sampling techniques in case study research

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.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

You Might Also Like:

Excellent and helpful. Best site to get a full understanding of Research methodology. I’m nolonger as “clueless “..😉

Excellent and helpful for junior researcher!

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.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

Statistical Sampling Case Study

To learn about sampling techniques in social science research, students practice tackling a real-world research problem through discussing a hypothetical case..

• To enable students to understand the benefits and drawbacks of various sampling techniques.
• To provide students with experience designing sampling methods to address a real-world research problem.

Class: Sociology 128: Models of Social Science Research

Introduction/Background: This course introduces sociology students to concepts and strategies in social science research. In week five, students learn about nonrandom and random sampling techniques (snowball sampling, simple random sampling, etc.). In discussion section later that week, students apply this knowledge to a hypothetical case study where a researcher aims to study the experiences of homeless people in the United States.

Before Class:

  Students learned about the pros and cons of various sampling techniques in lecture.

During Class:

• In discussion section, students received a handout about various types of sampling techniques, as well as a hypothetical research scenario about researching a population of homeless people in New York City. The instructor and students reviewed the sampling techniques they had learned, including what types of social science research questions each technique would enable researchers to answer.
• Students were then broken up into groups of 2-3. Guided by four questions on the handout, students analyzed the research problem and discussed within their groups the pros and cons of various sampling techniques. The instructor moved between the groups and provided feedback as students were deciding how to answer each question.
• Once each group determined how they would approach the problem, they shared out their choice with the class. The class discussed the benefits and drawbacks to each group's choices.

Students left class with a deep understanding of a hypothetical research scenario and the various considerations they would have to take into account when deciding how to sample a population. They would later use this knowledge in developing their group projects at the end of the semester.

The research scenario prompt, attached

Submitted by Matthew Clair, Teaching Fellow, Harvard Department of Sociology

Sampling in Qualitative Research

The chapter discusses different types of sampling methods used in qualitative research to select information-rich cases. Two types of sampling techniques are discussed in the past qualitative studies—the theoretical and the purposeful sampling techniques. The chapter illustrates these two types of sampling techniques relevant examples. The sample size estimation and the point of data saturation and data sufficiency are also discussed in the chapter. The chapter will help the scholars and researchers in selecting the right technique for their qualitative study.

• Related Documents

Sample size estimation and sampling techniques for selecting a representative sample

Qualitative sampling methods.

Qualitative sampling methods differ from quantitative sampling methods. It is important that one understands those differences, as well as, appropriate qualitative sampling techniques. Appropriate sampling choices enhance the rigor of qualitative research studies. These types of sampling strategies are presented, along with the pros and cons of each. Sample size and data saturation are discussed.

A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

Abstract Background Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context. Methods Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria. Results Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively. Conclusion The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

R2: A computer program for interval estimation, power Calculations, sample size estimation, and hypothesis testing in multiple regression

Barriers to self-care in elderly people with hypertension: a qualitative study.

Purpose Hypertension is the most common chronic disease throughout the world. Self-care is the key criteria in determining the final course of the disease. However, the majority of elderly people do not observe self-care behaviors. The purpose of this paper is to analyze the experiences of elderly people with hypertension in order to understand the barriers of their self-care behaviors. Design/methodology/approach This is a qualitative study with a conventional content analysis approach conducted in Tehran, Iran in 2017. Data collection was done among 23 participants – 14 elderly people; 6 cardiologists, geriatric physicians and nurses working in the cardiovascular ward; and 3 caregivers – who were selected by purposeful sampling. Using semi-structured, face-to-face interviews, data collection was continued until data saturation. Findings Three main categories, including attitude limitations, inefficient supportive network and desperation, all showed barriers to self-care by the experiences of elderly people with hypertension. Originality/value Lack of knowledge of the disease and its treatment process is one of the main barriers to self-care in elderly people with hypertension. Deficient supportive resources along with economic and family problems exacerbate the failure to do self-care behaviors.

Sample Size Estimation

Tamaño óptimo de la muestra.

Key words: Bias, estimation, population, sampleAbstract. The basics of sample size estimation process are described. Assuming the normal distribution, the procedures for estimation of sample size for the mean; with and without knowledge of the population variance, and population proportion are noted. Sample size for more than one population feature is also given.Palabras clave: Estimación, muestra, población, sesgoResumen. Se describen los fundamentos del proceso de la estimación del tamaño óptimo de la muestra. Suponiendo una distribución normal para una población, se notan los procedimientos de la estimación del tamaño óptimo de la muestra para la media muestral con y sin el conocimiento de la varianza poblacional. Se presenta el tamaño óptimo de la muestra con más de una característica poblacional.

P90 The problem with a heuristic approach to sample size estimation for time-to-failure endpoints involving three or more treatment groups

Sample size estimation for negative binomial regression comparing rates of recurrent events with unequal follow-up time, export citation format, share document.

What We Offer

With a comprehensive suite of qualitative and quantitative capabilities and 55 years of experience in the industry, Sago powers insights through adaptive solutions.

• Recruitment
• Communities
• Methodify® Automated research
• QualBoard® Digital Discussions
• QualMeeting® Digital Interviews
• Global Qualitative
• Global Quantitative
• In-Person Facilities
• Research Consulting
• Europe Solutions
• Neuromarketing Tools
• Trial & Jury Consulting

Who We Serve

Form deeper customer connections and make the process of answering your business questions easier. Sago delivers unparalleled access to the audiences you need through adaptive solutions and a consultative approach.

• Consumer Packaged Goods
• Financial Services
• Media Technology
• Medical Device Manufacturing
• Marketing Research

With a 55-year legacy of impact, Sago has proven we have what it takes to be a long-standing industry leader and partner. We continually advance our range of expertise to provide our clients with the highest level of confidence.​

• Global Offices
• Partnerships & Certifications
• News & Media
• Researcher Events

Sago Announces Launch of Sago Health to Elevate Healthcare Research

Sago Launches AI Video Summaries on QualBoard to Streamline Data Synthesis

Sago Executive Chairman Steve Schlesinger to Receive Quirk’s Lifetime Achievement Award

Drop into your new favorite insights rabbit hole and explore content created by the leading minds in market research.

