using nvivo for a literature review

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NVivo: Introduction and Basics

Overview of NVivo
Accessing NVivo
Getting Started
Coding in NVivo
Using Notes to Organize Ideas
Queries and Visualizations

How NVivo Can Help

Lit review tips & troubleshooting.

Other Resources

NVivo can help you produce a high-quality literature review. You can use it to:

Import source documents such as (includes citation managers such as EndNote or other reference management software)
Run quantitative text frequency searches. This identifies commonly occurring terms across all or any documents in your project file
Develop and test themes—before reading, as you read, and after reading
Brainstorm and refine themes using a variety of visual techniques
Capture sections of text as you read and classify them according to themes
Use Memos to annotate and manage your reading process
Create a centralized project file which stores your source documents, records of your research journey, and sections of text categorized by theme
Run search queries across some or all documents in your project file. These could be source documents, coded text, research notes, keywords, abstracts, etc.
NVivo and the Dissertation Literature Review : Video on using NVivo for literature reviews
Extending Your Literature Review With NVivo : Blog post on advanced techniques for using NVivo with literature reviews.

Text Readability

Something you will want to bear in mind here is "text readability".

A document is text-readable if a computer can recognise individual words. This allows users to copy or search any of the text.
Text files or Word documents (doc or docx files) are text-readable. Most recently-created PDFs are also text-readable.
Adobe Acrobat Pro and Microsoft Word can often create a text-readable version of a PDF.

Some reasons why text-readable PDF versions of source documents may not be available:

Some e-books and journal articles may be available as PDFs but are DRM-protected. This can prevent full-text access.
Old methods of scanning books or journal articles as PDFs. PDFs created in the past may not be text-readable. OCR may not work with old PDFs.
The printed document itself is of poor quality. For example, with documents typed with manual typewriters, a lack of uniformity may make text recognition impossible.
Copyright restrictions may prevent the creation or use of a PDF. More information regarding your copyright obligations as a user of material provided by Deakin is available by following this link.

Using Text-Readable PDF and Text Documents as Internal Files in NVivo

NVivo works best with text-readable PDFs or text files.

When importing text-readable PDFs or text files, NVivo creates new copies. It stores them as Internal Files.
Text-readable PDFs or text files are the easiest documents to Code in NVivo. The Coded text is also available for extra functions such as Word Frequency Searches.
Hence wherever you can access or create a text-readable pdf, you should.

Non-Readable PDFs

If you have access to a PDF but the full text is not readable (e.g. because of DRM or OCR problems), you have to make a choice.

You can import the PDF as an Internal File.
This will allow you to Code the PDF on-screen and see the results of your Coding as highlighting on the PDF.
However, you will be Coding by region instead of text. This means NVivo will take screenshots of the parts of the PDF you assign to Nodes. Thus NVivo's text-based functions (e.g. Word Frequency Searches and Text Queries) will not be available.
Alternatively, you can create an External File.
This allows you to populate a text document with key sections of text that you enter yourself.
You will not be able to read or see the actual document within NVivo. Nor will you see your Coding as highlighting on the document. But NVivo will be able to access the limited text you enter for Word Frequency Searches, Text Queries, etc.

What if I have no PDF?

Creating your own PDF versions of source documents is usually straightforward. But check for copyright restrictions first.
You can get free or inexpensive scanning apps for most smartphones. Try Clear Scan or Genius Scan.
Q: What if I only want to reference a small part of a hard-copy text? I don't want to go to the trouble of scanning a document.

A: Your best option is to create an External File and populate it yourself within NVivo.

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Last Updated: Jun 11, 2024 8:21 PM
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Research Data & Digital Scholarship

NVivo for Qualitative Data Analysis

Literature Reviews
Workshop Recordings
Resources/Tutorials
Keeping Notes in NVivo
Understanding Queries
References from reference management tools
Import/Export Bibliographies
Handout from Carleton College

The process of using NVivo for Literature Reviews can include:

Collecting your articles, ideally using a Citation Manager
Importing the citations from your citation manager or bibliography into NVivo
Importing the full-text PDFs of your articles into NVivo
Coding your article using themes and keywords
Using NVivo's queries to auto-code segments of your articles
Using Memos to annotate and manage your reading process
Using codes to select sentences for quoting and paraphrasing
Using memos to draft your literature review

Often the literature review process is inherently iterative, and will include importing and exporting to other formats. You may need to use OCR software to make some of your articles searchable by NVivo.

NVivo and the Dissertation Literature Review Video on using NVivo for literature reviews
Extending Your Literature Review With NVivo Blog post on advanced techniques for using NVivo with literature reviews.

Director of Research Data & Digital Scholarship

See schedule button for current dates and times. Appointments available in person and on zoom.

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Next: Workshop Recordings >>
Last Updated: Aug 12, 2024 4:59 PM
URL: https://guides.library.upenn.edu/nvivo

Adventures of a PhD candidate

Reflections on the thesis journey

Nvivo for a literature review: How and why

Recently, I decided to use Nvivo for my literature review. Firstly, Nvivo doesn’t take out all of the leg work of doing a literature review, for me it is an organisational tool. Secondly, I wish I had started this sooner!

I was reading about data analysis for my confirmation document and every book suggested familiarising yourself with the program you would use. At the time, I had no experience with Nvivo, I thought how am I going to get practise with no data? Some how along the way, I discovered that some people use it for their literature review. I decided this would be a fantastic, low pressure, method for familarising myself with the program. This actually had the unintended result of allowing myself to confidently say ‘Yes! I can do that!’ when offered Research Assistant work that would involve using Nvivo.

The next reason I decided to use Nvivo was I had no method for organising my quotes that I liked, beyond a spreadsheet and a word document. I personally didn’t like this method, as I felt I couldn’t search and categorise things the way I wanted. Nvivo allows me to categorise quotes under multiple ‘nodes’.

My research focuses on tourism and I wanted a method for sorting my articles (quickly) into: tourist, organisations, volunteers, and host communities. I also wanted a way of looking at how the methods intersected, along with the theoretical frameworks. I know this could probably be done in excel, but I couldn’t quickly access my quotes while searching for the above criteria (well, at least with my limited knowledge of excel).

The next section will require some knowledge of Nvivo and will contain screenshots of Nvivo 11 for Mac. Although they have similar features, I know the Windows version might look different and has extra features. Throughout the post below I have tried to provide the alternate names for the Nvivo 12 (Windows).

Importing references

Firstly, I import my articles under the ‘internals’ sources and into a folder called ‘articles’. I name each one with the authors name and year. In Nvivo 12 (Windows) I think the ‘internals’ folder is simply called ‘Files’.

This is a quick shot of what it looks like. If you already have a significant EndNote library you can import this, which is what I originally did. I code all of my articles using nodes kept in my ‘literature review’ folder. This will allow me to keep these nodes seperate from my data collection later.

At first, I was unsure of how to keep this current. My technique is to enter an article into EndNote, then manually import it into Nvivo. I then code it as I read. I think this is the most efficient method.

Assigning classifications to the references

As mentioned above, I originally wanted quick access to both sorting and finding quotes. To do this I use the ‘source’ classification available in Nvivo. This is called a ‘file classification’ in the Nvivo 12 for Windows.

As you can see, my source classification has a title: ‘Reference’ and different attributes listed below. These will import from Endnote (some of them). The most important reason for doing this is it allows me to do a ‘Matrix Coding Inquiry’ which I will now show you an example of output:

Screen Shot 2018-09-05 at 1.20.15 pm.png

In the left column you can see some nodes I chose to display for this example. The row across the top shows the ‘attributes’. The intersections of these show the number of times these have been coded together. If we look at the yellow box, we can see I have coded ‘Images of Africa’ three times in a reference that has the attribute ‘volunteer tourist’. This is useful to me if I want to write about literature on volunteer tourists discuss their host communities. I can double click on this and view the three times I have coded this and it looks like:

Screen Shot 2018-09-05 at 1.22.28 pm.png

This of course takes a fair amount of organisation, but it helps me to easily find intersections of different categories. You can also look at the intersection of two nodes in the same way.

If you don’t need to find specific intersections you can also view individual nodes:

Screen Shot 2018-09-05 at 1.25.40 pm.png

I wrote this post because I struggled to find the ‘how’ and ‘why’ of using Nvivo for a literature review. I would strongly recommend it, as it is a great tool for this purpose. I’m not an expert at Nvivo yet, but I really enjoy using it.

If you have any questions, please feel free to comment below!

