Sampling Technique Builder
Define who you want to study, answer a few questions about your situation, and see how each sampling technique fits your research question, design and population. Compare techniques side by side, then choose one and write your sampling plan. The builder never chooses for you.
Project details
Enter what you already know. Anything left blank is simply not compared.
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Your population
Who your findings are about, who you can reach, and how you will find them.
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Your situation
Answer what you can. Your answers explain how each technique relates to your situation; they don't choose one.
How techniques relate to your answers
No answers yet. Every technique is shown.
Simple random sampling
Probability sampling
Every member of the population has an equal chance of being selected, and participants are chosen at random from a complete list.
Systematic sampling
Probability sampling
Every kth member of an ordered list is selected, after a random starting point.
Stratified sampling
Probability sampling
The population is divided into non-overlapping subgroups (strata), such as year groups, and a random sample is drawn from each.
Cluster sampling
Probability sampling
The population is divided into naturally occurring groups (clusters), such as schools or towns. Clusters are selected at random, and everyone in the chosen clusters is included.
Multistage sampling
Probability sampling
Sampling happens in stages, such as selecting regions, then schools within them, then pupils within those schools, at random at each stage.
Convenience sampling
Non-probability sampling
Participants are chosen because they are easy to reach, such as students in the researcher's own class.
Purposive sampling
Non-probability sampling
Participants are deliberately chosen because they have characteristics or experience relevant to the research question.
Judgmental sampling
Non-probability sampling
Participants are chosen by the researcher's, or experts', judgement of who is most typical or most knowledgeable. Many texts treat it as another name for purposive sampling.
Quota sampling
Non-probability sampling
Participants are recruited, not at random, until set numbers (quotas) in each category are filled, usually in proportion to the population.
Snowball sampling
Non-probability sampling
Initial participants recruit further participants from among people they know, so the sample grows through their networks.
Volunteer sampling
Non-probability sampling
People choose to take part in response to an invitation, such as an advertisement or an email to a mailing list. Also called self-selection sampling.
Consecutive sampling
Non-probability sampling
Every eligible person who presents during a set period is invited, such as all patients attending a clinic over three months.
Theoretical sampling
Non-probability sampling
In grounded theory, participants or sites are chosen according to the concepts emerging from the analysis, and sampling continues until new data add little (saturation).
Your shortlist
Add the techniques you want to consider. Compare them below, then choose one yourself.
Your shortlist is empty. Add techniques from the list above or here.
How each technique fits your project
Each check explains why, names the parts of your project behind it, and says what you should justify.
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Compare techniques
Your shortlisted techniques side by side.
Add at least two techniques to your shortlist to compare them.
Your sampling plan
Why it suits your question, design and population, and how you will handle its limitations.
Step by step, how participants will be identified, selected and invited.
What share of the people you invite you expect to take part, such as 60. Base it on similar studies or a pilot.
Checks
Population For you to review
Describe your target population: everyone your findings are meant to apply to.
Unit of analysis For you to review
State your unit of analysis: what each case in your data is, such as a person, a class or a school.
Inclusion and exclusion criteria Worth checking
List who is eligible and who isn't. Clear criteria make your sample reproducible and your findings easier to interpret.
Technique chosen For you to review
You haven't chosen or shortlisted a technique yet.
Your project draft
This tool writes only the Sampling plan section. Everything else is shown as you entered it.
Sampling techniques explained
Every technique the builder covers. References for each will be added after academic review.
Probability sampling
Simple random sampling
Every member of the population has an equal chance of being selected, and participants are chosen at random from a complete list.
Learn more about simple random sampling
When it is commonly used
When a complete list of the population exists and findings need to be generalised to it.
Strengths
- Selection is free from researcher bias.
- Supports statistical inference about the population.
Limitations
- Needs a complete, accurate list of the population.
- Small subgroups may be missed or under-represented by chance.
Assumptions
- The sampling frame lists every member of the population.
- The people who take part don't differ systematically from those who decline.
Common sample sizes
Set by a sample size calculation for the estimate or test the study needs.
Examples
Randomly selecting 300 students from a university's enrolment list.
References
References awaiting academic review.
Systematic sampling
Every kth member of an ordered list is selected, after a random starting point.
Learn more about systematic sampling
When it is commonly used
When a complete list exists and a simpler procedure than random number selection is wanted.
Strengths
- Simple to carry out.
- Spreads the sample evenly across the list.
Limitations
- Needs a complete list.
- Biased if the list has a repeating pattern that matches the interval.
Assumptions
The order of the list isn't related to what the study measures.
Common sample sizes
Set by a sample size calculation; the interval is the population size divided by the sample size.
Examples
Selecting every 20th patient record, starting from a randomly chosen record.
References
References awaiting academic review.
Stratified sampling
The population is divided into non-overlapping subgroups (strata), such as year groups, and a random sample is drawn from each.
