Table Of ContentSurvey Sampling and Measurement
Edited by
N. KRISHNAN NAMBOODIRI
DEPARTMENT OF SOCIOLOGY
UNIVERSITY OF NORTH CAROLINA
CHAPEL HILL, NORTH CAROLINA
ACADEMIC PRESS New York San Francisco London 1978
A Subsidiary of Harcourt Brace Jovanovich, Publishers
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Library of Congress Cataloging in Publication Data
Symposium on Survey Sampling, 2d, University of North
Carolina, 1977.
Survey sampling and measurement.
(Quantitative studies in social relations)
Papers presented at the 2d Symposium on Survey
Sampling held at the Chapel Hill campus of the University
of North Carolina, Apr. 14-17,1977.
Includes index.
1. Sampling (Statistics)—Congresses. 2. Social
surveys—Congresses. I. Krishnan Namboodiri, N.
II. Title.
HA31.2.S97 1977 001.4'22 78-3345
ISBN 0-12-513350-2
PRINTED IN THE UNITED STATES OF AMERICA
List of Contributors
Numbers in parentheses indicate the pages on which the authors' contributions begin.
BARBARA A. BAILAR (69, 175), U.S. Bureau of the Census, Washing
ton, D.C. 20233
LEROY BAILEY (175), U.S. Bureau of the Census, Washington, D.C.
20233
D. BASU (267, 337), Department of Statistics, The Florida State Uni
versity, Tallahassee, Florida 32304
DWIGHT B. BROCK (121), Office of Statistical Research, National
Center for Health Statistics, 3700 East-West Highway, Hyattsville,
Maryland 20782
CAROL CORBY (175), Research Center for Measurement Methods,
U.S. Bureau of the Census, Washington, D.C. 20233
WILLIAM G. CUMBERLAND (293, 331), Division of Biostatistics,
School of Public Health, University of California, Los Angeles, Los
Angeles, California 90024
xiii
xiv List of Contributors
ESTELA BEE DAGUM (217), Seasonal Adjustment and Time Series
Analysis Staff, Statistics Canada, Tunney's Pasture, Ottawa, On
tario, Canada K1A OT6
STEPHEN E. FIENBERG (89), Department of Applied Statistics,
School of Statistics, University of Minnesota, St. Paul, Minnesota
55108
A. L. FINKNER* (45), U.S. Bureau of the Census, Washington, D.C.
20233
DANIEL H. FREEMAN, JR. (121), Department of Epidemiology and
Public Health (Biometry), School of Medicine, Yale University, New
Haven, Connecticut 06510
V. P. GODAMBE (311), Department of Statistics, University of Wa
terloo, Waterloo, Ontario, Canada N2L 3G1
MORRIS H. HANSEN (341), Westat Corporation, Rockville, Maryland
20852
H. O. HARTLEY (35), Institute of Statistics, Texas A & M University,
College Station, Texas 77843
D. G. HORVITZ (3), Statistical Sciences Group, Research Triangle Insti
tute, P.O. Box 12194, Research Triangle Park, North Carolina 27709
LESLIE KISH (13), Survey Research Center, Institute for Social Re
search, The University of Michigan, Ann Arbor, Michigan 48106
JAN KMENTA (107), Department of Economics, The University of Mi
chigan, Ann Arbor, Michigan 48109
C. MICHAEL LANPHIER (69), Department of Sociology, York Univer
sity, Downsview, Ontario, Canada M3J 1P3
WILLIAM G. MADOW (315, 341), 700 New Hampshire Avenue N.W.,
Washington, D.C. 20037
M. N. MURTHY (231), Statistical Institute for Asia and the Pacific, P.O.
Box 13, Akasaka, Tokyo 107, Japan
HAROLD NISSELSON (45), U.S. Bureau of the Census, Washington,
D.C. 20233
R. PLATEK (157), Household Surveys Development Division, Statistics
Canada, R. H. Coats Building, Tunney's Pasture, Ottawa, Ontario,
Canada K1A OT6
J. N. K. RAO (35, 323), Department of Mathematics, Arts Tower,
Carleton University, Ottawa, Ontario, Canada K1S 5B6
* Present address: Research Triangle Institute, Research Triangle Park, North Carolina
27709.
