Types of data

• What is primary data?

  data that a researcher has gathered themselves in their own study. it's collected for the purpose of their study
• Strengths of primary data

  • have more control over the collection of data so usually more accurate/valid
  • more up-to-date data as collected directly from participants
• Weaknesses of primary data

  • very time consuming + expensive
  • can be quite sensitive information so may make participants feel uncomfortable
  • may lead to researcher bias as the researcher is collecting it directly
• What is secondary data?

  data that a researcher has obtained from elsewhere or another piece of research to use to inform them in their own research; collected not for the purpose of this study
• Strengths of secondary data

  • time + cost effective as we can make use of data that has already been collected (so more practical)
  • less personal so don't have to ask/test participant directly
  • can often give a large sample of data
• Weaknesses of secondary data

  • may be inaccurate/outdated data (don't know how old it is)
  • may be misinterpreted
• How is the majority of qualitative data collected?

  case studies, interviews, questionnaires (open questions) and unstructured observations
• Strengths of qualitative data

  • can generate information about the reasons behind behaviour in detail = increased internal validity as we are able to find out why behaviour happened
  • not limited like numbers people are free to elaborate = increased internal validity as we are able to find out why a behaviour happened
• Weaknesses of qualitative data

  • subjective as it's description is more open to bias = decreases validity as interpretations of data are open to interpretation
• How is quantitative data usually gathered from

  experiments, closed questions, structured observations with behavioural categories and correlations
• Strengths of quantitative data

  • easy to make comparisons + check consistency of data = high external reliability
  • easy to analyse and make comparisons between different groups or conditions = can see whether any significant differences in findings
  • objective as it's purely numerical so less open to bias = increases validity as we are able to draw objective inferences w/o interpretation
• Weaknesses of quantitative data

  • cannot show insight into reasons behind behaviour = reduced internal validity as we don't know why behaviour occured