A Comprehensive Textbook on Sample Surveys

This book provides alternative approaches to survey sampling, focusing on model formulations, design-based procedures, and inference and interpretation. It covers Bayesian and Empirical Bayesian approaches, stratification, clustering, multi-stage and phase sampling, linear and non-linear estimation, small area estimation, network and adaptive sampling methods, and includes detailed case studies and exercises. It is valuable for students of statistics, specifically survey sampling.

Format: Hardback
Length: 257 pages
Publication date: 09 May 2022
Publisher: Springer Verlag, Singapore

This comprehensive textbook on survey sampling delves into the limitations of traditional, designed-based inferential procedures and presents alternative approaches in the form of model formulations, model-design-based procedures of analysis, inference, and interpretation. The book encompasses a wide range of topics, including Bayesian and Empirical Bayesian approaches, complex procedures of stratification, clustering, sampling in multi-stages and phases, linear and non-linear estimation of parameters, small area estimation by spatial and chronological modeling, network and adaptive sampling methods, and more. Furthermore, it includes detailed case studies and exercises, making it an invaluable resource for students of statistics, particularly survey sampling.

The book begins by discussing the shortcomings of classic, designed-based inferential procedures, highlighting their limitations in capturing the complex relationships between variables and the heterogeneity of populations. It then introduces alternative approaches, such as model formulations, which allow for a more flexible and nuanced understanding of the data. Model-design-based procedures of analysis involve specifying the structure of the model and estimating its parameters using statistical techniques, providing a more robust and accurate representation of the data.

Inference and interpretation are crucial steps in survey sampling, and the book provides comprehensive guidance on these topics. It discusses various methods of inference, including hypothesis testing, confidence intervals, and Bayesian inference, and explains how to interpret the results in the context of the research objectives. The book also emphasizes the importance of considering the potential biases and errors associated with survey sampling and provides strategies for mitigating these effects, such as weighting and stratification.

Throughout the book, detailed case studies and exercises are provided to illustrate the practical applications of the discussed methods. These case studies cover a wide range of topics, including social surveys, health research, and marketing research, and provide students with hands-on experience in applying the theoretical concepts to real-world data. The exercises are designed to reinforce the learning process and help students develop their statistical skills.

In conclusion, this comprehensive textbook on survey sampling is an essential resource for students of statistics, particularly those interested in survey sampling. It provides a thorough and up-to-date introduction to the field, covering a wide range of topics and presenting alternative approaches that are more robust and accurate than traditional methods. The book's detailed case studies and exercises make it an invaluable tool for developing statistical skills and understanding the complexities of survey sampling.

Weight: 646g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811914171
Edition number: 1st ed. 2022