List of Contributors XV
RICHARD M. ROYALL (293, 331), Department of Biostatistics, School
of Hygiene and Public Health, The Johns Hopkins University, Balti
more, Maryland 21205
J. SEDRANSK (143), Statistical Science Division, State University of
New York at Buffalo, Buffalo, New York 14214
B. V. SHAH (25), Statistical Sciences Group, Research Triangle Insti
tute, P.O. Box 12194, Research Triangle Park, North Carolina 27709
BAHADUR SINGH* (143), National Institute of Environmental Health
Sciences, Research Triangle Park, North Carolina 27709
M. P. SINGH (157), Statistics Canada, Tunney's Pasture, Ottawa, On
tario, Canada K1A OT6
T. M. F. SMITH (201), Department of Mathematics, University of
Southampton, Southampton, England S09 5NH
V. TREMBLAY (157), Centre de Sondage, Université de Montréal,
C.P. 6128, Montréal, Québec, Canada
P. VUAGNAT (255), Mathematics Section, University of Geneva, 11211
Geneva 24 Switzerland
* Present address: Department of Statistics, State University of New York at
Buffalo, Buffalo, New York 14214.
Preface
It is well known that when survey sampling was in its infancy, most
theorists were practitioners and most practitioners were theorists.
Problems encountered in practice fostered theory, and advances in theory
nurtured practice. This happy state of affairs does not seem to exist now.
Today, sampling theorists do not engage in conducting surveys, and prac
titioners ignore the new theoretical exhortations. Can anything be done to
bring theory and practice together again?
If there has occurred a separation between theory and practice, a simi
lar gap has opened up between "survey objectives" and "survey data
use." It is well recognized that even today sample surveys are largely
enumerative and descriptive in their objectives—to estimate a number of
totals, scores of proportions, and myriads of means. But once the initially
proclaimed objectives of the survey are met, the data are often made
available for public use, mostly through data banks. And in the hands of
John Q. Public, who is interested in analytic studies, survey data are, irre
spective of the complexity of the sample design involved, a random
sample from an infinite population characterized by a convenient proba-
xvii
xviii Preface
bility distribution. Multivariate models such as path models are fitted to
the data under such assumptions, and these models are then used in criti
cizing or formulating public policies, in revising or constructing substan
tive theories, in assessing changes, in forecasting the future, and for simi
lar mundane purposes. Is it legitimate to superimpose on sample survey
data convenient stochastic models? Is it safe to ignore the sample design
at the analysis stage?
With questions like these uppermost in our minds, four of us (A. Beza,
N. L. Johnson, N. K. Namboodiri, and H. B. Wells) at the Chapel Hill
campus of the University of North Carolina approached the National Sci
ence Foundation in 1976 with a request for financial support for a sympo
sium on survey sampling and measurement. Our request was granted, and
the symposium was held at Chapel Hill in April 1977. It was the Second
Symposium on Survey Sampling (SSSS), the first one having been the in
ternational Symposium on the Foundations of Survey Sampling held on
the campus in 1968.
This volume contains the invited papers presented at the 1977 sympo
sium. No attempt was made to record the informal discussions that took
place following the presentations; the authors were, however, encouraged
to revise their papers in light of the comments from the participants.
Formal discussion was organized only in one session, in which D. Basu
presented a paper on the relevance of randomization in data analysis and
R. M. Royall and W. G. Cumberland delivered one on an empirical study
of prediction theory in finite population sampling. These papers were dis
cussed by V. P. Godambe, W. G. Madow, and J. N. K. Rao. After the
symposium, the written versions of the discussants' comments were
made available to the authors for their reactions. The comments and the
authors' rejoinders are included in this volume.
The volume is divided into seven parts. In Part I, D. G. Horvitz, with
an eye toward improving the quality of sample surveys, makes a plea for
the creation of a computerized system of information on error estimates
associated with the design and execution of surveys, and L. Kish suggests
a realistic agenda for future work in survey sampling practice and theory.
Part II contains four papers, each dealing with specific methodological
problems. B. V. Shah advocates the use of the linear term in the Taylor
expansion to obtain approximate expression for the variance. H. O.
Hartley and J. N. K. Rao suggest a method for estimating the overall
(sampling plus nonsampling) variance of linear estimators in surveys
based on stratified multistage designs with equal-probability selection at
the last stage. A. L. Finkner and H. Nisselson discuss, on the basis of the
experience at the U.S. Bureau of the Census, several problems associated
with continuing cross-section surveys. C. M. Lanphier and B. A. Bailar
Preface XIX
describe special sampling-frame problems that were encountered in a
study of appropriate design strategies for a survey of surveys in the
United States.
Part HI contains three papers, each dealing with selected problems of
analysis of survey data. S. E. Fienberg reviews the design and execution
of the National Crime Survey and describes some stochastic models that
may be used in the analysis of the data from that source. He takes the po
sition that "it is unclear whether we need to take into account the com
plexities of the sample design when we try to model the victimization his
tories of individuals with common sociodemographic and geographic
characteristics." J. Kmenta discusses survey data analysis from an econ-
ometrician's standpoint; he gives particular attention to problems such as
how to formulate and test simultaneous equation models using cross-
sectional data. D. H. Freeman and D. B. Brock demonstrate, with data
from health surveys in the United States, that in the analysis of complex
survey data the use of appropriately estimated covariance matrices is
rewarding, because when they are used in the analysis it becomes pos
sible to detect nonzero differences between domain estimates, which may
otherwise go undetected.
