Sampling

Population – totality of all possible values of a particular characteristic for specified group of objects

Sample – part of a population selected according to a rule or plan

Sampling – process of choosing a representative portion of population

Reasons for sampling

• Practicality – time, money, personnel constraints

• Destruction of item/s to be studied

• Samples can be more thoroughly studied than whole populations

• Fewer errors are encountered in handling data

Types of sampling

• Probability samples– Samples selected by chance– All elements have an equal chance

of being chosen• Non-probability samples– Samples selected by judgment

Steps in Sampling

Define population

Determine homogeneity and size of the population

Choose an appropriate sampling method

Sampling methods

Simple random sampling

Simple, easy

Samples may be widely spread,

impractical for large populations, may

be less precise

Stratified random sampling

More efficient than simple random

sampling, allows for more

comprehensive data analysis

More information may be needed

about the population, more

tedious

Systematic random sampling with a random

start

Easy to do, quick and cheap, spreads

the sample over the entire population

May not be accurate if

unsuspected periodicity is

present in the population

Sampling methods

Cluster sampling

No need to list the elements, cheapest

to conduct

Not as efficient; elements close to each other often have similar

characteristics

Multi-stage sampling

Very efficient and minimizes sampling

error

May be tedious adn require collection of more information