• Case Studies
• Knowledge Kit

Revolutionizing Healthcare Research: A Q&A With Industry Experts

The Deciders April 2024: Michigan Voters in Union Households

Get in touch

• Account Logins

Different Types of Sampling Techniques in Qualitative Research

• Resources , Blog

Key Takeaways:

• Sampling techniques in qualitative research include purposive, convenience, snowball, and theoretical sampling.
• Choosing the right sampling technique significantly impacts the accuracy and reliability of the research results.
• It’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique for your qualitative research.

Qualitative research seeks to understand social phenomena from the perspective of those experiencing them. It involves collecting non-numerical data such as interviews, observations, and written documents to gain insights into human experiences, attitudes, and behaviors. While qualitative research can provide rich and nuanced insights, the accuracy and generalizability of findings depend on the quality of the sampling process. Sampling is a critical component of qualitative research as it involves selecting a group of participants who can provide valuable insights into the research questions.

This article explores different types of sampling techniques used in qualitative research. First, we’ll provide a comprehensive overview of four standard sampling techniques used in qualitative research. and then compare and contrast these techniques to provide guidance on choosing the most appropriate method for a particular study. Additionally, you’ll find best practices for sampling and learn about ethical considerations researchers need to consider in selecting a sample. Overall, this article aims to help researchers conduct effective and high-quality sampling in qualitative research.

In this Article:

• Purposive Sampling
• Convenience Sampling
• Snowball Sampling
• Theoretical Sampling

Factors to Consider When Choosing a Sampling Technique

Practical approaches to sampling: recommended practices, final thoughts, get expert guidance on your sample needs.

Want expert input on the best sampling technique for your qualitative research project? Book a consultation for trusted advice.

Request a consultation

4 Types of Sampling Techniques and Their Applications

Sampling is a crucial aspect of qualitative research as it determines the representativeness and credibility of the data collected. Several sampling techniques are used in qualitative research, each with strengths and weaknesses. In this section, let’s explore four standard sampling techniques used in qualitative research: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling. We’ll break down the definition of each technique, when to use it, and its advantages and disadvantages.

1. Purposive Sampling

Purposive sampling, or judgmental sampling, is a non-probability sampling technique commonly used in qualitative research. In purposive sampling, researchers intentionally select participants with specific characteristics or unique experiences related to the research question. The goal is to identify and recruit participants who can provide rich and diverse data to enhance the research findings.

Purposive sampling is used when researchers seek to identify individuals or groups with particular knowledge, skills, or experiences relevant to the research question. For instance, in a study examining the experiences of cancer patients undergoing chemotherapy, purposive sampling may be used to recruit participants who have undergone chemotherapy in the past year. Researchers can better understand the phenomenon under investigation by selecting individuals with relevant backgrounds.

Purposive Sampling: Strengths and Weaknesses

Purposive sampling is a powerful tool for researchers seeking to select participants who can provide valuable insight into their research question. This method is advantageous when studying groups with technical characteristics or experiences where a random selection of participants may yield different results.

One of the main advantages of purposive sampling is the ability to improve the quality and accuracy of data collected by selecting participants most relevant to the research question. This approach also enables researchers to collect data from diverse participants with unique perspectives and experiences related to the research question.

However, researchers should also be aware of potential bias when using purposive sampling. The researcher’s judgment may influence the selection of participants, resulting in a biased sample that does not accurately represent the broader population. Another disadvantage is that purposive sampling may not be representative of the more general population, which limits the generalizability of the findings. To guarantee the accuracy and dependability of data obtained through purposive sampling, researchers must provide a clear and transparent justification of their selection criteria and sampling approach. This entails outlining the specific characteristics or experiences required for participants to be included in the study and explaining the rationale behind these criteria. This level of transparency not only helps readers to evaluate the validity of the findings, but also enhances the replicability of the research.

2. Convenience Sampling  

When time and resources are limited, researchers may opt for convenience sampling as a quick and cost-effective way to recruit participants. In this non-probability sampling technique, participants are selected based on their accessibility and willingness to participate rather than their suitability for the research question. Qualitative research often uses this approach to generate various perspectives and experiences.

During the COVID-19 pandemic, convenience sampling was a valuable method for researchers to collect data quickly and efficiently from participants who were easily accessible and willing to participate. For example, in a study examining the experiences of university students during the pandemic, convenience sampling allowed researchers to recruit students who were available and willing to share their experiences quickly. While the pandemic may be over, convenience sampling during this time highlights its value in urgent situations where time and resources are limited.

Convenience Sampling: Strengths and Weaknesses

Convenience sampling offers several advantages to researchers, including its ease of implementation and cost-effectiveness. This technique allows researchers to quickly and efficiently recruit participants without spending time and resources identifying and contacting potential participants. Furthermore, convenience sampling can result in a diverse pool of participants, as individuals from various backgrounds and experiences may be more likely to participate.

While convenience sampling has the advantage of being efficient, researchers need to acknowledge its limitations. One of the primary drawbacks of convenience sampling is that it is susceptible to selection bias. Participants who are more easily accessible may not be representative of the broader population, which can limit the generalizability of the findings. Furthermore, convenience sampling may lead to issues with the reliability of the results, as it may not be possible to replicate the study using the same sample or a similar one.

To mitigate these limitations, researchers should carefully define the population of interest and ensure the sample is drawn from that population. For instance, if a study is investigating the experiences of individuals with a particular medical condition, researchers can recruit participants from specialized clinics or support groups for that condition. Researchers can also use statistical techniques such as stratified sampling or weighting to adjust for potential biases in the sample.

3. Snowball Sampling

Snowball sampling, also called referral sampling, is a unique approach researchers use to recruit participants in qualitative research. The technique involves identifying a few initial participants who meet the eligibility criteria and asking them to refer others they know who also fit the requirements. The sample size grows as referrals are added, creating a chain-like structure.

Snowball sampling enables researchers to reach out to individuals who may be hard to locate through traditional sampling methods, such as members of marginalized or hidden communities. For instance, in a study examining the experiences of undocumented immigrants, snowball sampling may be used to identify and recruit participants through referrals from other undocumented immigrants.

Snowball Sampling: Strengths and Weaknesses

Snowball sampling can produce in-depth and detailed data from participants with common characteristics or experiences. Since referrals are made within a network of individuals who share similarities, researchers can gain deep insights into a specific group’s attitudes, behaviors, and perspectives.

