Statistics for Real-Life Sample Surveys: Non-Simple-Random Samples and Weighted Data by Sergey Dorofeev – A Cambridge Un

Introduction

In the world of social research, market analysis, and public opinion polling, the ideal of a perfectly random sample is often just that—an ideal. Real-world surveys, especially those conducted across India's diverse population, frequently rely on samples that are far from simple random selections. Whether due to logistical constraints, cost considerations, or deliberate design choices, these 'non-simple-random samples' pose unique challenges. Statistics for Real-Life Sample Surveys: Non-Simple-Random Samples and Weighted Data by Sergey Dorofeev provides a practical and rigorous guide for navigating this complex terrain. Published by Cambridge University Press, this hardcover volume is an essential resource for researchers, data analysts, and students who need to work with imperfect but realistic survey data.

Book Overview

This book bridges the gap between textbook statistics and the messy reality of survey data. It begins by acknowledging that many samples used in social and commercial surveys are less random than assumed—sometimes by necessity, sometimes by design. Dorofeev systematically explains why this happens, how to measure the degree of non-randomness, and when and how to apply weighting to correct for biases. The book is not a theoretical treatise but a practical companion, filled with extended data examples that demonstrate techniques in action. It addresses the statistical adaptations needed to draw valid conclusions from such samples, making it a valuable tool for anyone who has ever grappled with survey data that does not conform to ideal assumptions.

Key Highlights

• Practical Focus: Emphasizes real-world applications over abstract theory, with numerous worked examples.
• Weighting Techniques: Detailed guidance on when and how to apply weighting to adjust for sample biases.
• Non-Randomness Assessment: Methods to evaluate the degree and impact of non-randomness in your sample.
• Statistical Adaptations: How to modify standard statistical procedures for use with non-simple-random samples.
• Extended Data Examples: Full case studies that illustrate the techniques in realistic contexts.

Inside the Book

The structure is designed for both reference and learning. Early chapters lay the groundwork by explaining why samples are often non-random and how to recognize different types of non-randomness. Subsequent chapters delve into the mechanics of weighting—including post-stratification, raking, and propensity score weighting—and the implications for variance estimation and hypothesis testing. The book also covers design effects, cluster sampling, and stratification, always with an eye on practical implementation. Each chapter concludes with summaries and references, making it easy to find specific methods when needed.

Key Topics

• Understanding non-simple-random samples in social and commercial surveys
• Assessing the degree of non-randomness and its impact on data quality
• Principles and methods of weighting: post-stratification, calibration, and propensity scores
• Variance estimation for weighted data
• Design effects and effective sample size
• Handling cluster sampling and stratified designs
• Practical solutions for common survey problems

Reader Benefits

Readers will gain the confidence to handle survey data that does not meet ideal assumptions. The book provides clear, step-by-step guidance for diagnosing sample issues and applying appropriate corrections. By working through the examples, researchers will learn to avoid common pitfalls and produce more reliable analyses. The emphasis on realistic solutions means that the methods can be immediately applied in fields such as market research, public health, education, and political polling. For Indian researchers dealing with complex survey designs—like those used by the National Sample Survey Office or in consumer panels—this book offers directly relevant tools.

Learning Outcomes

• Identify and classify different types of non-randomness in survey samples.
• Evaluate whether weighting is necessary and choose the appropriate method.
• Apply weighting techniques to adjust for selection bias and non-response.
• Compute correct standard errors and confidence intervals for weighted data.
• Interpret survey results accurately when dealing with complex samples.
• Design better surveys by understanding the implications of sampling choices.

Who Should Read

This book is written for practising researchers, data analysts, and statisticians who work with survey data in social sciences, marketing, public health, and government statistics. It is also suitable for advanced undergraduate and postgraduate students in statistics, economics, sociology, and business analytics who want to move beyond textbook examples. Professionals in market research firms, polling organizations, and data-driven companies will find it an indispensable desk reference. Indian readers involved in large-scale surveys—such as those conducted by the government, NGOs, or private sector—will benefit from the practical orientation.

About the Author

Sergey Dorofeev is a highly respected statistician with extensive experience in survey methodology and applied statistics. His work focuses on the practical challenges of analyzing real-world survey data, particularly when samples deviate from ideal random designs. Dorofeev's expertise bridges academic rigor and industry application, making his writing both authoritative and accessible. He has contributed to numerous research projects and publications, and his insights are valued by practitioners across multiple disciplines.

About the Publisher

Cambridge University Press is one of the world's oldest and most prestigious academic publishers, with a history dating back to 1534. Known for its rigorous editorial standards and commitment to scholarly excellence, Cambridge publishes works that advance knowledge in science, mathematics, social sciences, and humanities. This book reflects the Press's dedication to providing high-quality, practical resources for researchers and professionals globally.

Conclusion

Statistics for Real-Life Sample Surveys is more than a textbook—it is a survival guide for anyone who must make sense of imperfect survey data. In a country like India, where surveys often face logistical hurdles, cultural diversity, and resource constraints, the ability to handle non-random samples and weighted data is invaluable. Sergey Dorofeev's clear explanations, practical examples, and focus on realistic solutions make this hardcover volume a worthy addition to any researcher's library. Whether you are a student learning survey methods or a seasoned analyst facing complex data, this book will help you extract meaningful insights from the messy reality of real-life surveys.