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Sampling
Sampling:
In statistics, quality assurance and survey methodology, sampling is the selection of a subset (a statistical sample) of individuals from within a statistical population to estimate characteristics of the whole population. Or,
Sampling is a statistical procedure that is concerned with the selection of the individual observation; it helps us to make statistical inferences about the population
Most frequently used sampling methods are:
A. Simple Random Sampling:
A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of a simple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees. In this case, the population is all 250 employees, and the sample is random because each employee has an equal chance of being chosen.
Advantages of Simple random sampling:
The following are the advantages of simple random sampling:
- One of the great advantages of simple random sampling method is that it needs only a minimum knowledge of the study group of population in advance.
- It is free from errors in classification.
- This is suitable for data analysis which includes the use of inferential statistics.
- Simple random sampling is representative of the population.
- It is totally free from bias and prejudice.
- The method is simple to use.
- It is very easy to assess the sampling error in this method.
Disadvantages of simple random sampling:
Simple random sampling suffers from the following demerits:
- This method carries larger errors from the same sample size than that are found in stratified sampling.
- In simple random sampling, the selection of sample becomes impossible if the units or items are widely dispersed.
- One of the major disadvantages of simple random sampling method is that it cannot be employed where the units of the population are heterogenous in nature.
- This method lacks the use of available knowledge concerning the population.
- Sometimes, it is difficult to have a completely catalogued universe.
- It may be impossible to contact the cases which are very widely dispersed.
B. Convenience Sampling:
Convenience sampling is a non-probability sampling technique where subjects are selected because of their convenient accessibility and proximity to the researcher. The subects are selected just because they are easiest to recruit for the study and the researcher did not consider selecting subjects that are representative of the entire population.
In all forms of research, it would be ideal to test the entire population, but in most cases, the population is just too large that it is impossible to include every individual. This is the reason why most researchers rely on sampling techniques like convenience sampling, the most common of all sampling techniques. Many researchers prefer this sampling technique because it is fast, inexpensive, easy and the subjects are readily available.
Advantages of convenience sampling:
1. Availability of data: Based from the name itself, it can be attained on a convenient manner. In fact, subjects for this type of study can be just within the researcher. So the researcher does not need to do extra effort to gather data elsewhere. 2. Saves precious time: This technique would enable the gathering of data in a much shorter time compared to other methods. This is because it does not need to acquire an exhaustive research for the whole population. This method will only be given to a handful of people that are easily approachable. 3. Saves previous money: If you are going to conduct research, it normally requires you to spend a great deal of money to do it. With this option though, you can just collect data with the use of sampling technique. This is a great alternative when funding is not yet available. 4. Useful for Pilot studies: The technique used in convenience sampling will allow the gathering of primary data regarding the topic. Such findings can be used as pointers and should help in the decision for further actions.
Disadvantages of Convenience sampling:
1. Possible bias in data gathering: This method can get the views of a specific group of people and not the whole population. Hence, if some groups are over-represented or under-represented, this can affect the quality of data being gathered. 2. Possibility of Sampling Error: Since the selection process is already biased, there are inaccuracies that are bound to set in. This type of discrepancy is known as sampling error. 3. No generalized results: Using this method will lead to the difficulty of acquiring generalized conclusions that have been drawn from the research. This is because it is not possible to draw conclusions just by simply what a biased sample say. Most of all, it is not possible to formulate laws or rules, but identifying trends is. Likewise, it is not reliable to make a statement based on the misrepresentation of data from a chosen group of people alone. Convenience sampling is a method of collecting data samples from people or respondents who are easily accessible to the researcher. However the pros and cons of convenience
Advantages of Cluster sampling:
Can be cheaper than other sampling plans e.g. fewer travel expenses, administration costs.
- Feasibility: this sampling technique takes large populations into account. Since these groups are so large, deploying any other sampling plan would be very costly.
- Economy: The regular two major concerns of expenditure, i.e. travelling and listing are greatly reduced in this method. For example: compiling research information about every household in a city would be very costly, whereas compiling information about various blocks of the city will be more economical. Here, travelling as well as listing efforts will be greatly reduced.
- Reduced variability: When estimates are being considered by any other method, reduced variability in results is observed. This may not be an ideal situation every time.
- When sampling frame of all elements is not available we can resort only to the cluster sampling.
Disadvantages of Cluster sampling:
- Higher sampling error, which can be expressed in the so=called design effect, the ration between the number of subjects in the cluster study and the number of subjects in an equally reliable, randomly sampled unclustered study.
- Biased samples: If the group in population that is chosen as a sample has a biased opinion, then the entire population is inferred to have the same opinion. This may not be the actual case.
- The other probabilistic methods give fewer errors than this method. For this reason, it is discouraged for beginners.
E. Stratified Sampling:
Stratified sampling is a probability sampling method and a form of random sampling in which the population is divided into two or more groups (strata) according to one or more common attributes.
Stratified random sampling intends to guarantee that the sample represents specific sub- groups or strata. Accordingly, application of stratified sampling method involves dividing population into different sub-groups (strata) and selecting subjects from each strata in a proportionate manner.
For example: selecting sample group of 10 respondents by dividing the population into male and female strata in order to achieve equal representation of both genders in the sample group.
Stratified sampling can be divided into the following two groups: proportionate and disproportionate. Application of proportionate stratified random sampling technique involves determining the sample size in each stratum in a proportionate manner to the entire population.
In disproportionate stratified random sampling, on the contrary, numbers of subjects recruited from each stratum does not have to be proportionate to the total size of the population. Accordingly, application of proportionate stratified random sampling generates more accurate primary data compared to disproportionate sampling.
Advantages of stratified sampling:
- Stratified random sampling is superior to simple random sampling because the process of stratifying reduces sampling error and ensures a greater level of representation.
- Thanks to the choice of stratified random sampling adequate representation of all subgroups can be ensured.
- When there is homogeneity within the strata and heterogeneity between strata, the estimates can be as precise (or even more precise) as with use of simple random sampling.
Disadvantages of Stratified Sampling:
- The application of stratified random sampling requires the knowledge of strata membership a priori. The requirement to be able to easily distinguish between strata in the sample frame may create difficulties in practical levels.
- Research process may take longer and prove to be more expensive due to the extra stage in the sampling procedure.
- The choice of stratified sampling method adds certain complexity to the analysis plan.
Difference between cluster sampling and stratified sampling:
For a stratified random sample, a population is divided into stratum, or sub-population, before sampling. At first glance, the two techniques seem very similar. However, in cluster sampling the actual cluster is the sampling unit, in stratified sampling, analysis is done on elements within each strata. In cluster sampling, a researcher will only study selected clusters; with stratified sampling, a random sample is drawn from each strata.
- A researcher wants to conduct a survey among 10000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 4% margin of error? Z score is 1.96. Ans: 567.
2. A researcher wants to conduct a survey among 5000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 1% margin of error? Z score is 2.58. Ans: 3845
3. A researcher wants to conduct a survey among 7000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 2.5% margin of error? Z score is 1.65. Ans: 943
4. A researcher wants to conduct a survey among 3000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 3.2% margin of error? Z score is 2.58. Ans: 1055
5. A researcher wants to conduct a survey among 1000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 5% margin of error? Z score is 1.96. Ans: 278
6. A researcher wants to conduct a survey among 6000 patients regarding their perception
on quality of marketed medicine. How many patients does he need to approach at least if he can tolerate 1.5% margin of error? Z score is 2.17.
Ans: 2795.
