What is Sampling Methods
Sampling methods in a study are very crucial in defining the quality, and accuracy of your results. In simple terms, the act of choosing a representative of the whole is seen in sampling whereby a subgroup of people or any other item in a population may be chosen. The reliability and validity of your research results greatly depends on the sampling technique to use.
In carrying out a survey, an experiment, or a study, the sampling technique to adopt will influence the degree that your results can be generalized. There are a wide range of sampling techniques to choose from and sometimes it may prove to be difficult to identify the most appropriate sampling technique which best suits your research objectives.
In the following blog, we will learn about the various forms of sampling, their strengths and limitations as well as an example to follow when making a decision. With this knowledge on the nature of sampling techniques, you can be in a position to determine the most appropriate sampling technique to use in your study and improve the validity of your study.
What is Sampling?
A research is any process of making a selection of a group of people, objects or data out of a larger population as a representative of the population in a research. The primary objective is to arrive at a reasonable and representative sample under which the researchers can make deductions about the whole population without any need to research on all/everything.
The various methods of taking samples will allow the researcher to generalize data about a population without necessarily collecting all the required information. A statistical sampling plan is influential to the relevance and usefulness of an outcome.
Types of Sampling Methods
Sampling methods fall under two wide categories which include probability and non-probability sampling. The different types are characterised by different techniques and uses. Let’s break them down:
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Probability Sampling
Probability sampling is a method in which the individual chances of selecting each member of a population is known but not zero. This sampling would be best suited to quantitative research where the researcher wants to generalize the tests about a large population. Probability sampling methodology tends to be linked to statistical analysis since it leads to a smaller bias rate and a representative sample.
- Random Sampling Techniques
Simple random sampling is one of the most popular random samples. It entails selecting individuals in a population by chance, whereby the individuals are likely to have equal probability of being detected. The other techniques of random sampling involve stratified sampling, cluster sampling, and systematic sampling that all have different methods of subdivision or distribution of the population to derive a representative sample.
- Advantages of Probability Sampling:
- Makes statistical inferences possible
- Reduces bias
- Provides an equal chance of selection
- Disadvantages:
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- It is more costly in terms of time and money
- Might need exhaustive list of whole population
Examples of Probability Sampling Methods
- Simple Random Sampling: Choosing people by taking names out of a hat, or an automated value generator to select the study participants.
- Stratified Sampling: Separating the population into sub categories (strata) using some characteristics, and then selecting sample participants randomly using each category.
- Systematic Sampling: Sorting through the list of a population picking every nth person.
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Non-Probability Sampling
Non-probability sampling, on the contrary, involves methods in which people are chosen on the basis of more than random choice. Such approaches will not necessarily provide a representative sample and results can be said to be affected by a selection bias. Although the non-probability sampling is not as statistically strong as probability sampling it is commonly employed in exploratory research where rules on accurate generalization are not the major objective.
Convenience and Purposive Sampling
One of the non-probability sampling methods is convenience sampling, which is relatively easy to carry out and the fastest to implement. It entails the selection of participants that are readily available or those closest to me. The other type of sampling is purposive sampling (or judgmental sampling) which entails the selection of individuals on the basis of character or purpose. It is common in the development of research that examines specific communities with certain characteristics.
Advantages of Non-Probability Sampling:
- Less expensive and less angst-ridden
- Helpful in exploratory or qualitative study
Disadvantages:
- Great risk of bias
- Less applicable with the overall population
Examples of Non-Probability Sampling Methods
- Convenience Sampling: The interviewing of people passing/walking behind a given area or a prior established group of individuals.
- Purposive Sampling: Using experts in an area that you are asking about, or using other persons who qualify to give some criteria related to the research.
Probability vs Non-Probability Sampling: Which to Use?
The decision of probability vs non-probability sampling is based on the goals of the research. Probability sampling makes sense when you require a population representative, quantitative data, as is the case with surveys or large experiments. Conversely, non-probability methods of sampling are associated in most cases of qualitative studies or where sampling of a particular subgroup or a small population is intended where generalizability is not an issue.
When to Use Probability or Non-Probability Methods
- Probability Sampling is best when you would like to generalise findings to the rest of the population and you also possess a complete list of the population. It is generally adopted to analyze statistics, large surveys, and experiments in which the accuracy and representativeness of data is of importance.
- A non-probability sampling works excellently when you have a restricted budget, time, and vocation, or when you need to conduct an exploratory research. It is widely applied in the case studies or interviews or in the study of niche groups.
Overview of Sampling Techniques in Quantitative Research
The purpose of quantitative research is to be able to quantify a problem or be able to gain insight into some patterns or trends through a population. In quantitative research, sampling methods are based on getting a sample that represents considerable values of a population and which can be studied statistically. Such forms of studies should use probability sampling approaches such as random sampling, stratified, and systematic sampling.
With the probability sampling method, one has a chance to determine the possibility of minimizing bias, sampling error and ensure that the quantitative research data can be used to generalize to a larger population. This enhances the quality of reliability and validity of the research results.
Sampling Error and Bias
Sampling error can be described as the disparity between the sample attribute and populous attribute. Even with the most effective sampling methods, one will always be in danger of the sample failing to perfectly represent the population. At this point, the relevance of the selection of the appropriate sampling method appears.
Bias arises when some groups or individuals have been presented in excess or in shortage as compared to the sample and thus make flawed conclusions. The probability sampling usually minimizes the probability of bias because every individual stands an equal likelihood of selection. Conversely, the non-probability sampling techniques such as convenience sampling have higher probability of being biased given that they would be more subjective.
Examples of Sampling Methods in Research
Sampling methods are important determinants of research validity and reliability of research results. The following are some illustrations of good sampling techniques in research:
- Simple Random Sampling: A sample list contains the name of everyone in a population and a random number generator is used by researchers to pick the participants.
- Stratified Random Sampling: A sample list contains the name of everyone in a population and a random number generator is used by researchers to pick the participants.
- Cluster Sampling: The population is grouped into clusters (e.g. geographical areas), then a random choice of clusters is asked.
- Convenience Sampling: The researchers interview the easiest person to access e.g. in a classroom or in a place.
Importance of Choosing the Right Sampling Method
It is impossible to overemphasize the significance of selecting an appropriate method of sampling. The selected method of research guarantees the representativeness of the sample in relation to the population and promotes the results being more precise and authentic. As an illustration, in case the focus of study is about eating behavior among college students, a randomly-selected sample consisting of different colleges will give a wider and realistic insight into the study than a study that involves only surveying students of one university.
Unprofessional sampling, however, may result in sampling errors or bias, which interferes with the results and invalidates a research study.
Conclusion
To conclude, sampling techniques can be discussed as such a vital element of any research study, since it has a direct impact on the quality of findings and its credibility. Either using probability sampling as a practical tool with large scale surveys or non-probability as a tool of more focused qualitative research, the key thing is to match the appropriate tool with the purposes of the research.
Being familiar with the varieties of sampling types, techniques, and the way they are used, a researcher can guarantee the fact that his/her data collection process will be as efficient and devoid of any biases as possible. Proper choice of the sampling method will not only enable accurate research results but also its generalizability where the inferences of the sample can be applied to the rest of the population.