An update 3rd April 2020:

Nvivo 12 (for Mac) looks much more like Nvivo for Windows, and very different to some of the screenshots in this post. Please keep this in mind.

I have since made a new post with an update on my thoughts.

I have also made a YouTube video which may be of interest to you:

Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo

MSc, PhD, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers ‘cut and paste’ and use coloured pens to categorise data. Recently, the use of software specifically designed for qualitative data management greatly reduces technical sophistication and eases the laborious task, thus making the process relatively easier. A number of computer software packages has been developed to mechanise this ‘coding’ process as well as to search and retrieve data. This paper illustrates the ways in which NVivo can be used in the qualitative data analysis process. The basic features and primary tools of NVivo which assist qualitative researchers in managing and analysing their data are described.

QUALITATIVE RESEARCH IN MEDICINE

Qualitative research has seen an increased popularity in the last two decades and is becoming widely accepted across a wide range of medical and health disciplines, including health services research, health technology assessment, nursing, and allied health. 1 There has also been a corresponding rise in the reporting of qualitative research studies in medical and health related journals. 2

The increasing popularity of qualitative methods is a result of failure of quantitative methods to provide insight into in-depth information about the attitudes, beliefs, motives, or behaviours of people, for example in understanding the emotions, perceptions and actions of people who suffer from a medical condition. Qualitative methods explore the perspective and meaning of experiences, seek insight and identify the social structures or processes that explain people”s behavioural meaning. 1 , 3 Most importantly, qualitative research relies on extensive interaction with the people being studied, and often allows researchers to uncover unexpected or unanticipated information, which is not possible in the quantitative methods. In medical research, it is particularly useful, for example, in a health behaviour study whereby health or education policies can be effectively developed if reasons for behaviours are clearly understood when observed or investigated using qualitative methods. 4

ANALYSING QUALITATIVE DATA

Qualitative research yields mainly unstructured text-based data. These textual data could be interview transcripts, observation notes, diary entries, or medical and nursing records. In some cases, qualitative data can also include pictorial display, audio or video clips (e.g. audio and visual recordings of patients, radiology film, and surgery videos), or other multimedia materials. Data analysis is the part of qualitative research that most distinctively differentiates from quantitative research methods. It is not a technical exercise as in quantitative methods, but more of a dynamic, intuitive and creative process of inductive reasoning, thinking and theorising. 5 In contrast to quantitative research, which uses statistical methods, qualitative research focuses on the exploration of values, meanings, beliefs, thoughts, experiences, and feelings characteristic of the phenomenon under investigation. 6

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or categorising the data. Basically it involves making sense of huge amounts of data by reducing the volume of raw information, followed by identifying significant patterns, and finally drawing meaning from data and subsequently building a logical chain of evidence. 8

Coding or categorising the data is the most important stage in the qualitative data analysis process. Coding and data analysis are not synonymous, though coding is a crucial aspect of the qualitative data analysis process. Coding merely involves subdividing the huge amount of raw information or data, and subsequently assigning them into categories. 9 In simple terms, codes are tags or labels for allocating identified themes or topics from the data compiled in the study. Traditionally, coding was done manually, with the use of coloured pens to categorise data, and subsequently cutting and sorting the data. Given the advancement of software technology, electronic methods of coding data are increasingly used by qualitative researchers.

Nevertheless, the computer does not do the analysis for the researchers. Users still have to create the categories, code, decide what to collate, identify the patterns and draw meaning from the data. The use of computer software in qualitative data analysis is limited due to the nature of qualitative research itself in terms of the complexity of its unstructured data, the richness of the data and the way in which findings and theories emerge from the data. 10 The programme merely takes over the marking, cutting, and sorting tasks that qualitative researchers used to do with a pair of scissors, paper and note cards. It helps to maximise efficiency and speed up the process of grouping data according to categories and retrieving coded themes. Ultimately, the researcher still has to synthesise the data and interpret the meanings that were extracted from the data. Therefore, the use of computers in qualitative analysis merely made organisation, reduction and storage of data more efficient and manageable. The qualitative data analysis process is illustrated in Figure 1 .

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Qualitative data analysis flowchart

USING NVIVO IN QUALITATIVE DATA ANALYSIS

NVivo is one of the computer-assisted qualitative data analysis softwares (CAQDAS) developed by QSR International (Melbourne, Australia), the world’s largest qualitative research software developer. This software allows for qualitative inquiry beyond coding, sorting and retrieval of data. It was also designed to integrate coding with qualitative linking, shaping and modelling. The following sections discuss the fundamentals of the NVivo software (version 2.0) and illustrates the primary tools in NVivo which assist qualitative researchers in managing their data.

Key features of NVivo

To work with NVivo, first and foremost, the researcher has to create a Project to hold the data or study information. Once a project is created, the Project pad appears ( Figure 2 ). The project pad of NVivo has two main menus: Document browser and Node browser . In any project in NVivo, the researcher can create and explore documents and nodes, when the data is browsed, linked and coded. Both document and node browsers have an Attribute feature, which helps researchers to refer the characteristics of the data such as age, gender, marital status, ethnicity, etc.

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Project pad with documents tab selected

The document browser is the main work space for coding documents ( Figure 3 ). Documents in NVivo can be created inside the NVivo project or imported from MS Word or WordPad in a rich text (.rtf) format into the project. It can also be imported as a plain text file (.txt) from any word processor. Transcripts of interview data and observation notes are examples of documents that can be saved as individual documents in NVivo. In the document browser all the documents can be viewed in a database with short descriptions of each document.

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Document browser with coder and coding stripe activated

NVivo is also designed to allow the researcher to place a Hyperlink to other files (for example audio, video and image files, web pages, etc.) in the documents to capture conceptual links which are observed during the analysis. The readers can click on it and be taken to another part of the same document, or a separate file. A hyperlink is very much like a footnote.

The second menu is Node explorer ( Figure 4 ), which represents categories throughout the data. The codes are saved within the NVivo database as nodes. Nodes created in NVivo are equivalent to sticky notes that the researcher places on the document to indicate that a particular passage belongs to a certain theme or topic. Unlike sticky notes, the nodes in NVivo are retrievable, easily organised, and give flexibility to the researcher to either create, delete, alter or merge at any stage. There are two most common types of node: tree nodes (codes that are organised in a hierarchical structure) and free nodes (free standing and not associated with a structured framework of themes or concepts). Once the coding process is complete, the researcher can browse the nodes. To view all the quotes on a particular Node, select the particular node on the Node Explorer and click the Browse button ( Figure 5 ).

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Node explorer with a tree node highlighted

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Browsing a node

Coding in NVivo using Coder

Coding is done in the document browser. Coding involves the desegregation of textual data into segments, examining the data similarities and differences, and grouping together conceptually similar data in the respective nodes. 11 The organised list of nodes will appear with a click on the Coder button at the bottom of document browser window.

To code a segment of the text in a project document under a particular node, highlight the particular segment and drag the highlighted text to the desired node in the coder window ( Figure 3 ). The segments that have been coded to a particular node are highlighted in colours and nodes that have attached to a document turns bold. Multiple codes can be assigned to the same segment of text using the same process. Coding Stripes can be activated to view the quotes that are associated with the particular nodes. With the guide of highlighted text and coding stripes, the researcher can return to the data to do further coding or refine the coding.

Coding can be done with pre-constructed coding schemes where the nodes are first created using the Node explorer followed by coding using the coder. Alternatively, a bottom-up approach can be used where the researcher reads the documents and creates nodes when themes arise from the data as he or she codes.

Making and using memos

In analysing qualitative data, pieces of reflective thinking, ideas, theories, and concepts often emerge as the researcher reads through the data. NVivo allows the user the flexibility to record ideas about the research as they emerge in the Memos . Memos can be seen as add-on documents, treated as full status data and coded like any other documents. 12 Memos can be placed in a document or at a node. A memo itself can have memos (e.g. documents or nodes) linked to it, using DocLinks and NodeLinks .

Creating attributes

Attributes are characteristics (e.g. age, marital status, ethnicity, educational level, etc.) that the researcher associates with a document or node. Attributes have different values (for example, the values of the attribute for ethnicity are ‘Malay’, ‘Chinese’ and ‘Indian’). NVivo makes it possible to assign attributes to either document or node. Items in attributes can be added, removed or rearranged to help the researcher in making comparisons. Attributes are also integrated with the searching process; for example, linking the attributes to documents will enable the researcher to conduct searches pertaining to documents with specified characteristics ( Figure 6 ).

