documentation research approach

• Documentary Research: Definition, Types, Applications & Examples

Introduction

Over the years, social scientists have used documentary research to understand series of events that have occurred or happened in the past. Here, they explore available recovered or existing documents and material to get information and gain insight into a research question or particular topic.

In this article, we would define the concept of documentary research, the various types of documentary research, its applications, and some valid examples.

Let’s dive right in.

What is Documentary Research?

In simple terms, documentary research is a form of research that uses records to get accurate information about a particular subject. It is a systematic investigation and analysis of existing records or documents. These documents can be in written forms, visual/audio materials, photographs, videos books, etc.

Documentary research is a valuable approach used in exploring historical events, cultural phenomena, and societal trends to get deep insight into a topic, subject or research question.

Documentary research is somewhat similar to content analysis, which also entails studying existing information/documents.

One of the most vital considerations when using documentary research is the quality of the material being utilized, hence the danger of falling into the single-story phenomenon. 

To forestall this, the documents being reviewed must be assessed thoroughly before it is used. (see John Scott, A Matter of Record, 1990). The criteria for authenticity involves checking the documents thoroughly to ensure their genuineness.

List of Documentary Research Methods

• Social Research Studies: This form of documentary research is commonly used in social research studies. For instance, Karl Max used documentary research extensively for his research and the documents he used include The Royal Commission, Inland revenue reports, and Her Majesty Inspectors of Factory reports, to mention a few. Emile Durkheim one of the founders of sociology authored a book on suicide and his work was recognized as the first modern example of consistent use of documents for social research.
• Archival Inquiry: This is a field of sociology explored in documentary research. It entails using primary source documents stored in archives. This form of research is popular amongst historians and the archival documents are referred to as references in their research.
• Content Analysis: This method involves the examination and interpretation of content in documents like articles, books, and speeches in other to find a connection, verify events, and identify patterns or trends.
• Historical Analysis: This is the study and analysis of occurrences that took place in the past, but were documented in records like newspapers, government records, and diaries to understand past events accurately and use the information to understand the present.
• Textual Analysis: This form of analysis is focused on printed texts, in a bid to understand pictures, symbols, and language in other to understand events or occurrences that happened in the lives of the subject.
• Oral Tradition : Oral history involves gathering information via oral summations of people who had direct experience of the events or subject being researched. These interviews are recorded and transcribed, and then analyzed as documents.
• Ethnographic Research: This form of research involves documenting the daily experiences of people in their natural environment, in other to understand how interactions in their personal space affect or impacts their experiences.
• Comparative Analysis: Comparative analysis entails comparing documents from multiple sources to understand context, and periods and uncover any similarities or differences. The goal is to understand cultural or political variations.
• Cross-Sectional Analysis: Cross-sectional analysis involves reviewing documents from multiple perspectives to understand changes, trends, or developments over a specific period.
• Aesthetic Interpretation: This is analyzing visual documents, like paintings, photographs, and footage from videos. This is often used as a supplement to text to authenticate discoveries uncovered in text documents.

Understanding the Documentary Research Methodology

Documentary research involves several key steps, such as defining the objective or research question, identifying relevant resources, revising them, and drawing up a well-informed and accurate conclusion based on fact.

Here are some key points to help you understand the documentary research methodology:

• Purpose: The essence of documentary research is to review existing documents to have insight into a research problem or question. The documents reviewed include written texts, such as books, articles, letters, diaries, newspapers, official reports, government publications, and archival materials, and non-written materials like videos, audio recordings,  photographs, and digital documents.
• Data Collection: This phase is when researchers gather relevant documents required for the research topic. These documents are evaluated carefully based on credibility and relevance. 

Explore – Data Collection Methods: Definition + Steps to Do It

• Data Analysis: Here, the gathered documents are analyzed systematically using relevant document research methodologies. This involves reading, grouping similar resources, and extracting information based on similarities, trends, etc.
• Interpretation: After data analysis, the discoveries are interpreted and the answers are applied to the research question or objective.

Read More: What is Data Interpretation? + [Types, Methods & Tools]

• Ethical Considerations: Ethical principles should be considered when carrying out documentary research. Copyright and intellectual property rights should be respected and all necessary permissions should be obtained before using confidential materials.
• Strengths and Limitations: The documentary research methodology has several advantages. One of which is that it helps researchers study past events by providing relevant documentation that sheds light. It also offers rich and detailed insights into social, cultural, and historical contexts. However, as with every good thing, there are limitations, such as some form of biases in the selected documents, which could emanate from the author or source of the document, missing data, and validity of the findings.

Related: What are Ethical Practices in Market Research?

Applications of Documentary Research

The documentary research methodology has a broad range of applications across various disciplines. They include:

• Historical Research : Documentary research is used extensively in historical studies to explore past events, in other to predict the future. Researchers review historical documents, like letters, diaries, government records, newspapers, and photographs, to better understand historical narratives, social and cultural contexts, and see how individuals or communities conducted their activities in the past.
• Social Sciences: In social sciences, documentary research helps investigate social concepts and trends. Documents like surveys, census data, and organization records are studied and analyzed, in other to understand public opinion, social inequality, and organizational behavior.
• Legal Research: Documentary research plays a vital role in legal studies. Lawyers, legal scholars, policymakers, etc analyze legal documents, regulations, court cases, and legal antecedents all in a bid to understand the legal framework and ways in which law evolves. Documentary research can support legal arguments, influence the development of legal theories, and inform policy-making.
• Education Research: Documentary research is used to understand educational policies, curriculum development, and teaching practices. Researchers review educational documents, such as textbooks,  educational policies, and assessment materials, to access educational systems, approaches, and the effect of these on learning outcomes.

