Area sampling is a fundamental technique used in market research to gather data from a specific geographical area. This method is particularly useful when the population of interest is spread across a large geographical area and it is impractical or costly to conduct a simple random sample. Understanding area sampling is crucial for market researchers, statisticians, and anyone involved in data collection. It is a technique that can provide valuable insights into a population, while also saving time and resources.
Sampling, for the purposes of research, refers to any process by which members of a population are selected to participate in research. There are many methods for sampling, each with a slightly different purpose. Before you can obtain a sample, you must first identify a target population. The target population refers to all of the people who are the focus of a study. For example, a study about elementary school teacher burnout would include all elementary school teachers in the population. In some cases, you may need to consider an accessible population. This is a subset of the target population that can reasonably be accessed by the researcher for sampling. Oftentimes, researchers will use a sampling frame to facilitate their sampling methods. A sampling frame is a list of all of the members of the population.
Area sampling, also known as geographical sampling, is a sampling method where the researcher selects certain geographical areas and then conducts a survey within these areas. The selection of these areas can be random, systematic, or based on certain criteria.
Probability and Non-Probability Sampling
The two overarching approaches to sampling are probability sampling (random) and non-probability sampling. Common probability-based sampling methods include simple random sampling, stratified random sampling, cluster sampling and systematic sampling. Common non-probability-based sampling methods include purposive sampling, convenience sampling and snowball sampling.
Simple Random Sampling
Through simple random sampling (SRS), all members of the population have an equal chance of being selected. Therefore, this is a type of probability sampling. A rudimentary method of SRS is drawing names out of a hat. Each slip of paper has the same chance of being chosen on every draw. You could also use a random number generator to facilitate random selection from the population.
Simple random sampling assumes that all members of the population are accessible. If your population is "people in the United States" and you are attempting to sample via the Internet, members of the population without Internet access do not have a chance to be selected. This would not be an appropriate use of simple random sampling.
Researchers use SRS when the intention is to obtain a representative sample that can provide data for generalizing to the population. If members are chosen randomly, the sample is less subject to bias that may exist by non-random sampling methods. A random number generator (or equivalent process) is used to select all sampling locations. It can be used for any objective, including estimating or testing means, comparing means, proportions, etc., of two or more areas/processes, delineating boundaries, etc., but is one of the least efficient (though easiest) designs since it doesn't use any prior information or professional knowledge. It is primarily used in conjunction with other sampling designs, as the last stage of sampling in multi-stage projects (i.e., a sample of units is selected at the first stage and then subunits are selected from each unit), and for assigning units in experimental (e.g., intra-laboratory studies). Use when the area/process to sample is relatively homogeneous (i.e., no major patterns of contamination or "hot spots" expected) and there is no prior information or professional knowledge available; there is little to no prior information or professional judgment available; there is a need to protect against any type of selection bias (for example, when any professional judgment used to define 'areas' may be challenged); or it is not possible to do more than the simplest computations on the resulting data.
Stratified Sampling
Stratified sampling is a two-step sampling procedure. First, the population is divided into groups or strata. How this is done will depend on your specific population. Using the example of elementary school teachers, we could divide the teachers up based on state or school district, with each state (or school district) representing one strata. Next, members of each strata are selected for participation. When they are selected randomly from within each strata, it is called stratified random sampling.
Prior information about the area/process is used to create groups that are sampled independently using a random process.
Purposive Sampling
Purposive sampling is often used in studies where the aim is to gather information from a small population (especially rare or hard-to-find populations), as it allows the researcher to target specific individuals who have unique knowledge or experience. Naturally, this sampling method is quite prone to researcher bias and judgement error, and it’s unlikely to produce generalisable results, so it’s best suited to studies where the aim is to go deep rather than broad.
Convenience Sampling
Convenience sampling is a method where participants are selected based on their availability or accessibility. In other words, the sample is selected based on how convenient it is for the researcher to access it, as opposed to using a defined and objective process.
Naturally, convenience sampling provides a quick and easy way to gather data, as the sample is selected based on the individuals who are readily available or willing to participate. This makes it an attractive option if you’re particularly tight on resources and/or time. In some cases, you could use your judgement to engage with frequent shoppers, as well as rare or occasional shoppers, to understand what judgements drive the two behavioural extremes.
Selecting a Sampling Method
When choosing a sampling method, you need to consider your research aims, objectives and questions, as well as your resources and other practical constraints. Don’t let this get you down though – as long as your sampling choice is well explained and justified, and the limitations of your approach are clearly articulated, you’ll be on the right track.
In an ideal world, you’d want your sample to be perfectly representative of the population, as that would allow you to generalise your findings to the entire population. At the simplest level, sampling (within a research context) is the process of selecting a subset of participants from a larger group. For example, if your research involved assessing US consumers’ perceptions about a particular brand of laundry detergent, you wouldn’t be able to collect data from every single person that uses laundry detergent – but you could potentially collect data from a smaller subset of this group. In technical terms, the larger group is referred to as the population, and the subset (the group you’ll actually engage with in your research) is called the sample. Put another way, you can look at the population as a full cake and the sample as a single slice of that cake.
Conclusion
Sampling is a critical component of research methodology, allowing researchers to draw conclusions about a larger population by studying a manageable subset. The choice of sampling method—whether it be area sampling, simple random sampling, stratified sampling, purposive sampling, or convenience sampling—depends heavily on the research objectives, available resources, and the nature of the population. Each method has its own advantages and disadvantages, and understanding these is key to designing a robust study. Area sampling, for instance, is invaluable for geographically dispersed populations, while purposive sampling allows for in-depth study of specific groups. Ultimately, a well-chosen and justified sampling strategy is fundamental to producing reliable and meaningful research outcomes.