methodology document analysis

Document Analysis - How to Analyze Text Data for Research

Introduction

What is document analysis, where is document analysis used, how to perform document analysis, what is text analysis, atlas.ti as text analysis software.

In qualitative research , you can collect primary data through surveys , observations , or interviews , to name a few examples. In addition, you can rely on document analysis when the data already exists in secondary sources like books, public reports, or other archival records that are relevant to your research inquiry.

In this article, we will look at the role of document analysis, the relationship between document analysis and text analysis, and how text analysis software like ATLAS.ti can help you conduct qualitative research.

Document analysis is a systematic procedure used in qualitative research to review and interpret the information embedded in written materials. These materials, often referred to as “documents,” can encompass a wide range of physical and digital sources, such as newspapers, diaries, letters, policy documents, contracts, reports, transcripts, and many others.

At its core, document analysis involves critically examining these sources to gather insightful data and understand the context in which they were created. Research can perform sentiment analysis , text mining, and text categorization, to name a few methods. The goal is not just to derive facts from the documents, but also to understand the underlying nuances, motivations, and perspectives that they represent. For instance, a historical researcher may examine old letters not just to get a chronological account of events, but also to understand the emotions, beliefs, and values of people during that era.

Benefits of document analysis

There are several advantages to using document analysis in research:

• Authenticity : Since documents are typically created for purposes other than research, they can offer an unobtrusive and genuine insight into the topic at hand, without the potential biases introduced by direct observation or interviews.
• Availability : Documents, especially those in the public domain, are widely accessible, making it easier for researchers to source information.
• Cost-effectiveness : As these documents already exist, researchers can save time and resources compared to other data collection methods.

However, document analysis is not without challenges. One must ensure the documents are authentic and reliable. Furthermore, the researcher must be adept at discerning between objective facts and subjective interpretations present in the document.

Document analysis is a versatile method in qualitative research that offers a lens into the intricate layers of meaning, context, and perspective found within textual materials. Through careful and systematic examination, it unveils the richness and depth of the information housed in documents, providing a unique dimension to research findings.

Document analysis is employed in a myriad of sectors, serving various purposes to generate actionable insights. Whether it's understanding customer sentiments or gleaning insights from historical records, this method offers valuable information. Here are some examples of how document analysis is applied.

Analyzing surveys and their responses

A common use of document analysis in the business world revolves around customer surveys . These surveys are designed to collect data on the customer experience, seeking to understand how products or services meet or fall short of customer expectations.

By analyzing customer survey responses , companies can identify areas of improvement, gauge satisfaction levels, and make informed decisions to enhance the customer experience. Even if customer service teams designed a survey for a specific purpose, text analytics of the responses can focus on different angles to gather insights for new research questions.

Examining customer feedback through social media posts

In today's digital age, social media is a goldmine of customer feedback. Customers frequently share their experiences, both positive and negative, on platforms like Twitter, Facebook, and Instagram.

Through document analysis of social media posts, companies can get a real-time pulse of their customer sentiments. This not only helps in immediate issue resolution but also in shaping product or service strategies to align with customer preferences.

Interpreting customer support tickets

Another rich source of data is customer support tickets. These tickets often contain detailed descriptions of issues faced by customers, their frustrations, or sometimes their appreciation for assistance received.

By employing document analysis on these tickets, businesses can detect patterns, identify recurring issues, and work towards streamlining their support processes. This ensures a smoother and more satisfying customer experience.

Historical research and social studies

Beyond the world of business, document analysis plays a pivotal role in historical and social research. Scholars analyze old manuscripts, letters, and other archival materials to construct a narrative of past events, cultures, and civilizations.

As a result, document analysis is an ideal method for historical research since generating new data is less feasible than turning to existing sources for analysis. Researchers can not only examine historical narratives but also how those narratives were constructed in their own time.

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Performing document analysis is a structured process that ensures researchers can derive meaningful, qualitative insights by organizing source material into structured data . Here's a brief outline of the process:

• Define the research question
• Choose relevant documents
• Prepare and organize the documents
• Begin initial review and coding
• Analyze and interpret the data
• Present findings and draw conclusions

The process in detail

Before diving into the documents, it's crucial to have a clear research question or objective. This serves as the foundation for the entire analysis and guides the selection and review of documents. A well-defined question will focus the research, ensuring that the document analysis is targeted and relevant.

The next step is to identify and select documents that align with the research question. It's vital to ensure that these documents are credible, reliable, and pertinent to the research inquiry. The chosen materials can vary from official reports, personal diaries, to digital resources like social media data , depending on the nature of the research.

Once the documents are selected, they need to be organized in a manner that facilitates smooth analysis. This could mean categorizing documents by themes, chronology, or source types. Digital tools and data analysis software , such as ATLAS.ti, can assist in this phase, making the organization more efficient and helping researchers locate specific data when needed.

With everything in place, the researcher starts an initial review of the documents. During this phase, the emphasis is on identifying patterns, themes, or specific information relevant to the research question.

Coding involves assigning labels or tags to sections of the text to categorize the information. This step is iterative, and codes can be refined as the researcher delves deeper.

After coding, interesting patterns across codes can be analyzed. Here, researchers seek to draw meaningful connections between codes, identify overarching themes, and interpret the data in the context of the research question .

This is where the hidden insights and deeper understanding emerge, as researchers juxtapose various pieces of information and infer meaning from them.

Finally, after the intensive process of document analysis, the researcher consolidates their findings, crafting a narrative or report that presents the results. This might also involve visual representations like charts or graphs, especially when demonstrating patterns or trends.

Drawing conclusions involves synthesizing the insights gained from the analysis and offering answers or perspectives in relation to the original research question.

Ultimately, document analysis is a meticulous and iterative procedure. But with a clear plan and systematic approach, it becomes a potent tool in the researcher's arsenal, allowing them to uncover profound insights from textual data.

Text analysis, often referenced alongside document analysis, is a method that focuses on extracting meaningful information from textual data. While document analysis revolves around reviewing and interpreting data from various sources, text analysis hones in on the intricate details within these documents, enabling a deeper understanding. Both these methods are vital in fields such as linguistics, literature, social sciences, and business analytics.

In the context of document analysis, text analysis emerges as a nuanced exploration of the textual content. After documents have been sourced, be it from books, articles, social networks, or any other medium, they undergo a preprocessing phase. Here, irrelevant information is eliminated, errors are rectified, and the text may be translated or converted to ensure uniformity.

This cleaned text is then tokenized into smaller units like words or phrases, facilitating a granular review. Techniques specific to text analysis, such as topic modeling to determine discussed subjects or pattern recognition to identify trends, are applied.

The derived insights can be visualized using tools like graphs or charts, offering a clearer understanding of the content's depth. Interpretation follows, allowing researchers to draw actionable insights or theoretical conclusions based on both the broader document context and the specific text analysis.

Merging text analysis with document analysis presents unique challenges. With the proliferation of digital content, managing vast data sets becomes a significant hurdle. The inherent variability of language, laden with cultural nuances, idioms, and sometimes sarcasm, can make precise interpretation elusive.

Many text analysis tools exist that can facilitate the analytical process. ATLAS.ti offers a well-rounded, useful solution as a text analytics software . In this section, we'll highlight some of the tools that can help you conduct document analysis.

Word Frequencies

A word cloud can be a powerful text analytics tool to understand the nature of human language as it pertains to a particular context. Researchers can perform text mining on their unstructured text data to get a sense of what is being discussed. The Word Frequencies tool can also parse out specific parts of speech, facilitating more granular text extraction.

Sentiment Analysis

The Sentiment Analysis tool employs natural language processing (NLP) and machine learning to analyze text based on sentiment and facilitate natural language understanding. This is important for tasks such as, for example, analyzing customer reviews and assessing customer satisfaction, because you can quickly categorize large numbers of customer data records by their positive or negative sentiment.

AI Coding relies on massive amounts of training data to interpret text and automatically code large amounts of qualitative data. Rather than read each and every document line by line, you can turn to AI Coding to process your data and devote time to the more essential tasks of analysis such as critical reflection and interpretation.

These text analytics tools can be a powerful complement to research. When you're conducting document analysis to understand the meaning of text, AI Coding can help with providing a code structure or organization of data that helps to identify deeper insights.

AI Summaries

Dealing with large numbers of discrete documents can be a daunting task if done manually, especially if each document in your data set is lengthy and complicated. Simplifying the meaning of documents down to their essential insights can help researchers identify patterns in the data.

AI Summaries fills this role by using natural language processing algorithms to simplify data to its salient points. Text generated by AI Summaries are stored in memos attached to documents to illustrate pathways to coding and analysis or to highlight how the data conveys meaning.

Take advantage of ATLAS.ti's analysis tools with a free trial

Let our powerful data analysis interface make the most out of your data. Download a free trial today.

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• v.35(10); 2020 Dec

Document analysis in health policy research: the READ approach

Sarah l dalglish.

1 Department of International Health, Johns Hopkins School of Public Health, 615 N. Wolfe St, Baltimore, MD 21205, USA

2 Institute for Global Health, University College London, Institute for Global Health 3rd floor, 30 Guilford Street, London WC1N 1EH, UK

Hina Khalid

3 School of Humanities and Social Sciences, Information Technology University, Arfa Software Technology Park, Ferozepur Road, Lahore 54000, Pakistan

Shannon A McMahon

4 Heidelberg Institute of Global Health, Medical Faculty and University Hospital, University of Heidelberg, Im Neuenheimer Feld 130/3, 69120 Heidelberg, Germany

Associated Data

Document analysis is one of the most commonly used and powerful methods in health policy research. While existing qualitative research manuals offer direction for conducting document analysis, there has been little specific discussion about how to use this method to understand and analyse health policy. Drawing on guidance from other disciplines and our own research experience, we present a systematic approach for document analysis in health policy research called the READ approach: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We provide practical advice on each step, with consideration of epistemological and theoretical issues such as the socially constructed nature of documents and their role in modern bureaucracies. We provide examples of document analysis from two case studies from our work in Pakistan and Niger in which documents provided critical insight and advanced empirical and theoretical understanding of a health policy issue. Coding tools for each case study are included as Supplementary Files to inspire and guide future research. These case studies illustrate the value of rigorous document analysis to understand policy content and processes and discourse around policy, in ways that are either not possible using other methods, or greatly enrich other methods such as in-depth interviews and observation. Given the central nature of documents to health policy research and importance of reading them critically, the READ approach provides practical guidance on gaining the most out of documents and ensuring rigour in document analysis.