Examples of Documentary Research

• The Russian Revolution (1891 – 1924), With the aid of newspaper documents and personal diaries Orlando Figes, a British historian narrated the most important milestones of the revolution in that period and proffered a comprehensive portrait of everyday occurrences as it occurred then the book Figes. depicts how the Russian Revolution was a historical process that changed the lives of its people and had its influence globally.
• The Vietnam War . The 990 minutes audiovisual documentary by Ken Burns narrates the Vietnam War (1955-1975). Throughout 10 episodes, the military operations of the Vietnam War were addressed, as well as the opposition to the war by the US.
• Bios . Lives that marked yours: Luis Alberto Spinetta. This two-hour audiovisual documentary, produced by National Geographic, intimate and deeply details the life of  Luis Alberto Spineta, an artist referred to as one of the fathers of Argentine rock. His family was part of the production,the100 hours documentary was directed by Catarina Spinetta and she used recordings, and testimonies from family members to review her father’s childhood until his final moments.
• The Secret Decrees of the Dictatorship . This publication was released between March and May 2019, and more than 7000 secret decrees issued by the Military Juntas in Argentina between 1976-1983 were reviewed by the Data Unit of the news portal. These decrees signed by different dictators focused on deportations, the prohibition of books, and the sale of weapons. All of these materials were analyzed and presented along with eight notes, published in 2019.
• World War II in Photographs, David Boyle . This book is an example of aesthetic documentary research. 900 high-quality photographs from various sources were used to portray World War II (1939–1945). The images uncover the scenarios as the warfare took place. The images were arranged in chronological order with images of the steppes of Russia, the deserts of Africa, the jungles of the South Pacific, and the seas of the Arctic and each one of them has a detailed explanation of the course of events.
• The Silence of the Others . This documentary by the Spanish Pedro Almodóvar took 7 years to produce and over 450 hours of review of materials to uncover the crimes carried out during the Franco regime and the plight of the victims seeking justice. 
• The Berlin Wall . The border through a city, Thomas Flemming. This is another example of documentary research, with documents, photos, and illustrations, this book illustrates the history of the Berlin Wall. The daily life of the people who lived to the west and east of the city was portrayed in the book as well as the events that led to the fall of the border in 1989.

Purpose of Documentary Research

The purpose of documentary research is to gather verifiable evidence, that can help researchers understand clearly events that occurred in the past/present and also uncover new knowledge by analyzing existing documents and materials. It aids researchers in exploring topics that are difficult to decipher through other research methods and proffers a historical or contextual perspective on the subject being studied.

When to Use Documentary Research

Documentary research is best when researching events that occurred in the past, especially in instances where direct observation is not applicable. Here are some instances where documentary research is particularly useful:

• Historical Studies: Documentary research is ideal when conducting historical research. Researchers can then analyze historical records or documents left behind to better understand past events, chronologically.
• Exploratory Research: In cases where there are gaps in research studies. documentary research can serve as an exploratory method to fill gaps in knowledge by exploring different perspectives that can uncover new knowledge.
• Policy Analysis : Documentary research is useful in examining policies and similar regulations. By analyzing policy documents, over a period, researchers can measure the impact policies had or have on a particular subject. Based on their review of existing documents, they can make recommendations and supervise their implementation. This method is particularly useful in fields such as public policy, education, healthcare, and social welfare.
• Comparative Studies: Documentary research is useful for comparative analysis. Researchers can analyze documents from different sources and geographical locations to identify patterns, verify results or simply identify contradictions and uncover areas that require further investigation.

Characteristics of Documentary Research

• Uses Existing Documents: Documentary research is based on existing documents as a primary source of data. These documents can be written(letters, diaries, articles, books)or unwritten documents(videos, photographs, inscriptions). These documents are analyzed to gain insight and understanding into a specific phenomenon. 
• Non-Experimental In Nature: Documentary research does not involve manipulated variables, meaning that the researcher can not change the outcomes by directly intervening in the research. All the results derived are based on phenomena that have d occurred, which have documented records to attest to their occurrence. 
• Data Analysis: Documentary research involves rigorous data analysis, as researchers have to carefully read, extract relevant information, categorize data, and use qualitative/quantitative analysis to derive results.
• Interpretation of Findings: After data analysis. The findings of the research must be interpreted in a way that gives insight and deep understanding to anyone reading about the subject being researched. The interpretation phase involves synthesizing and relating the findings to the research questions or objectives.
• Contextual Understanding : Documentary research emphasizes the importance of understanding the social, cultural, and historical, events in the context, in which the documents were recorded, reviewed, and analyzed.By context we mean, the period, cultural norms, political climate, socio-economic factors, etc where the events being studied took place and under what circumstances. This contextual understanding helps to interpret the findings and draw accurate conclusions.
• Cross Reference and Validation: Documentary research is characterized by cross-referencing or triangulation, which involves using multiple sources or methods to corroborate findings. The combination of documentary research with other research methods strengthens the validity and reliability of their findings. This enhances the robustness of the research and helps minimize potential biases or inaccuracies.
• Ethical Considerations: Documentary research requires that researchers respect ethical guidelines and principles. Copyright and intellectual property laws must be adhered to and necessary permissions obtained when using sensitive or confidential documents, as well as the privacy and anonymity of individuals mentioned in the documents. 

