Sample

The most important concepts relating to the subject of  samples are  as follows (Ilmes, 2007):

Population

A population is the entire group of elements for which the conclusions of an investigation should be valid (e.g. all of the students of a course, all of the interactions documented by log-file between students and tutors, etc.). When all the elements of a population are investigated, it is called a census. If the population is the students of a course, then a census can normally be easily performed.

Sample

If a census is not possible (e.g. if the population is too large), a selection of the population (“sample”) can be made. When only these selected units are included in the investigation, it is called a partial survey.

Selection methods and sample types

There are different methods for the selection of elements (“sampling”):

Random sampling: In a random sample, the population should be known and defined exactly. Each member should be represented in the population once and thus have an equal or calculable probability of selection.

• Simple random sampling: Each member of the population has an equal chance of being selected. Example: A random number generator is used to select a sample of all students enrolled on a course. The resulting simple random sample allows the maximum possible representativeness, i. e. the sample reflects the composition of the population.
  
• Complex random sampling: Each member of the population does not have an equal chance of selection but a calculable one. One example for this kind of sampling is the stratified selection: Here, the population is divided into different „strata” which are homogeneous in respect of the variable of interest such as students of medicine,  sociology and information technology, with each member belonging exactly to only one stratum. Elements are selected from each stratum by random selection. The resulting stratified sampling has the advantage that elements from “small” strata are adequately represented in the sample.

Non-probability sampling: In non-probability sampling, elements are selected according to certain characteristics. Non-probability sampling techniques cannot be used to infer from the sample to the general population by applying statistical models.

• Typical case sampling: In typical case sampling, elements are considered characteristic for the population are selected.
• Quota sampling: In quota sampling the distribution of certain characteristics in the sample corresponds exactly to the distribution of these characteristics in the population. This requires that the distribution of specific characteristics within the population is known.
  

[1] [2]