Sampling is very important to perform any research. The accurate sampling methodology can impact the validity of your research. It is very important to choose the right method for a certain problem or a question. Your chosen method can bring exciting results based on your studies, and you can have great ideas to witness.
Most research studies are very important for some form of sampling. It means you only have to select some part of your study area representing the whole to extract meaningful insights. No one can study everything. For example, if you want to survey students of age 25 to 30, you can easily ask questions on the phone to almost hundreds of people of this particular age. Now, you can conclude people within that demographic group. In this kind of study, you would be focused on two groups, one large group and the second small group you are studying. The sampling is very important depending upon your type of study. There is no single way of doing it, but depending on your study, you can choose various methods. This article will explain how you can use different sampling methods to produce exciting results.
What is the importance of sampling methodology?
Sampling is very important to understand when studying a larger population; it is very important to use sampling methods in research studies of all types and sizes. Because if you can reduce time and effort, why would you not do that? It helps you study a larger population with the same resources. It remarkably opens up opportunities for you.
Sampling favours you to boost your research, so you are not limited by the cost, time, and complexity constraints in coming up with population sizes. Sapling allows you to perform things such as doing exit polls during elections, mapping the spread and effects of epidemics across a certain geographical area, and leading nationwide census research that delivers a portrait of society and culture.
How is sampling methodology beneficial in obtaining remarkable ideas?
Sampling saves money and time while being cheaper and more practical than reaching every member of a target population. It allows you to have a higher degree of control with lesser complexity. This can also eradicate exposure to non-sampling errors, including non-response bias. Non-random sampling is cheaper than random sampling because it reduces the cost of finding people and collecting data from them.
It helps characterise the populations under specific parameters, such as means, totals, and proportions. You must know that the sample might have errors since information is obtained from a sample that might not be concise with alternative samples from the same population.
What to look up to when sampling?
It would help if you considered the sample unit to use and with whom sampling will be applied.
- What is the size of the sample?
- What should be the size of a sample?
- How many people are involved?
- What are available frames?
- What method will be adopted, and how will the people be chosen?
How to choose and use the correct sampling methodology for the best results?
The selection of sampling methods depends on the problem you are solving. It requires you to identify the target population. You must be clear on the hypothesis or the null hypothesis you will test. You must clearly understand who, what, when, why, and how questions. Through this, you can be confident in designing proper tests and picking a sampling methodology to support the research purposes. Below are some steps that you can use to select the method best for your study.
- Define the target population: it is the first step in describing the sampling process to accurately define the targeted population.Usually, the population is related to the number of people living in a specific country.
- Research objective: Your objective will define your sample size and population of interest. It will be easier to get a random sample for a small sample, but you must check out your resources and budget for a larger sample.
- Availability of sampling frame: You must check the availability of the sampling frame. If there is no other availability, you can make a list of our own. You can select random sampling for systematic and cluster sampling.
- Study Design: You may consider the topic’s prevalence in the population and what is a suitable study design. For example, you can use a stratified sampling method for studying a population with a large ethnic group.
- Random Sampling: It is the one that answers our questions, as well as allows others to make use of our results. You can always select from a non-random sampling method when you cannot afford a non-random sampling method.
What types of sampling methodology are available?
Generally, we divide sampling into two types. Probability/random sampling and non-probability /non-random sampling. The below figure showcases sampling techniques.
Probability sampling methodology defines that every item in the population has an equal opportunity to be included in the sample. You can select a sample of your choice, but it can be costly in terms of time.
Non-probability sampling methodology is linked with case study research design, and qualitative research is normally associated with real-life phenomena. It does not make statistical references concerning a large population. A case study does not need to be representative, but a clear rationale must include some cases and individuals rather than others.
Below is a description of some strengths and weaknesses associated with each sampling technique:
- Positive Points: Least cost, least time-consuming, and most convenient
- Negative Points: Selection bias, non-representative sample, not suggested by descriptive and causal research
- Positive Points: Low cost, best for exploratory research
- Negative Points: Subjective research, not for generalisation
- Positive Points: The sample is controlled for specific characteristics
- Negative Points: No assurance
- Positive Points: Can assess rare characteristics
- Negative Points: Time-taking
- Positive Points: Deliver projectable results, easily understood
- Negative Points: Hard to develop sampling frame, costly, reduced precision
- Positive Points: improve representativeness, easier to implement than simple random sampling
- Negative Points: Can decrease representativeness
- Positive Points: Add all important subpopulations
- Negative Points: Hard to choose relevant stratification variables, not recommendable to stratify on multiple variables, expensive
- Positive Points: Feasible to implement, cost-effective
- Negative Points: Vague, tough to compute and interpret results
Depending on your goal, you can choose the best sampling methodology to deliver the best outcome. Remember that you will only come up with exciting ideas if you choose the right method.
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