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What are Sampling Methods? Techniques, Types, and Examples

Every type of research includes samples from which inferences are drawn. The sample could be biological specimens or a subset of a specific group or population selected for analysis. The goal is often to conclude the entire population based on the characteristics observed in the sample. Now, the question comes to mind: how does one collect the samples? Answer: Using sampling methods. Various sampling strategies are available to researchers to define and collect samples that will form the basis of their research study.

In a study focusing on individuals experiencing anxiety, gathering data from the entire population is practically impossible due to the widespread prevalence of anxiety. Consequently, a sample is carefully selected—a subset of individuals meant to represent (or not in some cases accurately) the demographics of those experiencing anxiety. The study’s outcomes hinge significantly on the chosen sample, emphasizing the critical importance of a thoughtful and precise selection process. The conclusions drawn about the broader population rely heavily on the selected sample’s characteristics and diversity.

Table of Contents

What is sampling?

Sampling involves the strategic selection of individuals or a subset from a population, aiming to derive statistical inferences and predict the characteristics of the entire population. It offers a pragmatic and practical approach to examining the features of the whole population, which would otherwise be difficult to achieve because studying the total population is expensive, time-consuming, and often impossible. Market researchers use various sampling methods to collect samples from a large population to acquire relevant insights. The best sampling strategy for research is determined by criteria such as the purpose of the study, available resources (time and money), and research hypothesis.

For example, if a pet food manufacturer wants to investigate the positive impact of a new cat food on feline growth, studying all the cats in the country is impractical. In such cases, employing an appropriate sampling technique from the extensive dataset allows the researcher to focus on a manageable subset. This enables the researcher to study the growth-promoting effects of the new pet food. This article will delve into the standard sampling methods and explore the situations in which each is most appropriately applied.

What are sampling methods or sampling techniques?

Sampling methods or sampling techniques in research are statistical methods for selecting a sample representative of the whole population to study the population’s characteristics. Sampling methods serve as invaluable tools for researchers, enabling the collection of meaningful data and facilitating analysis to identify distinctive features of the people. Different sampling strategies can be used based on the characteristics of the population, the study purpose, and the available resources. Now that we understand why sampling methods are essential in research, we review the various sample methods in the following sections.

Types of sampling methods  

Before we go into the specifics of each sampling method, it’s vital to understand terms like sample, sample frame, and sample space. In probability theory, the sample space comprises all possible outcomes of a random experiment, while the sample frame is the list or source guiding sample selection in statistical research. The  sample  represents the group of individuals participating in the study, forming the basis for the research findings. Selecting the correct sample is critical to ensuring the validity and reliability of any research; the sample should be representative of the population. 

There are two most common sampling methods: 

• Probability sampling: A sampling method in which each unit or element in the population has an equal chance of being selected in the final sample. This is called random sampling, emphasizing the random and non-zero probability nature of selecting samples. Such a sampling technique ensures a more representative and unbiased sample, enabling robust inferences about the entire population. 
• Non-probability sampling:  Another sampling method is non-probability sampling, which involves collecting data conveniently through a non-random selection based on predefined criteria. This offers a straightforward way to gather data, although the resulting sample may or may not accurately represent the entire population. 

  Irrespective of the research method you opt for, it is essential to explicitly state the chosen sampling technique in the methodology section of your research article. Now, we will explore the different characteristics of both sampling methods, along with various subtypes falling under these categories. 

What is probability sampling?  

The probability sampling method is based on the probability theory, which means that the sample selection criteria involve some random selection. The probability sampling method provides an equal opportunity for all elements or units within the entire sample space to be chosen. While it can be labor-intensive and expensive, the advantage lies in its ability to offer a more accurate representation of the population, thereby enhancing confidence in the inferences drawn in the research.   

Types of probability sampling  

Various probability sampling methods exist, such as simple random sampling, systematic sampling, stratified sampling, and clustered sampling. Here, we provide detailed discussions and illustrative examples for each of these sampling methods: 

• Simple random sampling:  In simple random sampling, each individual has an equal probability of being chosen, and each selection is independent of the others. Because the choice is entirely based on chance, this is also known as the method of chance selection. In the simple random sampling method, the sample frame comprises the entire population. 

For example,  A fitness sports brand is launching a new protein drink and aims to select 20 individuals from a 200-person fitness center to try it. Employing a simple random sampling approach, each of the 200 people is assigned a unique identifier. Of these, 20 individuals are then chosen by generating random numbers between 1 and 200, either manually or through a computer program. Matching these numbers with the individuals creates a randomly selected group of 20 people. This method minimizes sampling bias and ensures a representative subset of the entire population under study. 

• Systematic sampling:  The systematic sampling approach involves selecting units or elements at regular intervals from an ordered list of the population. Because the starting point of this sampling method is chosen at random, it is more convenient than essential random sampling. For a better understanding, consider the following example.  

For example, considering the previous model, individuals at the fitness facility are arranged alphabetically. The manufacturer then initiates the process by randomly selecting a starting point from the first ten positions, let’s say 8. Starting from the 8th position, every tenth person on the list is then chosen (e.g., 8, 18, 28, 38, and so forth) until a sample of 20 individuals is obtained.  

• Stratified sampling: Stratified sampling divides the population into subgroups (strata), and random samples are drawn from each stratum in proportion to its size in the population. Stratified sampling provides improved representation because each subgroup that differs in significant ways is included in the final sample. 

For example, Expanding on the previous simple random sampling example, suppose the manufacturer aims for a more comprehensive representation of genders in a sample of 200 people, consisting of 90 males, 80 females, and 30 others. The manufacturer categorizes the population into three gender strata (Male, Female, and Others). Within each group, random sampling is employed to select nine males, eight females, and three individuals from the others category, resulting in a well-rounded and representative sample of 200 individuals. 

• Clustered sampling: In this sampling method, the population is divided into clusters, and then a random sample of clusters is included in the final sample. Clustered sampling, distinct from stratified sampling, involves subgroups (clusters) that exhibit characteristics similar to the whole sample. In the case of small clusters, all members can be included in the final sample, whereas for larger clusters, individuals within each cluster may be sampled using the sampling above methods. This approach is referred to as multistage sampling. This sampling method is well-suited for large and widely distributed populations; however, there is a potential risk of sample error because ensuring that the sampled clusters truly represent the entire population can be challenging. 

For example, Researchers conducting a nationwide health study can select specific geographic clusters, like cities or regions, instead of trying to survey the entire population individually. Within each chosen cluster, they sample individuals, providing a representative subset without the logistical challenges of attempting a nationwide survey. 

Use s of probability sampling  

Probability sampling methods find widespread use across diverse research disciplines because of their ability to yield representative and unbiased samples. The advantages of employing probability sampling include the following: 

• Representativeness  

Probability sampling assures that every element in the population has a non-zero chance of being included in the sample, ensuring representativeness of the entire population and decreasing research bias to minimal to non-existent levels. The researcher can acquire higher-quality data via probability sampling, increasing confidence in the conclusions. 

• Statistical inference  

Statistical methods, like confidence intervals and hypothesis testing, depend on probability sampling to generalize findings from a sample to the broader population. Probability sampling methods ensure unbiased representation, allowing inferences about the population based on the characteristics of the sample. 

• Precision and reliability  

The use of probability sampling improves the precision and reliability of study results. Because the probability of selecting any single element/individual is known, the chance variations that may occur in non-probability sampling methods are reduced, resulting in more dependable and precise estimations. 

• Generalizability  

Probability sampling enables the researcher to generalize study findings to the entire population from which they were derived. The results produced through probability sampling methods are more likely to be applicable to the larger population, laying the foundation for making broad predictions or recommendations. 

• Minimization of Selection Bias  

By ensuring that each member of the population has an equal chance of being selected in the sample, probability sampling lowers the possibility of selection bias. This reduces the impact of systematic errors that may occur in non-probability sampling methods, where data may be skewed toward a specific demographic due to inadequate representation of each segment of the population. 

What is non-probability sampling?  

Non-probability sampling methods involve selecting individuals based on non-random criteria, often relying on the researcher’s judgment or predefined criteria. While it is easier and more economical, it tends to introduce sampling bias, resulting in weaker inferences compared to probability sampling techniques in research. 

Types of Non-probability Sampling   

Non-probability sampling methods are further classified as convenience sampling, consecutive sampling, quota sampling, purposive or judgmental sampling, and snowball sampling. Let’s explore these types of sampling methods in detail. 

• Convenience sampling:  In convenience sampling, individuals are recruited directly from the population based on the accessibility and proximity to the researcher. It is a simple, inexpensive, and practical method of sample selection, yet convenience sampling suffers from both sampling and selection bias due to a lack of appropriate population representation. 

For example, imagine you’re a researcher investigating smartphone usage patterns in your city. The most convenient way to select participants is by approaching people in a shopping mall on a weekday afternoon. However, this convenience sampling method may not be an accurate representation of the city’s overall smartphone usage patterns as the sample is limited to individuals present at the mall during weekdays, excluding those who visit on other days or never visit the mall.

• Consecutive sampling: Participants in consecutive sampling (or sequential sampling) are chosen based on their availability and desire to participate in the study as they become available. This strategy entails sequentially recruiting individuals who fulfill the researcher’s requirements. 

For example, In researching the prevalence of stroke in a hospital, instead of randomly selecting patients from the entire population, the researcher can opt to include all eligible patients admitted over three months. Participants are then consecutively recruited upon admission during that timeframe, forming the study sample. 

• Quota sampling:  The selection of individuals in quota sampling is based on non-random selection criteria in which only participants with certain traits or proportions that are representative of the population are included. Quota sampling involves setting predetermined quotas for specific subgroups based on key demographics or other relevant characteristics. This sampling method employs dividing the population into mutually exclusive subgroups and then selecting sample units until the set quota is reached.  

For example, In a survey on a college campus to assess student interest in a new policy, the researcher should establish quotas aligned with the distribution of student majors, ensuring representation from various academic disciplines. If the campus has 20% biology majors, 30% engineering majors, 20% business majors, and 30% liberal arts majors, participants should be recruited to mirror these proportions. 

• Purposive or judgmental sampling: In purposive sampling, the researcher leverages expertise to select a sample relevant to the study’s specific questions. This sampling method is commonly applied in qualitative research, mainly when aiming to understand a particular phenomenon, and is suitable for smaller population sizes. 

For example, imagine a researcher who wants to study public policy issues for a focus group. The researcher might purposely select participants with expertise in economics, law, and public administration to take advantage of their knowledge and ensure a depth of understanding.  

• Snowball sampling:  This sampling method is used when accessing the population is challenging. It involves collecting the sample through a chain-referral process, where each recruited candidate aids in finding others. These candidates share common traits, representing the targeted population. This method is often used in qualitative research, particularly when studying phenomena related to stigmatized or hidden populations. 

For example, In a study focusing on understanding the experiences and challenges of individuals in hidden or stigmatized communities (e.g., LGBTQ+ individuals in specific cultural contexts), the snowball sampling technique can be employed. The researcher initiates contact with one community member, who then assists in identifying additional candidates until the desired sample size is achieved.

Uses of non-probability sampling  

Non-probability sampling approaches are employed in qualitative or exploratory research where the goal is to investigate underlying population traits rather than generalizability. Non-probability sampling methods are also helpful for the following purposes: 

• Generating a hypothesis  

In the initial stages of exploratory research, non-probability methods such as purposive or convenience allow researchers to quickly gather information and generate hypothesis that helps build a future research plan.  

• Qualitative research  

Qualitative research is usually focused on understanding the depth and complexity of human experiences, behaviors, and perspectives. Non-probability methods like purposive or snowball sampling are commonly used to select participants with specific traits that are relevant to the research question.  

• Convenience and pragmatism  

Non-probability sampling methods are valuable when resource and time are limited or when preliminary data is required to test the pilot study. For example, conducting a survey at a local shopping mall to gather opinions on a consumer product due to the ease of access to potential participants.  

Probability vs Non-probability Sampling Methods  

Frequently asked questions  

• What is multistage sampling ? Multistage sampling is a form of probability sampling approach that involves the progressive selection of samples in stages, going from larger clusters to a small number of participants, making it suited for large-scale research with enormous population lists.  
• What are the methods of probability sampling? Probability sampling methods are simple random sampling, stratified random sampling, systematic sampling, cluster sampling, and multistage sampling.
• How to decide which type of sampling method to use? Choose a sampling method based on the goals, population, and resources. Probability for statistics and non-probability for efficiency or qualitative insights can be considered . Also, consider the population characteristics, size, and alignment with study objectives.
• What are the methods of non-probability sampling? Non-probability sampling methods are convenience sampling, consecutive sampling, purposive sampling, snowball sampling, and quota sampling.
• Why are sampling methods used in research? Sampling methods in research are employed to efficiently gather representative data from a subset of a larger population, enabling valid conclusions and generalizations while minimizing costs and time.  

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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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Sampling Methods: A guide for researchers

Affiliation.

• 1 Arizona School of Dentistry & Oral Health A.T. Still University, Mesa, AZ, USA [email protected].
• PMID: 37553279

Sampling is a critical element of research design. Different methods can be used for sample selection to ensure that members of the study population reflect both the source and target populations, including probability and non-probability sampling. Power and sample size are used to determine the number of subjects needed to answer the research question. Characteristics of individuals included in the sample population should be clearly defined to determine eligibility for study participation and improve power. Sample selection methods differ based on study design. The purpose of this short report is to review common sampling considerations and related errors.

Keywords: research design; sample size; sampling.

Copyright © 2023 The American Dental Hygienists’ Association.

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Systematic Sampling | A Step-by-Step Guide with Examples

Published on October 2, 2020 by Lauren Thomas . Revised on December 18, 2023.

Systematic sampling is a probability sampling method in which researchers select members of the population at a regular interval (or k ) determined in advance.

If the population order is random or random-like (e.g., alphabetical), then this method will give you a representative sample that can be used to draw conclusions about your population of interest.

Table of contents

When to use systematic sampling, step 1: define your population, step 2: decide on your sample size, step 3: calculate sampling interval k, step 4: select the sample and collect data, other interesting articles, frequently asked questions about systematic sampling.

Systematic sampling is a method that imitates many of the randomization benefits of simple random sampling , but is slightly easier to conduct.

You can use systematic sampling with a list of the entire population , like you would in simple random sampling. However, unlike with simple random sampling, you can also use this method when you’re unable to access a list of your population in advance.

Order of the population

When using systematic sampling with a population list, it’s essential to consider the order in which your population is listed to ensure that your sample is valid .

