Geographical methods Flashcards
What are the 7 sampling methods?
- Simple random sampling
- stratified random sampling
- systematic sampling
- cluster sampling
- Quota sampling
- convenience sampling
- snowball sampling
what are under probability sampling methods?
- Simple random sampling
- stratified random sampling
- systematic sampling
- cluster sampling
what are under non-probability sampling
- Quota sampling
- convenience sampling
- snowball sampling
What is the appropriate use and how to conduct of random sampling?
Appropriate use:
— When the whole population is available for a survey
— When every member has an equal chance of being selected
— When the area is large or time-limited
How to conduct:
— Use random tools like number generators or tables
— Select people based on generated numbers (e.g., 2nd, 5th person)
What is simple random sampling
Definition:
Simple random sampling is a method where each population member has an equal chance of being selected.
What is the appropriate use and how to conduct of systematic sampling?
Appropriate use:
— When sufficient representation of people is available
— When there’s a continuous stream of people (e.g., on the street)
How to conduct:
— Select individuals from a larger population starting at a random point, then choose with a fixed interval (e.g., every 1 male followed by 1 female)
What is systematic sampling
Definition:
Systematic sampling selects every nth item from a population based on a calculated interval.
What is the appropriate use and how to conduct of stratified sampling?
Appropriate use:
— When investigating specific subgroups
— When proportional representation of mixed characteristics is needed
How to conduct:
— Divide the population into relevant subgroups based on relevant characteristics (eg. gender, race)
— Calculate the sample size for each subgroup
— Select samples using random or systematic methods
What is stratified sampling
Definition:
Stratified sampling is a probability sampling method where the population is divided into distinct subgroups, and samples are drawn from each subgroup.
Pros and cons of systematic sampling!
Pros:
— Efficient and easy to implement
— Provides even population coverage
Cons:
— Can introduce bias if patterns align with the interval
— Limited randomness after the first selection
What is cluster sampling?
Cluster sampling refers to grouping a large population of people up into smaller groups. Random clusters are then selected to form a sample
Example:
Imagine a school with 20 classrooms. To do a survey, you randomly select 5 classrooms (clusters) and then survey all students in those selected classrooms, rather than surveying students from every classroom.
pros and cons of stratified sampling
Pros:
— Ensures proportional representation from each subgroup
— Reduces bias by sampling from all strata
Cons:
— Requires clear subgroup definitions, which can be challenging
— Can be time-consuming to collect and analyze data from multiple subgroups
Pros and cons of simple random sampling!
Pros:
— Unbiased results due to equal selection chance
— Simple and quick to implement
Cons:
— Can be costly for large populations
— May not represent specific subgroups efficiently
Pros and cons of cluster sampling!
Pros:
— Convenient and time-saving as it involves fewer groups
— Less hassle and complexity in organization
Cons:
— Might miss some minorities if they are not in chosen clusters
— Less accurate if clusters vary widely in characteristics
What is quota sampling?
A non-random selection technique where a researcher chooses a sample group to represent particular population characteristics
EG:
if you wanna sell a meal kit, you identify the different groups in the population - muslims, vegens, meat eaters. Then you take 200 people from each group and conduct research.