Key Messages

• Rigour in qualitative research is judged partly by the use of deliberate, systematic procedures; however, little specific guidance is available for analysing documents, a nonetheless common method in health policy research.
• Document analysis is useful for understanding policy content across time and geographies, documenting processes, triangulating with interviews and other sources of data, understanding how information and ideas are presented formally, and understanding issue framing, among other purposes.
• The READ (Ready materials, Extract data, Analyse data, Distil) approach provides a step-by-step guide to conducting document analysis for qualitative policy research.
• The READ approach can be adapted to different purposes and types of research, two examples of which are presented in this article, with sample tools in the Supplementary Materials .

Introduction

Document analysis (also called document review) is one of the most commonly used methods in health policy research; it is nearly impossible to conduct policy research without it. Writing in early 20th century, Weber (2015) identified the importance of formal, written documents as a key characteristic of the bureaucracies by which modern societies function, including in public health. Accordingly, critical social research has a long tradition of documentary review: Marx analysed official reports, laws, statues, census reports and newspapers and periodicals over a nearly 50-year period to come to his world-altering conclusions ( Harvey, 1990 ). Yet in much of social science research, ‘documents are placed at the margins of consideration,’ with privilege given to the spoken word via methods such as interviews, possibly due to the fact that many qualitative methods were developed in the anthropological tradition to study mainly pre-literate societies ( Prior, 2003 ). To date, little specific guidance is available to help health policy researchers make the most of these wells of information.

The term ‘documents’ is defined here broadly, following Prior, as physical or virtual artefacts designed by creators, for users, to function within a particular setting ( Prior, 2003 ). Documents exist not as standalone objects of study but must be understood in the social web of meaning within which they are produced and consumed. For example, some analysts distinguish between public documents (produced in the context of public sector activities), private documents (from business and civil society) and personal documents (created by or for individuals, and generally not meant for public consumption) ( Mogalakwe, 2009 ). Documents can be used in a number of ways throughout the research process ( Bowen, 2009 ). In the planning or study design phase, they can be used to gather background information and help refine the research question. Documents can also be used to spark ideas for disseminating research once it is complete, by observing the ways those who will use the research speak to and communicate ideas with one another.

Documents can also be used during data collection and analysis to help answer research questions. Recent health policy research shows that this can be done in at least four ways. Frequently, policy documents are reviewed to describe the content or categorize the approaches to specific health problems in existing policies, as in reviews of the composition of drowning prevention resources in the United States or policy responses to foetal alcohol spectrum disorder in South Africa ( Katchmarchi et al. , 2018 ; Adebiyi et al. , 2019 ). In other cases, non-policy documents are used to examine the implementation of health policies in real-world settings, as in a review of web sources and newspapers analysing the functioning of community health councils in New Zealand ( Gurung et al. , 2020 ). Perhaps less frequently, document analysis is used to analyse policy processes, as in an assessment of multi-sectoral planning process for nutrition in Burkina Faso ( Ouedraogo et al. , 2020 ). Finally, and most broadly, document analysis can be used to inform new policies, as in one study that assessed cigarette sticks as communication and branding ‘documents,’ to suggest avenues for further regulation and tobacco control activities ( Smith et al. , 2017 ).

This practice paper provides an overarching method for conducting document analysis, which can be adapted to a multitude of research questions and topics. Document analysis is used in most or all policy studies; the aim of this article is to provide a systematized method that will enhance procedural rigour. We provide an overview of document analysis, drawing on guidance from disciplines adjacent to public health, introduce the ‘READ’ approach to document analysis and provide two short case studies demonstrating how document analysis can be applied.

What is document analysis?

Document analysis is a systematic procedure for reviewing or evaluating documents, which can be used to provide context, generate questions, supplement other types of research data, track change over time and corroborate other sources ( Bowen, 2009 ). In one commonly cited approach in social research, Bowen recommends first skimming the documents to get an overview, then reading to identify relevant categories of analysis for the overall set of documents and finally interpreting the body of documents ( Bowen, 2009 ). Document analysis can include both quantitative and qualitative components: the approach presented here can be used with either set of methods, but we emphasize qualitative ones, which are more adapted to the socially constructed meaning-making inherent to collaborative exercises such as policymaking.

The study of documents as a research method is common to a number of social science disciplines—yet in many of these fields, including sociology ( Mogalakwe, 2009 ), anthropology ( Prior, 2003 ) and political science ( Wesley, 2010 ), document-based research is described as ill-considered and underutilized. Unsurprisingly, textual analysis is perhaps most developed in fields such as media studies, cultural studies and literary theory, all disciplines that recognize documents as ‘social facts’ that are created, consumed, shared and utilized in socially organized ways ( Atkinson and Coffey, 1997 ). Documents exist within social ‘fields of action,’ a term used to designate the environments within which individuals and groups interact. Documents are therefore not mere records of social life, but integral parts of it—and indeed can become agents in their own right ( Prior, 2003 ). Powerful entities also manipulate the nature and content of knowledge; therefore, gaps in available information must be understood as reflecting and potentially reinforcing societal power relations ( Bryman and Burgess, 1994 ).

Document analysis, like any research method, can be subject to concerns regarding validity, reliability, authenticity, motivated authorship, lack of representativity and so on. However, these can be mitigated or avoided using standard techniques to enhance qualitative rigour, such as triangulation (within documents and across methods and theoretical perspectives), ensuring adequate sample size or ‘engagement’ with the documents, member checking, peer debriefing and so on ( Maxwell, 2005 ).

Document analysis can be used as a standalone method, e.g. to analyse the contents of specific types of policy as they evolve over time and differ across geographies, but document analysis can also be powerfully combined with other types of methods to cross-validate (i.e. triangulate) and deepen the value of concurrent methods. As one guide to public policy research puts it, ‘almost all likely sources of information, data, and ideas fall into two general types: documents and people’ ( Bardach and Patashnik, 2015 ). Thus, researchers can ask interviewees to address questions that arise from policy documents and point the way to useful new documents. Bardach and Patashnik suggest alternating between documents and interviews as sources as information, as one tends to lead to the other, such as by scanning interviewees’ bookshelves and papers for titles and author names ( Bardach and Patashnik, 2015 ). Depending on your research questions, document analysis can be used in combination with different types of interviews ( Berner-Rodoreda et al. , 2018 ), observation ( Harvey, 2018 ), and quantitative analyses, among other common methods in policy research.

The READ approach

The READ approach to document analysis is a systematic procedure for collecting documents and gaining information from them in the context of health policy studies at any level (global, national, local, etc.). The steps consist of: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We describe each of these steps in turn.

Step 1. Ready your materials

At the outset, researchers must set parameters in terms of the nature and number (approximately) of documents they plan to analyse, based on the research question. How much time will you allocate to the document analysis, and what is the scope of your research question? Depending on the answers to these questions, criteria should be established around (1) the topic (a particular policy, programme, or health issue, narrowly defined according to the research question); (2) dates of inclusion (whether taking the long view of several decades, or zooming in on a specific event or period in time); and (3) an indicative list of places to search for documents (possibilities include databases such as Ministry archives; LexisNexis or other databases; online searches; and particularly interview subjects). For difficult-to-obtain working documents or otherwise non-public items, bringing a flash drive to interviews is one of the best ways to gain access to valuable documents.

For research focusing on a single policy or programme, you may review only a handful of documents. However, if you are looking at multiple policies, health issues, or contexts, or reviewing shorter documents (such as newspaper articles), you may look at hundreds, or even thousands of documents. When considering the number of documents you will analyse, you should make notes on the type of information you plan to extract from documents—i.e. what it is you hope to learn, and how this will help answer your research question(s). The initial criteria—and the data you seek to extract from documents—will likely evolve over the course of the research, as it becomes clear whether they will yield too few documents and information (a rare outcome), far too many documents and too much information (a much more common outcome) or documents that fail to address the research question; however, it is important to have a starting point to guide the search. If you find that the documents you need are unavailable, you may need to reassess your research questions or consider other methods of inquiry. If you have too many documents, you can either analyse a subset of these ( Panel 1 ) or adopt more stringent inclusion criteria.

Exploring the framing of diseases in Pakistani media

In Table 1 , we present a non-exhaustive list of the types of documents that can be included in document analyses of health policy issues. In most cases, this will mean written sources (policies, reports, articles). The types of documents to be analysed will vary by study and according to the research question, although in many cases, it will be useful to consult a mix of formal documents (such as official policies, laws or strategies), ‘gray literature’ (organizational materials such as reports, evaluations and white papers produced outside formal publication channels) and, whenever possible, informal or working documents (such as meeting notes, PowerPoint presentations and memoranda). These latter in particular can provide rich veins of insight into how policy actors are thinking through the issues under study, particularly for the lucky researcher who obtains working documents with ‘Track Changes.’ How you prioritize documents will depend on your research question: you may prioritize official policy documents if you are studying policy content, or you may prioritize informal documents if you are studying policy process.

Types of documents that can be consulted in studies of health policy

During this initial preparatory phase, we also recommend devising a file-naming system for your documents (e.g. Author.Date.Topic.Institution.PDF), so that documents can be easily retrieved throughout the research process. After extracting data and processing your documents the first time around, you will likely have additional ‘questions’ to ask your documents and need to consult them again. For this reason, it is important to clearly name source files and link filenames to the data that you are extracting (see sample naming conventions in the Supplementary Materials ).

Step 2. Extract data

Data can be extracted in a number of ways, and the method you select for doing so will depend on your research question and the nature of your documents. One simple way is to use an Excel spreadsheet where each row is a document and each column is a category of information you are seeking to extract, from more basic data such as the document title, author and date, to theoretical or conceptual categories deriving from your research question, operating theory or analytical framework (Panel 2). Documents can also be imported into thematic coding software such as Atlas.ti or NVivo, and data extracted that way. Alternatively, if the research question focuses on process, documents can be used to compile a timeline of events, to trace processes across time. Ask yourself, how can I organize these data in the most coherent manner? What are my priority categories? We have included two different examples of data extraction tools in the Supplementary Materials to this article to spark ideas.

