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Convenience Sampling: Definition, Examples and Tips

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Scientific and statistical research generally involves the study of large populations. An excellent example can be gathering socioeconomic & demographic data from a city- or region-wide population. There are two primary ways to obtain accurate data and study such gigantic populations- census and sampling. While census involves the complete enumeration of everyone in a population, sampling enumerates a subset.

Census and samples both have their benefits and drawback. Yet, when gathering data personally from million-strong populations becomes extremely time-consuming, accrues higher costs, and resource-intensive, population sampling remains the only feasible avenue.

population sampling

Convenience sampling is a sampling technique that belongs to non-probability sampling strategies. This article offers an in-depth look into all its aspects.

What is Convenience Sampling?

As the same suggests, convenience sampling involves collecting data from samples that can be accessed conveniently. There may be different scenarios where accessibility becomes a factor.

  • Preliminary stages or pilot studies of an extensive research & analysis endeavour may require information on a small scale. In certain cases, resource and time constraints can prevent large-scale sampling.
  • If we are asked to gather data from people in a disaster-struck region, collecting data from a major portion of the population may not always be possible. Accessibility and resources can become major obstacles in such scenarios.
  • For SMEs, it may not be feasible to conduct census-based research for analysing consumer information from city-wide populations.  

In all cases where careful sample selection and census studying is not possible, convenience sampling is handy.

Convenience sampling is a non-probabilistic sampling procedure. Unlike probabilistic sampling methods, convenience sampling is less objective & poses a higher risk of bias & error. Results from convenience sampling studies cannot be generalised to the wider population. As participants self-select for the studies, the results only apply to them.

The subjective judgement of the researcher is central in determining the sample to be studied in non-probabilistic strategies such as convenience sampling, rendering them less objective and accurate. And factors such as time and resource constraints, ease of accessibility, and nature of the research are key influencers of the researcher’s judgement in nearly every case of convenience sampling.

Convenience Sampling Technique: How Does It Work?

  • Researchers determine the purpose and objectives of their research.
  • Next, they define the target population. It is relatively simple for convenience sampling as you choose participants based on ease of accessibility and participants’ willingness.

Estimate the attributes of the target population for their research.

  • They then think about the most convenient method to gather data from the population.

Accessibility, cost, time & resource constraints, and the nature of the research influence the sampling strategy.

  • Researchers and surveyors come up with appropriate questions for target samples. Qualitative and quantitative research questions can be part of the convenience sampling-based survey.
  • Finally, the survey is conducted, and information is gathered from those willing to participate.

The convenience of access means one can access relevant subjects for study anywhere— in malls, on streets, at parks, in online forums & communities.

Features, Benefits & Drawbacks

  • Data collection in convenience sampling involves samples or parts of a population easily accessible by the researcher.
  • Researchers reach out to an easily accessible portion of a population and ask them to participate in the study.
  • Accessibility and availability of a population and time & resource constraints are primary concerns in these cases.
  • The biggest advantages of convenience sampling are its cost-effectiveness, speed, and relative ease.
  • Selection and motivation biases, potential sampling error, & poor generalizability are major drawbacks. The lack of definitive sampling frames and randomisation undermines generalising to a larger population.

Two other major drawbacks are poor participation and non-participant error (risk of failure due to unavailability of vital research-specific information). This is critical in hospital cases.

  • Pilot experiments, preliminary research, and exploratory research remain major applicational of convenience sampling.

Let’s look at certain real-life examples or use cases of convenience sampling.

Examples/Use-Cases of Convenience Sampling

The very nature of convenience sampling should make it easy to understand its applications and use cases. Whenever you decide to gather information from sources and populations readily accessible to you based on your subjective, arbitrary judgement, that’s convenience sampling. YOU, the surveyor or researcher, are using your knowledge and judgement in choosing the sampling source.  

Here are some examples.

  • Imagine yourself to be a researcher who wishes to make sense of the career goals of the students of a particular academic institution.

Say, the institute has a total population of 3000. It may not be feasible to gather info from every single one of those 3000 pupils. Authorities leave it to you to collect and collate accurate data. If you resort to gathering data from 100 students with whom you can interact relatively easily, then you are employing convenience sampling.

Your choice of 100 students is not based on randomness but on your critical judgement. You invite students, and the willing ones come forth to provide information. As per the norms of convenience sampling, you choose the easiest place to acquire data – at the institute’s entrance, in the common room, in the canteen, in the library, on the playground, etc.

  • Medical and clinical researchers often employ convenience sampling strategies. Samples are taken from patients or clinical cases readily available at a location (hospitals, medical record databases, customer or membership lists, etc.).
  • This sampling strategy also finds use in qualitative research. In such cases, the study participants’ motivation plays a big role in sample data.

Motivation bias may creep into the study. Participants may fabricate responses to produce the best outcome, counter cognitive dissonance, colour responses with emotions, or withhold information.

