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Respondents: the definition, meaning and the recruitment

Respondents: this article explains the concept of respondents in a practical way. The article starts with the definition and meaning of the word respondents, followed by a summary of the types of research in which they play an important role and a step-by-step plan for selecting the right ones. Enjoy reading!

What are respondents?

Writing a thesis means doing research. Research involves collecting data and insights to answer research questions or hypotheses . A common method in research is to collect responses from individuals who play an important role in the research. These individuals are known as “respondents” .

Definition of respondents

In the context of research, a respondent refers to the individual who participates in a research by completing surveys , questionnaires, interviews , or other data collection tools.

They are an essential part of the research process , as their input and perspectives help generate meaningful conclusions and enrich the overall findings of the study.

When do I need respondents for my research?

Students conducting a thesis or other academic research projects may need respondents in a variety of scenarios, such as:

Surveys and questionnaires

When researching the opinions, preferences, or behaviors of a specific group of people, students can design surveys or questionnaires to collect data directly from respondents.

Case studies

For in-depth analysis of an individual, group, organization or community, students can gather insights from respondents through interviews or focus groups.

Experimental studies

In experimental research, they are essential to provide data that allows students to evaluate the effects of certain variables or interventions.

Longitudinal studies

Students conducting surveys over a longer period of time can engage them to collect data at multiple points in time and observe changes or trends.

Qualitative research

In qualitative studies, students often involve them to explore their experiences, beliefs, and perceptions in depth.

Remember that the selection of suitable participants is very important for the validity and relevance of the research.

Ethical considerations, confidentiality and obtaining informed consent from a respondent should always be a priority to ensure the integrity of the research process.

How do I select the right respondents?

When conducting academic research, selecting the right participants is very important.

The choice of participants has a profound impact on the quality and validity of the findings obtained.

Ensuring that the selected individuals match the characteristics of ideal respondents contributes to the overall success of the research project.

Read more below about the characteristics of a good respondent.

Willingness

Ideal participants (respondents) show enthusiasm to participate in the survey, which improves data collection and response rates.

Relevant knowledge and experience

Participants with knowledge and experience relevant to the research topic provide valuable insights and credible information.

Representativeness

Ideal participants represent the target population, allowing for broader generalization of the research results.

Honesty and accuracy

Participants who provide honest and accurate answers contribute to the reliability of the data collected.

Responsiveness

Ideal participants actively engage in the research process and respond to surveys or interview requests in a timely manner.

Ensuring a diverse group of participants allows for a comprehensive understanding of different perspectives and contexts.

Reliability

Respondents who are reliable and committed to participation throughout the research process contribute to the consistency of the study.

Selecting with these characteristics not only strengthens the research results, but also promotes an enriching and comprehensive exploration of the chosen research topic.

Respondents recruitment: how do I select the right respondents?

Finding and recruiting suitable respondents for research can be a challenging task, especially for students undertaking academic studies. Here are some practical methods and useful tips to facilitate successful respondent recruitment:

Use social media

Make use of social media, forums and academic networks to reach potential respondents. Online platforms offer a wide range of individuals from diverse backgrounds who may want to participate in your research.

Clearly define the purpose of your research and the specific characteristics you are looking for in respondents. A targeted approach will attract people who actually match the requirements of your study.

Take advantage of networking

Leverage personal and professional connections, such as friends, family, classmates, or colleagues, to identify potential participants. These connections can lead you to individuals who are more willing to participate.

Collaborate

Collaborate with educational institutions, NGOs or civil society organizations related to your research topic. They can help you access their members or stakeholders as potential participants.

Offer respondents a reward for participating

Incentives, such as gift cards, vouchers or tokens of appreciation, can motivate people to participate. Make sure the incentives are relevant and attractive to your target participants.

Use clear communication

Use concise and engaging language in your recruitment messages when approaching potential particpants. Clearly explain the research objectives and state the benefits of their participation.

Respect the effort of respondents

Recognize the importance of their time and be transparent about the expected time commitment for their engagement.

Ensure anonymity of respondents

Assure them that their personal information will be kept strictly confidential and that their answers will remain anonymous.