Case study Documents tell part of the story in Niger

Document analyses are first and foremost exercises in close reading: documents should be read thoroughly, from start to finish, including annexes, which may seem tedious but which sometimes produce golden nuggets of information. Read for overall meaning as you extract specific data related to your research question. As you go along, you will begin to have ideas or build working theories about what you are learning and observing in the data. We suggest capturing these emerging theories in extended notes or ‘memos,’ as used in Grounded Theory methodology ( Charmaz, 2006 ); these can be useful analytical units in themselves and can also provide a basis for later report and article writing.

As you read more documents, you may find that your data extraction tool needs to be modified to capture all the relevant information (or to avoid wasting time capturing irrelevant information). This may require you to go back and seek information in documents you have already read and processed, which will be greatly facilitated by a coherent file-naming system. It is also useful to keep notes on other documents that are mentioned that should be tracked down (sometimes you can write the author for help). As a general rule, we suggest being parsimonious when selecting initial categories to extract from data. Simply reading the documents takes significant time in and of itself—make sure you think about how, exactly, the specific data you are extracting will be used and how it goes towards answering your research questions.

Step 3. Analyse data

As in all types of qualitative research, data collection and analysis are iterative and characterized by emergent design, meaning that developing findings continually inform whether and how to obtain and interpret data ( Creswell, 2013 ). In practice, this means that during the data extraction phase, the researcher is already analysing data and forming initial theories—as well as potentially modifying document selection criteria. However, only when data extraction is complete can one see the full picture. For example, are there any documents that you would have expected to find, but did not? Why do you think they might be missing? Are there temporal trends (i.e. similarities, differences or evolutions that stand out when documents are ordered chronologically)? What else do you notice? We provide a list of overarching questions you should think about when viewing your body of document as a whole ( Table 2 ).

Questions to ask your overall body of documents

HIV and viral hepatitis articles by main frames (%). Note: The percentage of articles is calculated by dividing the number of articles appearing in each frame for viral hepatitis and HIV by the respectivenumber of sampled articles for each disease (N = 137 for HIV; N = 117 for hepatitis). Time frame: 1 January 2006 to 30 September 2016

Representations of progress toward Millennium Development Goal 4 in Nigerien policy documents. Sources: clockwise from upper left: ( WHO 2006 ); ( Institut National de la Statistique 2010 ); ( Ministè re de la Santé Publique 2010 ); ( Unicef 2010 )

In addition to the meaning-making processes you are already engaged in during the data extraction process, in most cases, it will be useful to apply specific analysis methodologies to the overall corpus of your documents, such as policy analysis ( Buse et al. , 2005 ). An array of analysis methodologies can be used, both quantitative and qualitative, including case study methodology, thematic content analysis, discourse analysis, framework analysis and process tracing, which may require differing levels of familiarity and skills to apply (we highlight a few of these in the case studies below). Analysis can also be structured according to theoretical approaches. When it comes to analysing policies, process tracing can be particularly useful to combine multiple sources of information, establish a chronicle of events and reveal political and social processes, so as to create a narrative of the policy cycle ( Yin, 1994 ; Shiffman et al. , 2004 ). Practically, you will also want to take a holistic view of the documents’ ‘answers’ to the questions or analysis categories you applied during the data extraction phase. Overall, what did the documents ‘say’ about these thematic categories? What variation did you find within and between documents, and along which axes? Answers to these questions are best recorded by developing notes or memos, which again will come in handy as you write up your results.

As with all qualitative research, you will want to consider your own positionality towards the documents (and their sources and authors); it may be helpful to keep a ‘reflexivity’ memo documenting how your personal characteristics or pre-standing views might influence your analysis ( Watt, 2007 ).

Step 4. Distil your findings

You will know when you have completed your document review when one of the three things happens: (1) completeness (you feel satisfied you have obtained every document fitting your criteria—this is rare), (2) out of time (this means you should have used more specific criteria), and (3) saturation (you fully or sufficiently understand the phenomenon you are studying). In all cases, you should strive to make the third situation the reason for ending your document review, though this will not always mean you will have read and analysed every document fitting your criteria—just enough documents to feel confident you have found good answers to your research questions.

Now it is time to refine your findings. During the extraction phase, you did the equivalent of walking along the beach, noticing the beautiful shells, driftwood and sea glass, and picking them up along the way. During the analysis phase, you started sorting these items into different buckets (your analysis categories) and building increasingly detailed collections. Now you have returned home from the beach, and it is time to clean your objects, rinse them of sand and preserve only the best specimens for presentation. To do this, you can return to your memos, refine them, illustrate them with graphics and quotes and fill in any incomplete areas. It can also be illuminating to look across different strands of work: e.g. how did the content, style, authorship, or tone of arguments evolve over time? Can you illustrate which words, concepts or phrases were used by authors or author groups?

Results will often first be grouped by theoretical or analytic category, or presented as a policy narrative, interweaving strands from other methods you may have used (interviews, observation, etc.). It can also be helpful to create conceptual charts and graphs, especially as this corresponds to your analytical framework (Panels 1 and 2). If you have been keeping a timeline of events, you can seek out any missing information from other sources. Finally, ask yourself how the validity of your findings checks against what you have learned using other methods. The final products of the distillation process will vary by research study, but they will invariably allow you to state your findings relative to your research questions and to draw policy-relevant conclusions.

Document analysis is an essential component of health policy research—it is also relatively convenient and can be low cost. Using an organized system of analysis enhances the document analysis’s procedural rigour, allows for a fuller understanding of policy process and content and enhances the effectiveness of other methods such as interviews and non-participant observation. We propose the READ approach as a systematic method for interrogating documents and extracting study-relevant data that is flexible enough to accommodate many types of research questions. We hope that this article encourages discussion about how to make best use of data from documents when researching health policy questions.

Supplementary Data

Supplementary data are available at Health Policy and Planning online.

Supplementary Material

Czaa064_supplementary_data, acknowledgements.

The data extraction tool in the Supplementary Materials for the iCCM case study (Panel 2) was conceived of by the research team for the multi-country study ‘Policy Analysis of Community Case Management for Childhood and Newborn Illnesses’. The authors thank Sara Bennett and Daniela Rodriguez for granting permission to publish this tool. S.M. was supported by The Olympia-Morata-Programme of Heidelberg University. The funders had no role in the decision to publish, or preparation of the manuscript. The content is the responsibility of the authors and does not necessarily represent the views of any funder.

Conflict of interest statement . None declared.

Ethical approval. No ethical approval was required for this study.

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 3 August 2009

This article examines the function of documents as a data source in qualitative research and discusses document analysis procedure in the context of actual research experiences. Targeted to research novices, the article takes a nuts‐and‐bolts approach to document analysis. It describes the nature and forms of documents, outlines the advantages and limitations of document analysis, and offers specific examples of the use of documents in the research process. The application of document analysis to a grounded theory study is illustrated.

• Content analysis
• Grounded theory
• Thematic analysis
• Triangulation

Bowen, G.A. (2009), "Document Analysis as a Qualitative Research Method", Qualitative Research Journal , Vol. 9 No. 2, pp. 27-40. https://doi.org/10.3316/QRJ0902027

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The Basics of Document Analysis

Document analysis is the process of reviewing or evaluating documents both printed and electronic in a methodical manner. The document analysis method, like many other qualitative research methods, involves examining and interpreting data to uncover meaning, gain understanding, and come to a conclusion.

What is Meant by Document Analysis?

Document analysis pertains to the process of interpreting documents for an assessment topic by the researcher as a means of giving voice and meaning. In Document Analysis as a Qualitative Research Method by Glenn A. Bowen , document analysis is described as, “... a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material. Like other analytical methods in qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge.”

During the analysis of documents, the content is categorized into distinct themes, similar to the way transcripts from interviews or focus groups are analyzed. The documents may also be graded or scored using a rubric.

Document analysis is a social research method of great value, and it plays a crucial role in most triangulation methods, combining various methods to study a particular phenomenon.

>> View Webinar: How-To’s for Data Analysis

Documents fall into three main categories:

• Personal Documents: A personal account of an individual's beliefs, actions, and experiences. The following are examples: e-mails, calendars, scrapbooks, Facebook posts, incident reports, blogs, duty logs, newspapers, and reflections or journals.
• Public Records: Records of an organization's activities that are maintained continuously over time. These include mission statements, student transcripts, annual reports, student handbooks, policy manuals, syllabus, and strategic plans.
• Physical Evidence: Artifacts or items found within a study setting, also referred to as artifacts. Among these are posters, flyers, agendas, training materials, and handbooks.

The qualitative researcher generally makes use of two or more resources, each using a different data source and methodology, to achieve convergence and corroboration. An important purpose of triangulating evidence is to establish credibility through a convergence of evidence. Corroboration of findings across data sets reduces the possibility of bias, by examining data gathered in different ways.

It is important to note that document analysis differs from content analysis as content analysis refers to more than documents. As part of their definition for content analysis, Columbia Mailman School of Public Health states that, “Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents).

How Do You Do Document Analysis?

In order for a researcher to obtain reliable results from document analysis, a detailed planning process must be undertaken. The following is an outline of an eight-step planning process that should be employed in all textual analysis including document analysis techniques.

• Identify the texts you want to analyze such as samples, population, participants, and respondents.
• You should consider how texts will be accessed, paying attention to any cultural or linguistic barriers.
• Acknowledge and resolve biases.
• Acquire appropriate research skills.
• Strategize for ensuring credibility.
• Identify the data that is being sought.
• Take into account ethical issues.
• Keep a backup plan handy.

Researchers can use a wide variety of texts as part of their research, but the most common source is likely to be written material. Researchers often ask how many documents they should collect. There is an opinion that a wide selection of documents is preferable, but the issue should probably revolve more around the quality of the document than its quantity.

Why is Document Analysis Useful?

Different types of documents serve different purposes. They provide background information, indicate potential interview questions, serve as a mechanism for monitoring progress and tracking changes within a project, and allow for verification of any claims or progress made.

You can triangulate your claims about the phenomenon being studied using document analysis by using multiple sources and other research gathering methods.

Below are the advantages and disadvantages of document analysis

• Document analysis may assist researchers in determining what questions to ask your interviewees, as well as provide insight into what to watch out for during your participant observation.
• It is particularly useful to researchers who wish to focus on specific case studies
• It is inexpensive and quick in cases where data is easily obtainable.
• Documents provide specific and reliable data, unaffected by researchers' presence unlike with other research methods like participant observation.