4. Theoretical Sampling

Theoretical sampling is a sophisticated and strategic technique that can help researchers develop more in-depth and nuanced theories from their data. Instead of selecting participants based on convenience or accessibility, researchers using theoretical sampling choose participants based on their potential to contribute to the emerging themes and concepts in the data. This approach allows researchers to refine their research question and theory based on the data they collect rather than forcing their data to fit a preconceived idea.

Theoretical sampling is used when researchers conduct grounded theory research and have developed an initial theory or conceptual framework. In a study examining cancer survivors’ experiences, for example, theoretical sampling may be used to identify and recruit participants who can provide new insights into the coping strategies of survivors.

Theoretical Sampling: Strengths and Weaknesses

One of the significant advantages of theoretical sampling is that it allows researchers to refine their research question and theory based on emerging data. This means the research can be highly targeted and focused, leading to a deeper understanding of the phenomenon being studied. Additionally, theoretical sampling can generate rich and in-depth data, as participants are selected based on their potential to provide new insights into the research question.

Participants are selected based on their perceived ability to offer new perspectives on the research question. This means specific perspectives or experiences may be overrepresented in the sample, leading to an incomplete understanding of the phenomenon being studied. Additionally, theoretical sampling can be time-consuming and resource-intensive, as researchers must continuously analyze the data and recruit new participants.

To mitigate the potential for bias, researchers can take several steps. One way to reduce bias is to use a diverse team of researchers to analyze the data and make participant selection decisions. Having multiple perspectives and backgrounds can help prevent researchers from unconsciously selecting participants who fit their preconceived notions or biases.

Another solution would be to use reflexive sampling. Reflexive sampling involves selecting participants aware of the research process and provides insights into how their biases and experiences may influence their perspectives. By including participants who are reflexive about their subjectivity, researchers can generate more nuanced and self-aware findings.

Choosing the proper sampling technique is one of the most critical decisions a researcher makes when conducting a study. The preferred method can significantly impact the accuracy and reliability of the research results.

For instance, purposive sampling provides a more targeted and specific sample, which helps to answer research questions related to that particular population or phenomenon. However, this approach may also introduce bias by limiting the diversity of the sample.

Conversely, convenience sampling may offer a more diverse sample regarding demographics and backgrounds but may also introduce bias by selecting more willing or available participants.

Snowball sampling may help study hard-to-reach populations, but it can also limit the sample’s diversity as participants are selected based on their connections to existing participants.

Theoretical sampling may offer an opportunity to refine the research question and theory based on emerging data, but it can also be time-consuming and resource-intensive.

Additionally, the choice of sampling technique can impact the generalizability of the research findings. Therefore, it’s crucial to consider the potential impact on the bias, sample diversity, and generalizability when choosing a sampling technique. By doing so, researchers can select the most appropriate method for their research question and ensure the validity and reliability of their findings.

Tips for Selecting Participants

When selecting participants for a qualitative research study, it is crucial to consider the research question and the purpose of the study. In addition, researchers should identify the specific characteristics or criteria they seek in their sample and select participants accordingly.

One helpful tip for selecting participants is to use a pre-screening process to ensure potential participants meet the criteria for inclusion in the study. Another technique is using multiple recruitment methods to ensure the sample is diverse and representative of the studied population.

Ensuring Diversity in Samples

Diversity in the sample is important to ensure the study’s findings apply to a wide range of individuals and situations. One way to ensure diversity is to use stratified sampling, which involves dividing the population into subgroups and selecting participants from each subset. This helps establish that the sample is representative of the larger population.

Maintaining Ethical Considerations

When selecting participants for a qualitative research study, it is essential to ensure ethical considerations are taken into account. Researchers must ensure participants are fully informed about the study and provide their voluntary consent to participate. They must also ensure participants understand their rights and that their confidentiality and privacy will be protected.

A qualitative research study’s success hinges on its sampling technique’s effectiveness. The choice of sampling technique must be guided by the research question, the population being studied, and the purpose of the study. Whether purposive, convenience, snowball, or theoretical sampling, the primary goal is to ensure the validity and reliability of the study’s findings.

By thoughtfully weighing the pros and cons of each sampling technique, researchers can make informed decisions that lead to more reliable and accurate results. In conclusion, carefully selecting a sampling technique is integral to the success of a qualitative research study, and a thorough understanding of the available options can make all the difference in achieving high-quality research outcomes.

If you’re interested in improving your research and sampling methods, Sago offers a variety of solutions. Our qualitative research platforms, such as QualBoard and QualMeeting, can assist you in conducting research studies with precision and efficiency. Our robust global panel and recruitment options help you reach the right people. We also offer qualitative and quantitative research services to meet your research needs. Contact us today to learn more about how we can help improve your research outcomes.

Find the Right Sample for Your Qualitative Research

Trust our team to recruit the participants you need using the appropriate techniques. Book a consultation with our team to get started .

The Swing Voter Project Pennsylvania: April 2024

Navigating the Dynamic Future of Qualitative Research

How Connecting with Gen C Can Help Your Brand Grow

Quantifying Digital Detox Experiences Across the Globe

Digital Detox: How Different Generations Navigate Social Media Breaks

Building Trust Through Inclusive Healthcare Research Recruitment

The Swing Voter Project Wisconsin: March 2024

The Deciders February 2024: African American voters in North Carolina

Take a deep dive into your favorite market research topics

How can we help support you and your research needs?