If your population is in ascending or descending order, using systematic sampling should still give you a fairly representative sample, as it will include participants from both the bottom and top ends of the population.

For example, if you are sampling from a list of individuals ordered by age, systematic sampling will result in a population drawn from the entire age spectrum. If you instead used simple random sampling, it is possible (although unlikely) that you would end up with only younger or older individuals.

You should not use systematic sampling if your population is ordered cyclically or periodically, as your resulting sample cannot be guaranteed to be representative.

Systematic sampling without a population list

You can use systematic sampling to imitate the randomization of simple random sampling when you don’t have access to a full list of the population in advance.

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Like other methods of sampling, you must decide upon the population that you are studying.

In systematic sampling, you have two choices for data collection :

• You can select your sample ahead of time from a list and then approach the selected subjects to collect data, or
• You can approach every k th member of your target population to ask them to participate in your study.

Listing the population in advance

Ensure that your list contains the entire population and is not in a periodic or cyclic order. Ideally, it should be in a random or random-like (such as alphabetical) order, which will allow you to imitate the randomization benefits of simple random sampling .

Selecting your sample on the spot

If you cannot access a list in advance, but you are able to physically observe the population, you can also use systematic sampling to select subjects at the moment of data collection.

In this case, ensure that the timing and location of your sampling procedure covers the full population to avoid bias in the results.

Before you choose your interval, you must first decide on your sample size. It’s important to choose a representative number in order to avoid sampling bias . There are several different ways to choose a sample size, but one of the most common involves using a sample size calculator .

Once you have chosen your desired margin of error and confidence level , estimated total size of the population, and the standard deviation of the variables you are attempting to measure, this calculator will provide you with the sample size you should aim for.

When you know your target sample size, you can calculate your interval, k , by dividing your total estimated population size by your sample size. This can be a rough estimate rather than an exact calculation.

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If you already have a list of your population, randomly select a starting point on your list, and from there, select every k th member of the population to include in your sample.

If you don’t have a list, you choose every k th member of the population for your sample at the same time as collecting the data for your study.

As in simple random sampling , you should try to make sure every individual you have chosen for your sample actually participates in your study. If those who decide to participate do so for reasons connected with the variables that you are collecting, this could cause research bias to affect your study.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

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 .

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

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Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research

10 Pages Posted: 31 Jul 2018

Hamed Taherdoost

Hamta Group

Date Written: April 10, 2016

In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. This paper presents the steps to go through to conduct sampling. Furthermore, as there are different types of sampling techniques/methods, researcher needs to understand the differences to select the proper sampling method for the research. In the regards, this paper also presents the different types of sampling techniques and methods.

Keywords: Sampling Method, Sampling Technique, Research Methodology, Probability Sampling, Non-Probability Sampling

Suggested Citation: Suggested Citation

Hamed Taherdoost (Contact Author)

Hamta group ( email ).

Vancouver Canada

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Home » Sampling Methods – Types, Techniques and Examples

Sampling Methods – Types, Techniques and Examples

Table of Contents

Sampling refers to the process of selecting a subset of data from a larger population or dataset in order to analyze or make inferences about the whole population.

In other words, sampling involves taking a representative sample of data from a larger group or dataset in order to gain insights or draw conclusions about the entire group.

Sampling Methods

Sampling methods refer to the techniques used to select a subset of individuals or units from a larger population for the purpose of conducting statistical analysis or research.

Sampling is an essential part of the Research because it allows researchers to draw conclusions about a population without having to collect data from every member of that population, which can be time-consuming, expensive, or even impossible.

Types of Sampling Methods

Sampling can be broadly categorized into two main categories:

Probability Sampling

This type of sampling is based on the principles of random selection, and it involves selecting samples in a way that every member of the population has an equal chance of being included in the sample.. Probability sampling is commonly used in scientific research and statistical analysis, as it provides a representative sample that can be generalized to the larger population.

Type of Probability Sampling :

• Simple Random Sampling: In this method, every member of the population has an equal chance of being selected for the sample. This can be done using a random number generator or by drawing names out of a hat, for example.
• Systematic Sampling: In this method, the population is first divided into a list or sequence, and then every nth member is selected for the sample. For example, if every 10th person is selected from a list of 100 people, the sample would include 10 people.
• Stratified Sampling: In this method, the population is divided into subgroups or strata based on certain characteristics, and then a random sample is taken from each stratum. This is often used to ensure that the sample is representative of the population as a whole.
• Cluster Sampling: In this method, the population is divided into clusters or groups, and then a random sample of clusters is selected. Then, all members of the selected clusters are included in the sample.
• Multi-Stage Sampling : This method combines two or more sampling techniques. For example, a researcher may use stratified sampling to select clusters, and then use simple random sampling to select members within each cluster.

Non-probability Sampling

This type of sampling does not rely on random selection, and it involves selecting samples in a way that does not give every member of the population an equal chance of being included in the sample. Non-probability sampling is often used in qualitative research, where the aim is not to generalize findings to a larger population, but to gain an in-depth understanding of a particular phenomenon or group. Non-probability sampling methods can be quicker and more cost-effective than probability sampling methods, but they may also be subject to bias and may not be representative of the larger population.

Types of Non-probability Sampling :

• Convenience Sampling: In this method, participants are chosen based on their availability or willingness to participate. This method is easy and convenient but may not be representative of the population.
• Purposive Sampling: In this method, participants are selected based on specific criteria, such as their expertise or knowledge on a particular topic. This method is often used in qualitative research, but may not be representative of the population.
• Snowball Sampling: In this method, participants are recruited through referrals from other participants. This method is often used when the population is hard to reach, but may not be representative of the population.
• Quota Sampling: In this method, a predetermined number of participants are selected based on specific criteria, such as age or gender. This method is often used in market research, but may not be representative of the population.
• Volunteer Sampling: In this method, participants volunteer to participate in the study. This method is often used in research where participants are motivated by personal interest or altruism, but may not be representative of the population.

Applications of Sampling Methods

Applications of Sampling Methods from different fields:

• Psychology : Sampling methods are used in psychology research to study various aspects of human behavior and mental processes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and ethnicity. Random sampling may also be used to select participants for experimental studies.
• Sociology : Sampling methods are commonly used in sociological research to study social phenomena and relationships between individuals and groups. For example, researchers may use cluster sampling to select a sample of neighborhoods to study the effects of economic inequality on health outcomes. Stratified sampling may also be used to select a sample of participants that is representative of the population based on factors such as income, education, and occupation.
• Social sciences: Sampling methods are commonly used in social sciences to study human behavior and attitudes. For example, researchers may use stratified sampling to select a sample of participants that is representative of the population based on factors such as age, gender, and income.
• Marketing : Sampling methods are used in marketing research to collect data on consumer preferences, behavior, and attitudes. For example, researchers may use random sampling to select a sample of consumers to participate in a survey about a new product.
• Healthcare : Sampling methods are used in healthcare research to study the prevalence of diseases and risk factors, and to evaluate interventions. For example, researchers may use cluster sampling to select a sample of health clinics to participate in a study of the effectiveness of a new treatment.
• Environmental science: Sampling methods are used in environmental science to collect data on environmental variables such as water quality, air pollution, and soil composition. For example, researchers may use systematic sampling to collect soil samples at regular intervals across a field.
• Education : Sampling methods are used in education research to study student learning and achievement. For example, researchers may use stratified sampling to select a sample of schools that is representative of the population based on factors such as demographics and academic performance.

Examples of Sampling Methods

Probability Sampling Methods Examples:

• Simple random sampling Example : A researcher randomly selects participants from the population using a random number generator or drawing names from a hat.
• Stratified random sampling Example : A researcher divides the population into subgroups (strata) based on a characteristic of interest (e.g. age or income) and then randomly selects participants from each subgroup.
• Systematic sampling Example : A researcher selects participants at regular intervals from a list of the population.

Non-probability Sampling Methods Examples:

• Convenience sampling Example: A researcher selects participants who are conveniently available, such as students in a particular class or visitors to a shopping mall.
• Purposive sampling Example : A researcher selects participants who meet specific criteria, such as individuals who have been diagnosed with a particular medical condition.
• Snowball sampling Example : A researcher selects participants who are referred to them by other participants, such as friends or acquaintances.

How to Conduct Sampling Methods

some general steps to conduct sampling methods:

• Define the population: Identify the population of interest and clearly define its boundaries.
• Choose the sampling method: Select an appropriate sampling method based on the research question, characteristics of the population, and available resources.
• Determine the sample size: Determine the desired sample size based on statistical considerations such as margin of error, confidence level, or power analysis.
• Create a sampling frame: Develop a list of all individuals or elements in the population from which the sample will be drawn. The sampling frame should be comprehensive, accurate, and up-to-date.
• Select the sample: Use the chosen sampling method to select the sample from the sampling frame. The sample should be selected randomly, or if using a non-random method, every effort should be made to minimize bias and ensure that the sample is representative of the population.
• Collect data: Once the sample has been selected, collect data from each member of the sample using appropriate research methods (e.g., surveys, interviews, observations).
• Analyze the data: Analyze the data collected from the sample to draw conclusions about the population of interest.

When to use Sampling Methods

Sampling methods are used in research when it is not feasible or practical to study the entire population of interest. Sampling allows researchers to study a smaller group of individuals, known as a sample, and use the findings from the sample to make inferences about the larger population.

Sampling methods are particularly useful when:

• The population of interest is too large to study in its entirety.
• The cost and time required to study the entire population are prohibitive.
• The population is geographically dispersed or difficult to access.
• The research question requires specialized or hard-to-find individuals.
• The data collected is quantitative and statistical analyses are used to draw conclusions.

Purpose of Sampling Methods

The main purpose of sampling methods in research is to obtain a representative sample of individuals or elements from a larger population of interest, in order to make inferences about the population as a whole. By studying a smaller group of individuals, known as a sample, researchers can gather information about the population that would be difficult or impossible to obtain from studying the entire population.

Sampling methods allow researchers to:

• Study a smaller, more manageable group of individuals, which is typically less time-consuming and less expensive than studying the entire population.
• Reduce the potential for data collection errors and improve the accuracy of the results by minimizing sampling bias.
• Make inferences about the larger population with a certain degree of confidence, using statistical analyses of the data collected from the sample.
• Improve the generalizability and external validity of the findings by ensuring that the sample is representative of the population of interest.

Characteristics of Sampling Methods

Here are some characteristics of sampling methods:

• Randomness : Probability sampling methods are based on random selection, meaning that every member of the population has an equal chance of being selected. This helps to minimize bias and ensure that the sample is representative of the population.
• Representativeness : The goal of sampling is to obtain a sample that is representative of the larger population of interest. This means that the sample should reflect the characteristics of the population in terms of key demographic, behavioral, or other relevant variables.
• Size : The size of the sample should be large enough to provide sufficient statistical power for the research question at hand. The sample size should also be appropriate for the chosen sampling method and the level of precision desired.
• Efficiency : Sampling methods should be efficient in terms of time, cost, and resources required. The method chosen should be feasible given the available resources and time constraints.
• Bias : Sampling methods should aim to minimize bias and ensure that the sample is representative of the population of interest. Bias can be introduced through non-random selection or non-response, and can affect the validity and generalizability of the findings.
• Precision : Sampling methods should be precise in terms of providing estimates of the population parameters of interest. Precision is influenced by sample size, sampling method, and level of variability in the population.
• Validity : The validity of the sampling method is important for ensuring that the results obtained from the sample are accurate and can be generalized to the population of interest. Validity can be affected by sampling method, sample size, and the representativeness of the sample.

Advantages of Sampling Methods

Sampling methods have several advantages, including:

• Cost-Effective : Sampling methods are often much cheaper and less time-consuming than studying an entire population. By studying only a small subset of the population, researchers can gather valuable data without incurring the costs associated with studying the entire population.
• Convenience : Sampling methods are often more convenient than studying an entire population. For example, if a researcher wants to study the eating habits of people in a city, it would be very difficult and time-consuming to study every single person in the city. By using sampling methods, the researcher can obtain data from a smaller subset of people, making the study more feasible.
• Accuracy: When done correctly, sampling methods can be very accurate. By using appropriate sampling techniques, researchers can obtain a sample that is representative of the entire population. This allows them to make accurate generalizations about the population as a whole based on the data collected from the sample.
• Time-Saving: Sampling methods can save a lot of time compared to studying the entire population. By studying a smaller sample, researchers can collect data much more quickly than they could if they studied every single person in the population.
• Less Bias : Sampling methods can reduce bias in a study. If a researcher were to study the entire population, it would be very difficult to eliminate all sources of bias. However, by using appropriate sampling techniques, researchers can reduce bias and obtain a sample that is more representative of the entire population.

Limitations of Sampling Methods

• Sampling Error : Sampling error is the difference between the sample statistic and the population parameter. It is the result of selecting a sample rather than the entire population. The larger the sample, the lower the sampling error. However, no matter how large the sample size, there will always be some degree of sampling error.
• Selection Bias: Selection bias occurs when the sample is not representative of the population. This can happen if the sample is not selected randomly or if some groups are underrepresented in the sample. Selection bias can lead to inaccurate conclusions about the population.
• Non-response Bias : Non-response bias occurs when some members of the sample do not respond to the survey or study. This can result in a biased sample if the non-respondents differ from the respondents in important ways.
• Time and Cost : While sampling can be cost-effective, it can still be expensive and time-consuming to select a sample that is representative of the population. Depending on the sampling method used, it may take a long time to obtain a sample that is large enough and representative enough to be useful.
• Limited Information : Sampling can only provide information about the variables that are measured. It may not provide information about other variables that are relevant to the research question but were not measured.
• Generalization : The extent to which the findings from a sample can be generalized to the population depends on the representativeness of the sample. If the sample is not representative of the population, it may not be possible to generalize the findings to the population as a whole.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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What are sampling methods and how do you choose the best one?

Posted on 18th November 2020 by Mohamed Khalifa

This tutorial will introduce sampling methods and potential sampling errors to avoid when conducting medical research.

Introduction to sampling methods

Examples of different sampling methods, choosing the best sampling method.

It is important to understand why we sample the population; for example, studies are built to investigate the relationships between risk factors and disease. In other words, we want to find out if this is a true association, while still aiming for the minimum risk for errors such as: chance, bias or confounding .

However, it would not be feasible to experiment on the whole population, we would need to take a good sample and aim to reduce the risk of having errors by proper sampling technique.

What is a sampling frame?

A sampling frame is a record of the target population containing all participants of interest. In other words, it is a list from which we can extract a sample.