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Document attribute explorer

Search operation

The three most useful types of searches in NVivo are Single item (text, node, or attribute value), Boolean and Proximity searches. Single item search is particularly important, for example, if researchers want to ensure that every mention of the word ‘cure’ has been coded under the ‘Curability of cervical cancer’ tree node. Every paragraph in which this word is used can be viewed. The results of the search can also be compiled into a single document in the node browser and by viewing the coding stripe. The researcher can check whether each of the resulting passages has been coded under a particular node. This is particularly useful for the researcher to further determine whether conducting further coding is necessary.

Boolean searches combine codes using the logical terms like ‘and’, ‘or’ and ‘not’. Common Boolean searches are ‘or’ (also referred to as ‘combination’ or ‘union’) and ‘and’ (also called ‘intersection’). For example, the researcher may wish to search for a node and an attributed value, such as ‘ever screened for cervical cancer’ and ‘primary educated’. Search results can be displayed in matrix form and it is possible for the researcher to perform quantitative interpretations or simple counts to provide useful summaries of some aspects of the analysis. 13 Proximity searches are used to find places where two items (e.g. text patterns, attribute values, nodes) appear near each other in the text.

Using models to show relationships

Models or visualisations are an essential way to describe and explore relationships in qualitative research. NVivo provides a Modeler designated for visual exploration and explanation of relationships between various nodes and documents. In Model Explorer, the researcher can create, label and connect ideas or concepts. NVivo allows the user to create a model over time and have any number of layers to track the progress of theory development to enable the researcher to examine the stages in the model-building over time ( Figure 7 ). Any documents, nodes or attributes can be placed in a model and clicking on the item will enable the researcher to inspect its properties.

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Model explorer showing the perceived risk factors of cervical cancer

NVivo has clear advantages and can greatly enhance research quality as outlined above. It can ease the laborious task of data analysis which would otherwise be performed manually. The software certainly removes the tremendous amount of manual tasks and allows more time for the researcher to explore trends, identify themes, and make conclusions. Ultimately, analysis of qualitative data is now more systematic and much easier. In addition, NVivo is ideal for researchers working in a team as the software has a Merge tool that enables researchers that work in separate teams to bring their work together into one project.

The NVivo software has been revolutionised and enhanced recently. The newly released NVivo 7 (released March 2006) and NVivo 8 (released March 2008) are even more sophisticated, flexible, and enable more fluid analysis. These new softwares come with a more user-friendly interface that resembles the Microsoft Windows XP applications. Furthermore, they have new data handling capacities such as to enable tables or images embedded in rich text files to be imported and coded as well. In addition, the user can also import and work on rich text files in character based languages such as Chinese or Arabic.

To sum up, qualitative research undoubtedly has been advanced greatly by the development of CAQDAS. The use of qualitative methods in medical and health care research is postulated to grow exponentially in years to come with the further development of CAQDAS.

More information about the NVivo software

Detailed information about NVivo’s functionality is available at http://www.qsrinternational.com . The website also carries information about the latest versions of NVivo. Free demonstrations and tutorials are available for download.

ACKNOWLEDGEMENT

The examples in this paper were adapted from the data of the study funded by the Ministry of Science, Technology and Environment, Malaysia under the Intensification of Research in Priority Areas (IRPA) 06-02-1032 PR0024/09-06.

TERMINOLOGY

Attributes : An attribute is a property of a node, case or document. It is equivalent to a variable in quantitative analysis. An attribute (e.g. ethnicity) may have several values (e.g. Malay, Chinese, Indian, etc.). Any particular node, case or document may be assigned one value for each attribute. Similarities within or differences between groups can be identified using attributes. Attribute Explorer displays a table of all attributes assigned to a document, node or set.

CAQDAS : Computer Aided Qualitative Data Analysis. The CAQDAS programme assists data management and supports coding processes. The software does not really analyse data, but rather supports the qualitative analysis process. NVivo is one of the CAQDAS programmes; others include NUDIST, ATLAS-ti, AQUAD, ETHNOGRAPH and MAXQDA.

Code : A term that represents an idea, theme, theory, dimension, characteristic, etc., of the data.

Coder : A tool used to code a passage of text in a document under a particular node. The coder can be accessed from the Document or Node Browser .

Coding : The action of identifying a passage of text in a document that exemplifies ideas or concepts and connecting it to a node that represents that idea or concept. Multiple codes can be assigned to the same segment of text in a document.

Coding stripes : Coloured vertical lines displayed at the right-hand pane of a Document ; each is named with title of the node at which the text is coded.

DataLinks : A tool for linking the information in a document or node to the information outside the project, or between project documents. DocLinks , NodeLinks and DataBite Links are all forms of DataLink .

Document : A document in an NVivo project is an editable rich text or plain text file. It may be a transcription of project data or it may be a summary of such data or memos, notes or passages written by the researcher. The text in a document can be coded, may be given values of document attributes and may be linked (via DataLinks ) to other related documents, annotations, or external computer files. The Document Explorer shows the list of all project documents.

Memo : A document containing the researcher”s commentary flagged (linked) on any text in a Document or Node. Any files (text, audio or video, or picture data) can be linked via MemoLink .

Model : NVivo models are made up of symbols, usually representing items in the project, which are joined by lines or arrows, designed to represent the relationship between key elements in a field of study. Models are constructed in the Modeller .

Node : Relevant passages in the project”s documents are coded at nodes. A Node represents a code, theme, or idea about the data in a project. Nodes can be kept as Free Nodes (without organisation) or may be organised hierarchically in Trees (of categories and subcategories). Free nodes are free-standing and are not associated to themes or concepts. Early on in the project, tentative ideas may be stored in the Free Nodes area. Free nodes can be kept in a simple list and can be moved to a logical place in the Tree Node when higher levels of categories are discovered. Nodes can be given values of attributes according to the features of what they represent, and can be grouped in sets. Nodes can be organised (created, edited) in Node Explorer (a window listing all the project nodes and node sets). The Node Browser displays the node”s coding and allow the researcher to change the coding.

Project : Collection of all the files, documents, codes, nodes, attributes, etc. associated with a research project. The Project pad is a window in NVivo when a project is open which gives access to all the main functions of the programme.

Sets : Sets in NVivo hold shortcuts to any nodes or documents, as a way of holding those items together without actually combining them. Sets are used primarily as a way of indicating items that in some way are related conceptually or theoretically. It provides different ways of sorting and managing data.

Tree Node : Nodes organised hierarchically into trees to catalogue categories and subcategories.

Introduction to NVivo

NVivo can be a helpful tool to assist with literature reviews, as it allows you to organise, summarise and search the literature you have found on your particular topic. When conducting a literature review, if you have already used referencing software (e.g. EndNote) to store your references, then you can import them to NVivo using the instructions provided in the Importing from EndNote section of this module (or similar if using different referencing software). Alternatively, if you have not uploaded them to referencing software then you can import them from a saved location on your computer using the instructions provided in the Importing materials saved on your computer section of this module.

Once you have added your literature to an NVivo project, you can create codes as detailed in the Codes & coding page of this module. In particular, you may wish to consider creating some or all of the following codes (don’t forget to use sub codes as necessary; for example, you could create Qualitative research and Mixed method research codes under a code for Methodology ):

Definitions
Empirical studies
Methodology
Data collection
Limitations
Good quotes
Gaps or contradictions

You can also search your project materials in various ways, as detailed in the Searching page of this module. In addition, you may also like to make use of some of NVivo’s other functionality to make notes on your materials, and to create a working document, as detailed in the following sections. In order to follow some of the examples provided, you can either use the project you created in the Setting up a project page, or make use of the Environmental Change Down East sample project (as detailed in NVivo sample projects ).

Annotations

Annotations can be used for a range of purposes, including to highlight a word or phrase that needs further definition, or to clarify or comment on a particular aspect of an argument.

To add an annotation to the Analyzing Estuarine Shoreline Change file, for example, open the PDF and highlight the text you wish to annotate. Then either right click and choose ‘New Annotation’ from the menu that appears, or click on the down arrow for the ‘Annotations’ icon above the article (the first icon, which looks like a speech bubble) and choose ‘Add Annotation’. Either way, you can then type or your annotation.

To show or hide the list of annotations at any time, select or deselect the ‘Annotations’ check box on the menu at the top of the screen, or click on the down arrow for the ‘Annotations’ icon above the article and select or deselect ‘View Annotations’. If you wish to delete an annotation, make sure the annotations are displayed and then select the item number for the relevant annotation, right click and choose ‘Delete’. Note that you can view the list of documents you have annotated by clicking on Annotations in the Navigation View (in the ‘Notes’ group), and can double click on any of these to view the annotation(s).

