Understanding Target Population in Research

What is the target population?

Given that the target population represents the entire population for which any given study intends to examine, it is important to define this group, also referred to as the theoretical population.

Here is a partial list of example target population specifications or definitions:

•  Adults 18+ U.S. census representative
• Delta Airlines Diamond and Platinum Elite Frequent Flyers
• Individuals who are registered to vote in a primary or an election contest held in the state of South Carolina
• Certified public accountants with businesses located in Atlanta, New York, Miami
• First and second-generation Haitians living in Paris.

From this list, it is clear that having access to the total population of all the theoretical individuals in each of the above categories is virtually impossible regardless of which methodology you deploy. Therefore you need to evaluate the assets available to you for each of the target population groups.

Next, choose a sampling technique that will allow you to build a sample frame that will yield the sample size you require with the slightest sampling error and non-response bias from the data. It is also essential to avoid ambiguity when defining your target population. Take this political polling example:

‘Individuals who are registered to vote in a primary or an election contest held in the state of South Carolina’

If this were the general election, this would be a perfectly appropriate definition. If this were the democratic primary, it would still be valid. South Carolina is an open primary state where members of any party affiliation can vote for the other party’s candidate.

Since many states don’t hold open primaries, you must specify registered: Democrats, Independents, and so on.

Here is a partial list of example ambiguous target population definitions:

• Russians who have left the country
• People who have recently become mothers
• Frequent travelers
• Individuals who are “gifted.”

The list above is meant to highlight different cases where ambiguity can cloud the study design. In the first example:

‘Russians who have left the country.’

It is unknown where the Russians live, and it is not defined here from which country they have departed. So we might assume it’s Russia. But we could be mistaken. In the case of ‘Recent mothers,’ it is difficult to determine whether they have achieved this status through pregnancies for the first time or pregnancies, adoptions, foster programs, through marriage, or any number of other modern social scenarios.

Look at ‘Frequent travelers‘; you stop and ask, is this business travel or leisure? Is this overseas, or do trips to the next town count? Lastly, when it comes to people in the ‘Gifted’ category this day and age, in our estimation, doesn’t everybody get a gold star for just getting through life? Absolutely!

The point is this, be extra cautious and thorough when defining the target population and when beginning to map out your study, and it will guide you in setting up your sampling design. Otherwise, you may end up with ambiguities in your data that will be hard to resolve or impossible to conduct any meaningful analysis.

How does the target population differ from the population sample?

Why is the target population significant?

Having a working knowledge of the target population and taking steps to be “particular” in creating a clear and precise definition, we can discuss why this target audience is not the same as the sampled population.

It is more akin to what the sample would resemble if we had an infinite measure of time and access to unlimited resources.

The target population is essential for three primary reasons:

1. Sets clear direction on the scope and objective of the research and data types
2. Defines the characteristic variables of the individuals who qualify for the study
3. Provides the scope of the total population or universe for determining sample size

The target population is the master blueprint for the sample. It defines (variable characteristics) the overall set or sets of all items (people in market research) who will qualify for your study.

Next, the researcher will then identify the sampling methods for the survey respondents or study participants that will yield data with the least amount of bias. Ideally, if the study is quantitative, the researcher can make observations and draw statistical inferences from the data.

If it’s a qualitative study, the researcher will likely generalize the findings. The source input records for qualitative data analysis are generally not from a probability sample method and are not randomly selected.

Depending on the sampling methods chosen, you choose a sample frame to build your sample size. Then, executing a plan to reach each group of people from the target populations from a selection process defined in the sampling frame.

What are the considerations for the target population in online research?

All data collection methodologies carry with them their strengths and inherent weaknesses. These strengths and weaknesses are typically expressed in these terms

Due to advances in mobile and wireless technology and the availability of high-speed internet, including the most remote regions globally, today’s access to global audiences for research is growing in size.

Because of this, more businesses are interested in reaching their target market with an online questionnaire to conduct market research or see if they qualify for clinical trials or participate in a political poll.

You can conduct both probability and non-probability sampling methods online depending on the market research panel provider you choose, their sampling knowledge, and the available panel resources.

With planning, it is possible to have a probability sample of registered Democrats in Ohio or a cluster sample of restaurant owners ( 3-5 years in business and over 5 million in sales) in Atlanta, Chicago, and Pittsburgh.

As you design your online study, consider survey questions, and think about the target population, remember that your selection criteria should be precisely defined.

Each sampled population or cohort should have clearly defined variables from which to build quotas and targets. And whether you are going for large sample sizes or small, it’s your data analysis and target population that will guide you.