We regard sampling as selecting samples that act as representatives of a larger group of people for research or study. This process is bifurcated into Probability and Non-Probability Sampling types. We select random samples for representing the entire population under consideration in probability sampling. The researcher can choose samples from a larger population based on his judgment under non-probability sampling. While some researchers debate the non-probability sample’s inclusiveness over its counterpart, many believe in randomly choosing the outcome.
Let’s understand the concept of non-probability sampling to learn its process. Before moving further, you must know the Types of data with examples for better understanding.
Principles of Non-Probability Sampling
For using non-probability sampling, the researchers have to decide from theoretical and practical reasons. Based on the research strategy chosen for the conclusion, they must also decide if non-probability sampling is appropriate for their work.
- Theoretical Reasons: Non-Probability samples represent an important group of sampling types that easily gel with quantitative, qualitative, and mixed-method research strategies. Despite this, the researchers who follow a quantitative research strategy often believe in the superiority of the probability sampling technique over non-probability sampling. This is because the samples are not randomly selected for observation in non-probability sampling. Such reasons make researchers believe that the lack of access to the list of the population being studied compels them to use non-probability sampling.
However, in qualitative research strategy, when the researchers use purposive non-probability sampling, they receive strong theoretical reasons behind the choice of the samples, which are evaluated for the study. The researchers subjectively draw on theory and their practice for deciding the sample because they are interested in the samples’ intricacy under observation.
- Practical Reasons: When compared with probability sampling, the cost of procedures that are followed for including the samples of non-probability sampling is cheaper, quicker, and easier. The students at all levels of education use non-probability sampling techniques for their dissertations. However, the researchers treat the non-probability sampling method as an alternative to probability sampling so that there isn’t a need to abandon the study because it cannot meet the required criteria for probability sampling.
Another practical reason for choosing non-probability sampling is the expense and time needed for implementing the technique. It is empirically workable to study unfamiliar concepts using this method, even when the sponsored amount is less.
Knowing if non-probability sampling is appropriate for research, the researcher must deeply analyze his choice of strategy. He must consider the impact that the strategy will have on the question of ‘Is this sampling method appropriate’ for his study? The researcher’s job is not over when he realizes that non-probability sampling will guide the dissertation. After the first stage of deciding, selecting the right non-probability sampling technique becomes important. The next section discusses the sampling technique.
Types of Non-Probability Sampling
Different types of non-probability sampling are explained in this section.
- Convenience Sampling: In this type of nonprobability sampling, the samples are only considered because they are easily accessible to the researcher. The researcher only selects such a type of sampling because they can be easily recruited. Sometimes, the population under observation is so large that it becomes practically impossible to take the samples that represent the entire population. This is when the researchers don’t take the pain of searching for the samples that rightly represent the population and perform convenience sampling.
Advantage: Cost-effectiveness, speed, and ease of availability.
- Consecutive Sampling: Consecutive sampling is just like convenience sampling, but it has some exceptions. In this method, the researcher selects an individual or a group of samples for study. He studies them for a substantial amount of time, analyses the results, and moves on to another group or subject if required. As the samples are not studied directly at once, but consecutively one after the other, this method of nonprobability sampling is named Consecutive sampling. By following this technique, the researcher has time to work with different topics while making his research precise by collecting results that provide important insights.
Advantage: This allows a thorough study of the sample under consideration.
- Purposive Sampling: Purposive non-probability sampling is also known as Judgment sampling because the samples selected for the study are purely chosen based on the researchers’ credibility and knowledge. This means that the researchers have the power to select the people who (in their opinion) are fit for the research study. Because of these factors, researchers don’t count purposive sampling under the umbrella of scientific sampling methods. In this method, the researcher’s preconceived notions can affect the results of the research. Thus, scientists and researchers stay away from this technique because it involves an exorbitant amount of uncertainty.
Advantage: Low-cost method of sampling.
- Quota Sampling: We can understand this non-probability sampling method with the help of an example. Consider that a researcher wishes to understand the goals of women and men working in a company. The total count of the employees in the organization becomes the population of this study. Here, the population is 500 employees. From this population, the researcher will only need a stratum of the population to study the topic. To get the required sample, the population will have to be divided into groups of strata. We achieve this through Quota sampling.
Advantage: Only with one sample from a specific group can the researcher get an idea of the entire population.
- Snowball Sampling: During the research, researchers may find it difficult to locate a sample. This is when snowball non-probability sampling comes into the picture. It helps researchers in finding a sample hard to locate. This technique is widely used by researchers when the sample size is small and is not available easily. Like a referral program, the researcher struggles to find a suitable subject and then asks the subject to help the researcher find similar subjects. In this way, the study receives an acceptable size sample through references.
Advantage: Helps in locating hard-to-find samples.
- Modal Instance Sampling: While statistically considering a distribution, we know that mode is the most frequently occurring value. While using modal instance sampling, the researchers sample the most frequent (or ‘typical’) case. For instance, in many informal public opinion polls, the interviewer interviews the ‘typical’ voter. This is how modal instance sampling works. However, this structure of the method drags along many disadvantages with it. The modal case selection is highly ambiguous because it is difficult to find a case that typically represents the entire population. Most times, the variables (age, income, and education) under consideration are not the only ones to classify the modal sample (typical voter).
Advantage: Perfect for informal sampling.
- Expert Sampling: In this non-probability sampling method, the researcher assembles a sample of people who have demonstrable or known experience in a particular field that is being studied. Researchers choose expert sampling for their study because of two reasons. First, it is used to provide proof of the validity of the approach that the researcher has selected for the study. For example, suppose he has performed research using modal analysis but is doubtful of the criteria which have been used to define the modal instance. In that case, he can perform expert sampling of the study. The second reason for choosing expert sampling is to know the views of the expert in the matter under consideration.
Advantage: Expert opinions corroborate the researcher’s decision to implement a particular method of study.
- Heterogeneity Sampling: We use this non-probability sampling method when the researcher wishes to include every view or opinion in his study with no concern regarding the proportional representation of the views. We also know heterogeneity as diversity sampling. The researcher’s primary interest in using this sampling method is to get a broad spectrum of views and ideas without establishing the modal ones. Here, the idea is being sampled and not people.
Advantage: Instead of the population of people having the idea, we sample these people’s ideas for the study.
Applications of Non-Probability Sampling
Non Probability Sampling is used to show if a particular characteristic or trait is present in a population. Researchers only use this method of sampling when they have budget or time limitations for the study. Also, many a time, these types of research techniques generate results that do not generalize the total population under observation.
Some institutions which use the non-probability sampling techniques are listed below. They use it to consider the additional quality criteria and dimensions for their study. This corroborates their research with evidence.
- Government or Private Statistical Agencies
- Survey Research
- Market Research