What Is Convenience Sampling? | Definition & Examples

Convenience sampling is a non-probability sampling method where units are selected for inclusion in the sample because they are the easiest for the researcher to access.

This can be due to geographical proximity, availability at a given time, or willingness to participate in the research. Sometimes called accidental sampling, convenience sampling is a type of non-random sampling.

Example: Convenience sampling
Suppose you are researching public perception towards the city of Seattle. You have determined that a sample of 100 people is sufficient to answer your research question.

To collect your data, you stand at a subway station and approach passersby, asking them whether they want to participate in your research. You continue to ask until the sample size is reached.

Note: Make sure not to confuse random selection with stopping passersby at random.

  • In probability (or random) sampling, random selection means that each unit has an equal chance of being selected.
  • In convenience sampling, stopping people at random means that not everyone has an equal chance of forming part of your sample. For instance, here you have excluded people who did not pass through that subway station on the day and time you were collecting your data.

When to use convenience sampling

Convenience sampling is often used in qualitative and medical research studies.

In medical research, convenience sampling often involves selecting clinical cases or participants that are available around a particular location (such as a hospital) or a medical records database.

In qualitative research, convenience sampling is often used in social sciences and education where it’s convenient to use pre-existing groups, such as students.

Convenience sampling could be a good fit for your research if:

  • You want to get an idea of people’s attitudes and opinions
  • You want to run a test pilot for your survey
  • You want to generate hypotheses that can be tested in greater depth in future research

Be aware that convenience sampling can introduce several types of research bias, such as selection bias and sampling bias.

Convenience sampling examples

There are several ways to draw a convenience sample. Here are a few examples:

Example: Online convenience sampling

You are researching how parents use a popular online parenting forum. You want to find out if parents are likely to participate in discussions online or just “lurk,” as well as what kind of information they are seeking there.

Since it’s an online community, there is no membership list to use as a sampling frame. This is a good scenario for using convenience sampling. You decide to draw a convenience sample of 100 users.

You create a pop-up ad that invites users to complete your online survey, which the administrators agree to place on the website.To entice users to participate, a prize draw is mentioned in the ad.

Example: Convenience sampling based on location

Suppose you are researching why people visit Monroe Lake Recreation Area, a popular recreational destination in your county. To gather insights, you stand in a parking area and approach people at random, asking them if they would be interested in participating in a five-minute anonymous survey on their preferred recreational activities.

To maximize the number of responses, you also create flyers with a scannable QR code and a shortened URL link. You place them at the Welcome Center and other locations around the lake.

Crowdsourced convenience sampling

You are conducting research into attitudes toward depression. You are interested in the difference between collectivistic and individualistic cultures. As an early-career researcher, you do not have an extensive international network. You decide to use a crowdsourcing platform, like Amazon Mechanical Turk (MTurk).

MTurk covers a wide range of demographic populations both in the United States and internationally. This enables you to access a more diverse pool of respondents in exchange for monetary compensation.

Here, you set up a short survey for screening purposes. You can then separate out people who qualify for your longer survey, rewarding them with bonus pay. Alternatively, you can email participants if they qualify for the longer survey, or set location-specific criteria, so as to meet the collectivist or individualist criterion.

Example: Convenience sampling of a pre-existing group

You are doing a survey to investigate work satisfaction at a large camping gear company in your town. The manager has given you permission to conduct your research but cannot give you a list of all employees due to privacy regulations.

As you do not have a sampling frame, you cannot use probability sampling. Instead, you decide to use convenience sampling. You stand next to the coffee machine and approach random employees, asking them to fill in your quick survey.

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How to reduce bias in convenience sampling

Convenience samples are quite prone to research bias.

  • Since the researcher draws the sample based on convenience and not equal probability, convenience samples never result in a statistically balanced selection of the population. This leads to sampling bias.
  • Very often, participants are offered monetary or other incentives to complete a survey. If a reward is their only motivation, they may give inaccurate or false answers. This leads to response bias, social desirability bias, and self-selection bias.
  • Researchers are subjective in how they choose their participants (e.g., by stopping the passersby who appear friendliest). This leads to observer bias.

Despite these limitations, there are steps that you can take as a researcher to reduce bias in research. Here are a few options:

  • Describe in detail how you recruited your participants in the methodology section of your research paper to make your research reproducible and replicable.
  • Diversify your data collection by recruiting as many participants or cases as possible and use a sample size calculator to determine the appropriate sample size.
  • Distribute your surveys at different days and times, and use different methods for recruiting participants
  • Use appropriate descriptive analysis methods, rather than statistical analyses designed for probability samples

Overall, avoid overstating your research findings. Remember that findings based on a convenience sample only apply to the selected cases or participant group. By definition, they cannot be generalized to the target population.

Many research studies, particularly in the behavioral sciences, rely heavily on samples from undergraduate students. These have the potential for limited external validity and run the risk of including a disproportionately large number of “WEIRD” participants: Western, Educated, Industrialized, Rich and Democratic.

In a research context, crowdsourcing can help you avoid overly WEIRD samples because it draws from a large and diverse population.

Advantages and disadvantages of convenience sampling

As with any sampling method, convenience sampling has its advantages and disadvantages. It is important to be aware of these, so you can decide if it is the best fit for your research design.

  • Advantages of convenience sampling

Depending on your research design, there are advantages to using convenience sampling.

  • Convenience sampling is usually low-cost and easy, with subjects readily available.
  • In the absence of a sampling frame, convenience sampling allows researchers to gather data that would not have been possible otherwise.
  • If you’re conducting exploratory research, convenience sampling can help you gather data that can be used to generate a strong hypothesis or research question.
  • Disadvantages of convenience sampling

Convenience sampling has its disadvantages as well, and it’s not a good fit for every study.

  • Since the sample is not chosen through random selection, it is impossible that your sample will be fully representative of the population being studied. This undermines your ability to make generalizations from your sample to the population of interest.
  • Getting responses only from the participants who are easiest to contact and recruit leaves out many respondents. This affects the accuracy of your data and runs the risk that important cases are not detected, leading to undercoverage bias.
  • Convenience sampling relies on the subjective judgment of the researcher and the subjective motivations of the participants. This leads to a high risk of observer bias.
For any type of research, it’s important to be explicit about your sampling method, as well as its potential limitations and biases.

Frequently asked questions about convenience sampling

What is the difference between quota sampling and convenience sampling?

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

What is the difference between random sampling and convenience sampling?

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

What is non-probability sampling?

In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling.

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Kassiani Nikolopoulou

Kassiani has an academic background in Communication, Bioeconomy and Circular Economy. As a former journalist she enjoys turning complex scientific information into easily accessible articles to help students.