There are many ways to select a sample for your systematic investigation—some researchers rely on probability sampling techniques while others opt for non-probability sampling techniques like purposive sampling.
To successfully implement purposive sampling, the researcher must know the population of interest and match the aims and objectives of systematic investigation along with the qualities of the several subgroups. Use this article to discover how to implement purposive sampling in your systematic study.
Also known as subjective sampling, purposive sampling is a non-probability sampling technique where the researcher relies on their discretion to choose variables for the sample population. Here, the entire sampling process depends on the researcher’s judgment and knowledge of the context.
If done right, purposive sampling helps the researcher filter out irrelevant responses that do not fit into the context of the study. Once you spell out the criteria for the systematic investigation based on specific aims and objectives, you can go ahead to choose units or variables that can provide meaningful responses.
If your research requires specific information from a particular subset of your population of interest, then purposive sampling is the way to go. Also, if you’re dealing with a small population of interest, purposive sampling can help you have a representative sample for your research.
Adopting purposive sampling for your data is pretty straightforward. First, you need to spell out the qualifying conditions or criteria for your research sample. Once you have this, you can go ahead and reject any variable that doesn’t satisfy these conditions.
If you like, you can adopt one or more purposive sampling techniques depending on the goals and objectives of your research.
This method allows the researcher to compare a phenomenon to the typical behaviors of the population of interest.
The extreme technique allows the researcher to investigate outliers or deviations from the typical characteristics of variables in the research population.
For this method, the researcher picks one case for the systematic investigation and uses the outcomes to generalize other similar cases.
This technique allows you to discover members of the sample who have a shared characteristic or a shared set of features.
Maximum variation sampling is a purposive sampling technique used to examine a diverse range of cases relevant to a particular phenomenon or event. Unlike the homogeneous sampling method, the researcher selects variables that are incredibly different from each other to have truly diverse responses and research outcomes.
When To Use The Maximum Sampling Method
You should adopt the maximum sampling technique for your data if;
a. You’re looking for diverse views on the subject matter;
b. It’s impossible to collect random samples from your population of interest and;
c. You need a fully representative sample.
Here’s a simple example of how purposive sampling works in market research:
Before launching a new wine product, an organization conducts pilot testing to gather feedback from the market. For the sample population, the researcher chooses expert wine tasters to provide valuable feedback for product improvement.
Purposive sampling can also be used in educational research. Suppose you want to collect feedback from students on the pedagogical methods in their school. You go ahead to handpick the brightest students who can provide relevant information for your systematic investigation.
No. Purposive sampling and convenience sampling refer to different things. In purposive sampling, the researcher deeply analyzes the context of the systematic investigation and uses their discretion to choose variables that can provide relevant information.
In convenience sampling, the researcher uses accessibility and convenience to determine which variables make up the research sample. This means that they only collect data from participants who can be identified and approached with as little effort as possible.
Quota sampling entails creating a research sample consisting of variables that share specific traits or qualities. On the other hand, purposive sampling relies entirely on the knowledge and discretion of the researcher.
In terms of similarities, purposive and quota sampling are both non-probability methods and depend on the researcher’s discretion and knowledge to some degree. Purposive sampling would seek out people that have relevant research attributes. Quota sampling takes purposive sampling one step further by identifying categories that are important to the study and for which there is likely to be some variation.
Adopting purposive sampling for your research helps you to extract lots of information from research participants, especially when there are just a few of them. On the flip side, it can ruin your data collection process if you make subjective or generalized assumptions when selecting variables for your data collection process.
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