Sampling

Sampling and population are two important and often confused subjects in research. They can be defined as;

Population: Theoretically specified group of studied elements or the large group results are generalized to

Sample: Study participants, usually a smaller group or a subset of the population

Sampling: process of selective study participants

Sampling and Population Diagram

There are two types of sampling, probability and it's opposite. Within there these general types, there are many different examples and kinds. A few of them are listed below.

Probability Sampling

• Every member of the population has a known & equal chance of being selected for the study
• Random selection is a key element

1. Simple Random Sampling: Each member of population is assigned a number, then random drawing of numbers is done.
2. Systematic Random Sampling: Every kB element is selected (i.e. every 4th name is chosen)
3. Stratified Random Sampling:
   • Group of participants are in layers / levels
   • Break into groups, then randomly select from the groups

Non-Probability Sampling

• Not random
• Unequal chance of selection

• Snowball Sampling
• Participants refer others to the study
• Works well with hidden populations
• Convenience or Available Participants
• Just choosing participants because the people are available
• Often if a survey in the middle of the pub
• Quota
• The non-random parallel to stratified sampling
• Characteristic qualifies for study (i.e. demographic, age…etc)
• Purposive Sampling
• Criteria or diagnostic category qualifies for participation (not random)
• For example, finding information on people who have been molested and their cases have been brought to court