Non-probability sampling refers to sampling methods that do not involve random selection. This type of sampling relies on the subjective judgment of the researcher rather than randomization. While this approach may limit the generalizability of the findings to the larger population, it is often used in exploratory research, qualitative studies, and when random sampling is impractical or impossible.
What Is Non-probability Sampling?
Definition and Importance
Non-probability sampling is a sampling technique in which samples are selected based on the subjective judgment of the researcher rather than random selection. Unlike probability sampling, where each member of the population has a known, non-zero chance of being selected, non-probability sampling does not afford every individual in the population an equal or known chance of being included in the sample. This can be advantageous in exploratory research, pilot studies, and in situations where conducting a random sample is impractical or impossible.
Types of Non-probability Sampling
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Convenience Sampling: Selecting samples that are easiest to access. This is often used for preliminary research efforts.
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Judgmental (or Purposive) Sampling: Samples are chosen based on the researcher’s knowledge and judgment about which ones will be the most useful or representative.
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Quota Sampling: Ensuring that certain characteristics within the population are represented in the sample to certain proportions.
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Snowball Sampling: Existing study subjects recruit future subjects among their acquaintances, often used in hidden or hard-to-reach populations.
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Consecutive Sampling: Every subject meeting criteria is selected until the desired sample size is achieved.
Applicability
Non-probability sampling is applicable in various research contexts, particularly:
- Exploratory research
- Qualitative studies
- Case studies
- Pilot studies
- Situations where random sampling is not feasible due to time, cost, or access constraints
Comparisons with Probability Sampling
| Criteria | Probability Sampling | Non-probability Sampling |
|---|---|---|
| Selection Process | Random | Based on researcher’s judgment |
| Generalizability | High, results can be generalized to the population | Limited, results apply only to the sample |
| Use Case | When statistical inference is required | When exploratory insights are needed |
| Types | Simple random, stratified, cluster, systematic | Convenience, judgmental, quota, snowball |
Examples and Considerations
Example Scenario
In market research, a company might use convenience sampling by surveying people who visit a specific store because it requires less time and resources. This sample is perhaps not fully representative of all potential customers, but it provides quick and actionable insights.
Special Considerations
- Bias Risk: High potential for selection bias; the sample might not represent the entire population.
- Subjectivity: The researcher’s judgment plays a critical role, which could skew results.
- Generalizability: Limited, results derived from non-probability samples are often not generalizable beyond the sample studied.
Related Terms
- Probability Sampling: Sampling methods that involve random selection. Each member of the population has a known, non-zero chance of being selected.
- Bias: Systematic error introduced into sampling or testing by selecting or encouraging one outcome or answer over others.
- Sample Size: The number of observations or replicates included in the sample.
FAQs
1. What is the main difference between probability and non-probability sampling?
2. When is non-probability sampling preferred over probability sampling?
3. Can results from non-probability sampling be generalized to the entire population?
References
- Cohen, L., Manion, L., & Morrison, K. (2007). “Research Methods in Education.” Routledge.
- Flick, U. (2018). “An Introduction to Qualitative Research.” SAGE.
- Taherdoost, H. (2016). “Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research.” International Journal of Academic Research in Management.
Summary
Non-probability sampling methods provide practical solutions for researchers in scenarios where random sampling is not feasible. While these methods come with limitations like potential biases and reduced generalizability, they offer valuable insights in exploratory and qualitative research settings. Understanding the trade-offs between non-probability and probability sampling is crucial for designing robust and credible research studies.
