Systematiske og strukturerte litteratursøk
Systematic and structured literature search
Although there are countless different ways to find academic literature, structured literature searches in bibliographic databases remain a central methodological element in most literature reviews.
On these pages, you will find a brief, pragmatic introduction that will enable you to conduct good, structured searches in such databases. On this main page, we briefly describe a 5-step working method that will take you from your research question to a complete search. We will also explain some important concepts along the way.
The 5-step method for developing good literature searches is essentially the same, whether you are conducting a limited literature study at bachelor level or a full-fledged, publishable systematic literature review. The difference between these two primarily lies in the breadth of the research question, the number of databases you search in, and the sensitivity of the search strategy you develop.
The 5-step method is also, in a sense, an abstraction, in that in practice the steps may overlap significantly, and there is often a need to return to an earlier step as a result of what you have learned during one of the later stages.
At the bottom of the page, you will find a collection of examples tailored to different levels that show how the 5-step process can be applied.
The 5-step method
In the type of bibliographic databases we primarily work with in comprehensive searches for literature reviews, it is not effective to search using long phrases in natural language that intuitively describe what we are looking for. To build good, structured database searches, we must first identify the main thematic elements of the research question, or main concepts, for which we are searching for literature.
A structured database search consists of search terms organized into groups or 'boxes', where each box corresponds to a main thematic element or searchable concept. Within each box, we combine synonymous or nearly synonymous search terms using the Boolean operator OR. The various groups or boxes are all finally combined using the operator AND. We can visualize this as follows:
A simple example for illustration. Let's imagine we are looking for academic literature on measures or support schemes for gifted children with learning disabilities. In this research question, there are a number of significant words, but not all are equally suitable for including in a literature search. The best starting points are the concepts 'giftedness' and 'learning disabilities'. These become our main concepts. In our box model, it might look something like this:
Don't worry about the details of the search terms. They will be explained later and demonstrated in the examples. It is the structure we should note here, and perhaps especially the simplicity of the structure.
It is very important to take this first step seriously. A common mistake is to get lost in overly complex and unstable structures when starting a literature search.
Perhaps you have heard of PICO or other similar tools for formulating research questions? In some cases, they can be useful tools for identifying the main thematic elements of our research question. However, our experience is that they often do more harm than good, precisely because they tempt us to start with an overly complex structure.
Once we have identified the main concepts in our research question, we can start finding search terms corresponding to each of them. There are numerous different ways of finding search terms. Here, we will describe a handful that we find particularly useful.
But before we proceed, we need to know that there are two different types of search terms: controlled vocabulary search terms and 'free' text words. See our explanation of controlled and 'free' search terms.
A general piece of advice before we delve into the specific methods: take good notes during this process! There is little point in identifying good search terms if you do not collect and systematize them. It can also be a good idea to keep an eye on how often a given search term appears in the literature you consider most relevant. A good way to keep track is to draw a box for each of the main concepts you identified in Step 1, and then update it with the search terms you find. Feel free to use this template.
The first and most obvious method for finding search terms is to use your own and other knowledgeable people's ideas. What do we think are the terms most commonly used for our main concepts in academic literature? This method works better the more knowledge we have about the topic.
The second method involves using the database's controlled vocabulary search functionality. In bibliographic databases that use a controlled vocabulary ('subject headings', 'thesaurus', 'synonym dictionary'), there is always a function to search for terms in the controlled vocabulary. It tries to 'map' the words or phrases you enter to terms in the controlled vocabulary. The suggestions sometimes fit well; sometimes less so. After searching, you can click on a suggested term in the vocabulary and look for broader or more specific terms, related terms, read definitions, and so forth. It is worth investing time in this so that you can make good decisions about which controlled search terms to use.
The database interface for the controlled vocabulary can also help us find useful search terms that are not part of the controlled vocabulary as such, i.e. 'free' text words. When we have found a controlled search term we want to investigate and click on it, we typically find an explanation of how that term is used to index articles in the database. Sometimes there is a definition, and quite often also a list of synonyms. We usually find them under the heading Entry Terms or Used for. They can be handy to note down, for later use as text words in our search.
A third important method is to start with relevant articles we already have or that we have found by using more intuitive search tools. Academic search engines like Google Scholar, discovery tools like Oria, basic search in Ovid databases (Ovid Medline, Ovid Embase and Ovid PsycINFO), and some other tools (e.g. Keenious) can in slightly different ways receive longer phrases and more natural language as input, and based on that, find the most relevant articles for us.