Memos are documents in your project that you can use to paraphrase what you have read, as well as to store your own personal insights, observations, interpretation and notes. Each memo is generally linked to an item in your project, although you can also create memos which are not linked if wished.

If you have imported from referencing software such as EndNote, the default is for a memo to be created for each reference containing the abstract, keywords and notes. You can check if this is the case for a particular reference by right clicking on it, either in the List View at the middle or in the Details View at the right of screen, and choosing ‘Memo Link’ (or ‘Links’ and then ‘Memo Link’) from the menu. If a memo already exists you will have the option to open or delete the memo; if it doesn’t, you will have the option to link to a new or existing memo. Either way, you can add your own content to a memo by opening it and selecting the ‘Edit’ box above it (once you have finished, click the ‘Edit’ box again to turn the edit mode off). You can also view a list of memos in your project by selecting Memos (or a sub-folder of Memos , e.g. Literature in the sample project) in the Navigation View (in the ‘Notes’ group), and can double click to open any of these.

You might find that you prefer to code directly from the references in your project, or that you wish to paraphrase and add observations to memos for the items and to code from these instead (or you could do a combination of both).

Creating a literature review document

Finally, if you would like to keep everything together in your NVivo project then you may like to create a working document for your topic. To create this in a new folder called Literature Review , for example, first create this folder as a sub-folder of the existing Literature folder. Open this new folder and right click in the List View, then choose ‘New File’ and ‘New Document…’. Name the document and click ‘OK’, and a blank document will open in the Details View.

The basic Microsoft Word functions for use in your working document will be available in the menu in the ‘Edit’ tab at the top of the screen, and you can turn on or turn off the edit mode by clicking on the ‘Edit’ box as per memos. You can use this document to keep track of your literature search strategies, the databases you use and so on. You can save this document to your computer at a later date if wished by right clicking on it either in the List View or in the Details View, choosing ‘Export’ and ‘Export Document…’ (or just ‘Export Document…’), browsing to choose where you would like to save it and then selecting ‘OK’.

Skills Team Workshops: Using NVivo with EndNote for Literature Reviews

Skills workshops
Video tutorials

Using NVivo with EndNote

For literature reviews.

Whether you are a qualitative or quantitative researcher, EndNote and NVivo can help with the process of writing a literature review. EndNote users can export existing libraries (with attached pdfs) into NVivo so that all the bibliographic data is included. You can then use NVivo to search all of your papers in one go for a particular word (or related words), code passages of text so that you can analyse them together, graphically explore the most used terms and much more..

Staff and students at the University of Hull can download both NVivo and EndNote by visiting the Support Portal and using the search box.

These video tutorials covers:

Importing references and PDFs into EndNote and exporting them into NVivo
Coding passages of text under particular themes so that they can be analysed together
Searching all PDFs for words and related words
Looking at word frequencies to spot possible trends
Graphically analysing bibliography data to show patterns

The memos can be found in the Notes section of the folder pane down the left of the NVivo screen:

These memos can be useful for initial scoping exercises. For example you can create a folder for 'Scoping nodes' and just read and code the memos to get a feel for what is out there. These nodes can be quite broad at this stage. You can then decide which papers require more specific reading for more precise coding (when you are in a memo you can right-click and choose Links>Memo Link>Open Linked Item to open the associated PDF).

Whole text coding

Depending on your project, it may be useful to apply codes to the whole PDF. For example, the country the research took place in, the type of industry, the level of education etc. This can then be used when running coding queries i.e. look for thematic code x in papers also coded at whole text code y . It is a good idea to put these codes in their own folder.

Whole text codes cannot be applied by dragging and dropping. The easiest way to apply them is to right-click on the PDF name in list view and choose Code . You can create new codes or add to existing codes in the box that pops up.

Search folders

The bibliographic data imported from EndNote can be used to categorise your PDFs into folders. For example, you could have them in separate folders for each year or each group of 5 years, or you could categorise them by the journal they are in.

The Search folders video will show you how this feature works.

Sets can also be used to put groups of articles in. For example you could have sets for articles yet to be coded and a set for those that have been coded. The Sets video will show how to use this feature.

Using EndNote to collect literature

You can collect literature on mass from a database for export into EndNote. This blog post from a member of the Skills Team shows you how:

Collecting the literature blog post

If you are working off-campus, make sure you set up the correct information in your EndNote Preferences to use the Find Full Text feature.

Find Full Text off-campus via EZproxy

If you are working off-campus, you can use EZproxy to improve the success of the Find Full Text feature in EndNote.

To add these settings to EndNote, choose Edit (EndNote on macOS), Preferences and select Find Full Text. Then enter the following details:

Open URL Path:

https://zc7xc9dq8b.search.serialssolutions.com/

Authenticate with URL:

https://hull.idm.oclc.org

Even so, you will probably not find all of the PDFs you need that way. You will therefore have to download and individually attach each PDF either by:

Dragging the PDF directly onto the appropriate record (from your downloads folder or the downloads bar at the bottom of your window).
Selecting a record in EndNote and clicking Attach PDF in the PDF tab above your detail pane on the right, navigate to the file and select it.

Exporting from EndNote and importing to NVivo

These videos will run through the export and import process for transferring bibliographic data from EndNote to NVivo.

Exporting from EndNote

Importing into NVivo

This blog post will show you how to export the files from EndNote and import them into NVivo:

Also see: Exporting from EndNote to NVivo blog post

Coding your text

The simplest way to use NVivo to help with literature reviewing is to assign passages of text to particular codes (that you create) so that you can then view everything with that code in one place when you are writing it up.

The videos below you how to do this for interview data but the principle is exactly the same for the literature.

Coding theory
Starting to code
Developing your coding
Viewing your coding
Further coding techniques

Querying your PDFs and your coding

Some other useful features of NVivo that are useful when reviewing literature are the Text Search, Word Frequency and Coding queries.

Text Searching

The Text Search query allows you to search all of your imported PDFs for particular words or phrases. These can then then be saved as nodes and you can see them all in one place as for the coding done in the videos above. You can also see Word Trees that look at what words come before or after the words found - and then run further searches for regularly occurring phrases.

Word Frequencies

NVivo can run a query that looks at which words appear most often in your PDFs (or groups of PDFs). This may help you spot trends that you had not noticed when reading. You can also run text-search queries on the results of your word frequency query to see where the words appear.

Coding Queries

These can be used once you have done coding as described in the videos above. It will allow you to see where coding overlaps.

Again, the videos below show you how to do this for interview data but the principle is exactly the same for your literature. Just make sure you select your literature folder as the place to search (or it will also look at linked memos which contain the abstracts - see the Memos box on this page).

Text search query
Query options
Word frequency query
Coding query
Dynamic sets

Considerations for using NVivo with literature

All the bibliography data from the EndNote records is stored by NVivo and you can use it create some potentially useful charts.

The below videos cover additional considerations when working with bibliographic data.

Working with literature in NVivo

Querying the literature

Last Updated: Jul 30, 2024 5:31 PM
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How to use NVivo for your Literature Review

This article has been written to explain how to use NVivo for your literature review process when doing your PhD. The examples I give are based on the NVivo for Mac platform which is a slightly different version to the Windows option, the principles and processes however remain the same.

You might be asking, ‘why do I need to do this?’ Well, as a doctoral researcher you will be reading literally hundreds of articles and you’ll need some mechanism to file and capture the relevant bits. Also, as a future researcher you will need to revisit many of the articles you have read to produce your own journal submissions. NVivo will make the process more effective and efficient. It uses the principles of thematic analysis so you will also gain an excellent overview of that aspect of qualitative analysis. The two videos below are short presentations of the process and the remainder of the blog will give you a step-by-step guide.

Step by step videos, click to activate.

The step-by-step guide..

The first thing you must do is identify the appropriate literature to read. These could be journal articles, books, reports, websites or a range of other documents. An important aspect of the process is managing your references. I use a reference management platform called Refworks, I will be explaining its use in my next article. Before all else you will need to copy the citation of the article you are reviewing so that it can be pasted into NVivo, this will help you remember where the document came from. If this is your first review then you will need to create a new NVivo file, I recommend you name it the subject area you are focusing on.

With NVivo open, create a new document by clicking on the ‘Create’ tab, then select ‘Document’ this will be stored in ‘Source’, ‘Internal’ (see figure 1).

