thesis sampling techniques

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.

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Probability-based sampling methods

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

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

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

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

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

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

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

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

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

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

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

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

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

Non-probability-based sampling methods

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

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

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

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

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

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

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

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

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

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

How to choose a sampling method

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

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

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

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

Let’s recap…

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

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

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

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

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

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

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• What Is Non-Probability Sampling? | Types & Examples

What Is Non-Probability Sampling? | Types & Examples

Published on July 20, 2022 by Kassiani Nikolopoulou . Revised on June 22, 2023.

Non-probability sampling is a sampling method that uses non-random criteria like the availability, geographical proximity, or expert knowledge of the individuals you want to research in order to answer a research question.

Non-probability sampling is used when the population parameters are either unknown or not possible to individually identify. For example, visitors to a website that doesn’t require users to create an account could form part of a non-probability sample.

Note that this type of sampling is at higher risk for research biases than probability sampling, particularly sampling bias .

• In non-probability sampling , each unit in your target population does not have an equal chance of being included. Here, you can form your sample using other considerations, such as convenience or a particular characteristic.
• In probability sampling , each unit in your target population must have an equal chance of selection.

Table of contents

Types of non-probability sampling, non-probability sampling examples, probability vs. non-probability sampling, advantages and disadvantages of non-probability sampling, other interesting articles, frequently asked questions about non-probability sampling.

There are five common types of non-probability sampling:

Convenience sampling

Quota sampling, self-selection (volunteer) sampling, snowball sampling, purposive (judgmental) sampling.

Convenience sampling is primarily determined by convenience to the researcher.

This can include factors like:

• Ease of access
• Geographical proximity
• Existing contact within the population of interest

Convenience samples are sometimes called “accidental samples,” because participants can be selected for the sample simply because they happen to be nearby when the researcher is conducting the data collection .

In quota sampling , you select a predetermined number or proportion of units, called a quota. Your quota should comprise subgroups with specific characteristics (e.g., individuals, cases, or organizations) and should be selected in a non-random manner.

Your subgroups, called strata , should be mutually exclusive. Your estimation can be based on previous studies or on other existing data, if there are any. This helps you determine how many units should be chosen from each subgroup. In the data collection phase, you continue to recruit units until you reach your quota.

There are two types of quota sampling:

• Proportional quota sampling is used when the size of the population is known. This allows you to determine the quota of individuals that you need to include in your sample in order to be representative of your population.
• Non-proportional quota sampling is used when the size of the population is unknown. Here, it’s up to you to determine the quota of individuals that you are going to include in your sample in advance.

Note that quota sampling may sound similar to stratified sampling , a probability sampling method where you divide your population into subgroups that share a common characteristic.

The key difference here is that in stratified sampling, you take a random sample from each subgroup, while in quota sampling, the sample selection is non-random, usually via convenience sampling. In other words, who is included in the sample is left up to the subjective judgment of the researcher.

You stand at a convenient location, such as a busy shopping street, and randomly select people to talk to who appear to satisfy the age criterion. Once you stop them, you must first determine whether they do indeed fit the criteria of belonging to the predetermined age range and owning or renting a property in the suburb.

Self-selection sampling (also called volunteer sampling) relies on participants who voluntarily agree to be part of your research. This is common for samples that need people who meet specific criteria, as is often the case for medical or psychological research.

In self-selection sampling, volunteers are usually invited to participate through advertisements asking those who meet the requirements to sign up. Volunteers are recruited until a predetermined sample size is reached.

Self-selection or volunteer sampling involves two steps:

• Publicizing your need for subjects
• Checking the suitability of each subject and either inviting or rejecting them

Keep in mind that not all people who apply will be eligible for your research. There is a high chance that many applicants will not fully read or understand what your study is about, or may possess disqualifying factors. It’s important to double-check eligibility carefully before inviting any volunteers to form part of your sample.

Snowball sampling is used when the population you want to research is hard to reach, or there is no existing database or other sampling frame to help you find them. Research about socially marginalized groups such as drug addicts, homeless people, or sex workers often uses snowball sampling.

To conduct a snowball sample, you start by finding one person who is willing to participate in your research. You then ask them to introduce you to others.

Alternatively, your research may involve finding people who use a certain product or have experience in the area you are interested in. In these cases, you can also use networks of people to gain access to your population of interest.

