Advantages And Disadvantages Of Samplin
Advantages and disadvantages of sampling methods
Type
Description
Advantages
Disadvantages
Stratified
This requires one to be bias towards their data. For example, if you knew the proportion of pensioners in an area was 30% and you wanted to represent this in your sample you would ensure that pensioners’ answers only made up 30% of your sample.
It can be used with random or systematic sampling, and with point, line or area techniques
If the proportions of the sub-sets are known, it can generate results which are more representative of the whole population
It is very flexible and applicable to many geographical enquiries
Correlations and comparisons can be made between sub-sets.
The proportions of the sub-sets must be known and accurate if it is to work properly
It can be hard to stratify questionnaire data collection, accurate up to date population data may not be available and it may be hard to identify people's age or social background effectively
Random
In this method there is no bias in data, and every piece of data must have an equal chance of being selected. This would consist of using a random number generator/ chart and randomly selecting the sites at which investigated.
Can be used with large sample populations
Avoids bias
Can lead to poor representation of the overall parent population or area if large areas are not hit by the random numbers generated. This is made worse if the study area is very large
There may be practical constraints in terms of time available and access to certain parts of the study area
Systematic
There is some structure or underlying order to the way in which data is selected for sampling. Using the questionnaire for example, interviewing every 5th person.
It is more straight-forward than random sampling
A grid doesn't necessarily have to be used, sampling just has to be at uniform intervals
A good coverage of the study area can be more easily achieved than using random sampling
It is more biased, as not all members
Type
Description
Advantages
Disadvantages
Stratified
This requires one to be bias towards their data. For example, if you knew the proportion of pensioners in an area was 30% and you wanted to represent this in your sample you would ensure that pensioners’ answers only made up 30% of your sample.
It can be used with random or systematic sampling, and with point, line or area techniques
If the proportions of the sub-sets are known, it can generate results which are more representative of the whole population
It is very flexible and applicable to many geographical enquiries
Correlations and comparisons can be made between sub-sets.
The proportions of the sub-sets must be known and accurate if it is to work properly
It can be hard to stratify questionnaire data collection, accurate up to date population data may not be available and it may be hard to identify people's age or social background effectively
Random
In this method there is no bias in data, and every piece of data must have an equal chance of being selected. This would consist of using a random number generator/ chart and randomly selecting the sites at which investigated.
Can be used with large sample populations
Avoids bias
Can lead to poor representation of the overall parent population or area if large areas are not hit by the random numbers generated. This is made worse if the study area is very large
There may be practical constraints in terms of time available and access to certain parts of the study area
Systematic
There is some structure or underlying order to the way in which data is selected for sampling. Using the questionnaire for example, interviewing every 5th person.
It is more straight-forward than random sampling
A grid doesn't necessarily have to be used, sampling just has to be at uniform intervals
A good coverage of the study area can be more easily achieved than using random sampling
It is more biased, as not all members