Disadvantages

• It is likely that the documents researchers obtain are not complete or written objectively, requiring researchers to adopt a critical approach and not assume their contents are reliable or unbiased.
• There may be a risk of information overload due to the number of documents involved. Researchers often have difficulties determining what parts of each document are relevant to the topic being studied.
• It may be necessary to anonymize documents and compare them with other documents.

How NVivo Can Help with Document Analysis

Analyzing copious amounts of data and information can be a daunting and time-consuming prospect. Luckily, qualitative data analysis tools like NVivo can help!

NVivo’s AI-powered autocoding text analysis tool can help you efficiently analyze data and perform thematic analysis . By automatically detecting, grouping, and tagging noun phrases, you can quickly identify key themes throughout your documents – aiding in your evaluation.

Additionally, once you start coding part of your data, NVivo’s smart coding can take care of the rest for you by using machine learning to match your coding style. After your initial coding, you can run queries and create visualizations to expand on initial findings and gain deeper insights.

These features allow you to conduct data analysis on large amounts of documents – improving the efficiency of this qualitative research method. Learn more about these features in the webinar, NVivo 14: Thematic Analysis Using NVivo.

>> Watch Webinar NVivo 14: Thematic Analysis Using NVivo

Learn More About Document Analysis

Watch Twenty-Five Qualitative Researchers Share How-To's for Data Analysis

Recent Articles

Texts as Data I: Document Analysis

• First Online: 14 August 2019

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• Kari Karppinen 5 &
• Hallvard Moe 6  

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Collecting and analyzing policy and industry documents is a basic part of most media policy research. The convenience of using existing material produced by public or private institutions can make the use of such documents seem relatively straightforward. However, the selection of relevant documents, their availability, collection and analysis also present methodological problems that researchers need to be aware of. In this chapter, we discuss different understandings of what ‘documents’ are and review basic approaches to collecting and using policy and industry documents in media policy research. We then reflect on the advantages and hazards associated with collecting and using documents as research material. We emphasize the blurred nature of the distinction between ‘primary’ documents and ‘secondary’ interpretation, and point out that documents are always socially produced. All policy and industry documents frame issues in a certain light and only present one perspective into the issues and possible solutions, thus necessitating source criticism. Furthermore, the relative ease of accessing documentary sources does not mean that all potentially relevant documents (e.g. internal company documents) are equally readily available to researchers. In many contexts, access to documents can be deliberately blocked or limited. Using documents also entails limitations that stem from challenges with transparency and comparability. Finally, we note that documents leave silences: focusing on the visible exercise of official power, policy documents usually do not cover radical alternatives and policy options never considered in the first place. After discussing these challenges, the chapter presents a step-by-step process of collecting documents, from research design to accessing the documents, illustrated with the help of two cases studies.

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Scott, J. (Ed.). (2006). Documentary research . London: Sage.

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Karppinen, K., Moe, H. (2019). Texts as Data I: Document Analysis. In: Van den Bulck, H., Puppis, M., Donders, K., Van Audenhove, L. (eds) The Palgrave Handbook of Methods for Media Policy Research. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-16065-4_14

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March 9, 2016

• An Introduction to Document Analysis

Introduction

Document analysis is a form of qualitative research in which documents are interpreted by the researcher to give voice and meaning around an assessment topic (Bowen, 2009). Analyzing documents incorporates coding content into themes similar to how focus group or interview transcripts are analyzed (Bowen,2009). A rubric can also be used to grade or score document. There are three primary types of documents (O’Leary, 2014):

• Public Records: The official, ongoing records of an organization’s activities. Examples include student transcripts, mission statements, annual reports, policy manuals, student handbooks, strategic plans, and syllabi.
• Personal Documents: First-person accounts of an individual’s actions, experiences, and beliefs. Examples include calendars, e-mails, scrapbooks, blogs, Facebook posts, duty logs, incident reports, reflections/journals, and newspapers.
• Physical Evidence: Physical objects found within the study setting (often called artifacts). Examples include flyers, posters, agendas, handbooks, and training materials.

Document analysis is a social research method and is an important research tool in its own right, and is an invaluable part of most schemes of triangulation, the combination of methodologies in the study of the same phenomenon (Bowen, 2009). In order to seek convergence and corroboration, qualitative researchers usually use at least two resources through using different data sources and methods. The purpose of triangulating is to provide a confluence of evidence that breeds credibility (Bowen, 2009). Corroborating findings across data sets can reduce the impact of potential bias by examining information collected through different methods. Also, combining qualitative and quantitative sometimes included in document analysis called mixed-methods studies.  

Before actual document analysis takes place, the researcher must go through a detailed planning process in order to ensure reliable results. O’Leary outlines an 8-step planning process that should take place not just in document analysis, but all textual analysis (2014):

• Create a list of texts to explore (e.g., population, samples, respondents, participants).
• Consider how texts will be accessed with attention to linguistic or cultural barriers.
• Acknowledge and address biases.
• Develop appropriate skills for research.
• Consider strategies for ensuring credibility.
• Know the data one is searching for.
• Consider ethical issues (e.g., confidential documents).
• Have a backup plan.

A researcher can use a huge plethora of texts for research, although by far the most common is likely to be the use of written documents (O’Leary, 2014). There is the question of how many documents the researcher should gather. Bowen suggests that a wide array of documents is better, although the question should be more about quality of the document rather than quantity (Bowen, 2009). O’Leary also introduces two major issues to consider when beginning document analysis. The first is the issue of bias, both in the author or creator of the document, and the researcher as well (2014). The researcher must consider the subjectivity of the author and also the personal biases he or she may be bringing to the research. Bowen adds that the researcher must evaluate the original purpose of the document, such as the target audience (2009). He or she should also consider whether the author was a firsthand witness or used secondhand sources. Also important is determining whether the document was solicited, edited, and/or anonymous (Bowen, 2009). O’Leary’s second major issue is the “unwitting” evidence, or latent content, of the document. Latent content refers to the style, tone, agenda, facts or opinions that exist in the document. This is a key first step that the researcher must keep in mind (O’Leary, 2014). Bowen adds that documents should be assessed for their completeness; in other words, how selective or comprehensive their data is (2009). Also of paramount importance when evaluating documents is not to consider the data as “necessarily precise, accurate, or complete recordings of events that have occurred” (Bowen, 2009, p. 33). These issues are summed up in another eight-step process offered by O’Leary (2014):

• Gather relevant texts.
• Develop an organization and management scheme.
• Make copies of the originals for annotation.
• Asses authenticity of documents.
• Explore document’s agenda, biases.
• Explore background information (e.g., tone, style, purpose).
• Ask questions about document (e.g., Who produced it? Why? When? Type of data?).
• Explore content.

Step eight refers to the process of exploring the “witting” evidence, or the actual content of the documents, and O’Leary gives two major techniques for accomplishing this (2014). One is the interview technique. In this case, the researcher treats the document like a respondent or informant that provides the researcher with relevant information (O’Leary, 2014). The researcher “asks” questions then highlights the answer within the text. The other technique is noting occurrences, or content analysis, where the researcher quantifies the use of particular words, phrases and concepts (O’Leary, 2014). Essentially, the researcher determines what is being searched for, then documents and organizes the frequency and amount of occurrences within the document. The information is then organized into what is “related to central questions of the research” (Bowen, 2009, p. 32). Bowen notes that some experts object to this kind of analysis, saying that it obscures the interpretive process in the case of interview transcriptions (Bowen, 2009). However, Bowen reminds us that documents include a wide variety of types, and content analysis can be very useful for painting a broad, overall picture (2009). According to Bowen (2009), content analysis, then, is used as a “first-pass document review” (p. 32) that can provide the researcher a means of identifying meaningful and relevant passages.

In addition to content analysis, Bowen also notes thematic analysis, which can be considered a form of pattern recognition with the document’s data (2009). This analysis takes emerging themes and makes them into categories used for further analysis, making it a useful practice for grounded theory. It includes careful, focused reading and re-reading of data, as well as coding and category construction (Bowen, 2009). The emerging codes and themes may also serve to “integrate data gathered by different methods” (Bowen, 2009, p. 32). Bowen sums up the overall concept of document analysis as a process of “evaluating documents in such a way that empirical knowledge is produced and understanding is developed” (2009, p. 33). It is not just a process of lining up a collection of excerpts that convey whatever the researcher desires. The researcher must maintain a high level of objectivity and sensitivity in order for the document analysis results to be credible and valid (Bowen, 2009).

The Advantages of Document Analysis

There are many reasons why researchers choose to use document analysis. Firstly, document analysis is an efficient and effective way of gathering data because documents are manageable and practical resources. Documents are commonplace and come in a variety of forms, making documents a very accessible and reliable source of data. Obtaining and analysing documents is often far more cost efficient and time efficient than conducting your own research or experiments (Bowen, 2009). Also, documents are stable, “non-reactive” data sources, meaning that they can be read and reviewed multiple times and remain unchanged by the researcher’s influence or research process (Bowen, 2009, p. 31).

Document analysis is often used because of the many different ways it can support and strengthen research. Document analysis can be used in many different fields of research, as either a primary method of data collection or as a compliment to other methods. Documents can provide supplementary research data, making document analysis a useful and beneficial method for most research. Documents can provide background information and broad coverage of data, and are therefore helpful in contextualizing one’s research within its subject or field (Bowen, 2009). Documents can also contain data that no longer can be observed, provide details that informants have forgotten, and can track change and development. Document analysis can also point to questions that need to be asked or to situations that need to be observed, making the use of document analysis a way to ensure your research is critical and comprehensive (Bowen, 2009).

Concerns to Keep in Mind When Using Document Analysis

The disadvantages of using document analysis are not so much limitations as they are potential concerns to be aware of before choosing the method or when using it. An initial concern to consider is that documents are not created with data research agendas and therefore require some investigative skills. A document will not perfectly provide all of the necessary information required to answer your research questions. Some documents may only provide a small amount of useful data or sometimes none at all. Other documents may be incomplete, or their data may be inaccurate or inconsistent. Sometimes there are gaps or sparseness of documents, leading to more searching or reliance on additional documents then planned (Bowen, 2009). Also, some documents may not be available or easily accessible. For these reasons, it is important to evaluate the quality of your documents and to be prepared to encounter some challenges or gaps when employing document analysis.

Another concern to be aware of before beginning document analysis, and to keep in mind during, is the potential presence of biases, both in a document and from the researcher. Both Bowen and O’Leary state that it is important to thoroughly evaluate and investigate the subjectivity of documents and your understanding of their data in order to preserve the credibility of your research (2009; 2014).

