Dr Linda Omar
Sampling is a research technique used for selecting individual members or a subset of the population to make statistical inferences from them and estimate the characteristics of the whole population.
The population includes all members from a specified group and all possible outcomes or measurements that of interest. As the exact population depends on the scope of the study.
The sample consists of some observations drawn from the population, either as a part or a subset of the population. Hence, the sample can be considered as group of elements that participates in the study, while the sampling frame is the information that locates and defines the dimensions of the universe. A good sample should represent the population under study, be accurate and unbiased, and be of adequate size and reliability.
Sampling techniques are divided into two groups that are, probability and non-probability sampling, as follows:
- A) Probability sampling: involves random selection, allowing you to make statistical inferences about the whole population. Probability sampling allows us to quantify the standard error of estimates, confidence intervals to be informed and hypotheses to be formally tested. The main disadvantage is bias in selecting the sample and the costs involved in the survey. Generally, there are four types of probability sampling techniques:
Simple Random Sampling: In simple random sampling, each observation in the population is given an equal probability of selection and every possible sample of a given size has the same probability of being selected.
One possible method of selecting a simple random sample is to number each unit on the sampling frame sequentially and make the given selections by generating numbers from a random number generator. It may also involve with or without replacement. Replacement allows the units to be selected once. Without replacement is the most used method.
Cluster Sampling: Cluster sampling divides the population into multiple clusters for research. Researchers then select random groups with a simple random or systematic random sampling technique for data collection. There are two types of cluster sampling techniques: One-stage and two-stage cluster sampling.
Systematic Sampling: In systematic random sampling, the researcher first, randomly picks the first item from the population. Then, selects each item from the list. The procedure involved in systematic random sampling is very easy and can be done manually. The results represent the population unless certain characteristics of the population are repeated for every individual.
4 Stratified Sampling
In stratified sampling, the entire population is divided into multiple non-overlapping, homogeneous groups (strata) and randomly choose final members from the various strata for research.
Members in each of these groups should be distinct so that every member of all groups gets equal opportunity to be selected using simple probability. There are three types of stratified sampling techniques: Proportionate, disproportionate and optimal Stratified Sampling
- B) Non-Probability Sampling: It involves non-random selection based on convenience or other criteria, allowing you to easily collect initial data. Non-probability samples are preferred when accuracy in the results is not important.
If a non-probability sample is carried out carefully, then the bias in the results can be reduced. The main disadvantage is that it is dangerous to make inferences about the whole population. There are four types of non-probability sampling techniques:
- Convenience Sampling: is the easiest method of sampling and the participants are selected based on availability and willingness to participate in the survey. The results are prone to significant bias as the sample may not be representative of the population.
2.Judgmental or Purposive Sampling: a researcher relies on his/her judgment when choosing members of the population to participate in the study.
Researchers often believe that they can obtain a representative sample by using sound judgment, which will result in saving time and money. As the researcher’s knowledge is instrumental in creating a sample in this sampling technique, there are chances that the result obtained will be highly accurate with a minimum margin of error.
- Snowball Sampling: This method is commonly used in social sciences when investigating hard-to-reach groups. Existing subjects are asked to nominate further subjects known to them, so the sample increases in size like a rolling snowball.
This sampling method involves primary data sources nominating other potential primary sources to be used in the research. So, the method is based on referrals from initial subjects to generate additional subjects. Therefore, when applying this sampling method, members of the group are recruited via chain referral.
Quota Sampling: This method is mainly used by market researchers. The researchers divide the survey population into mutually exclusive subgroups.
These subgroups are selected with respect to certain known features, traits or interests. Samples from each subgroup are selected by the researcher.
The choice of which sampling technique to use depends on a variety of factors such as; the objectives and scope of the study, the method of data collection, the precision of the results, the availability of a sampling frame and the resources required to maintain the frame and availability of extra information about the members of the population.
In summary, reducing sampling error is the major goal of any selection technique. A sample should be big enough to answer the research questions, but not so big that the process of sampling becomes uneconomical.
In general, the larger the sample, the smaller the sampling error and the better the job you can do. Finally, the researcher should decide on the appropriate sampling method based on their research questions and objectives.
When choosing a company to undertake research, it is imperative to take into consideration the following factors; the company’s ability to adeptly formulate methodology, design questionnaires provide concise analysis and reduce data collected into actionable information.
At Medlico Research and Training Centre, we pride ourselves for having these critical competencies under one roof, our team can adequately attend to private organisations, the Civic society, Government Departments and their agencies as well as individual researchers.
Dr Linda Haj Omar is the CEO of Medlico Research & Training Centre. For more information/Enquiries: Visit: 4 Lanark Belgravia, Harare — Zimbabwe Tel: (+263) 242 702326/7, WhatsApp: +263 777 553011/12, Email: [email protected]