[image source_type=”attachment_id” source_value=”2932″ caption=”Figure 1: Creating a new document.” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

Name the document using ‘Author’ and ‘Year’ as the nomenclature (i.e., Shaw 2017). Paste the full reference at the top of the document, then copy and paste the abstract . Review your chosen piece of literature, then when you find something of interest, copy and paste it into the NVivo document as a new paragraph and note the page number. Pasting an exact copy gives you the option of either paraphrasing it at a later stage or using the direct quote in your work (this is why you need the page number). Once you have done this highlight the paragraph then give it a name (a node / code / theme). If it is a new node then right click your mouse and select ‘Code Selection’ then ‘Code Selection at New Node’, type in your node / code / theme name (this is likely to be a section heading you will use in your literature review), see figure 2a and 2b.

[image source_type=”attachment_id” source_value=”2930″ caption=”Figure 2a: Code selection Part I” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

[image source_type=”attachment_id” source_value=”2931″ caption=”Figure 2b: Code selection Part II” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

If it is an existing node or if you are not sure if you have used it before right click your mouse and select ‘Code Selection’ then ‘Code Selection at Existing Node’, look up the appropriate node then tick the box, see figure 3a and 3b.

[image source_type=”attachment_id” source_value=”2928″ caption=”Figure 3a: Code selection existing Part I” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”] [image source_type=”attachment_id” source_value=”2929″ caption=”Figure 3a: Code selection existing Part II” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

Continue the process until you have completed reviewing the chosen piece of literature. You can review what you have done by clicking on the ‘View’ tab, then ‘Coding Stipes’ then ‘All Nodes Coding’. This will present the nodes on the right hand side of the document. Note, each word, sentence or paragraph can have more than one theme (code) see figure 4a and 4b.

[image source_type=”attachment_id” source_value=”2927″ caption=”Figure 4a: Code stripes Part I” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”] [image source_type=”attachment_id” source_value=”2926″ caption=”Figure 4b: Code stripes Part II” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

Once you have reviewed all the articles, books, websites etc, you can start thinking about how to structure your literature review. To do this you must first review each node, just click on Nodes (left window), you will see all your nodes / themes, you can sort them by clicking on the reference tab, see figure 5.

[image source_type=”attachment_id” source_value=”2925″ caption=”Figure 5: Reviewing the nodes” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

When you click on the selected name of the node it will open showing you the aggregated data that you have saved (figure 6). This will illustrate all the authors (you have reviewed) that have given an opinion about the element of literature you have considered. You must then decide if it is worthy of a place in your literature review. If you click on the hyperlink it will open the original document, you can now retrieve the whole reference if required. You must create a list of all your chosen literature (I do this in Refworks, and as stated earlier I will be covering this in my next article).

[image source_type=”attachment_id” source_value=”2924″ caption=”Figure 6: Node output” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

If, while reviewing the nodes you decide one needs changing you can simply right click on the node to change the properties through the ‘Get Info’ function: this can only be done if name does already exist (see figure 7a and 7b). There are also ways to merge nodes or create a hierarchy from them, I won’t go through the details now but will cover them in a later article.

[image source_type=”attachment_id” source_value=”2923″ caption=”Figure 7a: Changing nodes Part I” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”] [image source_type=”attachment_id” source_value=”2922″ caption=”Figure 7b: Changing nodes Part II” align=”center” icon=”zoom” size=”large” fitMobile=”true” autoHeight=”true” quality=”100″ lightbox=”true”]

Some of you who who are familiar with NVivo might be asking, ‘why not upload the whole PDF, then create themes from that?’ Well, if you have a small number of pdfs to review then it should not be a problem, but if there is a substantial number (as you would have with a PhD) then you run the risk of making the platform unstable and it is prone to crashing (I learnt this by experience!). This is why I advocate copying only those sections which are important.

So, in summary the steps you have to take are as follows:

1. Create an NVivo Project.

2. Identify the literature you want to review.

3. Create a document in ‘Sources – Internal’.

4. Name your document: Author Year.

5. Copy the full reference into NVivo.

6. Copy the abstract into NVivo.

7. Identify words, sentences or paragraphs that are useful.

8. Copy and paste these useful words, sentences or paragraphs into NVivo.

9. Add the page number.

10. Highlight what you have copied and give it a node / theme.

11. Repeat process until literature review complete.

12. Sort and review nodes to develop the literature review structure.

Here is a pdf copy of this blog for you to download: NVivo and your Literature Review

Dr Alan Shaw is a Senior Lecturer and Marketing consultant focusing on a range of sectors. His main interests are in strategy development, social marketing, digital marketing, advertising, consumer behaviour and marketing application.

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Using NVivo in systematic reviews

Introduction
What is a systematic review?
NVivo and qualitative research

Using NVivo

Nvivo training, nvivo- "how to" videos.

On this page you will find very short videos, ranging from less than two minutes to almost nine minutes, covering the use of various facets of NVivo 11 for Windows. There are many videos available online about using NVivo which are generally much longer and more comprehensive. The links on the page below will take you to "snippets" of some of those longer videos and are intended to give focused information quickly and easily. These videos are not intended to replace the need for training but to supplement it. When playing the videos you will notice that only a small section of the video's time slider will be highlighted, that is the section that will play. If you move the slider outside of the highlighted area the entire video will become active.

Even before you upload your collected articles for review into NVivo you may have an idea of what concepts you wish to explore or you may want to brainstorm ideas. You can do this by creating a Mind Map (4:38). As well as giving you a visual aid a Mind Map can help you to create a node structure.

Importing sources from EndNote or Zotero into NVivo

Once you have done your document gathering and have your documents in an EndNote ( Exporting from EndNote (3:22) ; Importing into NVivo (5:49)) or Zotero (4:44) library they can be imported directly into NVivo

Nodes and coding

Nodes and Coding (2:18) are fundamental to understanding NVivo. Nodes are the containers within which the coded data sits.

Word Frequency Query and Text Search Query

To determine an appropriate node structure you may want to use Mind Maps or a Query. A Word Frequency Query (5:54) tells you which words are used most often in your chosen sources. A Text Search Query (6:48) allows you to search for specific words within your sources and allows you to see them in context.

Manual coding and Coding Stripes

While reading, sections of text can be Coded to Nodes. If a PDF document’s text cannot be recognized it can still be Coded By Region (0:53). Coding Stripes (2:01) show into which nodes elements of an article have been coded.

Framework Matrix and Matrix Coding Query

The Framework Matrix (6:47) and Matrix Coding Query (3:41) allow you to compare coded material across different demographics or among themes allowing patterns in the data to be discerned.

Explore Diagram

The Explore Diagram (1:39) helps you to visualise connections between articles

UQ Library training classes in NVivo Pro We offer training in IT, research skills, research management and publishing, and EndNote. We also have short lunchtime drop-in sessions covering topical subjects for your research. Check this regularly, or contact your librarian for details of upcoming sessions.
Training Manual: Essentials for Getting Started
Training Manual: Next Steps
QSR International: NVivo page QSR International's NVivo page with links to training resources and more general information.
Learning NVivo with LinkedIn Learning Learn to use NVivo Starter 11 for qualitative data analysis. Find out how to import, organize, analyze, and visualize text-based research data.
<< Previous: NVivo and qualitative research
Last Updated: Jun 13, 2024 9:01 AM
URL: https://guides.library.uq.edu.au/research-techniques/nvivo-use-systematic-review

DOI: 10.1007/s10742-022-00270-2
Corpus ID: 246433033

Using NVivoTM as a methodological tool for a literature review on nursing innovation: a step-by-step approach

Tina L. Rylee , S. Cavanagh
Published in Health Services & Outcomes… 1 February 2022

3 Citations

Understanding the health and well-being of women with multiple sclerosis, the experiences of people with liver disease of palliative and end‐of‐life care in the united kingdom—a systematic literature review and metasynthesis, media pembelajaran pai: definisi, sejarah, ragam dan model pengembangan, 25 references, undertaking a literature review: a step-by-step approach., communications of the association for information systems a hermeneutic approach for conducting literature reviews and literature searches a hermeneutic approach for conducting literature reviews and literature searches a hermeneutic approach for conducting literature reviews and literature searches.

Highly Influential

Innovative Behavior in Nursing Context: A Concept Analysis.

Using nvivo for literature reviews: the eight step pedagogy (n7+1), qualitative analysis techniques for the review of the literature, a software-assisted qualitative content analysis of news articles: example and reflections, writing a literature review, towards a methodology for developing evidence-informed management knowledge by means of systematic review, using grounded theory as a method for rigorously reviewing literature, maximizing transparency in a doctoral thesis1: the complexities of writing about the use of qsr*nvivo within a grounded theory study, related papers.

