Types of data

• Qualitative data

  Data that is expressed in words and non-numerical (however data can be converted into numbers for analysis.

  E.g Data from an unstructured interview
• Strengths of qualitative data

  • Produces more rich detail, it is much broader in scope and gives participants the opportunity to fully report their thoughts, feelings and opinions on a given subject.
  • Greater external validity, it provides the researcher with a more meaningful insight into participants worldview.
• Limitations of qualitative data

  • Difficult to analyse, it tends not the lend itself to being summarised statistically so patterns and comparisons within the data are difficult to identify.
  • Conclusions rely on subjective interpretations of the researcher and these may be subject to bias. particularly if the researcher has preconceptions about what they are expecting to find.
• Quantitative data

  Data that can be counted, usually given as numbers.
• Strengths of quantitative data

  • Simple to analyse so comparisons can be easily drawn.
  • Data in numerical form tends to be more objective and less open to bias.
• Limitations of quantitative data

  • Narrower in meaning and detail than qualitative data, so may fail to represent real life.
• Primary data

  Information which has been obtained first hand by the researcher for the purposes of a research project.
• Strengths of primary data

  • Data fits research, it is authentic and obtained from participants themselves for the purpose of a particular investigation. Questionnaires and interviews can be designed in such way that they specifically target the information that the researcher requires.
• Limitations of primary data

  • Requires time and effort. Conducting an experiment requires considerable planning, preparation and resources and this is a limitation when compared to secondary data which can be accessed in minutes.
• Secondary data

  Information that has already been collected by someone else, so pre-dates current research project.
• Strengths of secondary data

  • Inexpensive, easily accessed, requiring minimal effort. When examining secondary data the researcher may find that desired information already exists so no need to conduct primary data collection.
• Limitations of secondary data

  • May be substantial variation in the quality and accuracy of secondary data. Information may first look valuable and promising but further on in the investigation may be outdated and incomplete. Data may not match researchers needs or objective, so may challenge validity of any conclusions.
• Meta-analysis

  Process of combining findings from a number of studies on a particular topic. The aim is to produce an overall statistical conclusion based on a range of studies.
• Explain how meta-analysis and review are different?

  • Meta-analysis involves combining data from several research studies.
  • Whereas review involves data from a number of studies being compared and discussed.
• General statements about meta-analysis

  • Uses secondary data
  • All studies have investigated the same aims/hypotheses
• Strengths of meta-analysis

  • Allows us to create a larger more varied sample and results can be generalised across a much larger population, increasing validity.
• Limitation of meta-analysis

  • Prone to publication bias (file drawer problem). Researcher may not select all relevant studies, choosing to leave out studies with negative/non-significant results. Therefore conclusions from meta-analysis will be bias, because they only represent select data.