What makes a good sample?

A good sample should be a representative subset of the population we are interested in studying, therefore, with each participant having equal chance of being randomly selected into the study.

We could choose a sampling method based on whether we want to account for sampling bias; a random sampling method is often preferred over a non-random method for this reason. Random sampling examples include: simple, systematic, stratified, and cluster sampling. Non-random sampling methods are liable to bias, and common examples include: convenience, purposive, snowballing, and quota sampling. For the purposes of this blog we will be focusing on random sampling methods .

Example: We want to conduct an experimental trial in a small population such as: employees in a company, or students in a college. We include everyone in a list and use a random number generator to select the participants

Advantages: Generalisable results possible, random sampling, the sampling frame is the whole population, every participant has an equal probability of being selected

Disadvantages: Less precise than stratified method, less representative than the systematic method

Example: Every nth patient entering the out-patient clinic is selected and included in our sample

Advantages: More feasible than simple or stratified methods, sampling frame is not always required

Disadvantages:  Generalisability may decrease if baseline characteristics repeat across every nth participant

Example: We have a big population (a city) and we want to ensure representativeness of all groups with a pre-determined characteristic such as: age groups, ethnic origin, and gender

Advantages:  Inclusive of strata (subgroups), reliable and generalisable results

Disadvantages: Does not work well with multiple variables

Example: 10 schools have the same number of students across the county. We can randomly select 3 out of 10 schools as our clusters

Advantages: Readily doable with most budgets, does not require a sampling frame

Disadvantages: Results may not be reliable nor generalisable

How can you identify sampling errors?

Non-random selection increases the probability of sampling (selection) bias if the sample does not represent the population we want to study. We could avoid this by random sampling and ensuring representativeness of our sample with regards to sample size.

An inadequate sample size decreases the confidence in our results as we may think there is no significant difference when actually there is. This type two error results from having a small sample size, or from participants dropping out of the sample.

In medical research of disease, if we select people with certain diseases while strictly excluding participants with other co-morbidities, we run the risk of diagnostic purity bias where important sub-groups of the population are not represented.

Furthermore, measurement bias may occur during re-collection of risk factors by participants (recall bias) or assessment of outcome where people who live longer are associated with treatment success, when in fact people who died were not included in the sample or data analysis (survivors bias).

By following the steps below we could choose the best sampling method for our study in an orderly fashion.

Research objectiveness

Firstly, a refined research question and goal would help us define our population of interest. If our calculated sample size is small then it would be easier to get a random sample. If, however, the sample size is large, then we should check if our budget and resources can handle a random sampling method.

Sampling frame availability

Secondly, we need to check for availability of a sampling frame (Simple), if not, could we make a list of our own (Stratified). If neither option is possible, we could still use other random sampling methods, for instance, systematic or cluster sampling.

Study design

Moreover, we could consider the prevalence of the topic (exposure or outcome) in the population, and what would be the suitable study design. In addition, checking if our target population is widely varied in its baseline characteristics. For example, a population with large ethnic subgroups could best be studied using a stratified sampling method.

Random sampling

Finally, the best sampling method is always the one that could best answer our research question while also allowing for others to make use of our results (generalisability of results). When we cannot afford a random sampling method, we can always choose from the non-random sampling methods.

To sum up, we now understand that choosing between random or non-random sampling methods is multifactorial. We might often be tempted to choose a convenience sample from the start, but that would not only decrease precision of our results, and would make us miss out on producing research that is more robust and reliable.

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Mohamed Khalifa

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No Comments on What are sampling methods and how do you choose the best one?

Thank you for this overview. A concise approach for research.

really helps! am an ecology student preparing to write my lab report for sampling.

I learned a lot to the given presentation.. It’s very comprehensive… Thanks for sharing…

Very informative and useful for my study. Thank you

Oversimplified info on sampling methods. Probabilistic of the sampling and sampling of samples by chance does rest solely on the random methods. Factors such as the random visits or presentation of the potential participants at clinics or sites could be sufficiently random in nature and should be used for the sake of efficiency and feasibility. Nevertheless, this approach has to be taken only after careful thoughts. Representativeness of the study samples have to be checked at the end or during reporting by comparing it to the published larger studies or register of some kind in/from the local population.

Thank you so much Mr.mohamed very useful and informative article

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Sampling Methods & Strategies 101

Everything you need to know (including examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | January 2023

If you’re new to research, sooner or later you’re bound to wander into the intimidating world of sampling methods and strategies. If you find yourself on this page, chances are you’re feeling a little overwhelmed or confused. Fear not – in this post we’ll unpack sampling in straightforward language , along with loads of examples .

Overview: Sampling Methods & Strategies

• What is sampling in a research context?
• The two overarching approaches

Simple random sampling

Stratified random sampling, cluster sampling, systematic sampling, purposive sampling, convenience sampling, snowball sampling.

• How to choose the right sampling method

What (exactly) is sampling?

At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group . For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent (good luck with that!) – but you could potentially collect data from a smaller subset of this group.

In technical terms, the larger group is referred to as the population , and the subset (the group you’ll actually engage with in your research) is called the sample . Put another way, you can look at the population as a full cake and the sample as a single slice of that cake. In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. In other words, you’d want to cut a perfect cross-sectional slice of cake, such that the slice reflects every layer of the cake in perfect proportion.

Achieving a truly representative sample is, unfortunately, a little trickier than slicing a cake, as there are many practical challenges and obstacles to achieving this in a real-world setting. Thankfully though, you don’t always need to have a perfectly representative sample – it all depends on the specific research aims of each study – so don’t stress yourself out about that just yet!

With the concept of sampling broadly defined, let’s look at the different approaches to sampling to get a better understanding of what it all looks like in practice.

The two overarching sampling approaches

At the highest level, there are two approaches to sampling: probability sampling and non-probability sampling . Within each of these, there are a variety of sampling methods , which we’ll explore a little later.

Probability sampling involves selecting participants (or any unit of interest) on a statistically random basis , which is why it’s also called “random sampling”. In other words, the selection of each individual participant is based on a pre-determined process (not the discretion of the researcher). As a result, this approach achieves a random sample.

Probability-based sampling methods are most commonly used in quantitative research , especially when it’s important to achieve a representative sample that allows the researcher to generalise their findings.

Non-probability sampling , on the other hand, refers to sampling methods in which the selection of participants is not statistically random . In other words, the selection of individual participants is based on the discretion and judgment of the researcher, rather than on a pre-determined process.

Non-probability sampling methods are commonly used in qualitative research , where the richness and depth of the data are more important than the generalisability of the findings.

If that all sounds a little too conceptual and fluffy, don’t worry. Let’s take a look at some actual sampling methods to make it more tangible.

Need a helping hand?

Probability-based sampling methods

First, we’ll look at four common probability-based (random) sampling methods:

Importantly, this is not a comprehensive list of all the probability sampling methods – these are just four of the most common ones. So, if you’re interested in adopting a probability-based sampling approach, be sure to explore all the options.

Simple random sampling involves selecting participants in a completely random fashion , where each participant has an equal chance of being selected. Basically, this sampling method is the equivalent of pulling names out of a hat , except that you can do it digitally. For example, if you had a list of 500 people, you could use a random number generator to draw a list of 50 numbers (each number, reflecting a participant) and then use that dataset as your sample.

Thanks to its simplicity, simple random sampling is easy to implement , and as a consequence, is typically quite cheap and efficient . Given that the selection process is completely random, the results can be generalised fairly reliably. However, this also means it can hide the impact of large subgroups within the data, which can result in minority subgroups having little representation in the results – if any at all. To address this, one needs to take a slightly different approach, which we’ll look at next.

Stratified random sampling is similar to simple random sampling, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly , but from within certain pre-defined subgroups (i.e., strata) that share a common trait . For example, you might divide the population into strata based on gender, ethnicity, age range or level of education, and then select randomly from each group.

The benefit of this sampling method is that it gives you more control over the impact of large subgroups (strata) within the population. For example, if a population comprises 80% males and 20% females, you may want to “balance” this skew out by selecting a random sample from an equal number of males and females. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So, depending on your research aims, the stratified approach could work well.

Next on the list is cluster sampling. As the name suggests, this sampling method involves sampling from naturally occurring, mutually exclusive clusters within a population – for example, area codes within a city or cities within a country. Once the clusters are defined, a set of clusters are randomly selected and then a set of participants are randomly selected from each cluster.

Now, you’re probably wondering, “how is cluster sampling different from stratified random sampling?”. Well, let’s look at the previous example where each cluster reflects an area code in a given city.

With cluster sampling, you would collect data from clusters of participants in a handful of area codes (let’s say 5 neighbourhoods). Conversely, with stratified random sampling, you would need to collect data from all over the city (i.e., many more neighbourhoods). You’d still achieve the same sample size either way (let’s say 200 people, for example), but with stratified sampling, you’d need to do a lot more running around, as participants would be scattered across a vast geographic area. As a result, cluster sampling is often the more practical and economical option.

If that all sounds a little mind-bending, you can use the following general rule of thumb. If a population is relatively homogeneous , cluster sampling will often be adequate. Conversely, if a population is quite heterogeneous (i.e., diverse), stratified sampling will generally be more appropriate.

The last probability sampling method we’ll look at is systematic sampling. This method simply involves selecting participants at a set interval , starting from a random point .

For example, if you have a list of students that reflects the population of a university, you could systematically sample that population by selecting participants at an interval of 8 . In other words, you would randomly select a starting point – let’s say student number 40 – followed by student 48, 56, 64, etc.

What’s important with systematic sampling is that the population list you select from needs to be randomly ordered . If there are underlying patterns in the list (for example, if the list is ordered by gender, IQ, age, etc.), this will result in a non-random sample, which would defeat the purpose of adopting this sampling method. Of course, you could safeguard against this by “shuffling” your population list using a random number generator or similar tool.

Non-probability-based sampling methods

Right, now that we’ve looked at a few probability-based sampling methods, let’s look at three non-probability methods :

Again, this is not an exhaustive list of all possible sampling methods, so be sure to explore further if you’re interested in adopting a non-probability sampling approach.

First up, we’ve got purposive sampling – also known as judgment , selective or subjective sampling. Again, the name provides some clues, as this method involves the researcher selecting participants using his or her own judgement , based on the purpose of the study (i.e., the research aims).

For example, suppose your research aims were to understand the perceptions of hyper-loyal customers of a particular retail store. In that case, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes .

Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience . Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad .

Next up, we have convenience sampling. As the name suggests, with this method, participants are selected based on their availability or accessibility . In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.

Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. However, as you’d expect, this sampling method is unlikely to produce a representative sample and will of course be vulnerable to researcher bias , so it’s important to approach it with caution.

Last but not least, we have the snowball sampling method. This method relies on referrals from initial participants to recruit additional participants. In other words, the initial subjects form the first (small) snowball and each additional subject recruited through referral is added to the snowball, making it larger as it rolls along .

Snowball sampling is often used in research contexts where it’s difficult to identify and access a particular population. For example, people with a rare medical condition or members of an exclusive group. It can also be useful in cases where the research topic is sensitive or taboo and people are unlikely to open up unless they’re referred by someone they trust.

Simply put, snowball sampling is ideal for research that involves reaching hard-to-access populations . But, keep in mind that, once again, it’s a sampling method that’s highly prone to researcher bias and is unlikely to produce a representative sample. So, make sure that it aligns with your research aims and questions before adopting this method.

How to choose a sampling method

Now that we’ve looked at a few popular sampling methods (both probability and non-probability based), the obvious question is, “ how do I choose the right sampling method for my study?”. When selecting a sampling method for your research project, you’ll need to consider two important factors: your research aims and your resources .

As with all research design and methodology choices, your sampling approach needs to be guided by and aligned with your research aims, objectives and research questions – in other words, your golden thread. Specifically, you need to consider whether your research aims are primarily concerned with producing generalisable findings (in which case, you’ll likely opt for a probability-based sampling method) or with achieving rich , deep insights (in which case, a non-probability-based approach could be more practical). Typically, quantitative studies lean toward the former, while qualitative studies aim for the latter, so be sure to consider your broader methodology as well.

The second factor you need to consider is your resources and, more generally, the practical constraints at play. If, for example, you have easy, free access to a large sample at your workplace or university and a healthy budget to help you attract participants, that will open up multiple options in terms of sampling methods. Conversely, if you’re cash-strapped, short on time and don’t have unfettered access to your population of interest, you may be restricted to convenience or referral-based methods.

In short, be ready for trade-offs – you won’t always be able to utilise the “perfect” sampling method for your study, and that’s okay. Much like all the other methodological choices you’ll make as part of your study, you’ll often need to compromise and accept practical trade-offs when it comes to sampling. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.

Let’s recap…

In this post, we’ve covered the basics of sampling within the context of a typical research project.

• Sampling refers to the process of defining a subgroup (sample) from the larger group of interest (population).
• The two overarching approaches to sampling are probability sampling (random) and non-probability sampling .
• Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling.
• Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
• When choosing a sampling method, you need to consider your research aims , objectives and questions, as well as your resources and other practical constraints .

If you’d like to see an example of a sampling strategy in action, be sure to check out our research methodology chapter sample .

Last but not least, if you need hands-on help with your sampling (or any other aspect of your research), take a look at our 1-on-1 coaching service , where we guide you through each step of the research process, at your own pace.

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Grad Coach tutorials are excellent – I recommend them to everyone doing research. I will be working with a sample of imprisoned women and now have a much clearer idea concerning sampling. Thank you to all at Grad Coach for generously sharing your expertise with students.

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Sampling Methods In Reseach: Types, Techniques, & Examples

Saul Mcleod, PhD

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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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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.

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The Sample and Sampling Techniques

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A Sampling-Based Method to Estimate the Volume of Solution Space for Linear Arithmetic Constraints

• Published: 04 July 2024

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• Yan-Feng Xie 1 , 2 ,
• Chun-Ming Yuan 1 , 2 &
• Rui-Juan Jing 3  

The linear arithmetic constraints play important roles in many research fields. Estimating the volume of their solution spaces has specific applications, such as programming verification, linear programming, polyhedral optimization, and so on. In this paper, the authors provide an efficient estimation for the volume of the solution space for linear arithmetic constraints. This method sums up the estimations for volumes of oblique cones centered along randomly generated rays. The error analysis is provided to improve the accuracy.

