Every research effort focuses on a particular group, based on its aims and objectives—this focus group is commonly referred to as the population of interest.

Because it’s impossible to study all existing variables at the same time, a researcher must identify a broad group that can provide relevant information for the research. For example, research about wild cats in Africa will rightly exclude cows, dogs, and birds, as they do not satisfy the immediate interest of the systematic investigation.

When you kickstart your research with the right population of interest, it has a positive influence on your research sample and the outcomes of your systematic investigation.

A population of interest is the subset of the general population from which the researcher draws out the sample for a systematic investigation. In other words, it is the primary focus of a researcher, and it provides all the data needed to reach reasonable conclusions in the course of an investigation.

For example, during market research, your population of interest might be all middle-income earners aged 30–45 or all lactating mothers between ages 25 and 27. After you nail this part, you can go ahead to build a survey, poll, or quiz that resonates with them as your target audience and prospective customers.

Different factors influence a researcher’s choice of a population of interest, including the aims and objective of the investigation, accessibility of the subgroup, and the size of their budget. Regardless of these factors, the most important thing is that you end up with a population of interest with enough data and inferences for your research.

Also known as the population parameter of interest, the parameter of interest is a statistical value that gives you more information about the research sample or population being studied. In other words, these parameters define and describe a given research population.

For example, suppose you want to discover the average income of all lactating mothers in a particular area. This is a parameter because it refers to the entire population of lactating mothers in that context and not just a small group of people.

Researchers use a population of interest and parameters of interest to gather relevant responses during a systematic investigation. Despite the close relationship of these terms, they do not mean the same thing.

While the population of interest refers to the specific subgroup in a research context that can provide the data needed for the systematic investigation, a parameter of interest is the exact information you need from the research population. Let’s look at an example.

Suppose an organization is conducting market research to discover the average income of GenZers in a particular location. In this case, all of the GenZers in that location form the population of interest, while “average income” is the parameter of interest.

Choosing the right population of interest is a critical step in any research. If you settle on a population that cannot provide relevant data, it could ruin your research outcomes.

First, you should ensure that your population of interest is diverse enough and represents different subgroups. For example, if your research focuses on women aged 18–25, the different ages should be represented in your population.

Other things you should do/have at the back of your mind include:

- Spell out the aims and objectives of your research.
- Make sure the target population reflects your research aims and objectives.
- Choose your research sample well in advance.

Once you have the right population of interest, the next step is choosing the sample size for your systematic investigation.

Sampling is an important research technique that involves choosing representatives from your population of interest to participate in the data collection process. It helps you save time and cost because you don’t have to collect responses from every member of the target population before reaching valid judgments.

Let’s look at some things you should consider as you choose the sample size for your research.

- First, spell out the specific parameters you want to measure using the sample.
- Always assume a margin of error with your assumptions and different calculations.
- Estimate the cost of sampling the target population.
- Understand the variety within the population you’d like to measure.
- Account for the response rate if your target audience

You should take six essential steps to choose the right sample size, especially if you’re dealing with continuous data.

Step 1: Determine the size of your population.

While you might not know the exact number, you need to be clear about who does and doesn’t fit into your group. You can have an estimated range for your sample size.

Step 2: Determine the margin of error or confidence interval.

Since you’re dealing with estimates in your data sets, errors are inevitable. You can set how much difference you’ll allow between the mean number of your sample and the mean number of your population.

Step 3: Determine the confidence level in your data sets.

Confidence level, in this case, simply refers to how confident you want to be that the actual mean of your data falls within your margin of error. Your confidence interval can be 90% confident, 95% confident, and 99% confident.

Step 4: Determine the Standard Deviation

Since you have your estimates, you can take a calculated guess about how much the responses you receive will vary from each other and the mean number. If you don’t like to play the guessing game, you can assume that your standard deviation is 0.5, suggesting that your sample size is large enough.