BEFORE YOU GO

Have you considered how to harness AI in your research process? Check out our on-demand webinar for everything you need to know

Impacts of noise pollution from high-speed rail and road on bird diversity: a case study in a protected area of Italy

• Research Article
• Open access
• Published: 20 April 2024

Cite this article

You have full access to this open access article

• Ester Bergamini 1 , 2   na1 ,
• Sofia Prandelli   ORCID: orcid.org/0009-0007-0922-5790 2   na1 ,
• Fausto Minelli 3 &
• Roberto Cazzolla Gatti 2 , 4  

195 Accesses

Explore all metrics

The disturbance of infrastructures may affect biological communities that are exposed to them. This study assesses the impact of high-speed (highway and railway) infrastructures in a protected study site, the Natural Reserve Fontanili di Corte Valle Re (Emilia–Romagna, Italy). We compared bird diversity with sound intensity and frequency in three sampling areas, increasingly distant from the infrastructures at the border with the reserve, during the last 4 years (2019–2022), monitoring sedentary, nesting, and migratory bird species. We hypothesize a decreasing diversity closer to the source of disturbance, which is mostly attributable to noise pollution. Our findings confirmed this trend, and we show that, in particular, disturbance seems to influence species richness more than the total abundance of birds. We also discovered that highway disturbance was much higher than railway in terms of frequency and duration. In light of these results, we suggest that some species, which have a behavioral ecology strongly based on singing to communicate with each other for their reproductive and defensive strategies, may suffer more from constant acoustic disturbance. The installation of effective noise barriers to shield the sound produced by the highways should be considered a mandatory request not only in proximity to houses but also in the vicinity of protected areas.

Avoid common mistakes on your manuscript.

Introduction

Anthropogenic disturbance has been widely studied in ecology since its effects have consequences on communities and their composition (Ausprey et al. 2023 ). According to Pickett et al. ( 1989 ), disturbance can be defined as a change in the ecosystem caused by an external factor to the level of interest. Another well-accepted idea is that disturbance, as every process that causes loss of biomass in a community (Grime 1977 ), also has a negative impact on a community and its elements.

Previous studies showed that the proximity to disturbing infrastructures is one of the biggest causes of biodiversity loss (Van Der Zande et al. 1980 ). In particular, the number of negative effects of roads and railways is high when considering mortality due to vehicle collisions, particularly for reptiles, amphibians, and mammals. Although birds showed contrasting effects on infrastructure’s presence, the impact of the acoustic disturbance may be relevant for these taxa, even if poorly investigated. A recent study from Ausprey et al. shows how any kind of disturbance, in agricultural landscapes, leads to a decline in species and that sensitivities to anthropogenic disturbance are species-specific (Ausprey et al. 2023 ). The decrease in abundance and species diversity in birds is particularly documented at short distances, up to 1 km from the source of disturbance (Benítez-López et al. 2010 ). The disturbance from viable infrastructures is mostly acoustic and its consequences are proportional to the magnitude of the noise. There is preliminary evidence that birds are less affected by train passage, which is discontinuous than by the motor vehicles on the highways, which is quite constant (Wiącek et al. 2015 ). Species that possess communicative skills and base their reproductive behaviors mostly on vocalization are certainly the most affected (Lucas et al. 2017 ). A study conducted by Reijnen and Foppen ( 2006 ) showed that an important level of disturbance, such as big and busy roads, is the main cause of negative abundance trends of birds, compared to the presence of the infrastructure itself.

At the same time, there might be a positive effect that increases bird diversity due to the landscape’s heterogeneity created by roads and railways (Morelli et al. 2014 ; Kajazer-Bonk et al. 2019 ). Close to the infrastructure, woody habitats would be side by side with crops, leading to an increase in the amount of food availability. Moreover, the traffic could lead to a reduction in the number of predators, while artificial light would extend diurnal activities in winter. Having different habitats also means having more nesting sites and chances of hiding (Kajzer-Bonk et al. 2019 ).

This study aims to analyze the impact of acoustic disturbance produced by high-speed road and rail on bird diversity of migratory, nesting, and sedentary species of a protected area in northern Italy. We hypothesize that both species abundance and richness increase with incremental distance from the source of disturbance. On the other hand, we evaluated how the mid-domain effect could lead to a higher abundance and diversity in the core of the reserve and a decrease of them at the edge (Colwell and Lees 2000 ).

Materials and methods

The study was conducted in the bird-ringing site of Natural Reserve Fontanili di Corte Valle Re, in Italy. The site (coordinates 44.7672 N, 10.5328 E) has an area of 37 hectares and is located in the municipality of Campegine (Reggio Emilia). Data were collected at an increasing distance from two big infrastructures: the A1 highway and the high-speed railway Milano–Bologna, both adjacent to the protected area. The presence of specimens was studied for the reproductive and migratory seasons, from autumn 2019 to autumn 2022. The monitoring was done for the MonITRing Project of the National Centre of Ringing (CNI), of the Superior Institute for Environmental Protection and Research (ISPRA).

The protected area is one of the last flatland springs remaining in its natural state. Around the regional Reserve, water crosses cultivated fields and, near the springs, it leads to the growth of hydrophilic plants and reeds. The woods in which nets were located is mainly composed of black alder ( Alnus glutinosa ), grey sallow ( Salix cinerea ), alder buckthorn ( Rhamnus frangula ), black elderberry ( Sambucus nigra ), field elm ( Ulmus minor ), blackthorn ( Prunus spinosa ), English hawthorn ( Crataegus laevigata ), and dogwood ( Cornus sanguinea ). Different microhabitats allow the presence of a rich and varied fauna (Parchi Emilia Centrale 2022 ). The vulnerability of the Reserve, besides the rarity of its flatlands, is due to the closeness to the A1 highway, one of the busiest in Italy, and the adjacent high-speed railroad. The area is also part of the EU Natura 2000 network (Mézard et al., 2009 ) within the Area of ecological restoration Fontanili Media Pianura Reggiana and it is a Site of Community Importance (SCI), in which habitats are protected (Ambiente, Regione Emilia Romagna 2022 ).

Sampling methods and data analysis

The catch of the specimens took place using mist nets that have a mesh size of 16 mm, height of 2.5 m, and width of 12 m. Four of them were placed in three spots hereafter denominated Near, Intermediate, and Far, for a total net length of 48 m in each of them. The three transects were all placed perpendicular to the viable infrastructures and in areas where plant cover and composition are mostly the same.

Highways and railways can be considered the main sources of disturbance since the Reserve has few visitors per year and the trails are far from the nets. The seasonal presence of hunters in the surrounding areas may be considered an additional disturbance factor for the birds and may push them into the Reserve’s core. Distance from the Reserve and high-speed ways was 160 m from the nearest net (Near), 320 m from the intermediate one (Intermediate), and 425 m from the farthest one (Far) (Fig. 1 ). To ensure comparability, we checked that the three areas, although different in shape, have similar vegetation and are surrounded by the same type of agricultural field. One main difference among the three sampling sites is that the Near site is surrounded by a slightly higher forest cover, in an L shape, than the other two sites. We also confirmed that the three sampling points were located in areas with similar vegetation and environmental conditions.

