C1 - Data Collection

• Population is the whole set of items that are of interest.
• Census observes or measures every member of a population.
• 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:

  • it should give a completely accurate result

  Disadvantages:

  • time consuming and expensive
  • cannot be used when the testing process destroys the item
  • hard to process large quantity of data
• Sample -

  Advantages:

  • less time consuming and expensive than a census
  • fewer people have to respond
  • less data to process than in a census

  Disadvantages:

  • the data may not be as accurate
  • the sample may not be large enough to give information about small sub-groups of the population
• The size of the sample can affect the validity of any conclusions drawn:

  • the size of the sample depends on the required accuracy and available resources
  • the larger the sample, the more accurate it is, but you will need greater resources
  • if the population is very 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
• Individual units of a population are known as sampling units.
• Often sampling units of a population are individually named or numbered to form a list called a sampling frame.
• In random sampling, every member of the population has an equal chance of being selected. The sample should therefore be representative of the population. Random sampling also helps to remove bias from a sample.
• A simple random sample is one where every sample size has an equal chance of being selected.
• In systematic sampling, the required elements are chosen at regular intervals from an ordered list.
• In stratified sampling, the population is divided into mutually exclusive strata and a random sample is taken from each.
• Simple random sampling -

  Advantages:

  • free of bias
  • easy and cheap to implement for small populations and samples
  • each sampling unit has an equal chance of selection

  Disadvantages:

  • not suitable when the population or sample size is large as it is potentially time consuming, disruptive and expensive
  • a sampling frame is needed
• Systematic sampling -

  Advantages:

  • simple and quick to use
  • suitable for large samples and populations

  Disadvantages:

  • a sampling frame is needed
  • it can introduce bias if the sampling frame is not random
• Stratified sampling -

  Advantages:

  • sample accurately reflects the population structure
  • guarantees proportional representation of groups within a population

  Disadvantages:

  • population must be clearly classified into distinct strata
  • selection within each stratum suffers from the same disadvantages as simple random sampling
• In quota sampling, an interviewer or researcher selects a sample that reflects the characteristics of the whole population.
• Opportunity sampling consists of taking the sample from people who are available at the time the study is carried out and who fit the criteria you are looking for.
• Quota sampling -

  Advantages:

  • allows a small sample to still be representative of the population
  • no sampling frame required
  • quick, easy and inexpensive
  • allows for easy comparison between different groups within a population

  Disadvantages:

  • 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, which adds time and expense
  • non-responses are not recorded as such
• Opportunity sampling -

  Advantages:

  • easy to carry out
  • inexpensive

  Disadvantages:

  • unlikely to provide a representative sample
  • highly dependent on individual researcher
• Variables or data associated with numerical observations are called quantitative variables/data.
• Variables or data associated with non-numerical observations are called qualitative variables/data.
• A variable that can take any value in a given range is a continuous variable.
• A variable that can take only specific values in a given range is a discrete variable.
• When data is presented in a grouped frequency table, the specific data values are not shown. The groups are more commonly known as classes:

  • Class boundaries tell you the maximum and minimum values that belong in each class.
  • The midpoint is the average of the class boundaries.
  • The class width is the difference between the upper and lower class boundaries.