Use a strategic approach

If you’re having trouble recruiting, consider sending follow-up messages or reminders to those who initially expressed interest but haven’t responded yet.

Respondents Recruitment challenges

Respondents play a vital role in academic research, but there are several challenges researchers may face. Understanding these common challenges is essential for successful data collection and meaningful research outcomes. Some of the obstacles commonly encountered when dealing with respondents are explained below:

Limited Availability of respondents

They may have busy schedules or other commitments that make it difficult to find time to participate in research. This can lead to problems in obtaining a sufficient sample size within the desired time period.

Some potential particpants may choose not to participate in the survey, which may lead to non-response bias. This bias can affect the representativeness of the collected data and potentially distort the study results.

Some individuals may be reluctant or disinterested in participating in research because of privacy concerns, skepticism about the purpose of the research, or a lack of interest in the topic.

Challenges in recruitment process

Finding and approaching suitable participants can be a challenge, especially for niche or specific research topics. It may take creative approaches and persistence to identify and recruit the right participants.

Survey fatigue of respondents

Now it’s your turn

What do you think? Do you recognize the explanation about the subject of respondents? Have you ever participated in a survey? Which characteristics of a respondent do you consider most important? Have you ever written a thesis for which you needed a respondent? What recruitment methods did you use? What tips can you share with (future) thesis students to help them find good particpants? Do you have other tips or comments?

Share your experience and knowledge in the comments box below.

More information

• Barton, J., Bain, C., Hennekens, C. H., Rosner, B., Belanger, C., Roth, A., & Speizer, F. E. (1980). Characteristics of respondents and non-respondents to a mailed questionnaire . American Journal of Public Health, 70(8), 823-825.
• Kaiser, K. (2009). Protecting respondent confidentiality in qualitative research . Qualitative health research, 19(11), 1632-1641.
• Scott, J. (2008). Children as respondents: The challenge for quantitative methods . In Research with children (pp. 103-124). Routledge .
• Montabon, F., Daugherty, P. J., & Chen, H. (2018). Setting standards for single respondent survey design . Journal of Supply Chain Management , 54(1), 35-41.

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Ben Janse is a young professional working at ToolsHero as Content Manager. He is also an International Business student at Rotterdam Business School where he focusses on analyzing and developing management models. Thanks to his theoretical and practical knowledge, he knows how to distinguish main- and side issues and to make the essence of each article clearly visible.

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Harvard University Program on Survey Research

• How to Frame and Explain the Survey Data Used in a Thesis

Surveys are a special research tool with strengths, weaknesses, and a language all of their own. There are many different steps to designing and conducting a survey, and survey researchers have specific ways of describing what they do.

This handout, based on an annual workshop offered by the Program on Survey Research at Harvard, is geared toward undergraduate honors thesis writers using survey data.

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A Comprehensive Guide on the Respondents of the Study

Introduction

Welcome to The Knowledge Nest, your trusted source for in-depth information on various topics in the field of Community and Society. In this comprehensive guide, we will delve into the fascinating world of respondents in research and sampling, providing you with valuable insights and detailed explanations.

Understanding Research and Sampling

Research plays a vital role in gaining knowledge and understanding about different aspects of our society. To carry out effective research, it is essential to gather data from specific individuals or groups known as respondents. These respondents represent a particular population that the research aims to study.

To ensure accurate and reliable results, researchers employ sampling techniques to select the appropriate respondents. Sampling involves carefully choosing a subset of the population that can represent the entire population of interest. The selection process is crucial as it impacts the generalizability and validity of the research findings.

The Role of Respondents in Research

The role of respondents in research cannot be underestimated. They are the key participants who provide critical information that directly contributes to the research objectives. The data collected from the respondents help researchers to analyze and draw meaningful conclusions, making their participation invaluable.

Researchers often employ various methods to gather data from respondents, such as surveys, interviews, observations, or experiments. Each method has its strengths and limitations, depending on the research goals and the nature of the population being studied.

Types of Respondents

1. general population.

