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Sampling

Here are some sampling techniques:

  • Random sampling: this happens when we choose our sample randomly. It is also called representative sampling or probability sampling.

    But sometimes we might what to sort out the whole population a bit before we take our sample. For example, instead of a sample including all students, we might only want to sample from post graduate students at Monash University. This would result in a stratified random sample. We would then need to say, when presenting our results, that the limitations of the research would be that the sample can only really be generalised to all post graduate students at Monash University and not to all post graduate students in Australia.

  • Purposive sampling: This type of sampling involves choosing a particular group of subjects to compare with another group. So the sampling is purposive or deliberate including only those subjects to be compared.
  • Stratified sampling: As mentioned above, this type of sample is selected from a more restricted group, and some categorisation has taken place so that the particular group investigated is better represented. A highly stratified sampling technique involves selecting specific numbers for each category, eg: 48 male informants (12 in each of the four social classes). Each group of 12 might then be further divided into a 10-12 age group, a 14-17 age group and a 30-55 age group.(Punch 1998, p. 146)

Good sampling is fundamental to producing strong research and it must be carefully decided before any data is collected. Discuss your sampling technique with your supervisor to ensure that your research can be valued.

How will you do the research?
  • Ask people for their experiences (interviews)?
  • Ask people for their details and perceptions (questionnaires)?
  • Ask people to do a test, then some learning, then another test (pre- and post-test)?
  • Ask people if you can watch them (ethnographic observation, field notes, video clips)?
  • Develop some material/s to test and measure?
  • Develop some code in IT to test?
Who will collect your data?
  • Ideally it should be you. That is the best way of knowing what your data is like.
When will you collect it?
  • How long will the data collection take?
  • When will it suit the participants?
  • Do you have to travel somewhere?
  • Is the research site accessible or inaccessible due to climate or politics?
  • Do you have permission to do research in the country, region, business?
How will you collect it?
  • by listening (recording) the participant,
  • by observing, by testing (e.g., a student or end-user,
  • by measuring the occurrence of something (e.g., weather, soil samples, disease symptoms),
  • by investigating the structure of something (e.g. a company, government body),
  • by investigating the history of something (e.g. gathering data from libraries, museums, collections, etc)
How much data should you collect?
  • This varies across disciplines; it is best to have too much.
  • If it is quantitative data be sure that you have sufficient numbers for statistical analysis
  • For a descriptive case study or ethnographic observation, collect as much as you can, even if you do not use it all.
What instruments will you need?
  • Pre and post tests - existing validated tests or new trialed tests (validated how)?
  • Interview schedules - piloted and checked for ambiguity (by whom), etc
  • Questionnaires - trialed and checked for ambiguity (by whom), etc
  • Laboratory animals - ordered in time for the experiments (which appropriate funding, permissions, licences)
  • Special chemicals - ordered in time for the experiments (which appropriate funding, permissions, licences)
  • Special equipment - ordered in time for the research (which appropriate funding, permissions, licences)
How will you prepare your data for analysis?
  • Transcribe it from tape-recordings?
  • Code it for themes using software for qualitative analysis
  • Code it for statistical analysis
  • Trial it and observe results, modify and re-trial
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