Sampling Strategy
Choosing participants for your research
Sampling strategy determines how you select units (e.g., people, organisations, cases) from the population of interest. This section covers the main probability and non-probability sampling techniques used in quantitative dissertations.
The sampling strategy that you select in your dissertation should naturally flow from your chosen research design and research methods, as well as taking into account issues of research ethics. To set the sampling strategy that you will use in your dissertation, you need to follow three steps: (a) understand the key terms and basic principles; (b) determine which sampling technique you will use to select the units that will make up your sample; and (c) consider the practicalities of choosing such a sampling strategy for your dissertation (e.g., what time you have available, what access you have, etc.).
In Sampling: The basics, we include terms such as units/cases/objects, sample, sampling frame, population, sample size, random sampling, sampling bias, amongst other terms. If you are already confident that you understand these basic principles of sampling, we introduce you to the two major groups of sampling techniques that you could use to select the units that you will include in your sample:
Probability sampling techniques, which include simple random sampling, systematic random sampling and stratified random sampling.
Non-probability sampling, which include quota sampling, self-selection sampling, convenience sampling, snowball sampling and purposive sampling.
We explain what each of these types of sampling technique are, how to create them, and their advantages and disadvantages. If there is more about sampling that you would like to know about, please leave feedback. Alternately, click on the articles below:
- Sampling: The Basics The key terms and basic principles of sampling, from populations and units to sampling frames and sample size Also known as: population, units, cases, sampling frame, sample size, sampling bias
- Sampling Strategy: A dissertation guide Setting the sampling strategy for your dissertation in three steps, from basic principles to practicalities Also known as: choosing a sampling strategy, sampling guide, practicalities
- How to structure the Sampling Strategy section of your dissertation The four steps to a well-structured Sampling Strategy section: describe, explain, state and justify Also known as: writing up, structure, Chapter Three, Research Strategy chapter
- Probability sampling The principles of probability sampling and how to create a probability sample for your dissertation Also known as: random sampling, statistical inference, representative sample
- Simple random sampling Every member of the population has an equal chance of being selected Also known as: SRS, lottery, equal chance, basic random
- Systematic random sampling Selecting every nth member from a randomly ordered list Also known as: nth selection, interval, every kth, fixed interval, periodic
- Stratified random sampling Dividing the population into subgroups (strata) before random selection Also known as: proportionate, disproportionate, stratification, subgroups, strata
- Non-probability sampling The principles of non-probability sampling and when to use it in your dissertation Also known as: non-random, judgement sampling, theoretical sampling
- Quota sampling Ensuring specific subgroups are represented in set proportions Also known as: proportional representation, non-random stratified, controlled selection
- Convenience sampling Selecting participants based on ease of access and availability Also known as: accidental, haphazard, opportunity, grab, available subjects
- Purposive sampling Selecting participants based on the researcher's judgement of who is most useful Also known as: judgmental, selective, subjective, expert choice, deliberate
- Self-selection sampling Individuals choose whether to participate in the research themselves Also known as: volunteer, opt-in, voluntary response
- Snowball sampling Existing participants recruit future participants from their networks Also known as: chain referral, network, respondent-driven, referral, word of mouth
- Total population sampling Studying every member of the entire population of interest Also known as: census, complete enumeration, whole population, exhaustive
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