Step 5: Find the Z-Score using the confidence level. Here’s a simple Z-Score table you can use for this. For example, these are the Z-scores for the most common confidence levels:

90% – Z Score = 1.645

95% – Z Score = 1.96

99% – Z Score = 2.576

Step 6: Apply the sample size formula to get the projected sample size for your data sets.

Sample Size = (Z-score)2 * StdDev*(1-StdDev) / (margin of error)2

Let’s see how this works in real-time:

Z-Score: 1.96

SD: 0.5

The Margin of Error: 5%

Sample Size = [(1.96×1.96) × (0.5 × 0.5)] ÷ (0.05×0.05)

(3.8416 × 0.25) ÷ 0.0025

0.9604 ÷ 0.0025 = 384.16

This means you need 385 respondents.

- Some researchers fail to account for a margin of error with their data estimates which affects the final sample size estimation.
- Failure to spell out the specific aims and objectives of your research can affect the selection of your sample size.
- Using the wrong estimation for your standard deviation can lead to having a sample size that’s too large or too small.
- Choosing a target population with little or no variation can exclude several subgroups from your sample size and affect the collected responses.

In simple terms, a sampling frame is a list of the different parameters you want to study in your population of interest. If possible, the sampling frame includes the specific name of everyone or everything your research would explore.

How does a sampling frame differ from the data sample size? While the data sample size only tells you the number of people you need to collect responses from, a sampling frame gives you specific information about who these people are, where you can find them, plus other demographic information. For example, your sample size can be 350, while the sampling frame is the list of all Black American women in San Francisco University.

The goal of a reasonable sampling frame is to provide a bird’ eye view of what is included in your systematic investigation and the things you should ignore. Depending on the type of your research, you can create alphanumeric tags for the different variables and parameters in the frame.

So how do you create a sampling frame for your research? First, you must have done the background work to determine your population of interest, population parameters, and sample size.

- Next, list all the variables (individuals or other research subjects) included in the target population. If you’re studying flowers, for instance, you should list all the species of flowers that can be found within the target geographical location.
- In your sampling frame, include the contact information, map location, or any other information that can help you locate the different units on your list. You can also add the demographic data of the different variables in the frame.
- Exclude the information of any variable that is not part of your target population.
- Organize all the information in a logical and systematic order. Don’t forget to add numeric or alphanumeric tags for all the units in the frame.

- In some cases, some units or variables in the population are not represented in the frame.
- Some researchers might include non-members of the population of interest in the sampling frame, which affects the quality of information.
- Some units are listed more than once in the sample.
- Due to information inadequacy, some sampling frames list clusters instead of the individual information of the different units.

Population sampling techniques are broadly categorized as probability sampling and non-probability sampling. Probability sampling is a method of selecting variables into the research sample based on the rules of probability. This means that every variable in the population of interest has a fair chance of being part of the research sample.

On the other hand, non-probability sampling relies on the researcher’s bias to choose population members for the systematic investigation. The researcher can consider both external and internal factors to decide which variable is added to the sample.

These techniques are further split into different sub-categories, which we will look at in this section.

Simple random sampling is a probability sampling method where the researcher spontaneously chooses which sample variables from the population of interest. It is often described as the most unbiased sampling method because every variable has an equal chance of being included in the sample.

Simple random sampling happens via the lottery method or the use of random numbers. In the lottery method, the researcher assigns numbers to every variable, throws these numbers into a box, and draws numbers from this box to randomly select samples. The other method involves numbering the population and creating a random numbers table from which the samples are chosen.

**Advantages of Simple Random Sampling **

- It reduces the level of bias in the research sample, which allows for more objective outcomes.
- It doesn’t require any technical expertise.

**Disadvantages of Simple Random Sampling **

- It could lead to bias, especially when you’re dealing with a small data set.
- Random sampling is time-consuming.

In cluster sampling, the researcher splits the population of interest into subsets known as clusters and randomly selects members into the sample from each cluster. Typically, the clusters emerge from naturally occurring groups that have some demographic similarities. For example, an investigator can create clusters based on gender, location, income levels, or level of education.