The reason that the issues surrounding document analysis are concerns and not disadvantages is that they can be easily avoided by having a clear process that incorporates evaluative steps and measures, as previously mentioned above and exemplified by O’Leary’s two eight-step processes. As long as a researcher begins document analysis knowing what the method entails and has a clear process planned, the advantages of document analysis are likely to far outweigh the amount of issues that may arise.

References:

Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal, 9(2), 27-40. doi:10.3316/QRJ0902027 O’Leary, Z. (2014). The essential guide to doing your research project (2nd ed.). Thousand Oaks, CA: SAGE Publications, Inc.

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Published: March 9, 2016

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I found the document so interesting. Thank you

Dear Triad Your article was very insightful. I am currently researching about document analysis to make it my methodology strategy to analyze a web application. I would be glad if you had any more material regarding this subject to share.

Very helpful.

Thank you for this valuable information. I request for more such information in qualitative analysis.

I benefited from this article so much . thank you for taking your time to write and share it.

This is really helpful for understanding the basic concept of document analysis. Really impressive!

This is one of the good way to remove difficulties during writing the research

Hi, valuable information herein. My research is qualitative and I want to take a number of pictures which I will then use to formulate questions for the interview guide. My question is this, how do I formulate the document analysis checklist?

Regards, Nancy

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Document Analysis as a Qualitative Research Method

2009, Qualitative Research Journal

This article examines the function of documents as a data source in qualitative research and discusses document analysis procedure in the context of actual research experiences. Targeted to research novices, the article takes a nuts-and-bolts approach to document analysis. It describes the nature and forms of documents, outlines the advantages and limitations of document analysis, and offers specific examples of the use of documents in the research process. The application of document analysis to a grounded theory study is illustrated.

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Article Contents

Introduction, what is document analysis, the read approach, supplementary data, acknowledgements.

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Document analysis in health policy research: the READ approach

• Article contents
• Figures & tables

Sarah L Dalglish, Hina Khalid, Shannon A McMahon, Document analysis in health policy research: the READ approach, Health Policy and Planning , Volume 35, Issue 10, December 2020, Pages 1424–1431, https://doi.org/10.1093/heapol/czaa064

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Document analysis is one of the most commonly used and powerful methods in health policy research. While existing qualitative research manuals offer direction for conducting document analysis, there has been little specific discussion about how to use this method to understand and analyse health policy. Drawing on guidance from other disciplines and our own research experience, we present a systematic approach for document analysis in health policy research called the READ approach: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We provide practical advice on each step, with consideration of epistemological and theoretical issues such as the socially constructed nature of documents and their role in modern bureaucracies. We provide examples of document analysis from two case studies from our work in Pakistan and Niger in which documents provided critical insight and advanced empirical and theoretical understanding of a health policy issue. Coding tools for each case study are included as Supplementary Files to inspire and guide future research. These case studies illustrate the value of rigorous document analysis to understand policy content and processes and discourse around policy, in ways that are either not possible using other methods, or greatly enrich other methods such as in-depth interviews and observation. Given the central nature of documents to health policy research and importance of reading them critically, the READ approach provides practical guidance on gaining the most out of documents and ensuring rigour in document analysis.

Rigour in qualitative research is judged partly by the use of deliberate, systematic procedures; however, little specific guidance is available for analysing documents, a nonetheless common method in health policy research.

Document analysis is useful for understanding policy content across time and geographies, documenting processes, triangulating with interviews and other sources of data, understanding how information and ideas are presented formally, and understanding issue framing, among other purposes.

The READ (Ready materials, Extract data, Analyse data, Distil) approach provides a step-by-step guide to conducting document analysis for qualitative policy research.

The READ approach can be adapted to different purposes and types of research, two examples of which are presented in this article, with sample tools in the Supplementary Materials .

Document analysis (also called document review) is one of the most commonly used methods in health policy research; it is nearly impossible to conduct policy research without it. Writing in early 20th century, Weber (2015) identified the importance of formal, written documents as a key characteristic of the bureaucracies by which modern societies function, including in public health. Accordingly, critical social research has a long tradition of documentary review: Marx analysed official reports, laws, statues, census reports and newspapers and periodicals over a nearly 50-year period to come to his world-altering conclusions ( Harvey, 1990 ). Yet in much of social science research, ‘documents are placed at the margins of consideration,’ with privilege given to the spoken word via methods such as interviews, possibly due to the fact that many qualitative methods were developed in the anthropological tradition to study mainly pre-literate societies ( Prior, 2003 ). To date, little specific guidance is available to help health policy researchers make the most of these wells of information.

The term ‘documents’ is defined here broadly, following Prior, as physical or virtual artefacts designed by creators, for users, to function within a particular setting ( Prior, 2003 ). Documents exist not as standalone objects of study but must be understood in the social web of meaning within which they are produced and consumed. For example, some analysts distinguish between public documents (produced in the context of public sector activities), private documents (from business and civil society) and personal documents (created by or for individuals, and generally not meant for public consumption) ( Mogalakwe, 2009 ). Documents can be used in a number of ways throughout the research process ( Bowen, 2009 ). In the planning or study design phase, they can be used to gather background information and help refine the research question. Documents can also be used to spark ideas for disseminating research once it is complete, by observing the ways those who will use the research speak to and communicate ideas with one another.

Documents can also be used during data collection and analysis to help answer research questions. Recent health policy research shows that this can be done in at least four ways. Frequently, policy documents are reviewed to describe the content or categorize the approaches to specific health problems in existing policies, as in reviews of the composition of drowning prevention resources in the United States or policy responses to foetal alcohol spectrum disorder in South Africa ( Katchmarchi et al. , 2018 ; Adebiyi et al. , 2019 ). In other cases, non-policy documents are used to examine the implementation of health policies in real-world settings, as in a review of web sources and newspapers analysing the functioning of community health councils in New Zealand ( Gurung et al. , 2020 ). Perhaps less frequently, document analysis is used to analyse policy processes, as in an assessment of multi-sectoral planning process for nutrition in Burkina Faso ( Ouedraogo et al. , 2020 ). Finally, and most broadly, document analysis can be used to inform new policies, as in one study that assessed cigarette sticks as communication and branding ‘documents,’ to suggest avenues for further regulation and tobacco control activities ( Smith et al. , 2017 ).

This practice paper provides an overarching method for conducting document analysis, which can be adapted to a multitude of research questions and topics. Document analysis is used in most or all policy studies; the aim of this article is to provide a systematized method that will enhance procedural rigour. We provide an overview of document analysis, drawing on guidance from disciplines adjacent to public health, introduce the ‘READ’ approach to document analysis and provide two short case studies demonstrating how document analysis can be applied.

Document analysis is a systematic procedure for reviewing or evaluating documents, which can be used to provide context, generate questions, supplement other types of research data, track change over time and corroborate other sources ( Bowen, 2009 ). In one commonly cited approach in social research, Bowen recommends first skimming the documents to get an overview, then reading to identify relevant categories of analysis for the overall set of documents and finally interpreting the body of documents ( Bowen, 2009 ). Document analysis can include both quantitative and qualitative components: the approach presented here can be used with either set of methods, but we emphasize qualitative ones, which are more adapted to the socially constructed meaning-making inherent to collaborative exercises such as policymaking.

The study of documents as a research method is common to a number of social science disciplines—yet in many of these fields, including sociology ( Mogalakwe, 2009 ), anthropology ( Prior, 2003 ) and political science ( Wesley, 2010 ), document-based research is described as ill-considered and underutilized. Unsurprisingly, textual analysis is perhaps most developed in fields such as media studies, cultural studies and literary theory, all disciplines that recognize documents as ‘social facts’ that are created, consumed, shared and utilized in socially organized ways ( Atkinson and Coffey, 1997 ). Documents exist within social ‘fields of action,’ a term used to designate the environments within which individuals and groups interact. Documents are therefore not mere records of social life, but integral parts of it—and indeed can become agents in their own right ( Prior, 2003 ). Powerful entities also manipulate the nature and content of knowledge; therefore, gaps in available information must be understood as reflecting and potentially reinforcing societal power relations ( Bryman and Burgess, 1994 ).

Document analysis, like any research method, can be subject to concerns regarding validity, reliability, authenticity, motivated authorship, lack of representativity and so on. However, these can be mitigated or avoided using standard techniques to enhance qualitative rigour, such as triangulation (within documents and across methods and theoretical perspectives), ensuring adequate sample size or ‘engagement’ with the documents, member checking, peer debriefing and so on ( Maxwell, 2005 ).

Document analysis can be used as a standalone method, e.g. to analyse the contents of specific types of policy as they evolve over time and differ across geographies, but document analysis can also be powerfully combined with other types of methods to cross-validate (i.e. triangulate) and deepen the value of concurrent methods. As one guide to public policy research puts it, ‘almost all likely sources of information, data, and ideas fall into two general types: documents and people’ ( Bardach and Patashnik, 2015 ). Thus, researchers can ask interviewees to address questions that arise from policy documents and point the way to useful new documents. Bardach and Patashnik suggest alternating between documents and interviews as sources as information, as one tends to lead to the other, such as by scanning interviewees’ bookshelves and papers for titles and author names ( Bardach and Patashnik, 2015 ). Depending on your research questions, document analysis can be used in combination with different types of interviews ( Berner-Rodoreda et al. , 2018 ), observation ( Harvey, 2018 ), and quantitative analyses, among other common methods in policy research.

The READ approach to document analysis is a systematic procedure for collecting documents and gaining information from them in the context of health policy studies at any level (global, national, local, etc.). The steps consist of: (1) ready your materials, (2) extract data, (3) analyse data and (4) distil your findings. We describe each of these steps in turn.

Step 1. Ready your materials

At the outset, researchers must set parameters in terms of the nature and number (approximately) of documents they plan to analyse, based on the research question. How much time will you allocate to the document analysis, and what is the scope of your research question? Depending on the answers to these questions, criteria should be established around (1) the topic (a particular policy, programme, or health issue, narrowly defined according to the research question); (2) dates of inclusion (whether taking the long view of several decades, or zooming in on a specific event or period in time); and (3) an indicative list of places to search for documents (possibilities include databases such as Ministry archives; LexisNexis or other databases; online searches; and particularly interview subjects). For difficult-to-obtain working documents or otherwise non-public items, bringing a flash drive to interviews is one of the best ways to gain access to valuable documents.