The general population refers to the overall group of individuals that the research aims to study and understand. It consists of people from different backgrounds, cultures, age groups, and socioeconomic statuses. Gathering data from the general population allows researchers to gain a broad perspective on the topic of interest.

2. Targeted Groups

In many studies, researchers specifically focus on particular groups within the general population. These targeted groups could include specific demographics, such as age, gender, occupation, or geographic location. By narrowing down the population, researchers can gain more precise insights into the research topic, allowing for focused analysis and conclusions.

3. Experts and Professionals

Respondents in the form of experts and professionals play a crucial role in research related to Community and Society. These individuals possess specialized knowledge and experience in their respective fields. Their insights and perspectives are often sought after to further enhance the understanding of specific subject areas.

4. Organizations and Institutions

While individuals make up the majority of research respondents, organizations and institutions can also be valuable sources of information. Research conducted by or in collaboration with these entities can provide a comprehensive understanding of societal systems, policies, and practices.

Ensuring Data Quality

When it comes to research and sampling, ensuring data quality is of utmost importance. Researchers employ various strategies to ensure the accuracy and reliability of the data collected from respondents. Some common techniques include:

• Random sampling: Selecting respondents randomly to avoid bias.
• Pilot testing: Conducting a smaller-scale test to identify any potential issues or limitations in data collection methods.
• Data validation: Cross-checking responses with other sources or using validation techniques to confirm the accuracy of the gathered data.
• Data anonymization: Protecting the privacy and confidentiality of respondents by anonymizing their identities and ensuring data security.

By now, you have gained a comprehensive understanding of the role and importance of respondents in research and sampling. They hold the key to unlocking valuable insights into the complex fabric of our society. The Knowledge Nest is committed to providing you with accurate and detailed information on various subjects, always aiming to empower and inform.

Stay tuned for more in-depth guides and resources, as we continue to explore the vast field of Community and Society. Remember, knowledge is power, and The Knowledge Nest is your gateway to acquiring knowledge and enhancing your understanding of the world around us.

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Sampling: how to select participants in my research study? *

Jeovany martínez-mesa.

1 Faculdade Meridional (IMED) - Passo Fundo (RS), Brazil.

David Alejandro González-Chica

2 University of Adelaide - Adelaide, Australia.

Rodrigo Pereira Duquia

3 Universidade Federal de Ciências da Saúde de Porto Alegre (UFCSPA) - Porto Alegre (RS), Brazil.

Renan Rangel Bonamigo

João luiz bastos.

4 Universidade Federal de Santa Catarina (UFSC) - Florianópolis (RS), Brazil.

In this paper, the basic elements related to the selection of participants for a health research are discussed. Sample representativeness, sample frame, types of sampling, as well as the impact that non-respondents may have on results of a study are described. The whole discussion is supported by practical examples to facilitate the reader's understanding.

To introduce readers to issues related to sampling.

INTRODUCTION

The essential topics related to the selection of participants for a health research are: 1) whether to work with samples or include the whole reference population in the study (census); 2) the sample basis; 3) the sampling process and 4) the potential effects nonrespondents might have on study results. We will refer to each of these aspects with theoretical and practical examples for better understanding in the sections that follow.

TO SAMPLE OR NOT TO SAMPLE

In a previous paper, we discussed the necessary parameters on which to estimate the sample size. 1 We define sample as a finite part or subset of participants drawn from the target population. In turn, the target population corresponds to the entire set of subjects whose characteristics are of interest to the research team. Based on results obtained from a sample, researchers may draw their conclusions about the target population with a certain level of confidence, following a process called statistical inference. When the sample contains fewer individuals than the minimum necessary, but the representativeness is preserved, statistical inference may be compromised in terms of precision (prevalence studies) and/or statistical power to detect the associations of interest. 1 On the other hand, samples without representativeness may not be a reliable source to draw conclusions about the reference population (i.e., statistical inference is not deemed possible), even if the sample size reaches the required number of participants. Lack of representativeness can occur as a result of flawed selection procedures (sampling bias) or when the probability of refusal/non-participation in the study is related to the object of research (nonresponse bias). 1 , 2

Although most studies are performed using samples, whether or not they represent any target population, census-based estimates should be preferred whenever possible. 3 , 4 For instance, if all cases of melanoma are available on a national or regional database, and information on the potential risk factors are also available, it would be preferable to conduct a census instead of investigating a sample.