Typical cluster sampling methods include single-stage cluster sampling, two-stage cluster sampling, and multi-stage cluster sampling. In single-stage cluster sampling, the researcher allows every variable in the selected clusters to participate in the systematic investigation.

The researcher splits the population into naturally occurring groups for the two-stage cluster sampling method and chooses random samples from each cluster. Multi-stage clustering creates a highly specific research sample by splitting clusters into smaller groups based on their natural occurrence plus the research objectives.

**Advantages of Cluster Sampling**

- It helps you to cut down the time and costs involved in sampling.
- It increases your chances of having a genuinely diversified research sample.

**Disadvantages of Cluster Sampling **

- The quality of data is easily affected by researcher bias, especially in multi-stage sampling.
- It requires a high level of technical know-how.

For You: Cluster Sampling Guide: Types, Methods, Examples & Uses

Systematic sampling is one of the most common methods of probability sampling in research. First, the researcher chooses a random starting point within the population of interest and then selects a variable into the sample at regular fixed intervals. Here’s an example that illustrates this point.

Suppose you have a population of interest with 200 variables. Using systematic sampling, the researcher chooses the 10th member of the population and every 5th member afterward. So, you have a sample like this: 10, 15, 20, 25, 30…

**Advantages of Systematic Sampling **

- It is simple to implement and easy to understand.
- Systematic sampling ensures that the entire research population is evenly sampled.

**Disadvantages of Systematic Sampling **

- It only works for a large-sized research population.
- Systematic sampling requires a population with a natural amount of randomness.

Random stratified sampling is quite similar to cluster sampling. Here. the researcher splits the population of interest into multiple homogenous groups known as strata, and chooses the sample from the different strata. Something to note here is that while the groups are internally homogenous, they are externally non-overlapping and represent the entire population.

**Advantages of Stratified Random Sampling **

- It produces more accurate results compared to other methods of probability sampling.
- Stratified random sampling is a convenient method of collecting data from a large population.

**Disadvantages of Stratified Random Sampling **

- It is pretty difficult to organize and process results from stratified random sampling.
- Stratified random sampling is not suitable for every type of systematic investigation.

Convenience sampling happens when the researcher retrieves information from only members of the target population who are easily accessible. Typically, researchers favor convenience sampling when they are dealing with a large target audience, and it is impossible to reach everyone.

**Advantages of Convenience Sampling**

- It allows you to collect data quickly for your research
- Convenience sampling is time and cost-effective.

**Disadvantages of Convenience Sampling**

- All the subsets in your population of interest might not be represented in the research sample.
- The quality of data is affected by researcher bias.

Also known as authoritative or purposive sampling, it happens when the researcher chooses the sample data based on his or her expertise or existing knowledge. This type of probability sampling requires a small target population where only a limited number of variables have the characteristics required.

Quota sampling is a type of nonprobability sampling where the researcher collects data in a specific proportion from different groups in the target population. For example, if your target audience consists of 60% male and 40% female, then the research sample would be in this ratio.

Snowball sampling is a non-probability sampling technique that allows researchers to find variables with rare characteristics. Here, the researcher depends on the existing variables in the research sample to find other participants that potentially fit into the systematic investigation.

**Conclusion**

In this article, we’ve looked at the population of interest and other related concepts that affect the success of any research. More than identifying the right target population, you must know how to select a sample with relevant variables for your research. If done right, sampling reduces the chances of errors in your final results.

You may also like:

In this article, we are going to discuss the concept of non-probability sampling, its advantages and disadvantages, and where it can be used

Read this article to learn more about the types, advantages and disadvantages of this researcher sampling technique.

In this article, we’d look at why you should adopt convenience sampling in your research and how to reduce the effects of convenience...

In this article, we’d show you how to get a heterogenous sample for diverse data and also touch on the different types of stratified sampling