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Anuja Cabraal (Phd)

NVivo for your Literature Review: Managing reading lists

This is the second in a series of posts about using NVivo for your literature review. In this post, I will discuss ways NVivo can help simplify and manage reading lists. It might sound frivolous but think about all the different places you might have jotted down books or references you want to read…. well, NVivo is actually an excellent way of keeping all that information together!

When it comes to doing a literature review, there are a number of stages we go through and a wide variety of resources we use. The first is finding relevant references. Here are some potential resources to do this:

Browsing the shelves
Journal databases
Library databases
Library liaison officers for your subject area (I know they have them at universities in Australia, not sure about overseas)
Google scholar
Online search engines

Recommendations

Readings suggested by other people

I used to have this information stored in a number of different places, including:

Research journal (a journal where I would write anything PhD related throughout my doctorate).
Email: There was an option where the library site would email me the list that came up in my searches.
Print outs: I would also sometimes print these things out and keep them in a separate folder, highlighting the important ones, and crossing some off my list as I went along.

Now, however, NVivo can be used to store this kind of information, which means it is all together in one place! I suggest creating two folders in the “sources – internals” section of NVivo. One called “reading list” which has a list of all the items you want to read. This way, everything you want to read is together in one place. The other folder is called “references”, where you will store the actual articles/books/literature you are going to use for the literature review. You can see these folders on the left hand side of the image provided below.

Online search

The image you see above is a search I did for the term “NVivo” on a library website. I first captured the image on the website, then imported it into NVivo (External data → From Other Sources → From NCapture).

Other sources

You might also get references by word of mouth, browsing shelves (a favourite of mine when deep in the throws of research; It is amazing what gems you can find)! To bring these into NVivo, I suggest that you create a document, and simply type them in. the document works exactly like Word does (in fact, you could even bring in a word document), the bonus of bringing it into NVivo is that you can code it!

Code your reading list!

That’s right…. I suggest you CODE your reading list as you bring each item in. It generally takes no more than a couple of minutes to do (depending on how long the lists are of course)! Create a node called “reading list” so that you know it is for that purpose, and then underneath that, create three nodes:

Finished reading
Maybe/only if time

Then, you simply code the relevant sources into the relevant slot. For example, if there is text that I think is a “must read” that relates to theoretical perspectives, I code it to must read AND theoretical perspectives.

This way, when you are looking for what to read next on a particular topic (lets say methodology), you simply do a search in NVivo for everything you have coded into “Must read” and “methodology” and start with those. When you have finished reading that reference, simply uncode it from “must read” and code it into the “finished reading” node.

This can really help simply the storage of all the different “bits” that seem to be part of a literature review. It certainly helped me stay focussed on the essential readings, and can also save time, because it means you don’t have to keep running the same searches over again in case you misplaced the information!

These are some of the things I have done, and found helpful when using NVivo for a literature review. I will keep posting ideas, and will also write a post about how to code and note take using NVivo, specifically for a literature review.

I now also run a full day and half-day masterclass on Using NVivo for a Literature Review. If you or your university are interested in hosting a workshop, please feel free to contact me.

Published by Dr. Anuja Cabraal

Writer. Qualitative researcher. View all posts by Dr. Anuja Cabraal

12 thoughts on “ NVivo for your Literature Review: Managing reading lists ”

Reblogged this on the pointy edge and commented: Practical advice for managing your reading “inbox”. Of course, code your reading list – that’s a really good idea.

Hi Kath, Thanks for the comment and the reblog! Anuja

Hi, Perhaps you could correct the sentence “When you have finished reading that references, simply uncode it from “must read” and code it into the “read” node.” accordingly to the picture and change it to “When you have finished reading that references, simply uncode it from “must read” and code it into the “finished reading” node”.

Congratulations for your job. It is really useful

Thanks Ernani, I have corrected the error. Happy to hear you find this useful.

I’ve used #Nvivo for a literature review exercise before (coding my notes and reviewing), but never thought about using it as a long term organisational tool for this. However, I’ve been thinking that I need to get my reading more organised for the future, and these are some really interesting ideas. Thanks!

Great post! Thanks! I m just starting (trying to start) my literature review. I have all the papers I used in my PhD courses, and now I want to be well-organized. I am struggling to decide which tools (software) to use. I’ll definitely use EndNote, and I didn’t know if it will be ok to annotate and put keywords directly on this software…Now that I know that Nvivo can be so useful!!! I think I will try! 🙂 ….Now there is Nvivo 10 available….I think it will be worthy to pay for it…hein? Thanks again!

My pleasure! By the way, it might be worthwhile finding out if your university has a site licence for the program. If they do, you might not need to pay for it yourself.

Pingback: NVivo for Literature Reviews | PennWIC

Hi! I am interested in my university (university of South Carolina) hosting a workshop or webinar. Can you give me more information on how to make that happen?

This is really helpful practical advice – obvious/simple ideas are usually the best – thankyou – I am not a confident social media person but am now going to look for the follow up post you mention on coding and note-taking in Nvivo for lit review as that is what I am trying to get sorted at present. Thanks again.

Anuja, thanks for this post. You’ve made some awesome suggestions. I was wondering why would I use NCapture instead of just PDF-ing a web-page (for example) and importing the PDF? Hope that makes sense.

I’m just about to start my Literature review so this has been very timely. Thanks

Constructing and Conducting an Impactful Literature Review with Dr. Thomas

Writing a literature review can be one of the most daunting aspects of the academic research process—and one of the most misunderstood. Dr. Robert Thomas , Lecturer in Marketing and Strategy at Cardiff University’s Cardiff Business School in Wales, is a subject-matter expert for SAGE Publishing who has written a book (“Turn Your Literature Review into an Argument – Little Quick Fixes”) and developed an online course (“Conduct a Literature Review”) that focus on this crucial stage of the academic process.

In a webinar hosted by Citavi , Conducting and Constructing a Literature Review for Maximum Impact , Dr. Thomas took an audience on a deep dive into the literature review process. He outlined the different types of literature reviews, what an effective literature review should aim to achieve, how to select and organize material, and how to craft a compelling review that establishes the foundation for an entire research project or dissertation.

He also emphasized the idea that a literature review is more than a summary of source materials. It’s an opportunity to refine your research question, demonstrate to reviewers that you have the capabilities to recognize and evaluate significant scholarship in your field, and identify gaps in the knowledge that your research can address.

Key Recommendations for Literature Reviews

Literature reviews do not simply summarize the work of others; they are a 2,000- to 5,000-word document that presents your research question and aims within the context of other scholarship to establish a foundation for the rest of your work.
To select material for a literature review, you should conduct keyword searches on journals in the field to begin capturing potential sources and gain an understanding of major contributors to previous research.
Aim for a “point of saturation” with the number of sources ultimately included in the literature review to demonstrate breadth and depth of reading. Dr. Thomas recommends including at least 10-15 sources in a literature review, although you may read and evaluate many more than this.
Adopt a robust note-taking process that encourages critical thinking about sources during reading such as the Cornell Notetaking Method or by using a writing organization tool like Citavi.
Take care not to construct your literature review as an opinion piece that only covers supportive source materials. Instead, Dr. Thomas recommends introducing an element of argumentation by including sources that present counterarguments, then rebutting them with additional evidence.
Dr. Thomas emphasizes that you must paraphrase sources using your own words, not AI summaries or other ghost-writing tools. Writing original paraphrases develops your understanding of arguments and evidence presented by scholars in your field while also reducing the risk of accidental plagiarism.

“[A literature review] is a chance for you to embrace and become part of the argument.” – Dr. Robert Thomas

Developing an Impactful Literature Review

Dr. Thomas recommends four different types of literature reviews:

Systematic Review This type of review has a pre-defined research question and aims to summarize as much of the scholarship on a particular question as possible. An example of this type of review is a meta-analysis which draws on research into a particular problem and then evaluates findings according to a common standard to arrive at an “answer”, or at least a general summary of findings across the literature. This type of analysis is usually conducted by experts in the field.

Chronological Review As the name suggests, chronological reviews evaluate scholarship in the field in a linear progression from the earliest historical sources to contemporary research. The idea is to demonstrate the importance of a field of study by tracing its development from the past to the present.

Methodological Review This type of review is rare. Instead of looking at theories or research outcomes in a given field, the methodological review looks at the steps taken by researchers to develop the evidence for their theories. A methodological review, for example, may evaluate sampling techniques or data analysis procedures.