In this way, the process of snowball sampling begins. You started by attending the meeting, where you met someone who could then put you in touch with others in the group.

Purposive sampling is a blanket term for several sampling techniques that choose participants deliberately due to qualities they possess. It is also called judgmental sampling, because it relies on the judgment of the researcher to select the units (e.g., people, cases, or organizations studied).

Purposive sampling is common in qualitative and mixed methods research designs, especially when considering specific issues with unique cases.

Common purposive sampling techniques include:

• Maximum variation (heterogeneous) sampling

Homogeneous sampling

Typical case sampling.

• Extreme (or deviant) case sampling

Critical case sampling

Expert sampling.

These can either be used on their own or in combination with other purposive sampling techniques.

Maximum variation sampling

The idea behind maximum variation sampling is to look at a subject from all possible angles in order to achieve greater understanding. Also known as heterogeneous sampling, it involves selecting candidates across a broad spectrum relating to the topic of study. This helps you capture a wide range of perspectives and identifies common themes evident across the sample.

Homogeneous sampling , unlike maximum variation sampling, aims to achieve a sample whose units share characteristics, such as a group of people that are similar in terms of age, culture, or job. The idea here is to focus on this similarity, investigating how it relates to the topic you are researching.

A typical case sample is composed of people who can be regarded as “typical” for a community or phenomenon. A typical case sample allows you to develop a profile of what would generally be agreed as being “average” or “normal.”

Typical case samples are often used when large communities or complex problems are investigated. In this way, you can gain an understanding in a relatively short time, even if you are not familiar with what’s going on yourself.

Note that the purpose of typical case sampling is to describe and illustrate what is typical to those unfamiliar with the setting or situation. The purpose is not to make generalized statements about the experiences of all participants. In other words, typical case sampling allows you to compare samples, not generalize samples to populations.

Extreme (deviant) case sampling

Extreme (or deviant) case sampling uses extreme cases of a particular phenomenon ( outliers ). This can mean remarkable failures, successes, or crises, as well as any event, organization, or individual that appears to be the “exception to the rule.” Extreme case sampling is most often used when researchers are developing best-practice guidelines.

Note that extreme case sampling usually occurs in combination with other sampling strategies. The process of identifying extreme or deviant cases usually occurs after some portion of data collection and analysis has already been completed.

Critical case sampling is used where a single case (or a small number of cases) can be critical or decisive in explaining the phenomenon of interest. It is often used in exploratory research , or in research with limited resources.

There are a few cues that can help show you whether or not a case is critical, such as:

• “If it happens here, it will happen anywhere”
• “If that group is having problems, then all groups are having problems”

It is critical to ensure that your cases fit these criteria prior to proceeding with this sampling method.

Expert sampling involves selecting a sample based on demonstrable experience, knowledge, or expertise of participants. This expertise may be a good way to compensate for a lack of observational evidence or to gather information during the exploratory phase of your research.

Alternatively, your research may be focused on individuals who possess exactly this expertise, similar to ethnographic research .

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There are a few methods you can use to draw a non-probability sample, such as:

Social media

River sampling, street research.

Suppose you are researching the motivations of digital nomads (young professionals working solely in an online environment). You are curious what led them to adopt this lifestyle.

Since your population of interest is located all over the globe, it clearly isn’t feasible to conduct your study in person. Instead, you decide to use social media, finding your participants through snowball sampling.

You start by identifying social media sites that cater to digital nomads, such as Facebook groups, blogs, or freelance job sites. You ask the administrators for permission to post a call for participants with information about your research, encouraging readers to share the call with peers.

You are part of a research group investigating online behavior and cyberbullying, in particular among users aged 15 to 30 in your state. You are collecting data in two ways, using an online survey.

You first place a link to your survey in an online news article about cyber-hate published by local media. Second, you create an advertising campaign through social media, targeted at users aged 15 to 30 and linking back to your survey. To entice users to participate, a prize draw (movie tickets) is mentioned in all ads. The survey and the campaign are active for the same length of time.

These two data collection methods are river samples. The name refers to the idea of researchers dipping into the traffic flow of a website, catching some of the users floating by.

You are interested in the level of knowledge about myocardial infarction symptoms among the general population.

