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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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Document Analysis

• First Online: 02 January 2023

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• Benjamin Kutsyuruba 4  

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This chapter describes the document analysis approach. As a qualitative method, document analysis entails a systematic procedure for reviewing and evaluating documents through finding, selecting, appraising (making sense of), and synthesizing data contained within them. This chapter outlines the brief history, method and use of document analysis, provides an outline of its process, strengths and limitations, and application, and offers further readings, resources, and suggestions for student engagement activities.

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Additional Reading

Kutsyuruba, B. (2017). Examining education reforms through document analysis methodology. In I. Silova, A. Korzh, S. Kovalchuk, & N. Sobe (Eds.), Reimagining Utopias: Theory and method for educational research in post-socialist contexts (pp. 199–214). Sense.

Kutsyuruba, B., Christou, T., Heggie, L., Murray, J., & Deluca, C. (2015). Teacher collaborative inquiry in Ontario: An analysis of provincial and school board policies and support documents. Canadian Journal of Educational Administration and Policy, 172 , 1–38.

Kutsyuruba, B., Godden, L., & Tregunna, L. (2014). Curbing the early-career attrition: A pan-Canadian document analysis of teacher induction and mentorship programs. Canadian Journal of Educational Administration and Policy, 161 , 1–42.

Segeren, A., & Kutsyuruba, B. (2012). Twenty years and counting: An examination of the development of equity and inclusive education policy in Ontario (1990–2010). Canadian Journal of Educational Administration and Policy, 136 , 1–38.

Online Resources

Document Analysis: A How To Guide (12:27 min) https://www.youtube.com/watch?v=vOsE9saR_ck

Document Analysis with Philip Adu (1:16:40 min) https://youtu.be/bLKBffW5JPU

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Kutsyuruba, B. (2023). Document Analysis. In: Okoko, J.M., Tunison, S., Walker, K.D. (eds) Varieties of Qualitative Research Methods. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-04394-9_23

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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.

• < Previous

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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Document Analysis as a Qualitative Research Method

Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 3 August 2009

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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

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Document Analysis as a Qualitative Research Method

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• 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.

And So It Was Written

Author: Triad 3

Published: March 9, 2016

Word Count: 1626

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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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