✰ All items in any field of inquiry constitute a ‘Universe’ or ‘Population.’ Sampling is required if the universe of the population under study is too large.
✰ A sample design is a definite plan for obtaining a sample from a given population
✰ The validity of research results much depends upon the quality of the sample drawn.
✰ If the sample is biased or lopsided then the results cannot be trusted or generalized.
The main benefits of sampling are as follows:
✰ Reduction in overall cost of research.
✰ Less time-consuming and in certain cases, this is desirable as well.
✰ In case, the population is consistent, this becomes even more desirable.
To start with, let’s have a look at some terminology:
• Population • Sample • Sampling • Parameter • Statistics
Population:
✰ A Population is a collection of elements or items about which we wish to make an inference. The number of elements or items in the population is the size of the population. Let Population is denoted by N.
Sample:
✰ A sample is Basically a subset of the population. It is selected to represent the whole population. The number of elements or items in the sample is the size of the sample. If the Population is denoted by N, then the sample is denoted by n.
Sampling:
✰ Sampling is the process to draw a sample from a given population.
Parameter:
✰ The characteristic of the population is called a parameter.
Statistics:
✰ The characteristic of the sample is called statistics.
Example of Sampling in research methodology.
Types of sampling techniques:
There are two types of sampling methods:
1. Probability sampling 2. Non-probability sampling
Probability sampling and Non-probability samplings are the following types:
Non- Probability Sampling and Probability Sampling
1.Probability sampling
✰ Probability sampling is also known as “random sampling”. In probability sampling design, every item of the population has an equal chance of selection in the sample and all choices are independent of one another. Here, independence means that selection of one element is neither being affected by the selection of other elements nor it will affect the other elements.
Types of Probability sampling:
1• Simple random sampling 2• Systematic sampling 3• Stratified sampling 4• Cluster sampling 5• Area sampling 6• Multi stage sampling
1.1 Simple random sampling
✰ Every element or item of the population has a known and equal chance of being selected in the sample. It is used when the population is homogenous.
Note: if we have to select a sample of 100 items from a population of 11,000 items, then we can put the names or numbers of all the 11,000 items on slips of paper and conduct a lottery.
1.2 Systematic sampling
✰ In systematic sampling design, the selection process starts by picking some random point in the list and then every nth element is selected until the desired number is secured. Where n is calculated by formula,
K = (Size of population)/(size of sample)
Suppose Population size = 1100 and sample size is 100 then K = 1100/100 = 110 Every 110th element is selected from population and place into sample.
Note:
In the above example, first, pick a random element from the population. Here 2nd element is picked and placed into the sample after that select every 3rd element from the population and place it into the sample.
1.3 Stratified sampling
✰ • In case, the population is heterogeneous, then a stratified sampling technique is applied so as to obtain a representative sample.
✰ • In stratified random sampling, the Population is divided into mutually exclusive groups or strata and random samples are drawn from each group.
✰ • The population within a stratum is homogeneous with respect to the characteristics under study.
✰ • The population in a particular stratum may be in proportion to its population.
✰ • Suppose there are 1000 students in a school, 700 of them study science and 300 students study commerce. In a sample of 100, 70 students will be from science and 30 from commerce, i.e., in the same ratio as in the overall population.
1.4 Cluster sampling
✰ • The simple random and stratified sampling is adopted in situations when population size is small and units are identifiable. But if the population is larger, the researcher can go for cluster sampling.
✰ • In cluster sampling, the population is divided into mutually exclusive groups or clusters, where each cluster is representative of the population as a whole.
✰ • After that randomly select some of the clusters and make the sample.
Difference between Stratified and cluster sampling:
Stratified sampling
Cluster sampling
Definition
In this, the population is divided into homogenous groups and then sample is randomly taken from each groups or clusters.
In this, the population is divided into heterogeneous groups and then all the individuals of a few groups or clusters are randomly selected.
Groups
The population is divided into different groups which is externally heterogeneous but internally homogeneous.
The population is divided into different groups which are externally homogeneous but internally heterogeneous.
sample
From each group or strata, elements are randomly selected.
