Secondary Research Methods

Secondary Research Methods

Definition

  • Secondary research methods involve using data that has already been collected by others.
  • This type of research is useful for gaining a broader understanding of a topic.
  • It can validate primary research findings and provide context for new research.

Existing Statistical Data

  • Existing statistical data refers to numerical data that has already been collected.
  • This data often comes from government databases, research studies, or organisational records.
  • Pros: large amounts of data available, often from reliable sources.
  • Cons: may not be tailored to your specific research question, and the quality or relevance of data may vary.

Literature Reviews

  • Literature reviews involve a detailed exploration of existing academic literature related to a topic.
  • This can include academic journal articles, books, and conference papers.
  • Pros: can identify gaps in existing research and provide context for your study.
  • Cons: can be time-consuming to find and analyse relevant literature.

Media and Document Analysis

  • Media and document analysis involves critiquing media sources such as newspapers, films, and online content, or organisational documents like policies, minutes of meetings etc.
  • It can provide insight into public opinion, societal trends, or internal company perspectives.
  • Pros: easy to access and may provide a cultural or societal perspective.
  • Cons: may contain bias and may not be directly applicable to your research question.

Internet Research

  • Internet research involves accessing online information related to your topic.
  • This can include websites, online publications, blogs, social media sites, forums, and digital archives.
  • Pros: can access a vast amount of information quickly, and often free.
  • Cons: quality and credibility of information varies greatly, and outdated content is common.

When using secondary research, it is crucial to evaluate the source’s credibility, check for potential bias, and understand the context in which the data was collected. Keeping ethical considerations in mind such as intellectual property rights and data privacy is also essential.