Enhanced descriptions from Syndetics:

Large surveys are becoming increasingly available for public use, and researchers are often faced with the need to analyse complex survey data to address key scientific issues. For proper analysis it is also important to be aware of the different aspects of the design of complex surveys. Practical Methods for Design and Analysis of Complex Surveys features intermediate and advanced statistical techniques for use in designing and analysing complex surveys. This extensively updated edition features much new material, and detailed practical exercises with links to a Web site, helping instructors and enabling use for distance learning.
* Provides a comprehensive introduction to sampling and estimation in descriptive surveys, including design effect statistic and use of auxiliary data.
* Includes detailed coverage of complex survey analysis, including design-based ANOVA and logistic regression with GEE estimation.
* Contains much new material, including handling of non-sampling errors, and model-assisted estimation for domains.
* Features detailed real-li fe case studies, such as multilevel modeling in a multinational educational survey.
* Supported by a Web site containing software codes, real data sets, computerized exercises with solutions, and online training materials.


Practical Methods for Design and Analysis of Complex Surveys provides a useful practical resource for researchers and practitioners working in the planning, implementation or analysis of complex surveys and opinion polls, including business, educational, health, social, and socio-economic surveys and official statistics. In addition, the book is well suited for use on intermediate and advanced courses in survey sampling.

Table of contents provided by Syndetics

• Preface (p. vii)
• 1 Introduction (p. 1)
• 2 Basic Sampling Techniques (p. 9)
• 2.1 Basic definitions (p. 12)
• 2.2 The Province '91 population (p. 18)
• 2.3 Simple random sampling and design effect (p. 22)
• 2.4 Systematic sampling and intra-class correlation (p. 37)
• 2.5 Selection with probability proportional to size (p. 49)
• 3 Further Use of Auxiliary Information (p. 59)
• 3.1 Stratified sampling (p. 61)
• 3.2 Cluster sampling (p. 70)
• 3.3 Model-assisted estimation (p. 87)
• 3.4 Efficiency comparison using design effects (p. 105)
• 4 Handling Nonsampling Errors (p. 111)
• 4.1 Reweighting (p. 115)
• 4.2 Imputation (p. 121)
• 4.3 Chapter summary and further reading (p. 127)
• 5 Linearization and Sample Reuse in Variance Estimation (p. 131)
• 5.1 The Mini-Finland Health Survey (p. 132)
• 5.2 Ratio estimators (p. 138)
• 5.3 Linearization method (p. 141)
• 5.4 Sample reuse methods (p. 148)
• 5.5 Comparison of variance estimators (p. 163)
• 5.6 The Occupational Health Care Survey (p. 166)
• 5.7 Linearization method for covariance-matrix estimation (p. 171)
• 5.8 Chapter summary and further reading (p. 184)
• 6 Model-assisted Estimation for Domains (p. 187)
• 6.1 Framework for domain estimation (p. 187)
• 6.2 Estimator type and model choice (p. 195)
• 6.3 Construction of estimators and model specification (p. 198)
• 6.4 Further comparison of estimators (p. 207)
• 6.5 Chapter summary and further reading (p. 211)
• 7 Analysis of One-way and Two-way Tables (p. 215)
• 7.1 Introductory example (p. 216)
• 7.2 Simple goodness-of-fit test (p. 222)
• 7.3 Preliminaries for tests for two-way tables (p. 232)
• 7.4 Test of homogeneity (p. 236)
• 7.5 Test of independence (p. 245)
• 7.6 Chapter summary and further reading (p. 254)
• 8 Multivariate Survey Analysis (p. 257)
• 8.1 Range of methods (p. 257)
• 8.2 Types of models and options for analysis (p. 261)
• 8.3 Analysis of categorical data (p. 269)
• 8.4 Logistic and linear regression (p. 283)
• 8.5 Chapter summary and further reading (p. 296)
• 9 More Detailed Case Studies (p. 299)
• 9.1 Monitoring quality in a long-term transport survey (p. 300)
• 9.2 Estimation of mean salary in a business survey (p. 306)
• 9.3 Model selection in a socioeconomic survey (p. 312)
• 9.4 Multi-level modelling in an educational survey (p. 321)
• References (p. 331)
• Author Index (p. 339)
• Subject Index (p. 343)

Reviews provided by Syndetics

CHOICE Review

Here is a very informative and clearly written book on the design and analysis of complex surveys. An attractive feature is the integration of survey analysis and survey sampling in one volume, which provides the reader with useful and practical tools for sampling methodology and estimation. Lehtonen and Pahkinen present valuable material on the gains from the use of available auxiliary information for efficient sampling and estimation, and on the conceptual separation of analytical surveys and descriptive surveys. The authors cover sampling design, the design-efficient statistic, missing data and how to handle them, making use of auxiliary information, variance and covariance estimation, design-based analysis of frequency tables, and multivariate procedures for complex surveys. This application-oriented book presents concepts and relevant topics through case studies and fully worked examples taken from such sources as health, social, business, socioeconomic, and educational surveys. Useful references; subject and author indexes. Two appendixes treat software for survey analysis and bootstrap variance approximation. There are no exercises or problems. Overall, a very useful book. Highly recommended. Undergraduate through faculty. D. V. Chopra; Wichita State University

Author notes provided by Syndetics

Risto Lehtonen and Erkki Pahkinen are the authors of Practical Methods for Design and Analysis of Complex Surveys, 2nd Edition, published by Wiley.