Advantages of Documentary Research

• Access to Existing Data: In documentary research, existing data is readily available for review and analysis. There is no need to collect new data, via surveys and the like which can take time or require intensive resources. This makes documentary research a cost-effective and efficient method.
• Rich and Dynamic Data: Documents and materials used in documentary research offer a rich pool of information and insights. This method covers a wide range of topics, periods, and perspectives. There is access to primary sources, such as original letters or historical documents, as well as secondary sources like scholarly articles or reports. This variety of data allows for a comprehensive and clear understanding of the research topic.
• Longitudinal and Historical Perspectives: Documentary research allows researchers to study phenomena over extended periods and explore historical contexts. By examining documents spanning different periods, researchers can analyze patterns, trends, changes, and continuity across social, cultural, or organizational aspects. 
• Non-Intrusive Method : Since documentary research relies on existing documents, there is no direct involvement with research subjects or settings. Hence there is no need to disturb or manipulate the research environment or intrude on the lives of individuals. This makes it an ethical and practical method, especially for sensitive or personal topics.
• Exploration of Inaccessible or Historical Data: Documentary research allows researchers to access data that cannot be duplicated anymore due to timelapse and changing circumstances. For instance, researchers can analyze archived documents, historical records, or rare texts which provide unique insights into the past or specific contexts. 
• Large-Scale Data Analysis : Documentary research deals with or involves large volumes of data. Numerous documents, texts, or media materials to identify patterns, themes, or trends can be examined. This exposure to extensive data sets enables comprehensive analysis and enhances the reliability of research findings.

Limitations of Documentary Research

• The Danger of Biased Perspectives: The documents used in documentary research are subject to bias, as they could reflect the perspectives, agendas, or limitations of the authors or organizations that produced them. Critical evaluation is necessary to ensure the credibility of the documents.
• No Control Over Data Collection : Documentary research relies on existing data that may not have been aimed at the research question it is being applied to. As researchers have limited control over the collection process, there is the potential for missing or incomplete information.
• Subjective Interpretation: Documents analyzed require interpretation of findings, which can be subjective as different researchers can interpret the same document differently, leading to variations in findings and conclusions.

Documentary research is a valuable form of research methodology as it provides access to existing documents and materials for analysis and interpretation. There are many advantages of these methods, such as diverse sources of data, historical perspectives, and access to large volumes of data from analysis.

However, there are also limitations like biases based on the author’s perspective, no control over data collection, and challenges in interpretation. A clear understanding of the pros and cons of this research method would help users make informed decisions on how to apply documentary research to their subject of study.

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• cross-sectional study
• data analysis
• data collection methods
• data interpretation
• documentary research
• Angela Kayode-Sanni

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Research Methodologies Guide

• Action Research
• Bibliometrics
• Case Studies
• Content Analysis
• Digital Scholarship This link opens in a new window
• Documentary
• Ethnography
• Focus Groups
• Grounded Theory
• Life Histories/Autobiographies
• Longitudinal
• Participant Observation
• Qualitative Research (General)
• Quasi-Experimental Design
• Usability Studies

Documentary Research

According to Scott & Marshall (2015), Documentary Research is

" Research that uses personal and official documents as a source material. Documents... may include such things as newspapers, diaries, stamps, directories, handbills, maps, government statistical publications, photographs, paintings, gramophone records, tapes, and computer files. "

Documentary research is often conducted by social scientists to assess a set of documents for historical or social value, or to create a larger narrative through the study of multiple documents surrounding an event or individual. 

Documentary research is often related to Content Analysis research methodologies. 

For more information, browse the selected resources below:

Books and articles

• Documentary Research in the Social Sciences by Malcolm Tight Publication Date: 2019 From diaries and letters to surveys and interview transcripts, documents are a cornerstone of social science research. This book guides you through the documentary research process, from choosing the best research design, through data collection and analysis, to publishing and sharing research findings.
• Documentary Research by Gary McCulloch Publication Date: 2004 Documentary sources have become increasingly neglected in education and the social sciences. This book seeks to emphasise their potential value and importance for an understanding of modern societies, while also recognising their limitations, and explores their relationship with other research strategies.
• An Introduction to Documentary Research A brief introduction to documentary research from the American Educational Research Association.
• Documentary Research [Reference] An encyclopedia entry on Documentary Research from the Encyclopedia of Curriculum Studies.
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Home » Documentary Analysis – Methods, Applications and Examples

Documentary Analysis – Methods, Applications and Examples

Table of Contents

Documentary Analysis

Definition:

Documentary analysis, also referred to as document analysis , is a systematic procedure for reviewing or evaluating documents. This method involves a detailed review of the documents to extract themes or patterns relevant to the research topic .