Map of the study site, the regional Natural Reserve Fontanili di Corte Valle Re, with the indication of the three sampling points in which the mist nets were located and the high-speed ways

Acoustic disturbance has been measured in the center of three transects using professional digital phonometers with a range of measures from 30 to 130 dBA and a sensitivity of 0.1 dBA, during optimal weather conditions, without wind or adverse meteorological conditions. The phonometers were set in the center of the three transects, detecting values at the same time for periods of 30 min and three repetitions during the day. We measured the acoustic disturbance in randomly selected days to provide empirical evidence of the differential acoustic impacts in the three sampling areas. We assumed that the acoustic disturbance throughout the year is similar to the average noise detected during the randomly selected days of recordings. We have good reasons to believe that our assumption is correct because the only variable factor is the wind and it is quite unpredictable during the seasons, while the railway and highway traffic noise remains almost constant throughout the year. The measurements returned 3573 records — two measurements every second — for every point, for a total of 10,719. To separate noise pollution caused respectively by railway and highway infrastructure, data were separated by calculating, for each study point, the value corresponding to the 95th percentile, dividing them into average noise values and peak noise values.

Bird captures were made using the standard procedure for birds’ ringing. The nets were opened before sunrise and monitored six times in the following hours. Ancillary data were collected including time of the catch, ring code, recapture, species name, Euring specific code, status, molt, plumage, weight, age, sex, fat, muscle, measurements of tarsus, chord, and third remiges’ length. The equipment included a bird identification guide, rings of various sizes, banding pliers, different rules, and a portable scale. Data has then been transferred to electronic datasets.

The statistical analysis, performed with the software R and ggplot package (R Core Team 2016 ), included the calculation of the AED index ( Absolute Effective Diversity ), to estimate the number of species observed in an area, including those not detected (Cazzolla Gatti et al. 2020 ). We also compared temporal trends, during years and seasons, between the three areas. To understand whether the acoustic disturbance affects species diversity and abundance Wilcoxon’s test (Wilcoxon 1992 ) was applied.

In the three sampling nets, 41 different species of birds were captured and ringed, and their relative abundance was recorded (Table 1 ). The three sampling sites showed differences in their richness and abundance, with higher values always for the Far net (Table 2 ). Some of the species protected under the Bird Directive 2009/147/CE have been ringed and collected in this study: the marsh warbler ( Acrocephalus palustris ), the pied flycatcher ( Ficedula hypoleuca) , the red-backed shrike ( Lanius collurio ), the nightingale ( Luscinia megarhynchos ), and the song thrush ( Turdus philomelos ).

To better analyze the contribution of resident and migratory species to the total richness and abundance in the three sampling areas, we separate species collected in Spring from those collected in Autumn (Figure 2 ). In terms of species richness, the Far net shows higher values for both periods ( p = 0.021 between Near and Far sampling points), whereas Spring abundance shows lower values in the Far net compared to the other sampling sites ( p = 0.036).

Bird species richness (S) and total abundance (N) in the whole study period (a and d, respectively), in Spring (b and e), and Autumn (c and f). The median (central line in the box) represents the values that fall between the second (lower quartile value) and the third quartile (upper quartile value). The whiskers include data that lie between the 10th percentile (lower bound) and the lower quartile value, and between the upper quartile and the 90th percentile. Points outside the lower and upper bounds are outliers

Time series of noise pollution recordings show a clear separation of the three sampling nets, with higher values (in dB) at the Near sampling point (Figure 3 ). Then, the time series were separated into peak noise values and average noise values. This separation of noise pollution allowed us to clearly show that the disturbance caused by the passage of high-speed trains (Fig. 3 b) is scattered (because each recorded peak corresponded to a monitored time of train arrival), whereas the disturbance caused by the noise of motor vehicles on the highway is constant (Fig. 3 c). In both cases, the Near sampling point recorded the highest noise pollution, which was higher than at the Intermediate and Far nets both for railways and highways (Fig. 4 ).

Time series of acoustic disturbances (in dB) ( a ) separated as peaks noise values ( b ) and average noise values ( c ). To separate the noise pollution caused respectively by railway and highway infrastructure, acoustic recordings were separated by calculating, for each sampling point, the value corresponding to the 95th percentile, dividing them into average noise values and peak noise values

Noise pollution (in dB) distribution ( a ) separated into peak values distribution ( b ) and average values distribution ( c ). The median (central line in the box) represents the values that fall between the second (lower quartile value) and the third quartile (upper quartile value). The whiskers include data that lie between the 10th percentile (lower bound) and the lower quartile value, and between the upper quartile and 90th percentile. Points outside the lower and upper bounds are outliers

We analyzed the impact of a highway and a high-speed railway on the species diversity of a protected area in Italy, from autumn 2019 to autumn 2022.

Species richness decreases in the proximity of the sources of disturbance. In fact, bird diversity near the infrastructures is lower than the one observed in the more distant point, as confirmed also by previous studies: bird species richness is strongly affected by human disturbance such as agriculture, forest harvesting, roads, and urban and industrial areas in the surroundings of a habitat (Zhang et al. 2013 ). The significant differences between the nearest and farthest sampling points confirm our hypothesis of a higher impact due to high-speed ways, although the intermediate sampling net shows contrasting results attributable to the mid-domain effect that may lead to a higher abundance and diversity in the core of a reserve and a decrease of them at its edge (Colwell and Lees 2000 ).

In our study, bird abundance was significantly higher in the central area compared to the sampling point near the disturbance source. This is probably due to the mid-domain and edge effects, which lead the specimens to concentrate at the core of the Reserve, seeking protection in a less disturbed habitat. Bird diversity in the center of the Reserve was even a bit higher than at the farthest sampling point from noise pollution, and this may be because this point is close to the limit of the protected area and to the agricultural fields in which hunting seasonally takes place.

