Imagine that you’re a C-suite executive at an organization trying to decide on new safety features for children to implement in your next vehicle model. In your journey to meet these demands, you’ll want to talk to moms of small children in America to discover what safety features they seek when vehicle shopping. You don’t have the time or the budget to speak to every mom in America, nor would it be productive to do so, but you can get a good idea from a specific group that would represent the overall population of moms of small children in America. This can be achieved through a variety of population sampling methods.
Population sampling, specifically for market research, is when you take the entire population of whom you want to derive conclusions and select a fraction of that group to represent the overall population. Instead of interviewing millions of moms in America, you perhaps interview a few thousand or even a few hundred to paint the picture of what you’re trying to learn. Population sampling is an essential aspect of market research because it allows researchers to gain insight into their population of interest without interviewing an entire population. There are several population sampling methods, each with advantages and disadvantages. In this article, we will look at the three most accurate population sampling methods.
Simple Random Sampling
One of the most common population sampling methods is random sampling. This involves randomly selecting a subset of participants from the population you want to learn about. Some consider this the most accurate population sampling method because it prevents research bias, allowing for an impartial population representation. You can think of simple random sampling like the lottery, where individuals are assigned a specific number. Then you randomly draw numbers, representing who will end up in your overall sample. For example, if you want a selection of 15 students from a group of 150, you’d assign each student a number, put all 150 numbers in a hat, and then randomly choose 15 of those numbers for your sample.
In larger populations such as all moms in the US, this requires the assistance of computers that can randomly generate participants at a larger scale. Unfortunately, there’s no way to achieve a 100% accuracy rate in simple random sampling because it’d require researchers to survey the entire population versus just a sample, so you must implement a plus or minus variance which is the difference between the actual number and the number you observed during the study.
The stratified population sampling method requires that the population be divided into different strata or subgroups based on specific characteristics. These characteristics include race, age, gender identity, geographic location, etc. After they are split up, individuals are randomly selected from each stratum or subgroup to be included in the overall sample. By selecting a sample from each stratum, researchers can ensure that their overall sample represents the entire population.
Stratified sampling is typically used in diverse populations where researchers must ensure that their sample accurately reflects specific characteristics of the overall population. This ensures that all elements are provided equal representation in their overall sample. For example, if a vitamin company wanted to determine how their products are used in different age groups, they may divide adults into subgroups by age and then randomly select equal amounts of individuals from each age group for their overall sample.
The last population sampling method is systematic sampling. Systematic sampling requires the researcher to randomly select a starting point from the population and then pick every “Nth” individual to be included in the overall sample.
This is considered a probability sampling method because the individuals are selected at predetermined intervals. For example, you probably experienced systematic sampling in school when your teacher had you count off by fours and then put you in groups by that number. Every student who was the 1st number was the team leader, and the following numbers had different duties based on their number. This is an equal-opportunity, low-stress way to divide individuals.
Systematic sampling is beneficial when randomly selecting members from a population is challenging. It’s a quick, simple, and often more accurate technique than simple random sampling because of the probability of including all population members. This population sampling method is used in surveys with large, dispersed, and difficult-to-reach populations.
Different population sampling methods are abundant for researchers depending on the needs of the study. Let’s go back to the example at the beginning of this article. Out of the following population sampling methods, simple random sampling, stratified sampling, or systematic sampling, which would be the most beneficial in determining the top safety features to implement in your next vehicle model?
As a researcher or decision-maker, you must consider your research project’s most cost-effective and beneficial methods before implementing them. You can always utilize our dedicated service team at InnovateMR if you feel you have a project that requires a hard-to-reach demographic to walk you through your research endeavor from inception to completion.
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