For research focusing on a single policy or programme, you may review only a handful of documents. However, if you are looking at multiple policies, health issues, or contexts, or reviewing shorter documents (such as newspaper articles), you may look at hundreds, or even thousands of documents. When considering the number of documents you will analyse, you should make notes on the type of information you plan to extract from documents—i.e. what it is you hope to learn, and how this will help answer your research question(s). The initial criteria—and the data you seek to extract from documents—will likely evolve over the course of the research, as it becomes clear whether they will yield too few documents and information (a rare outcome), far too many documents and too much information (a much more common outcome) or documents that fail to address the research question; however, it is important to have a starting point to guide the search. If you find that the documents you need are unavailable, you may need to reassess your research questions or consider other methods of inquiry. If you have too many documents, you can either analyse a subset of these ( Panel 1 ) or adopt more stringent inclusion criteria.

Exploring the framing of diseases in Pakistani media

In Table 1 , we present a non-exhaustive list of the types of documents that can be included in document analyses of health policy issues. In most cases, this will mean written sources (policies, reports, articles). The types of documents to be analysed will vary by study and according to the research question, although in many cases, it will be useful to consult a mix of formal documents (such as official policies, laws or strategies), ‘gray literature’ (organizational materials such as reports, evaluations and white papers produced outside formal publication channels) and, whenever possible, informal or working documents (such as meeting notes, PowerPoint presentations and memoranda). These latter in particular can provide rich veins of insight into how policy actors are thinking through the issues under study, particularly for the lucky researcher who obtains working documents with ‘Track Changes.’ How you prioritize documents will depend on your research question: you may prioritize official policy documents if you are studying policy content, or you may prioritize informal documents if you are studying policy process.

Types of documents that can be consulted in studies of health policy

During this initial preparatory phase, we also recommend devising a file-naming system for your documents (e.g. Author.Date.Topic.Institution.PDF), so that documents can be easily retrieved throughout the research process. After extracting data and processing your documents the first time around, you will likely have additional ‘questions’ to ask your documents and need to consult them again. For this reason, it is important to clearly name source files and link filenames to the data that you are extracting (see sample naming conventions in the Supplementary Materials ).

Step 2. Extract data

Data can be extracted in a number of ways, and the method you select for doing so will depend on your research question and the nature of your documents. One simple way is to use an Excel spreadsheet where each row is a document and each column is a category of information you are seeking to extract, from more basic data such as the document title, author and date, to theoretical or conceptual categories deriving from your research question, operating theory or analytical framework (Panel 2). Documents can also be imported into thematic coding software such as Atlas.ti or NVivo, and data extracted that way. Alternatively, if the research question focuses on process, documents can be used to compile a timeline of events, to trace processes across time. Ask yourself, how can I organize these data in the most coherent manner? What are my priority categories? We have included two different examples of data extraction tools in the Supplementary Materials to this article to spark ideas.

Case study Documents tell part of the story in Niger

Document analyses are first and foremost exercises in close reading: documents should be read thoroughly, from start to finish, including annexes, which may seem tedious but which sometimes produce golden nuggets of information. Read for overall meaning as you extract specific data related to your research question. As you go along, you will begin to have ideas or build working theories about what you are learning and observing in the data. We suggest capturing these emerging theories in extended notes or ‘memos,’ as used in Grounded Theory methodology ( Charmaz, 2006 ); these can be useful analytical units in themselves and can also provide a basis for later report and article writing.

As you read more documents, you may find that your data extraction tool needs to be modified to capture all the relevant information (or to avoid wasting time capturing irrelevant information). This may require you to go back and seek information in documents you have already read and processed, which will be greatly facilitated by a coherent file-naming system. It is also useful to keep notes on other documents that are mentioned that should be tracked down (sometimes you can write the author for help). As a general rule, we suggest being parsimonious when selecting initial categories to extract from data. Simply reading the documents takes significant time in and of itself—make sure you think about how, exactly, the specific data you are extracting will be used and how it goes towards answering your research questions.

Step 3. Analyse data

As in all types of qualitative research, data collection and analysis are iterative and characterized by emergent design, meaning that developing findings continually inform whether and how to obtain and interpret data ( Creswell, 2013 ). In practice, this means that during the data extraction phase, the researcher is already analysing data and forming initial theories—as well as potentially modifying document selection criteria. However, only when data extraction is complete can one see the full picture. For example, are there any documents that you would have expected to find, but did not? Why do you think they might be missing? Are there temporal trends (i.e. similarities, differences or evolutions that stand out when documents are ordered chronologically)? What else do you notice? We provide a list of overarching questions you should think about when viewing your body of document as a whole ( Table 2 ).

Questions to ask your overall body of documents

HIV and viral hepatitis articles by main frames (%). Note: The percentage of articles is calculated by dividing the number of articles appearing in each frame for viral hepatitis and HIV by the respectivenumber of sampled articles for each disease (N = 137 for HIV; N = 117 for hepatitis). Time frame: 1 January 2006 to 30 September 2016

Representations of progress toward Millennium Development Goal 4 in Nigerien policy documents. Sources: clockwise from upper left: ( WHO 2006 ); ( Institut National de la Statistique 2010 ); ( Ministè re de la Santé Publique 2010 ); ( Unicef 2010 )

In addition to the meaning-making processes you are already engaged in during the data extraction process, in most cases, it will be useful to apply specific analysis methodologies to the overall corpus of your documents, such as policy analysis ( Buse et al. , 2005 ). An array of analysis methodologies can be used, both quantitative and qualitative, including case study methodology, thematic content analysis, discourse analysis, framework analysis and process tracing, which may require differing levels of familiarity and skills to apply (we highlight a few of these in the case studies below). Analysis can also be structured according to theoretical approaches. When it comes to analysing policies, process tracing can be particularly useful to combine multiple sources of information, establish a chronicle of events and reveal political and social processes, so as to create a narrative of the policy cycle ( Yin, 1994 ; Shiffman et al. , 2004 ). Practically, you will also want to take a holistic view of the documents’ ‘answers’ to the questions or analysis categories you applied during the data extraction phase. Overall, what did the documents ‘say’ about these thematic categories? What variation did you find within and between documents, and along which axes? Answers to these questions are best recorded by developing notes or memos, which again will come in handy as you write up your results.

As with all qualitative research, you will want to consider your own positionality towards the documents (and their sources and authors); it may be helpful to keep a ‘reflexivity’ memo documenting how your personal characteristics or pre-standing views might influence your analysis ( Watt, 2007 ).

Step 4. Distil your findings

You will know when you have completed your document review when one of the three things happens: (1) completeness (you feel satisfied you have obtained every document fitting your criteria—this is rare), (2) out of time (this means you should have used more specific criteria), and (3) saturation (you fully or sufficiently understand the phenomenon you are studying). In all cases, you should strive to make the third situation the reason for ending your document review, though this will not always mean you will have read and analysed every document fitting your criteria—just enough documents to feel confident you have found good answers to your research questions.

Now it is time to refine your findings. During the extraction phase, you did the equivalent of walking along the beach, noticing the beautiful shells, driftwood and sea glass, and picking them up along the way. During the analysis phase, you started sorting these items into different buckets (your analysis categories) and building increasingly detailed collections. Now you have returned home from the beach, and it is time to clean your objects, rinse them of sand and preserve only the best specimens for presentation. To do this, you can return to your memos, refine them, illustrate them with graphics and quotes and fill in any incomplete areas. It can also be illuminating to look across different strands of work: e.g. how did the content, style, authorship, or tone of arguments evolve over time? Can you illustrate which words, concepts or phrases were used by authors or author groups?

Results will often first be grouped by theoretical or analytic category, or presented as a policy narrative, interweaving strands from other methods you may have used (interviews, observation, etc.). It can also be helpful to create conceptual charts and graphs, especially as this corresponds to your analytical framework (Panels 1 and 2). If you have been keeping a timeline of events, you can seek out any missing information from other sources. Finally, ask yourself how the validity of your findings checks against what you have learned using other methods. The final products of the distillation process will vary by research study, but they will invariably allow you to state your findings relative to your research questions and to draw policy-relevant conclusions.

Document analysis is an essential component of health policy research—it is also relatively convenient and can be low cost. Using an organized system of analysis enhances the document analysis’s procedural rigour, allows for a fuller understanding of policy process and content and enhances the effectiveness of other methods such as interviews and non-participant observation. We propose the READ approach as a systematic method for interrogating documents and extracting study-relevant data that is flexible enough to accommodate many types of research questions. We hope that this article encourages discussion about how to make best use of data from documents when researching health policy questions.

Supplementary data are available at Health Policy and Planning online.

The data extraction tool in the Supplementary Materials for the iCCM case study (Panel 2) was conceived of by the research team for the multi-country study ‘Policy Analysis of Community Case Management for Childhood and Newborn Illnesses’. The authors thank Sara Bennett and Daniela Rodriguez for granting permission to publish this tool. S.M. was supported by The Olympia-Morata-Programme of Heidelberg University. The funders had no role in the decision to publish, or preparation of the manuscript. The content is the responsibility of the authors and does not necessarily represent the views of any funder.

Conflict of interest statement . None declared.

Ethical approval. No ethical approval was required for this study.

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Monday, January 20, 2020

A QDA recipe? A ten-step approach for qualitative document analysis using MAXQDA

Guest post by Professional MAXQDA Trainer Dr. Daniel Rasch .

Introduction

Qualitative text or document analysis has evolved into one of the most used qualitative methods across several disciplines ( Kuckartz, 2014 & Mayring, 2010). Its straightforward structure and procedure enable the researcher to adapt the method to his or her special case – nearly to every need.

This article proposes a recipe of ten simple steps for conducting qualitative document analyses (QDA) using MAXQDA (see table 1 for an overview).

Table 1: Overview of the “QDA recipe”

The ten steps for conducting qualitative document analyses using MAXQDA

Step 1: the research question(s).

As always, research begins with the question(s). Three aspects should be covered when dealing with the research question(s):

• What do you want to find out exactly,
• what relevance does your research on this exact question have, and
• what contribution is your research going to make to your discipline?

Highlight these questions in your introduction and make your research stand out.

Step 2: Data collection and data sampling

After you have decided on the questions, you should think about how to answer them. What kind of qualitative data will best answer your question? Interviews – how many and with whom? Documents – which ones and where to collect them from?