However, there are several theoretical and practical reasons that prevent us from carrying out census-based surveys, including:

• Ethical issues: it is unethical to include a greater number of individuals than that effectively required;
• Budgetary limitations: the high costs of a census survey often limits its use as a strategy to select participants for a study;
• Logistics: censuses often impose great challenges in terms of required staff, equipment, etc. to conduct the study;
• Time restrictions: the amount of time needed to plan and conduct a census-based survey may be excessive; and,
• Unknown target population size: if the study objective is to investigate the presence of premalignant skin lesions in illicit drugs users, lack of information on all existing users makes it impossible to conduct a census-based study.

All these reasons explain why samples are more frequently used. However, researchers must be aware that sample results can be affected by the random error (or sampling error). 3 To exemplify this concept, we will consider a research study aiming to estimate the prevalence of premalignant skin lesions (outcome) among individuals >18 years residing in a specific city (target population). The city has a total population of 4,000 adults, but the investigator decided to collect data on a representative sample of 400 participants, detecting an 8% prevalence of premalignant skin lesions. A week later, the researcher selects another sample of 400 participants from the same target population to confirm the results, but this time observes a 12% prevalence of premalignant skin lesions. Based on these findings, is it possible to assume that the prevalence of lesions increased from the first to the second week? The answer is probably not. Each time we select a new sample, it is very likely to obtain a different result. These fluctuations are attributed to the "random error." They occur because individuals composing different samples are not the same, even though they were selected from the same target population. Therefore, the parameters of interest may vary randomly from one sample to another. Despite this fluctuation, if it were possible to obtain 100 different samples of the same population, approximately 95 of them would provide prevalence estimates very close to the real estimate in the target population - the value that we would observe if we investigated all the 4,000 adults residing in the city. Thus, during the sample size estimation the investigator must specify in advance the highest or maximum acceptable random error value in the study. Most population-based studies use a random error ranging from 2 to 5 percentage points. Nevertheless, the researcher should be aware that the smaller the random error considered in the study, the larger the required sample size. 1

SAMPLE FRAME

The sample frame is the group of individuals that can be selected from the target population given the sampling process used in the study. For example, to identify cases of cutaneous melanoma the researcher may consider to utilize as sample frame the national cancer registry system or the anatomopathological records of skin biopsies. Given that the sample may represent only a portion of the target population, the researcher needs to examine carefully whether the selected sample frame fits the study objectives or hypotheses, and especially if there are strategies to overcome the sample frame limitations (see Chart 1 for examples and possible limitations).

Examples of sample frames and potential limitations as regards representativeness

Sampling can be defined as the process through which individuals or sampling units are selected from the sample frame. The sampling strategy needs to be specified in advance, given that the sampling method may affect the sample size estimation. 1 , 5 Without a rigorous sampling plan the estimates derived from the study may be biased (selection bias). 3

TYPES OF SAMPLING

In figure 1 , we depict a summary of the main sampling types. There are two major sampling types: probabilistic and nonprobabilistic.

Sampling types used in scientific studies

NONPROBABILISTIC SAMPLING

In the context of nonprobabilistic sampling, the likelihood of selecting some individuals from the target population is null. This type of sampling does not render a representative sample; therefore, the observed results are usually not generalizable to the target population. Still, unrepresentative samples may be useful for some specific research objectives, and may help answer particular research questions, as well as contribute to the generation of new hypotheses. 4 The different types of nonprobabilistic sampling are detailed below.

Convenience sampling : the participants are consecutively selected in order of apperance according to their convenient accessibility (also known as consecutive sampling). The sampling process comes to an end when the total amount of participants (sample saturation) and/or the time limit (time saturation) are reached. Randomized clinical trials are usually based on convenience sampling. After sampling, participants are usually randomly allocated to the intervention or control group (randomization). 3 Although randomization is a probabilistic process to obtain two comparable groups (treatment and control), the samples used in these studies are generally not representative of the target population.

