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Sampling Methods – Types, Techniques and Examples

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Sampling is a critical process in research, allowing researchers to draw conclusions about a larger population by examining a smaller, manageable subset. Sampling methods are essential for producing reliable, representative data without needing to survey an entire population. This guide covers various types of sampling methods, key techniques, and practical examples to help you select the most suitable method for your research.

Sampling is the process of selecting a subset of individuals or items from a larger population to make inferences about that population. Researchers use sampling to collect data more efficiently and to generalize findings to the entire group without surveying everyone.

Key Objectives of Sampling :

• Reduce Costs and Time : Sampling allows for efficient data collection by focusing on a representative subset.
• Improve Accuracy : Smaller, well-designed samples can lead to more accurate, focused data collection.
• Ensure Representativeness : By carefully selecting a sample, researchers can ensure that the findings are relevant to the larger population.

Types of Sampling Methods

Sampling methods can be broadly classified into two categories: probability sampling and non-probability sampling .

1. Probability Sampling

In probability sampling, every individual or item in the population has a known, non-zero chance of being selected. This type of sampling is often used when researchers aim for unbiased, generalizable results.

Examples of Probability Sampling :

• Simple random sampling
• Stratified sampling
• Systematic sampling
• Cluster sampling

2. Non-Probability Sampling

In non-probability sampling, individuals are selected based on specific characteristics or convenience rather than random selection. This method is suitable for exploratory research where generalizability is less critical.

Examples of Non-Probability Sampling :

• Convenience sampling
• Quota sampling
• Snowball sampling
• Purposive sampling

Techniques and Examples for Each Sampling Method

Probability sampling techniques.

• Technique : Each individual in the population has an equal chance of being selected. Researchers use random number generators or random selection tools to choose participants.
• Example : A school administrator randomly selects 50 students from a list of all students to survey about cafeteria satisfaction.
• Technique : The population is divided into subgroups (strata) based on a characteristic (e.g., age, gender), and random samples are taken from each subgroup.
• Example : In a study on employee satisfaction, researchers divide employees into departments (e.g., sales, HR, finance) and randomly select employees from each department.
• Technique : A starting point is randomly selected, and then every kth individual is chosen from a list. This method is often used when there’s a fixed pattern or order in the population list.
• Example : A researcher wants to survey a population of 1,000 people and decides to select every 10th person on a sorted list after a random start.
• Technique : The population is divided into clusters (groups) that are randomly selected. All individuals within selected clusters are then included in the sample.
• Example : In a national health study, a researcher randomly selects specific cities (clusters) and surveys all residents within those cities.

Non-Probability Sampling Techniques

• Technique : Participants are selected based on availability or ease of access, making it a fast and easy sampling method.
• Example : A psychology student surveys classmates because they are easily accessible and available for quick data collection.
• Technique : The population is divided into categories (e.g., age, gender), and a specified number of participants from each category is chosen non-randomly.
• Example : A researcher studying consumer preferences might set a quota to survey 50 men and 50 women in a shopping mall.
• Technique : Participants recruit other participants, making it useful for studying hard-to-reach populations.
• Example : In a study on experiences of ex-convicts, initial participants refer other ex-convicts they know, expanding the sample.
• Technique : Participants are selected based on specific criteria or characteristics relevant to the study’s purpose.
• Example : In a study on the effects of leadership training, a researcher selects participants who hold managerial positions to gain insights specific to leaders.

When to Use Each Sampling Method

• Simple Random Sampling : Use when you need a fully representative sample, especially if the population is homogeneous and a sampling frame is available.
• Stratified Sampling : Best when studying specific subgroups within a population, as it ensures representation across key characteristics.
• Systematic Sampling : Suitable when you have a large population list and need a simple yet systematic approach, especially if the list has no inherent order.
• Cluster Sampling : Useful for large, geographically dispersed populations; ideal when it’s impractical to survey individuals directly.
• Convenience Sampling : Ideal for exploratory studies, pilot tests, or when time and resources are limited.
• Quota Sampling : Use when studying demographic or categorical diversity, especially when you need specific representation within the sample.
• Snowball Sampling : Ideal for reaching hidden, hard-to-reach, or marginalized populations.
• Purposive Sampling : Best when studying a specific, well-defined population or a unique group that directly relates to the research question.

Examples of Sampling in Research Studies

• Objective : Investigate student study habits across grade levels.
• Sampling Method : Stratified sampling, where students are divided into grades (strata) and randomly sampled from each grade.
• Objective : Examine patient satisfaction in a hospital network.
• Sampling Method : Cluster sampling, where hospitals (clusters) are selected, and all patients within selected hospitals are surveyed.
• Objective : Understand shopping preferences among young adults.
• Sampling Method : Convenience sampling, where young adults at a popular mall are surveyed.
• Objective : Study the experiences of refugees in a new country.
• Sampling Method : Snowball sampling, where initial participants (refugees) refer others in their community.

