types of data

• Qualitative data


  Data which is displayed in words, is non-numerical
• Strengths of qualitative data


  • More richness and depth of detail
  • Allows participants to further develop their opinions hence has greater external validity
  • A more meaningful insight into the participants' views is achieved
• Limitations of qualitative data

  • Difficult to analyse
  • Difficult to make comparisons with other data
  • Researcher bias presented as conclusions rely on the subjective interpretations of the researcher (interpretative bias)
• Quantitative data

  Data that is displayed numerically, not in words
• Strengths of quantitative data

  • Can be analysed statistically so converted to graphs or charts
  • This makes it easy to make comparisons with other data
• Limitations of quantitative data

  • Lack of depth in detail
  • No meaningful insight into participants' views
  • As participants are not able to develop their opinions the results have low external validity
• Primary data


  Information obtained first hand by the researcher for an investigation
• Strengths of primary data

  • Targets the exact information which the researcher needs, so the data fits their aims and objectives
• Limitations of primary data

  • Requires time and effort
  • Can be expensive
• Secondary data


  Information collected by someone else other than the researcher yet is used by the researcher for their investigation
• Strengths of secondary data

  • Expensive
  • Data is accessed so requires minimal effort to collect
• Limitations of secondary data

  • It may be likely that the data is outdated or incomplete
  • The data may not be reliable- the researcher was not there when the study was conducted so is likely to be unsure of the validity of the results
• meta analysis 


  researcher combines results from many different studies and uses the data to form an overall view of the subject they are investigating
• strengths of meta analysis

  more generalisability - a larger amount of data is studied
• limitations of meta analysis 


  publication bias - researcher intentionally doesn’t publish all the data from the relevant studies and chooses to leave out the negative results

  • gives a false representation of what the researcher was investigating