What is a population of interest?

When starting a research study, one of the key things you must do is to identify your population of interest. But what is a population of interest in statistics?

A population of interest refers to the group a researcher wants to study and draw conclusions or inferences about. 

Members of a population of interest have common characteristics of interest to the researcher.

For example, in research, to determine the average monthly sales for a specific type of electronics company within a state, the population of interest is ‘all electronic companies' in the particular state. 

However, since the population of interest, in this case, is too large (900) to include feedback from all population members, the researcher picks a random sample of 90 electronic companies within the state to represent the entire population of interest. 

The parameter of interest or population parameter of interest refers to a value of interest related to a specific research population. This is the focus of the researcher who is seeking information about the population or research sample being studied.

Both the population of interest and parameters of interest are things researchers need to take into consideration when collecting relevant responses during a systematic investigation. While the two terms have a close relationship, it's important to understand specifically what they relate to. 

The population of interest is the entire group in a research context that a researcher wants to study or generalize the findings to. On the other hand, a parameter of interest is the specific information required from the research operation.

For example, in a study investigating the average monthly sales of electronic companies in a particular state, the population of interest is all electronic companies in that state. In contrast, the parameter of interest is the 'average monthly sales.'

If a researcher collects data from the wrong population of interest, the conclusions will not be usable. Choosing the right population of interest distinguishes between a useful, valid research study and an expensive waste of time.

Here's how to identify a population of interest:

Identify and agree on the target population

Determining the population parameters is the most crucial step when selecting a population of interest. That is defining and agreeing upon a set of criteria or characteristics that members of the population should have in common. Criteria can be inclusion or exclusion. 

Inclusion criteria

It is the criteria that all members of the population of interest must have in common. An example would be research that seeks to determine the demand level for inventory management systems. In that case, the individuals studied must know how inventory is managed and the relevant systems or software relating to their respective companies.

Exclusion criteria

This refers to characteristics that make individuals or phenomena being studied ineligible for inclusion in the population of interest. While exclusion criteria is not automatically the opposite of inclusion criteria, some individuals or institutions may meet inclusion criteria but still get excluded from the population of interest due to some other characteristic.

For instance, research seeking to ascertain the demand for online shopping platforms among households will consider anyone responsible for shopping in a household as the population of interest. However, it may exclude elderly senior citizens if they have a low take-up of online shopping.

In this case, age would be an exclusion criterion that is not the opposite of the inclusion criterion (i.e., being responsible for shopping in a household).

Identify and know the sample in advance

You must apply the criteria early on after identifying and agreeing on the target population. Ensure that the population of interest and the sample from which you collect data are the same.

If you use an online survey approach, incorporate filter questions to ensure that your population of interest meets the inclusion criteria and removes those that meet the exclusion criteria from your sample.

Select a sample from the population

After establishing the population of interest, use the right sampling technique to get a sample for inclusion in the study.

The population of interest is a specific subgroup within a population of inference or the entire population. However, it will still be too large and difficult to collect data from every population member feasibly.

Sampling helps identify representatives from which you will collect data to reflect the views of the wider market.

Here's how to choose a sample from the population of interest:

Settle on population parameters to estimate

Define your inclusion and exclusion criteria, ensuring you can clearly justify them. 

For example, research on the customer experience of using a particular app will consider anyone who has used the app as the population of interest.

However, the researcher may narrow it down to participants who have used the app within the past month. It maximizes the chances that the participant can clearly recall their perceptions of the app.

Assume a margin of error with results

Determining the exact size of a sample is seldom possible, and researchers should anticipate that their estimates will be approximate. As a result, it is important to account for a certain margin of error.

Beware of the variability of the population you want to measure 

Populations often vary in size. But if you have a large population of interest, it doesn't mean you should collect a very large sample. If the population is relatively stable, you can pick a small sample from a large population of interest.

Consider the response rate

A significant part of the sample will be non-responsive regardless of when you reach out to them and the incentives you offer. People may be unavailable or simply uninterested in participating in your research. Therefore, consider increasing your sample size to cover the high levels of refusal. 

The most commonly used sampling techniques are:

Probability sampling

Probability sampling uses the theory of probability to choose a sample. It includes everyone, with all having an equal chance of being chosen. This type of sample has no bias whatsoever, and everyone in the population has an equal chance to be part of the research.

There are four types of probability sampling, including:

Simple random sampling

This method involves a spontaneous selection of the sample variables from the population of interest. It is highly unbiased since each member has an equal opportunity of being part of the sample.

Simple random sampling can be achieved using random numbers. 

Number the population and pick random numbers to select samples. It can also be achieved through a lottery by assigning numbers to every variable, throwing them into a real or virtual ‘box,’ and drawing the numbers from the box to randomly pick samples. This method reduces bias in the research sample, allowing for more objective outcomes.