The chapters in Part IV deal with nonresponse, undercoverage, and re
lated problems. B. Singh and J. Sedransk consider, from a Bayesian view
point, inferences concerning the mean of a finite population, under a
two-phase sampling scheme with post-stratification, and analyze as a spe
cial case of this general approach the problem of nonresponse in surveys.
R. Platek, M. P. Singh, and V. Tremblay use the concept of response
probabilities to develop response-nonresponse error components under
various commonly used methods for adjusting for nonresponse. B. A.
Bailar, L. Bailey, and C. Corby describe adjustment procedures devel
oped at the U.S. Bureau of the Census to handle nonresponse, undercov
erage, and the like.
Part V contains two papers, both dealing with time series analysis.
T. M. F. Smith examines the application of classical time series models
of the autoregressive integrated moving average (AIMA) type to data
from repeated surveys. E. B. Dagum discusses a method developed at
Statistics Canada for estimating changes in seasonal variations in eco
nomic time series such as monthly data on labor force.
In Part VI, chapters by M. N. Murthy and P. Vuagnat deal with what
may be called applications of sample survey data and methods. Murthy
points out that the state of affairs with respect to the availability of reli
able data for use in national planning in developing countries is indeed
alarming. He then suggests ways to improve the situation. Vuagnat dis
cusses the problems in the application of sampling methods in geology.
XX Preface
Part VII deals with the gap between current survey practices and recent
theoretical developments. D. Basu expounds the eyebrow-raising thesis
that "at the analysis stage, we have no need to concern ourselves with the
exact nature of the [sample] design. . . ." R. M. Royall and W. G. Cum
berland supply empirical material relevant to their claim that "analysis
based on prediction models and directed to specific samples can reveal re
lationships which are essential in making inferences, but which are con
cealed in analyses which entail averaging over all possible samples." The
comments on these two chapters and the rejoinders reveal that theorists
themselves are not in agreement with each other on many crucial issues.
In the final chapter, M. H. Hansen and W. G. Madow review the efforts of
theorists to replace the "conventional survey practice," namely the prob
ability sampling approach, with the framework of traditional statistical
theory, in which models and distributions are assumed, and analysis is
guided by these assumptions. The Hansen-Madow position is that in
large-scale sample surveys the essentially assumption-free approach of
"the conventional sampling practice" has substantial advantages over its
suggested alternative, since the objective of such surveys is to obtain rela
tively high precision in inferences concerning finite, highly heterogenous
populations. They concede, however, that when the aim is to make infer
ences about causal systems, model-based approaches do have a role to
play.
Many interesting topics receive little or no attention herein. For this,
the subjective sampling plan adopted by the symposium organizers is to
blame.
It is hoped that this volume will be of interest to survey statisticians
as well as to survey data users. If it stimulates thoughtful and courageous
attack on some of the unresolved problems in survey sampling, its
mission will have been amply fulfilled.
ACKNOWLEDGMENTS
The symposium was sponsored by the Institute for Research in Social
Science, and the Departments of Statistics, Sociology, and Biostatistics,
University of North Carolina at Chapel Hill. The sponsors are grateful to
the National Science Foundation for its generous grant (Grant Number
Soc 76-23208) which made the symposium possible.
The sponsors are also grateful to the authors for their careful prepara
tion of the chapters. The views expressed are, however, those of the
authors and not necessarily those of the sponsors or of the National
Science Foundation.
Preface xxi
Local arrangements were made by W. H. Heriford and his staff at the
Extension division of the University of North Carolina at Chapel Hill.
O. A. Andrew of the Department of Sociology unstintingly contributed her
time and talent to help with the organization, to assist the participants
in all possible ways, and in several other tangible and intangible respects.
S. Morton served as symposium secretary. The sponsors are thankful
to these persons and to Vice Chancellor Lyle V. Jones who took time out
from his busy schedule to welcome the participants. Thanks are also due
to those who served as session chairmen: R. J. Carroll, J. Grizzle, D. M.
Hawkins, N. K. Namboodiri, W. G. Cocharan, M. Francis, J. Murphy,
G. Simmons, G. Koch, J. Sedransk, S. L. Stokes, H. B. Wells, and
A. Beza.
Finally the writer of these lines, in his capacity as the symposium
chairman, wishes to express his appreciation to A. Beza for his assistance
in drafting the proposal to the National Science Foundation, and to the
three cochairmen of the symposium (A. Beza, N. L. Johnson, and H. B.
Wells) for their invaluable contributions in developing the program and
selecting the speakers and participants.
N.K.N.
