Data analysis: Kinds of data

• Qualitative data is non-numerical data that describes qualities, experiences, or opinions.
• Quantitative data is numerical data that can be measured and analysed statistically.
• Strengths of qualitative data:

  • Rich, in-depth detail → Provides insight into thoughts, emotions, and experiences.
  • Greater external validity → Provides more real-life, meaningful insight that numbers are unable to do.
• Weaknesses of qualitative data:

  • Difficult to analyse statistically → Cannot easily compare large datasets.
  • Subjective interpretation → Can be influenced by researcher bias.
• Strengths of quantitative data:

  • Easier to analyse and compare → Allows for statistical testing (e.g., averages, correlations).
  • More reliable → Less open to researcher bias and more objective.
• Weaknesses of quantitative data:

  • Lacks depth → Does not explain why participants behave in a certain way.
  • Can oversimplify behaviour → Ignores complexity (e.g., reducing mental health to a number).
• Primary data is data collected first-hand by the researcher for their specific study.

  • It is gathered directly from the participants as part of an experiment, self-report or observation.
• Secondary data is data that has already been collected by someone else and is then used by a researcher.

  • Such data already exists before the investigation and may include the work of other psychologists (journals, books etc.) or government statistics.
• Strengths of primary data:

  • Fits the research aim → Designed specifically for the study’s purpose.
  • Up-to-date and relevant → More reliable than using old research.
• Weaknesses of primary data:

  • Time-consuming and expensive → Requires more effort to collect as an experiment needs to be conducted, for example, which requires considerable planning, preparation and resources.
  • May have small sample sizes → Harder to generalise findings.
• Strengths of secondary data:

  • Quick and cost-effective → Saves time as data is already available.
  • Large sample sizes → Often more representative than primary data.
• Weaknesses of secondary data:

  • May not be specific to research aims → Data may be outdated or irrelevant.
  • Lack of control → The original study may have used biased methods or is incomplete.