In an ideal world, a survey would collect data from every individual in a population. However, in most cases this isn’t feasible because of limited resources and time. So we need to use a subset of the population to draw a conclusion.
Identifying the right subset of population to work with is an art. This should be a carefully identified group that is representative of the whole population.
Why is sampling so important?
Sampling a part of the population often works as a more effective surveying tool than a full-blown census, which involves interviewing everyone in a population. You can examine a single area in greater depth with a smaller sample, as opposed to trying to find patterns in a huge pool of data. The latter is like trying to find a needle in a haystack.
- Did you know?Sampling and surveying a representative part of the population is often more effective than asking everyone.
- There are many different sampling methods, and which one you choose will depend on the sampling techniques, the context, your research purpose, and various other factors.
Before we dive into the sampling techniques, there are three things to keep in mind while constructing a sample:
Ensuring that all units in the survey are not similar to each other is a tall order, but it's important. To be a truly representative sample, the entire group must represent the spectrum of diversity within the population.
It's also important that survey respondents have been tracked on a case-by-case basis before going ahead with the survey. A good idea is to administer a consistency test for a sample, for example a pilot test, where you compare the individual units of the sample with the whole to make sure it properly represents the parent population's characteristics.
Many factors determine the size and the structure of a population. Researchers need to discuss these limitations and maintain transparency about the procedures followed while selecting the sample so the survey results are viewed with the right perspective.
Now let's get to the different sampling techniques.
Sampling methods can be categorized under two main headers: probability sampling and non-probability sampling.
Probability sampling techniques
This happens when each entity of the survey has a definite, non-zero probability of being incorporated into a sample. We call this a "definitive sample".
Probability samples are selected to be highly representative of the population. This way, they provide the most valid or credible results because they reflect the characteristics of that particular population.
There are three main kinds of probability sampling techniques: random, systematic, and stratified.
- WhenThis is the most straighforward sampling method. Random sampling is used with vast populations where every member is chosen independently from the rest.
- HowThis technique is easy to conduct because each subject is selected independently of the other members of the population.
- WhenIn cases where our population is logically homogenous.
- HowIn this method, we are viewing our sample as logically homogenous - i.e, all the units in the population attribute an equal interest for the surveyor. This technique involves ordering all individuals in a sequence and selecting individuals from regular intervals—say every fifth element in the set.
- Customers of the coffee shops
- People who visit on weekday mornings.
- WhenIn cases where you can divide your population into characteristics of importance for research.
- HowStratified sampling involves splitting the population into layers according to one or several characteristics. The intention here is to attempt to recreate the statistical features of the population on a smaller scale. Before sampling, the target population is divided into characteristics of importance for research: for example, by gender, social class, education level, religion, etc. Then the sample is created to simulate the target population.
Non-probability sampling techniques
Non-probability sampling techniques are the opposite of probabily sampling techniques in that it gives individuals in the sample an unequal chance of being selected.
Most surveys are not based on probability techniques, but rather on finding a suitable collection of respondents to complete the survey. In non-probability sampling, the relationship between the target population and the survey sample is immeasurable and the potential bias cannot be measured.
In these techniques, the selection is not completely randomized, thus the resultant sample is not truly representative of the entire population.
- WhenThe researcher decides which population they want to include in a sample based on his existing knowledge or professional judgement. It’s alternatively also called as purposive sampling.
- WhenThis technique is used when the target population is rare. Members of the target population recruit other members of the population for the survey.
- HowJust as snowballs roll in and gather mass, a sample constructed in this way will grow as you move through the process of conducting the survey.
- WhenThe sample is designed to include a designated number of people with certain specified characteristics.
- HowQuota sampling is the non-probability equivalent of stratified sampling that we discussed earlier. It starts with characterizing the population based on certain desired features and assigns a quota to each subset of the population.
- WhenDuring preliminary research efforts.
- HowThis sample is composed of, as the name suggests, people who are conventient to contact. Whoever persons are easily accessible are selected to complete the survey.
For the inital stages, probability sampling can have some sense of superiority, but its costs can be prohibitive. During these early stages of a study, non-probability techniques are probably a better way to give you an idea of what you are dealing with.
Once you have successfully selected your sample, you can start surveying by selecting from any of Zoho Survey’s 200 expert-verified templates and choosing from our list of 25 different question types.