Four probability sampling methods used in market research
Simple Random Sampling Method
There are generally two principal ways to make a random selection when building a sampling frame. When the sample size is smaller, one standard method is to use simple random sampling, which gives every individual in the target population an equal chance of being selected by generating a series of random numbers.
This is one of the most common sampling methods in market research in which all the elements (people, organizations, etc.) of the population to be investigated have the same chance of being selected for the sample.
For example, let’s say we have 10,000 members of our hiking club, and we want to survey them to explore new trail destinations (but we don’t want to read 10,000 reviews). We can decide to send the survey to a simple random sample of 400 “fans” to make sure that we will have a response from all types of members.
Simple random sampling is commonly used to identify customer satisfaction (opinion surveys), members of organizations (general exploratory surveys), etc.
When you have a larger population, deploy a systematic approach described below or use cluster or stratified methods.
Cluster sampling involves two stages. In stage one, the market researcher selects a certain number of groups or clusters of people to question or interview. In stage two, a random sample within each cluster is selected for the actual study.
Cluster sampling works best when a random sampling method of an entire population is too expensive, impossible, or extremely complicated. This method is a less expensive and faster way to collect market research information. However, since it’s not a completely random sampling, you are more likely to generate a sampling error.
Unlike strata sampling, where each stratum has a unique characteristic (sex, age, region, organization size, etc.), clusters or groups are the opposite. It is about making groups similar to each other. This method is used when we have technical difficulty in accessing all types of subjects in our population.
A cluster or a conglomerate is a “typical” group of our population; (that is, we could divide the population into X very similar groups, and then study only one or some of the groups exhaustively (all its subjects or making a random sample within the group).
One example of good use for this research sampling method may involve collecting customer preferences for a large, national hotel chain. It would be difficult, expensive, and time-consuming to collect information about every customer visiting every location of a hotel chain.
However, you can select a dozen locations around the country in stage one of a cluster sample and then randomly select guests at each of those 12 locations over the course of a month for your B2B research.
Perhaps you are collecting insights about new hotel services you’re thinking about adding. Customer preferences shared through such a cluster sample would probably be reasonably representative and usable for making such decisions.
Precision is not extremely important in this case, and therefore, the cost and time savings would outweigh the need for conducting a completely randomized survey.
Stratified sampling is where the overall population is divided into mutually exclusive groups before a random sample is selected. You might want to subdivide your group by gender, race, income level, or age. Each person can only belong to one stratum or group.
For example, we see that among our clients, there are 60% women and 40% men. If we want to survey a sample with these proportions, we will design the sample carefully, for example, by investigating 600 women and 400 men in a total sample of 1000 people.
Businesses or organizations looking for a high level of precision or the ability to analyze information within smaller subgroups and the overall population may want to invest in stratified sampling. Since a representative group will be selected from each stratum, the actual sample can be smaller, saving time and money.
Customer Experience and Brand Tracking Studies are most often stratified to ensure representation from all customer segments. However, they may be defined.
Depending on your population and research goals, you’ll want to decide if you will use a proportionate or disproportionate stratification. Proportionate stratification can increase your precision because the actual sampling fraction of people will be proportionate to your entire population, which may not be the case in a completely randomized sample.
Disproportionate stratification can help market researchers when there are significant variances among the strata. You may gain precision for a particular survey measure; however, this precision may not carry across other components of the research.
Systematic sampling is an easy version of probability sampling because researchers select every nth individual on a population list. It is the technique that focuses on choosing a random selection of the first element for the sample.
Then later components are selected using fixed or systematic intervals until the desired sample size is obtained. As long as the population list does not contain any pre-organized groups, the resulting sampling should be representative.
This method of sampling is simple, fast, and effective in most market research situations. All that is necessary is a list of the population, a starting point, and a sampling interval.
For example, if you want to collect data from a trade association with 10,000 members, you can select every 100th person (sampling interval) on a membership roster to create a survey group of 100.
One example of a potential problem with systematic sampling would be a list that is organized before the sample is selected.
For instance, if you are questioning coaches and players of an adult sporting league about tournament locations, and the list is made up of team sub-lists that always place two coaches followed by 20 team members, you run the risk of either soliciting feedback from all coaches or no coaches depending on your interval selection.