There is no research without information and resources. It is important to recognise that finding resources and information goes beyond simply having a skill. One of the key literacies in this process is search engine literacy. Search engine literacy involves knowing how to use search engines effectively to locate accurate and relevant information. This includes using advanced search techniques, evaluating the credibility of sources, and avoiding misinformation. Search engine literacy can be seen as transliteracy, as it utilises a combination of information, digital, and communication skills (Le Deuff, 2018).
Your search goals dictate the approach you take.
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For instance, a systematic review requires comprehensive searches, while quick information needs a more targeted approach. Heuristics, or search strategies, vary accordingly. Advanced techniques like Boolean operators are useful for systematic reviews, while simple keyword searches work for quick lookups. The systems you use, such as PubMed for medical research or Google Scholar for general searches, should align with your goals and heuristics. |
Adopted from: Gusenbauer, M., & Haddaway, N. R. (2021). What every researcher should know about searching–clarified concepts, search advice, and an agenda to improve finding in academia. Research Synthesis Methods, 12(2), 136-147. https://doi.org/10.1002/jrsm.1457
Effective searching requires a good match between these three components to improve search efficiency and outcomes. Your goals should dictate the heuristics you use, and the heuristics should be supported by the systems you choose.
When these components are well-matched, your search is more likely to be efficient and successful.
Choose the best search method for your research:
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Why Effective Search Matters? Enhances Research Quality. Ensures you find accurate and relevant information Saves Time. Helps you quickly locate the information you need. Broadens Knowledge. Exposes you to a wider range of sources and perspectives Supports Evidence-Based Decisions. Provides a solid foundation for making informed choices |
Traditional Search Methods
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Boolean Search Search uses logical operators (AND, OR, NOT) to combine or exclude keywords in a search, refining the results.
How does it work? It focuses on the relationships between keywords. |
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Keyword Search Search involves using specific words or phrases to search for information anywhere in the text (title, abstract, full text). How does it work? It searches for the exact keywords entered by the user. |
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Subject Headings Subject headings are controlled vocabulary terms assigned by indexers to describe the content of documents. How does it work? It uses standardised terms to ensure consistency and accuracy in search results. |
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Citation Chaining (Snowballing) Citation networking enables the discovery of additional relevant sources by examining the papers referenced by the authors you read or by checking who later cited those papers. Every citation can guide you to another piece of your research (Stapleton, 2024). This method of referencing can lead you to scholarly journals and publications that you might not have discovered through a standard database search.
How does it work? Begin with a highly relevant article and check its references (backward search) and the “cited by” feature (forward search) to discover additional sources. |
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Common challenges you may face:
Finding the right keywords to represent your research topic Creating effective Boolean search queries , which can be complex and take time Organising many search results can feel overwhelming Databases often update their content, making it hard to keep up Balancing: Time spent searching vs. analysing results, Focus on the depth of information and the range of sources and on your topic vs. exploring related areas Evaluating source credibility to ensure reliable information Avoiding information overload |
AI-Assited Search Methods
Semantic search goes beyond keywords to understand the intent and contextual meaning behind a query. It uses advanced techniques like natural language processing and knowledge networks to capture relationships between concepts.
| AI-Powered Search Engines: like Google Scholar use advanced algorithms to help find relevant academic papers quickly and efficiently. These search engines analyse vast amounts of data to understand the context and relevance of research papers. They can rank search results based on factors like citation counts, publication dates, and relevance to your query. |
| Natural Language Processing (NLP): NLP is a branch of AI that helps machines understand and interpret human language. When applied to search engines, NLP allows you to use natural, conversational language to find information. For example, instead of using specific keywords, you can ask a question. The search engine can understand the context and provide relevant results. |
Hybrid Search
Hybrid search combines the strengths of both lexical and semantic search methods. By doing so, it offers more accurate and relevant search results, particularly for complex queries where users may not know the exact terms to use.