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Xie, YF., Yuan, CM. & Jing, RJ. A Sampling-Based Method to Estimate the Volume of Solution Space for Linear Arithmetic Constraints. J Syst Sci Complex (2024). https://doi.org/10.1007/s11424-024-3425-4

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Enhancing the sample diversity of snowball samples: Recommendations from a research project on anti-dam movements in Southeast Asia

Julian kirchherr.

1 Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands

2 School of Geography and the Environment, University of Oxford, Oxford, United Kingdom

Katrina Charles

Associated data.

All data underlying the study are within the paper and its Supporting Information files.

Snowball sampling is a commonly employed sampling method in qualitative research; however, the diversity of samples generated via this method has repeatedly been questioned. Scholars have posited several anecdotally based recommendations for enhancing the diversity of snowball samples. In this study, we performed the first quantitative, medium- N analysis of snowball sampling to identify pathways to sample diversity, analysing 211 reach-outs conducted via snowball sampling, resulting in 81 interviews; these interviews were administered between April and August 2015 for a research project on anti-dam movements in Southeast Asia. Based upon this analysis, we were able to refine and enhance the previous recommendations (e.g., showcasing novel evidence on the value of multiple seeds or face-to-face interviews). This paper may thus be of particular interest to scholars employing or intending to employ snowball sampling.

Introduction

Snowball sampling is a commonly employed sampling method in qualitative research, used in medical science and in various social sciences, including sociology, political science, anthropology and human geography [ 1 – 3 ]. As is typical of terms adopted by a variety of fields, however, the phrase ‘snowball sampling’ is used inconsistently across disciplines [ 4 ]. The most frequently employed definition, suggested by Patton [ 5 ], Atkinson and Flint [ 6 ], Cohen and Arieli [ 7 ] and Bhattacherjee [ 8 ], is as a sampling method in which one interviewee gives the researcher the name of at least one more potential interviewee. That interviewee, in turn, provides the name of at least one more potential interviewee, and so on, with the sample growing like a rolling snowball if more than one referral per interviewee is provided.

This definition can initially seem self-explanatory, which may explain why snowball sampling is rarely discussed in most peer-reviewed papers that employ it. Various scholars use snowball sampling in their empirical work, but most provide only limited information on the method (see, e.g., [ 9 – 13 ]). Similarly, qualitative research textbooks often lack substantive discussion of snowball sampling (e.g., [ 8 , 14 – 19 ]). Bailey [ 14 ], for instance, devotes only a half-page of his 595-page book on social research methods to snowball sampling, acknowledging that ‘snowball sampling procedures have been rather loosely codified’ ([ 14 ], p. 96), an observation echoed by Penrod et al. [ 3 ].

This paper focuses on snowball sampling procedures, which we define as those actions undertaken to initiate, progress and terminate the snowball sample [ 1 , 20 ]. Despite the lack of substantive writing on snowball sampling as a method, several authors [ 2 , 3 , 21 ] have provided recommendations for enhancing a sample’s diversity in snowball sampling procedures (we discuss this further in Section 4). However, as this advice is not based on a quantitative analysis of evidence, but only on anecdotal evidence, there is a risk that these recommendations are based on coincidence. The aim of this paper is to provide advice on enhancing the sample diversity of a snowball sample. This advice is grounded in a medium- N analysis of relevant evidence, thus reducing the probability of positing advice that is based on coincidence [ 22 ]. A medium- N analysis is generally based on 10–100 cases, whereas anecdotal evidence is usually based only on a handful of cases [ 23 , 24 ]. At the core of our work, we provide descriptive analyses of various commonly prescribed strategies for enhancing the sample diversity of a snowball sample. These analyses are based on reach-outs to 211 individuals via snowball sampling for a research project on anti-dam movements in Southeast Asia, resulting in 81 interviews conducted between April and August 2015. As far as we are aware, ours is the first medium- N analysis to focus on enhancing the sample diversity of a snowball sample.

The remainder of this paper is organised as follows: in Section 2, we discuss snowball sampling as a method; in Section 3, we present the research project on anti-dam movements in Southeast Asia that served as the basis for our medium- N analysis on snowball sampling procedures; in Section 4, we present and discuss insights on snowball sampling procedures based upon this analysis as well as our resulting recommendations; finally, in Section 5, we summarise our argument.

Throughout this paper, we employ social science methodology terminology. We define key terms for this paper such as ‘snowball sampling’ or ‘sampling’, since these terms are not consistently codified in the scholarly literature. Due to limited space, however, we refrain from defining terms we have deemed common in this field of study, referring only to the relevant literature.

On snowball sampling

Traditional sampling methods are comprised of two elements [ 25 , 26 ]. First, a full set of data sources is defined, creating a list of the members of the population to be studied, known as a sampling frame. Second, a specific sample of data is collected from this sampling frame. Snowball sampling defies both elements, since it does not rely upon a sampling frame [ 27 ] (which may indicate that a different term for snowball sampling would be more accurate). Snowball sampling is often employed when no sampling frame can be constructed.

Researchers frequently cannot construct a sampling frame if a difficult-to-reach population is to be studied. Difficult-to-reach-populations are also referred to as ‘hard-to-reach-populations’ [ 28 ], ‘hidden populations’ [ 29 ] or ‘concealed populations’ [ 21 ] in the scholarly literature. Although not all scholars may agree that these terms are interchangeable, we deem them interchangeable for the purposes of this paper. For further discussion of this terminology, see [ 30 , 31 ].

A difficult-to-reach population does not wish to be found or contacted (e.g., illegal drug users, illegal migrants, prostitutes or homeless people [ 6 , 31 ]). Snowball sampling was originally used by researchers to study the structure of social networks [ 32 ]. The earliest empirical account of snowball sampling is from 1955 [ 33 ], with snowball sampling first described as a method in 1958 [ 34 ]. While it is still used to study the structure of social networks [ 35 ], over the last few decades, the method’s key purpose has largely transformed ‘into […] an expedient for locating members of a [difficult-to-reach] population’ ([ 36 ], p. 141).

Researchers grounded in quantitative thinking, such as Lijphart [ 37 ] and King et al. [ 38 ], tend to view the drawing of a random sample from a sampling frame as the gold standard of data collection. Even these researchers may nevertheless consider non-probability sampling methods, such as snowball sampling, a ‘necessary and irreplaceable sampling [method]’ ([ 39 ], p. 367) when confronted with difficult-to-reach populations, particularly if the dismissal of snowball sampling would mean that no research could be conducted at all. Ultimately, ‘an important topic is worth studying even if very little [access to] information is available’ ([ 38 ], p. 6). Still, some of those grounded in quantitative thinking call snowball sampling a method ‘at the margin of research practice’ ([ 6 ], p. 1), since the lack of a sampling frame means that, unlike individuals in a random sample, individuals in a population of interest do not have the same probability of being included in the final sample. Findings from a snowball sample would therefore not be generalisable [ 40 ] (on generalisability, see [ 41 ]).

Several qualitative scholars rebut such criticism. Creswell, for instance, notes that ‘the intent [of qualitative research] is not to generalise to a population, but to develop an in-depth [and contextualised] exploration of a central phenomenon’ ([ 42 ], p. 203). Others [ 1 , 39 ] specifically oppose quantitative scholars’ negative framing of snowball sampling, arguing that this method would ‘generate a unique type of social knowledge’ ([ 1 ], p. 327). Due to the diversity of perspectives gathered, this knowledge would be particularly valuable for an in-depth and contextualised exploration of a central phenomenon. We therefore define the diversity of a sample as a measure of the range of viewpoints that have been gathered on a central phenomenon.

Researchers critical of snowball sampling respond to this defence by arguing that the method is unable to ensure sample diversity, which is a necessary condition for valid research findings. Indeed, some scholars have stated that snowball samples underrepresent and may even exclude those least keen to cooperate, since referrals may not materialise in an interview if a potential interviewee is only somewhat keen or not at all keen to be interviewed [ 3 , 43 ]. Similarly, potential interviewees with smaller networks may be underrepresented, as they are less likely to be referred for an interview [ 31 , 44 ]. Those with smaller networks may also be in a specific network whose different perspectives may be of interest but are excluded in the final sample. Meanwhile, snowball sampling is said to over represent those interviewees (and their respective networks) that the interviewer spoke with first; the relevant literature refers to this as ‘anchoring’ [ 20 , 39 ].

We do not aim to argue the ‘validity’ of the method, but rather to inform snowball sampling methodologies in order to promote sample diversity. From a qualitative perspective, ‘validity’ can be defined as ‘the correctness or credibility of a description, conclusion, explanation, interpretation or other sort of account’ ([ 45 ], p. 87), while quantitative researchers frequently use the terms ‘generalisability’ and ‘(external) validity’ interchangeably [ 46 , 47 ]. The term ‘validity’ is contested among qualitative researchers, and some qualitative researchers entirely reject the concept for qualitative work [ 48 , 49 ]. We do not aim to resolve this debate via this paper; instead, we focus on the (seemingly less-contested) term ‘sample diversity’. While we acknowledge that this term is not codified in qualitative textbooks such as the SAGE Encyclopedia of Qualitative Research Methods , sample diversity is considered desirable by the various qualitative scholars we reviewed. Boulton and Fitzpatrick demand, for instance, that qualitative researchers ‘ensure that the full diversity of individuals […] is included [in their sample]’ ([ 50 ], p. 84), a mandate echoed by other scholars [ 16 , 51 – 53 ].

In order to operationalise the concept of sample diversity, we used five key methodological recommendations to inform our research. In this paper, we use quantitative analyses from our experiences with snowball sampling to further reflect on these recommendations, which are briefly described below.

Prior personal contacts of the researcher are required

Patton ([ 5 ], p. 176) notes that snowball sampling ‘begins by asking well-situated people: “Who knows a lot about ____? Who should I talk to?”‘. In the absence of a sampling frame for the population of interest, however, the researcher must retain at least some prior personal or professional contacts in the population of interest which can serve as the seeds of the snowball sample [ 2 , 54 ]. Waters contends that building a diverse snowball sample ‘depend[s] almost exclusively on the researcher’s [prior personal or professional] contacts’ ([ 39 ], p. 372).

Sample seed diversity is important

Morgan [ 21 ] has claimed that the ‘best defence’ against a lack of sample diversity is to begin the sample with seeds that are as diverse as possible. Others echo this advice [ 3 , 39 , 55 ], arguing that it is ‘compulsory for the researcher to ensure that the initial set of respondents is sufficiently varied’ ([ 55 ], p. 55). The term ‘chain referral sampling’ has been used for snowball samples that are strategically built via multiple varying seeds [ 3 ].

Technology means face-to-face interviews are no longer required

Some researchers have argued that face-to-face interviews are obsolete. For instance, over 25 years ago, it was claimed there were ‘no remarkable differences’ ([ 56 ], p. 211) between information collected via telephone and information collected via face-to-face interviews. The increasing use of telecommunications in recent years is likely to have further reduced barriers to remote interviewing, and various scholars [ 57 , 58 ] continue to claim that ‘evidence is lacking that [telephone interviews] produce lower quality data’ ([ 59 ], p. 391). In particular, they have highlighted the benefits of using Skype for semi-structured interviews [ 57 ].

However, for snowball sampling, face-to-face interviews help to generate the trust that scholars claim is required in order to gain referrals [ 1 , 31 , 39 , 60 ]. Noy argues that ‘the quality of the referring process is naturally related to the quality of the interaction: […] if the researcher did not win the informant’s trust […], the chances the latter will supply the former referrals decrease’ ([ 1 ], p. 334).

Persistence is necessary to secure interviews

Although the value of persistence may be considered self-evident by some scholars, it is seen by multiple academics [ 61 – 63 ] as a central virtue of qualitative researchers. Many young career scholars who embrace snowball sampling are likely to hear such advice as, ‘If you cannot interview your envisaged interviewees initially, don’t give up!’. A ‘helpful hint’ for qualitative researchers seeking informants is, ‘Persevere–repeat contact’ [ 64 ].

More waves of sampling are required to access more reluctant interviewees

As a remedy for snowball sampling’s previously discussed bias towards excluding those least keen to be interviewed, multiple scholars suggest pursuing a snowball sample for multiple waves (with a new sampling wave reached once an interviewee introduces the interviewer to one or more potential interviewees) [ 65 – 68 ]. Those suggesting this remedy assume that pursuing more waves increases the likelihood of being referred to an interviewee from a particularly difficult-to-reach population who is at least somewhat keen to be interviewed.

Approval for this study was granted by the Central University Research Ethics Committee (CUREC) of the University of Oxford. Our population of interest for our research project were stakeholders in Southeast Asia’s dam industry. Since ‘the most dramatic conflicts over how to pursue sustainable development’ ([ 69 ], p. 83) have occurred over the construction of large dams, we see this industry as a conflict environment with widely varying viewpoints. A conflict environment is one in which people perceive their goals and interests to be contradicted by the goals or interests of the opposing side [ 70 ]. The major conflicting parties in the dam industry tend to be local and international non-governmental organisations (NGOs) and academics (usually keen not to construct a particular dam) versus international donors, the private sector and governments (usually keen to construct a particular dam) [ 71 , 72 ]. Each sub-population operating in a conflict environment can be considered difficult to reach since fear and mistrust are often pervasive [ 7 ]. Snowball sampling is a suitable research method in conflict environments because the introductions through trusted social networks that are at the core of this method can help interviewees to overcome fear and mistrust, which, in turn, ensures access [ 7 ]. This access is needed to gather the widely varying viewpoints in the hydropower industry, in particular viewpoints with regards to what constitutes just resettlement [ 73 , 74 ]. Based on this rationale, we chose snowball sampling as the main method for our research.

In order to ensure sample diversity for our research project on anti-dam movements in Southeast Asia, we aimed to gather perspectives mostly from six main sub-populations: (1) local NGOs, (2) international NGOs, (3) international donors, (4) academia, (5) the private sector and (6) the government. We hypothesized that ‘dam developers’, a main sub-category of the interviewee category ‘private sector’, would be the most significant challenge to ensuring the diversity of our sample. Early in our process, many of the scholars with whom we discussed our research project argued that it would be impossible to interview a dam developer from a Chinese institution; meanwhile, researchers from a comparable research project that ended approximately when our project started reported being unable to interview any dam developers from European institutions. We also initially failed to collect data from dam developers: for instance, a survey we initiated that was distributed by Aqua~Media (host of a major global dam developer conference) to more than 1,500 dam developers yielded just five responses, only one of which was complete. We considered this weak response rate to be due, at least in part, to the dam industry’s negative view of academicians since the publication of Ansar et al. [ 75 ], which Nombre ([ 76 ], p. 1), the president of the International Commission on Large Dams (ICOLD), called ‘[highly] misleading’.

