A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. Handbook of Statistical Data Editing and Imputation equips readers with the essential statistical procedures for detecting and correcting inconsistencies and filling in missing values with estimates. The authors supply an easily accessible treatment of the existing methodology in this field, featuring an overview of common errors encountered in practice and techniques for resolving these issues. The book begins with an overview of methods and strategies for statistical data editing and imputation. Subsequent chapters provide detailed treatment of the central theoretical methods and modern applications, with topics of coverage including: Localization of errors in continuous data, with an outline of selective editing strategies, automatic editing for systematic and random errors, and other relevant state-of-the-art methods Extensions of automatic editing to categorical data and integer data The basic framework for imputation, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values More advanced imputation methods, including imputation under edit restraints Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. The discussion concludes with a summary of the main concepts and a real-world example that incorporates realistic data along with professional insight into common challenges and best practices. Handbook of Statistical Data Editing and Imputation is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.

Table of Contents:
Preface ix 1 Introduction to Statistical Data Editing and Imputation 1 1.1 Introduction 1 1.2 Statistical Data Editing and Imputation in the Statistical Process 4 1.3 Data, Errors, Missing Data, and Edits 6 1.4 Basic Methods for Statistical Data Editing and Imputation 13 1.5 An Edit and Imputation Strategy 17 References 21 2 Methods for Deductive Correction 23 2.1 Introduction 23 2.2 Theory and Applications 24 2.3 Examples 27 2.4 Summary 55 References 55 3 Automatic Editing of Continuous Data 57 3.1 Introduction 57 3.2 Automatic Error Localization of Random Errors 59 3.3 Aspects of the Fellegi–Holt Paradigm 63 3.4 Algorithms Based on the Fellegi–Holt Paradigm 65 3.5 Summary 101 3.A Appendix: Chernikova’s Algorithm 103 References 104 4 Automatic Editing: Extensions to Categorical Data 111 4.1 Introduction 111 4.2 The Error Localization Problem for Mixed Data 112 4.3 The Fellegi–Holt Approach 115 4.4 A Branch-and-Bound Algorithm for Automatic Editing of Mixed Data 129 4.5 The Nearest-Neighbor Imputation Methodology 140 References 158 5 Automatic Editing: Extensions to Integer Data 161 5.1 Introduction 161 5.2 An Illustration of the Error Localization Problem for Integer Data 162 5.3 Fourier–Motzkin Elimination in Integer Data 163 5.4 Error Localization in Categorical, Continuous, and Integer Data 172 5.5 A Heuristic Procedure 182 5.6 Computational Results 183 5.7 Discussion 187 References 189 6 Selective Editing 191 6.1 Introduction 191 6.2 Historical Notes 193 6.3 Micro-selection: The Score Function Approach 195 6.4 Selection at the Macro-level 208 6.5 Interactive Editing 212 6.6 Summary and Conclusions 217 References 219 7 Imputation 223 7.1 Introduction 223 7.2 General Issues in Applying Imputation Methods 226 7.3 Regression Imputation 230 7.4 Ratio Imputation 244 7.5 (Group) Mean Imputation 246 7.6 Hot Deck Donor Imputation 249 7.7 A General Imputation Model 255 7.8 Imputation of Longitudinal Data 261 7.9 Approaches to Variance Estimation with Imputed Data 264 7.10 Fractional Imputation 271 References 272 8 Multivariate Imputation 277 8.1 Introduction 277 8.2 Multivariate Imputation Models 280 8.3 Maximum Likelihood Estimation in the Presence of Missing Data 285 8.4 Example: The Public Libraries 295 References 297 9 Imputation Under Edit Constraints 299 9.1 Introduction 299 9.2 Deductive Imputation 301 9.3 The Ratio Hot Deck Method 311 9.4 Imputing from a Dirichlet Distribution 313 9.5 Imputing from a Singular Normal Distribution 318 9.6 An Imputation Approach Based on Fourier–Motzkin Elimination 334 9.7 A Sequential Regression Approach 338 9.8 Calibrated Imputation of Numerical Data Under Linear Edit Restrictions 343 9.9 Calibrated Hot Deck Imputation Subject to Edit Restrictions 349 References 358 10 Adjustment of Imputed Data 361 10.1 Introduction 361 10.2 Adjustment of Numerical Variables 362 10.3 Adjustment of Mixed Continuous and Categorical Data 377 References 389 11 Practical Applications 391 11.1 Introduction 391 11.2 Automatic Editing of Environmental Costs 391 11.3 The EUREDIT Project: An Evaluation Study 400 11.4 Selective Editing in the Dutch Agricultural Census 420 References 426 Index 429