Sampling and it's types

 

Sampling

It is the method or process of data collection in which data is collected from the representative part of whole population. It is the selection of the sample from the whole population in order to estimate the characteristics of the population.

For eg:

a.      A cook can taste a spoon of rice or vegetable whether it is properly cooked or not.

b.     A pathologist or doctor examines a few drops of blood to draw the conclusion about the whole body.

c.      A businessman gives order for the commodities by examining only small sample of the same commodity.

Advantages of Sampling:

a.      The cost of sampling is minimum.

b.     It takes less time in collecting, editing, classification analysis and interpretation of data.

c.      More trained and skilled manpower can be used to collect accurate information.

d.     It is applicable in case of large size population.

e.      It is applicable if the elements need to be destroyed in case of testing.

Disadvantages of Sampling:

a.      Wrong and unreliable conclusion may be obtained.

b.     It cannot give accurate results if the sample survey is conducted by unskilled, untrained and illiterate person.

c.      It the population is too heterogeneous, it may be impossible to use the sampling technique.

d.     It may give wrong conclusion if the sample selected from the population is not the representative.

Methods of Sampling:

The important methods of sampling are given below:

a.      Probability Sampling

b.     Non-probability Sampling

A. Probability Sampling

Probability sampling is a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. All the members have an equal opportunity to be a part of the sample. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, you need to use a probability sampling technique. In this method, units of the population are selected under the law of probability.

There are four main types of probability sample.

a.      Simple Random Sampling

One of the best probability sampling techniques that helps in saving time and resources, is the Simple Random Sampling method. It is the simplest and most common method of sampling. It is a reliable method of obtaining information where every single member of a population is chosen randomly, merely by chance. Each individual has the same probability of being chosen to be a part of a sample.
For example, in an schools of 500 students, if the teacher decides on conducting team building activities, it is highly likely that they would prefer picking chits out of a bowl. In this case, each of the 500 students has an equal opportunity of being selected.

b.     Systematic Random Sampling

Researchers use the systematic sampling method to choose the sample members of a population at regular intervals. This method is used when: i. Complete list of the population from which the sample drawn is available.

ii. Population is large, scattered and non-homogeneous

It requires the selection of a starting point for the sample and sample size that can be repeated at regular intervals. This type of sampling method has a predefined range, and hence this sampling technique is the least time-consuming.
For example, a researcher intends to collect a systematic sample of 500 people in a population of 5000. He/she numbers each element of the population from 1-5000 and will choose every 10th individual to be a part of the sample (Total population/ Sample Size = 5000/500 = 10).

c.      Stratified Random Sampling

 Stratified random sampling is a method in which the researcher divides the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized and then draw a sample from each group separately. It is used in heterogeneous population. In this method, the population is first divided into subgroups (or strata) who all share a similar characteristic. It is used when we might reasonably expect the measurement of interest to vary between the different subgroups, and we want to ensure representation from all the subgroups. It improves the accuracy and representativeness of the results by reducing sampling bias. However, it requires knowledge of the appropriate characteristics of the sampling frame (the details of which are not always available), and it can be difficult to decide which characteristic(s) to stratify by.
For example, a researcher looking to analyze the characteristics of people belonging to different annual income divisions will create strata (groups) according to the annual family income. Eg – less than $20,000, $21,000 – $30,000, $31,000 to $40,000, $41,000 to $50,000, etc. By doing this, the researcher concludes the characteristics of people belonging to different income groups. Marketers can analyze which income groups to target and which ones to eliminate to create a roadmap that would bear fruitful results.

d. Cluster Sampling

 Cluster sampling is a method where the researchers divide the entire population into sections or clusters that represent a population. Clusters are identified and included in a sample based on demographic parameters like age, sex, location, etc. This makes it very simple for a survey creator to derive effective inference from the feedback. Cluster sampling can be more efficient that simple random sampling, especially where a study takes place over a wide geographical region. 

 

B. Non-Probability Sampling

 In non-probability sampling, the researcher chooses members for research at random. This sampling method is not a fixed or predefined selection process. This makes it difficult for all elements of a population to have equal opportunities to be included in a sample. The units of the population are not selected under the rule of probability. Non-probability sampling techniques are often appropriate for exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

a.      Convenience Sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher. The investigator selects the sample elements on the basis of his or her convenience. It is also known as accidental sampling because sample is chose accidentally. The investigator choses the closest person as respondents. It is not a scientific plan ans also does not have any definite plan. The selection is totally biased.

b.     Purposive or Judgement Sampling

Also known as selective, or subjective, sampling, this technique relies on the judgement of the researcher when choosing who to ask to participate. Researchers may implicitly thus choose a “representative” sample to suit their needs, or specifically approach individuals with certain characteristics. This approach is often used by the media when canvassing the public for opinions and in qualitative research. It is often used in qualitative research, where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences. It is useful for situations where we need to reach a targeted sample quickly and proportional sampling is not a primary concern.

Judgement sampling has the advantage of being time-and cost-effective to perform whilst resulting in a range of responses (particularly useful in qualitative research). However, in addition to volunteer bias, it is also prone to errors of judgement by the researcher and the findings, whilst being potentially broad, will not necessarily be representative.

c.      Quota Sampling

In this method, sample is selected according to some fixed quota. It is similar to stratified random sampling but sample items are chosen accidentally not randomly. This method of sampling is often used by market researchers. Interviewers are given a quota of subjects of a specified type to attempt to recruit. For example, an interviewer might be told to go out and select 20 adult men, 20 adult women, 10 teenage girls and 10 teenage boys so that they could interview them about their television viewing. Ideally the quotas chosen would proportionally represent the characteristics of the underlying population.

d. 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. For example, when carrying out a survey of risk behaviours amongst intravenous drug users, participants may be asked to nominate other users to be interviewed.

Snowball sampling can be effective when a sampling frame is difficult to identify. However, by selecting friends and acquaintances of subjects already investigated, there is a significant risk of selection bias (choosing a large number of people with similar characteristics or views to the initial individual identified).
 

Adopted from:

a.    https://www.scribbr.com/methodology/sampling-methods/

b.    https://www.questionpro.com/blog/types-of-sampling-for-social-research/

c.    https://www.healthknowledge.org.uk/public-health-textbook/research-methods/1a-epidemiology/methods-of-sampling-population