None of our researchers had significant direct links to the dam industry upon the start of the project; however, we did retain a variety of indirect links. Our researchers had past links to a management consultancy that serves various dam industry players, (more limited) links to an international donor working in the hydropower sector and links to activists in Myanmar advocating against dam projects.

After a favourable ethics review of our study by the CUREC of the University of Oxford, we commenced semi-structured interviews in April 2015, mostly via cold calls (we include cold e-mails in the term ‘cold calls’ throughout this paper). Initially, we conducted research via telephone only. We then undertook field research in Singapore, Myanmar and Thailand from June to August 2015 and terminated our data collection in late August 2015.

In total, 81 semi-structured interviews were carried out during this period. From a qualitative perspective, this is a relatively large sample size (for instance, the average qualitative PhD dissertation is based on 31 interviews [ 77 ]); from a quantitative perspective, however, the sample size is quite small [ 78 ]. Of our 81 interviews, 48 (59%) were conducted via telephone, 26 (32%) face-to-face and 7 (9%) online, either via e-mail or an online survey. Most of our interviews (57%) were carried out in July in Myanmar. Of our 81 interviewees, only 24 (30%) were women. Researchers who employ snowball sampling frequently employ personal/professional contact seeds and cold call seeds to build their sample (e.g., [ 2 , 79 , 80 ] with a seed defined as the starting point of a sample [ 65 ]). Of the 81 interviews analysed, 53 (65%) were rooted in a personal or professional contact ( Fig 1 ) (i.e. the seed of the interview pathway was a contact we had already retained prior to the research project). The remaining 28 (35%) interviews were rooted in cold calls.

Given the sensitive nature of the interview topic, all interviewees were assured anonymity. Thus, all of the interviews are coded, with the first letter indicating the mode of interview ( T for telephone, F for face-to-face, O for online survey or e-mail), the second letter indicating the category of interviewee ( A for academia, G for government, I for international donor, NI for international NGO, NL for national NGO, P for private sector) and the sequence of numbers indicating the interview number within a particular mode. Researcher A is indicated by RA , Researcher B by RB ; CON represents a conference event. Bold type indicates that an interview was completed, while X that an interview was not completed.

As outlined in the previous section, snowball sampling is sometimes criticised for producing samples that lack sample diversity. To address this criticism, we reviewed the (scarce) literature on enhancing sample diversity via snowball sampling procedures prior to commencing our study. Upon reflection during our research, we chose to pursue our analysis retrospectively in order to challenge some of the recommendations provided in literature. Our analysis is structured alongside the five core pieces of advice found in this literature ( Table 1 ). Our results are based on a quantitative analysis of the 81 interviews we conducted. Although we endeavoured to include all interview attempts, some initial cold calls may have been overlooked in this retrospective approach. Therefore, some of our analysis, particularly in Section 4.4, may be too optimistic. Overall, we were able reconstruct 211 reach-out attempts.

Sample diversity is measured by representation from five identified sub-groups.

Results and discussion

On prior personal and professional contacts.

Our analysis provides evidence that sample diversity can be reached even if no prior personal or professional contacts to the population of interest have been retained. The seeds of the interviews are depicted in Fig 2 , with the left side of the figure depicting the 53 interviews based on a personal or professional contact and the right side depicting the 28 interviews that were based on cold calls. This figure shows two main points of interest: first, both types of seeds include interviews in each interview category; second, the interview sub-category ‘dam developer’, which we hypothesised would be the most difficult to include in the sample, is also covered by both types of seeds. We can therefore conclude that a diverse sample could have been built even if we had relied solely on cold calls.

It is acknowledged, however, that building a snowball sample from cold calls is particularly labour-intensive [ 39 ]: in our research, only 25% of our cold calls led to an interview, compared to 62% of the referrals. Significant differences in the value of referrals persist from one interviewee group to another ( Fig 3 ). We measure the value of referrals via a concept we call ‘network premium’. To gauge the network premium, we subtracted the cold call response rate (i.e., the number of interviews initiated via cold calls divided by the total number of cold calls) from the referral response rate (i.e. the number of interviews initiated via referrals divided by the total number of referrals). Referrals were the most valuable when contacting international donors and private sector players, with network premiums of 74% and 52%, respectively, indicating that these groups are particularly difficult-to-reach populations.

(1) Unable to retrace for 13 identified reach-outs if initiated via referral or cold call; four reach-outs coded as ‘Other’. (2) Unable to retrace for one interview carried out via referral coded as ‘Other’. (3) Including personal contacts and contacts via conferences. (4) Referral response rate–Cold call response rate.

The overall results from these analyses are encouraging for scholars interested in researching a population to which no personal or professional contacts are retained prior to the research project. While personal or professional contacts maintained to the research population of interest can accelerate the research endeavour, our results also showcase that (at least for our topic of interest) a diverse sample can be built from cold calls if a researcher is willing to invest some time in reach-outs.

On seed variation

Our research confirms the scholars’ advice that seed diversity is important. Fig 4 (a variation of Fig 2 ) depicts the completed interviews from a seed perspective, with RA, RB and cold calls as the three main seeds of the sample. The sample built via RA, who has a background in the private sector, is largely biased towards this sector, with 47% of all interviews seeded via RA private sector interviews. RB conducted 57% of interviews, whose background is closest to local NGOs, were with local NGOs. Meanwhile, the sample built via cold calls indicates no significant biases towards any interviewee category. Interviews based on the network of RB included one (TNL17) with a leading activist from a remote area of Myanmar who provided unique insights into the early days of an anti-dam campaign. This insight helped us to develop a narrative of the campaign that was not skewed to the later days of the campaign and the activists prominent in these later days. The sample diversity ensured via RB was thus central to the quality of our research.

It is noteworthy that the three different seeds in Fig 4 include interviews in all interviewee categories, including the sub-category ‘dam developer’ (the sole exception is the interviewee category ‘international NGO, which contains zero interviews for RB). This indicates that, at least for our topic of interest, a fairly diverse sample can be generated even if the researcher is unable to vary her or his seed, although the overall data suggest that seed variation can significantly enhance sample diversity. Fig 3 may therefore be viewed as a case for collaboration among researchers; if researchers with different backgrounds and different personal and professional contacts to the population of interest begin to collaborate, such collaborations are bound to contribute to sample diversity.

On face-to-face interviews

Our descriptive analysis provides evidence to further support the argument that face-to-face interviews are redundant, with our data indicating that face-to-face interviews can lead to more sought referrals than telephone interviews (perhaps since trust may be more readily established via face-to-face conversations than over the telephone). Fig 5 aims to quantify the value of face-to-face interviews. Overall, 30 (37%) of our interviews were initiated via prior face-to-face conversations, while prior telephone conversations and online contact each led to only eight interviews (10%). An examination shows that of the nine interviews conducted with dam developers, the interviewee sub-category deemed most difficult to access, seven (78%) were initiated via prior face-to-face interviews, while not a single telephone interview led to a referral to a dam developer. These interviews proved to be essential for our research. For instance, one Chinese dam developer challenged a claim from numerous NGOs that his company would not engage with NGOs, which, in turn, allowed us to present a more balanced portrayal of the interplay between Chinese dam developers and NGOs.

(1) Comprises interviews with those already retaining a personal or professional contact prior to the research project.

While our research did not investigate whether face-to-face interviews lead to lower-quality data than telephone interviews, our data provide tentative evidence that face-to-face interviews are not obsolete; they can still be helpful for those employing or intending to employ snowball sampling, since these interviews can lead to more sought referrals and thus enhanced sample diversity. We acknowledge, however, that this finding may not be true for all populations. For instance, studies on individuals with sexually transmitted diseases have found that these interviewees (particularly men) tend to report more truthfully in an audio-computer-assisted self-interview (ACASI) than in a face-to-face interview, since interviewees tend to be more comfortable reporting on sexually transmitted diseases to a computer than to a live person [ 81 , 82 ].

On persistence

Our data suggest that persistence can indeed enhance sample diversity, but we can also conclude that excessive persistence does not necessarily yield dividends. Instead of distributing a great many interview reminders during our study, we reached out to the majority of our proposed interview subjects only once. Nevertheless, the scarce data we collected regarding persistence indicates its value. We map this data in Fig 6 , with the left side depicting our success rate in relation to the number of reach-outs (either one, two or three) and the right side depicting a deep dive on success rates achieved with two reach-outs (distinguishing between reach-out attempts to unknown potential interviewees and those to whom we were referred by other interviewees). We sent one interview reminder to 28 of our proposed interviewees. This led to 10 additional interviews, a success rate of 36%, equalling 12% of the total interviews analysed for this paper. Reminders appear to be only somewhat more helpful when contacting referrals in comparison to their usefulness with cold calls–a single reminder led to an interview in 39% of our cases for the former group and 38% for the latter. One of the most valuable interviews for our research gained via a reminder was with the CEO of a Burmese dam developer. This interviewee compared Chinese and European dam developers in Myanmar, which helped us to further refine our narrative on social-safeguard policy adherence by Chinese dam developers in Myanmar.

(1) Number of reach-outs unknown for 32 reach-outs. Eight potential interviewees responded, but refused interview.

Excessive persistence, however, does not appear to be worthwhile. We sent three reminders to seven of our envisaged interviewees, but as Fig 6 shows, this did not lead to a single additional interview. While our data does not suggest that excessive persistence is helpful to researchers, it may also not be recommended for ethical reasons. A potential interviewee who does not respond to an interview request after two reach-outs may be indicating via this non-response that she or he is not interested in participating in the research. If a single request remains unanswered, the researcher may hypothesise that, for instance, the e-mail was overlooked, a hypothesis particularly likely when conducting interviews with time-pressed leaders of organisations. Indeed, all 10 interviews only carried out upon the second reach-out were interviews with interviewees in management positions.

Our data on persistence provide some evidence that those employing or intending to employ snowball sampling can enhance sample diversity if every reach-out is carefully tracked and followed by a reminder. We typically sent a reminder after one week if no response was obtained upon the first reach-out. This persistence may help to include those least keen to be interviewed for a research endeavour.

Our data show some evidence that, for our topic of study, pursuing interviews for even a few waves provided the perspectives of particularly difficult-to-reach populations and thus achieved sample diversity. More than 60% of our interviews were conducted in the zeroth or first wave ( Fig 7 ). These include seven of the nine interviews conducted with dam developers, the sub-category we deemed most challenging to interview. The remaining two interviews with dam developers were conducted in the second wave. However, not a single interview with a dam developer was carried out in the third wave and beyond, although a fifth of our total interviews were carried out in the third or later waves. Pursuing interviews for multiple waves nevertheless yielded novel insights. For instance, interview FNL12, which was conducted in the sixth wave, yielded insights on small dam construction in Myanmar–a topic of (some) interest to our research endeavour, but not covered in detail by previous interviews. Furthermore, we note that our finding regarding the limited value of multiple waves may also be specific to our population, with this finding perhaps indicating a low degree of network segmentation in the population in question [ 83 ]. Meanwhile, a high degree of network segmentation may impede the pursuance of multiple waves, since interviewees may lack the suitable contacts for a referral [ 84 ].

While additional waves can lead to novel insights, our overall data on waves provide some evidence that the number of waves pursued is not a definitive indicator for sample diversity. Even very few waves can yield access to particularly difficult-to-access populations.

Our quantitative analysis of pathways to delivering sample diversity in snowball samples yielded the following revisions to the literature’s recommendations:

• Prior personal contacts are not essential for achieving sample diversity but tend to be helpful, as generating new contacts during research can be labour-intensive.
• Sample seed diversity is important to achieving sample diversity.
• Face-to-face interviews build trust and can help to generate further referrals.
• Persistence (within reason) is helpful in securing interviews.
• Sample diversity is not necessarily enhanced if a seed is advanced over numerous waves.

We do not claim that these insights are comprehensive, but we believe that these interpretations of our data may serve as a starting point for future scholars using snowball sampling procedures. All of the analyses presented in this section are based only on descriptive statistics. This means, for instance, that we cannot control for confounds such as effort [ 85 ]. An experimental research design would yield the most robust insights on sampling procedures to enhance the sampling diversity of a snowball sample (with, for instance, one research project staffed with scholars with relevant personal or professional contacts and another staffed with scholars without relevant contacts).

Overall, this work aims to advance the literature on snowball sampling as a qualitative sampling approach. While snowball sampling procedures may qualify ‘as the least “sexy” facet of qualitative research’ ([ 1 ], p. 328), these procedures are ‘not self-evident or obvious’ ([ 20 ], p. 141), since the snowball sample does not ‘somehow magically’ ([ 20 ], p. 143) start, proceed and terminate when a scholar attempts to develop a diverse sample. Rather, continuous, deliberate effort by the researcher(s) is required. Our paper has attempted to provide some insights on this effort.

Unfortunately, we developed the idea to write this paper only during the course of our research project, and thus some of our data may be skewed. For instance, we may not have been able to trace all original reach-out attempts and our data on persistence may therefore be biased. Some of those scholars grounded in quantitative thinking may also claim that the insights outlined in Section 4 lack external validity since our sample size is relatively small from a quantitative methodological perspective. In addition, our population was very specific and thus may not be comparable to other difficult-to-reach populations, and we also did not adopt an experimental research design as described above. Hence, we encourage scholars to replicate our findings via their respective research projects that employ snowball sampling. With many scholars claiming to feel more pressed than ever to deliver research results with maximum efficiency, we hope that these initial descriptive analyses of snowball sampling procedures provide some valuable insights to those employing or intending to employ this method and aiming to improve their management of it.

Supporting information

Acknowledgments.

We wish to thank our reviewers at PLOS ONE who provided constructive thoughts on this piece of work. We also thank Ralf van Santen for his outstanding contributions to this work as a research assistant.

Funding Statement

The authors received no specific funding for this work.