Documents used in this type of analysis can include a wide variety of materials such as text (words) and images that have been recorded without a researcher’s intervention. The domain of document analysis, therefore, includes all kinds of texts – books, newspapers, letters, study reports, diaries, and more, as well as images like maps, photographs, and films.

Documentary analysis provides valuable insight and a unique perspective on the past, contextualizing the present and providing a baseline for future studies. It is also an essential tool in case studies and when direct observation or participant observation is not possible.

The process usually involves several steps:

• Sourcing : This involves identifying the document or source, its origin, and the context in which it was created.
• Contextualizing : This involves understanding the social, economic, political, and cultural circumstances during the time the document was created.
• Interrogating : This involves asking a series of questions to help understand the document better. For example, who is the author? What is the purpose of the document? Who is the intended audience?
• Making inferences : This involves understanding what the document says (either directly or indirectly) about the topic under study.
• Checking for reliability and validity : Just like other research methods, documentary analysis also involves checking for the validity and reliability of the documents being analyzed.

Documentary Analysis Methods

Documentary analysis as a qualitative research method involves a systematic process. Here are the main steps you would generally follow:

Defining the Research Question

Before you start any research , you need a clear and focused research question . This will guide your decision on what documents you need to analyze and what you’re looking for within them.

Selecting the Documents

Once you know what you’re looking for, you can start to select the relevant documents. These can be a wide range of materials – books, newspapers, letters, official reports, diaries, transcripts of speeches, archival materials, websites, social media posts, and more. They can be primary sources (directly from the time/place/person you are studying) or secondary sources (analyses created by others).

Reading and Interpreting the Documents

You need to closely read the selected documents to identify the themes and patterns that relate to your research question. This might involve content analysis (looking at what is explicitly stated) and discourse analysis (looking at what is implicitly stated or implied). You need to understand the context in which the document was created, the author’s purpose, and the audience’s perspective.

Coding and Categorizing the Data

After the initial reading, the data (text) can be broken down into smaller parts or “codes.” These codes can then be categorized based on their similarities and differences. This process of coding helps in organizing the data and identifying patterns or themes.

Analyzing the Data

Once the data is organized, it can be analyzed to make sense of it. This can involve comparing the data with existing theories, examining relationships between categories, or explaining the data in relation to the research question.

Validating the Findings

The researcher needs to ensure that the findings are accurate and credible. This might involve triangulating the data (comparing it with other sources or types of data), considering alternative explanations, or seeking feedback from others.

Reporting the Findings

The final step is to report the findings in a clear, structured way. This should include a description of the methods used, the findings, and the researcher’s interpretations and conclusions.

Applications of Documentary Analysis

Documentary analysis is widely used across a variety of fields and disciplines due to its flexible and comprehensive nature. Here are some specific applications:

Historical Research

Documentary analysis is a fundamental method in historical research. Historians use documents to reconstruct past events, understand historical contexts, and interpret the motivations and actions of historical figures. Documents analyzed may include personal letters, diaries, official records, newspaper articles, photographs, and more.

Social Science Research

Sociologists, anthropologists, and political scientists use documentary analysis to understand social phenomena, cultural practices, political events, and more. This might involve analyzing government policies, organizational records, media reports, social media posts, and other documents.

Legal Research

In law, documentary analysis is used in case analysis and statutory interpretation. Legal practitioners and scholars analyze court decisions, statutes, regulations, and other legal documents.

Business and Market Research

Companies often analyze documents to gather business intelligence, understand market trends, and make strategic decisions. This might involve analyzing competitor reports, industry news, market research studies, and more.

Media and Communication Studies

Scholars in these fields might analyze media content (e.g., news reports, advertisements, social media posts) to understand media narratives, public opinion, and communication practices.

Literary and Film Studies

In these fields, the “documents” might be novels, poems, films, or scripts. Scholars analyze these texts to interpret their meaning, understand their cultural context, and critique their form and content.

Educational Research

Educational researchers may analyze curricula, textbooks, lesson plans, and other educational documents to understand educational practices and policies.

Health Research

Health researchers may analyze medical records, health policies, clinical guidelines, and other documents to study health behaviors, healthcare delivery, and health outcomes.

Examples of Documentary Analysis

Some Examples of Documentary Analysis might be:

• Example 1 : A historian studying the causes of World War I might analyze diplomatic correspondence, government records, newspaper articles, and personal diaries from the period leading up to the war.
• Example 2 : A policy analyst trying to understand the impact of a new public health policy might analyze the policy document itself, as well as related government reports, statements from public health officials, and news media coverage of the policy.
• Example 3 : A market researcher studying consumer trends might analyze social media posts, customer reviews, industry reports, and news articles related to the market they’re studying.
• Example 4 : An education researcher might analyze curriculum documents, textbooks, and lesson plans to understand how a particular subject is being taught in schools. They might also analyze policy documents to understand the broader educational policy context.
• Example 5 : A criminologist studying hate crimes might analyze police reports, court records, news reports, and social media posts to understand patterns in hate crimes, as well as societal and institutional responses to them.
• Example 6 : A journalist writing a feature article on homelessness might analyze government reports on homelessness, policy documents related to housing and social services, news articles on homelessness, and social media posts from people experiencing homelessness.
• Example 7 : A literary critic studying a particular author might analyze their novels, letters, interviews, and reviews of their work to gain insight into their themes, writing style, influences, and reception.

