In educational research, sampling design adheres to the same foundational logic as general research methodology, but it is contextualised around educational populations such as students, teachers, school leaders, or institutions. It is not a procedural afterthought; rather, it is a methodological decision that must be explicitly aligned with the research problem, research questions, and overall research design.
Sampling design serves several key functions:
- It determines who or what constitutes the source of data (e.g., learners, classrooms, schools).
- It ensures the appropriateness and credibility of the findings.
- It enables the researcher to operationalise the intent of the study whether that intent is to generalise, explain, explore, or generate theory.
Crucially, sampling design must be coherent with the research design. Different research designs imply different logics of sampling:
- In quantitative designs (e.g., experimental, survey), sampling strategies such as probability sampling (e.g., simple random, stratified) are typically employed to enhance representativeness and generalisability.
- In qualitative designs, sampling is usually purposeful and criterion-based, focusing on depth, richness, and relevance of data rather than representativeness.
For example, if the research aim is to develop a theory that explains an educational phenomenon, a Grounded Theory design is appropriate. In this case, theoretical sampling is used because participants are selected iteratively based on their potential to contribute to the emerging theory. Sampling decisions are made continuously throughout the study, guided by the evolving analysis rather than predetermined at the outset.
In short, an effective sampling design in education is not chosen in isolation as it is logically derived from the research purpose and tightly integrated with the research design, ensuring methodological congruence and the production of meaningful, defensible findings.
The process typically involves defining three levels:
- Population: The broad group of interest sharing a characteristic (e.g., all primary and secondary teachers in a country, or all undergraduate students).
- Target Population: The specific subset you can reasonably identify and study (e.g., primary teachers with a specific certification, or undergraduates at three specific universities).
- Sample: The actual individuals from that target population who are selected to participate in your study.
Educational research often relies heavily on non-probability sampling because getting a complete, randomized list of all students or teachers (a sampling frame) is rarely feasible. For example, researchers might use convenience sampling by distributing a questionnaire in a specific class or teacher’s social media group via WhatsApp or Telegram group. Alternatively, they might use purposive extreme case sampling to study specific subgroups, like selecting only Dean’s List students to understand high-achievement study habits.
Sampling design is the overarching framework of your sampling process. While a “sampling scheme” is the specific technique used to select units (like the river sampling – which is one types of convenience sampling), a sampling design includes the entire structure: the number and types of schemes, the sample size, and the relationship between participants.
An effective sampling design involves three main pillars:
1. The Sampling Scheme (The “How”)
This is the method used to select your sample from the population. It generally falls into two categories:
- Probability Sampling: Every member has a known and equal chance of being selected (e.g., simple random or stratified sampling). This is ideal for statistical generalization.
- Non-Probability Sampling: Selection is based on accessibility or specific criteria (e.g., convenience, river, or purposeful sampling). This is used when a full list of the population is unavailable.
2. Sample Size (The “How Many”)
This determines the statistical power and the depth of your data.
Some Examples of Non-Randomised Sampling (Not Purposive Sampling)
1. Convenience Sampling
The researcher selects participants who are the easiest to reach or most available. It is often used for pilot testing or when resources are limited.
- Example 1 for convenience sampling: A university lecturer surveying students in their own classroom to get quick feedback on a new teaching tool. It is appropriate to use convenience sampling, but it is often better to use purposive sampling.
- Convenience Sampling: You choose participants because they are easy to reach (e.g., “I am teaching this class, so I will study this class”).
- Purposive Sampling: You choose participants because they fit the purpose of the study (e.g., “I am studying my class on how AI helps struggling writers but I will specifically select the 5 students in my class who have the lowest writing scores“).
- Example 2 for convenience sampling: A company representative standing in a shopping mall and asking passersby for their opinion on a new product.
2. Quota (Ratio) Sampling
While “ratio” is often used in stratified sampling (one type of probability sampling) in random context, in a non-random context, it usually refers to Quota Sampling. For quota sampling, the researcher ensures the sample reflects specific proportions (ratios) of certain traits in the population (e.g., 50% male, 50% female).
