Probability in sampling refers to the likelihood or chance that a particular element or individual from a population will be selected to be part of a sample.
Two key concepts of probability sampling are:
- Random selection: Probability sampling involves random selection, where each element of the population has an independent and equal chance to be chosen for the sample. Random selection makes sure that the sample is representative and unbiased.
- Known probabilities: In probability sampling, each element’s probability of selection is known and can be quantified, which allows researchers to calculate sampling errors and make statistical inferences.
Advantages of probability sampling include:
- Representativeness: Probability sampling ensures that each member of the population has a chance of being included, leading to a more representative sample.
- Generalizability: Findings from a probability sample are more likely to be generalizable to the broader population, enhancing the study’s external validity.
Limitations of probability sampling:
- Logistical challenges: Implementing proper random sampling can be challenging, especially in large populations or when access to the entire population is difficult.
- Resource intensive: Conducting probability sampling, especially with complex designs, may require more resources, time, and effort than non-probability sampling methods.
- Potential for non-response bias: If selected individuals choose not to participate in the study, there is a risk of non-response bias, which may affect the sample’s representativeness.
In research, the choice between probability and non-probability sampling depends on the research objectives, the nature of the population, and practical considerations. Probability sampling is often preferred when the goal is to make statistical inferences and generalize findings to the broader population.
List of recommended resources #
For a broad overview #
This article by Mohamed Elfil and Ahmed Negida explains the different sampling methods in clinical research to get a better understanding of the generalizability of clinical research findings.
This blog on Scribbr by Kassiani Nikolopoulou gives an overview of probability sampling, its various types and examples. It also details the advantages and disadvantages of probability sampling, along with the basic difference between probability and non-probability sampling.
For in depth understanding #
This chapter from the book Sampling Essentials by Sage Publications gives an in-depth understanding of the different types of probability sampling as well as the differences between them. It also details the strengths and weaknesses of each probability sampling method.
This book by Steven K. Thompson as part of Wiley Series in Probability and Statistics gives an up-to-date and in-depth understanding of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations.
Case study #
This paper by Ramiro Flores Cruz studies the COVID-19 high-frequency phone survey (HFPS) 2020 conducted in 13 Latin American countries: Argentina, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Paraguay, and Peru. All national samples were based on a dual frame of cell and landline phones, and selected as a one-stage probability sample, with geographic stratification of landline numbers.
This report published by the World Bank describes the results of a phone survey completed in August and September of 2022, showing the precarity of living conditions and livelihood options across Yemen, but highlighting worse conditions amongst parts of the population. The survey draws on a probability sample of 1,297 respondents, 623 of whom are based in rural areas, while 480 and 193 are living in urban and semi-urban areas respectively.