For a week, you stand in a shopping mall and stop passersby, asking them whether they would be willing to take part in your research. To try to allow as broad a range of respondents as possible to be included, you interview equal numbers of people from Monday to Friday during working hours.

Sampling methods can be broadly divided into two types:

• Probability sampling : When the sample is drawn in such a way that each unit in the population has an equal chance of selection
• Non-probability sampling : When you select the units for your sample with other considerations in mind, such as convenience or geographical proximity

Probability sampling

For many types of analysis, it is important that the statistical analysis is conducted from a random probability sample from the population of interest. For the sample to qualify as random, each unit must have an equal chance (i.e., equal probability) of being selected.

When you use a random selection method (e.g., a drawing) and ensure that you have a sufficiently large sample, your sample is more likely to be representative, and the results generalizable.

Non-probability sampling

Non-probability sampling designs are used when the sample needs to be collected based on a specific characteristic of the population (e.g., people with diabetes).

Unlike probability sampling, the goal is not to achieve objectivity in the selection of samples, or to make statistical inferences. Rather, the goal is to apply the results only to a certain subsection or organization. These are used in both quantitative and qualitative research.

It is important to be aware of the advantages and disadvantages of non-probability sampling and to understand how they can play a role in your study design.

Advantages of non-probability sampling

Depending on your research design, there are advantages to choosing non-probability sampling.

• Non-probability sampling does not require a sampling frame, so your subjects are often readily available. This can make non-probability sampling quicker and easier to carry out.
• Non-probability sampling allows you to target particular groups within your population. In certain types of research, it is vital that certain units be included in your sample. For example, many kinds of medical research rely on people with a specific health issue.
• Although it is not possible to make statistical inferences from the sample to the population, non-probability sampling methods can provide researchers with the data to make other types of generalizations from the sample being studied.

Disadvantages of non-probability sampling

Non-probability sampling has some downsides as well. These include the following:

• Non-probability samples are extremely unlikely to be representative of the population studied. This undermines the generalizability and validity of your results.
• As some units in the population have no chance of being included in the sample, undercoverage bias is likely.
• Furthermore, since the selection of units included in the sample is often based on ease of access, sampling bias is common as well.
• While the subjective judgment of the researcher in choosing who makes up the sample can be an advantage, it also increases the risk of observer bias .

You can mitigate the disadvantages of non-probability sampling by describing your choices in the methodology section of your dissertation . Specifically, explain how your sample would differ from one that was randomly selected and mention any subjects who might be excluded or overrepresented in your sample.

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

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

Research bias

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

When your population is large in size, geographically dispersed, or difficult to contact, it’s necessary to use a sampling method .

This allows you to gather information from a smaller part of the population (i.e., the sample) and make accurate statements by using statistical analysis. A few sampling methods include simple random sampling , convenience sampling , and snowball sampling .

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

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National Institute for Minamata Disease [Japan]

National Institute for Environmental Studies [Japan]

Niigata Institute for Technology [Japan]

Osaka Prefecture University [Japan]

IDEA Consultants Inc. [Japan]

Office of Mercury Management, Environmental Health Department, Ministry of the Environment Government of Japan [Japan]

Corresponding author

2024 Volume 4 Pages 55-68

• Published: 2024 Received: December 12, 2023 Available on J-STAGE: August 27, 2024 Accepted: June 29, 2024 Advance online publication: - Revised: -

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Three monitoring methods, including manual monitoring methods based on active and passive samplings and an automatic active monitoring method, for atmospheric mercury (Hg) concentrations are recommended in the guidance on monitoring Hg based on Article 22 of the Minamata Convention on Mercury, which entered into force in 2017. However, among these methods, the dataset obtained by manual monitoring based on active sampling and automatic active monitoring has not yet been verified. Parallel observations using these two methods were conducted in one month for each season from May 2021 to February 2022 at urban and rural sites in Japan. The main objective of this study was to evaluate the comparability of the observations obtained using the two methods. Because the sampling duration of the manual monitoring method based on active sampling using the Japanese monitoring network was 24 h, the data on the daily mean concentrations of atmospheric Hg obtained by both methods were compared, and their consistency was evaluated using t -test, correlation analysis, and Bland–Altman analysis.

