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## A beginner's Guide to Sampling Methods in Medical Research | |||||||||||||||||||||||||||||||||||||

Critical Comments in Biomedicine | |||||||||||||||||||||||||||||||||||||

Article 4, Volume 2, Issue 2 - Serial Number 4, September 2021
PDF (2.22 MB)
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Document Type: Article | |||||||||||||||||||||||||||||||||||||

DOI: https://doi.org/10.18502/ccb.v2i2.7397 | |||||||||||||||||||||||||||||||||||||

Authors | |||||||||||||||||||||||||||||||||||||

Moslem Basti^{} ; Farzan Madadizadeh ^{} ^{}
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^{}Center For Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School Of Public Health, Shahid Sadoughi University Of Medical Sciences, Yazd, Iran. | |||||||||||||||||||||||||||||||||||||

Abstract | |||||||||||||||||||||||||||||||||||||

Background: Sampling methods are one of the main components of each research. Familiarity with a variety of sampling methods is essential for researchers.Objective: The main purpose of this study was to teach different probabilistic and non-probabilistic sampling methods to improve the knowledge of researchers in conducting more accurate research.Methods: In this tutorial article, useful information about each sampling method, as well as how to properly use each method and its strengths and weaknesses were provided.Results: Five cases of probabilistic sampling methods and four cases of non-probabilistic sampling methods that are common were taught. Probabilistic sampling included simple random sampling, stratified random sampling, cluster sampling, systematic random sampling, and multi-stage random sampling. In addition to introducing each method, its strengths and weaknesses were also mentioned.Conclusion: Probabilistic sampling methods despite limiting assumptions provide more reliable results. Therefore, if it is possible, researchers should use probabilistic sampling methods for the accuracy of the study. | |||||||||||||||||||||||||||||||||||||

Keywords | |||||||||||||||||||||||||||||||||||||

Sampling; Probability Samples; Sampling Study | |||||||||||||||||||||||||||||||||||||

