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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.
Convenience sampling is a sampling technique that belongs to non-probability sampling strategies. This article offers an in-depth look into all its aspects.
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.
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.
Estimate the attributes of the target population for their research.
Accessibility, cost, time & resource constraints, and the nature of the research influence the sampling strategy.
The convenience of access means one can access relevant subjects for study anywhere— in malls, on streets, at parks, in online forums & communities.
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.
Let’s look at certain real-life examples or 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.
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.
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.
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.
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.
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 and pilot testing are both common in medical research. Both investigations aim to maximise the probability of success of the main investigation or research.
Convenience sampling reduces the time, effort, and resources for preliminary studies.
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 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.
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.
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.
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 depends specifically on the location and accessibility of data sources. This is why it is quite challenging to replicate sampling results.
Data is replicable and generalisable in simple random sampling. Randomisation also eliminates bias.
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.
Below are the biggest advantages of adopting a convenience sampling strategy.
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.
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.
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.
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 biggest limitations of convenience sampling are the lack of randomisation, high subjectivity & bias, poor representation of the larger population, and lack of generalizability.
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.
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.
Respondents looking to make a positive impression may provide biased information. This distorts data and outcomes.
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.
Due to all the subjectivity and bias involved, convenience sampling data is unsuitable for generalisation or mapping onto a larger population.
Researcher subjectivity, the effect of ease of accessibility, and heavy bias make it challenging to reproduce results.
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.
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.
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.
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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.
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.
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.
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.
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.