  • An excellent use case for convenience sampling is gauging the public response to a product launch or brand event.

The surveyor or researcher can stand in front of a store where the new product is being sold or a mall. They can ask people entering or exiting the establishment about their opinion and experience through survey questions. That’s convenience sampling in action.

  • It may sometimes be necessary to run a pilot study to determine whether it is worth investing resources in large-scale research. Decision-makers want to get an initial inkling of audience reactions, the market climate, consumer needs, etc.

In all such cases, convenience sampling comes in handy. If researchers take special care in gathering data, the pilot research can be successful. Data from the pilot study influences the design, methodology, analytical processes, and resources available for the larger study.

  • Some of the most well-known examples of convenience sampling are:
  • Business representatives stop random people on the street to complete a survey or questionnaire,
  • Online surveys by companies,
  • Recording consumer responses by providing free samples of products

When can you use Convenience Sampling?

In the preceding sections, we have presented several examples and use cases for convenience sampling. Thus, their use cases may already be intuitive to you. Let’s go through the most prominent use cases once more.

Preliminary Research & Pilot Testing

Preliminary research and pilot testing are both common in medical research. Both investigations aim to maximise the probability of success of the main investigation or research.

  • Preliminary research helps refine investigation procedures and assess feasibility, cost, time taken, acceptance levels, and responses. These studies aid in gathering up-to-date data swiftly to evaluate an appropriate size of the random samples for the main trial.

Convenience sampling reduces the time, effort, and resources for preliminary studies.

  • The same goes for pilot studies. They precede field trials of any medical research. Pilot studies test every aspect of the study procedures, including participant selection criteria, data collection & collation procedures, exposure to research constraints, quality control, and data processing.

A pilot study’s target population and scope are obviously small compared to the actual trial. Convenience sampling, thus, becomes an effective technique.

Exploratory Research

Exploratory research identifies and understands the different parameters & attributes of a subject for developing an effective statistical research framework. These kinds of research explore unknown, poorly defined, and poorly understood subjects & phenomena.

Exploratory research aims to draw insights and interpret different aspects of a subject. The aim is not to draw conclusions but glean intuitive information and develop a tentative hypothesis, which will be useful for subsequent explanatory studies.

Exploratory researches are generally unstructured, low-cost, predominantly qualitative, and possesses no hard-and-fast rules. Naturally, convenience sampling strategies are most suitable for these studies.

Resource Constraints

The major reasons for choosing convenience sampling are time, cost, and resource constraints. You may not have the budget to reach out to most of a population, face tight deadlines, or have a very narrow timeframe to finish your research. In such cases, carefully-construed convenience sampling strategies are an excellent choice.

Accessibility Challenges

Convenience sampling may be the only practical way of gathering data if accessibility and/or availability of data units is a factor. If you are asked to gather data about victims of heavy flooding in an area, convenience sampling may be the only way out.

Research Generalizability is not the Primary Goal

Convenience sampling is subjective, with heavy biases, poor participation, and non-participant error. Thus, Sampling data is not considered representative of the target population and cannot be generalised. Sample estimates are not apt reflections of population response; generalisations can lead to gross inaccuracy.

However, if research generalizability is not a factor, convenience samples can be used without worries.

Convenience Sampling vs Simple Random Sampling

  • While it may seem random, convenience sampling is pseudo-random and subject to the researcher’s choice. At the end of the day, the participants are not chosen at random but as per the convenience of the researcher or ease of access. In addition to the proximity, the willingness of the study participants is also a key factor.

Convenience sampling depends specifically on the location and accessibility of data sources. This is why it is quite challenging to replicate sampling results.

  • This is not the case with simple random sampling. It is a probability-based method where researchers choose an appropriate sample size and employ genuine randomisation techniques to gather data. This randomisation makes the sample data representative of the larger population.

Data is replicable and generalisable in simple random sampling. Randomisation also eliminates bias.

  • Convenience sampling finds application in hypothesis formulation, pilot testing, and exploring the aspects of an unexplored research subject.

Simple random sampling is best used to acquire data for developing context and drawing generalisations about the wider population.

Now, it is time we looked at the advantages and disadvantages of convenience sampling.

What are the advantages & disadvantages of Convenience Sampling?

The Advantages of Convenience Sampling

Below are the biggest advantages of adopting a convenience sampling strategy.

  • Swift Data Collection

Whenever time is a constraint and generalisation is not a factor, researchers choose convenience sampling. The sampling process is very simple compared to other non-probabilistic and probabilistic techniques. This simplicity makes data collection very fast.

  • Cost Effective

There may not be enough resources to gather data from a large population. Cost may become an obstacle in many cases. Convenience sampling is the most cost-effective choice in such cases, as researchers can choose the most convenient way to acquire data. No heavy investments are necessary in these cases.

