Chapter 1 - Data Collection Flashcards
What is a population?
The whole set of items that are of interest (the thing being surveyed is the population)
What is a census?
A census observes or measures every member of a population
What is a sample?
A sample is a selection of observations taken from a subset of the population, which is used to find out information about the population as a whole
Census advantages:
- Gives a completely accurately result
- Representative of everyone (and smaller subgroups)
Census disadvantages:
- Time consuming
- Expensive
- Cannot be used when the testing process destroys the item
- Hard to process large quantities of data
Sample advantages:
- Less time consuming
- Less expensive
- Fewer people have to respond
- Less data to process
Sample disadvantages:
- The data may be less accurate
- The sample may not be large enough to represent small subgroups
- The results could be biased
What are individual units of a population known as?
Sampling units (i.e. a person in a larger survey is a sampling unit)
What is a sampling frame?
A list of individually numbered or named sampling units of a population
What does the size of a sample depend on?
The required accuracy and available resources
How is the validity of a sample affected by its size?
- Generally, the larger the sample, the more accurate it is (but you will require greater resources)
- If the population is varied, you need a larger sample than if the population were uniform
- Different samples can lead to different conclusions due to the natural variation in a population
Why do we random sample?
We randomly sample because it means every member of the population has an equal chance of being selected. The sample should therefore be representative of the population. It also helps to remove bias from the sample
What are the 3 methods of random sampling?
- Simple random sampling
- Stratified sampling
- Systematic sampling
Simple random sampling:
To carry out a simple random sample, you need a sampling frame, usually a list of people or things, Each person or thing is allocated a unique number and a selection of these numbers is chosen at random. Selections can be made using random number generators or lottery style sampling (e.g. pulled from a hat)
Stratified sampling:
In stratified sampling, the population is divided into mutually exclusive strata (distinct subgroups of the population e.g. males and females) and a random sample is taken from each. The number selected from each stratum is reflective of the proportion of that stratum within the population
Systematic sampling:
In systematic sampling, the required sampling units are selected from an ordered list, and made at regular, chosen intervals
The size of the interval depends upon the number in the population, as well as the number desired from the sample. Divide the population by the sample number and round down, then use this value as the difference between the ordered terms, after selecting a starting point less than this value
Advantages of simple random sampling:
- Free of bias
- Easy and cheap to implement for small populations and small samples
- Each sampling unit has a known and equal chance of selection
Disadvantages of simple random sampling:
- Not suitable when the population size or the sample size is large as it is potentially time consuming, disruptive and expensive
- A sampling frame is needed
- Could exclude minorities
Advantages of Systematic sampling:
- Simple and quick to use
- Suitable for large samples and large populations
- Cheap and easy
Disadvantages of Systematic sampling:
- A sampling frame is needed
- It can introduce bias if the sampling frame is not random
Advantages of Stratified sampling:
- Sample accurately reflects the population structure
- Guarantees proportional representation of groups within a population
- Small sample sizes required - saves time and money
Disadvantages of Stratified sampling:
- Population must be clearly classified into distinct strata
- Selection within each stratum suffers from the same disadvantages as simple random sampling
- The requirement of a sampling frame means it’s time consuming and expensive
What are the 2 types of non-random sampling?
- Quota Sampling
- Opportunity Sampling
Quota Sampling:
In quota sampling, the interviewer/researcher first determines the different characteristics of the populations that they wish to represent. These will be mutually exclusive in the same way that the strata for a stratified sample are
It is then determined how many people you wish to question from each group. (this can be determined in the same manner as a stratified sample)
As an interviewer, you would then meet members of the population, assess which strata they fall into, and then allocate them into the appropriate quota
Once you have met your quota for a group, you no longer include any further members into that group
You continue this process until your quota for each group is filled