Learn more about stratified sampling
When it is commonly used
When subgroups must be represented, or compared, and each member's subgroup is known in advance.
Strengths
- Ensures every subgroup is represented.
- Can give more precise estimates than a simple random sample of the same size.
Limitations
- Needs to know each member's subgroup before sampling.
- Unequal sampling across strata requires weighting in the analysis.
Assumptions
- Every member belongs to exactly one stratum.
- The characteristic used for the strata is recorded in the sampling frame.
Common sample sizes
Set by a sample size calculation, allocated across strata in proportion or by design.
Examples
Sampling students randomly within each faculty, in proportion to faculty size.
References
References awaiting academic review.
Cluster sampling
The population is divided into naturally occurring groups (clusters), such as schools or towns. Clusters are selected at random, and everyone in the chosen clusters is included.
Learn more about cluster sampling
When it is commonly used
When the population is spread widely, or there is a list of groups but not of individuals.
Strengths
- Practical and cheaper for widely spread populations.
- Needs a list of clusters rather than of every individual.
Limitations
- Less precise than a simple random sample of the same size, because members of a cluster tend to be alike.
- The analysis must account for clustering.
Assumptions
- Clusters are broadly similar to each other.
- A complete list of clusters exists.
Common sample sizes
Larger than a simple random sample for the same precision, because of clustering.
Examples
Randomly selecting 15 schools and surveying every teacher in them.
References
References awaiting academic review.
Multistage sampling
Sampling happens in stages, such as selecting regions, then schools within them, then pupils within those schools, at random at each stage.
Learn more about multistage sampling
When it is commonly used
For large, widely spread populations where no single list exists.
Strengths
- Practical for national or large-scale studies.
- Lists are only needed at each stage, not for the whole population.
Limitations
- Complex to design and analyse.
- Sampling error builds up at each stage.
Assumptions
- A list exists at each stage.
- Random selection is used at every stage.
Common sample sizes
Often large; set by a sample size calculation that allows for each stage.
Examples
Selecting districts, then households, then one adult per household for a national survey.
References
References awaiting academic review.
Non-probability sampling
Convenience sampling
Participants are chosen because they are easy to reach, such as students in the researcher's own class.
Learn more about convenience sampling
When it is commonly used
In pilot studies, exploratory work, or when time and access are very limited.
Strengths
- Quick and inexpensive.
- Useful for piloting instruments and procedures.
Limitations
- The sample is unlikely to represent the population.
- Findings can't be generalised statistically.
Assumptions
The people easiest to reach are similar enough to the population for the study's purpose.
Common sample sizes
Varies widely; often whoever is available in the time.
Examples
Surveying students who pass through the library on one afternoon.
References
References awaiting academic review.
Purposive sampling
Participants are deliberately chosen because they have characteristics or experience relevant to the research question.
Learn more about purposive sampling
When it is commonly used
In qualitative research, where information-rich cases matter more than representativeness.
Strengths
- Focuses on people who can answer the question in depth.
- Allows deliberate variety, such as contrasting cases.
Limitations
- Depends on the researcher's judgement, which can introduce bias.
- Findings can't be generalised statistically.
Assumptions
- The researcher can identify who holds relevant experience.
- The selection criteria can be stated and justified.
Common sample sizes
Usually small; sampling continues until the question is answered in enough depth.
Examples
Interviewing nurses who have worked night shifts for at least a year.
References
References awaiting academic review.
Judgmental sampling
Participants are chosen by the researcher's, or experts', judgement of who is most typical or most knowledgeable. Many texts treat it as another name for purposive sampling.
Learn more about judgmental sampling
When it is commonly used
When expert judgement is needed to identify typical or key cases, such as selecting representative schools.
Strengths
- Draws on expert knowledge of the population.
- Quick when experts are available.
Limitations
- Relies heavily on the judgement of whoever selects.
- Findings can't be generalised statistically.
Assumptions
- The person judging knows the population well.
- Their judgement of what is typical is sound.
Common sample sizes
Usually small.
Examples
Asking a head of department to nominate the most experienced teachers.
References
References awaiting academic review.
Quota sampling
Participants are recruited, not at random, until set numbers (quotas) in each category are filled, usually in proportion to the population.
Learn more about quota sampling
When it is commonly used
When categories should be represented in proportion, but no list exists for random sampling.
Strengths
- Ensures each category is represented.
- Quicker and cheaper than stratified sampling.
Limitations
- Selection within each quota isn't random, so bias remains.
- Needs to know the population's proportions.
Assumptions
- The proportions of each category in the population are known.
- People selected within each quota are similar to the rest of their category.
Common sample sizes
Set by quotas proportional to the population's characteristics.
Examples
Interviewing shoppers until 50 men and 50 women in each of three age groups have taken part.
References
References awaiting academic review.
Snowball sampling
Initial participants recruit further participants from among people they know, so the sample grows through their networks.