Example: A researcher wants to study students satisfaction of using university library. If they know the university has the ratio of 60% female and 40% male users/students, they recruit exactly 60 female students and 40 male students from the library lobby or library user log until those “quotas” are filled.
3. Snowball Sampling
Used when the target population is “hidden” or difficult to locate. Existing participants recruit or refer others from their social circle who meet the criteria.
Example: A study on the experiences of university students using neuroenhancer drugs. The researcher finds one participant, who then introduces them to others in their “community”.
Some Examples of Non-Randomised Sampling (Purposive Sampling Techniques)
1. Theoretical Sampling
It is a qualitative sampling strategy where you select new participants or data sources based on the interpretative theory that is emerging from your ongoing data analysis. It is a specific type of purposive (or purposeful) sampling. It is the principal sampling method used in the grounded theory approach. Instead of aiming to simply increase the overall sample size, the goal is to collect specific data that helps develop emergent themes, assess their relevance, and refine your concepts.
Example: A researcher studying “burnout” starts by interviewing medical students until reaching theoretical saturation (the point where concepts are dense, relationships are stable, and new data no longer adds explanatory power). After discovering that “lack of support” is a key theme, they specifically seek out medical students who work in high-support environments during their internship to see if the theory holds.
2. Deviant (Extreme) Case Sampling
This method focuses on “outliers” or cases that are unusual, highly successful, or notable failures. It is a specific type of purposive (or purposeful) sampling. Studying these extremes can provide unique insights that “average” cases cannot.
Example: A study on “academic success” that specifically interviews students with the highest possible GPAs and students who have dropped out, rather than those with average grades.
3. Intensity sampling
It is a purposive, non-random sampling strategy used to select cases that manifest the phenomenon of interest intensely, but not extremely. In qualitative research, while “Deviant Case” sampling looks at the outliers (the absolute best or worst), Intensity Sampling focuses on “rich” cases that are excellent examples of the situation of being so unusual that they no longer represent the general population.
Example: A study on the perceived impact of case-based learning on student engagement, researchers would not interview a student who hates school or a student who is a genius. Instead, they would select students who are consistently engaged and vocal in class. They provide “intense” data about how the method works because they are actively experiencing it, but they are still “normal” students.
4. Criterion Sampling
It is a specific type of purposive (or purposeful) sampling. It involves actively selecting participants who meet a predetermined, important criterion because that characteristic makes them “information-rich” regarding your research topic
Example: Selecting only individuals who have used a specific software for more than 10 hours a week for a user experience study.
5. Typical case sampling
It involves selecting participants who represent what is considered “average” or “normal” to illustrate a standard experience. It is a specific type of purposive (or purposeful) sampling.
Example: If a researcher wanted to study how a new curriculum benefits the average student, they would purposefully select only students with average grades.
6. Maximum Variation Sampling
Maximum variation sampling involves intentionally selecting participants or cases that are as different from one another as possible along a specific dimension. So, it is a specific type of purposive (or purposeful) sampling. While it might seem counterintuitive to look for such extreme differences, the primary goal is actually to identify core, shared patterns that remain consistent despite that high level of diversity. By deliberately expanding the range of variation, researchers can capture the true breadth of a phenomenon and confidently identify common themes that cut across different environments.
Example: A study exploring how teachers adapt to a new digital grading system. Instead of selecting a random group of teachers, the researcher purposefully selects a brand new first-year teacher, a mid-career teacher, and a veteran teacher with 30 years of experience. The goal is to capture as much diversity as possible along the dimension of “teaching experience.” If the researcher finds that all three of these vastly different teachers struggle with the exact same software issue, that finding is very strong because it represents a shared pattern that cuts across their differences.
7. Expert Sampling
Expert sampling is a type of purposive sampling where a researcher specifically select participants who possess a high degree of specialized knowledge or expertise in a study area. It is commonly used in the early stages of research to help shape the research questions and the study’s design.
Example: To study problematic mobile social media use among students, a researcher would not use expert sampling to select the undergraduate participants. However, a researcher might use it to select a small group of psychology professors or media researchers to review and validate the survey questionnaire before distributing it for data collection.
