Non-random sampling refers to a variety of sampling techniques where not all units in the population have an equal chance of being selected. This is in contrast to random sampling, which relies on probabilistic methods to ensure each unit has an equal chance of selection.
Historical Context
The concept of non-random sampling has evolved alongside the development of statistical science. Initially used in qualitative research, these techniques have been applied widely due to their practicality and ease of implementation in scenarios where random sampling is difficult or impossible.
Types of Non-Random Sampling
-
Convenience Sampling
- Definition: Sampling units that are conveniently available to the researcher.
- Example: Surveying people at a shopping mall.
-
Judgmental or Purposive Sampling
- Definition: Sampling based on the judgment of the researcher about which units are most representative or useful.
- Example: Selecting experts for an opinion poll.
-
Quota Sampling
- Definition: Sampling wherein certain quotas or targets for different subgroups within the population are set.
- Example: Ensuring a survey includes 50 males and 50 females.
-
Snowball Sampling
- Definition: Sampling existing study subjects recruit future subjects from among their acquaintances.
- Example: Using initial survey respondents to refer others.
Key Events
- 1940s: Increased use of non-random sampling during World War II for practical data collection.
- 1960s-1970s: Extensive use in market research and sociology for gathering specific opinions or behaviors.
- 2000s-Present: Application in online and mobile research where traditional random sampling is impractical.
Detailed Explanations
Merits of Non-Random Sampling:
- Practicality: Easier and quicker to administer.
- Cost-effective: Generally less costly than random sampling.
- Targeted: Useful when the research focuses on specific characteristics or a particular subset of the population.
Demerits of Non-Random Sampling:
- Bias: Higher potential for selection bias.
- Generalizability: Findings may not be generalizable to the wider population.
- Statistical Inference: Limits the ability to make precise statistical inferences.
Mathematical Models/Charts
graph TD;
A[Total Population] --> B[Convenience Sampling];
A --> C[Judgmental Sampling];
A --> D[Quota Sampling];
A --> E[Snowball Sampling];
Importance and Applicability
Non-random sampling techniques are crucial in situations where random sampling is unfeasible. Their use spans several fields:
- Market Research: To understand consumer preferences.
- Healthcare Studies: For recruiting patients with specific conditions.
- Sociological Studies: For gathering data on specific subpopulations.
Examples
- A study on dietary habits using convenience sampling from a local gym.
- Purposive sampling to gather opinions from climate change experts.
- Quota sampling in political polling to ensure representation of demographic groups.
Considerations
When employing non-random sampling, researchers should consider:
- Bias: Measures to mitigate bias should be implemented.
- Ethics: Ethical considerations, especially in snowball sampling.
- Validation: Validating findings through triangulation with other data sources.
Related Terms
- Random Sampling: A sampling technique where every unit has an equal chance of being selected.
- Sampling Bias: The bias introduced by selecting non-representative samples.
- Stratified Sampling: Dividing the population into subgroups and then sampling from each.
Comparisons
- Random vs. Non-Random Sampling: Random sampling aims for generalizability and unbiased estimates, while non-random sampling is often easier and more targeted but at the cost of potential bias.
Interesting Facts
- Despite its limitations, non-random sampling is frequently used in online research due to the sheer volume and accessibility of respondents.
Inspirational Stories
- Social Media Surveys: During COVID-19, researchers used snowball sampling via social media to quickly gather data on public health compliance, helping inform policy decisions.
Famous Quotes
- “The goal is to turn data into information, and information into insight.” – Carly Fiorina
Proverbs and Clichés
- “You can’t see the forest for the trees.”
- “Don’t put all your eggs in one basket.”
Expressions
- “Bias Towards Action”: Making decisions and taking actions despite imperfect data.
- [“Cherry-Picking”](https://financedictionarypro.com/definitions/c/cherry-picking/ ““Cherry-Picking””): Selecting data that supports one’s hypothesis while ignoring contradictory data.
Jargon
- [“Sampling Frame”](https://financedictionarypro.com/definitions/s/sampling-frame/ ““Sampling Frame””): The source material from which a sample is drawn.
- [“Selection Bias”](https://financedictionarypro.com/definitions/s/selection-bias/ ““Selection Bias””): Bias introduced by the method of selecting samples.
Slang
- “Data Dredging”: The practice of analyzing large volumes of data with the aim of finding patterns, often without a pre-specified hypothesis.
FAQs
-
What is non-random sampling? Non-random sampling refers to sampling methods where not every unit has an equal chance of being selected.
-
Why use non-random sampling? It is practical, cost-effective, and useful for targeting specific subgroups or when random sampling is infeasible.
-
What are the risks of non-random sampling? There is a higher risk of selection bias and less generalizability to the broader population.
References
- Cochran, W.G. (1977). “Sampling Techniques.” John Wiley & Sons.
- Patton, M.Q. (1990). “Qualitative Evaluation and Research Methods.” SAGE Publications.
Summary
Non-random sampling techniques are a set of methods used when random sampling isn’t practical or feasible. While these methods have their limitations, they are invaluable for specific types of research and in situations where targeted data collection is necessary. Understanding the advantages and limitations of non-random sampling can help researchers design better studies and mitigate the inherent biases associated with these methods.
This comprehensive overview of non-random sampling should provide a solid foundation for understanding this critical research methodology, its applications, and considerations.
