If you’re a market researcher or academic researcher, you probably need groups of people to interview, survey, or conduct experiments with. To accomplish this, knowing what kind of sampling techniques are most suitable for your research is key to obtaining reliable, credible, and valid findings!
In this article, you’ll learn about randomized vs. non-randomized sampling so that you can select the most suitable method and strategies for your needs.
What Is Sampling?
To begin, it’s important to understand what sampling is and how it differs from a target population. A population (or target population) is the group of people your study aims to generalize or draw conclusions from.
If you’re conducting an experiment on a new drug for people suffering from type 2 diabetes in New Zealand, then your target population is people suffering from type 2 diabetes in New Zealand.
A sample is the number of people that participate in your study for your intended purposes. Who these participants/subjects are will depend on what you’re investigating.
To conduct the experiment from the previous example, your sample might include 200–1,000 diabetic people in New Zealand. The number of people will largely depend on your study, methods used, and how you’re going to prove the reliability of your data, which will be discussed later.
What Is Randomized Sampling?
Randomized sampling, also called probability sampling, is a sampling method that selects a portion of a target population randomly. This method is most suitable if you need a sample that represents an entire population.
Random sampling is most often used in quantitative research, which aims to use data and figures to represent a large population.
Four Types of Randomized Sampling
1. Simple Random Sampling
In a simple random sample, all persons in a population have an equal chance of being selected. To employ this method, you can use any random generator or lottery method to select potential participants.
You need 100 students from a local elementary school for your research. After gaining permission from the school and relevant authorities, each student is assigned a number. You then use a random number generator online to select 100 students from the school.
Be sure to check your sample for any patterns that could create bias in your results. Using the above example, if your study aims to measure literacy rates among elementary school children ages 5–12, but your random generator selects 75 students under the age of 7 (with the remaining 25 being 8–12), then your data is not going to be representative of your target population.
2. Systematic Sampling
Similar to the random sampling strategy, this method also assigns a number to every member in a population. However, instead of using a lottery method or random generator to select participants, you create a system.
You need 100 students from a local elementary school for your research. After gaining permission from the school and relevant authorities, each student is assigned a number. This time, you create a system where 10 students from each class (assuming there are 10 classes) are selected by their teachers to participate in your study.
Be careful of potential patterns in your population sample that you did not account for. In the above example, if there are more classes for grade 5 than all other grades, there will be a higher number of students aged 11 and 12. Alternatively, maybe the teachers selected their students while other groups of students were not present in the class – so the entire population didn’t have an equal chance of being selected.
3. Stratified Sampling
To conduct a stratified sampling method, you must divide your target population into subpopulations (e.g., gender, age, race, or nationality). This sampling method is particularly useful if you have a diverse target population, which is why it’s commonly used in political polling.
You need 100 students from a local elementary school for your research. After gaining permission from the school and relevant authorities, you group the students into subpopulations based on grade level. After grouping them into five subpopulations (one for each grade) you select 20 students from each population via a random generator, lottery, or selective sampling technique.
This method is not suitable if the target population cannot be clearly partitioned into subpopulations. Additionally, you must scale the sample sizes to the subgroup sizes.
4. Cluster Sampling
Similar to stratified sampling, you’ll divide your target population into subpopulations/subgroups. However, instead of each group having distinct characteristics (e.g., groups based on age or gender), they’re each representative of the entire target population (mini populations of the larger target population). From there, you can select which subgroup you’ll use as your sample.
This strategy is effective if you have a large target population that’s spaced out geographically, helping to cut time and costs.
You have 15 elementary schools in your local district and need a study sample of 400 students. Instead of going to each school and using one of the above strategies for each one, you select only four schools (using a random or selective sampling method) as your sample population.
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You must be careful of potential errors or biases in the sample to ensure it represents the entire population. For example, if the four schools you randomly selected are all geographically located in similar areas or they’re all in affluent neighborhoods, then your data is not representative of all neighbors or schools with students from varying socioeconomic backgrounds.
What Is Non-Randomized Sampling?
In non-randomized sampling, specific criteria are being measured, observed, or investigated, and individuals in the target population do not have an equal chance of being selected.
This method is usually easier and more cost-effective than randomized sampling. However, it can leave more room for bias, which is why you should still aim to ensure that the sample is reflective of the population.
This sampling method is mostly used in qualitative research, which aims to gain greater knowledge and understanding of a phenomenon.
Four Types of Non-Randomized Sampling
1. Convenience Sampling
As can be inferred by the name, convenience sampling involves using participants that are conveniently accessible to the researcher. While this is a cost-effective way to conduct research, it’s usually not representative of the target population. As a result, the findings are not reliable or generalizable, and the sample usually contains sampling and selection bias.
You’re a graduate student studying student mental health at your university. You conduct interviews with your fellow graduate peers in your department to gather data and information for your research.
Your sample is not representative of the entire population size. In this example, it would be key to interview students from all departments and levels.
2. Voluntary Response Sampling
In this strategy, you have an open invitation to the public to volunteer in your study. For instance, you might share an invitation through posts on social media or fliers around your neighborhood or campus.
Using the above example of studying mental health at your university, you decide to put up fliers in your library and class buildings and share invites on social media platforms to recruit your peers to answer a survey.
This method is almost always biased in some way because some people are more likely to volunteer than others (e.g., people with a strong opinion on your research topic are more likely to volunteer), which can result in self-selection bias.
In the above example, for instance, you may have a large response from people who struggle with mental health because it relates to them more than others.
3. Purposive Sampling
In this strategy, you use your judgment to select your study participants. This is commonly used to save time and money, as well as when there are a limited number of people in the target population to select.
You’re studying mental health at your university, so you go to the busiest street on your campus and stop people to fill out a survey. The people you stop are solely based on your judgment of whether or not you think they’re suitable to participate.
This method is prone to errors due to the researcher’s judgment, unreliability, bias, and inability to generalize research findings. For this reason, it may not be suitable for most research.
4. Snowball Sampling
In this technique, you ask a number of people to participate in your study. You then ask those participants to recruit a select number of people they think would be suitable for the study as well. As a result, the number of participants snowballs as more people recruit others to join.
You’re studying mental health at your university but are only taking online classes, and the campus is closed. To overcome this, you send an email to students in your online class asking them to participate. To those who respond to the survey, you ask that they invite five other students to participate.
The snowball method can continue until you have your desired sample size. This strategy is particularly useful if you do not have access to enough people who are suitable for your research, as in the example.
This strategy can create bias in your sample since you’re relying on the judgment of your participants to recruit more participants. Additionally, your sample is at risk for not being representative of your target population.
How to Select a Sampling Method
Which sampling method you use will be based on whether your research is qualitative or quantitative, the size of your target population, the time and funds you have to allocate, and your access to potential participants.
Overall, you should select a sampling method that ensures your findings have the highest level of reliability, credibility, and validity and the lowest level of bias.