Total abundance shows a significant growing trend moving away from the source of disturbance, confirming the hypothesis that infrastructures represent the major reason for differences in bird species. Although a general increase is evident, it is not always linear and may reflect the different distribution of species within the reserve and their different behaviors. Birds that vocalize more could live further away from the source of noise pollution, which would interfere with the correct communication of individuals and consequently with their reproductive strategies, often based on an elaborate execution of the song, as already confirmed by different studies: with increasing background noise levels, males of European robin proved to be more likely to move away from the noise source and changed their singing behavior gradually (McLaughlin and Kunc 2013 ), and the number of birds per study area was shown to increase with the distance from the roads (Polak et al. 2013 ). Generally, highway noise has an impact on birds within several hundred meters (up to 1 km) from the source, despite visual disturbance and vehicular pollutants extended for a shorter distance from highways (Dooling and Popper 2016 ; Benítez-López A. et al. 2010 ).

In our sampling sites, we found a significantly higher bird abundance during autumn than spring and this may be due to migratory species that are probably less affected by anthropic noise while sedentary species are the most impacted. Anthropogenic noise pollution has been observed in other studies to play stronger effects on breeding birds than, in opposition to our results, wintering species, assuming these different responses may reflect species differences in acoustic communications (Wang et al. 2013 ; Catchpole et al. 2003 ). Breeding birds often need an elevated number of frequencies and types of acoustic contacts to complete the entire breeding cycle, such as mate attraction, territorial advertisement, or parent-offspring communication (Wang et al. 2013 ).

The absolute and effective diversity index (AED), which provides a comparable estimation of species missed from the sampling, confirms an important increase in the number of species moving away from high-speed ways. Some of these species could have different habitat preferences, while others, probably present in the reserve, have never been captured for random reasons. The index provided a value of potentially 50.81 living in the Reserve, which seems reliable as many species have been identified besides the 41 species detected in this study. The continuation of monitoring will certainly improve this work, allowing a higher bird species detection.

The highway A1 Milan–Naples and high-speed railway Milan–Bologna are highly busy causing a strong acoustic disturbance as measured by our phonometers. This may also be the reason why the reserve is rarely visited by people, as nature appreciation is enhanced by natural sounds and decreased by extraneous noises produced by road traffic, for instance (Chau et al. 2010 ).

Noise peaks in correspondence with the scattered passage of high-speed and regional trains seem to be less impacting for bird communication than the constant noise produced by the highway although both may importantly contribute to the acoustic disturbance.

It has been documented how the background acoustic disturbance — which grows exponentially in combination with the transit of high-speed trains or with the use of car horns — has repercussions on birdlife (Pickett et al. 1989 ; Parris and Schneider 2009 ; Lucas et al. 2017 ).

Reijnen et al. ( 1995 ) showed evidence that, in woodland, noise is probably the most critical factor in causing reduced densities close to roads. In regression analysis, using vehicle noise and visibility as response variables, the noise seemed to be the best and, in many species, the only predictor of observed depressed densities in the proximity of the road. Similarly, in open agricultural fields, several species of breeding birds showed a density reduction of almost 100% due to dense traffic, possibly causing an important loss of species richness (Reijnen et al. 1997 ), with total population density reduced by 39% in open agricultural grasslands and 35% in woodlands. Several studies also on non-bird species support the finding that, generally, animals change their distributions in response to anthropogenic noise, as the observation of Sonoran pronghorn’s behavior in avoiding loud areas due to military jet overflights (Barber et al. 2009 ). High levels of noise have been shown to affect wildlife physiological aspects, including temporary and permanent hearing loss (Barber et al. 2009 ).

In light of our findings and previous studies, to better protect birdlife and wildlife in general, it would be important to limit the extent of the disturbance by setting anti-noise barriers alongside the infrastructures. Most of the scientific literature is in line in acknowledging that anti-noise walls can reduce acoustic pollution of 5-15 dB: the volume of the sound pressure which a wall can absorb is directly proportional to the size of the sound pressure of the incoming source, considering that the decibel scale is logarithmic, even a small numeric variations translate into a major changes in sound perception (Hranický et al. 2016 ). According to Adamec et al. ( 2011 ), on average, well-designed barriers reduce noise by approximately 4 or more dB, depending on the geometry of traffic noise propagation.

Different types of noise barriers can be considered a valid help: reflecting walls with a smooth surface or absorbing walls with rugged folds are useful not only for the reflection of sound waves but also for their direct limitation; however, it is important to note that reflective barriers are often made with transparent materials and may be a danger for birds if not appropriate visual deterrent is placed (Hranický et al. 2016 ). Another related issue can occur where noise barriers are built only on one side of the track and animals can remain trapped on the infrastructure, but a simple solution can be the placement of stickers with dark contours of predatory birds for deterrence and further the integration of these structures with elevated ecological corridors (Hranický et al. 2016 ). In this specific case, noise barriers should reach a maximum height equivalent to the height of the tree crowns, as they could otherwise create an additional obstacle to the passage of moving birds.

Overall, our study was conducted in a relatively longer timespan (4 years) than most of the previous studies (Reijen et al. 2006 ; Wiacek et al. 2015 ) and, therefore, provides reliable evidence that road and rail traffic, particularly on high-speed ways, is one of the main causes of the loss of bird diversity, even in protected areas.

Data Availability

The data that support the findings of this study are available from the corresponding author, SP, upon reasonable request.

Adamec V, Jedlicka J, Licbinský R (2011) The possibilities for mitigating negative effects of transport. Trans Transport Sci 4(3):135

Article   Google Scholar  

Ausprey IJ, Newell FL, Robinson SK (2023) Sensitivity of tropical montane birds to anthropogenic disturbance and management strategies for their conservation in agricultural landscapes. Conservation Biol 37(6):e14136

Barber JR, Fristrup KM, Brown CL, Hardy AR, Angeloni LM, Crooks KR (2009) Conserving the wild life therein: protecting park fauna from anthropogenic noise. Park Sci 26(3):26–31

Google Scholar  

Benítez-López A, Alkemade R, Verweij PA (2010) The impacts of roads and other infrastructure on mammal and bird populations: a meta-analysis. Biol Conservation 143(6):1307–1316

Catchpole CK, Slater PJ (2003) Bird song: biological themes and variations. Cambridge University Press

Chau KC, Lam KC, Marafa LM (2010) Visitors’ response to extraneous noise in countryside recreation areas. Noise Contr Eng J 58(5):484–492

Colwell RK, Lees DC (2000) The mid-domain effect: geometric constraints on the geography of species richness. Trends Ecol Evol 15(2):70–76