Once we then have found a handful of articles we know are relevant to our research question, we can use them to find good search terms for more structured and comprehensive searches in bibliographic databases. Look for words in the title and see which words or phrases the authors or publishers have chosen for the article. Additionally, you can look up the articles individually in a database, click your way to the complete database entry, and note which controlled vocabulary search terms the article is indexed with. We have created a separate little guide for how you can find search terms from articles.
All these methods are illustrated in the examples below.
Now we are ready to construct our first keyword search in a proper bibliographic database. First, we choose the database we believe best covers our research question. Based on the main concepts, which we have organized and illustrated in our box structure, we select the database we think best covers our research question. If you are unsure which one it is, please contact us at the library. The examples below can give you an idea about which databases are the first choice for various topics.
In a first attempt to build a good search, it is most important to proceed step by step: One main concept in the research question at a time, and one search keyword at a time. The reference databases we use for structured searches all have an interface that lets us see all our previous searches in a given session – a so-called search history. When we search one keyword at a time, we can see how many results each individual search keyword contributes, and we can later use the search history to combine all the different search keywords with OR and AND, according to the structure and the main concepts we have come up with.
It is crucial to stick to this step-by-step way of working. It is a common mistake to try to enter the entire search with all the keywords in combination as one search string. If we do that, we quickly lose control, and it becomes impossible for both ourselves and others to make a good assessment of the search quality.
In all our examples, we demonstrate this step-by-step way of working.
In a first attempt to build a search, it is less important that we include all conceivable relevant search keywords. We first use the most obvious ones from Step 2. This can give us an impression of how large the literature is in the area, and we can, both through the process of building the first search and by looking a bit in the results lists, get ideas for more good search keywords, so that we can build an even better search in the next round.
It is highly unlikely that our first attempt at building a well-structured search for our topic in a bibliographic database will be entirely optimal. Developing good searches is an iterative process. What we learn from working through and seeing the results of the first attempt will give us a good starting point for improving the search.
To assess how successful our first attempt was, we evaluate how well the search succeeded in capturing the relevant literature. This requires that we skim through the search results to gain an overall impression. When making such an assessment, you should be aware that the perceived relevance of the results is worse than what you are used to from more intuitive search tools like Google Scholar, since results lists are typically not sorted by relevance.
Additionally, you can check if your search captured the examples of relevant literature you had already identified beforehand, or which you have found through phrase searches using the more intuitive tools.
The most common way to improve on a first attempt is probably to supplement our search with more search terms. Sometimes we find that certain search terms do not work as well as we thought they would, and therefore should be removed. Often, the new search terms we add will be 'free' text words.
Since text words do not have a fixed form, as controlled vocabulary terms do, we must be especially attentive to variations in the form and composition of words. We can account for variations using techniques such as truncation, wildcards and proximity operators. In addition, we must also consider which metadata fields we want to search for text words in. If we only search for them in the title and authors' keywords, we get a narrower and more focused search than if we also search in the abstract field.
Precisely how a search can be improved will vary greatly, so we recommend that you look at some of the examples below, to get a sense of both what it can look like in practice, and what the variations can involve. We do want to note, however, that the improvements we demonstrate in the examples are somewhat abbreviated, compared to the typical process of building a search, which often entails making improvements over two or three rounds.
No bibliographic database contains absolutely everything. To ensure that we do not miss important relevant sources, we usually need to search in multiple databases.
The number of databases we search in will depend on the purpose of our search and our level of study. For most literature studies at bachelor level, it is often sufficient to use one database. For a master's thesis, it is usually necessary to search in more than one database. Discuss the need to conduct your literature search in multiple databases with your supervisor.
If you choose to search in more than one bibliographic database, the trick is to keep the search as similar as possible across databases. The 'free' text words you searched with in the first database can usually be reused when you search in other databases. However, some aspects of the original search must be 'translated' to other databases. Not all bibliographic databases use the same controlled vocabulary, so an important step when we 'translate' a search strategy is to look up the corresponding controlled vocabular search terms in the relevant controlled vocabulary. We must also be careful with special characters and operators like wildcards. These can vary from database to database and must also be 'translated'.
Examples
Here you will find a collection of level-adapted examples that demonstrate how the 5-step method is applied. The examples also show the practical search construction in the databases. The examples are based on different research topics from various subjects, and with different levels of ambition regarding delimitation and sensitivity.