Data Availability

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Open Access

Study Protocol

Ngulluk Moort, Ngulluk Boodja, Ngulluk Wirin (our family, our country, our spirit): An Aboriginal Participatory Action Research study protocol

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Telethon Kids Institute, University of Western Australia, Perth, Australia, Faculty of Health, University of Canberra, Canberra, Australia

Roles Conceptualization, Investigation, Methodology, Writing – review & editing

Affiliation Telethon Kids Institute, University of Western Australia, Perth, Australia

Roles Conceptualization, Funding acquisition, Methodology, Writing – review & editing

Roles Conceptualization, Funding acquisition, Investigation, Methodology, Writing – review & editing

Affiliations Telethon Kids Institute, University of Western Australia, Perth, Australia, Faculty of Health Sciences, Curtin University, Perth, Australia

Affiliation Australian Centre for Child Protection, University of South Australia, Adelaide, Australia

Affiliations Telethon Kids Institute, University of Western Australia, Perth, Australia, Faculty of Health Sciences, Curtin University, Perth, Australia, Ngangk Yira Institute for Change, Murdoch University, Murdoch, Australia

• Sharynne Lee Hamilton, 
• Larissa Jones, 
• Millie Penny, 
• Charmaine Pell, 
• Nicole Ilich, 
• Carol Michie, 
• Raewyn Mutch, 
• Melissa O’Donnell, 
• Carrington Shepherd, 
• Brad Farrant

• Published: July 3, 2024
• https://doi.org/10.1371/journal.pone.0301237
• Peer Review
• Reader Comments

Globally, Indigenous children have historical and contemporary connections with government child protection services that have caused significant harm to their long-term health and wellbeing. Innovative, culturally secure and recovery focussed service provision is required. This paper describes a research protocol that has been designed by Indigenous researchers led by Indigenous Elders, to explore culturally secure care planning and service delivery in out-of-home care agencies in Australia. Using participatory action research methods, we will collect data using a variety of forums, including focus groups and semi-structured interviews. These data will explore the challenges for out-of-home care agencies in providing culturally secure care-planning, cultural activity and resources, and explore solutions to address factors that influence health and can assist to redress social inequities for Indigenous children. We aim to recruit approximately 100 participants for the qualitative study and 40 participants for the quantitative survey. Study participants will initially be recruited using purposive sampling, and as the study progresses will be recruited using a mixture of purposive and convenience sampling techniques. The rich data that this study is expected to yield, will inform ways to collect cultural information about Indigenous children and ways to provide cultural connections and activities that will have benefit to Indigenous children and families, and a broad range of social services.

Citation: Hamilton SL, Jones L, Penny M, Pell C, Ilich N, Michie C, et al. (2024) Ngulluk Moort, Ngulluk Boodja, Ngulluk Wirin (our family, our country, our spirit): An Aboriginal Participatory Action Research study protocol. PLoS ONE 19(7): e0301237. https://doi.org/10.1371/journal.pone.0301237

Editor: Emma Campbell, PLOS: Public Library of Science, UNITED KINGDOM

Received: March 5, 2024; Accepted: March 11, 2024; Published: July 3, 2024

Copyright: © 2024 Hamilton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: National Health and Medical Research Council, Ideas Grant #2010384.

Competing interests: I have read the journal’s policy and the authors of this manuscript have no competing interests to declare.

Introduction

Across Australia, Aboriginal and Torres Strait Islander (hereafter, respectfully termed Aboriginal) children are admitted to out-of-home care at just over 10 times the rate of their non-Indigenous peers [ 1 ] and are twice as likely to experience poor outcomes than their non-Indigenous peers, particularly in relation to mental health, and long-term wellbeing [ 2 – 7 ]. The inequities and subsequent poor outcomes for Aboriginal children in out-of-home care are largely attributed to a transfer of trauma across families resulting from policies of forced removal, which then contributes to the contemporary high rates of Aboriginal children removed from their families [ 2 , 3 ]. Families who have been affected by these policies, commonly experience greater rates of problematic alcohol and other drug use, criminality, mental health challenges, and have low levels of social capital and support, with clinically significant behavioural problems more likely among future generations [ 7 ]. The leading causes of infant removal in Aboriginal families today includes substance misuse and maternal mental health issues, often linked to unmitigated trauma associated with repeated child removal across generations, and subsequent high levels of distrust between the Aboriginal community and state child protection services [ 8 – 11 ].

When children enter the out-of-home care system, their care planning is critical to ensuring their ‘best interests’ are met in out-of-home care, and they are afforded opportunities and supported to outcomes that are greater than if they had remained with their families. Consistently though, across states and territories, Australian child protection systems have failed children in out-of-home care. This is evident in the more than 50 reports and inquiries into child protection services over the last 50 years, including a Royal Commission [ 4 ] into the sexual abuse of children in institutions and foster care; an Australian Senate inquiry [ 5 ], and an extensive inquiry by the Human Rights and Equal Opportunities Commission [ 6 ] into the policies of forced removal of Aboriginal children from their kin, countries and communities.

There is global scholarship, detailing the impact on colonised Indigenous peoples and communities where dispossession and child removal have featured [ 12 – 14 ]. Research details poorer health and social and wellbeing outcomes for colonised Indigenous peoples and many authors have called for harm reparation [ 4 , 6 ]. Addressing persistent social and health inequities requires considering both the contexts in which disparities exist, and in the case of colonised Indigenous peoples, finding innovative and culturally secure means of rectifying those inequities, which includes considering community perspectives of both the ‘causes of the causes’ and for finding solutions to address inequities [ 15 , 16 ].

Contemporary child protection landscape

In Australia, the Aboriginal and Torres Strait Islander Child Placement Principle [ 17 ] provides structured guidance for the placement of Aboriginal children removed from their families. The aim is to ensure, wherever possible, that children are placed with immediate or extended kin, or with an Aboriginal or Torres Strait Islander carer to assist to maintain connections to their kin, cultural knowledge, language and cultural activity [ 17 – 20 ]. Latest national figures though, show that in 2020, 57.8% of Aboriginal and Torres Strait Islander children are placed in non-Indigenous carer arrangements [ 1 ], with little tangible change to these statistics over the last five years [ 9 ]. This is of great concern. When Aboriginal children are not connected to their families and kin, and are denied opportunities for cultural immersion through activity and community interaction, there are negative consequences for their life-long health and wellbeing outcomes [ 18 , 20 ].

Removing a child from their parents and family members by statutory services such as child protection authorities (often accompanied by police) is, invariably, a traumatic experience, and associated with significant losses for children. This includes a loss of personal space (family homes and bedrooms, for example), possessions, connections with broader family and friends and access to educational and social activities like school, sport or extra-curricular activity [ 3 , 21 ]. Families, and the community services that advocate their needs with child protection services, including Aboriginal Community Controlled Organisations [ 20 ] can face significant relational, institutional and structural challenges to their service delivery [ 10 , 11 , 22 ]. Aboriginal Elders and senior leaders have consistently voiced their concern for Aboriginal children taken into out-of-home care and for the continuing erosion of knowledge of children’s connections, identity, language and cultural practices, fueling internalized racism [ 3 , 20 , 23 , 24 ]. It is well established that Aboriginal children who know their kin, culture and community experience better health and life outcomes in the long term [ 20 ].

For Aboriginal children in out-of-home care, cultural planning is crucial for supporting and maintaining connections to kin and community, language, cultural activity and cultural knowledge. In addition to the ensuring the inclusion of children’s voices in decisions that affect them when developing cultural support plans, the amount, type and quality of information is important for them to be beneficial to children [ 3 , 17 – 19 ]. It is also important to consider where and how cultural information is ascertained, and to ensure it is collected collaboratively with Elders and Aboriginal people specific to a child’s kin and community. In Western Australia, where this research is being conducted, the Department for Communities has responsibility for cultural planning and providing cultural connections for Aboriginal children in out-of-home care [ 25 ].

Aboriginal children are most likely to be notified to the Department for Communities for neglect and emotional abuse [ 25 ], and consistent with the national figures, around half of the Aboriginal children in care are placed with non-Indigenous carers. This is despite the calls by Aboriginal organisations and leaders for keeping Aboriginal children with their families and communities, in adherence to the Aboriginal and Torres Strait Islander Child Placement Principle [ 3 , 19 , 24 ]. Currently, the national policy landscape in child protection services is shifting, with a commitment to working with the national peak body for representing the interests of Aboriginal children and their families, the Secretariat of National Aboriginal and Islander Child Care, on a 10-year roadmap [ 25 , 26 ]. This is designed to give greater focus to the historical and systemic issues in the child protection system, by giving an increased participatory role when decisions are made about Aboriginal children and their families [ 20 , 26 ]. Regardless of these innovative policy reforms, it is likely to be some time before progress will be seen in reducing the number of Aboriginal children living in non-Indigenous care arrangements with mainstream out-of-home care agencies. This underscores the importance of working to ensure Aboriginal children in mainstream agency care are connected to their kin and culture, and that agencies have access to relevant and meaningful cultural training and support [ 3 , 18 , 20 , 24 ].

Research has found the value and focus placed on the importance of cultural connections and cultural immersion activities for Aboriginal children is significantly different between Aboriginal and non-Indigenous agencies, including child protection workers [ 18 , 20 ]. Mainstream agencies identify that there are many competing factors, such as needs that are considered a higher priority for children, that impact whether there is a greater or lesser focus on cultural connections and cultural security [ 20 ]. The Aboriginal workforce, however regardless of competing factors, places much greater value on cultural connection and cultural activity as fundamental for providing care that benefits Aboriginal children [ 18 , 20 ].

The Department of Communities currently provide a commitment to developing and maintaining children’s connection with culture and kin and develop cultural support plans in collaboration with a range of internal and external Aboriginal professional and community stakeholders, including Elders and community leaders specific to a child’s community [ 25 ]. There is no known research into how child protection services conduct and manage cultural information gathering, nor how/whether this information is disseminated to relevant parties who care for children in Australia. However, Aboriginal children’s cultural plans have been found to be failing to provide cultural connections and activities [ 3 ]; and to have omissions/losses that negatively impact the long-term health and wellbeing of Aboriginal children [ 19 , 20 , 26 ].

A critical component for developing strong cultural identities of Aboriginal children in out-of-home care is that cultural support plans consider their place and relationships in culture and community [ 3 , 17 , 18 , 24 , 26 ]. Moreover, reunification should be prioritised in a culturally secure way [ 18 , 20 ]. Given the likelihood of significant layers of intergenerational trauma for Aboriginal children, cultural plans should be flexible, well-funded and consider recovery-focused and strength-based care and should incorporate input from children in care, their parents and kinship networks [ 17 , 18 ]. Strong cultural programs that support connection to traditional lands and Elders, language, kin, community inclusion, cultural activities and cultural knowledge are fundamental components for increasing resilience and wellbeing for Aboriginal children [ 3 , 17 – 19 ].

Applying cultural lenses

The Ngulluk Koolunga Ngulluk Koort framework, comprehensively described elsewhere [ 23 ], brings together Perth Elders’ and Aboriginal community perspectives of what is important for the development of strong Aboriginal children; the aspects of kin and community that are protective for Aboriginal children; and what Aboriginal children need for happy and healthy futures. The framework is transformational and commits to decolonising research, policy and service delivery through the provision of education and awareness about the differences in Aboriginal and other worldviews and values. Further, the way these differences are respected, to increase the capability of governments and services to develop competency for ensuring culturally secure practices are optimal when working with Aboriginal families [ 23 ]. The research identified three priority areas that are of concern to the community: (1) child protection system involvement and the impact of child removal; (2) the importance of early childhood education and care, and early schooling; and (3) housing security and homelessness for Aboriginal families.

This research protocol represents a continuation of the translation of the Elders framework and the focus on child protection/child removal research as a priority. The research draws on the community co-designed set of principles and practice recommendations for child protection work [described elsewhere: 24]. These highlight the importance of kin, culture and community and the child protection sector (government and non-government agencies) focusing on developing the Aboriginal workforce and community-controlled organisations and embracing community-identified strategies to address the social issues faced by many Aboriginal families.

The principles and practice recommendations provide practical ways of working with the Aboriginal community toward better practice in child protection service provision. The history of past child removal policies and the associated distrust of child protection services among Aboriginal communities, means that it is critically important that interventions for Aboriginal children removed from their families and placed in the care of non-Indigenous agencies and carers are co-designed by children and families and community members, are culturally secure, place-based, family-centered, and focused on building on the strengths of Aboriginal people and culture [ 3 , 9 , 18 , 19 , 23 ].

Toward recovery

There is consensus among Aboriginal leaders and scholars that a recovery-focussed, family-centered and intergenerational approach to interventions is required to understand, and interrupt, the ongoing removal of Aboriginal children and the associated adverse health and wellbeing outcomes [ 3 , 19 ]. For Aboriginal children involved with statutory authorities, community led recovery approaches have been identified as central to redressing the harm that continues to reverberate consequences across communities [ 27 ]. As it stands, the long-term health and wellbeing outcomes for Aboriginal children in foster care are poor, with youth detention and adult prisons an inevitable and at times lethal trajectory for many [ 28 , 29 ]. There is a dire need for strength-based recovery-focussed policies and solutions for Aboriginal children involved with statutory systems in Australia to improve their health and wellbeing.

Recovery capital was first conceptualised as strength-based assessment tools that measure the range of internal and external resources that can be used to initiate and sustain recovery from alcohol and other drug problems and mental health issues [ 30 ]. Recovery is conceptualised at three levels: personal, social and community. Personal recovery capital represents an individual’s personal skills, abilities and personal resources including self-esteem, self-efficacy, coping mechanisms and resilience [ 31 ]. It includes individual communication skills, interpersonal and educational/vocational skills, problem-solving capacities, hope, optimism and goals. Social recovery capital refers to the recovery supports available to individuals that allow for identification of intimate relationships, family networks and broader social relationship. Community recovery capital refers to the tangible influences on recovery such as having access to safe housing, meaningful opportunities and accessible services [ 30 , 31 ]. Generally, the recovery capital approach has been targeted towards adult populations. Little is known about the benefits of establishing and building on the recovery capital assets possessed by children and youth [ 32 ].

A recovery capital model constitutes a strength-based framework to assess recovery while being cognisant of trauma and subsequent mental health problems. It offers solutions and hope to the problems that are well established in children who experience child protection interventions [ 33 ]. However, the recovery capital model does not systematically consider how assessments for recovery capital assets can be applied to Indigenous children and young people living in out-of-home care, including those whose pathways find them involved with the criminal justice system. There is much potential for collecting information that allows for measuring the cultural assets of Aboriginal children, or what has been conceptualised as ‘justice capital’ [ 34 ].

The Ngulluk Moort, Ngulluk Boodja, Ngulluk Wirin (Our family, Our Country, Our Spirit) study is being conducted between 2022 and 2026 in partnership with three mainstream out-of-home care agencies and focuses on Aboriginal children living in non-Indigenous care arrangements in Perth and surrounding districts. Cultural governance and advisory groups are in place, community consultation continues to be undertaken, and initial focus groups (described below: 2.2) have been conducted. This paper describes an Aboriginal Elder-and community-led research protocol, that has been co-designed to bring together the complex dimensions associated with Aboriginal family and kinship structures, cultural connections, planning and activities. The aim is to provide out-of-home care agency workers and non-Indigenous foster carers looking after Aboriginal children, with an opportunity to have direct access to children’s Elders and Aboriginal community connections, cultural knowledge and cultural activities and resources.

