what to do when your entire population for sampling is less than fifty

Sampling Techniques

Sampling helps a lot in inquiry. It is one of the virtually important factors which determines the accurateness of your inquiry/survey result. If anything goes wrong with your sample then information technology will be straight reflected in the final issue. There are lot of techniques which aid us to gather sample depending upon the need and situation. This weblog post tries to explain some of those techniques.

To start with, let's have a look on some basic terminology

Population

Sample

Sampling

Population is the collection of the elements which has some or the other characteristic in common. Number of elements in the population is the size of the population.

Sample is the subset of the population. The procedure of selecting a sample is known as sampling. Number of elements in the sample is the sample size.

Sampling

In that location are lot of sampling techniques which are grouped into two categories equally

• Probability Sampling
• Non- Probability Sampling

The difference lies between the in a higher place two is whether the sample selection is based on randomization or not. With randomization, every chemical element gets equal chance to exist picked up and to be office of sample for study.

Probability Sampling

This Sampling technique uses randomization to brand sure that every element of the population gets an equal chance to be part of the selected sample. It's alternatively known as random sampling.

Uncomplicated Random Sampling

Stratified sampling

Systematic sampling

Cluster Sampling

Multi stage Sampling

Simple Random Sampling: Every element has an equal adventure of getting selected to be the office sample. It is used when we don't have whatsoever kind of prior information about the target population.

For example: Random selection of 20 students from form of 50 student. Each student has equal take a chance of getting selected. Here probability of selection is 1/fifty

Unmarried Random Sampling

Stratified Sampling

This technique divides the elements of the population into small subgroups (strata) based on the similarity in such a fashion that the elements within the group are homogeneous and heterogeneous among the other subgroups formed. And then the elements are randomly selected from each of these strata. We need to have prior information about the population to create subgroups.

Stratified Sampling

Cluster Sampling

Our entire population is divided into clusters or sections and so the clusters are randomly selected. All the elements of the cluster are used for sampling. Clusters are identified using details such as historic period, sexual practice, location etc.

Cluster sampling can be washed in following means:

· Unmarried Phase Cluster Sampling

Entire cluster is selected randomly for sampling.

Single Stage Cluster Sampling

· 2 Phase Cluster Sampling

Hither first we randomly select clusters and then from those selected clusters we randomly select elements for sampling

Two Stage Cluster Sampling

Systematic Clustering

Here the selection of elements is systematic and non random except the first element. Elements of a sample are chosen at regular intervals of population. All the elements are put together in a sequence showtime where each element has the equal take chances of being selected.

For a sample of size n, we dissever our population of size Due north into subgroups of k elements.

We select our first element randomly from the first subgroup of k elements.

To select other elements of sample, perform following:

We know number of elements in each grouping is k i.e North/n

So if our get-go chemical element is n1 then

Second element is n1+g i.eastward n2

Tertiary chemical element n2+k i.e n3 and and then on..

Taking an example of N=20, northward=5

No of elements in each of the subgroups is N/n i.e 20/5 =iv= m

Now, randomly select showtime element from the commencement subgroup.

If we select n1= 3

n2 = n1+1000 = iii+4 = vii

n3 = n2+1000 = 7+4 = 11

Systematic Clustering

Multi-Stage Sampling

It is the combination of one or more than methods described above.

Population is divided into multiple clusters and then these clusters are further divided and grouped into various sub groups (strata) based on similarity. One or more than clusters can be randomly selected from each stratum. This process continues until the cluster tin't exist divided anymore. For example land can be divided into states, cities, urban and rural and all the areas with similar characteristics can be merged together to form a strata.

Multi-Stage Sampling

Not-Probability Sampling

Information technology does not rely on randomization. This technique is more reliant on the researcher's ability to select elements for a sample. Issue of sampling might be biased and makes difficult for all the elements of population to be part of the sample equally. This blazon of sampling is also known as non-random sampling.

Convenience Sampling

Purposive Sampling

Quota Sampling

Referral /Snowball Sampling

Convenience Sampling

Here the samples are selected based on the availability. This method is used when the availability of sample is rare and besides costly. And then based on the convenience samples are selected.

For example: Researchers prefer this during the initial stages of survey research, equally it's quick and like shooting fish in a barrel to deliver results.

Purposive Sampling

This is based on the intention or the purpose of study. Only those elements will be selected from the population which suits the best for the purpose of our report.

For Example: If we want to understand the thought process of the people who are interested in pursuing master's degree and then the choice criteria would be "Are you interested for Masters in..?"

All the people who respond with a "No" will exist excluded from our sample.

Quota Sampling

This type of sampling depends of some pre-fix standard. Information technology selects the representative sample from the population. Proportion of characteristics/ trait in sample should be same as population. Elements are selected until exact proportions of certain types of data is obtained or sufficient data in different categories is collected.

For example: If our population has 45% females and 55% males then our sample should reverberate the aforementioned percentage of males and females.

Referral /Snowball Sampling

This technique is used in the situations where the population is completely unknown and rare.

Therefore we volition take the help from the first element which nosotros select for the population and ask him to recommend other elements who will fit the clarification of the sample needed.

So this referral technique goes on, increasing the size of population like a snowball.

Referral /Snowball Sampling

For example: Information technology'due south used in situations of highly sensitive topics like HIV Aids where people will not openly discuss and participate in surveys to share information almost HIV Aids.

Not all the victims will respond to the questions asked so researchers can contact people they know or volunteers to go far touch on with the victims and collect information

Helps in situations where we do not accept the admission to sufficient people with the characteristics nosotros are seeking. It starts with finding people to study.

Hope at present y'all all have a good idea most sampling and it's techniques.

Thanks for reading!

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Source: https://towardsdatascience.com/sampling-techniques-a4e34111d808

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