At this point, you can already start thinking about validity: are you going to use a representative or a biased sample? Check the different options for sampling and its effects on validity ( Krippendorff, 2019 ).

Step 3: Select and prepare the data

For this step, MAXQDA 2020 is an excellent tool to help you prepare the selected data for any further steps . Whatever type of qualitative data you choose, you can import it into MAXQDA and then you can have MAXQDA assist in transcribing it. In the end, qualitative document analysis is all about written forms of communication (Kuckartz, 2014).

Figure 1: Import the data you have chosen or selected

Step 4: Codebook development

It takes time to develop a solid codebook. Working deductively, the process is a little easier with codes deriving from the theoretical considerations in the context of your research. Inductively, there are various steps you can use, ranging from creative coding to in-vivo-codes.

Content-wise, you can apply all sorts of codes, such as themes or evaluations, two of the most commonly used styles of content analysis (see thematic and evaluative content analysis in Kuckartz, 2014).

Figure 2: coding options in MAXQDA

• a brief definition,
• a long definition,
• criteria for when to use the code, 
• criteria for when not to use the code, and
• an example.

Using MAXQDA’s code memos simplify the process of creating and maintaining a good codebook . First, you can always go back to the codes and view and review your codebook within your project, and second, you can simply export the codebook as an attachment or appendix for publication purposes (use: Reports > Codebook ).

Figure 3: Creating a new code with code memo

Step 5: Unitizing and coding instructions

Before the process of coding starts, it is necessary to decide on the units of, as well as the rules for, coding. It is especially important to decide on your unit of coding (sentences, paragraphs, quasi-sentences, etc.). Coding rules help to keep this choice consistent and support you to stick to your research question(s) because every passage you code and every memo you write should be done in order to answer your research question(s). Decision rules should be added: what are you going to do if a passage does not fit in your subcodes but should be coded because it is important for your research question?

Step 6: Trial, training, reliability

Trial runs are of major importance. Not only do they show you, which codes work and which do not, but they also help you to rethink your choices in terms of the unit of coding, the content of the codebook, and reliability. Since there are different options for the latter, stick to what works best for you: either a qualitative comparison of what you have coded or quantitative indicators like Krippendorff’s alpha if need be .

You can test yourself or a team you work with and there might even be some situations, where a reliability test is not helpful or needed. When testing the codebook, be sure to test the variability of your collected documents and be sure that the entire codebook is tested. 

MAXQDA helps you compare different forms of agreement for more an unlimited number of texts, divided into two different document groups (one document group coded by coder 1, a second document group coded by coder 2 – be aware, that you can also test yourself and be coder 2 yourself).

Figure 4: Intercoder agreement

Step 7: Revision and modification

After checking, which codes work and which do not, you can revise the codebook and modify it. As Schreier puts it: “No coding frame (codebook – DR) is perfect” (Schreier, 2012: 147).

Step 8: Coding

There are many different coding strategies, but one thing is for sure: qualitative work needs time and reading, as well as working with the material over and over again.

One coding strategy might be to first make yourself comfortable with the documents and start coding after second or third reading only. Another strategy is to concentrate on some of your codes first and do a second round of coding with the other codes later.

Step 9: Analyze and compare

Analyze and compare – these two words are the essence of the qualitative analysis at this step. At the core of each qualitative document analysis is the description of the content and the comparison of these contents between the documents you analyze.

After everything has been coded, you can make use of different analysis strategies: paraphrase, write summaries, look for intersections of codes, patterns of likeliness between the documents using simple or complex queries.

Figure 5: different analysis strategies in MAXQDA

Step 10: Interpretation and presentation

Reporting and summarizing qualitative findings is difficult. Most often, we find simple descriptions of the content with the use of quotations, paraphrases or other references to the text. However, MAXQDA makes it fast and easier with many options to choose from . The easiest way is to generate a table to sum up your findings – if your data or the findings allow for this.

MAXQDA offers several options: either map relations of codes, documents or memos with the MAXMaps , create matrices between codes and documents ( Code Matrix Browser ) or codes and codes ( Code Relations Browser ) to display the distribution of codes inside your data or even using different colors to map the distribution of codes or single documents.

Figure 6: Visual Tools for presentation

The Code Matrix Browser also enables you to quantify the qualitative data using two clicks. You can export these numbers for further analysis with statistical packages, to run causal relation and effect calculations, such as regressions or correlations ( Rasch, 2018 ).

Summary and adoption

Qualitative document analysis is one of the most popular techniques and adaptable to nearly every field. MAXQDA is a software tool that offers many options to make your analysis and therefore your research easier .

The recipe works best for theory-driven, deductive coding. However, it can be also used for inductive, explorative work by switching some of these steps around: for example, your codebook development might be one step to do during or after the trial and testing, since codes are developed inductively during the coding process. Still, it is important to define these codes properly.

The above-mentioned recipe has been used as a basis for several publications by the author. Starting with simple comparison of qualitative and quantitative text analysis ( Boräng et al., 2014 ), to the usage of the qualitative data as a basis for regression models ( Eising et al., 2015 ; Eising et al., 2017 ) to a book using mixed methods and therefore both qualitative and quantitative data analysis ( Rasch, 2018 ).

About the author

Daniel Rasch is a post-doctoral researcher in political science at the German University of Administrative Sciences, Speyer. He received his Ph.D. with a mixed methods analysis of lobbyists‘ success in the European Union. He focuses on the quantification of qualitative data. He is an experienced MAXQDA lecturer and has been a Professional MAXQDA Trainer since 2012.

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What is document analysis?

Document or Documentary analysis is a social research method and is an important research tool in its own right and is an invaluable part of most schemes of triangulation. It refers to the various procedures involved in analyzing and interpreting data generated from the examination of documents and records relevant to a particular study. In other words, documentary work involves reading lots of written material (it helps to scan the documents onto a computer and use a qualitative analysis package). A document is something that we can read and which relates to some aspect of the social world. Official documents are intended to be read as objective statements of fact but they are themselves socially produced.

How does document analysis work in public health?

Use of documentary analysis has become quite popular within public health research, especially if you are trying to evaluate the impact of an initiative, for example a committee led venture to increase immunisation uptake in an area or a board led approach to reduce sexual ill-health or increase physical activity during a major event like the Olympics or Rugby World Cup. In this situation, you could take a 'qualitative' approach, utilising what is known as a 'realist viewpoint'. This involves establishing ‘a priori’ set of criteria to investigate whilst enabling the analysis to be guided by the data that emerges from familiarisation with the borough plans’ material. Data would be extracted relating to pre-agreed named terms covering the scope and scale of action plans, perhaps evidence of governance arrangements and minutes of the groups used to deliver the plans. To quantify the process, number and frequency of meetings and email exchanges may be included. This approach may then be supported with follow up interviews or surveys of the parties involved in delivering the plans.

Sources of Documents:

• Public records
• Private papers
• Visual documents
• Minutes of meetings (plus emails etc which indicate the frequency of those meetings - that can help to quantify the process - and governance arrangements)
• Strategies, policies, action plans by public bodies or organisations

The term 'biography' has two meanings in social research. Firstly, it is a particular style of interviewing, where the informant is encouraged to describe how his or her life (or some aspect of it) has changed and developed over time. In doing so, they reflect his/her own conception of self, identity and personal history. Secondly, 'biography' refers to a work that draws on whatever materials are available to an author to represent an account of a person's life and achievements. Narrative analysis is used to elicit results. This is a form of analysis used for chronologically told stories. It focuses on how elements are sequenced, why some elements are evaluated differently from others and how the past shapes perceptions of the present and how the present shapes perceptions of the past and of course, how both shape perceptions of the future. It is especially used in feminist research.

Types of Analysis

Quantitative:, content analysis, qualitative:.

• Discourse analysis
• Interpretative analysis
• Conversation analysis

Grounded Theory

Content analysis is like a social survey but uses a sample of images rather than people.  It is a technique for gathering and analyzing content of text.  Generally speaking, it consists of the following steps:

• Choose a question which can be measured with variables.
• Devise your unit of analysis (amount of text that's assigned a code - e.g. each daily newspaper could be a unit) and design your code book. 
• Make a sampling frame, choosing the cases to analyse that are representative and unbiased. To get a sampling frame, search for relevant cases in contemporary or historical archives. The sample has to be representative, yet small enough for analyzing in depth. You define your population (which can be words, paragraphs, sentences or all articles in a certain period of time) and sampling element.  Very often you are counting words - e.g. how many times does the word 'hooligan' appear in articles sensationalizing the reporting of disturbances at football matches?  
• Code all the cases and analyze the resulting data.
• Produce semi-quantitative results using cross-tabulations, charts or graphs and where there are few cases, use tables.
• Report in a standard 'scientific' format.

This coding is sometimes known as 'manifest coding' and measures 4 characteristics:

• Frequency  - e.g. how many times is the subject, phrase or word mentioned?
• Direction - i.e. the direction of messages in the content along some continuum - e.g. positive, negative.
• Intensity - i.e. strength or poser of a message in a direction.
• Space - i.e. size of space on a newspaper page, time on television, placement in social media

Content analysis is formal and systematic. It lends structure to your research. Variables are categorised in a precise manner so you can count them and intercoder reliability is commonly reported with the results of content analysis studies. However, content analysis ignores context and multiple meanings.  

Semiotics is a science that studies the life of signs in society. It is the opposite to the postivist method of content analysis. It is used a lot in media analysis.

In semiotics, the analyst seeks to connect the signifier (an expression which can be words, a picture or sound) with what is signified (another word, description or image). The use of language is noted as it is considered to be a description of actions. As part of language, certain signs match up with certain meanings. Semiotics seeks to understand the underlining messages in visual texts. It is related to discourse analysis and forms the basis for interpretive analysis.

Discourse Analysis

This is concerned with the production of meaning through talk and texts. Language is viewed as the topic of the research and how people use language to construct their accounts of the social world is important.

Intrepretative Analysis

This aims to capture hidden meaning and ambiguity. It looks how messages are encoded, latent or hidden. You are also acutely aware of who the audience is.

Conversation Analysis

This is concerned with the underlying structures of talk in interaction and with the achievement of interaction.

This is inductive, interpretative and can be social constructionalist. Central focus is on inductively generating novel theoretical ideas or hypotheses from the data. These new theories arise out of the data and are supported by the data. So they are said to be grounded.

Evaluation and Interpretation

Authenticity

Is it genuine, complete, reliable and of unquestioned authorship?

