How to Choose a Sampling Technique for Research?

How to Choose a Sampling Technique for Research?

Before choosing a Sampling Technique for research, you must first understand your target population and why you select a specific sample. Sampling techniques come in different types, such as non-probability, probability, and homogeneous.

Homogeneous sampling:

Homogeneous research sampling methods involve selecting a small group of subjects or units that are similar in many ways. The technique is most commonly used when the research goal is to understand or describe a specific group. This method is sometimes used in surveys and focus groups.

Advantages of using homogeneous samples:

The advantages of using homogeneous samples include avoiding the noise of sociodemographic factors and ethnic confounds that can affect results. This method also improves the accuracy and quality of data collected. However, the drawbacks of using a nonprobability sample include that estimates are not generally applicable to the target population. Probability samples, on the other hand, do not have this limitation.

Maximum variation sampling is another purposive sampling technique that examines a broad range of cases. This method allows researchers to gain insight from various angles and perspectives. On the other hand, homogeneous sampling is the opposite of maximum variation sampling. This method will enable researchers to squeeze out information from a small number of subjects and describe their impact.

Stratified random sampling:

Stratified random sampling is a method for distributing samples evenly across a population. This method helps to minimise costs and improve efficiency. In addition to being effective in quantitative data collection, stratified random sampling can be used in qualitative research. The process allows researchers to account for minority groups while ensuring that the sample is representative of the population’s general characteristics.

One of the main advantages of stratified sampling:

One of the main advantages of stratified sampling is its precision. For instance, a researcher may want to study a student’s scientific interest. For this study, they might divide the population into two strata based on gender. In other words, the researcher would choose 800 male students and 1,200 female students. Then, they would select 15 per cent of the population from each stratum.

The disadvantages of stratified sampling are that it is not appropriate for every study. Because it requires researchers to identify all members of the population and classify each into subgroups, stratified random sampling may not be a good choice for many studies. Stratified random sampling may also be time-consuming and costly because it involves an extra stage in the sampling process. In addition, it complicates the analysis plan and adds another layer of complexity.

Non-probability sampling:

Non-probability sampling is a good choice for research projects, especially in areas where the population is difficult to reach. In addition, it is cost-effective and convenient. This method is best for studying minority groups or those unwilling to participate in a formal research study.

Non-probability sampling is often used during the design stage of a research project. For example, a survey researcher may want to conduct an exploratory study by contacting a small number of people who resemble the study subjects. This technique is also used in full-blown research projects, which are usually qualitative, to gain an in-depth understanding.

Another type of non-probability sampling is quota sampling:

Another type of non-probability sampling is quota sampling. This type of sampling is more restrictive but aims to produce the same results as probability sampling. Quotas may be based on population proportions, for example, ten men and ten women in a sample of 20.

Quota sampling:

Quota sampling is a non-probability technique that allows researchers to select a sample based on a specific quota. This can ensure that underrepresented groups are represented. This technique also helps to minimise sampling error and selection bias. In many instances, quota samples will produce results comparable to those obtained through probability sampling.

Quota sampling is a popular technique because it is cheaper and faster than stratified sampling. It also allows researchers to choose the exact number of sample groups that best represent the population they are studying. However, quotas must be completed to ensure actionable results.

Author Bio:

Owen ingram is a research-based content writer who works for Cognizantt, a globally recognized E-Commerce-SEO and Research Prospect; a Dissertatie schrijven diensten tegen de beste prijzen in het Verenigd Koninkrijk Mr Owen ingram holds a PhD degree in mass communication. He loves to express his views on various issues, including education, technology, and more.