When to use Documentary Analysis

Documentary analysis can be used in a variety of research contexts, including but not limited to:

• When direct access to research subjects is limited : If you are unable to conduct interviews or observations due to geographical, logistical, or ethical constraints, documentary analysis can provide an alternative source of data.
• When studying the past : Documents can provide a valuable window into historical events, cultures, and perspectives. This is particularly useful when the people involved in these events are no longer available for interviews or when physical evidence is lacking.
• When corroborating other sources of data : If you have collected data through interviews, surveys, or observations, analyzing documents can provide additional evidence to support or challenge your findings. This process of triangulation can enhance the validity of your research.
• When seeking to understand the context : Documents can provide background information that helps situate your research within a broader social, cultural, historical, or institutional context. This can be important for interpreting your other data and for making your research relevant to a wider audience.
• When the documents are the focus of the research : In some cases, the documents themselves might be the subject of your research. For example, you might be studying how a particular topic is represented in the media, how an author’s work has evolved over time, or how a government policy was developed.
• When resources are limited : Compared to methods like experiments or large-scale surveys, documentary analysis can often be conducted with relatively limited resources. It can be a particularly useful method for students, independent researchers, and others who are working with tight budgets.
• When providing an audit trail for future researchers : Documents provide a record of events, decisions, or conditions at specific points in time. They can serve as an audit trail for future researchers who want to understand the circumstances surrounding a particular event or period.

Purpose of Documentary Analysis

The purpose of documentary analysis in research can be multifold. Here are some key reasons why a researcher might choose to use this method:

• Understanding Context : Documents can provide rich contextual information about the period, environment, or culture under investigation. This can be especially useful for historical research, where the context is often key to understanding the events or trends being studied.
• Direct Source of Data : Documents can serve as primary sources of data. For instance, a letter from a historical figure can give unique insights into their thoughts, feelings, and motivations. A company’s annual report can offer firsthand information about its performance and strategy.
• Corroboration and Verification : Documentary analysis can be used to validate and cross-verify findings derived from other research methods. For example, if interviews suggest a particular outcome, relevant documents can be reviewed to confirm the accuracy of this finding.
• Substituting for Other Methods : When access to the field or subjects is not possible due to various constraints (geographical, logistical, or ethical), documentary analysis can serve as an alternative to methods like observation or interviews.
• Unobtrusive Method : Unlike some other research methods, documentary analysis doesn’t require interaction with subjects, and therefore doesn’t risk altering the behavior of those subjects.
• Longitudinal Analysis : Documents can be used to study change over time. For example, a researcher might analyze census data from multiple decades to study demographic changes.
• Providing Rich, Qualitative Data : Documents often provide qualitative data that can help researchers understand complex issues in depth. For example, a policy document might reveal not just the details of the policy, but also the underlying beliefs and attitudes that shaped it.

Advantages of Documentary Analysis

Documentary analysis offers several advantages as a research method:

• Unobtrusive : As a non-reactive method, documentary analysis does not require direct interaction with human subjects, which means that the research doesn’t affect or influence the subjects’ behavior.
• Rich Historical and Contextual Data : Documents can provide a wealth of historical and contextual information. They allow researchers to examine events and perspectives from the past, even from periods long before modern research methods were established.
• Efficiency and Accessibility : Many documents are readily accessible, especially with the proliferation of digital archives and databases. This accessibility can often make documentary analysis a more efficient method than others that require data collection from human subjects.
• Cost-Effective : Compared to other methods, documentary analysis can be relatively inexpensive. It generally requires fewer resources than conducting experiments, surveys, or fieldwork.
• Permanent Record : Documents provide a permanent record that can be reviewed multiple times. This allows for repeated analysis and verification of the data.
• Versatility : A wide variety of documents can be analyzed, from historical texts to contemporary digital content, providing flexibility and applicability to a broad range of research questions and fields.
• Ability to Cross-Verify (Triangulate) Data : Documentary analysis can be used alongside other methods as a means of triangulating data, thus adding validity and reliability to the research.

Limitations of Documentary Analysis

While documentary analysis offers several benefits as a research method, it also has its limitations. It’s important to keep these in mind when deciding to use documentary analysis and when interpreting your findings:

• Authenticity : Not all documents are genuine, and sometimes it can be challenging to verify the authenticity of a document, particularly for historical research.
• Bias and Subjectivity : All documents are products of their time and their authors. They may reflect personal, cultural, political, or institutional biases, and these biases can affect the information they contain and how it is presented.
• Incomplete or Missing Information : Documents may not provide all the information you need for your research. There may be gaps in the record, or crucial information may have been omitted, intentionally or unintentionally.
• Access and Availability : Not all documents are readily available for analysis. Some may be restricted due to privacy, confidentiality, or security considerations. Others may be difficult to locate or access, particularly historical documents that haven’t been digitized.
• Interpretation : Interpreting documents, particularly historical ones, can be challenging. You need to understand the context in which the document was created, including the social, cultural, political, and personal factors that might have influenced its content.
• Time-Consuming : While documentary analysis can be cost-effective, it can also be time-consuming, especially if you have a large number of documents to analyze or if the documents are lengthy or complex.
• Lack of Control Over Data : Unlike methods where the researcher collects the data themselves (e.g., through experiments or surveys), with documentary analysis, you have no control over what data is available. You are reliant on what others have chosen to record and preserve.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Documentary Research: What it is, methodology & free examples

Social scientists often conduct documentary research. Its primary use is to assess various documents in the interest of social or historical value. Researchers also conduct documentary research to study multiple documents surrounding events or individuals.

