A “population of interest” is defined as the group of data that is being considered for a study or statistical reasoning. Many research studies require specific groups of interest to conclude and make decisions based on their findings. This group of interest is known as a sample. Sampling is a method deployed to select respondents.
For example, if you are interested in the average time a person between the age of 30-35 takes to recover from a certain ailment after consuming a certain 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 draw conclusions about.
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. For any research study to be effective, it is imperative to select the population of interest that is truly representative of the entire population. Before you begin your study, the target population must be identified and agreed upon. Selecting and knowing your sample well in advance 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.
Deciding upon a suitable sample 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 expect a margin of error while making assumptions based on the results of a sample.
- Know the cost of sampling the given population. Understanding the cost of sampling 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 large.
- Take into account the response rate of your population. A response rate of 20% is considered ‘good’ for an online research study.
- Sampling renders a mechanism for gathering data without surveying the whole targeted population.
- 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. Broadly speaking, there are two methodologies you can apply: Probability sampling and Non-probability sampling.
This is a method of determining a sample where you select the sample objects from a population-based on the theory of probability. Everyone is included in the sample, with everyone having an equal chance of being selected. There is no bias whatsoever in this type of sample. Every person in the population has a chance to be a part of the research.
Probability sampling can be further categorized into 4 types:
Simple random sampling:
Simple random sampling 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 purely chosen randomly and every member has the same probability of being chosen.
Cluster sampling is a method where respondents are grouped into clusters. These clusters may be defined based on demographic parameters such as age, sex, location, etc.
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.
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.
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 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
Non-probability sampling can further be classified into four distinct types:
- 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.
- 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 objective are selected.
- 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.
- Quota sampling: Quota sampling is a method of obtaining a sample where the researcher has the privilege to select a sample based on their strata. In this method, two people cannot exist under two different conditions.
In most cases, out of the total population of interest, insights can be only gathered from your pre-defined samples. This comes with its advantages and disadvantages. Here are a few listed below.