Sampling is the process where a subset of individuals or elements are selected from a larger population to represent and make inferences about that population. In research, sampling is a critical step that influences the generalizability and validity of study findings. The goal is to obtain a representative and unbiased sample that accurately reflects the characteristics of the broader population.
Three key concepts in sampling are:
- Population: Population refers to the entire group of individuals, elements, or units that meet the criteria for inclusion in the study. It is the larger group to which researchers want to generalize their findings.
- Sample: The sample is the subset of the population selected for the study. It is the group from which data is collected and analyzed.
- Sampling frame: The sampling frame is a list or representation of the elements in the population from which the sample is to be drawn. It serves as the basis for selecting the actual sample.
Some common sampling methods include:
- Random sampling: In random sampling, every individual or element has an equal chance to get selected for the sample. This method helps to get a representative sample and reduces bias.
- Stratified sampling: This involves dividing the population into subgroups or strata based on a specified characteristic (e.g., age, gender, socioeconomic status) and randomly selecting samples from each stratum. It ensures representation from various segments of the population.
- Convenience sampling: Convenience sampling involves selecting individuals who are readily available or easily accessible. While convenient, this method may introduce bias as it fails to ensure a random or representative sample.
- Snowball sampling: In snowball sampling, existing participants refer additional individuals to participate in the study. This method is often used when the population is challenging to reach directly.
- Systematic sampling: Systematic sampling involves selecting every nth individual from a list after a random start. This method is straightforward and can be more efficient than random sampling in certain situations.
List of recommended resources #
For a broad overview #
This blog on Scribbr gives an overview of the various sampling techniques in data collection in research. It differentiates between the key concepts of population and sample and describes the different sampling methods of probability and non-probability sampling.
This article by Dana P. Turner focuses on the common ways data are observed. It describes several common sampling designs, their uses, and the common problems encountered while sampling data.
For in depth understanding #
This chapter distinguishes between probabilistic and non-probabilistic sampling methods, teaches bases for stratifying samples and provides an all-round understanding of the use of sampling in marketing research.
Written by Changbao Wu and Mary E. Thompson, this book provides an in-depth understanding of sampling theory and practice used in research and survey sampling. It bridges the gap between survey data analysis and statistical methods of other fields.
Case study #
This study by Juan Munoz, Jose Munoz and Sergio Olivieri presents an innovative methodology based on the use of big data for sampling design of a representative survey of Venezuelan migrants and host communities’ populations in Ecuador.
This paper by Yele Maweki Batana, Takaaki Masaki, Shohei Nakamura and Mervy Ever Viboudoulou Vilpoux proposes monetary poverty and inequality estimates for Kinshasa using a new Kinshasa household survey implemented in 2018. Due to the obsolescence of the sampling frame, the survey was sampled using satellite imagery.