Sampling for Social Research: Basic Terms and Concepts
In social research, the process of sampling is a crucial step that involves selecting a subset of individuals or elements from a larger population. This subset, known as the sample, serves as a representative snapshot, allowing researchers to draw inferences about the broader population. To embark on this journey, it’s essential to grasp the fundamental terms and concepts in sampling. This post aims to provide a brief overview of fifteen of the most basic terms and concepts.
1. Population:
The population, also referred to as the target population, is the entire group that the researcher is interested in studying. It could be a specific demographic, community, or any defined group that shares common characteristics. For example, your population may be ‘university students’ or ‘drug addicts’.
2. Sample:
A sample is a subset of the population selected for the study. Clearly, a study of university students would not involve the participation of every student. Hence the need for a sample. The goal of sampling is to choose a representative sample that accurately reflects the characteristics of the larger population.
3. Sampling Frame:
The sampling frame is the list or representation of the elements in the population from which the sample is drawn. It serves as the basis for selecting potential participants.
4. Sampling Unit:
A sampling unit is an individual element or group within the population that is considered during the sampling process. It could be a person, household, organization, or any distinct unit depending on the research context.
5. Sampling Method:
Sampling methods are the procedures used to select individuals or elements for the sample. There are various sampling techniques, each with its strengths and limitations. Common methods include random sampling, stratified sampling, and convenience sampling.
6. Random Sampling:
Random sampling is a method where each member of the population has an equal chance of being included in the sample. This method minimizes bias and ensures that the sample is representative of the population.
7. Stratified Sampling:
Stratified sampling involves dividing the population into subgroups or strata based on certain characteristics and then randomly selecting samples from each stratum. This ensures representation from different segments of the population.
8. Convenience Sampling:
Convenience sampling involves selecting participants based on their availability or accessibility. While it is quick and easy, it may lead to a non-representative sample.
9. Sampling Bias:
Sampling bias occurs when the sample selected is not representative of the population, leading to inaccurate and skewed results. It can arise from flaws in the sampling method or the sampling frame.
10. Sample Size:
Sample size refers to the number of individuals or elements included in the sample. Determining an appropriate sample size is crucial for the reliability and generalizability of study findings.
11. Sampling Error:
Sampling error is the difference between the characteristics of the sample and the population. It is an inherent part of sampling and can be reduced by increasing the sample size.
12. Representative Sample:
A representative sample accurately mirrors the key characteristics of the population. Achieving representativeness is essential for generalizing study findings to the broader population.
13. Non-probability Sampling:
Non-probability sampling methods do not involve random selection. Examples include convenience sampling, purposive sampling, and snowball sampling. While they are less rigorous, they are often practical in certain research contexts.
14. Probability Sampling:
Probability sampling methods involve random selection and provide a known probability of each element being included in the sample. This enhances the generalizability of study results to the entire population.
15. Sampling Saturation:
Sampling saturation occurs when additional data collection does not yield new or meaningful information. Researchers may reach a point where the sample is deemed sufficient to answer the research questions.
Summary
Mastering the basic terms and concepts in sampling lays a solid foundation for researchers embarking on social research endeavours. Whether aiming to understand a specific community, demographic, or social phenomenon, the careful selection of a representative sample is the key to unlocking meaningful insights. As researchers commence their sampling, they contribute to the robustness and validity of social research, enriching our understanding of the diverse and dynamic patterns of human experiences and behaviours.
Recommended reading
Latpate, R., Kshirsagar, J., Gupta, V. K., & Chandra, G. (2021). Advanced sampling methods. Singapore: Springer. (click to view on Amazon).
Advance methods discussed in the book have tremendous applications in ecology, environmental science, health science, forestry, bio-sciences, and humanities. This book is targeted as a text for undergraduate and graduate students of statistics, as well as researchers in various disciplines.