Sample surveys is a major source of statistical information in economics and finance. When policymakers, researchers, and analysts need targeted data-based evidence, surveys can be deployed quickly, at relatively low cost, tailored to specific policy questions and used to capture information that other data sources often miss—subjective dimensions such as perceptions, expectations, values, and attitudes. From economics and finance to social and living conditions, surveys generate robust, decision-ready insights. And when based on representative samples, they yield accurate estimates for many finite populations, from households and individuals to businesses or financial institutions.

At the same time, the survey environment is changing rapidly. The growth of administrative and digital data sources, combined with survey fatigue and stronger concerns about confidentiality, has led to declining response rates and increasing operational difficulties, challenging the relevance of the survey instrument and raising questions about how surveys should be designed and used.

Even so, surveys are not obsolete, but they must evolve within a multi-source statistical ecosystem. For example, mixed-mode data collection can better match respondent preferences; greater use of administrative records can shorten questionnaires and improve precision; and modern data science tools—including AI—can support more efficient production and analysis.

This book provides a practical guide to survey work from design to analysis. It presents the theoretical foundations of probability-based sampling and inference, while emphasising the real constraints of survey operations—budget, time, staffing and respondent burden. It is intended for readers who need both methodological grounding and workable approaches for producing accurate estimates in statistical practice.

Key Features:

• Promote sample surveys as a core data source for research and decision-making in economics and finance.
• Serve as a practical handbook for academics and practitioners, covering both survey design and analysis methods.
• Illustrate key theoretical concepts with numerical applications using dedicated R packages for survey analysis.
• Explain how modern data science tools—including AI—can reshape survey design, implementation, and inference in the years ahead.