Handbook of Statistical Data Editing and

This book provides a comprehensive overview of the entire edit and imputation process for detecting and correcting errors in survey research. The authors begin with an introduction to the problem of errors and missing values in survey data and then go on to explore the methods for correcting systematic errors, identifying random errors, and error localization in numerical and categorical data. Next, an intricate discussion of selective editing outlines various mechanisms for identifying the appropriate resources for treating data errors. A basic framework for imputation is provided in the next chapter with a breakdown of key methods and models along with a comparison of imputation with the weighting approach to correct missing values. The remaining chapters delve into more advanced topics in imputation methodology as well as new developments on imputation under edit constraints and benchmarking. Each chapter organizes the presented information in uniform components, with an introduction, outline of key theory and formulae, illustration of algorithms, a concise summary of key points, and a reference section listing additional resources on the topic. This presentation solidifies the bookrs"s goal of serving as a practical, one-stop reference on data editing and imputation.

Ton De Waal, PhD, is Head of the Department of Methodology at Statistics Netherlands, where he has also worked at the Division of Business Statistics. Dr. de Waal has written numerous papers in his areas of research interest, which include statistical data editing and imputation for business surveys and statistical disclosure control. Jeroen Pannekoek, PhD, is Senior Researcher in the Department of Methodology at Statistics Netherlands, where he currently leads the research program on data processing methodologies. He has published several papers on discrete data models, measurement errors, interviewer effects, and disclosure control methods. Sander Scholtus, MSc, is Researcher in the Department of Methodology at Statistics Netherlands. He has conducted extensive research on heuristic methods and algorithms for detecting and correcting errors in survey data.

Introduction to statistical data editing and imputation
Introduction
Statistical data editing and imputation in the statistical process
Data, errors, missing data and edits
Basic methods for statistical data editing and imputation
An edit and imputation strategy
Methods for deductive correction
Introduction
Theory and applications
Examples
Summary
Automatic editing of continuous data
Introduction
Automatic error localisation of random errors
Aspects of the Fellegi-Holt paradigm
Algorithms based on the Fellegi-Holt paradigm
Summary
Automatic editing: extensions to categorical data
Introduction
The error localisation problem for mixed data
The Fellegi-Holt approach
A branch-and-bound algorithm for automatic editing of mixed data
The Nearest-neighbour Imputation Methodology
Automatic editing: extensions to integer data
Introduction
An illustration of the error localisation problem for integer data
Fourier-Motzkin elimination in integer data
Error localisation in categorical, continuous and integer data
A heuristic procedure
Computational results
Discussion
Selective editing
Introduction
Historical notes
Micro-selection: the score function approach
Selection at macro-level
Interactive editing
Summary and conclusions
Imputation
Introduction
General issues in applying imputation methods
Regression imputation
Ratio imputation
(Group) mean imputation
Hot deck donor imputation
A general imputation model
Imputation of longitudinal data
Approaches to variance estimation with imputed data
Fractional imputation
Multivariate imputation
Introduction
Multivariate imputation models
Maximum likelihood estimation in the presence of missing data
Example: the public libraries
Imputation under edit constraints
Introduction
Deductive imputation
The ratio hot deck method
Imputing from a Dirichlet distribution
Imputing from a singular normal distribution
An imputation approach based on Fourier-Motzkin elimination
A sequential regression approach
Calibrated imputation of numerical data under linear edit restrictions
Calibrated hot deck imputation subject to edit restrictions
Adjustment of imputed data
Introduction
Adjustment of numerical variables
Adjustment of mixed continuous and categorical data
Practical applications
Introduction
Automatic editing of environmental costs
The EUREDIT project: an evaluation study
Selective editing in the Dutch Agricultural Census
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Handbook of Statistical Data Editing and Imputation

Summary

Author Biography

Table of Contents

Supplemental Materials