The Four Subtypes of Probability Sampling

 

dataSpring-subtypes-probability-samplingPreviously, we discussed the two main types of survey sampling: probability and non-probability sampling. These two types are crucial to gauge data and factors better. However, using any of the two will depend on the goals of the study, and each of them has sub-types. Knowing which of these subtypes your research falls into will make your study cost-efficient and help you gain the best result. We'll discuss these sub-types in two parts. For the first part, we'll discuss the different probability sampling subtypes.

Probability Sampling Subtypes 

There are four main subtypes under our first sampling method that are mainly used for quantitative research. These produce results that represent the whole of the population.  

  • Simple random sampling 

Simple random sampling is the most straightforward method of probability sampling. This can be done through the lottery method, where you choose the sample at random by drawing or using a computer program that simulates the same action, and the random number method, where you assign every individual a number. By using a random number generator or random number table, you can randomly pick a subset of the population.

Example:
If you have a sampling frame of 100 individuals, labeled from 0 to 99, use the groups of three digits from the random table to pick your sample. Then, if the first three numbers from the random number table were 094, select the individual labeled “94”, and so on.  

  • Systematic sampling

Systematic sampling is similar to simple random sampling, but it is slightly easier to conduct. Each member of the population is listed with a number, but instead of randomly generating numbers, individuals are selected at a regular interval.

Example:
If you want to select 500 individuals from a population of 5000, you can select every tenth person in the population to build a systematically sampled population.

  • Stratified sampling

Through stratified sampling, the population is divided into subpopulations or subgroups called “strata” that may differ in important ways. This could be based on relevant characteristics such as gender, age bracket, income bracket, and job role, to name a few. This allows researchers to draw more precise conclusions by ensuring that every subgroup is properly represented in the sample. You can use random or systematic sampling to select a sample from each subgroup.

Example:
If you are to conduct research on the level of education amongst women in a community, one can identify different population groups based on ethnicity, gender, religion, and income level. Random or systematic sampling will be done to select a sample from each subgroup.

  • Cluster sampling

Cluster sampling also involves dividing the population into subgroups known as “clusters”, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups. 

Example:
There are ten schools across the country, and all have roughly the same number of students. You may not have the capability to travel to every school to collect data, so you use random sampling to select three schools – these are then your clusters.

Discover on our next blog the different sub-types for non-probability sampling. In the meantime check out our 3 Tips to Improve Your Online Research Interviews, and if you are planning to conduct a survey, you can read more about the Survey Software Must-Haves: 6 Basic question Types. See you at our next one!

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