Sometimes, researchers resort to collecting data from the most accessible variables in the population of interest—this process is known as convenience sampling. While convenience sampling leaves lots of room for bias, it also helps you speed up your data collection process and get access to the information you need on time.
In this article, we’d look at different reasons you might have to adopt convenience sampling in your research, the best ways to go about it, and how to reduce the effects of convenience sampling bias in your data.
Convenience sampling or accidental sampling is a non-probability sampling method where the researcher selects sample members from only available and easily accessible participants.
Here, the investigator makes little or no effort to connect with the different clusters or sub-groups in the population. Instead, they gather information from any member they can find—no other criteria determines which variable joins the sample.
There are three primary reasons why a researcher would opt for convenience sampling—population size, timeframe, and accessibility.
The researcher might opt for convenience sampling if the population of interest is large, with numerous clusters or strata. Also, if they need to complete the systematic investigation in a short timeframe or do not have access to all the sub-groups, they can collect responses from only the subsets they find.
While convenience sampling is the most common research sampling method, it sure isn’t the most effective. If you only collect data from easily accessible variables, you’d certainly miss out on minority groups, and this affects the validity of your research outcomes. For example, if you send out a voter apathy survey to only your friends and extrapolate the results to the entire community, this passes as convenience sampling.
Convenience sampling is a non-probability sampling technique that involves selecting your research sample based on convenience and accessibility. This means that the researcher draws the sample from the part of the population close to hand. On the flip side, simple random sampling is a probability sampling technique where all the variables have the same chance of being part of the sample population.
In convenience sampling, the researcher relies on accessibility to determine the variables in the research sample. For simple random sampling, the researcher works out the idle sample size and applies the lottery method or the random numbers method to choose variables for the sample.
Convenience sampling is best for pilot testing and hypothesis generation, while simple random sampling is best for research contexts requiring generalizations about a larger group.
Simple random sampling eliminates sample bias because it spells out the method of selecting the research variables. On the other hand, convenience sampling leaves a lot to the researcher’s discretion, which leads to several biases.
Convenience sampling speeds up the research process, helping you to save time and cost. You don’t need to invest lots of resources to collect responses from the members of the research population. It also allows for immediate research outcomes.
Simple random sampling is the best method for collecting data from a large population of interest. It gives a fair representation of the variables in the target population and helps you eliminate researcher bias.
When should you use convenience sampling as opposed to other methods like stratified sampling or cluster sampling? One of the most obvious answers is you should opt for convenience sampling when dealing with a large sample size within a limited timeframe.
Organizations might opt for convenience sampling to measure brand perception and awareness within their target market. In this case, you only need to randomly pick variables from the population, collect their responses, and analyze them for valid research outcomes.
Convenience sampling also comes in handy during concept testing, which happens before any product goes live. It allows the organization to confirm the feasibility and viability of every idea or product before the launch.
Unlike other sampling methods, convenience sampling only requires that you find members of the population who are willing to participate in your research, without any parameters or selection criteria.
It’s also very easy to pull off—you can create and send out a Formplus survey to participants and gather the data you need seamlessly.
Before starting your data collection process, you can use convenience sampling for pilot testing and hypothesis generation. You collect information from some target population members and use this information to make necessary adjustments to your systematic investigation. You can also use these results to generate a hypothesis that can be confirmed or refuted in your research.
Since you’re collecting data from an accessible population, you can quickly process results and arrive at research outcomes on time. Researchers usually just pull whoever is willing to participate from the closest physical location or through their website and get the data they need.
Convenience sampling is a flexible process with little or no rules to follow. You don’t need to work yourself by over calculating sample fractions or ideal sample size. You are free to choose your sample from the population however you want.
One of the significant limitations of convenience sampling is that it subjects your data collection to bias, affecting the quality of your responses. While you can’t entirely remove bias from this method, there are several things you can do to reduce its impact. Let’s look at a couple of them.
Convenience sampling is the easiest sampling method, and you don’t need expert skills or complex procedures to pull it off. Here’s a simple guide on how to use this method in your systematic investigation.
Step 1: Determine the population of interest for your research.
Step 2: Select the participants for your data collection process.
At different points, researchers embrace convenience sampling to help them collect information fast. A real-life example is the “Pepsi Challenge” marketing campaign that happens at large shopping malls randomly. Let’s look at other possible scenarios of convenience sampling.
Example 1: A researcher wants to know how many women in a community use smartphones. He decides to go to a popular mall for middle-income mothers and record their responses to collect necessary data. By doing this, he leaves out every woman who wasn’t at the mall during the data collection process.
Example 2: To collect feedback on a new product idea, the researcher selects some target population members and gathers valuable responses from them ahead of the actual product launch.
Example 3: A company wants to donate money to 100 orphanages in its city. It selects the 100 most popular orphanages and makes donations to them.
Like convenience sampling, quota sampling is also a non-probability sampling method.
In quota sampling, the researcher segments the population of interest into mutually exclusive sub-groups called quotas and relies on their judgment to decide what variables form the sample. It results in a tailored sample that’s proportionate to some characteristic or trait of the population.
On the other hand, convenience sampling involves collecting data from the members of the research sample who are easily accessible. This means that the researcher samples members of the population of interest because they are “convenient” data sources.
While convenience sampling isn’t the best way to have a representative sample for your systematic investigation, it can help you save time when you’re dealing with large sample size. Of course, you need to make a conscious effort to limit the bias in your sample.
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