Speaker 1: In this video, we're going to unpack the jargon-filled world of sampling and sampling methods. We'll explain what sampling is, explore the most popular sampling methods, and unpack how to choose the right sampling method for your study. By the end of this video, you'll have a clear foundational understanding of sampling so that you can make informed decisions for your research project. By the way, if you're currently working on a dissertation, thesis, or research project, be sure to grab our free dissertation templates to help fast-track your write-up. These tried and tested templates provide a detailed roadmap to guide you through each chapter, section by section. If that sounds helpful, you can find the link in the description below. To kick things off, let's start with the what. So what exactly does sampling mean within a research context? Well, at the simplest level, sampling is the process of selecting a subset of participants from a larger group. For example, if your research aimed to assess U.S. 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. Yeah, good luck with that. But you could plausibly 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—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. Hmm, can you see what my mind is on? Well, in an ideal world, you would want your sample to be perfectly representative of the population, as that would allow you to generalize your findings to the entire population. In other words, you'd want to cut a perfect cross-sectional slice of that cake, such that the slice reflects every layer of the cake in exact proportion. Unfortunately, achieving a truly representative sample is 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 largely depends on the specific research aims of each study, so don't stress yourself out about that just yet. By the way, if you want to learn more about research aims, objectives, and questions, check out our explainer video up here or click on the link in the description. All right, 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. 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 other 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 predetermined process and not the discretion of the researcher. As a result of this process-driven approach, probability sampling achieves a random sample. Probability-based sampling methods are most commonly used in quantitative research—in other words, numbers and statistics-based research. This is especially true for studies where the aim is to produce generalizable findings—in other words, to produce findings that allow you to draw conclusions about the broader population of interest, not just the sample itself. Right, now let's look at the second approach—non-probability sampling. Non-probability sampling, as the name suggests, consists of sampling methods in which participant selection 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 predetermined process. Non-probability sampling methods are commonly used in qualitative research where the richness and depth of the data are more important than the generalizability of the findings. If that all sounds a little too conceptual and fluffy, don't worry. Next, we'll take a look at some actual sampling methods to make it a little more tangible. To kick things off, we'll look at three popular probability-based random sampling methods. Specifically, we'll explore simple random sampling, stratified random sampling, and cluster sampling. Importantly, this is not a comprehensive list of all possible probability sampling methods. These are just the three most common ones, so if you're interested in adopting a probability-based sampling approach, be sure to explore all of the options. First up, we've got simple random sampling. 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 data set as your sample. Thanks to its simplicity, simple random sampling is easy to implement and, as a consequence, is typically quite cheap and efficient. Because the selection process is completely random, you can generalize your results 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. This may or may not be an issue, depending on what you're trying to achieve, so it's important to always consider your research aims and research questions when you're deciding which sampling method to use. We'll explore that a little later in more detail. Next in line, we've got stratified random sampling. Stratified random sampling is similar to the previous method, but it kicks things up a notch. As the name suggests, stratified sampling involves selecting participants randomly, but from within certain predefined subgroups that share a common trait. These are called strata. For example, you could stratify a given population based on gender, ethnicity, or level of education, and then select participants randomly from each group. The benefit of this sampling method is that it gives you more control over the impact of large subgroups, large 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 an equal number of male and female participants. This would, of course, reduce the representativeness of the sample, but it would allow you to identify differences between subgroups. So once again, you need to think about your research aims, as well as the nature of the population that you're interested in, so that you can make the right sampling choice. Next up, let's look at 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 five neighborhoods. Conversely, with stratified random sampling, you would need to collect data from all over the city, in other words, many more neighborhoods. Either way, you'd still achieve the same sample size, 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 more vast geographic area. As a result, cluster sampling is often the more practical and economical option, especially when the population spans a large geographic area. 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 do the trick. Conversely, if a population is generally quite heterogeneous, in other words, diverse, stratified sampling will often be more appropriate. If you're enjoying this video so far, please help us out by hitting that like button. You can also subscribe for loads of plain language actionable advice. If you're new to research, check out our free dissertation writing course, which covers everything that you need to get started on your research project. As always, links are down in the description. Right, now that we've looked at a few probability-based sampling methods, it's time to explore some non-probability methods. The three that we will be looking at are purposive sampling, convenient sampling, and snowball sampling. First up, we've got purposive sampling, also known as judgment or subjective sampling. The names here provides some clues, as this sampling method involves the researcher selecting participants using their own judgment based on the purpose of the study, that is to say, 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 judgment to engage with frequent shoppers, as well as rare or occasional shoppers, and then analyze the resultant data to understand what perceptions and attitudes drive the two behavioral extremes, frequent versus rare shopping. 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 research bias and judgment error, and it's unlikely to produce generalizable results, so it's best suited to studies where the aim is to go narrow and deep rather than broad. Next up, let's look at convenient 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 predefined or objective or consistent process. Naturally, convenient sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available and willing. This makes it an attractive option if you're particularly tight on resources or time, but as you would expect, this sampling method is unlikely to produce a representative sample and will, of course, be vulnerable to research bias, so it's important to approach it with caution. By the way, if you want to learn more about research bias, check out our video about that up here, or you can hit the link in the description. Last but certainly 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 from the first small snowball and each additional subject recruited through a referral is added to the snowball, making it larger as it rolls along. Snowball sampling is often used in situations 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 to by someone that 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 research questions before you start rolling your snowball down the hill. 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 method of sampling for my study? This is a big question and we could do an entire video covering this, but I'll try to cover the essentials here. 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. I know I'm starting to sound like a stuck record here, but this alignment is really important. Specifically, you need to consider whether your research aims are primarily concerned with producing generalizable findings, in which case you'll likely opt for a probability-based sampling method, or if they're more focused on developing rich, deep insights, in which case a non-probability-based approach could be more practical. Typically, quantitative studies lean towards the former while qualitative studies lean towards 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. For example, if you have easy, free access to a large sample at your workplace or university, along with a healthy budget to help attract your 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. Importantly, you need to be ready for trade-offs. You won't always be able to utilize 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 trade-offs when it comes to sampling. Don't let this get you down, though. As long as your sampling choices are well explained and justified, and the limitations of your approach are clearly articulated, you'll be on the right track. Every study has its limitations, so don't try to hide yours. By the way, if you want to learn more about research limitations, we've got videos covering that, too. Link's in the description. All right, we've covered a lot of ground in this video. Let's quickly recap the key takeaways. Sampling is the process of defining a subgroup, a sample, from the larger group of interest, the population. The two overarching approaches to sampling are probability sampling, random sampling, and non-probability sampling. Popular probability-based sampling methods include simple random sampling, stratified random sampling, and cluster sampling. Popular non-probability-based sampling methods include purposive sampling, convenient sampling, and snowball sampling. When choosing a sampling method, you need to take into account your research aims, objectives, and questions, as well as your resources and other practical constraints. Keep these points in mind as you plan your research, and you'll be on the path to sampling success. If you got value from this video, please hit that like button to help more students find this content. For more videos like this, check out the Grad Coach channel and subscribe for plain language, actionable research tips and advice every week. Also, if you're looking for one-on-one support with your dissertation, thesis, or research project, be sure to check out our private coaching service, where we hold your hand throughout the research process, step by step. You can learn more about that and book a free initial consultation at gradcoach.com.