Non-probability sampling

Definition

Non-probability sampling is a technique where individuals are selected for a study based on non-random criteria, meaning not every individual has a known or equal chance of being included. This method is often used in social research where researchers prioritize specific characteristics or traits over random selection. Because it lacks randomization, the findings may be less generalizable to the broader population, but it can provide valuable insights in exploratory research or when studying hard-to-reach groups.

5 Must Know Facts For Your Next Test

1. Non-probability sampling does not allow for the calculation of sampling error, which makes it difficult to determine how representative the sample is of the population.
2. This method can be beneficial for qualitative research as it allows researchers to focus on specific groups that might have unique insights or experiences.
3. Common forms of non-probability sampling include convenience sampling, purposive sampling, and snowball sampling.
4. Researchers using non-probability sampling must be cautious when making inferences about the broader population since the results may not be applicable beyond the sample studied.
5. Non-probability sampling is often used in pilot studies or preliminary research to gather insights before conducting more rigorous studies with probability sampling.

Review Questions

• How does non-probability sampling differ from probability sampling, and what are some advantages and disadvantages of using this method?
  • Non-probability sampling differs from probability sampling in that not all individuals have a known or equal chance of being selected. An advantage of non-probability sampling is that it can be more practical and cost-effective for certain research scenarios, especially when studying hard-to-reach populations. However, a major disadvantage is that the results may not be generalizable to the wider population due to potential biases in sample selection.
• In what situations might a researcher choose to use non-probability sampling instead of probability sampling?
  • A researcher might choose non-probability sampling when they are conducting exploratory research and are interested in gaining insights from specific groups that are difficult to reach through random methods. For instance, if a study aims to understand the experiences of a marginalized community, non-probability techniques like purposive or snowball sampling would allow researchers to gather detailed qualitative data from individuals who meet specific criteria. This tailored approach can yield richer information compared to a broad but shallow probabilistic sample.
• Critically assess the implications of using non-probability sampling on the validity and reliability of research findings.
  • Using non-probability sampling can significantly impact the validity and reliability of research findings. Because this method does not ensure equal representation of all individuals in the population, it may introduce biases that affect the accuracy of conclusions drawn from the data. Researchers must critically evaluate how their choice of sample affects their results and consider whether their findings can be reliably generalized. In contexts where generalizability is essential, incorporating follow-up studies with probability samples could help validate initial insights gained from non-probability methods.

Related terms

Convenience sampling: A type of non-probability sampling where participants are selected based on their easy availability and proximity to the researcher.

Purposive sampling: A non-probability sampling method where participants are selected based on specific characteristics or qualities relevant to the research study.

Quota sampling: A non-probability sampling technique that ensures certain characteristics are represented in the sample to meet predefined quotas.