Sampling in research
Sampling in research is the process of drawing sample of a desired size from the population. A sample is a specimen that is representative of the population from which it has been selected. A sample can be taken from a population for various end-uses: in statistics sample of data is taken to draw conclusion about the whole data set; in research sample is taken to draw conclusions about the population parameters; in medical science a sample of the drugs is tested to know its efficiency and side effects. You should keep in mind that sample is an estimate of the characteristics of the total population. You should use the best possible method of sampling to make it representative of the population.
Sampling in research can be done randomly or non-randomly. The researchers prefer to do random sampling if it is possible. In other words, random sampling is more reliable, accurate, and better representative of the population. Random sampling is not always possible and hence the researchers imply non-random sampling techniques.
Sampling in day-to-day life
Sampling is a process that we also do in our day-to-day life. When we go for grocery and randomly take a bite of an apple and find it sweet we want to buy the lot. This one apple we tasted is a sample that we have selected from the whole population. In various grocery stores product samples are sold free of cost to lure the customers. Sampling thus helps us make better decisions.
Sample is taken for a variety of reasons, some benefits of sampling in research are as follows:
- A sample can help you save time as well as human and financial resources. Cost effective methods have to be used in research, medicine, statistics and other areas of study. Time is also limited and one has to draw the conclusions. Studying the whole population can be a lengthy process and often impractical too. The processing and analysis of the data in statistics can be made easier with sampling, on the other hand, the results of the research will take years and years to come out.
- Sampling makes the process of research and statistical studies flexible. Without sampling you cannot setup high aims in research.
- A sample also helps in getting the results faster, in many studies the results should be reached to the audience so that they can get benefits from the findings. For example in medical science the results of the experiments can help the humanity in many ways, taking too long time may offset the importance and the scope of that research.
- Sampling sometimes becomes more accurate and effective than the population because of the ease of processing data. The researcher cannot handle the data of entire population and accuracy becomes questionable.
Types of Sampling
|Random sampling||Non-random sampling||Mixed sampling|
|Simple random sampling||Quota||Systematic sampling|
|Stratified random sampling||Accidental|
The types of sampling mentioned here are mostly used in research and statistics. There are many other types of sampling that are used in other areas of study.
Aims in Selecting a Sample
Good sample is a useful mean to accomplish a research but badly selected sample can ruin the entire research. The data has to be selected form the sample that you have drawn. Accuracy in sample selection is therefore crucial for the validity and reliability of your research.
In selecting a sample you should try to:
- Make sure that your sample is selected in the best possible manner. Your sample represents the population and it should fulfill this purpose.
- Avoid conscious or unconscious biases in the sample. The sample should cover the whole population and for this purpose the accurate sampling technique should be used.
- Ensure that every unit in the population has an equal chance of being selected.
Shortcomings of Sampling
Sampling is a trade-off between gains and loses. There are certain disadvantages of sampling, regardless of these drawbacks sampling is still used in several fields.
- Sampling may not be representative of the population because sample is only a part of the population and not the population itself.
- Sampling increases the chances of bias in research due to more manipulation.
- In non-random sampling the degree of generalizability is highly questionable.
- Sample should have to be big enough to represent the population. Very small sample in proportion to the population cannot be representative of the population.
In short, sampling is a trade off between accuracy and saving of resources. Your sample should be a good representative of the population so you can reap more benefits from it.
- Kumar, R., (2011). Research Methodology: A Step-by-Step Guide for Beginners, Sage Publications: 4th Ed., New Delhi. P-p- 175-180.