What is documentary research?

Documentary research is the research conducted through the use of official documents or personal documents as the source of information.

Documents can include anything from the following: 

• Directories
• Government statistical publications
• Gramophone records
• Photographs
• Computer files

The above may not fit the traditional bill of a “document”, but companies can use them towards documentary research since they contain information.

Documentary research is similar to content analysis, which involves studying existing information recorded in media, texts, and physical items. Here, data collection from people is not required to conduct research. Hence, this is a prime example of secondary research.

It is essential to consider the quality of the documents while using it as evidence on social relations and social meanings. Keep in mind that, unlike surveys and research interviews, the records are originally published/generated without keeping the purpose of research in mind. It is good practice to cross-verify documents against other similar documents before reaching a decision.

Documentary research examples

Bellow, we can find a few real-life examples of documentary research applied to companies’ daily events.

1. Social research studies

Although documentary research is not used extensively today, it is the go-to research method to conduct social research studies. For example, Karl Marx and Emile Durkheim used documentary research extensively for their research.

Karl Marx used documents like:

• Her Majesty Inspectors of Factories Reports
• Royal Commission
• Inland Revenue Reports

There’s also a record of his use of reports by the Medical Officer of the Privy Council, reports on children’s employment in factories, the Corn-laws, the Banking Acts, and Census Reports for Wales and England to name a few.

Durkheim, one of the founders of Sociology, wrote a book on suicide, which is recognized as the first modern example of a methodical and consistent use of documents for social research.

2. Archival inquiry

The field of sociology has a popular, longstanding tradition of documentary inquiry. Many historians refer to and rely on primary documents for their research. Historians give historical documents more emphasis while conducting research. Of course, as we evolve, virtual documents like emails will play a significant role in research activities conducted by these researchers.

3. Aesthetic interpretation

Documentary research is not limited to text only. Pictures, paintings, videos, audio files, monuments are also used to conduct research. Documentary researchers use these tools in addition to texts while studying social sciences. The use of these tools adds to the authenticity of the textual research, or may very well point out deviations in the findings.

This deviation suggests that investigators research more to draw accurate conclusions.

Documentary research methodology

Documentary research, if conducted thoroughly, can help develop a hypothesis or prove or disprove an existing theory. This of course depends on the methodology applied and the depth of research conducted. The researcher must conduct his/her own secondary research to analyze the contents before extracting it. They must handle the data scientifically.

Follow this four-step approach to control the quality of the content:

The authenticity of the documents

The credibility of the documents

Representativeness of the documents

The meaning derived from the documents

Let’s take a look at these in detail.

Authenticity implies whether the document’s origin is reliable, is the evidence genuine, are the intentions sincere, and what were the commitments to creating the document. The authenticity of the source is the primary criterion of documentary research.

Credibility means the subjective and objective components that make one believe the source of information and whether the data is free from distortion and error. The information must be trustworthy and must have some level of expertise.

Representativeness refers to whether the document represents a more extensive collection of the data point, and it is an aggregation of the topic being studied. That said, documents get distorted with time due to the inclusion of new factors, and a check has to be made to ensure the documents are representative.

Meaning means whether the findings are understandable and clear to be called evidence. The goal of examining documents is to understand its significance and meaning. Researchers must find out whether the document fits within the historical context or not.

Advantages of documentary study

Here are the advantages of the documentary research method:

• Data readily available: Data is readily available in various sources. You only need to know where to look and how to use it. The data is available in different forms, and harnessing it is the real challenge.
• Inexpensive and economical: The data for research is already collected and published in either print or other forms. The researcher does not need to spend money and time like they do to collect market research insights and gather data. They need to search for and compile the available data from different sources.
• Saves time: Conducting market research is time-consuming. Responses will not come in quickly as expected, and gathering global responses will take a huge amount of time. If you have all the reference documents available (or you know where to find them), research is relatively quick.
• Non-bias: Primary data collection tends to be biased. This bias depends on a lot of factors like the age of the respondents, the time they take the survey, their mentality while taking the survey, their gender, their feelings towards certain ideas, to name a few. The list goes on and on when it comes to surveying bias.
• A researcher is not necessary during data collection: The researcher doesn’t need to be present during data collection. It is practically impossible for the researcher to be present at every point of the data source, especially thinking about the various data sources.
• Useful for hypothesis: Use historical data to draw inferences of the current or future events. Conclusions can be drawn from the experience of past events and data available for them. 