Article   CAS   Google Scholar  

Commissione europea, Direzione generale dell’Ambiente, Mézard N, Sundseth K, Wegefelt S (2009) Natura 2000: proteggere la biodiversità in Europa, (S. Wegefelt, editor, N. Mézard, translato) Ufficio delle pubblicazioni

Connell JH (1978) Diversity in tropical rain forests and coral reefs: high diversity of trees and corals is maintained only in a nonequilibrium state. Science 199(4335):1302–1310

Dooling RJ, Popper AN (2016, July) Some lessons from the effects of highway noise on birds. In Proceedings of Meetings on Acoustics (Vol. 27, No. 1). AIP Publishing

EUNIS, European Environment Agency, 2022. https://eunis.eea.europa.eu/ , last access 31/08/2022

Fahrig L, Rytwinski T (2009) Effects of roads on animal abundance: an empirical review and synthesis. Ecol Soc 14(1):20

Gatti RC (2018) Biodiversity in time and space. Nova Science Publishers, Incorporated

Gatti RC, Amoroso N, Monaco A (2020) Estimating and comparing biodiversity with a single universal metric. Ecol Model 424:109020

Grime JP (1977) Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am Nat 111(982):1169–1194

Hranický PM, Kendra M, Skrúcaný T (2016) Efficiency of anti-noise barriers in railway transport. Tech Issues 3(2016):43–52

Ispra Ambiente, 2022. https://www.isprambiente.gov.it/ , last access 3/05/2022

Kajzer-Bonk J, Skórka P, Bonk M, Lenda M, Rożej-Pabijan E, Wantuch M, Moroń D (2019) The effect of railways on bird diversity in farmland. Environ Sci Pollut Res 26:31086–31098

Lucas PS, de Carvalho RG, Grilo C (2017) Railway disturbances on wildlife: types, effects, and mitigation measures. Railway Ecol, 81-99

McLaughlin KE, Kunc HP (2013) Experimentally increased noise levels change spatial and singing behaviour. Biol Lett 9(1):20120771

Morelli F, Beim M, Jerzak L, Jones D, Tryjanowski P (2014) Can roads, railways and related structures have positive effects on birds?–A review. Trans Res Part D Trans Environ 30:21–31

Parchi EC (2022)  http://www.parchiemiliacentrale.it/ , last access 16/06/2022

Parris KM, Schneider A (2009) Impacts of traffic noise and traffic volume on birds of roadside habitats. Ecol Soc 14(1):23

Pickett STA, Kolasa J, Armesto JJ, Collins SL (1989) The ecological concept of disturbance and its expression at various hierarchical levels. Oikos 54:129–136

Polak M, Wiącek J, Kucharczyk M, Orzechowski R (2013) The effect of road traffic on a breeding community of woodland birds. Eur J Forest Res 132:931–941

R Core Team. (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/

Regione ER (2022) https://ambiente.regione.emilia-romagna.it/it/ , last access 14/06/2022

Reijnen R, Foppen RUUD (2006) Impact of road traffic on breeding bird populations. Ecol Trans Managing Mob Environ 48:255–274

Reijnen R, Foppen R, Braak CT, Thissen J (1995) The effects of car traffic on breeding bird populations in woodland. III. Reduction of density in relation to the proximity of main roads. J Appl Ecol 32:187–202

Reijnen R, Foppen R, Veenbaas G (1997) Disturbance by traffic of breeding birds: evaluation of the effect and considerations in planning and managing road corridors. Biodivers Cons 6:567–581

Shannon CE (1948) A mathematical theory of communication. Bell System Tech J 27(3):379–423

Simpson EH (1949) Measurement of diversity. Nature 163(4148):688–688

Van der Zande AN, Ter Keurs WJ, Van der Weijden WJ (1980) The impact of roads on the densities of four bird species in an open field habitat—evidence of a long-distance effect. Biol Conservation 18(4):299–321

Wang Y, Ding P, Chen S, Zheng G (2013) Nestedness of bird assemblages on urban woodlots: implications for conservation. Landscape Urban Plan 111:59–67

Wiącek J, Polak M, Filipiuk M, Kucharczyk M, Bohatkiewicz J (2015) Do birds avoid railroads as has been found for roads? Environ Manage 56:643–652

Wilcoxon F (1992) Individual comparisons by ranking methods. Springer, New York, pp 196–202

Zhang J, Kissling WD, He F (2013) Local forest structure, climate and human disturbance determine regional distribution of boreal bird species richness in Alberta Canada. J Biogeograph 40(6):1131–1142

Download references

Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement.

Author information

Ester Bergamini and Sofia Prandelli equally contributed and shared the first authorship.

Authors and Affiliations

Department of Biology, University of Pisa, Pisa, Italy

Ester Bergamini

Department of Biological, Geological, BIOME-Biodiversity and Macroecology Lab, and Environmental Sciences (BiGeA), University of Bologna, Bologna, Italy

Ester Bergamini, Sofia Prandelli & Roberto Cazzolla Gatti

Ente di Gestione per i Parchi e la Biodiversità-Emilia Centrale, Modena, Italy

Fausto Minelli

Biological, Geological and Environmental Sciences Department (BiGeA), BIOME Lab, University of Bologna, Bologna, Italy

Roberto Cazzolla Gatti

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Ester Bergamini, Sofia Prandelli, Fausto Minelli, and Roberto Cazzolla Gatti. The first draft of the manuscript was written by Ester Bergamini with the supervision of Roberto Cazzolla Gatti, and all authors worked on manuscript revision and finalization with the lead of Sofia Prandelli. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sofia Prandelli .

Ethics declarations

Ethical approval.

We certify that the manuscript titled “Impacts of noise pollution from high-speed rail and road on bird diversity: a case study in a protected area of Italy” has been entirely our original work except otherwise indicated, and it does not infringe the copyright of any third party. Its submission to Environmental Science and Pollution Research implies that it has not been published previously, that it is not under consideration for publication elsewhere, that its publication is approved by all authors, and that, if accepted, will not be published elsewhere in the same form, in English or any other language, without the written consent of the Publisher. The research activities have been conducted in collaboration with the Natural Reserve Fontanili di Corte Valle Re, which received authorization from ISPRA (Italian Superior Institute for Environmental Protection and Research) to capture and monitor birds (Determinazione di Servizio n. 7307 of 17/05/2018 (2018-2020) and 5720 of 01/04/2021 (2021-2023).

