Once you have a bibliographic management system in place, you can begin your reading and coding the literature for relevance to your research question. This section will help you create a code to skim, scan and select literature efficiently and effectively.
Students often think that a research topic is established after reading the literature. However, reading the literature is best done after defining a research question. A well written research question helps you to quickly read and scan the literature for new ideas or ‘research gaps’ while remaining focussed on your topic. You may alter and narrow the scope of your research question as you progress through the research process while remaining confident that you are answering your question directly.
The categories you need to code your literature come directly from the terms of your research question. The key terms of your research question become the major areas of your literature review. The categories of literature need to form a logical sequence of ideas that lead to a coherent, well-argued position.
Example: The bodies of literature relevant to answering the research question: ‘What factors characterise a successful mentoring relationship for minority students?’ would include:
Then you need to establish a system for coding reference material for each category. Coding allows you to categorise literature according to themes and sub-themes, such as relevant topics, points of view, research inter-relationships, or new or challenging ideas and theories. Using the coding system helps you avoid writing notes on areas of interest that aren’t directly relevant to your research question.
To begin, establish a coding system that is meaningful to you as you plan the first version of your literature review outline (headings, paragraphs etc.). Consider using:
You can also use software such as Leximancer (a paid product – not Notre Dame subscribed) or the free web-based Voyant Tools to help you with coding your literature. Voyant Tools can also be downloaded and installed on a desktop computer. These products can examine body of text and produce a ranked list of terms based on frequency and related occurrence. These terms are then visually represented to show connections between concepts.