Population
- all the items we are interested in
Sample
- a subset of items chosen from a population
Sampling frame
- when sampling units are of a population are individually named or numbered in a list
Census testing
Adv - give completely accurate result
Disadv - time consuming, can’t be used when testing involves destruction, large volume of data
Sample testing
Adv - less expensive, less time consuming, less data
Disadv - data may not be accurate, sample may not represent small sub groups of pop.
Simple random sampling
- firstly you need a sampling frame
- assign each item a number from 1 to N
- use a random no. generator to select ‘n’ unique numbers
- choose the items corresponding to these numbers to form the sample
Advantages of simple random sampling
- bias free
- easy and cheap to implement for small populations
- each sampling unit has an equal chance of being selected
Disadvantages of simple random sampling
- not suitable for large population size
- sample may not accurately reflect population
- sampling frame is needed
Systematic sampling
In systematic sampling the elements are chosen at regular intervals from an ordered list
1. you need a sampling frame
2. Assign each item a number from 1 to N
3. Starting at a random item between 1 and k, take every k^th element to form the sample
k = pop size/samp size
Advantages of systematic sampling
- simple and quick to use
- suitable for large samples and populations
Disadvantages of systematic sampling
- can introduce bias if sampling frame is small and not random, as patterns can be picked up in the data
- a sampling frame is needed
Stratified sampling
- Population divided into groups(strata) and a simple random sample is carried out in each group
1. You need a sampling frame and a distinct strata, the same proportion n/N is sampled from each strata
2. Within each strata, each item is assigned a different number and a random no. generator is used to select the number of unique numbers required.
3. Choose the items corresponding to these numbers to form the sample
Advantages of stratified sampling
- sample accurately reflects population structure
- guarantees proportional representation of groups within population
Disadvantages of stratified sampling
- sampling frame is needed and population must be clearly classified into distinct stratas
- selection within each stratum suffers from same disadvantages as simple random sampling
Quota Sampling
Population divided into groups according to characteristic
1. A quota of items/people in each group is set to try and reflect the group’s proportion in the whole population (quotas are calculated in the same way as stratified sampling)
2. Interviewer selects the actual sampling units until the quotas are reached
3. Once a quota is full, ignore subsequent sampling units that meet the characteristic
Advantages and of quota sampling
- Allows a small sample to be still be representative of population
- No sampling frame required
- Relatively quick, easy and inexpensive
Disadvantages of quota sampling
- non-random sampling can introduce bias
- population must be divided into groups which can be costly or inaccurate
- can depend on knowledge/expertise of researcher
Opportunity sampling
Sample taken from people who are available at time of study, who meet criteria
Advantages of opportunity sampling
- easy to carry out
- inexpensive
Disadvantages of opportunity sampling
- Unlikely to provide a representative sample
- Highly dependant on individual researcher