Purposive sampling: this is used when a diverse sample is necessary or the opinion of experts in a particular field is the topic of interest. This technique was used in the study by Roubille et al, in which recommendations for the treatment of comorbidities in patients with rheumatoid arthritis, psoriasis, and psoriatic arthritis were made based on the opinion of a group of experts. 6

Quota sampling: according to this sampling technique, the population is first classified by characteristics such as gender, age, etc. Subsequently, sampling units are selected to complete each quota. For example, in the study by Larkin et al., the combination of vemurafenib and cobimetinib versus placebo was tested in patients with locally-advanced melanoma, stage IIIC or IV, with BRAF mutation. 7 The study recruited 495 patients from 135 health centers located in several countries. In this type of study, each center has a "quota" of patients.

"Snowball" sampling : in this case, the researcher selects an initial group of individuals. Then, these participants indicate other potential members with similar characteristics to take part in the study. This is frequently used in studies investigating special populations, for example, those including illicit drugs users, as was the case of the study by Gonçalves et al, which assessed 27 users of cocaine and crack in combination with marijuana. 8

PROBABILISTIC SAMPLING

In the context of probabilistic sampling, all units of the target population have a nonzero probability to take part in the study. If all participants are equally likely to be selected in the study, equiprobabilistic sampling is being used, and the odds of being selected by the research team may be expressed by the formula: P=1/N, where P equals the probability of taking part in the study and N corresponds to the size of the target population. The main types of probabilistic sampling are described below.

Simple random sampling: in this case, we have a full list of sample units or participants (sample basis), and we randomly select individuals using a table of random numbers. An example is the study by Pimenta et al, in which the authors obtained a listing from the Health Department of all elderly enrolled in the Family Health Strategy and, by simple random sampling, selected a sample of 449 participants. 9

Systematic random sampling: in this case, participants are selected from fixed intervals previously defined from a ranked list of participants. For example, in the study of Kelbore et al, children who were assisted at the Pediatric Dermatology Service were selected to evaluate factors associated with atopic dermatitis, selecting always the second child by consulting order. 10

Stratified sampling: in this type of sampling, the target population is first divided into separate strata. Then, samples are selected within each stratum, either through simple or systematic sampling. The total number of individuals to be selected in each stratum can be fixed or proportional to the size of each stratum. Each individual may be equally likely to be selected to participate in the study. However, the fixed method usually involves the use of sampling weights in the statistical analysis (inverse of the probability of selection or 1/P). An example is the study conducted in South Australia to investigate factors associated with vitamin D deficiency in preschool children. Using the national census as the sample frame, households were randomly selected in each stratum and all children in the age group of interest identified in the selected houses were investigated. 11

Cluster sampling: in this type of probabilistic sampling, groups such as health facilities, schools, etc., are sampled. In the above-mentioned study, the selection of households is an example of cluster sampling. 11

Complex or multi-stage sampling: This probabilistic sampling method combines different strategies in the selection of the sample units. An example is the study of Duquia et al. to assess the prevalence and factors associated with the use of sunscreen in adults. The sampling process included two stages. 12 Using the 2000 Brazilian demographic census as sampling frame, all 404 census tracts from Pelotas (Southern Brazil) were listed in ascending order of family income. A sample of 120 tracts were systematically selected (first sampling stage units). In the second stage, 12 households in each of these census tract (second sampling stage units) were systematically drawn. All adult residents in these households were included in the study (third sampling stage units). All these stages have to be considered in the statistical analysis to provide correct estimates.

NONRESPONDENTS

Frequently, sample sizes are increased by 10% to compensate for potential nonresponses (refusals/losses). 1 Let us imagine that in a study to assess the prevalence of premalignant skin lesions there is a higher percentage of nonrespondents among men (10%) than among women (1%). If the highest percentage of nonresponse occurs because these men are not at home during the scheduled visits, and these participants are more likely to be exposed to the sun, the number of skin lesions will be underestimated. For this reason, it is strongly recommended to collect and describe some basic characteristics of nonrespondents (sex, age, etc.) so they can be compared to the respondents to evaluate whether the results may have been affected by this systematic error.