The primary objectives of the research are:

• To work with Aboriginal Elders and community, alongside stakeholders from out-of-home care agencies, to provide cultural knowledge, resources and activities to bolster current cultural support plans for Aboriginal children in their care.
• To work with non-Indigenous foster carers and out-of-home care agency staff to develop a suite of culturally secure training and workforce support materials.
• To provide recommendations from the research that can assist address structural challenges to collecting and sharing cultural information for Aboriginal children in care with non-Indigenous foster carers.

This research employs a mixed methods approach utilising qualitative and quantitative methods.

Qualitative study

There is increasing global recognition of the imperative of including Indigenous perspectives in research. This is a response to the acknowledged historical shortcomings of research practice to protect Indigenous peoples from the continued legacies of colonisation [ 35 , 36 ]. As such, using culturally secure research methods and practices is critical, to ensure research is conducted in a way that is sympathetic, respectful and ethically sound from the perspective of participants as well as prioritising Indigenous world views, wisdom, knowledge and science to inform the right way of growing up Aboriginal children [ 23 ].

This research, led by the Ngulluk Koolunga Ngulluk Koort Elder child protection expert knowledge holders (hereafter, Elder expert knowledge holders), has been conceptualised and is delivered by the Elders (MP, CP), with a team of Australian Aboriginal study investigators and researchers (SLH, LJ, CM), a senior Māori academic and researcher (RM) and is supported by non-Indigenous investigators and researchers who have previously been involved in co-designing innovative, and high-quality participatory action health research (NI, MOD, CS, BF). The research places Aboriginal Elders and community at the centre by using an Aboriginal Participatory Action Research framework [ 36 ] ( Fig 1 ) and incorporating an Aboriginal worldview and knowledge framework [ 36 , 37 ]. The Aboriginal Participatory Action Research process supports a forum for combining post-colonial and hybrid knowledge in ways that inform interventions, theories, and advocacy [ 36 ]. Knowledge sharing and learning is supported through the Aboriginal Participatory Action Research process as it shifts power, shares resources, and establishes community ownership over research outcomes [ 36 ]. The Aboriginal Participatory Action Research process also supports our work and relationships with teams of non-Indigenous leaders and staff who are providing foster care services for Aboriginal children, and who provide advice and input into the policy and practice development of both their own organisations and government child protection services.

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https://doi.org/10.1371/journal.pone.0301237.g001

Addressing the persistent health and social inequities for Aboriginal children in out-of-home care requires consideration of the broader social and economic contexts in which disparities manifest and exploring the means to rectify those inequities [ 16 ]. Moreover, exploring innovative and culturally appropriate ways that are inclusive of community perspectives for identifying problems and solutions, or the ‘causes of the causes’ [ 15 , 16 ], to address factors that influence health and social inequity. Co-designing research with and for communities using an Aboriginal Participatory Action Research approach is one way to do this. Using a strength-based co-designed approach recognises the cultural wisdom, knowledge and expertise held by the Elders, and positions ideas of family and cultural aspiration as central to the well-being of the whole community and a flourishing future. Aboriginal Participatory Action Research utilises cyclical, dynamic, and reflective processes that aid research implementation and enable community-driven solutions and is consistent with approaches that are inclusive and respectful of Aboriginal forms of wisdom and knowledge [ 36 ].

The Elder expert knowledge holders provide the cultural governance for the research. The Aboriginal Participatory Action Research design, as can be seen in Fig 1 , places the community as central to all aspects of the research. We have established an Aboriginal Community Advisory Group, made up of Aboriginal community members with lived experience of out-of-home care along with a Professional Aboriginal Advisory Group comprised of Aboriginal representatives in the community sector whose work intersect with child protection services. The Elders and the research team are accountable to the broader Aboriginal community of Perth [ 23 ], and present all research activity at Big Elder Meetings, meetings which bring together a wider network of Elders for consultation, codesign and endorsement of activity. As Fig 1 shows, the Aboriginal Participatory Action Research process is an iterative, dynamic, inclusive process of co-design and consultation which encourages partnerships, and through continual evaluation and refinement, offers the best opportunity for research translation which is beneficial for participants and community [ 36 ]. The arrows show the dynamic interactions between the research objectives, the research team, the Elder expert knowledge holders, Big Elder meetings, the out-of-home care agency staff and carers, and the community. It highlights the constant, circular motion of co-design, consultation, and evaluation through to research translation.

The founding work with the agencies in this research identified that there is a significant gap in knowledge and connection with the Aboriginal community for providing optimal cultural care for Aboriginal children. As such, the development and design of this research has been informed by the following question:

Can the provision of Elder and Aboriginal community-led knowledge-sharing forums and cultural training with out-of-home care agency staff, assist to develop sustainable ways to provide cultural connections and cultural activity for Aboriginal children in the care of non-Indigenous carers?

The focus of all aspects of this research will ask questions across three key evaluation areas—research process, research outcomes, and research sustainability. First, in relation to the research process we ask: did the research work in achieving the desired aims and outcomes? Using a variety of research methods and forums we will explore whether the research is achieving the desired outcomes across its implementation, and whether it is acceptable to the Aboriginal Elders and community. In the second key area, research outcomes, we ask: was the research effective for participants? We will examine outcomes across a range of groups, including Aboriginal Elders and the community, Aboriginal children in out-of-home care, and non-Indigenous foster carers and agency staff. In the third key area, research sustainability, we ask: has this research influenced systemic change and has it provided a model for cultural competency training that could be rolled out in mainstream out-of-home care agencies?

Participants.

The qualitative study is comprised of multiple data collection sets including out-of-home care agency staff providing case management and support to foster carers, agency non-Indigenous foster carers with Aboriginal children in their care, and participants from the Aboriginal community. Participant recruitment commenced on 16 November 2022 and will continue over the next 12 months. To date, 51 agency staff, and 27 non-Indigenous foster carers have been recruited and participated in focus groups and individual interviews. It is expected that around 100 participants will be recruited.

Informed consent.

Fully informed written consent has been and will continue to be obtained from all study participants. Participants meet with research team members and are provided with an information statement and consent form. They are given the opportunity to ask questions about the research before providing written consent. Participation is voluntary and participants are informed that they can withdraw from the research at any point without negative consequences to their participation in any program or service. Participants are given an assurance of confidentiality in all publicly available information and peer-reviewed publications. They are advised that data and identifying information will be deidentified or coded as soon as possible, and only deidentified data will be stored on password protected computers and files that will be exclusively accessible to members of the research team.

Data collection.

Out-of-home care agency staff will participate in several rounds of structured focus groups, led by the Elder expert knowledge holders and Aboriginal research team members. Two rounds of focus groups have been conducted. The first aimed to establish the barriers and facilitators to accessing cultural connections, knowledge, and activities for Aboriginal children in their care. The focus groups were semi-structured, exploring four themes: 1. cultural connection; 2. cultural activity; 3. cultural planning, and 4. the research partnership(s). The focus group data has provided a baseline with which to conceptualise and co-design formal and informal cultural training opportunities for the agency workers and carers.

These focus group data have been presented by the research team to the Elder expert knowledge holders, and to the cultural advisory groups and focus group participants for further data interpretation. This has informed the codesign of the necessary elements identified by the out of home care agencies for cultural training.

Focus groups will be conducted twice a year across the life of the research, for feedback, evaluation, and continual refinement of research activity. Multiple sets of semi-structured interviews will be undertaken with non-Indigenous foster carers, for the purposes of establishing the current cultural connections, resources, and activities they can provide to the children in their care, what is needed and wanted, what can be done to fill any gaps in resources and activities.

Data will be collected using forums such as yarning circles, and individual interviews employing a social yarning and research topic yarning approach [ 34 , 38 ]. Yarning has become an established research method, both in Australian and global Indigenous studies, providing a safe place for Aboriginal people to share their feelings, hopes and fears through storytelling [ 38 ]. It is a fluid process of knowledge sharing and respectful communication that is flexible, allowing for adaptations that might be required to support language or literacy difference, and is suitable for both Aboriginal and non-Indigenous participants [ 34 ]. Yarning creates relationships and often reveals rich, insightful, and valuable contexts that may not be identified in traditional forms of Western interviewing [ 38 ]. Using yarning as a data collection method will allow for hearing the complexities which may exist at the intersection of the personal and community lives of participants. Aboriginal research staff will have primary responsibility for data collection to ensure that data collection activity and interactions with Aboriginal participants are culturally relevant and secure.

Data analysis.

Qualitative analysis, data sorting, and coding will be conducted using NVivo software. A coding framework will be developed, partly from a priori theoretical perspective and partly from the main themes established through close reading and thematic analysis of transcripts. Multiple research team members will be involved in developing the coding frame to avoid idiosyncratic interpretation. Analysis will then be thematic, by application of this coding frame to interview transcripts and notes. The study’s Aboriginal cultural guidance groups and community members will be supported to engage with the analysis phase.

The cyclical nature of the Aboriginal Participatory Action Research ( Fig 1 ) process includes confirming with participants the interpretations and findings of qualitative research to ensure accuracy of the views represented [ 36 ]. Analysed qualitative data will be prepared for discussion, community consultation and feedback of research findings to the Aboriginal community/s, key stakeholders, and service providers. Data will also be verified against the Consolidated Criteria for Reporting Qualitative research checklist [ 39 ].

Quantitative study

Informed by the intitial focus group data, a ‘Cultural Knowing, Being and Doing’ survey has been co-designed to examine organisation level comparisons over time in relation to cultural activity, cultural planning and needs. The survey will be sent to participants twice a year for three years to evaluate changes in knowledge and assess the benefits of the cultural information and activities provided over the life of the research to agency staff and carers. This offers an opportunity for continual refinement and improvement of the research activity with our partner organisations as well as providing, in the longer term, an opportunity to explore changes across the three organisations.

Quantitative data will be collected with around 40 agency staff who have been purposely selected through participation in the focus groups. New staff will be brought into the research as participants for both focus groups and survey participation. Agency staff were provided with an overview of the content and aims of the survey during the intitial focus groups. Information and consent to participate in the survey is required via an online consent form to proceed undertaking the survey and contact details for the ethics body and the research team are provided.

SurveyMonkey will be used to develop an online, plain language survey. SurveyMonkey supports data collection through desktop and mobile applications and can be easily implemented using laptops or iPads. Where necessary the survey will be printed, and responses to hardcopy forms will be manually entered into the database.

Data analysis and triangulation.

Quantitative data will be analysed using SPSS software Version 27. We will, primarily, undertake univariable descriptive statistics and test changes in knowledge over time, with a detailed analysis plan to be developed in collaboration with the Elder expert knowledge holders, study’s cultural advisory groups and Aboriginal community members.

Triangulation of qualitative and quantitative data will be conducted over the course of the research by the study investigators, partner organisations, members of the research team and the community. This will provide a framework for the application of interpretative lenses, which will maintain a constant culturally mediated effect on what is being heard and read and translated. The findings from the survey are expected to provide information and guidance for the agencies’ own professional cultural practices and training needs in the future.

This research is being conducted in accordance with and approval from the Western Australian Aboriginal Health Ethics Committee (#1137) and has reciprocal ethical approval from the University of Western Australia. Ethics amendments have been and will continue to be submitted as required for the duration of the research.

The research will be conducted in accordance with the National Health and Medical Research Council’s National Statement on Ethical Conduct in Human Research and Guidelines for Ethical Conduct in Aboriginal and Torres Strait Islander Health Research [ 40 ]. All data records will be managed according to National Health and Medical Research Council ethical guidelines and the universities standards and protocols. Audio recordings will be transferred for transcription via a share-file held on the Telethon Kids Institute’s secure drive. All data records, including audio recordings and transcripts, consent forms and survey data will be managed according to ethical requirements. Consent forms will be stored separately from any data collection forms. All hard copy and electronic data will be securely archived for a minimum of 7 years after the date of publication or research program completion, whichever is the latter, and in accordance with the requirements of the ethics bodies.

The research will honour the rights of Aboriginal peoples to have control over their cultural intellectual property, communities, resources, and Country in the creation, collection, access, analysis, interpretation, management, dissemination, and reuse of data [ 41 ].

Evaluation translation and dissemination

This paper describes the protocol for an innovative co-designed Aboriginal Participatory Action Research [ 36 ] study, designed to mitigate the likelihood for Aboriginal children living in out-of-home care with non-Indigenous foster parents being disconnected from their cultural connections and activities [ 9 , 17 – 20 , 23 , 24 ]. Furthermore, to reduce the chances of experiencing poorer life outcomes [ 2 ], mitigating the grief and loss children experience when they are removed and stemming the erosive consequences of being disconnected from family, language and cultural practices, and participating in community activities. The study is Aboriginal Elder and community-led and takes a family-centered intergenerational approach designed to understand the needs of out-of-home care agencies for providing culturally secure care for Aboriginal children living with non-Indigenous foster carers.

The unique Aboriginal Participatory Action Research [ 36 ] design shown in Fig 1 includes an iterative and constant evaluation using reflective practice. An evaluation framework for the study has been codesigned by the research team and the Elder expert knowledge holders, and this will continue to be refined throughout the research codesign process. We used a program logic approach [ 42 ] to draft the framework. Logic models identify the existing evidence that drives the intentions or aims of the research, research operations and activities allowing for accurate measurement of whether intended outcomes were achieved [ 42 ]. It is an approach which encourages stakeholders to develop a common understanding of how a program is intended to operate to achieve its objectives. In essence, a program logic approach is linear and draws a clear line from the intended aims of the research, the activities undertaken and the intended outcomes [ 42 ]. The evaluation, which will be undertaken across the life of the research through the study’s participatory action research processes, will allow us to capture unintended positive or negative consequences that result from research delivery, the general benefits or challenges for stakeholders and participants and allows flexibility to adapt the research activity as needed.