Integrative Review This is the most common type of literature review, which draws on aspects of the prior three types of literature review to produce a document that is broad in historical scope while also justifying the methodological approach you plan to take in your research. An integrative review also combines theory and data to provide a focus for the overall project.

Dr. Thomas recommends a step-by-step process for developing an integrative literature review that can establish your credibility—and confidence—as a researcher in any field.

Step 1: Locating and Capturing Sources First, Dr. Thomas emphasized that a literature review should draw on a wide range of sources including contemporary journals as well as books. He recommended conducting keyword searches on journals and in databases to begin identifying sources.

Step 2: Taking Robust Notes that Spark Critical Evaluation Once you’ve identified potential sources, you need to read and take notes. Dr. Thomas recommends using a structured notetaking approach such as the Cornell Notetaking System — one that captures essential data and facts about the source as well as allowing you to record your thoughts, questions, and criticisms of the material. Taking notes is not just about summarizing the text you read, Dr. Thomas explains, but about ensuring that what you read can be “reduced down to what it means to you and the work [you are doing].”

Step 3: From Broad Ideas to Fine Details A theoretical framework places your proposed research within the context of a body of scholarship and shows how you plan to produce new knowledge that can address gaps in a specific area of the field. Dr. Thomas gave the example of a researcher who wants to explain why people purchase Omega luxury watches.

In this example, the theoretical framework would begin with looking at evidence for the broader issue of consumer decision-making and the influence of marketing, then gradually digging down to evidence that drives purchases in the luxury watch market. From there, you would plan how to demonstrate the factors that lead people to choose Omega watches in particular.

Step 4: Maintaining Authenticity as an Author Literature reviews require a great deal of analysis and synthesis of information. When developing your project, it can be tempting to make use of AI summaries or other ghost-writing tools to quickly produce an evaluation of a source.

Dr. Thomas emphasizes that you must avoid this at all costs. First, creating your own summary or paraphrase of a source’s theories helps you internalize and better understand it. Second, using ghost-writing tools can lead to plagiarism.

Step 5: Making the Case for Your Contribution to the Field Finally, Dr. Thomas emphasizes that a literature review offers you an opportunity to present a balanced view of your subject area that acknowledges opposing evidence or perspectives. “You will [receive more credit] for actually accrediting those who do not support what you do than you will for creating something descriptive that [only] supports your research question.”

However, in addition to evaluating opposing evidence, you must also provide an effective rebuttal that demonstrates why your research ultimately matters. The ultimate goal of your literature review is to guide your readers through a labyrinth of texts, theories, and ideas toward the unique contribution you plan to make as a scholar.

Develop a More Impactful Literature Review with Citavi

Citavi supports every aspect of the literature review development process – from finding, evaluating, and taking notes on sources to constructing outlines, citations, and bibliographies.

Step 1: Finding Sources with Citavi To help with locating sources, Citavi makes it possible for you to begin capturing potential sources from anywhere on the web or within the app itself. Carry out keyword searches of freely available databases — or data sources your institution has licensed — from within Citavi. Enter an IBSN or DOI number to automatically generate a citation. Import details about source materials you’ve found on the web using the Picker browser extension or drag-and-drop PDFs and references from other systems straight into your Citavi project. Citavi will also generate a bibliography as you add references and automatically generate citations that meet many different style guidelines.

Step 2: Writing and Organizing Notes with Citavi Citavi is also designed to account for the changing nature of the internet. When you import a website address as a reference, Citavi will automatically generate a PDF of that page at the time you accessed it. This way, if the reference is changed or taken offline, you’ll still have access to the original information.

With Citavi, you can record notes, questions, quotations, comments, and summaries within the record for each reference — even on the PDF version of the document itself. These notes can then be organized into an outline which you can use for the next phase of the literature review process: establishing a theoretical framework for the research you plan to conduct.

Step 3: Building Frameworks with Citavi Citavi makes it possible for you to begin building this “funnel” framework as you read and organize your source materials. You can build an outline for your literature review using the notes you have keyed into your project — an outline that’s easily exportable to a Word document, complete with citations and links – and then begin writing.

Step 4: Maintaining Authenticity as an Author with Citavi By automatically connecting your sources to references you make, Citavi helps reduce the risk of unintentional plagiarism in your work.

If you’re looking for more ways to accelerate your literature review, Citavi also integrates with qualitative data analysis software , NVivo, which can help you identify themes in your sources. Watch this webinar to learn more about how using NVivo and Citavi together helps you go beyond simple reference management and creates a springboard to analyze your literature, connect it to your empirical data, analyze it with NVivo’s research tools, and publish your work faster:

>>Watch on-demand webinar: Accelerating your Literature Review with Citavi and NVivo 14

Access additional free literature resources here: Lumivero - Accelerating Your Literature Review

Make the literature review process more manageable with the all-in-one referencing and writing solution designed for individual researchers or teams. Download a free trial of Citavi or buy a subscription today.

MIT researchers release a repository of AI risks

Man looking at big data represented by binary code and data symbols like graphs.

Which specific risks should a person, company or government consider when using an AI system, or crafting rules to govern its use? It’s not an easy question to answer. If it’s an AI with control over critical infrastructure, there’s the obvious risk to human safety. But what about an AI designed to score exams, sort resumes or verify travel documents at immigration control? Those each carry their own, categorically different risks, albeit risks no less severe.

In crafting laws to regulate AI, like the EU AI Act or California’s SB 1047 , policymakers have struggled to come to a consensus on which risks the laws should cover. To help provide a guidepost for them, as well as for stakeholders across the AI industry and academia, MIT researchers have developed what they’re calling an AI “risk repository” — a sort of database of AI risks.

“This is an attempt to rigorously curate and analyze AI risks into a publicly accessible, comprehensive, extensible and categorized risk database that anyone can copy and use, and that will be kept up to date over time,” Peter Slattery, a researcher at MIT’s FutureTech group and lead on the AI risk repository project, told TechCrunch. “We created it now because we needed it for our project, and had realized that many others needed it, too.”

Slattery says that the AI risk repository, which includes over 700 AI risks grouped by causal factors (e.g. intentionality), domains (e.g. discrimination) and subdomains (e.g. disinformation and cyberattacks), was born out of a desire to understand the overlaps and disconnects in AI safety research. Other risk frameworks exist. But they cover only a fraction of the risks identified in the repository, Slattery says, and these omissions could have major consequences for AI development, usage and policymaking.

“People may assume there is a consensus on AI risks, but our findings suggest otherwise,” Slattery added. “We found that the average frameworks mentioned just 34% of the 23 risk subdomains we identified, and nearly a quarter covered less than 20%. No document or overview mentioned all 23 risk subdomains, and the most comprehensive covered only 70%. When the literature is this fragmented, we shouldn’t assume that we are all on the same page about these risks.”

To build the repository, the MIT researchers worked with colleagues at the University of Queensland, the nonprofit Future of Life Institute, KU Leuven and AI startup Harmony Intelligence to scour academic databases and retrieve thousands of documents relating to AI risk evaluations.

The researchers found that the third-party frameworks they canvassed mentioned certain risks more often than others. For example, over 70% of the frameworks included the privacy and security implications of AI, whereas only 44% covered misinformation. And while over 50% discussed the forms of discrimination and misrepresentation that AI could perpetuate, only 12% talked about “pollution of the information ecosystem” — i.e. the increasing volume of AI-generated spam.

“A takeaway for researchers and policymakers, and anyone working with risks, is that this database could provide a foundation to build on when doing more specific work,” Slattery said. “Before this, people like us had two choices. They could invest significant time to review the scattered literature to develop a comprehensive overview, or they could use a limited number of existing frameworks, which might miss relevant risks. Now they have a more comprehensive database, so our repository will hopefully save time and increase oversight.”

But will anyone use it? It’s true that AI regulation around the world today is at best a hodgepodge: a spectrum of different approaches disunified in their goals. Had an AI risk repository like MIT’s existed before, would it have changed anything? Could it have? That’s tough to say.

Another fair question to ask is whether simply being aligned on the risks that AI poses is enough to spur moves toward competently regulating it. Many safety evaluations for AI systems have significant limitations , and a database of risks won’t necessarily solve that problem.

The MIT researchers plan to try, though. Neil Thompson, head of the FutureTech lab, tells TechCrunch that the group plans in its next phase of research to use the repository to evaluate how well different AI risks are being addressed.

