Types of Sampling Techniques in Research Design

Sampling entails selecting members or a subset of a target population in a study. Researchers estimate the characteristics of the entire population based on the sample and make statistical inferences regarding the whole population. Scholars and researchers must understand what research sampling is and the various methods of obtaining representative sample units to achieve their research goals and objectives. In need of data analysis services? We specialize in providing comprehensive sampling techniques to support your research needs. Our team ensures that your study receives accurate and representative data by utilizing our sampling strategies to align with your research objectives for reliable outcomes.

In research designs, sampling is a time-convenient and cost-effective technique that speeds up the research process and saves time, money, and other resources compared to studying each member of the target population.

What is Research Sampling?

Research sampling is the process of selecting a subset or a group of participants from whom to collect data during research. The sample should be representative of the population/group from which it was drawn. There are different types of sampling methods that researchers can use to obtain representative samples. This article contains examples of the different types of sampling techniques in research design.

Methods of Sampling in Statistics

It is hardly possible to collect data from every member of the target population or group in a research study. One should select a sample or real study participants to represent the entire population/group. It is fundamental to decide on the sampling procedure that will produce an ideal representative sample to draw valid conclusions. There are two main categories of sampling in research design; probability sampling and non-probability sampling.

(1). Probability Sampling Techniques in Research

The probability sampling technique is where the researcher sets particular criteria and allows every member of the population/group an equal chance of selection to participate in the study. The probability sampling method is mostly used in quantitative research and produces rigorous statistical inferences. The main probability sampling methods include:

(a). Simple random sampling methods

The simple random sampling method is appropriate for generating results that ideally represent the entire population. The simple random sample implies that every member of the population has an equal chance of selection based on chance. The sampling procedure helps to save time and other resources during research.

For instance, if the management in a company of 300 employees offers to take employees on vacation or for team building activities, they can use the random number generator, pick pieces of paper or bottle tops out of a bowl, or any other technique entirely based on chance to give each employee an equal chance of being selected. In this example, the sampling frame should include all the members of the group (the 300 employees).

(b). Cluster sampling methods

In cluster sampling methods, the researcher devices the target population in subgroups or clusters based on demographic factors such as race, gender, or geographical location. Each subgroup should have similar features as the entire sample. If the formed clusters are too large for effective data collection, the researcher can sample individuals from each cluster or subgroup using two-stage cluster sampling methods. In the above example, if the company wishes to take 3 of the employees for vacation but it has 10 offices with almost the same number of employees with similar roles, the manager could use random sampling to select three offices (cluster sample) at a time and two-stage cluster sampling to select the 10 employees from each office (the participants) to attend the activity.

(c). Stratified random sampling

Stratified sampling is where the researcher divides the population into non-overlapping subgroups to represent the entire population. To draw precise conclusions, one should ensure that each subgroup is well represented in the random sample. The subgroups/strata can be organized depending on certain characteristics such as job roles, gender, age, or education level. If the researcher wants a gender-balanced sample from a group of 300 employees containing 200 women and 100 men, the population is divided into two strata. Stratified random sampling is then used to select 20 women and 10 men to provide a representative sample size of 30 participants.

(d). Systematic sampling techniques

In systematic sampling, the researcher enlists each target group member with a number and selects the participants at regular intervals. It is the least time-consuming random selection method owing to its predefined range. If researchers want to select a systematic sample of 30 from a population of 300 employees, they would assign numbers to each member from 1-300 and select each 10th individual to be part of the representative sample.

Probability sampling methods can be used to reduce sample bias or when dealing with a large and diverse target population. The sampling techniques help in planning and creating an accurate sample from which to derive well-defined data for the study.