The observation values obtained by the two methods were consistent (correlation coefficients=0.99 or higher) in all seasons, despite the large seasonal variation in meteorological conditions, and the unpaired t -test indicated that there were no differences between them in each season. Moreover, the Bland–Altman analysis showed that more than 96% of the data points were found to be within the 95% limit of agreement. Therefore, the manual monitoring method based on active sampling used in this study was in better agreement with the automatic active monitoring method. These results indicate that the data obtained by both methods are comparable. Additionally, manual monitoring based on active sampling can be used to build mercury monitoring networks at a lower cost than automatic active monitoring. Overall, the data obtained by both methods were proven to be beneficial for the effectiveness evaluation of the Minamata Convention.

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Thesis: The Impacts of Simplifying Science and How to Achieve Understanding

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Logan Hunt defended her Barrett Honors College thesis entitled, "The Impacts of Simplifying Science and How to Achieve Understanding" in Spring 2023 in front of committee members Jane Maienschein and Risa Schnebly.  https://keep.lib.asu.edu/items/183942

Simplifying science means more than just making science understandable for people of lower chronological age, it also encompasses making science more accessible to people with a lower educational age. Through their “Embryo Tales,” Ask a Biologist discusses topics such as fetal alcohol syndrome, ectopic pregnancies, polio, etc. and the science behind them in an easy-to-understand manner. The Ask a Biologist materials are directed at a younger audience in terms of educational age compared to most textbooks and other sources, which allows them to communicate information to people who otherwise may not comprehend the science at hand. As Ask a Biologist states, their main goal is to “increase communication between scientists and the public” (Ask a biologist). They increase the cognition of the public by using a readability level checker to keep each sentence easy to understand, implementing well thought-out analogies throughout the article, incorporating helpful pictures, and including an engaging, related story at the beginning of each article. This thesis explains studies both for and agains those techniques aiming to make science-related topics more understandable. The thesis encompasses some of my own Embryo Tales with an analysis of them, highlights my role in shaping Embryo Tales into what they are today, and also details how I will apply what I learned to my career as a future physician.

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Simulation modeling to assess line transect distance sampling under a range of translocation scenarios

The accuracy of posttranslocation population monitoring methods is critical to assessing long-term success in translocation programs. Translocation can produce unique challenges to monitoring efforts; therefore, it is important to understand the flexibility and robustness of commonly used monitoring methods. In Florida, USA, thousands of gopher tortoises Gopherus polyphemus have been, and continue to be, translocated from development sites to permitted recipient sites. These recipient sites create a broad range of potential monitoring scenarios due to variability in soft-release strategies, habitat conditions, and population demographics. Line transect distance sampling is an effective method for monitoring natural tortoise populations, but it is currently untested for translocated populations. We therefore produced 3,024 individual-based, spatially explicit scenarios of translocated tortoise populations that differed in recipient site and tortoise population properties, based on real-world examples, literature review, and expert opinion. We virtually sampled simulated tortoise populations by using line transect distance sampling methods and built a Bayesian hierarchical model to estimate the population density for each simulation, which incorporated individual-level covariates (i.e., burrow width and burrow occupancy). Line transect distance sampling was largely appropriate for the conditions that typify gopher tortoise recipient sites, particularly when detection probability on the transect lines was greater than or equal to 0.85. Designing the layout of transects relative to the orientation of soft-release pens, to avoid possible sampling biases that lead to extreme outliers in estimates of tortoise densities, resulted in more accurate population estimates. We also suggest that use of individual-level covariates, applied using a Bayesian framework as demonstrated in our study, may improve the applicability of line transect distance sampling surveys in a variety of contexts and that simulation can be a powerful tool for assessing survey design in complex sampling situations.

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Elizabeth a. hunter, phd, research ecologist.

Multi-Camera Calibration in Motion Capture Systems: Seeking Solutions for Room-Sized Environments

Abstract: This bachelor thesis explores solutions for multi-camera calibration in room-sized motion capture systems using OpenCV. The setup includes approximately 4x5 meters environment.

Multi-Camera Calibration in Motion Capture Systems for Room-Sized Environments

In this article, we will discuss the process and challenges of multi-camera calibration in motion capture systems, specifically for room-sized environments. The focus will be on understanding the key concepts, techniques, and best practices to achieve accurate and reliable calibration results.