Disadvantages of the method

Here are the disadvantages of the documentary research method:

• Limited data: Data is not always available, especially when you need to cross-verify a theory or strengthen your argument based on different forms of data.
• Inaccuracies: As the data is historical and published, there is almost no way of ascertaining if the data is accurate or not. 
• Incomplete documents: Often, documents can be incomplete, and there is no way of knowing if there are additional documents to refer to on the subject.
• Data out of context: The data that the researcher refers to may be out of context and may not be in line with the concept the researcher is trying to study. Its because the research goal is not thought of when creating the original data. Often, researchers have to make do with the available data at hand.

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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

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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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Documentation in Reports and Research Papers

• An Introduction to Punctuation
• Ph.D., Rhetoric and English, University of Georgia
• M.A., Modern English and American Literature, University of Leicester
• B.A., English, State University of New York

In a report or  research paper , documentation is the evidence  provided for information and ideas borrowed from others. That evidence includes both primary sources  and secondary sources .

There are numerous documentation styles and formats, including MLA style (used for research in the humanities), APA style (psychology, sociology, education), Chicago style (history), and ACS style (chemistry).

Examples and Observations

• Adrienne Escoe "Documentation has many meanings, from the broad—anything written in any medium—to the narrow—policies and procedures manuals or perhaps records." ( T he Practical Guide to People-Friendly Documentation , 2nd. ed. ASQ Quality Press, 2001)
• Kristin R. Woolever "An issue more important than documentation form is knowing when to document. In brief, anything that is copied needs to be documented... "Perhaps the best tip for knowing when to document is to use common sense. If writers are careful to give credit where it is due and to provide the reader with easy access to all the source material, the text is probably documented appropriately." ( About Writing: A Rhetoric for Advanced Writers . Wadsworth, 1991)

Note-Taking and Documentation During the Research Process

• Linda Smoak Schwartz "The most important thing to remember when you take notes from your sources is that you must clearly distinguish between quoted, paraphrased , and summarized material that must be documented in your paper and ideas that do not require documentation because they are considered general knowledge about that subject." ( The Wadsworth Guide to MLA Documentation , 2nd ed. Wadsworth, 2011)

Library Resources Versus Internet Resources

• Susan K. Miller-Cochran and Rochelle L. Rodrigo "When you are reviewing and analyzing your resources, keep in mind that the library/Internet distinction is not quite as simple as it might seem at first. The Internet is where students often turn when they are having difficulty getting started. Many instructors warn students against using Internet resources because they are easily alterable and because anyone can construct and publish a Web site. These points are important to remember, but it is essential to use clear evaluative criteria when you are looking at any resource. Print resources can be self-published as well. Analyzing how easily a resource is changed, how often it is changed, who changed it, who reviews it, and who is responsible for the content will help you choose resources that are reliable and credible, wherever you might find them." ( The Wadsworth Guide to Research, Documentation , rev. ed. Wadsworth, 2011)

Parenthetical Documentation

• Joseph F. Trimmer "You may decide to vary the pattern of documentation by presenting the information from a source and placing the author's name and page number in parentheses at the end of the sentence. This method is particularly useful if you have already established the identity of your source in a previous sentence and now want to develop the author's idea in some detail without having to clutter your sentences with constant references to his or her name.​" ( A Guide to MLA Documentation , 9th ed. Wadsworth, 2012)
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Practice Research Process: Documentation and Publication

• First Online: 20 May 2022

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• Robin Nelson 2  

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This chapter addresses two aspects: documentation of Practice Research, and publication and archive potential. In both, the affordances of digital culture are noted as facilitators, even to the extent of non-linear, interactive models of access to research insights. Taking the example of CREW’s “Hamlet Encounters” project, the first part illustrates how documentation arises in the process of professional practice/Practice Research and how it might be deployed by way of evidence through several iterations of a project without telos . The second part reviews established and new places for publication of Practice Research—in print journals and in new hybrid modes which incorporate audiovisuals, with or without commentary. The residual need for a sustainable archive of praxis publications is marked.

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In Bulley and Sahin, 2021 : 08.

See also Jürgens and Fernandes “Choreographic practice-as-research” in Arlander et al., eds, 2018 : 262–274.

See https://www.alexanderwhitley.com/digital-body , accessed 13/05/21.

Doktor der Künste, 2018, University of Music and Performing Arts, Graz, Austria.

www.crewonline.org

See clips on Vimeo/YouTube and http://www.crewonline.org/art/projects

Iterations have been shown at the Shakespeare Festival, Gdansk (Aug, 2017), KVS Brussels (May, 2018) and IFTR, Belgrade (July, 2018).

Eric Joris, Chiel Kattenbelt, Aneta Mancewicz and Robin Nelson, subsequently joined by Joris Weijdom.

A additional production planned for Amsterdam in 2021 was postponed owing to Covid 19.

All images from Hamlet Encounters are reprinted by permission of Eric Joris and CREW.

For Blast Theory’s coinage and use of “orchestration,” see Benford and Giannachi, 2011 : 209–224 ff .

For an account of that tradition, see Bowers, 1966 [1940].

In addition to those cited below, the following publications might be considered: Journal of Sonic Studies, PARSE, PARtake, RUUKU, Vis . Some are region or language-specific but others aspire to be international in scope.