Broadly, key indicators of success will be measured around Aboriginal Elder and community satisfaction, acceptability and endorsement of research outcomes and research translation. For Aboriginal children in care, success will be measured in terms of increases in cultural knowledge, connection to family and kin and opportunities for cultural activity which will be measured using a justice capital scale ( Fig 2 ). Considering justice capital as they relate to aspects of cultural strengths and assets that are known, when absent, to contribute to child removal, and when present are protective factors for children is important. Given the likelihood of significant layers of intergenerational trauma for Aboriginal children, this measure will consider what is required to support connection to the cultural elements known to improve health and wellbeing and provide opportunities for a meaningful and culturally-connected life [ 17 , 43 – 46 ].

https://doi.org/10.1371/journal.pone.0301237.g002

The statements have been designed following extensive consultation with Elders and the Aboriginal community in Perth for both justice-involved children and those living away from home in statutory care. SLH (first author) has worked closely with Recovery Capital development experts to ensure congruence with the measures and scoring of the REC CAP [ 47 ] Scale.

For Aboriginal young people living in non-Indigenous child protection care arrangements, ensuring that all the cultural aspects that determine their health and wellbeing are considered is crucial. The higher the score, the more likely their recovery journey will provide opportunities for meaningful, happy and culturally connected lives. Lower scores provide important information about what needs to be sought to strengthen a child’s cultural connections, activity and knowledge. The justice capital scale is a self-assessment tool, and Aboriginal agency staff will support children to fill in the scale at two points in the research; prior to and after providing cultural connections, activities and resources.

For non-Indigenous foster carers, measures will be collected during qualitative interviews. We will explore foster carer views and experiences of research acceptability, engagement and inclusion. Measures will be qualitative, exploring relationships and engagement with the Elders and research team. We will explore whether carers are more connected to and engaged with the Aboriginal community and events with Aboriginal children in their care, and whether they have more knowledge about the family connections of children in their care. The ultimate measure of success will be how well the research bolsters cultural care plans that benefit children’s connections, cultural knowledge and cultural activity as identified using the justice capital assets scale ( Fig 2 ).

Evaluation of the research for out-of-home care agency staff will explore their experiences and views about their observations of the impact of the research on children and carers as well as the benefits and challenges for foster carers and the agency staff participating in the research. In addition to the survey as an evaluation tool that allows us to refine the research activity and tailor to the needs of our partner agencies, we will use a variety of mediums such as interviews, focus groups to measure research engagement through ongoing attendance and participation. We will also explore whether there is an increase in cultural awareness and culturally secure practices through the provision of relevant and meaningful cultural training and support [ 48 ]. It is planned to develop training that both imparts knowledge through truth-telling and exploring history, exploring contemporary practice while at the same time engaging the participants in critical reflection of both their own worldviews and potential racial biases with an aim to boost cultural humility and culturally secure practice [ 48 ].

Evaluation and translation will aim to explore the factors that are required to address systemic barriers, known to impact the outcomes for Aboriginal children in care, when cultural support planning is not done well [ 3 , 18 , 19 , 46 ]. Ultimately, the translation of this research and its effectiveness depends on how well the research relationships across the agencies, and the multiple stakeholders and participants are maintained within the Aboriginal Participatory Action Research framework [ 36 ], by understanding how well the research advocates and supports broader systemic change in the out-of-home care sector. Children could find connections with their families able to provide them kinship care, potentially increasing adherence to the Aboriginal and Torres Strait Islander Child Placement Principle [ 17 ]. There is also potential for giving an increased role in knowledge and information sharing to Aboriginal families, communities and organisations, consistent with the Department of Communities commitment to the objectives of the National Voice for our Children 10-year roadmap [ 26 ]. There is potential to provide greater knowledge and understanding about the importance placed on culture and connection for Aboriginal children in out-of-home care, bringing the differences in views between Aboriginal people and non-Indigenous agencies and child protection workers closer together [ 26 ]. The aim is for trust and confidence to be built in the Aboriginal community, and healing and reparations for past harm, by harnessing the extensive networks that exist in the Aboriginal community and promoting the potential for strong families and networks that lessen child removals for future generations and increase the chance of long-term positive social and health outcomes for Aboriginal children unable to live at home with their families.

Acknowledgments

We acknowledge, first and foremost the extensive contribution of the Telethon Kids Institute Ngulluk Koolunga Ngulluk Koort Honorary Elders/Co-Researchers: Aunty Oriel Green, Uncle Allan Kickett (RIP), Aunty Muriel Bowie, Uncle Albert McNamara, Kerry Hunt, Aunty Charmaine Pell and Aunty Millie Penny. We wish to acknowledge the sad passing of Uncle Allan Kickett during the writing of this publication. Uncle Allan was a member of the Child Protection Elder Sub-Group, alongside Aunty Millie Penny and Aunty Charmaine Pell. Uncle Allan’s contributions to our research, to his professional career, and to the Aboriginal community have been extensive and we thank him for his unwavering support and for sharing his extensive knowledge and wisdom. We thank the members of the study’s advisory groups and community representatives for their support and cultural guidance for the research. We acknowledge and thank Professor David Best and Professor Emily Hennessy for their support and advice developing the justice capital assets scale. We acknowledge the work of out-of-home care organisations in Western Australia for their contribution to this research. We acknowledge the commitment, willingness and enthusiasm of agency staff for embracing culturally secure practices for Aboriginal children in their care.

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9 of DU’s Coolest Undergrad Research Projects

Emma atkinson, supported by the undergraduate research center, dozens of students got the chance to present their work at this spring’s showcase..

From science to the arts, students from departments across the University of Denver gathered this spring to present their research projects at the annual Undergraduate Research Showcase.

The Undergraduate Research Center awarded many of these students grants to conduct their research, which culminated in a diverse display of creative and engaging projects. The research represents the a dvancing intellectual growth dimension of the 4D Experience. 

“DU’s undergraduate research activities are truly premier, providing students with rigorous opportunities to work with thought leaders from across campus—flexing their muscles as thought explorers, translating and learning critical skills, and building the confidence to tackle the problems of tomorrow,” says Corinne Lengsfeld, senior vice provost for research and graduate education.

Here are descriptions of nine of this year’s coolest research projects, along with what students told us about their experience.

Saint Brigit and Her Habits: Exploring Queerness in Early Medieval Ireland

Jacqueline Stephenson

“By examining how scholars have used a lens of queerness and mediation to analyze key hagiographies—first of saints, more broadly, and then of female saints—and using these approaches to examine the inherently queer actions, positions and roles found in Saint Brigit’s First Life, we can gain a clearer view of societal views towards queerness during the early medieval period and in early medieval Ireland, in particular.

In doing so, this thesis will help chip away at the monolithic view of the period as well as the queer erasure within—demonstrating that queerness has always been a fundamental part of human society.”

What was the best part about doing this research?

The best part about doing this research was actually contributing to the scholarship and demonstrating that queerness has always been here, even in the places we might least expect. Additionally, seeing concepts we often don't associate with religion and this period were fascinating.

An example is one of her miracles being essentially an abortion, where a young unmarried woman didn't want to be pregnant and came to Saint Brigit, who made the fetus disappear. This is literally a celebrated miracle and not condemned—something shocking considering present-day religious attitudes toward abortion.

Questioning the Effectiveness of the Olympic Truce

Vincent Pandey

“A modern model for peace is the Olympic Truce, a United Nations General Assembly resolution that calls for the pausing and prevention of new conflicts from one week before the Olympic Games through one week after the Paralympic Games. Some argue that the symbolic nature of the truce allows it to create moments of peace in conflicts, while others argue that it is nothing more than a gesture of goodwill that has not actually been used for peace.

My research question is: Has the Olympic Truce ever been successfully implemented to prevent the onset of new conflict or in creating a ceasefire during an ongoing conflict? I use conflict data and case studies to determine the prevalence of conflict during Olympic Truce periods and four case studies to analyze attempts to use the Olympic Truce and some of the challenges faced.”

Hawai'i Uncovered

Lauren Tapper

“Through photography, Hawai'i Uncovered aims to explore the conflicting aspects of identity in Hawai'i, reveal the true characteristics of the state and separate its perception by tourists from the reality that locals know all too well. Photos were taken at popular tourist and lesser known ‘locals-only’ destinations on multiple of the Hawaiian Islands. The photos focused mostly on the way that people interact and exist within these places and amongst each other through a candid and observational lens.

The idea of community and belonging are both the saving grace and downfall of these islands, allowing many to be proud and excited about who they are while also leaving some cast out and forgotten. Both the romanticized and ignored aspects of these islands are what make the state unlike anywhere else in the world and are essential in defining Hawai'i in an honest fashion.”

How did the idea for this research come about?

I got the idea for this research as someone who was born and raised in Hawaii and came to the mainland to notice that there was a huge discrepancy between what people think they know about Hawaii versus what is true, or at least what I know to be true.

Flower Study

Haven Hinds

“This project is a study of extinct flowers, their histories, environments, biology, colors and assigned personalities—manifested in 3D models and a digital book. The goal was to select six flowers with interesting histories and/or cultural connections. Since the flowers can, for the most part, not be seen or kept, they were created in Blender as accurately as possible. To give people a means of connection with the flowers, each was assigned a personality based on different factors. These factors could be the colors they possess, where geographically they thrived, parts of their history or biological factors.

To allow these personalities to further flourish, color spaces were created in Affinity Designer to be applied not only as the background of the models but also as the background of half of the book’s pages. Each person who interacts with the book is encouraged to pick out flowers they connect to and create their own garden of these now-gone flowers.”

The best part about doing this research was finding out some of the love stories connected to these flowers. I am a hopeless romantic, so I enjoyed even the tragic ending to the ancient stories connected to the flowers. Their stories allowed me the opportunity to give the flowers personality traits, allowing others to further connect.

A Midsummer Night’s Dream Specialty Props

“ʽA Midsummer Night’s Dream’ is one of Shakespeare’s most renowned plays, steeped in a world of dreams and magic, and will be the second show in the Department of Theatre’s winter quarter season. This show has the goal of bringing in college audiences that have not had the full experience of witnessing live theatre and who may have previous, negative notions about Shakespearean plays. The goal was to transform our audience for the two hours they witnessed this play into people that can enter a world of magic, try something new, appreciate it, challenge their own thinking and revitalize an art that took a heavy blow in recent years.

In order to achieve this fantastical reality onstage, I worked closely with the costume designer (Janice Lacek) and director (Sabin Epstein) to research, design, prototype and fabricate specialty props and costumes to create the multiple distinct worlds present in ‘A Midsummer Night’s Dream.’ By the end of the project, I designed, prototyped and fabricated five large-scale specialty props sporting at least 50 separate pieces, each using advanced painting and texturing methods, LEDs lights and wiring techniques, foam molding, paper mâché, and wig making techniques.”

Immersive Stargazing: Leveraging VR for Astronomy Education

“This project leverages virtual reality (VR) technology to create an immersive stargazing experience that makes the wonders of the cosmos accessible to all. By integrating VR with educational strategies, we aim to revolutionize the teaching and experience of astronomy. Our primary objective is to enhance astronomy education by developing a VR platform that transcends conventional teaching methods, making celestial phenomena accessible and engaging for users from diverse educational and geographical backgrounds.

This initiative will democratize access to astronomy, promote STEM education and introduce users to advanced technological learning tools. The approach involves constructing detailed celestial models and integrating them into a user-friendly VR interface.”

Flamethrower Vol. 1-3: Innovation in Multidisciplinary Electronic and Acoustic Music

Trevor Briggs

“Flamethrower Vol. 1-3 is a series of three EPs (short albums) that combine jazz, classical and commercial electronic music to produce an artistic work in which the perception, function and musical context of electronic instruments is challenged. I approached these compositions treating electronic components as instruments, writing out scores as I would for brass and wind instruments. I also utilized cutting-edge electronic hardware to generate degenerated, fragmented and evolving sounds.

This type of in-depth sound design allowed me to write, perform and record novel parts for unique instruments. The remainder of the sounds on these recordings incorporate sampling of field recordings to generate new instruments or soundscapes and unconventional recordings of woodwinds (saxophone, clarinet, flute, bass clarinet and alto flute) from Professor Remy Le Boeuf, my faculty partner.”

The best parts of this project were tiny moments where I was really able to see how the final product was coming together. These times came through all parts of the writing and production process. I recorded Professor Remy Le Boeuf on woodwinds a few times for the album, and those sessions were full of those moments. There's a lot of momentary joy in making music with other people. As a recording engineer and producer, I have been feeling very lucky and grateful that I'm the one that gets to capture and shape these moments for others to listen to.

Technology and Homelessness: How Accessibility and Blockchain Technology Could Impact the Unhoused

“This paper discusses how the unhoused population suffers at the hand of technological inequality despite being relatively offline. It presents theories on how this would change if we reapproached how technology is used to assist the unhoused. It suggests implementing blockchain as a resource as well as modifying the websites built for the unhoused.

Employees at shelters are interviewed for this paper about their experiences with using digital resources to rehouse and restabilize the vulnerable. They are asked how the sites can be improved for more optimized use. The sites are also tested against current user experience (UX) standards for accessibility.”

My idea for this research came from two different sources. The first was the approximately 100 hours of volunteer work that I did with a local organization that works with the unhoused. I worked with caseworkers and other employees to get a better understanding of how the organization was run and what major needs were held by the unhoused population. It opened my eyes to a lot of problems that I hadn’t thought about before, especially how difficult it is to escape homelessness and why it is so difficult. 

The second source came later, when I was already working on my thesis paper. I was studying abroad in Greece and took a really interesting UX design class. That class made me start thinking about what UX looked like throughout my life, so when I was looking at government sites to understand where the unhoused would need to be using personal identification, I started noticing all these UX problems that would be easy to fix, which was baffling and frustrating to me, so I decided to add a second part to my paper.

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN

“Suicide remains a global health concern for the field of health, which urgently needs innovative approaches for early detection and intervention. This paper focuses on identifying suicidal intentions in SuicideWatch Reddit posts and presents a novel approach to detect suicide using the cutting-edge RoBERTa-CNN model, a variant of RoBERTa (Robustly Optimized BERT Approach). RoBERTa is a language model that captures textual information and forms semantic relationships within texts.

By adding the Convolution Neural Network (CNN) head, RoBERTa enhances its ability to capture important patterns from heavy datasets. To evaluate RoBERTa-CNN, we experimented on the Suicide and Depression Detection dataset and obtained solid results. For example, RoBERTa-CNN achieves 98% mean accuracy with the standard deviation (STD) of 0.0009. It also reaches over 97.5% mean area under the curve (AUC) value with an STD of 0.0013. Then, RoBERTa-CNN outperforms competitive methods, demonstrating the robustness and ability to capture nuanced linguistic patterns for suicidal intentions. Hence, RoBERTa-CNN can detect suicide intention on text data very well.”

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