Advantages and Disadvantages of Each Method

Tips for choosing the right sampling method.

• Define Your Research Goals : Clarify whether you need a representative sample or a specific target group to meet the objectives.
• Consider Resources : Time, budget, and accessibility influence the feasibility of sampling methods.
• Evaluate Population Characteristics : Large, diverse populations may require stratified or cluster sampling, while homogeneous populations might benefit from simple random sampling.
• Assess Generalizability : If generalizing results to a larger population is important, prioritize probability sampling methods.
• Address Ethical Concerns : Ensure ethical considerations for sensitive populations, especially when using snowball or purposive sampling.

Sampling is a cornerstone of research design, allowing researchers to make informed conclusions about populations through carefully selected samples. Whether using probability or non-probability sampling, understanding each method’s strengths and limitations can help researchers choose the best approach for their study. With well-chosen sampling methods, researchers can collect reliable data, make meaningful inferences, and contribute valuable insights to their fields.

• Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Sage Publications.
• Babbie, E. (2020). The Practice of Social Research . Cengage Learning.
• Fowler, F. J. (2014). Survey Research Methods . Sage Publications.
• Lohr, S. (2021). Sampling: Design and Analysis . Chapman and Hall/CRC.
• Patton, M. Q. (2015). Qualitative Research & Evaluation Methods . Sage Publications.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Saul McLeod, PhD

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BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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On This Page:

Sampling methods in psychology refer to strategies used to select a subset of individuals (a sample) from a larger population, to study and draw inferences about the entire population. Common methods include random sampling, stratified sampling, cluster sampling, and convenience sampling. Proper sampling ensures representative, generalizable, and valid research results.

• Sampling : the process of selecting a representative group from the population under study.
• Target population : the total group of individuals from which the sample might be drawn.
• Sample: a subset of individuals selected from a larger population for study or investigation. Those included in the sample are termed “participants.”
• Generalizability : the ability to apply research findings from a sample to the broader target population, contingent on the sample being representative of that population.

For instance, if the advert for volunteers is published in the New York Times, this limits how much the study’s findings can be generalized to the whole population, because NYT readers may not represent the entire population in certain respects (e.g., politically, socio-economically).

The Purpose of Sampling

We are interested in learning about large groups of people with something in common in psychological research. We call the group interested in studying our “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.

One of the problems that can occur when selecting a sample from a target population is sampling bias. Sampling bias refers to situations where the sample does not reflect the characteristics of the target population.

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 the national lottery. If the “population” is everyone who bought a lottery ticket, then everyone has an equal chance of winning the lottery (assuming they all have one ticket each).

Random samples require naming or numbering the target population and then using some raffle method to choose those to make up the sample. Random samples are the best method of selecting your sample from the population of interest.

• The advantages are that your sample should represent the target population and eliminate sampling bias.
• The disadvantage is that it is very difficult to achieve (i.e., time, effort, and money).

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.

For example, students studying English Literature may spend more money on books than engineering students, so if we use a large percentage of English students or engineering students, our results will not be accurate.

We have to determine the relative percentage of each group at a university, e.g., Engineering 10%, Social Sciences 15%, English 20%, Sciences 25%, Languages 10%, Law 5%, and Medicine 15%. The sample must then contain all these groups in the same proportion as the target population (university students).

• The disadvantage of stratified sampling is that gathering such a sample would be extremely time-consuming and difficult to do. This method is rarely used in Psychology.
• However, the advantage is that the sample should be highly representative of the target population, and therefore we can generalize from the results obtained.

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.

• This is a quick and easy way of choosing participants (advantage)
• It may not provide a representative sample and could be biased (disadvantage).

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, you list all the population members and then decide upon a sample you would like. By dividing the number of people in the population by the number of people you want in your sample, you get a number we will call 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.

• The advantage of this method is that it should provide a representative sample.

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 refers to the consistency and reproducibility of research findings across different occasions, researchers, or instruments. A small sample size may lead to inconsistent results due to increased susceptibility to random error or the influence of outliers. In contrast, a larger sample minimizes these errors, promoting more reliable results.

Validity pertains to the accuracy and truthfulness of research findings. For a study to be valid, it should accurately measure what it intends to do. A small, unrepresentative sample can compromise external validity, meaning the results don’t generalize well to the larger population. A larger sample captures more variability, ensuring that specific subgroups or anomalies don’t overly influence results.

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 : While increasing the sample size generally leads to improved accuracy and precision, there’s a point where adding more participants yields only marginal benefits. For instance, going from 50 to 500 participants might significantly boost a study’s robustness, but jumping from 10,000 to 10,500 might not offer a comparable advantage, especially considering the added costs.