Sampling Methods

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CX 101: Sampling Methods

When you want to get information from customers, it might seem nice to be able to ask every single customer. To make that happen, you would need every customer to agree to be surveyed, and it would take an extreme amount of time, effort, and money to then ask every customer your survey questions. Even then, you would have an inordinate amount of data to sift through. It’s true that you could definitively make claims about what your customers are saying, but it’s not actually necessary to go through this level of work. In fact, most likely, it’s not possible to survey every single customer.

Instead of surveying every single person you want feedback from, most people use a concept called sampling instead and rely on sampling methods to research a group. Sampling allows you to get information from a group of people, and when done correctly, the information is also generalizable and usable. We’ll walk you through sampling, types of sampling methods, and how to begin using some of these techniques. 

What Is Sampling?

Sampling is using a group of your population to understand the population as a whole. Think of sampling as you would with sampling a cake. To see if a whole cake is delicious, you can usually tell by eating a slice of the cake. That slice of the cake can tell you a lot about the taste, texture, consistency, and overall balance of the cake—and it’s much easier to eat just a slice instead of an entire cake. Sampling for surveys works much the same way. 

You take a group of your population and survey just them. It’s typically much more manageable and affordable to do so when you’re doing large scale research. From there, your data team will be able to analyze the data from the sample—which is typically a smaller amount that’s easier to glean important insights from. The insights from sampling—if your sampling is done correctly—can then tell you about the whole group you’re researching. And it can help you gather these insights at a fraction of the cost and much less effort than it would take to survey the entire group. 

Difference Between Population and Sample

To better understand sampling methods, it’s important to distinguish between the population and the sample. The population is the entire group of people you want to learn about and to be able to draw conclusions about. For example, if you wanted to determine how your customers felt about a new product, your population would be every single customer that’s purchased the new product from you. If you wanted to research the grocery shopping habits of single mothers, your population would be every single mother. 

The sample is a representative group of your population that will be participating in your research or survey. The key is that the sample has to be an accurate representation of your population. For example, if you were researching the grocery shopping habits of single mothers, you couldn’t go to a local grocery store and survey every person who walked in. You would get data, but it wouldn’t be data about the population you’re trying to study. As with the cake analogy, the sample or slice has to accurately represent the entire cake. 

It’s important to remember that population doesn’t necessarily mean “big” and sample means “small.” Populations can be defined by so many factors: geography, age, gender, income, and so many more factors. You can have a tiny population of just a particular set of customers or a large population like the entire adult population of North America. The larger, more dispersed, or more diverse your population is, the harder it will be to sample. 

What Are Sampling Methods?

When you want to do a survey or perform research, you’ll need to use sampling methods to determine who will be a part of your sample and how it will be related to your population. Carefully consider how you will select a sample that is as representative of your population as possible. In general, there are two categories of sampling methods: probability sampling and non-probability sampling. 

Probability sampling is when each member of the population has an equal chance of being selected to be included in the sample. The sample participants are chosen randomly, and the results from the survey are generalizable to the population as a whole. Probability sampling methods are typically more accurate than others, but they are also more time consuming and expensive to make possible. 

On the other hand, non-probability sampling is when each member of the population does not have a chance of being selected. With these sampling methods, you could choose your sample based on convenience or other limiting criteria that make it so that every person isn’t eligible to be selected.

For example, if you wanted to study all of your customers, it would be a non-probability approach to then just select a sample of customers who have subscribed to an email list. In this situation, you would be limiting who could be selected to those on a list, which may or may not be accurate to your entire population. With non-probability sampling, it’s generally much more affordable and easier to do research, but you do run the risk of accumulating higher amounts of sampling error and reducing the likelihood of having a generalizable sample. 

Probability Sampling Methods

To perform a probability sampling survey, there are several methods that are commonly used. These are some of the most commonly used probability sampling methods: 

Simple Random Sampling

Simple random sampling is the simplest way to get a sample where every member of the population had an equal chance of being selected. To do a simple random sample, you will choose a way to randomly select a certain number of people from your population to survey. Some common methods include using a random number generator, drawing a name out of a hat or bowl, or any other type of chance. 

For example, you could number each customer you’ve had and use a random number generator to determine who will be a part of your sample. You could use a list generator to select certain customers from a list of names or emails. However you do it, the key is that it’s random. 

Systematic Sampling

Using simple random sampling can be extremely time consuming with a large population, so many will instead use systematic sampling. Systematic sampling is using some sort of designated system to choose randomly. For example, you could number all of your customers and choose the tenth individual. Choosing systematically saves you time and effort but still provides you with a random sample. 

Stratified Sampling

Stratified sampling is most useful when you have groups of people who should be sampled from equally. First, you divide your population into groups that don’t overlap (i.e. people from one group can’t be in another group). From there, you’ll randomly select a sample from each group. 

For example, if you were looking at your customers, you might want to break them up by annual income to see if that affects what you’re researching. Your stratified groups would then be done by income, and you would select a small sample from within each group. 

Cluster Sampling

Cluster sampling also involves splitting your population into groups, but these groups should be split randomly if possible. Then, instead of selecting from each group, you will randomly select groups and sample everyone in the group. For example, an airline might randomly select a certain number of flights each day and survey every passenger on those flights. 

Non-Probability Sampling Methods

Since probability sampling can be time consuming, some people will use non-probability sampling methods instead. These methods are generally not generalizable to the whole population as they may or may not be an accurate representation of the population. 

Convenience Sampling

Convenience sampling is choosing a sample based on ease of access. Instead of choosing from a population randomly, you choose from a population based on who is easy to communicate with. For example, standing in front of a grocery store and surveying everyone who walks past is convenience sampling. Not every member of your population has an equal chance to be chosen, and your data will only represent one day at one grocery store.

Choosing customers based on being subscribed to newsletters or who follow your company on Instagram could also be convenience sampling (if your population is larger than just “those who follow us on Instagram”) because it’s all about ease of access. 

Voluntary Response Sampling

Voluntary response sampling is when you select a sample based on who wants to be a part of the sample. The individuals can voluntarily choose to respond or not respond based on a general call for responses. For example, you could send out an email to every customer and ask them to join the study. Those with strong opinions or interest would be the most likely to join, which could mean your population isn’t representative. 

Purposive sampling

Purposive sampling selects a sample based on what a researcher decides. Essentially a researcher will be the one to determine if someone is in the sample or not. For example, you could put out a survey, and the researcher would then only look at the surveys for people who they decided met a certain criteria: like having purchased the most recent product. 

Snowball Sampling

Snowball sampling is used when a population is hard to reach. For example, if your research requires data from shelterless people, you may have a hard time reaching them for a survey. Snowball sampling is when you use just a few individuals you can find from this group or even choose participants based on whose family or associates you can contact. While snowball sampling isn’t random, it can be useful for certain populations that you may not be able to survey in another way. 

The Bottom Line

Overall, there are many sampling methods to choose from when planning your surveys. The end goal is to try to get your sample to be as representative as possible of your overall population, so you can use the results to generalize about the population and make conclusions. Poor sampling will give poor results. After all, as we all know, if we put crappy data in, we get crappy results, which don’t benefit anyone. Choose a representative sample instead for beneficial results
See how InMoment can help you with your sampling and survey efforts to help you choose the right sampling methods to get a representative sample.

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