Credibility

Is the document free from error or distortion?

Representativeness

Can the documents available be said to constitute a representative sample of the documents that originally existed?

What is the surface meaning? Is there a deeper/semiotic meaning?

Further reading

Robson, C. Real World Research. 3rd edition. Chichester,Wiley:2011.

Richie, J, Lewis J, (eds). Qualitative Research Practice, London: 2003.

Berger A. Media Analysis Techniques. The Sage Commtext Series, Newbury Park: 1991.

Bryman A. Social Research Methods. Oxford University Press:2001. See chapters 17-19.

Gribbs G. Qualitative Data Analysis: Explorations with Nvivo. Open University Press:2002.

Leedy, P. Practical Research: Planning and Design. 6th Edition. Merril, New Jersey, 1997.

Seale, C. Researching Society and Culture. Sage:2001. See chapters 18 - 21.

Wimmer, R.D. & Dominick, J. R. Mass Media Research: An Introduction. Belmont:1983.

And an example of where I've used it:

Heffernan C. 2001. "The Irish media and the lack of public debate on new reproductive technologies (NRTs) in Ireland", Health, 5 (3):355-371. http://hea.sagepub.com/cgi/content/abstract/5/3/355

The Monitoring and Evaluation Toolkit

Document analysis.

A useful tool for data checking, often combined with analysis of project records and interviewing, is document analysis.

This technique can also be used in theory-based evaluations. By analysing key documentation and reports relating to your project and the problem it is tackling, you can get a better understanding of the context and the factors that are causing the problem. You can also triangulate your findings and check your assumptions by asking whether they are borne out in the documentation.

Illustration

So, your survey of a local village has shown that men have attended family planning courses along with their wives and that they are now regularly using contraceptives. Yet your analysis of local reports (e.g. hospital records) show that there is an increase in unplanned births, teenage pregnancies and the spread of infectious diseases. The documentation appears to contradict your survey and you may need to ask some discreet questions of villagers in order to get to the bottom of this.

What counts as document analysis?

The points raised below draw heavily on the work of Glenn A. Bowen (‘Document Analysis as a Qualitative Research Method’)

Document analysis is a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material

Documents that may be used for systematic evaluation as part of a study take a variety of forms including:

advertisements; agendas, attendance registers, and minutes of meetings; manuals; background papers; books and brochures; diaries and journals; event programs (i.e.,printed outlines); internal correspondence; letters and memoranda; maps and charts; newspapers (clippings/ articles); press releases; program proposals, application forms, and summaries; radio and television program scripts; organisational or institutional reports; survey data; and various public records. 

Scrapbooks and photo albums can also furnish documentary material for research purposes. These types of documents are found in libraries, newspaper archives, historical society offices, and organisational or institutional files.

Why use this technique?

Document analysis helps you to triangulate the claims about your project because it allows you to refer to multiple sources and to combine this document review with, for example, interviews.

So, documentary evidence drawn from school brochures and school websites in Namibia may tell you that the overriding priroty of schools in Winhoek is on pupil safety.

Yet police records may reveal that there is a high incidence of rape of young school girls, including by secondary school teachers. Interviews may be required to tease out why the documentary evidence is contradictory.

Documents serve various purposes. They provide context and historical background, point to possible interview questions, offer a means of tracking developments and monitoring progress over the course of a project, and provide a means of verifying (or questioning) that progress or indeed other claims.

Advantages & Disadvantages

• Document analysis helps you focus on the questions you might ask in interviews and also helps you understand what to look out for with participant observation.
• It is particularly useful when you want to drill down and focus on a particular case study, be it a particular patient, school pupil, village, sub-sector or workshop.
• It is relatively cost-effective and rapid where data is readily available (e.g. via the internet).
• Documents offer specific and stable data, which is unaffected by the presence of researchers (with participant observation by contrast, project participants may behave differently when they know they are being observed).

Disadvantages

• Documents may not be complete or written in an objective fashion so you will have to adopt a critical stance and not assume that the information contained within them is precise or unbiased.
• The number of documents involved can lead to information overload. Which parts of which document are most relevant to your question? Which is largely about the progress of your project and the factors that have helped or hindered that progress?
• Documents may need to be anonymised and scrutinised against other documents. Is the one document that you end up using most representative or is it stating something very different to all the other documents? If so, why is this? Is the source or research design or the purpose of the document different?
• Note that artifact analysis can be an alternative or complement to document analysis. It involves interpreting different artifacts such as tools, sculptures, weapons or even pieces of equipment. For ways of analysing such artifacts, see for example the National Archives website .

Where to next?

Click here to return to the top of the page and here to return to Step 3 (Data Checking).

KB5042421: CrowdStrike issue impacting Windows endpoints causing an 0x50 or 0x7E error message on a blue screen

Microsoft has identified an issue impacting Windows endpoints that are running the CrowdStrike Falcon agent. These endpoints might encounter error messages 0x50 or 0x7E on a blue screen and experience a continual restarting state.

We have received reports of successful recovery from some customers attempting multiple restart operations on affected Windows endpoints.

We are working with CrowdStrike to provide the most up-to-date information available on this issue. Please check back for updates on this ongoing issue. 

Important:  We have released a USB tool to help automate this manual repair process. For more information, see  New recovery tool to help with CrowdStrike issue impacting Windows devices .

To resolve this issue, follow these instructions for your version of Windows.

Hold the power button for 10 seconds to turn off your device and then press the power button again to turn on your device.

On the Windows sign-in screen, press and hold the  Shift key while you select  Power >  Restart .

Restart your device. Note  You may be asked to enter your  BitLocker recovery key . When the device restarts, continue pressing F4 and then it will log you in to safe mode. Please note, for some devices, you need to press F11 to log in through safe mode.

Once in safe mode, right-click Start , click  Run , type  cmd  in the Open box, and then click OK .

If your system drive is different than C:\, type C: and then press Enter . This will switch you to the C:\ drive.

Type the following command and then press Enter:

CD C:\Windows\System32\drivers\CrowdStrike

Note In this example, C is your system drive. This will change to the CrowdStrike directory.

Once in the CrowdStrike directory, locate the file matching “C-00000291*.sys”. To do this, type the following command and then press Enter :

dir C-00000291*.sys

Permanently delete the file(s) found. To do this, type the following command and then press Enter .

del C-00000291*.sys

Manually search for any files that match “C-00000291*.sys” and delete them.

Restart your device.

On the Windows sign-in screen, press and hold the  Shift  key while you select  Power   >  Restart .

Restart your device. Note  You may be asked to enter your  BitLocker recovery key .

When the device restarts, continue pressing F4 and then it will log you in to safe mode.

Once in safe mode, right-click Start , click  Run , type  cmd  in the Open box, and then click  OK .

Type in the following command and then press Enter :

Note  In this example C is your system drive. This will change to the CrowdStrike directory.

Recovery methods

If you receive the Windows Recovery screen, use one of the following methods to recover your device.

Method 1: Use Enable safe mode

Hold the power button for 10 seconds to turn off your device and thenpress the power button again to turn on your device.

On the Windows sign-in screen, press and hold the  Shift  key while you select  Power >   Restart .

After your device restarts to the  Choose an option  screen, select  Troubleshoot  >  Advanced options  >  Startup Settings  >  Enable safe mode . Then, restart your device. Note  You might be asked to enter your  BitLocker recovery key . When the device restarts, continue pressing F4 and then it will log you in to safe mode. Please note, for some devices, you need to press F11 to log in through safe mode.

If the screen asks for a BitLocker recovery key, use your phone and log on to  https://aka.ms/aadrecoverykey . Log on with your Email ID and domain account password to find the BitLocker recovery key associated with your device. To locate your BitLocker recovery key, click Manage Devices > View Bitlocker Keys > Show recovery key .

If your system drive is different than C:\, type C: and then press  Enter . This will switch you to the C:\ drive.

Type the following command and then press Enter :

Tip:  CD C:\Windows\System32\drivers\CrowdStrike

Note  In this example, C is your system drive. This will change to the CrowdStrike directory.

After your device restarts to the  Choose an option  screen, select  Troubleshoot  >  Advanced options  >  Startup Settings  >  Enable safe mode .  Then restart your device again. Note  You might be asked to enter your  BitLocker recovery key . When the device restarts, continue pressing F4 and then it will log you into safe mode. Please note, for some devices, you need to press F11 to log in through safe mode.

If the screen asks for a BitLocker recovery key, then use your phone and log on to  https://aka.ms/aadrecoverykey . Log on with your Email ID and domain account password to find the bit locker recovery key associated with your device. To locate your BitLocker recovery key, click Manage Devices > View Bitlocker Keys > Show recovery key .

Select the name of the device where you see the BitLocker prompt. In the expanded window, select View BitLocker Keys . Go back to your device and input the BitLocker key that you see on your phone or secondary device.

Note  In this example, C is your system drive. This will change to the CrowdStrike directory.

Method 2: Use System Restore

After your device restarts to the  Choose an option  screen, select  Troubleshoot  >  Advanced options  >  System Restore .

If the screen asks for a BitLocker recovery key, use your phone and log on to  https://aka.ms/aadrecoverykey . Login with your email id and domain account password to find the bit locker recovery key associated with your device. To locate your BitLocker recovery key, click Manage Devices > View Bitlocker Keys > Show recovery key .

Click Next  on System Restore.

Select the Restore option in the list, click  Next , and then click  Finish .

Click Yes  to confirm the restore. Note  This will perform just the Windows system restore and personal data should not be impacted. This process might take up to 15 minutes to complete.

If the screen asks for a BitLocker recovery key, use your phone and log on to  https://aka.ms/aadrecoverykey . Log in with your Email ID and domain account password to find the bit locker recovery key associated with your device. To locate your BitLocker recovery key, click Manage Devices > View Bitlocker Keys > Show recovery key .

Select the Restore option in the list, click Next , and then click  Finish .​​​​​​​

Contact CrowdStrike

If after following the above steps, if you still experience issues logging into your device, please reach out to CrowdStrike for additional assistance.

Start your PC in safe mode in Windows

Third-party information disclaimer

The third-party products that this article discusses are manufactured by companies that are independent of Microsoft. We make no warranty, implied or otherwise, about the performance or reliability of these products.

We provide third-party contact information to help you find technical support. This contact information may change without notice. We do not guarantee the accuracy of this third-party contact information.

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