Sampling Plan

Definition

What is a sampling plan?

A sampling plan is a method that is widely used in research investigations to provide an overview for doing research. It specifies which group will be polled, the sample size, and how respondents will be selected from the population.

Sampling plans should be structured so that the generated data contains a representative sample of the variables of interest and allows for the answers to all questions indicated in the goals.

After developing the sampling strategy, it is time to verify and then pass on to the parties responsible for execution.

What is a sampling plan?

What are the types of sampling plans?

When researching a group of people, it is challenging to gather data from every member of that group. The sample is the set of people who will take part in the study.

There are two types of sampling plans: probability sampling and non-probability sampling.

Probability sampling

Probability sampling is a sampling approach in which a researcher selects a few criteria and selects members of a population at random. With this selection criteria, all members have an equal chance of being included in the sample.

Probability sampling techniques are the best option for producing results that are representative of the general population.

There are four different kinds of probability samples:

  • Simple random sampling. It is a reliable way of gathering information in which every individual element of the population is selected randomly. Each person has the same chance of being chosen to be a part of this program.  
  • Systematic sampling. Systematic sampling is comparable to a simple random sample, although it is usually more convenient to conduct. Every member of the population is assigned a number, and then the members are chosen at regular intervals.   
  • Stratified sampling. The researcher splits the population into subgroups that do not overlap but still represent the total population using stratified random sampling. These groups are sorted before selection, and then a sample is drawn from each subgroup separately.  
  • Cluster sampling. Cluster sampling also entails segmenting the population into segments, but each part should share similar features with the rest of the sample. Rather than picking people from each subgroup, you choose entire groupings at random. This strategy helps deal with large, dispersed populations.   

Non-probability sampling

The researcher selects participants for research at random in non-probability sampling. This sampling approach is not a predetermined or fixed selection process. As a result, it is difficult for all population members to have equal chances of being included in a sample.

Non-probability sampling can be divided into four types, each of which explains the aim of this sampling approach in further detail:

  • Convenience sampling. A convenience sample is made up of people who are most conveniently accessible.
  • Judgmental or purposive sampling. This form of sampling, also known as judgment sampling, entails the researcher using their knowledge to choose the most relevant sample to the research’s goals.
  • Snowball sampling. Snowball sampling is a technique in which research participants recruit other people to participate in a test or study. It’s employed when finding potential participants is difficult.
  • Quota sampling. In this sampling technique, members are chosen based on predetermined criteria. The created sample would have the same characteristics as the overall population because it is formed based on particular attributes. It is a quick way to collect samples.

 

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