Although it doesn't require technical expertise, it is time-consuming. It could also cause bias, especially if the data set is small. 

Cluster sampling

Cluster sampling groups the population of interest into clusters, followed by randomly selecting members from each subset into the sample. The clusters are defined from naturally occurring groups based on demographic parameters such as income levels, level of education, gender, location, age, etc. 

Cluster sampling has three subtypes: single-stage, two-stage, and multi-stage cluster sampling. 

Single-stage cluster sampling allows every variable in the chosen clusters to participate in the research study.

The two-stage method involves splitting the population into naturally occurring groups and selecting random samples from each subset.

In multi-stage clustering, the researcher splits clusters into smaller groups based on their natural occurrence and the research objectives to create a highly specific sample. 

Cluster sampling helps cut down on costs and time involved in sampling. The method also increases the chances of getting a genuinely diversified research sample. However, cluster sampling requires a high level of technical expertise.

Researcher bias, e.g., prioritizing certain clusters over others due to personal preference, may affect the quality of data, especially in the multi-stage cluster sampling method. 

Systematic sampling

Systematic sampling involves choosing individuals at equal intervals from the population. The researcher picks a random starting point within the population of interest and selects a respondent to be included in the sample at regular fixed intervals.

For instance, if you have a population of 150, you can choose the 15th member of the population and every 10th member afterward. Therefore, you will pick the numbers 15, 25, 35, 45, 55, up to 145.

Systematic sampling is easy to understand and simple to implement. It ensures even sampling of the entire research population. However, the population must be large sized and have a natural amount of randomness.

Stratified random sampling

Stratified random sampling involves splitting the population of interest into distinctive but predefined parameters. The researcher splits the respondents into multiple homogenous groups and selects the sample from the different groups.

Stratified random sampling gives more accurate results than other probability sampling methods. It is also convenient for collecting data from a large population. However, it is not ideal for all types of systematic investigation. Its results can be hard to organize and process as it requires accurate knowledge of population characteristics, which may not always be available.

Non-probability sampling

Probability sampling requires the availability of complete and correct population databases from which the researcher can identify and draw the random sample. Unfortunately, that is rarely possible, especially in research that examines consumer behavior.

Non-probability sampling is ideal for such situations since it does not yield a sample representing the population. This method involves selecting the sample based on the preference of the researcher. Due to its convenience and practicability, the method is commonly used in business and marketing research.

Examples of non-probability sampling methods include:

Convenience sampling

This method involves retrieving details only from members of the target population who the researcher can access easily.

Sample selection is based on proximity and not representativeness. The method allows the researcher to collect data quickly for their research. It also saves on cost and time. However, the research sample obtained through this method may not represent all the subsets of your population of interest.

The method is also prone to researcher bias. Also, it lacks generalizability due to its lack of representation, limiting the study's usefulness and its conclusions. 

Judgmental sampling

Judgmental sampling involves choosing sample data based on the researcher's expertise or existing knowledge. In short, you judge and develop the sample based on the nature of the study and how you understand your target audience.

Judgmental sampling only chooses the variable that fits the research criteria and end objectives. Like convenience sampling, it has significant challenges with researcher bias and corresponding issues with the reliability and generalizability of research findings.

Snowball sampling

This method works like a snowball which gathers more snow around itself as it picks up pace. The researcher relies on the existing variables in the research sample to find other participants that potentially fit into the systemic investigation. The method tasks the respondents to give referrals or recruit samples for the research once they participate in the study.

Snowball sampling can, however, lead to significant sampling bias. Participants in the study may share certain characteristics or have common social networks, which can result in a sample that is not representative of the broader population.

Quota sampling

Quota sampling involves collecting data in a specific proportion from various groups in the target population. For instance, if the target audience has 70% male and 30% female, the researcher samples in this ratio.

Quota sampling can lead to significant sampling bias if the quotas do not represent the population of interest. The sample may not accurately represent the population if quotas are set based on the researcher's assumptions rather than actual demographic characteristics.

Sampling in a population of interest has several advantages, including:

• If done correctly, it ensures high accuracy, i.e., low chances of sampling errors, and will capture the views and behavior of the wider population.

• Sampling is more economically viable than surveying the entire population.

• Like the previous point, sampling saves time since the researcher surveys only a portion of the entire population.

Researchers cannot study all existing variables or respondents at the same time. They must identify a target group that can offer relevant information for the research.

Selecting the right population of interest increases the validity of research data and can support informed decision-making. However, most populations of interest are large, and thus uneconomical and time-consuming to survey each member individually.

Ultimately, you need to identify an appropriate sampling technique to select and survey a smaller group to represent your population of interest.

The margin of error refers to the percentage points that sample results will vary from the real population value. It determines the amount of confidence you have that your sample is representative of the results you would get by surveying the total population.

When defining the breadth of a population of interest, you start with the characteristics, traits, and qualities that the population you are gathering information about has in common.