Data Collection Flashcards

1
Q

What is a population?

A

The whole set of items that are of interest

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2
Q

What is a sample?

A

Some subsets of the population intended

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3
Q

What is a sampling unit?

A

Each individual thing that can be sampled in the population

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4
Q

What is a sampling frame?

A

The list formed when sampling units are named/numbered

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5
Q

What is a census?

A

Data collected from the entire population

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6
Q

Pros and cons of a census

A

Pros
- gives a completely accurate result
Cons
- time consuming
- expensive
- cannot be used when testing involves destruction
- large vol. of data to process

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7
Q

Pros and cons of a sample

A

Pros
- cheaper
- quicker
- less data to process
Cons
- data=x accurate
- data=x large enough to represent small subgroups

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8
Q

What is random sampling?

A

When each sampling units name in the sampling frame has an equal chance of being chosen=avoids bias

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9
Q

Simple random sampling

A

What is it:
Every sample has an equal chance of being selected.

Method:
Assign each item a number from 1 to n (n=no. Items)
Use random number generator, or lottery sampling’ (names in a hat) to generate the required sample size. Ignore repeats.
Select items that correspond to the number.

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10
Q

Pros and cons of simple random sampling

A

Pros
- bias free.
- easy and cheap to implement.
- each number has a known equal chance of being selected.

Cons
• Not suitable when population size is large.
• Sampling frame needed.

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11
Q

Systematic random sampling

A

What is it :
Required elements are chosen at regular intervals in ordered list.

Method:
Assign each item a number from 1 to
n (where n items)
Calc k:
k =pop size (N)/samp size (n)
Generate a random number between 1 and k as your starting number
Then select every kth element

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12
Q

Pros and cons of systematic random sampling

A

Pros
- Simple and quick to use.
• Suitable for large samples/ populations.

Cons
- Sampling frame needed.
• Can introduce bias if sampling frame not random.

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13
Q

Stratified random sampling

A

What is it :
Population divided into groups (strata) and a simple random sample carried out in each group.
sample size (n)
Same proportion/population size (N)
sampled from each strata.
Used when sample is large and population naturally divides into groups.

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14
Q

Pros and cons of stratified random sampling

A

Pros
• Reflects population structure.
• Guarantees proportional representation of groups within population.

Cons
• Population must be clearly classified into distinct strata.
- Selection within each stratum
suffers from same disadvantages as simple random sampling.

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15
Q

Quota sampling (non-random)

A

What is it :
Population divided into groups.
A quota of items/people in each group reflects the group’s proportion in the whole population.
Interviewer selects the actual sampling units. (eg advertise) until the quota is full. Ignore items from a group once sample is full.

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16
Q

Pros and cons quota sampling

A

Pros
Allows small sample to still be representative of population.
No sampling frame required.
Quick, easy, inexpensive.
• Allows for easy comparison between different groups in population.

Cons
• Non-random sampling can introduce bias.
• Population must be divided into groups, which can be costly or inaccurate.
• Increasing scope of study increases number of groups, adding time/expense.
• Non-responses are not recorded.

17
Q

Opportunity sampling (non-random)

A

Sample taken from people who are available at time of study, who meet criteria.
Eg stand somewhere and ask passers by

18
Q

Pros and cons of opportunity sampling

A

Pros
- Easy to carry out.
• Inexpensive

Cons
• Unlikely to provide a representative sample.
• Highly dependent on individual researcher.