(2). Non-probability Sampling Methods in Qualitative and Exploratory Research

Non-probability sampling techniques involve selecting participants based on the statistician's capabilities rather than a predefined criterion. The non-probability sample selection implies that group members do not have the same probability of being selected to participate in the study. Although non-probability sampling is cheaper and easier to conduct, it has greater risks of human bias and does not accommodate sampling errors. The sampling methods are mostly used in qualitative and exploratory research to gain an initial understanding of a population that has not been adequately researched before. Non-probability sampling methods include:

(a). Convenience sampling techniques

A convenience sample is based on how easy it is for the researcher to access the participants. The selection is based on proximity rather than representativeness. Although convenience sampling is a cheap and easy way to gather data, especially at the initial data collection stages in research, it cannot produce generalizable results because there is no proof that the sample represents the target population. The non-probability sampling technique is used in cases where there are time and cost constraints in data collection.

Market researchers can use convenience sampling methods to survey the customers who visit a particular mall. They can do so by issuing questionnaires to the customers to fill out to understand their purchasing behavior. However, because the researcher may stand at the entrance or exit to survey the few customers who pass by, there is no way they can generalize their findings to the entire population of buyers.

(b). Purposive sampling methodology

In judgmental sampling, also known as purposive sampling, the researcher uses his expertise and knowledge of the target audience to select a sample that is most helpful to accomplish the purpose of the study. The method of sampling is mostly used in qualitative research involving a small specific population. Judgment sampling helps scholars and researchers gain detailed knowledge about a phenomenon rather than making statistical inferences. The desired sample size is selected based on defined criteria.

For instance, if a researcher wants to understand the thought patterns and opinions of expectant women about maternity experience, the sample frame would include only the expectant women. Those who are not pregnant are excluded from the study.

(c). Snowball sampling techniques

Snowball sampling is used when the target population is inaccessible, or the participants are hardly traceable. One uses snowballs to recruit other participants for the study. In such a case, the researchers can interview a few categories who are reachable to generate results. For instance, in a study that involves a sensitive topic like HIV/AIDs, subjects may not be willing to participate openly. One can use snowball sampling to survey the few volunteers who can link the researcher to other victims they may know.

(d). Quota sampling methods

The selection of participants in quota sampling depends on a pre-test standard or specific attributes. The technique operates under the assumption that the sample has the same qualities as the total population.

Frequently Asked Questions (FAQs) About Sampling Methods in Research Design

Some of the FAQs about sampling methods include?

(1). How can I select the right sampling method for my research?

In research design, it is fundamental to choose a method of sampling that will effectively achieve the study objectives. The steps to follow when choosing an appropriate sampling method include:

• Define the research goals and objectives while considering the desired precision, cost, accuracy, and time implications.
• Determine the best sampling technique that will help achieve the study goals without significant bias.
• Review and test the chosen sampling methods to be sure they can achieve the study objectives.

(2). What is a sampling bias?

A sampling bias occurs when not every member of a target group/population is given an equal chance of selection. Some of the members systematically stand a greater chance of selection than others.

(3). What are some of the possible sources of bias in sampling?

Sampling bias may result from sources such as:

• Deviation from pre-agreed sampling rules and criteria.
• Replacing selected subjects with others, especially when they are out of reach by the time the study commences.
• Low response rates.
• Omitting participants from groups or populations that are hardly reachable.
• Using an outdated list as the study's sample frame.

(4). Are there substantial differences between sampling bias and sampling error?

A sampling error is a single instance of inaccuracy where the estimated sample is not representative of the target population. In contrast, a sampling bias is a consistent error affecting several samples.

(5). What are the benefits of sampling in research designs?

In research, sampling is used to make inferences or draw conclusions about a target group or population. The samples are cost-effective, less time-consuming, convenient, and more manageable when collecting data than surveying the entire target population. Using different sampling methods helps to save time, finances, and other limited resources.

To choose the most appropriate sampling method, one must evaluate their research goals and objectives, allocated timeline for the study, budget, and the type of participants required to gather data. Selecting the most suitable sampling procedure saves time and other limited resources to effectively and efficiently achieve the study's aims, goals, and objectives.

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