# data[].attributes.headline
Answer all of the questions as a group as quickly as possible

# data[].attributes.subject
Answer all of the questions as a group as quickly as possible

# data[].attributes.headline
Check your Blackboard group number, open the worksheet and work together to complete it

# data[].attributes.subject
Check your Blackboard group number, open the worksheet and work together to complete it

# data[].attributes.body
Group 1 - contexts 1 & 2

Group 2- contexts 1 & 2

Group 3- contexts 3 & 4

Group 4- contexts 3 & 4

# data[].attributes.body
Cluster sampling

# data[].attributes.body
1.Cluster sampling
adv: Good for very large populations where representative clusters (groups) are available. Useful to avoid unnecessary traveling for face-to-face data collection.
disadv: Less representative of a population than a stratified sample

# data[].attributes.body
1.Cluster sampling
Because the two companies have a large crowd, cluster sampling can be used to avoid some unnecessary face-to-face communication.

# data[].attributes.body
2.Random sampling

# data[].attributes.body
Cluster sampling
Because the target group of our research is quiet large and difficult to interview.
Advantages
Good for very large populations where representative clusters (groups) are available
Disadvantages
Less representative of a population than a stratified sample.

# data[].attributes.body
Stratified sampling
advantages
1 Useful if your target population is a diverse group of people

2 Minimises selection bias (the researcher choosing certain people, usually without being aware of doing so, for any number of reasons)
disadvantages
1 Can be difficult to identify subgroups as pre-existing information about population needed to identify subgroups that smaller scale researchers do not always have

2 Time consuming and complicated

3 Need a large population