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
Advantages
- 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
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