When designing a market research study and considering goals and objects the next step is to determine the target population to be studied. The target population the entire population, or set, that will be considered qualified for data analysis. Having a clear definition of this group is necessary before planning your sample design as it will be an important factor driving your sampling methodology, feasibility, and sample size.
What is a target population?
Given the target population represents the entire population for which any given study wishes to examine, it is important to be precise in defining this group also referred to sometimes as the theoretical population.
Here is a partial list of example target population definitions:
- US Adults 18+ Census Rep
- Delta Airlines Diamond and Platinum Elite Frequent Flyers
- Registered Voters in South Carolina
- Certified Public Accounts 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 and choose a sampling technique that will allow you to build a sample frame that will yield that sample size you require with the least sampling error and non-response bias from the data.
It is also important to avoid ambiguity in defining your target population. Take for example “Registered Voters in South Carolina” If this were the general election this would be a perfectly appropriate definition. If this were the Democratic primary however it would still be valid as South Carolina is an Open Primary State we member of any party affiliate can vote for the other parties candidate.
Here is a partial list of example ambiguous target population definitions:
- Russians who have never left the country
- Recent Mothers
- Frequent Travelers
- People who are Gifted
Here again, this list above is meant to highlight different cases where ambiguity can cloud the study design. In the first example, we don’t know where the Russians are living. We have not defined what country they have left. We might assume it’s Russia. But we could be mistaken. In the case of “Recent Mothers”, it is difficult to determine whether we are talking about pregnancies for the first time or pregnancies and/or adoptions or via foster programs, etc.
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? And finally, when it comes to people in the gifted category this day and age. In our book 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 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 kind of meaningful analysis.
How does the target population differ from the sample?
Why is the target population important? With a working knowledge of what the target population is and how you should 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 actually more like what the sample would be if we had an infinite measure of time and access to unlimited resources.
The target population is important for three primary reasons:
- Sets clear direction on the scope and objective of the research and data types
- Defines the characteristic variables of the individuals who qualify for the study
- 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) overall the set or sets of all items (or people in the case of market research) who will qualify for your study.
The researcher then will identify what sampling methods should be considered for the survey respondents or study participants that will yield data with the least amount of bias. If the study is quantitative, ideally the research can make observations and draw statistical inferences from the data. If it’s a qualitative study, the researcher will likely generalize the findings. The qualitative data analysis is not normally a probability sample method and randomly selected.
Depending on the sampling methods chosen, a sample frame is built and a plan is executed to reach each group of people from the target populations from a selecting process defined in the sampling frame.
What are the considerations for the target population in online research?
All data collection methodologies carry with them strengths and inherent weaknesses. These strengths and weaknesses are typically expressed in these terms:
- Data Collection Time (Field Time)
- Data Collection Costs (Fielding Costs)
- Response Rate
- Non-Response Bias
- Selection Bias (Sample Frame Error)
- Data Processing Time (Cleaning, Validating, Coding)
Due to advances in mobile and wireless technology and the availability to highspeed internet in even the most remote regions of the world today access to global research audiences 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 still conduct both probability and non-probability sampling methods online depending on the panel provider you choose and the sampling knowledge and panel assets. So it 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 and you 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 ging for large sample sizes or small, it’s your data analysis and target population that will guide you.
Jim Whaley is CEO of OvationMR and posts frequently on The Standard Ovation and other Industry Blogs. Ovation MR is a global provider of first-party data for those seeking solutions that require information for informed business decisions. Ovation MR is a leader in delivering insights and reliable results across a variety of industry sectors around the globe consistently for market research professionals and management consultants.Visit: https://www.ovationmr.com.
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