A “population of interest” is defined as the population/group from which a researcher tries to draw conclusions. It is a subset of the general population that the surveyor wants to know more about.
Many research studies require specific groups of interest to make decisions based on their findings. This group of interest is known as a sample. Sampling is a method deployed to select survey respondents.
For example: If you are interested in the average time a person between the age of 30-35 takes to recover from a specific ailment after consuming a particular type of medication, the population of interest is all persons between 30-35 year-olds with that ailment using that medication.
The population of interest is not limited to the human population only. It is a group of aspects that have something in common. It can be objects, animals, measurements, etc. having many characteristics within a group.
For example, a medical study examines the spread of a specific disease in street dogs in a city. Here, street dogs belonging to that city are the population of interest. This population of interest or sample represents the entire population you want to conclude.
Sampling is a powerful technique of collecting opinions from a wide range of people, chosen from a particular group, with the effort to know more about an entire group in general. Deciding upon a suitable sample from your population of interest depends on several key factors:
- Firstly, decide on what population parameters you would like to estimate.
- Don’t expect the estimates taken from a sample to be exact. Always assume a margin of error while making assumptions based on the results of a sample.
- Know the cost of sampling the given population. Understanding this cost helps us determine how precise our estimates should be.
- Know how variable the population you want to measure is. It is not necessary to assume that a large sample is required if the population of interest is substantial.
- Take into account the response rate of your population. A response rate of 20% is considered ‘good’ for an online research study.
For any research study to be practical, it is imperative to select the population of interest that is truly representative of the whole population.
- Before you begin your research study, identify and agree upon the target population.
- Choose and know your sample well in advance. This will go a long way in eliminating any feedback that will be deemed useless for your study.
- If your survey goal is to understand the effectiveness of a product or service, then the target population should be the customers who have used it or best fits who will use the product/service.
- It would be very costly and time-consuming to collect data from the whole population of your target market. By accurately sampling your population of interest, it is possible to build an actual picture of the target market using common trends from the results.
- Sampling renders a mechanism for gathering data without surveying the whole targeted population. Read more on survey data collection.
- The population of interest is the entire unit of people you consider for the study. A sample is a subset of this group that represents the population.
- Sampling reduces survey fatigue, as it is used to prevent surveyors from conducting too many surveys, thereby increasing response rates.
- It is also much cheaper and time-saving than surveying the entire bunch.
- Tracking patterns of response rates for different groups will help determine how many respondents to select.
- The study is not only limited to the selected part but applies to the whole targeted population.
Now that you understand you can’t survey the entire population of interest due to various factors, you must adopt one of the best-suited sample selection methodologies for your research study. There are two methodologies you can apply: Probability sampling and Non-probability sampling.
Probability sampling means selecting the sample based on the theory of probability. The sample includes everyone, with all having an equal chance of being selected. There is no bias whatsoever in this type of sample. Every person in the population has an opportunity to be a part of the research.
What are the four types of probability sampling?
- Simple random sampling: This method is the simplest way of selecting a sample. Here, every member has an equal chance of being a part of the sample. The objects in this sample are chosen randomly, and every member has the same probability of being selected. Read more about simple random sampling.
- Cluster sampling: This is a method where respondents are grouped into clusters. These clusters may be defined based on demographic parameters such as age, sex, location, etc. Read more about cluster sampling.
- Systematic sampling: In systematic sampling, the individuals are chosen at equal intervals from the population. A starting point is selected, and then respondents are picked at pre-defined sample intervals. Read more about systematic sampling.
- Stratified random sampling: Stratified random sampling is a process of splitting the respondents into distinctive but pre-defined parameters. In this method, the respondents don’t overlap but collectively represent the whole population. Read more about stratified random sampling.
Non Probability sampling to determine the population of interest:
The non-probability sampling method uses the researcher’s preference regarding selecting a sample. This method of sampling is derived mostly based on the researcher’s ability to access to this sample. Here the members of the population do not have an equal chance of being a part of the sample.
What are the four types of Non-probability sampling?
- Convenience sampling: As the name suggests, convenience sampling stands for the convenience at which the researcher can reach the respondent. Researchers do not have the authority of selecting the samples and is purely done on the grounds of proximity and not representativeness. Read more about convenience sampling.
- Judgemental/purposive sampling: In judgemental sampling, the researcher judges and develops his sample on the nature of the study and understanding of his target audience. Only people who fit the research criteria and end objectives are selected. Read more about judgemental sampling.
- Snowball sampling: As a snowball picks up pace, it gathers more snow around itself. Similarly, with snowball sampling, the respondents are tasked to provide referrals or recruit samples for the study once they finish participation in the study. Read more about snowball sampling.
- Quota sampling: Quota sampling is a method of obtaining a sample where the researcher has the privilege to select an example based on their strata. In this method, two people cannot exist under two different conditions. Read more about quota sampling.
What are the advantages of sampling in a population of interest?
In most cases, out of the total population of interest, insights can be only gathered from your pre-defined samples. Here are the top nine advantages:
- Very accurate – low chances of errors (if sampled well)
- Economically viable
- Highly reliable
- High suitability ratio towards the different surveys
- It takes less time compared to surveying the entire population
- Reduced resource deployment
- Intensive & exhaustive data
- Apply properties to a larger population
- Ideal when the population of interest is enormous
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