Probability Sampling: Exploring The Options Available
Consider that you’re looking to run a study to identify the most popular colour from a jar of boiled sweets. The jar contains black, red, orange, green and yellow sweets.
Let’s say you randomly pick the sweets to ensure that each colour has an equal and fair chance. By doing this, you’ve already performed an example of probability sampling in practice.
At this point, you might be asking, how? Well, in our example, each colour represents a unique characteristic within a population. So, by using probability sampling, you will guarantee that each colour has an equal and fair chance of being selected for your study.
Then, if you go on to conclude that the black sweet is the most popular amongst all the sweets, you can be confident in the accuracy of your statement. Why? Because it’s based on the representative sample of the entire population of sweets.
In this blog, we’ll explore in more detail what probability sampling is, the different methods, the tools needed for probability sampling and how to conduct it.
Probability vs non-probability
When it comes to sampling, it’s important to understand the differences between these two methods, so you can select the best approach for your needs.
Operating under the principle of randomness, where every individual or item within a population has an equal, but low chance of being selected, probability sampling provides a solid foundation for researchers to make more accurate predictions and informed decisions.
By contrast, non-probability sampling, though not precise and reliant on the judgement of researchers, can be useful in situations where there is a lack of time and limited resources.
For example, consider if we were looking to survey the leisure habits of people living in Gloucestershire. It would be next to impossible to survey every individual in the county and collect their information. This is where we would use a sample to represent the whole population. And this sample can be picked either randomly or systematically, using different sampling methods.
Probability sampling methods
When it comes to collating accurate data from a diverse population, probability sampling is one of the better choices. Many methods can be used, which will help ensure each member has a non-zero chance of being selected in a sample.
Here are some options to think about.
Simple random sampling
Consider if you put all the items of the population into a hat and began picking out a sample without looking.
Simple random sampling operates on pure chance, with each member of that population having an equal and independent chance of being selected. Therefore, this approach ensures an unbiased representation, making it ideal for use in situations where the population is relatively homogenous.
For example, let’s say you wanted to find out the preferences of smartphone users in a city. If there were 20,000 smartphone users in that city, assigning each of them a unique number and using a random number generator to select 1000 users would represent a simple random sample. Consequently, each smartphone user would have an equal chance of being part of the survey, providing a snapshot of the city’s diverse preferences.
Systematic sampling is all about choosing the ‘kth’ member from a list after randomly choosing a starting point.
For example, if you wanted to survey every 10th customer coming into a store, you could start with a random first customer and then select every 10th person after that.
Such an approach provides a systematic yet unbiased way to sample large populations.
For situations where you have a diverse population, and it can be divided into distinct subgroups (strata) based on specific demographic characteristics (like age, gender, or income), stratified sampling is the most appropriate method of choice.
Here researchers will initially divide the population into strata and then randomly select samples from each stratum. This ensures that there is representation from every subgroup, leading to a more detailed and accurate analysis.
In cluster sampling, researchers will look to divide the population into clusters, such as geographical regions or schools. They will then randomly select whole clusters to be part of the sample, with surveyors interviewing every individual within those selected clusters.
This method is particularly beneficial when it’s challenging to create a complete list of the population, yet simpler to group them into clusters.
Every one of these probability sampling methods serves a different research need. The most important thing to remember is to choose a method that aligns with the specific characteristics of your population and the objectives of your study.
Probability sampling tools
In today’s digital age, everything is accessible with just a few clicks, making research easier and more efficient.
Here are some of the tools you can use.
Random number generators
When it comes to simple random sampling, the use of online random number generators can be a great help.
With online random number generators researchers can generate a list of random numbers corresponding to unique identifiers in the population. These numbers can then help to determine the selected individuals or items, to ensure a perfectly random sample.
Geographic Information Systems (GIS) Software
Tools such as GIS software helps researchers to conduct stratified sampling based on geographical regions. By overlaying population data on maps, researchers can visually analyse the population distribution and build strata for more targeted sampling.
The use of survey software is also valuable, as these platforms help to streamline the sampling process. For example, with the SmartSurvey platform, you can create engaging surveys, collect data, and analyse and act on the insights you get back.
The use of advanced survey platforms not only makes the overall process more time-efficient, but also enhances the efficiency and accuracy of the data you have to work with.
Benefits and limitations of probability sampling
As with any method, while there are many advantages to using probability sampling, there are some disadvantages too. So, it’s prudent to be aware of these, so you can make a better-informed decision about whether it’s the best approach for you.
Since it ensures that your sample mirrors the entire population, you’re left with results that are more applicable in the wider context.
Given that it’s based on chance, probability sampling avoids sampling bias. It also enhances the reliability of your data.
The more accurate representation helps you to generalise your findings. This means that the conclusions drawn from your sample can be extended to the wider population.
Precise statistical analysis
The known probabilities for each member of the sample also enables statistical calculations.
The need for a complete population list
To conduct probability sampling properly, it’s necessary to have a comprehensive list of the entire population, which can be a little challenging in some cases.
In situations where you’re dealing with a large population, it can be a bit time-consuming and resource intensive. You might also need a lot of monetary back-ups to facilitate the study.
Complex to implement
Methods including stratified or multistage sampling, can be complex to design. So, you might need adequate training and expertise to make it work.
How to conduct probability sampling
Having got to grips with the different probability sampling methods and the benefits and limitations of this approach, it’s useful to know how to carry it out.
Here are some pointers to help you.
Define your population
The first thing you need to do is clearly define your target population.
You need to be as detailed and specific as you can, outlining the characteristics and boundaries of your group.
Select a method
You need to base your choice on the nature of your study and the characteristics of your population.
Remember that your choice of method should align with your research objectives.
Create your sampling frame
Next you need to create a list containing all the elements of your population, which is called your sampling frame. This stage is important, as it forms the basis for selecting the sample.
Your frame should be up-to-date and accurate.
Assign unique identifiers
After this you’ll want to add some distinct numbers or codes to each element in your frame. Again, this is important, as these play a vital role in your selection process, ensuring that every element has a fair chance of being selected.
Randomly select samples
Your selection should also correspond to the unique identifiers you’ve assigned to your elements.
These random numbers help guide the choice of your samples. For instance, if you’re employing simple random sampling, you can use a random number generator to pick specific numbers, indicating the selected elements.
Collect data from the samples
Next, you’ll want to be collecting data from your samples, whether that’s surveys, interviews, observations, or any other data collection method relevant to your research.
You also need to be double-checking everything for consistency and accuracy throughout.
Analysis and interpretation
You need to be using appropriate statistical techniques for this stage.
Researchers can use the results from this analysis to make conclusions about the entire population. But you must still double check that everything is valid and reliable.
Report your results
Whatever way you choose to communicate your results, whether that’s through presentations, reports or academic papers, you must clearly outline the method you’ve used, your sample size, frame, and any limitations you encountered during the sampling process.
If you’re to ensure credibility, then transparency in your reporting process is crucial.
Depending on the type of research you’re carrying out, if you want to avoid sampling bias and ensure your results are as accurate and representative as you can make them, then probability sampling is the right approach for you. And while some of the probability sampling methods can be a bit complex to implement, with the right know how, resources and processes this can be easily alleviated - making this approach well worth the effort.