Introduction

Motion capture systems are widely used in various fields, including animation, robotics, sports science, and healthcare. These systems typically consist of multiple cameras placed around the environment to track the movement of objects or individuals. Calibrating these cameras is crucial for accurate and consistent data collection.

Challenges in Room-Sized Environments

Room-sized environments present unique challenges for multi-camera calibration. Factors such as occlusion, limited visibility, and varying lighting conditions can affect the accuracy of the calibration process. In this article, we will explore these challenges and discuss potential solutions.

Key Concepts in Multi-Camera Calibration

Multi-camera calibration involves determining the intrinsic and extrinsic parameters of each camera in the system. Intrinsic parameters include focal length, optical center, and lens distortion, while extrinsic parameters describe the position and orientation of the camera in the environment.

Calibration Techniques

Various calibration techniques can be employed for multi-camera systems. These include planar target-based methods, such as the Direct Linear Transform (DLT), and non-planar target-based methods, such as the Camera Calibration Toolbox (CCT). We will discuss these techniques and their advantages and disadvantages.

Best Practices for Room-Sized Environments

To achieve accurate and reliable calibration results in room-sized environments, it is essential to follow best practices. These include selecting appropriate calibration targets, ensuring adequate camera coverage, controlling lighting conditions, and accounting for occlusion and limited visibility.

Example: Calibrating a 4x5 Meter Room-Sized Environment

Let's consider a room-sized environment measuring approximately 4x5 meters. To calibrate the motion capture system in this environment, we can follow these steps:

• Select a suitable calibration target, such as a checkerboard pattern.
• Place the target at various locations throughout the environment, ensuring that each camera can see at least two target corners.
• Capture images of the target from each camera.
• Use a calibration tool, such as the Camera Calibration Toolbox, to estimate the intrinsic and extrinsic parameters of each camera.
• Repeat the process to improve calibration accuracy and robustness.

Multi-camera calibration in motion capture systems for room-sized environments presents unique challenges. By understanding the key concepts, techniques, and best practices, researchers and practitioners can achieve accurate and reliable calibration results, ensuring high-quality data collection for various applications.

Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330-1334.

Bouguet, J.-Y. (2004). Camera Calibration Toolbox for Matlab. Retrieved from http://www.vision.caltech.edu/bouguetj/calib_doc/

Delve deeper into multi-camera calibration techniques and their implementation in room-sized motion capture systems using OpenCV.

Combining suspense and fallback loading in react.

In this article, we'll explore how to use React's Suspense and fallback components to improve the user experience during data fetching.

Tags: :  OpenCV Computer Vision Motion Capture Calibration

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Environmental Science: Atmospheres

Characterization of atmospheric microplastics in hangzhou, a megacity of the yangtze river delta, china.

Microplastics (MPs) have become a key environmental issue over the last few decades. However, while previous studies have mainly focused on aquatic MPs pollution, research on atmospheric MPs remains limited. To expand our knowledge of atmospheric MPs, we collected atmospheric samples using active and dry deposition techniques during one year in an urban environment in the megacity of Hangzhou, China. MPs were identified in the samples using a range of analytical and optical techniques. The concentrations of MPs on the filters collected using active sampling ranged from 0.37-8.9 particles/m3, with an annual mean of 3.2 ± 0.5 particles/m3. The dry deposition rate of atmospheric MPs ranged from 441.18-3181.8 particles/m2/day, with an annual mean of 1387.8 ± 237.7 particles/m2/day. Fiber MPs was the most predominant type while a few film-type MPs were identified. Raman microspectrometer analysis identified that tires (27.0% of MPs) and polyethylene terephthalate (PET, 19.7% of MPs) were the dominant MPs types. Finally, we estimated that the annual dry deposition rate of MPs in Hangzhou urban area was 16.9 ± 2.9 tons. Exploring abundance and deposition of MPs helps to evaluate their potential threat to human health or aquatic ecology, which finally contributes to development of MPs control measures.

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L. Xu, J. Li, S. Yang, Z. Li, Y. Liu, Y. Zhao, D. Liu, A. C. Targino, Z. Zheng, M. Yu, P. Xu, Y. Sun and W. Li, Environ. Sci.: Atmos. , 2024, Accepted Manuscript , DOI: 10.1039/D4EA00069B

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