“Our repository will help us in the next step of our research, when we will be evaluating how well different risks are being addressed,” Thompson said. “We plan to use this to identify shortcomings in organizational responses. For instance, if everyone focuses on one type of risk while overlooking others of similar importance, that’s something we should notice and address.

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IMAGES

Overview and Key Background
(PDF) Conducting a Literature Review of PDFs using NVivo
Using nvivo for literature review
Using NVivo for Literature Reviews: The Eight Step Pedagogy (N7+1
Using NVivo for you Literature Review
How I'm using NVivo for my literature review

COMMENTS

Extending Your Literature Review With NVivo
A focus on thinking about your literature review in relation to NVivo (28 minute video) Kristi Jackson. Accelerating your Literature Review in NVivo (30 minutes) Stacy Penna, Ed.D. However, the latest versions NVivo have added some new possibilities for analyzing your literature. You can use the social network feature to explore relationships ...
Using NVivo TM as a methodological tool for a literature review on
Purpose This paper describes a step-by-step process on how to conduct a literature review using a qualitative analysis approach in conducting a literature review using NVivo to drive the analysis and explore the state of nursing innovation. Data synthesis This manuscript makes a unique contribution highlighting the importance of a comprehensive literature review following conceptual and ...
Using NVivo for Literature Reviews
NVivo can help you produce a high-quality literature review. You can use it to: Import source documents such as (includes citation managers such as EndNote or other reference management software) Run quantitative text frequency searches. This identifies commonly occurring terms across all or any documents in your project file
Guides: NVivo for Qualitative Data Analysis: Literature Reviews
The process of using NVivo for Literature Reviews can include: Collecting your articles, ideally using a Citation Manager. Importing the citations from your citation manager or bibliography into NVivo. Importing the full-text PDFs of your articles into NVivo. Coding your article using themes and keywords. Using NVivo's queries to auto-code ...
Nvivo for a literature review: How and why
Throughout the post below I have tried to provide the alternate names for the Nvivo 12 (Windows). Importing references. Firstly, I import my articles under the 'internals' sources and into a folder called 'articles'. I name each one with the authors name and year. In Nvivo 12 (Windows) I think the 'internals' folder is simply called ...
Data Analysis in Qualitative Research: A Brief Guide to Using Nvivo
Qualitative data is often subjective, rich, and consists of in-depth information normally presented in the form of words. Analysing qualitative data entails reading a large amount of transcripts looking for similarities or differences, and subsequently finding themes and developing categories. Traditionally, researchers 'cut and paste' and ...
Using NVivo™ for Literature Reviews: The Eight Step Pedagogy (N7+1)
Using N7+1 to Write a Literature Review. This section outlines the pedagogy of N7+1 (O'N eill & Booth, 2017) using Jackson's. (2014) concept of transparency- in -motion. The N7+1 pedagogy has ...
Literature Review
Accelerating Your Literature Review with Citavi & NVivo - Silvana di Gregorio, PhD - Lumivero, and Stacy Penna, EdD - Lumivero. Organizing Information in Your Field of Study - Umit Gunes, PhD - Duke University. Using Citavi to Support Your Arguments - Stacy Penna, EdD - Lumivero (a session from our Research and Technical Writing ...
Literature reviews
Introduction to NVivo. NVivo can be a helpful tool to assist with literature reviews, as it allows you to organise, summarise and search the literature you have found on your particular topic. When conducting a literature review, if you have already used referencing software (e.g. EndNote) to store your references, then you can import them to ...
PDF Using Nvivo and EndNote for literature reviews
First select Data in the navigation view, then highlight Files so it is shown in list view. Right mouse click in the white space of the Files list view, select New File then New Document OR. From the Create ribbon select Document. Name the document when the dialogue box opens and select OK.
Using NVivo for Literature Reviews: The Eight Step Pedagogy (N7+1)
To combat this dilemma our paper outlines the N7+1 approach to using Nvivo11™ for literature reviews. Through this approach researchers can develop an "auditable footprint," keep everything in one place, and go paperless. Keywords . Doctoral Research, Novice Researchers, NVivo™, Literature Review, Transparency, QDAS, Data Analysis,
Tackling the literature review
NVivo is a good tool to use when conducting a literature review. It allows you to manage your sources, identify themes and helps you to make connections between sources. Using NVivo also means you can go back easily, and review your literature review as you go. Lee Fallin - a library skills advisor from the University of Hull has written ...
Using NVivo with EndNote for Literature Reviews
Using NVivo with EndNote for Literature Reviews. Whether you are a qualitative or quantitative researcher, EndNote and NVivo can help with the process of writing a literature review. EndNote users can export existing libraries (with attached pdfs) into NVivo so that all the bibliographic data is included. You can then use NVivo to search all of ...
(PDF) Conducting a Literature Review using NVivo
The literature review is generally conducted according to a methodology developed by Warnes (2018) in his research paper entitled Conducting a Literature Review Using NVivo. Warnes developed his ...
how to use NVivo for your Literature Review
Copy and paste these useful words, sentences or paragraphs into NVivo. 9. Add the page number. 10. Highlight what you have copied and give it a node / theme. 11. Repeat process until literature review complete. 12. Sort and review nodes to develop the literature review structure.
Library Guides: Using NVivo in systematic reviews: Using NVivo
On this page you will find very short videos, ranging from less than two minutes to almost nine minutes, covering the use of various facets of NVivo 11 for Windows. There are many videos available online about using NVivo which are generally much longer and more comprehensive. The links on the page below will take you to "snippets" of some of ...
"Using NVivo™ for Literature Reviews: The Eight Step ...
While a literature review is a necessary milestone to be completed by all researchers in a timely and efficient manner, it is often one of the most difficult aspects of the research journey. ... M. M., Booth, S. R., & Lamb, J. T. (2018). Using NVivo™ for Literature Reviews: The Eight Step Pedagogy (N7+1). The Qualitative Report, 23(13), 21-39 ...
From screening to synthesis: using nvivo to enhance transparency in
The methods for using nvivo in the different stages of QES are discussed in terms of screening, data extraction, synthesis and quality appraisal. These insights could guide and encourage other researchers in its use in managing evidence synthesis. ... Literature review and descriptive articles: Include qualitative and mixed methods ...
PDF USING NVIVO FOR YOUR LITERATURE REVIEW
Figure 2: List of Articles in Literature Review NVIVO Project In the left hand column is a list of books and memos relating to a literature review on using computers for analysing qualitative data. The books are represented as Proxy Documents. NVIVO automatically marks out Proxy Documents by adding '(proxy)' next to a title of such a document.
Using NVivoTM as a methodological tool for a literature review on
A six-step process to conduct a literature review using a qualitative analytical program using NVivo to drive the analysis and explore the state of nursing innovation is described. This paper describes a step-by-step process on how to conduct a literature review using a qualitative analysis approach in conducting a literature review using NVivo to drive the analysis and explore the state of ...
NVivo for your Literature Review: Managing reading lists
This is the second in a series of posts about using NVivo for your literature review. In this post, I will discuss ways NVivo can help simplify and manage reading lists. It might sound frivolous but think about all the different places you might have jotted down books or references you want to read…. well, NVivo is actually an excellent way ...
Using NVivo for literature review
I have been taught to use NVivo, a qualitative data analysis software, for writing literature reviews. So far, my set-up is that I create a NVivo blank document for each source (article, book chapter, etc.), and then am copying over snippets from each source into the document so that I can code said snippets into nodes which correspond to sections or themes in my literature review.
Constructing and Conducting an Impactful Literature Review with Dr
If you're looking for more ways to accelerate your literature review, Citavi also integrates with qualitative data analysis software, NVivo, which can help you identify themes in your sources.Watch this webinar to learn more about how using NVivo and Citavi together helps you go beyond simple reference management and creates a springboard to analyze your literature, connect it to your ...
Artificial Intelligence in Education: A Systematic Literature Review
The study employs a systematic review of literature, focusing on works by eminent scholars such as Lee, Memarian, and Yuan, selected from the Scopus database spanning from 1986 to 2024.
MIT researchers release a repository of AI risks
They could invest significant time to review the scattered literature to develop a comprehensive overview, or they could use a limited number of existing frameworks, which might miss relevant risks.
Factors that Affect Pilot Response Times to Alerts: Findings From a
This paper compiles the findings from both a literature review and a survey of NASA Aviation Safety Reporting System (ASRS) reports, documenting that the confluence of factors affecting pilot responses to alerts results in response times that are often longer and more variable than might otherwise be expected.