From all the groups or clusters, a few groups or clusters are randomly selected.
Used when
The population is heterogeneous and small.
The population is large.
1.5 Area sampling
✰ Area sampling is quite similar to cluster sampling and is often talked about when the total geographical area of interest happens to be a big one.
✰ In area sampling, we first divide the total area into a number of smaller non-overlapping areas, generally called geographical clusters, then a number of these smaller areas are randomly selected, and all units in these small areas are included in the sample.
✰ Area sampling is especially helpful where we do not have the list of the population concerned.
1.6 Multi stage sampling
✰ Multi stage sampling can be a complex form of cluster sampling.
✰ This technique is meant for big inquiries extending to a considerably large geographical area like an entire country.
✰ Under multi-stage sampling the first stage may be to select large primary sampling units such as states, within each selected state, a few districts may be selected, and then, within each district, a few cities may be selected, and then finally within each city, certain families may be selected.
✰ If the technique of random sampling is applied at all stages, the sampling procedure is described as multi-stage random sampling.
2. Non-Probability sampling
✰ In a non-probability sampling design, not all item of the population has an equal chance of being selected in the sample. Non-probability sampling is based on non-randomization. It is a non-structured sample and due to some convenience, the researcher of the inquiry purposively chooses the particular items of the population for constituting a sample.
✰ In quota sampling, the members are selected according to some specific characteristics or traits chosen by a researcher or we can say that sampling depends on some pre-set standard.
✰ The researcher selects the representative sample from the population.
✰ The proportion of characteristics/traits in the sample should be the same as the population.
✰ The sample is selected from a population-based on specific characteristics, which is convenient to researcher.
✰ Researcher conveniently select population member. However, the outcomes may not be generalized to larger population.
Note:
If our population has 60% males and 40% females then our sample should reflect the same percentage of males and females. Let the sample size is 100 then out of 100 the researcher select 60 males and 40 females which are conveniently available.
2.2 Convenience sampling
✰ It involves selecting samples based on convenience.
✰ This method is used when the availability of samples is rare and also costly.
✰ In convenience sampling, the researcher chooses the population member which is conveniently or easily available.
✰ This is an easy and inexpensive way to gather the initial data.
✰ The sample is not true representation of whole population so it cannot produce generalized result.
Note:
If researcher has to select a sample of 100 items from a population of 11,000 items, then the researcher select 100 items which is conveniently available.
2.3 Judgment or purposive sampling
✰ It is the evidence from the term itself that researchers become judgemental to choosing a sample. Here, a researcher uses their own knowledge, expertise, or past experience to select the sample.
✰ it can be used for descriptive research or historical research
✰ Judgment sampling is used quite frequently in qualitative research where the desire happens to be to develop hypotheses rather than to generalize to larger populations.
✰ For example, if the researcher wants to research the effect of anger on Health, then the researcher deliberately selects the person who easily becomes angry in their day-to-day life.
2.4 Snowball sampling
✰ Snowball sampling is also called Network/ chain sampling/ referral sampling.
✰ In snowball sampling, the researcher selects one participant from the population and asks him to suggest else who might be willing or appropriate for the study.
✰ Snowball sampling is used in situations where it is difficult to identify the members of the sample.
2.5 Accidental sampling
✰ It is used in market research
✰ Rather than carefully determining or obtaining, the researcher selects the element that is close at hand.
✰ The researcher may decide to select members of the population that are easily accessible, readily available, willing to participate, available in close proximity, etc.
✰ for example, surveying the first 50 people passing on a street
Note:
The size of the sample depends on the more heterogeneous or diverse population.
2.6 Dimensional sampling
✰ It is the extension of quota sampling.
✰ The researcher takes into account several characteristics e.g. gender, age, income, residence, and education.
✰ The researcher must ensure that there is at least one person in the study representing each of the chosen characteristics.
Note:
Out of 30 people the researcher ensures they have interviewed 5 people that are of a certain gender, 5 a certain age group, and 5 who have income between 25,000 to 40,000.
