PREPARATION OF A FLOW CHART BASED ON SELECTION OF SAMPLE FROM A POPULATION

PREPARATION OF A FLOW CHART BASED ON SELECTION OF SAMPLE FROM A POPULATION

    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 process of selecting a sample is known as sampling. Number of elements in the sample is the sample size.

There are lot of sampling techniques which are grouped into two categories as

Probability Sampling

Non- Probability Sampling

Probability Sampling

This Sampling technique uses randomization to make 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.

Simple Random Sampling

Stratified sampling

Systematic sampling

Cluster Sampling

Simple Random Sampling

 Every element has an equal chance of getting selected to be the part sample. It is used when we don’t have any kind of prior information about the target population.

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

 Stratified Sampling

This technique divides the elements of the population into small subgroups (strata) based on the similarity in such a way 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.

  Cluster Sampling

Our entire population is divided into clusters or sections and then the clusters are randomly selected. All the elements of the cluster are used for sampling. Clusters are identified using details such as age, sex, location etc.

Cluster sampling can be done in following ways:

Multi-Stage Sampling

It is the combination of one or more methods .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 clusters can be randomly selected from each stratum. This process continues until the cluster can’t be divided anymore. For example country 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 phase sampling

Multiphase sampling is an extension of two‐phase sampling, also known as double sampling. Multiphase sampling must be distinguished from multistage sampling since, in multiphase sampling, the different phases of observation relate to sample units of the same type, while in multistage sampling, the sample units are of different types at different stages.

Systematic sampling

Systematic sampling is a type of probability sampling method in which sample members from a larger population are selected according to a random starting point but with a fixed, periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

                Non-Probability Sampling

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

Convenience Sampling

Purposive Sampling

Quota Sampling

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 also costly. So based on the convenience samples are selected.

For example: Researchers prefer this during the initial stages of survey research, as it’s quick and easy 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 study.

For Example: If we want to understand the thought process of the people who are interested in pursuing master’s degree then the selection criteria would be “Are you interested for Masters in..?”

All the people who respond with a “No” will be excluded from our sample.

Quota Sampling

This type of sampling depends of some pre-set standard. It 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 reflect the same percentage of males and females.

Snowball Sampling

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

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

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