Cost Effective
  • Great for Preliminary, Pilot, & Exploratory Research

Small-scale research for testing trial procedures, facets of an unknown subject, or understanding the target audience can conveniently use convenience sampling. Pilot data for primary research can be obtained swiftly and conveniently.

  • Fewer Rules & Simpler Means Easy Participation

Some of the biggest conveniences of convenience sampling are simplicity and lack of hard-and-fast rules. Anybody can participate, and it is easy to create future samples. Moreover, online convenience sampling makes it possible to acquire data from across the globe.

The above are the biggest advantages that make convenience sampling so popular despite the following drawbacks.

The Drawbacks of Convenience Sampling

The biggest limitations of convenience sampling are the lack of randomisation, high subjectivity & bias, poor representation of the larger population, and lack of generalizability.

  • Sampling & Motivation Bias

Sampling bias may creep in as the data is collected from those willing to participate and the place. The subjectivity of the researcher in choosing respondents and the underlying motive of the subjects inject bias into the gathered data.

  • Selection Bias

If not chosen carefully, convenience sampling can be subject to selection bias. People who know more about the subject may be represented in a study. The subjectivity of the researcher may be behind such selection bias.

Selection Bias
  • Positivity Bias

Respondents looking to make a positive impression may provide biased information. This distorts data and outcomes.

  • Poor External Validity

Lack of probability-based techniques, bias and no randomisation damage credibility and affect external validity. Other researchers and researchers may not consider findings to be of any proper use.

  • Inability to Generalise

Due to all the subjectivity and bias involved, convenience sampling data is unsuitable for generalisation or mapping onto a larger population.

  • Poor Reproducibility

Researcher subjectivity, the effect of ease of accessibility, and heavy bias make it challenging to reproduce results.

  • Poor Participation & Non-Participation Error

Small sample sizes can lead to inadequate data collection. Furthermore, subjectivity and location convenience can lead to non-participation errors as data vital for research can be missed.

There are ways to counter the drawbacks of the convenience strategy, however.

How to Avoid the Disadvantages of Convenience Sampling?

Reducing errors, the different kinds of biases and finding the best possible respondents are the best ways to counter the drawbacks of convenience sampling. Here are some ways to counter biases and reduce skewness.

  1. Large sample sizes can reduce bias and reduce participation errors. This will help you gather a variety of data from many different responses.
  2. Use a large sample size and cross-validate 50% of the collected data. Compare one half with the other half.
  3. Use probability sampling in tandem with convenience sampling. This will improve the credibility and external validity of the gathered information.
  4. Make sure to include both qualitative and quantitative questions in the sampling survey.
  5. Go for multiple samples. Carry out the same survey or experiment on different crowds of respondents. This expands the data set.

And with that, we finally wrap up this guide to convenience sampling. Hope it was an informative and interesting read for everyone. Use this quick guide to convenience sampling as convenient and follow the rules & tips carefully to gather pertinent data.

However, if finding relevant data or completing a research project seems too problematic, connect with’s experts today. Genuine PhD-qualified academic experts stand ready to aid you through every aspect of your research.

All the best!

Frequently Asked Question (FAQs)

What is convenience sampling?

It is a non-probability sampling method where researchers choose the most easy-to-access and convenient data sources. Simply put, they choose data subjects which can be easily accessed or interacted with as well as on the subject’s willingness to participate.

How is convenience sampling different from other sampling methods?

Convenience sampling is substantially subjective, overtly simplistic, not randomised, and location- & event specific. The method is non-probabilistic and prone to errors. Compared to commonly used probabilistic methods, convenience sampling is much less reliable and non-representative of the target population.

Why might researchers choose convenience sampling?

Time & resource constraints and accessibility issues are the biggest reasons for choosing convenience sampling. Also, convenience sampling is the easiest way to gather data if external validity and generalisation are not essential.

What are the potential limitations of convenience sampling?

Data collected using convenience sampling is non-representative of the target population and thus not generalisable. It is also more prone to non-participant bias, poor participation, motivation bias, positivity bias, and selection bias.

How does convenience sampling affect the generalizability of research findings?

The subjectivity of convenience sampling, location & event-specific nature of data, and high probability of errors & bias make convenience sampling data poor representatives of the larger population. Research findings based on such data do not possess much credibility and cannot be generalised with much validity.

Hi, I am Mark, a Literature writer by profession. Fueled by a lifelong passion for Literature, story, and creative expression, I went on to get a PhD in creative writing. Over all these years, my passion has helped me manage a publication of my write ups in prominent websites and e-magazines. I have also been working part-time as a writing expert for for 5+ years now. It’s fun to guide students on academic write ups and bag those top grades like a pro. Apart from my professional life, I am a big-time foodie and travel enthusiast in my personal life. So, when I am not working, I am probably travelling places to try regional delicacies and sharing my experiences with people through my blog. 

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