Sampling Methods In Research: Types, Techniques, & Examples

The Purpose of Sampling

Psychologists study large groups of people who share a characteristic relevant to the research question. This group is called the target population.

In some types of research, the target population might be as broad as all humans. Still, in other types of research, the target population might be a smaller group, such as teenagers, preschool children, or people who misuse drugs.

Studying every person in a target population is more or less impossible. Hence, psychologists select a sample or sub-group of the population that is likely to be representative of the target population we are interested in.

This is important because we want to generalize from the sample to the target population. The more representative the sample, the more confident the researcher can be that the results can be generalized to the target population.

A key risk when selecting a sample is sampling bias. This is a systematic difference between those included in the sample and those excluded, so some groups are over- or under-represented.

Common sources include under-coverage, where the sampling frame omits part of the population, and non-response, where people selected decline or drop out. Both can skew results even when the original selection method was sound.

Many psychology studies have a biased sample because they have used an opportunity sample that comprises university students as their participants (e.g., Asch).

OK, so you’ve thought up this brilliant psychological study and designed it perfectly. But who will you try it out on, and how will you select your participants?

There are various sampling methods. The one chosen will depend on a number of factors (such as time, money, etc.).

Random Sampling

Random sampling is a type of probability sampling where everyone in the entire target population has an equal chance of being selected.

This is similar to a national lottery. If everyone who enters holds one ticket, then everyone has an equal chance of winning.

Random samples require naming or numbering every member of the target population. A raffle method, such as a random number generator, is then used to choose the sample.

Random sampling is regarded as the ideal method because, in principle, it eliminates systematic sampling bias.

• Strengths: It is the least biased method, and the sample should represent the target population.
• Weaknesses: A complete list of the population is needed, which is time-consuming and costly to obtain. Even a perfectly random draw can be reintroduced to bias if people who refuse to take part differ from those who agree.

Stratified Sampling

During stratified sampling, the researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative.

A list is made of each variable (e.g., IQ, gender, etc.) that might have an effect on the research. For example, if we are interested in the money spent on books by undergraduates, then the main subject studied may be an important variable.

Students studying English Literature, for example, may spend more money on books than engineering students. Using a large percentage of either group would therefore skew the results.

The researcher determines the relative percentage of each group at the university, then draws the sample so it contains every group in that same proportion:

• Engineering: 10%
• Social Sciences: 15%
• English: 20%
• Sciences: 25%
• Languages: 10%
• Law: 5%
• Medicine: 15%

• Strengths: The sample should be highly representative of the target population, so results can be generalized with confidence.
• Weaknesses: Gathering such a sample is extremely time-consuming, which is why this method is rarely used in psychology.

Opportunity Sampling

Opportunity sampling is a method in which participants are chosen based on their ease of availability and proximity to the researcher, rather than using random or systematic criteria. It’s a type of convenience sampling.

An opportunity sample is obtained by asking members of the population of interest if they would participate in your research. An example would be selecting a sample of students from those coming out of the library.

• Strengths: It is a quick and easy way of choosing participants.
• Weaknesses: It may not provide a representative sample and could be biased.

Systematic Sampling

Systematic sampling is a method where every nth individual is selected from a list or sequence to form a sample, ensuring even and regular intervals between chosen subjects.

Participants are systematically selected (i.e., orderly/logical) from the target population, like every nth participant on a list of names.

To take a systematic sample, list all the population members and decide on the desired sample size. Divide the population size by the sample size to get the sampling interval, called n.

If you take every nth name, you will get a systematic sample of the correct size. If, for example, you wanted to sample 150 children from a school of 1,500, you would take every 10th name.

• Strengths: It is quicker than drawing individual random numbers and still spreads the sample evenly across the whole list.
• Weaknesses: If the list has a hidden pattern that lines up with the sampling interval, the sample can end up skewed. For example, every 10th house on a street might be a corner house.

Other Sampling Techniques

Beyond random, stratified, opportunity, and systematic sampling, several other techniques are widely used in psychological research.

Cluster Sampling

Cluster sampling divides the population into naturally occurring groups, called clusters, such as schools, hospitals, or geographic regions, and randomly selects whole clusters to study.

Rather than contacting every registered voter in a country, a researcher might randomly select one or two regions. All voters in those regions are then interviewed by telephone.

To study teaching practices across a city’s 200 primary schools, a researcher could randomly select 20 schools as the clusters. Every teacher in those schools is then observed, rather than sampling individual teachers from all 200 schools.

