Sample, Universe, Population


A sub-section of the population a representation of the population inference is generalized

Process of Sampling

  1. Define the population
  2. Develop Sampling Frame
  3. Select a Samling Method
  4. Determine sample size
  5. Execute the sampling process

Sampling Population Sample

Sampling Techniques

  1. Fixed vs sequential
  2. Probability vs non-probability
  3. Attributes vs

Probability Sampling Vs Non-probability Sampling

Every element has a chance of being in the samplenot equal chance
sample is randomsample is chosen by researcher according to their convenience
representative of the populationnot representative
graph TB
A(Sampling Methods)  
A-->C(Non Probability)
B-->Z(Simple Random <br> Simple Stratified <br> Cluster <br> Systematic)
C-->X(Purposive <br> Snowball<br>Convenience)

Simple Random

Stratified Random Sampling

builds up from simple random divides the population into groups depending on characteristic. groups = stratas and then random sampling is performed (Each subject only one strata; different stratas can have different number of subjects)

Cluster Random Sample

A cluster is obtained by first dividing the population into randomly chosen sub-groups (clusters) a random assortment of clusters = the sample in stratified - there is no common characteristic needed

Systematic Sampling

Every k th element This method simply involves selecting participants at a set interval, starting from a random point.