This book develops methods for two key problems in the analysis of
large-scale surveys: dealing with incomplete data and making
inferences about sparsely represented subdomains. The presentation
is committed to two particular methods, multiple imputation for
missing data and multivariate composition for small-area
estimation. The methods are presented as developments of
established approaches by attending to their deficiencies. Thus the
change to more efficient methods can be gradual, sensitive to the
management priorities in large research organisations and
multidisciplinary teams and to other reasons for inertia. The
typical setting of each problem is addressed first, and then the
constituency of the applications is widened to reinforce the view
that the general method is essential for modern survey analysis.
The general tone of the book is not "from theory to practice," but
"from current practice to better practice." The third part of the
book, a single chapter, presents a method for efficient estimation
under model uncertainty. It is inspired by the solution for
small-area estimation and is an example of "from good practice to
better theory."
A strength of the presentation is chapters of case studies, one
for each problem. Whenever possible, turning to examples and
illustrations is preferred to the theoretical argument. The book is
suitable for graduate students and researchers who are acquainted
with the fundamentals of sampling theory and have a good grounding
in statistical computing, or in conjunction with an intensive
period of learning and establishing one's own a modern computing
and graphical environment that would serve the reader for most of
the analytical work inthe future.
While some analysts might regard data imperfections and
deficiencies, such as nonresponse and limited sample size, as
someone else's failure that bars effective and valid analysis, this
book presents them as respectable analytical and inferential
challenges, opportunities to harness the computing power into
service of high-quality socially relevant statistics.
Overriding in this approach is the general principlea "to do the
best, for the consumer of statistical information, that can be done
with what is available. The reputation that government statistics
is a rigid procedure-based and operation-centred activity, distant
from the mainstream of statistical theory and practice, is refuted
most resolutely.
After leaving De Montfort University in 2004 where he was a
Senior Research Fellow in Statistics, Nick Longford founded the
statistical research and consulting company SNTL in Leicester,
England. He was awarded the first Campion Fellowship (2000a "02)
for methodological research in United Kingdom government
statistics. He has served as Associate Editor of the Journal of the
Royal Statistical Society, Series A, and the Journal of Educational
and Behavioral Statistics and as an Editor of the Journal of
Multivariate Analysis. He is a member of the Editorial Board of the
British Journal of Mathematical and Statistical Psychology. He is
the author of two other monographs, Random Coefficient Models
(Oxford University Press, 1993) and Models for Uncertainty in
Educational Testing (Springer-Verlag, 1995).
From the reviews:
"Ultimately, this book serves as an excellent reference source
to guide and improve statistical practice in survey settings
exhibiting theseproblems." Psychometrika
"I am convinced this book will be useful to practitioners...[and
a] valuable resource for future research in this field." Jan Kordos
in Statistics in Transition, Vol. 7, No. 5, June 2006
"To sum up, I think this is an excellent book and it thoroughly
covers methods to deal with incomplete data problems and small-area
estimation. It is a useful and suitable book for survey
statisticians, as well as for researchers and graduate students
interested on sampling designs." Ramon Cleries Soler in Statistics
and Operations Research Transactions, Vol. 30, No. 1, January-June
2006
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