Multistage sampling extends this logic for very large populations. It combines several steps in sequence: randomly selecting regions, then schools, then classes, then individual pupils within each class.

• Strengths: Cluster sampling is far cheaper and more practical for large, spread-out populations. Data collection concentrates in a few locations, and only a list of clusters is needed, not every individual.
• Weaknesses: A selected cluster can differ systematically from the rest of the population. Cluster sampling therefore usually carries more sampling error than a simple random sample, because people within one cluster tend to resemble each other.

Volunteer Sampling

In volunteer, or self-selected, sampling, participants actively choose to take part, usually by responding to an advertisement or notice.

Because people who want to take part differ from those who do not, self-selection limits how far the findings generalize. The experiences of people who would never volunteer are simply missing from the data.

Hazan and Shaver’s (1987) “Love Quiz” study recruited a self-selecting sample of newspaper readers who chose to send in their answers. This is a recognized limitation of that attachment study, since readers who respond to a love questionnaire may not represent people in general.

• Strengths: Volunteer sampling is easy to organise and ethically straightforward, since participants have chosen to take part. It is useful for reaching people willing to discuss a specific topic.
• Weaknesses: Rosenthal and Rosnow (1975) found volunteers tend to be better educated, more sociable, and more approval-seeking than non-volunteers. They may also hold more extreme opinions or try to “look good,” which biases the results.

Quota Sampling

Quota sampling is the non-probability counterpart of stratified sampling. The population is divided into subgroups, and a fixed quota of participants is set for each.

Unlike stratified sampling, participants within each subgroup are selected non-randomly, usually by opportunity, until every quota is filled.

A street surveyor might be told to obtain 25 men and 25 women. Once 25 men have completed the survey, the surveyor stops approaching men and continues with women only, until that quota is also full.

• Strengths: Quota sampling prevents any one group from being over-represented. It is quicker and cheaper than stratified sampling, because no full list or random draw is needed.
• Weaknesses: Selection within each quota is left to the interviewer, so quota sampling can smuggle in interviewer bias and cannot support proper statistical inference.

Purposive Sampling

In purposive, or judgement, sampling, the researcher deliberately approaches individuals expected to offer the most detailed or most relevant information for the study.

It is common in qualitative research, where the aim is rich, information-dense data from people with direct experience of the phenomenon. The goal is depth, not a statistically representative sample.

To study the lived experience of early-onset Parkinson’s disease, a researcher might purposively recruit people diagnosed before age 50. They hold relevant experience that a random cross-section of the public would not.

• Strengths: Purposive sampling yields rich, targeted data efficiently, and it is well matched to qualitative inquiry and to studying specialist or expert groups.
• Weaknesses: The researcher’s own judgement, and potential prejudices, determines who counts as “appropriate,” which can bias the sample.

Snowball Sampling

Snowball sampling starts with a few participants who then recruit others they know, so the sample grows through chains of personal referral.

Goodman (1961) gave the method its formal statistical treatment, and Biernacki and Waldorf (1981) developed it as a rigorous technique for studying concealed populations.

To investigate illegal drug use, for example, a researcher might recruit one or two users. These contacts then vouch for the study to acquaintances, reaching a hidden population that no register lists.

• Strengths: Snowball sampling reaches hidden or hard-to-access populations, such as drug users or members of secretive groups, that other methods cannot. Trust carried along referral chains raises participation.
• Weaknesses: Members recruit others like themselves from within their own social networks, so the sample is almost inevitably biased and unrepresentative of the wider population.

Sample size

The sample size is a critical factor in determining the reliability and validity of a study’s findings. While increasing the sample size can enhance the generalizability of results, it’s also essential to balance practical considerations, such as resource constraints and diminishing returns from ever-larger samples.

Reliability and Validity

• Reliability is the consistency of findings across occasions, researchers, or instruments. A small sample is more vulnerable to random error and outliers, while a larger sample produces more reliable results.

• Validity is the accuracy of research findings. A small, unrepresentative sample compromises external validity, so a larger sample that captures more variability generalizes better to the wider population.

Practical Considerations

• Resource Constraints: Larger samples demand more time, money, and resources. Data collection becomes more extensive, data analysis more complex, and logistics more challenging.

• Diminishing Returns: Beyond a certain point, adding more participants yields only marginal benefit. Going from 50 to 500 participants can transform a study’s robustness, but going from 10,000 to 10,500 adds little for the extra cost.