Consent to participate

We affirm that all authors have participated in the research work and are fully aware of ethical responsibilities.

Consent for publication

We affirm that all authors have agreed to the submission of the paper to ESPR and are fully aware of ethical responsibilities.

Additionally, this manuscript has not been submitted to a preprint server before submitting it to Environmental Science and Pollution Research.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: Philippe Garrigues

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Bergamini, E., Prandelli, S., Minelli, F. et al. Impacts of noise pollution from high-speed rail and road on bird diversity: a case study in a protected area of Italy. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-33372-0

Download citation

Received : 20 June 2023

Accepted : 13 April 2024

Published : 20 April 2024

DOI : https://doi.org/10.1007/s11356-024-33372-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Disturbance
• Infrastructure
• Biodiversity
• Find a journal
• Publish with us
• Track your research

• Support Dal
• Current Students
• Faculty & Staff
• Family & Friends
• Agricultural Campus (Truro)
• Halifax Campuses
• Campus Maps
• Brightspace

Dalhousie University

• Student Life
• Media Centre
• DAL Magazine

Most Commented

News archive.

• February 2024
• January 2024
• December 2023
• November 2023
• October 2023

Taking research to new places: Killam scholar trades Atlantic coast for expansive mountain ranges

Mia Samardzic - April 22, 2024

This article is part of a series profiling the inaugural Killam International Research Award recipients who traveled abroad in 2023. 

This time last year, biology PhD student Sam Walmsley was carrying out his research on an endangered population of northern bottlenose whales found in waters close to Nova Scotia — but he was far from the Atlantic coast himself.

Walmsley was one of three recipients selected for the inaugural Killam International Research Award launched in the fall of 2022, which provides rich global experiences for exceptional Killam scholars by offsetting the costs of undertaking research outside of Canada for up to three months.

The award gave Walmsley the opportunity to travel to Zurich, Switzerland from March to May of last year, where he worked with professor Dr. Adrian Jaeggi, who leads a lab investigating the evolution and ecology of human behaviour.

Understanding whale societies

Walmsley’s research aims to uncover the evolutionary origins and functions of whale societies, which share some similarities with human societies that aren’t found in any other animal groups.

“Understanding why these similarities arose despite such different physical environments will allow us to explain the evolution of these traits across species,” he says.

“Studying whales can offer a unique window into the origins of social living, meaning that we can ask questions like 'Why do some species form friendships and not others?' or 'What prompts individuals to cooperate with non-relatives?' For whales specifically, understanding how individuals interact with one another and share knowledge can become important for conservation. For example, social patterns may affect a population’s susceptibility for disease, or influence risky group movements as seen in mass stranding events.”

The Killam International Research Award exposed Walmsley to cutting-edge quantitative tools developed by researchers at the University of Zurich, serving as a lens into evolutionary history and helping to explain the origins of social complexity.

“Though originally applied to human evolution, applying this method to whales allows us to ask new questions like 'Were ancient whale species solitary, or did they rely on others?' This allowed me to expand my research beyond Nova Scotian waters to compare and model societies from whale and dolphin species all around the world.”

International immersion

The opportunity to expand his research in a major way wasn’t the only highlight of Walmsley’s trip.

“It was thrilling to immerse myself in the academic environment at the University of Zurich and the cultural environment of Zurich itself. Outside of my studies, I was able to explore the local mountains, consume many delicious cheeses, and even float down the Limmat River in an inflatable boat with my lab mates one Sunday afternoon. I am enormously grateful to professor Jaeggi and the members of his lab who made it such an interesting and enjoyable experience,” he says.

Walmsley describes being a Killam Scholar as an incredible privilege, granting him the opportunity to explore ideas with global experts in his field.

“Applying the new methods I was exposed to in Zurich to whale populations allows me to expand the scope of my thesis and situate my study of northern bottlenose whales in a wider evolutionary context,” he says. “There is a growing recognition that human impacts can disrupt the social structure of wild animals, which in turn can have consequences for the survival of the population. By monitoring and learning from these whales, I hope that we can continue to ensure their protection for years to come.”

Learn more about the Killam advantage and opportunities at Dalhousie University.

Recommended reading:  New Dal‑based Killam award opens (lab) door for tumour researcher   (from the same series)

Dal News welcomes discussion from members of the Dalhousie community and beyond, but urge comment writers to be respectful and refrain from personal attacks. False or unsubstantiated allegations, libellous statements and offensive language are not allowed. External links must be appropriate and relevant to the subject being discussed.

We encourage commenters to use their real first and last names.

Please note that comments that appear on the site are not the opinion of Dal News or Dalhousie University but only of the comment writer. The editors reserve the right to post, or not to post comments, edit or not edit, at their discretion.

Halifax, Nova Scotia, Canada  B3H 4R2 1-902-494-2211

Agricultural Campus Truro, Nova Scotia, Canada  B2N 5E3 1-902-893-6600

• Campus Directory
• Student Career Services
• Employment with Dalhousie
• For Parents
• For Employers
• Privacy Statement
• Terms of Use

Dalhousie University Halifax, Nova Scotia, Canada B3H 4R2 1.902.494.2211

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• An Bras Dermatol
• v.91(3); May-Jun 2016

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

Jeovany martínez-mesa.

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

David Alejandro González-Chica

2 University of Adelaide - Adelaide, Australia.

Rodrigo Pereira Duquia

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

Renan Rangel Bonamigo

João luiz bastos.

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

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

To introduce readers to issues related to sampling.

INTRODUCTION

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

TO SAMPLE OR NOT TO SAMPLE

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

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

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

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

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

SAMPLE FRAME

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

Examples of sample frames and potential limitations as regards representativeness

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

TYPES OF SAMPLING

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

Sampling types used in scientific studies

NONPROBABILISTIC SAMPLING

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

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

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

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

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

PROBABILISTIC SAMPLING

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

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

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

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

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

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

NONRESPONDENTS

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

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

SAMPLING METHOD ACCORDING TO THE TYPE OF STUDY

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

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

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

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

Financial Support: None.

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