What is sampling?
Sampling is a strategy for picking individuals or subgroups from the population to make statistical inferences about the characteristics of the whole population. Sampling is a method that reduces costs and serves as the basis of every research enterprise. This article discusses probability and non-probability sampling techniques.
 Probability and Non-Probability sampling
Broadly, there are two types of sampling: probability and non-probability sampling. These types of probability and non-probability sampling, are discussed in detail below.
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Probability sampling:
The probability sampling technique is an approach to sampling where the researcher selects a number of criteria and randomly chooses a number from the entire population. This sampling technique enables all the members of the population to have an equal chance of being selected as a sample.
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Non-probability sampling:
In this non-probability sampling technique, all aspects of the population do not have an equal chance of being selected. This non-probability sampling approach doesn’t follow a prearranged procedure for the selection.
Types of probability sampling
The probability sampling has four kinds of techniques.
1. Simple random sampling technique
The simple random sampling technique is one of the best techniques due to its ability to save time and resources. In this type of sampling technique, every single member of the population has an equal chance of being selected.
2. Cluster sampling
In this process, the researcher splits the population into parts or clusters that imitate the population. Demographic information such as gender, age, location, etc. is used to discover the clusters. This discovery helps the researcher generate real implications from the responses of the participants.
3. Systematic sampling
The selection of samples from the community is made at regular intervals in this type of sampling approach. For this purpose, it is decided to select the initial point of the sample and determine the sample size, which could be repeated at regular intervals of time. Due to its set range, this sampling method takes the least time.
4. Stratified random sampling
In this type of sampling technique, the entire population is split into non-overlapping small groups. And from each small group, a sample is selected randomly.
Advantages of probability sampling
- Sampling bias is reduced.
Using the probability sampling approach, the researcher’s bias is minimal. Since in probability sampling, each sample has an equal chance of being selected, it results in high-quality data collection.
- different population: since in this type of sampling, proper representation is made from every segment of the population, the data is not tilted towards a specific demographic.
- Develop a precise sample: the planning and creation of an accurate sample are helped by probability sampling. It helps in obtaining well-defined information.
Types of non-probability sampling
Non-probability sampling is usually done in qualitative research. The sample is an important aspect of qualitative research because of its representativeness and dependability. Qualitative research employs a variety of methodologies, each with merits and downsides. This section examines the four common sampling procedures used in types of non-probability sampling: purposive sampling, convenience sampling, snowball sampling, and theoretical sampling.
1. Convenience sampling
The researcher includes the most accessible people in this sampling strategy. One of the reasons for using the convenience sampling technique is that resources are limited. As a result, only the most cost-effective participants are chosen. Because the participants are chosen, convenience sampling is an affordable method for data collection. But the problem is that we are not sure if the sample represents the population or not. Therefore, the results cannot be generalized. There may be bias on the part of the researcher when he or she relies on this sampling technique.
Convenience Sampling: Advantages and Drawbacks
Convenience sampling has various advantages for researchers, including its simplicity and low cost. Researchers can use this strategy to swiftly and efficiently recruit people without spending time and money identifying and contacting potential volunteers. Furthermore, because people from different backgrounds and experiences are more likely to engage, convenience sampling can result in a varied pool of participants.
While convenience sampling is convenient, researchers must recognize its limitations. One of the most significant disadvantages of convenience sampling is that it is prone to selection bias. Participants who are more easily accessible may not be typical of the overall population, limiting the findings’ generalizability. Additionally, convenience sampling may cause problems with the reliability of the results.
2. Purposive or judgmental sampling technique
In this type of non-probability sampling technique, the researcher uses his or her experience to select the sample that is most relevant to their objectives. This type of non-probability sampling technique is most frequently applied in qualitative research because the researcher plans and wishes to collect exact information regarding a specific phenomenon instead of making statistical judgments when there is small population.
An effective purposive sample should essentially clarify inclusion criteria as well as justification. Always describe your inclusion and exclusion criteria, and keep an eye out for observer bias in your reasoning. For example, you’d like to learn more about the perspectives and experiences of impaired students at your university, so you purposely select a diverse group of students.
3. Snowball sampling
In this type of non-probability sampling technique, researchers recruit participants. The method entails identifying a few initial participants who match the eligibility criteria and requesting that they suggest others they know who also meet the criteria. As referrals are added, the sample size increases, forming a chain-like structure.
Snowball sampling allows researchers to reach out to people who might be difficult to contact using typical sampling methods, such as members of marginalized or hidden populations. For example, in research on illegal immigrants’ experiences, snowball sampling might be used to discover and enroll participants through referrals from other undocumented immigrants.
Snowball Sampling: Advantages and disadvantages
Snowball sampling can yield extensive data from participants who have similar qualities or experiences. Researchers can acquire comprehensive insights into a specific group’s beliefs, actions, and viewpoints because referrals are made within a network of people who share similarities.
4. Quota sampling
The basis of the quota sampling technique is the non-random selection of predefined numbers of units. This is called quota. The population is split into mutually exclusive groups known as strata, and then sample units are recruited until the completion of the quota. These sample units have unique qualities, which are determined before constructing a stratum. Who and what makes your sample is the goal of quota sampling
5. Theoretical Sampling
Theoretical sampling is an advanced and strategic strategy that can assist researchers in developing more detailed and nuanced theories from their data. Rather than choosing participants based on convenience or accessibility, theoretical sampling researchers choose individuals based on their ability to contribute to the developing themes and concepts in the data. Instead of pushing evidence to match a preconceived idea, this strategy allows researchers to revise their study question and theory based on the facts they acquire.
Theoretical sampling is employed when researchers undertake grounded theory research and have formed an initial theory or conceptual framework. In research on cancer survivors’ experiences, for example, theoretical sampling may be used to select and recruit people who can contribute fresh insights into survivors’ coping mechanisms.
Theoretical Sampling: Strengths and Weaknesses
One of the most notable advantages of theoretical sampling is that it allows researchers to fine-tune their study question and theory in response to new data. This means that the research can be very targeted and focused, leading to a better understanding of the topic being examined. Furthermore, theoretical sampling can yield rich and in-depth data since participants are chosen based on their ability to contribute fresh insights into the research subject.
Uses of types of non-probability sampling
The following types of non-probability sampling techniques can be advantageously used in the following situations:
- Establishment of hypothesis: When limited to no prior knowledge is available, researchers use the non-probability sampling method to form an assumption. This strategy in non-probability sampling allows for the immediate delivery of data while also establishing a foundation for future investigations.
- Exploratory research: This sample strategy is commonly used by researchers when undertaking qualitative research, pilot studies, or exploratory research.
- Budget and time restrictions: The non-probability sampling method is used when there are budget and time limits and initial data must be collected. Because the survey design is not rigorous, it is easy to select participants randomly and request that they complete the questionnaire or any surveys.