Full Text | |||||||||||||||||||||||||||||||||||||

It is costly, time consuming and beyond the ability of a researcher to collect information from the whole population; therefore, the best solution is to follow efficient sampling procedures. Sampling has several advantages and the most important one is saving time and money. It also requires less equipment and facilities. It can be said that the most important problem of sampling is the possibility of bias To achieve an ideal and suitable sample, the following points are need to be considered: 1. A clear definition of the statistical population 2. The format and framework of sampling 3- Characteristics of the statistical population to generalize the findings 4- Logical sample size 5- Adoption of appropriate sampling techniques 6- The rate of non-response according to the sample size Before introducing the types of probabilistic sampling, statistical population and the sample are elaborated below.
Researchers choose the type of sampling method based on the purpose of their research and the capabilities of the statistical population. If the purpose of sampling is to estimate population parameters, it is necessary to use probabilistic sampling In general, sampling techniques are divided into probabilistic and non-probabilistic methods. Given sampling is an important issue in medical research,
As a reliable sampling technique, it offers equal chance for individuals and objects to represent the whole population.
In this sampling method, each member of the statistical population has an equal chance to enter the sample or be selected in the study. The correct method recommended for selecting a simple random sample includes the use of a random number table or generated random numbers using statistical software There are two types of simple random sampling 1. Simple random sampling with replacement (SRS+R) 2. Simple random sampling without replacement (SRS-R) In the SRS with replacement, each member has the chance to be re-elected more than once; while in random sampling without replacement, each member has a chance to be selected only once.
Advantages of SRS method include minimization of the selection bias and simplification of the analysis. It is easy to estimate the accuracy obtained from the sample, but care needs to be taken to avoid sampling error; since random selection may lead to a sample that does not represent the statistical population. Moreover, this sampling method is not suitable for research aimed at subgroup analysis and may provide biased estimators
Stratified random sampling is a probabilistic sampling method in which the population is first divided into independent subgroups (strata) that have members with identical characteristics in each strata, and then a random sample is selected from each strata This method is suitable in studies where the researcher intends to compare different subgroups. Therefore, in this type of classification, the quantities based on which they are classified (such as age group) should be related to the desired characteristics and attributes to ensure sampling efficiency. In addition, it is more dynamic than simple random sampling; since it requires fewer samples
1- This method is more efficient than other sampling methods, 2- It is easier to use 3- It needs a smaller sample size [1].
Cluster random sampling is the result of dividing a population into similar clusters and heterogeneous populations within each cluster and then randomly selecting several clusters or groups. This sampling method is a two-step process CRS is divided into one-stage and multi-stage according to the method of implementation. In one-stage cluster sampling, clusters are selected only randomly among all elements. Multi-stage cluster sampling is an extended form of single-stage cluster sampling, so that it is carried out in several stages.
The main idea of SYRS is to regularly select a number of members on a list. Suppose we want to select names from a long list. A simple way is to select the appropriate distance (systematic sampling interval or sampling fraction) and select the names at equal intervals along the list. Systematic random sampling is often easier to perform than simple random sampling and also reduces the possibility of error Steps of sampling in SYRS method are as follows: selecting the statistical population, selecting the sample size, assigning a value to each member of the sample, determining appropriate distance between the sample (sampling fraction), choosing a random starting point, and finally starting the sampling systematically For example, suppose we want to systematically select a random sample with size 8 from a statistical population with size 24. First all the population units are numbered, then the sampling fraction (SF) is calculated by dividing preset sample size (n) by the size of the population (N),(SF=8/24=3). In the next step, the number of the first unit of sample (random starting point) will be randomly selected from 1 to 24, then the number of the next units is obtained by adding SF. For example, if the number of the first unit of the sample is 2, the number of the next units will be 5,8,11,14,17,20,23.
Depending on the characteristics of the community and the purpose of the research, the desired sample is sometimes selected in several stages. In this method, a sample of n1 is selected from a community of N people. Then, a smaller group is selected from n1; hence two-stage, three-stage, … and m stage can be define accordingly. This random sampling is suitable when the statistical population is too large
In NPS, the chances of selecting people or objects are not the same. In this type of sampling, there is a selection bias because the selection of individuals by the researcher is conscious and arbitrary. Therefore, estimating the sample error will be difficult due to non-random selection of individuals and it is impossible to obtain an accurate estimate of the sample error. Moreover, the results obtained from the sample cannot be generalized to the total population, because the obtained sample does not represent the community. NPS is cheaper and easier than probabilistic sampling, but the results are of lower validity than probabilistic sampling
Convenience sampling is a type of NPS in which the researcher studies the part of the statistical population that is more accessible. The results of this type of study cannot be attributed to the statistical population; since the sample size is not a true representative of the population. It is also suitable for the initial pilot study
In the QS, first the population is divided into separate subgroups, and then individuals are selected from the subgroups based on the proportion to size sampling. In this type of sampling, sample selection is non-random and often unreliable. Moreover, there is a selection bias error; since everyone in the community does not the same selection chance, making it the least popular sampling method. Quota sampling is usually used when time is short, there is no sampling framework, study budget is limited, and sampling accuracy is not a matter. In QS, it is possible to determine how many people are selected from each subgroup. The researcher places people in demographic groups based on age and gender variables. When the quota of a demographic group is saturated, sampling is completed
In statistical and epidemiological research, SS is a technique, in which the study group introduce other people, the study group grows like a rolling snowball. This sampling technique is often used in secret populations that are difficult to access, like drug addicts. This method has many limitations, because it does not have a specific framework and is time consuming
JS is also called purposive sampling and is selected non-randomly to achieve a specific goal. In this method, the researcher preferably uses people who have the necessary experience and knowledge in the field, so in JS, the researcher studies people who thinks are more suitable for study. This method is used when the number of people with a particular experience is small. It is also used in pilot studies. This type of sampling method has almost the same disadvantages as the convenience sampling method; therefore, the results are not of high validity
Determining the type of sampling method is one of the important components of medical studies. Therefore, researchers are expected to improve knowledge of sampling methods and how to use them (random and non-random). As a result, they have more mastery in choosing the type of sampling method in their studies. The sampling method and its logic is directly related to external validity, so that the external validity will decrease if the researcher makes a mistake in choosing the sampling frame or selecting participants. Therefore, researchers should also pay attention to external validity. In sampling, both internal and external validity should be high. External validity is highly sensitive to selection bias, so in
We would like to thank, Yazd University of Medical Sciences, especially the school of Health, which by holding scientific workshops, Encourages us to publish medical research articles.
Conceptualization: Madadizadeh F Formal analysis: Basti M, Madadizadeh F Investigation: Basti M, Madadizadeh F Methodology: Madadizadeh F Supervision: Madadizadeh F Writing – original draft: Basti M, Madadizadeh F Writing – review & editing: Basti M, Madadizadeh F
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
There were no conflict of interest. | |||||||||||||||||||||||||||||||||||||

References | |||||||||||||||||||||||||||||||||||||

[10] Brewerton PM, Millward LJ. Organizational research methods: A guide for students and researchers: Sage; 2001 | |||||||||||||||||||||||||||||||||||||

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