Navigator’s Kit: Sampling Procedures – Concepts & Best Practices
Video Description
Introduction
In this training video, Dr. Sérgio Moreira explores sampling procedures, a foundational yet often misunderstood aspect of social research and impact assessment. Responding to one of the most common practitioner questions, “What sample size do I need?”, he unpacks the reasoning behind sampling decisions, showing how thoughtful design can turn complex methodological choices into practical tools for better, more credible data collection.
Section 1 – Why Sampling Matters
Sampling is more than a technical exercise. It determines how accurately we can understand a community, assess project impacts, and make evidence-based decisions. While a census captures every individual in a population, it is rarely feasible. Sampling offers a resource-efficient alternative, if it is done rigorously. The goal is to achieve representativeness, ensuring the sample reflects the population’s diversity across key social, economic, and geographic characteristics.
Section 2 – Core Concepts and Key Questions
The video introduces three critical questions practitioners must answer when designing a non-probabilistic sample:
- What are the key characteristics to ensure representativeness?
Practitioners must identify the demographic, socio-economic, or behavioral traits most relevant to the study—such as gender, proximity to a project site, or household type. - What is the appropriate sample size?
Sample size depends on study objectives and the level of comparison desired (e.g., between communities or within a municipality). Sérgio illustrates how small adjustments to sample design can strengthen the reliability of findings. - What sampling procedures will ensure unbiased data collection?
Through examples like random routes, door-to-door selection, and field scheduling, Sérgio explains how to minimize bias while balancing practical constraints.
Section 3 – Bringing Sampling to Life: A Case Example
Drawing on a baseline community survey for a mining project, the video demonstrates how to operationalize these principles. The team identified three key sample characteristics, geography, gender, and type of residence, and applied structured rules to guarantee balanced representation. They alternated between convenience sampling and random selection, enforced return visits for non-responses, and tracked participation rates to ensure quality and credibility.
Section 4 – Managing Field Teams and Ethical Practice
Beyond methodology, Sérgio emphasizes the human side of data collection. Effective sampling depends on well-trained, motivated field teams who understand social performance principles and community dynamics. He discusses:
- The value of flexibility, adapting procedures to fit context without compromising rigor.
- The importance of motivation and reflection, encouraging teams to see each survey as part of a collective portrait of community life.
- Managing refusals as opportunities for dialogue rather than rejection.
- Using feedback loops between data collectors and analysts to maintain alignment and improve quality in real time.
Closing
High-quality sampling is the cornerstone of credible research. As Sérgio concludes, “garbage in, garbage out”, no amount of sophisticated analysis can fix poor data collection. By integrating methodological rigor with empathy, reflection, and adaptive management, practitioners can produce evidence that genuinely represents communities and supports informed, ethical decision-making.