Learn more about snowball sampling
When it is commonly used
When the population is hidden or hard to reach, such as people in stigmatised groups.
Strengths
- Reaches people who couldn't be found otherwise.
- Trust within networks can help recruitment.
Limitations
- The sample reflects the first participants' networks, so similar people are recruited.
- Findings can't be generalised statistically.
Assumptions
- Members of the population know one another.
- Participants are willing to refer others.
Common sample sizes
Varies; often small to moderate.
Examples
Recruiting informal carers, each of whom introduces other carers they know.
References
References awaiting academic review.
Volunteer sampling
People choose to take part in response to an invitation, such as an advertisement or an email to a mailing list. Also called self-selection sampling.
Learn more about volunteer sampling
When it is commonly used
When a study is advertised widely, or participation needs strong commitment.
Strengths
- Participants are willing and motivated.
- Easy to recruit through adverts and online.
Limitations
- Volunteers often differ from people who don't volunteer.
- Findings can't be generalised statistically.
Assumptions
Volunteers don't differ from the population in ways that matter to the study.
Common sample sizes
Varies with the reach of the invitation.
Examples
Recruiting participants through posters for a study of exercise habits.
References
References awaiting academic review.
Consecutive sampling
Every eligible person who presents during a set period is invited, such as all patients attending a clinic over three months.
Learn more about consecutive sampling
When it is commonly used
In clinical and service settings, where eligible people arrive over time.
Strengths
- Reduces the researcher's own selection bias compared with convenience sampling.
- Fits naturally into routine settings.
Limitations
- Depends on who attends during the period.
- Seasonal or time-related patterns can bias the sample.
Assumptions
The people attending during the period are typical of those attending at other times.
Common sample sizes
Everyone eligible during the period; the period is set to reach the size needed.
Examples
Inviting every new patient at a diabetes clinic between January and March.
References
References awaiting academic review.
Theoretical sampling
In grounded theory, participants or sites are chosen according to the concepts emerging from the analysis, and sampling continues until new data add little (saturation).
Learn more about theoretical sampling
When it is commonly used
In grounded theory studies, where sampling and analysis happen together.
Strengths
- Directs data collection to where it best develops the theory.
- Supports a well-grounded theory.
Limitations
- The sample can't be fully planned in advance.
- Needs analysis to run alongside data collection.
Assumptions
- Analysis happens alongside data collection.
- Saturation can be recognised and justified.
Common sample sizes
Not fixed in advance; sampling continues until saturation.
Examples
Interviewing newly qualified managers, then experienced managers once a concept about confidence emerges.
References
References awaiting academic review.
Next: Sample size
How many participants you need, with the working shown.
What should I do next?
Next in the research journey: data.
Questionnaire Builder
Assembles a structured questionnaire from your variables and indicators, explains where every question comes from, and exports it to Word, PDF and Markdown.
SPSS Research Lab
Connects a research question and selected analysis to IBM SPSS Statistics procedures, output tables, assumptions and reporting tools.
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Research Table Builder
Builds publication-ready research tables in APA, IEEE, Harvard, Chicago or MLA style, from your data, your results or your project, ready to paste into Word.
SPSS: From data preparation to reporting
A connected guide to preparing research data, choosing a supported analysis, running its IBM SPSS Statistics procedure, checking assumptions, reading output and reporting results.
How the builder works
- The builder reads your project draft: research question, objectives, variables, hypotheses, research design and research onion choices. Enter them in the first step; later versions will bring them across from the other research tools.
- Your answers to the decision questions only narrow the explanations. Every technique stays available, and none is ever chosen for you.
- Compatibility checks compare each technique with your project and population, and say why it fits, what it assumes, and what to justify. They never rank or score techniques.
- The builder writes only the sampling plan section of your project draft, which later tools, such as the sample size calculator and questionnaire builder, will read.
What this tool can't do
- It never chooses a sampling technique for you. Answers narrow the explanations; the choice and its reason are yours.
- It doesn't calculate sample size. That needs a sample size calculation for your analysis, which a later tool will provide.
- Compatibility checks compare your project's wording and choices with typical practice. They can't judge whether you can actually reach your population, or whether your approach is ethical.
- Some terms are used differently by different authors: judgmental and purposive sampling, for example, are often treated as the same technique.
- Nothing is saved unless you start a project in your research workspace, which keeps it in this browser only. Otherwise your plan is lost when you leave the page.
Awaiting review
- Academic review: the definitions, strengths, limitations and assumptions of each technique.
- Sampling methodology review: the typical designs, research onion choices and decision-assistant traits for each technique, and the plan checks.
- Examples: each technique's example, typical sample sizes, time and resource descriptions.
- References: each technique needs references from the methodology literature. None have been added yet.
Privacy
Everything stays in your browser. Nothing is sent anywhere or saved, and it is cleared when you leave the page. If you start a project in your research workspace, your work is also saved in this browser, and only this browser, until you delete the project.