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Research studies often try to find correlations, differences, or characteristics of large groups of people. Often, it’s not viable to collect data from everyone in this group. In such cases, researchers must take it upon themselves to select an appropriate sample group to represent their target population.
A sample group is a subset of a population. Studies will have a distinct target population from which the sample group must be selected. For example, if your research study is about the prevalence of depression in adolescents in France, your sample group must be selected from the population of adolescents in France.
It is important that researchers choose a sampling method appropriate to their study in order to choose their sample group. Choosing the right sample group is vital as the wrong sample group will not provide an accurate representation of their target population.
Sampling methods are divided into two broad categories, and they are as follows:
Probability Sampling: In probability sampling, a sample group is selected through a random and unbiased process.
Non-Probability Sampling: This method, on the other hand, involves processes of selecting a sample group through non-random processes, usually according to the researcher’s judgment.
In this article, we will specifically be delving into non-probability sampling and its characteristics.
Unlike probability sampling, which uses methods of “random selection” to select a sample group, non-probability sampling is where the researcher uses their judgment to select this group.
Hence, all members of a population (or target population) do not have an equal and known chance of being selected as a respondent.
Oftentimes, non-probability sampling is used by researchers when probability sampling methods are not feasible. Probability sampling is better suited for quantitative research as it is more reflective of the larger population, however, not all researchers may have the means to carry it out, and in such cases, non-probability sampling is required.
Here, we will discuss five types of non-probability sampling, which are;
As the name suggests, convenience sampling involves the collection of data where it is most readily available to the researcher. Convenience sampling is also known as accidental sampling because respondents are chosen whenever and wherever they are met. Convenience sampling is used in cases where certain target populations aren’t easily accessible or when researchers have a limited time frame.
For example, if a study about the consumption patterns of coffee is conducted, a researcher may choose to visit different coffee shops in order to collect information from customers at these shops.
While employing the method of convenience sampling, it is important that the researcher avoids turning his sample group into a biased one, as this will lead to inaccurate findings in the study.
Quota sampling is used in cases where the study aims to represent variables such as age, gender, income, occupation, or any other certain group. As this method of sampling involves the grouping of people with certain similar characteristics, it can be considered a form or proportionate stratified sampling (a component of probability sampling). However, in this case, the predetermined proportion of people will be sampled from different groups based on convenience.
For example, if you want to conduct a study with 55% male participants, you may choose to include male participants readily available to you until you have reached the required number/size of your quota.
As mentioned earlier, most researchers are interested in particular target populations. Purposive sampling, also known as judgemental sampling, is a method in which researchers choose respondents based on who is in the best position to provide the required information.
For example, if you were to conduct a study on the spending habits of millionaires, you would only be able to acquire first-hand information from those who have built their net worth into the millions, and therefore, those people must be chosen as respondents.
Snowball sampling is a method of non-probability sampling that is used when the target population of the study is inaccessible or hard to find. In this case, the researcher contacts someone who meets the criteria to be included in their sample group and then asks them to recommend other potential respondents who they may know who meet the criteria.
For example, if I’m doing a study on Native Americans, an ethnic group that is only 1.6% of the American population, it may be advantageous for me to use the snowball sampling method. After finding one or a few Native Americans, they may be able to give me a list of contacts of more Native Americans that they may know (relatives or friends, for instance).
Consecutive sampling, also known as total enumerative sampling, is a method of sampling where a researcher works with a sample group over a period of time, then picks a new sample group to work with again, and so on until they’ve acquired the amount of data they require. This is similar to convenience sampling, with the difference that there are multiple sample groups over the course of the study.
This sampling type is commonly used in various fields like market research, customer experience, healthcare, and more. Here are some examples of each type of non-probability sampling in various research methods.
One way to employ convenience sampling in market research is by conducting interviews with customers at a popular shopping center. For example, a brand can gather opinions from shoppers on a Saturday afternoon and leverage the convenience of a high-traffic area such as a shopping center.
A brand can establish quotas based on particular customer demographics, such as marital status or income, to gather targeted feedback on a new service.
A healthcare research team can use purposive sampling to select healthcare professionals specializing in a particular medical field that aligns with the research subject. This ensures a knowledgeable sample.
A software company can use this sampling type by identifying early users of new software and asking them to refer potential users for a survey. This method helps you explore a niche market where initial participants have connections with other users interested in the product.
Non-probability sampling is a suitable method when you have limited access to the entire population. Let’s see in what other circumstances you should use this sampling type.
The impact of non-probability sampling on successful research depends on various factors. The sampling types enable you to seamlessly conduct exploratory or qualitative research and focus on specific subgroups within the population. It allows you to intentionally target specific sample groups, which helps you study rare groups not represented in large populations.
The impact is contingent on the alignment of the sampling method with research goals and managing potential biases.
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