank L. Schmidt, Management and Organization, University of Iowa
Herbert Weisberg, Political Science, The Ohio State University
Publisher
Sara Miller McCune, Sage Publications, Inc.
INSTRUCTIONS TO POTENTIAL CONTRIBUTORS
For guidelines on submission of a monograph proposal to this series, please write
Michael S. Lewis-Beck, Editor
Sage QASS Series
Department of Political Science
University of Iowa
Iowa City, IA 52242
Page 1
Series / Number 07-008
Analysis of Ordinal Data
David K. Hildebrand
University of Pennsylvania
James D. Laing
University of Pennsylvania and Stanford University
Howard Rosenthal
Carnegie-Mellon University
Picture 2
SAGE PUBLICATIONS
The International Professional Publishers
Newbury Park London New Delhi
Page 2
Copyright 1977 by Sage Publications, Inc.
Printed in the United States of America
All rights reserved. No part of this book may be reproduced
or utilized in any form or by any means, electronic or mechanical,
including photocopying, recording, or by any
information storage and retrieval system, without permission in writing
from the publisher.
For information address:
Picture 3
SAGE Publications, Inc.
2455 Teller Road
Newbury Park, California 91320
E-mail: order@sagepub.com
SAGE Publications Ltd.
6 Bonhill Street
London EC2A 4PU
United Kingdom
SAGE Publications India Pvt. Ltd.
M-32 Market
Greater Kailash I
New Delhi 110 048 India
International Standard Book Number 0-8039-0795-8
Library of Congress Catalog Card No. L.C. 77-72857
97 98 99 00 01 02 03 28 27 26 25 24 23 22
When citing a professional paper, please use the proper form. Remember to cite the
correct Sage University Paper series title and include the paper number. One of the
two following formats can be adapted (depending on the style manual used):
(1) IVERSEN, GUDMUND R. and NORPOTH, HELMUT (1976) "Analysis of Variance."
Sage University Paper series on Quantitative Applications in the Social Sciences, 07-001.
Beverly Hills and London: Sage Publications.
OR
(2) Iversen, Gudmund R. and Norpoth, Helmut. 1976. Analysis of Variance. Sage University
Paper series on Quantitative Applications in the Social Sciences, series no. 07-001.
Beverly Hills and London: Sage Publications.
Page 3
Contents
Editor's Introduction
5
1. Ordinal Measurement
7
Picture 4
Three Issues in the Analysis of Ordinal Variables
10
2. Bivariate Prediction for Single Observations
17
Picture 5
Population versus Sample
17
Picture 6
Predicting With Knowledge of the Independent Variable State
18
Picture 7
Predicting Without Information About the Independent Variable State
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