Often, in study protocols, refusal to participate or sign the informed consent is considered an "exclusion criteria". However, this is not correct, as these individuals are eligible for the study and need to be reported as "nonrespondents".

SAMPLING METHOD ACCORDING TO THE TYPE OF STUDY

In general, clinical trials aim to obtain a homogeneous sample which is not necessarily representative of any target population. Clinical trials often recruit those participants who are most likely to benefit from the intervention. 3 Thus, the more strict criteria for inclusion and exclusion of subjects in clinical trials often make it difficult to locate participants: after verification of the eligibility criteria, just one out of ten possible candidates will enter the study. Therefore, clinical trials usually show limitations to generalize the results to the entire population of patients with the disease, but only to those with similar characteristics to the sample included in the study. These peculiarities in clinical trials justify the necessity of conducting a multicenter and/or global studiesto accelerate the recruitment rate and to reach, in a shorter time, the number of patients required for the study. 13

In turn, in observational studies to build a solid sampling plan is important because of the great heterogeneity usually observed in the target population. Therefore, this heterogeneity has to be also reflected in the sample. A cross-sectional population-based study aiming to assess disease estimates or identify risk factors often uses complex probabilistic sampling, because the sample representativeness is crucial. However, in a case-control study, we face the challenge of selecting two different samples for the same study. One sample is formed by the cases, which are identified based on the diagnosis of the disease of interest. The other consists of controls, which need to be representative of the population that originated the cases. Improper selection of control individuals may introduce selection bias in the results. Thus, the concern with representativeness in this type of study is established based on the relationship between cases and controls (comparability).

In cohort studies, individuals are recruited based on the exposure (exposed and unexposed subjects), and they are followed over time to evaluate the occurrence of the outcome of interest. At baseline, the sample can be selected from a representative sample (population-based cohort studies) or a non-representative sample. However, in the successive follow-ups of the cohort member, study participants must be a representative sample of those included in the baseline. 14 , 15 In this type of study, losses over time may cause follow-up bias.

Researchers need to decide during the planning stage of the study if they will work with the entire target population or a sample. Working with a sample involves different steps, including sample size estimation, identification of the sample frame, and selection of the sampling method to be adopted.

Financial Support: None.

* Study performed at Faculdade Meridional - Escola de Medicina (IMED) - Passo Fundo (RS), Brazil.

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• Sampling Methods | Types, Techniques, & Examples

Sampling Methods | Types, Techniques, & Examples

Published on 3 May 2022 by Shona McCombes . Revised on 10 October 2022.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample. The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. There are two types of sampling methods:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group. It minimises the risk of selection bias .
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis.

Table of contents

Population vs sample, probability sampling methods, non-probability sampling methods, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, and many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample.

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

You are doing research on working conditions at Company X. Your population is all 1,000 employees of the company. Your sampling frame is the company’s HR database, which lists the names and contact details of every employee.

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample , every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters.

In a non-probability sample , individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalisable results.

You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university.

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g., by responding to a public online survey).

Voluntary response samples are always at least somewhat biased, as some people will inherently be more likely to volunteer than others.

You send out the survey to all students at your university and many students decide to complete it. This can certainly give you some insight into the topic, but the people who responded are more likely to be those who have strong opinions about the student support services, so you can’t be sure that their opinions are representative of all students.

3. Purposive sampling

Purposive sampling , also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion.

You want to know more about the opinions and experiences of students with a disability at your university, so you purposely select a number of students with different support needs in order to gather a varied range of data on their experiences with student services.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to ‘snowballs’ as you get in contact with more people.

You are researching experiences of homelessness in your city. Since there is no list of all homeless people in the city, probability sampling isn’t possible. You meet one person who agrees to participate in the research, and she puts you in contact with other homeless people she knows in the area.

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling , and quota sampling .

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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