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See: Kultur, http://kultur.eprints.org ; Kultivate Project, https://vads.ac.uk/customizations/global/pages/kultur2group/projects/kultivate/index.html ; Defiant Objects, https://defiantobjects.wordpress.com , accessed 25/01/21.

Winchester University Press, http://www.experimentsandintensities.com/published/vol-3/ , accessed 25/01/21.

http://www.experimentsandintensities.com/published/vol-3/ , accessed 25/01/2.

Solleveld, 2012 : 78.

SAR Correspondents, https://societyforartisticresearch.org/correspondents/sar-correspondents/ , accessed 23/01/21.

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Bulley, James & Şahin, Özden (2021) Practice Research – Report 1: What is practice research? and Report 2: How can practice research be shared? . London: PRAG-UK, 2021. https://doi.org/10.23636/1347 , accessed, 03/06/21.

deLahunta, Scott & Barnard, Philip J (2018) “Seeing the ‘Choreographic Mind’: three analytic lenses developed to probe and notate creative processes in dance”. In The Neurocognition of Dance: Mind, Movement and Motor Skills , 2nd edition. Eds. Blaesing, B et al . London: Taylor & Francis: 88–114.

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Nelson, R. (2022). Practice Research Process: Documentation and Publication. In: Practice as Research in the Arts (and Beyond). Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-90542-2_5

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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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Exploring documentation in Person‐centred care: A content analysis of care plans

Doris lydahl.

1 Department of Sociology and Work Science, University of Gothenburg, Gothenburg Sweden

Nicky Britten

2 University of Exeter Medical School, Exeter UK

3 Sahlgrenska Academy, University of Gothenburg, Gothenburg Sweden

4 Centre for Person‐Centred Care, Institute of Health and Care Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg Sweden

5 Department of Anaesthetics, Surgery and Intensive Care, Sahlgrenska University Hospital, Östra, Gothenburg Sweden

6 Institute of Nursing and Health Promotion, Oslo Metropolitan University, Oslo Norway

Öncel Naldemirci

7 Department of Social Work, Umeå University, Umeå Sweden

Helen Lloyd

8 School of Psychology, University of Plymouth, Plymouth UK

Birgit Heckemann

Associated data.

The data that support the findings of this study are available from the corresponding author, [BH], upon reasonable request.

Person‐centred care is a growing imperative in healthcare, but the documentation of person‐centred care is challenging. According to the Gothenburg Framework of Person‐centred Care, care should be documented in continuously revised care plans and based on patients’ personally formulated goals and resources to secure a continuous partnership.

This study aimed to examine care plans produced within a randomised controlled trial that tested a person‐centred care intervention in older people with acute coronary syndrome. Nurses with training in the theory and practice of person‐centred care had written the care plans.

We conducted a secondary analysis of care plans developed in a randomised controlled trial for assessing person‐centred care in patients with acute coronary syndrome (Myocardial Infarct [MI] or unstable angina pectoris). The study sample included 84 patients, with three care plans for each patient from inpatient (T1), outpatient (T2) and primary care (T3), that is, a total of 252 care plans. We conducted a descriptive quantitative content analysis of the care plans to examine the reported patients' life‐world and medical/health resources and goals.

The analysis illustrates the differences and overlaps between life‐world and medical/health goals and resources. The documented goals and resources change over time: life‐world goals and resources decreased with time as medical/health goals and resources documentation increased.

Conclusions

This paper illustrates that in the setting of a randomised controlled trial, nurses with training in person‐centred care recorded fewer life‐world and more medical/health goals over time. Placing life‐world goals at the top of the goal hierarchy enables alignment with medical/health goals. Further research should explore whether the goals and resources documented in care plans accurately reflect patients' wishes as they transition along the care chain.

Trial registration: Swedish registry, Researchweb.org, ID NR 65 791.

What does this research add to existing knowledge in gerontology?

The documentation of life‐world and medical/health goals and resources is variable and changes over the course of a patient's journey.

What are the implications of this new knowledge for nursing care with older people?

The differences between life‐world and medical/health goals require more consideration. Patients' resources that support their recovery and goal attainment should also receive more attention.

How could the findings be used to influence policy or practice or research or education?

Person‐centred care training should highlight the differences between goals and resources and how to record these more clearly and assertively.

1. INTRODUCTION

Internationally and across all healthcare settings, providing person‐centred care is a growing imperative (Britten et al., 2017 ), not least in older people nursing (Dewing, 2008 ; Kindblom et al., 2021 ; Sundler et al., 2020 ). Tailoring care to the patient's individual wants and needs and jointly setting goals are essential to person‐centred care (Ekman et al., 2011 ).

In 2011, Ekman et al. ( 2011 ) developed the Gothenburg Framework for Person‐centred Care (gPCC) at the University of Gothenburg (Sweden). The gPCC, which has been widely implemented (Britten et al., 2017 ; Ekman et al., 2021 ; Håkansson Eklund et al., 2019 ), consists of three routines that facilitate the initiation, integration, and safeguarding of person‐centred care in daily clinical practice. The first routine involves initiating a partnership with the patient by eliciting the patient narrative, that is, the person's account of their illness, symptoms, and impact on their life. Especially important for this routine is identifying the patient's own resources (Ekman et al., 2011 ). The second routine concerns working the partnership through shared decision‐making and establishing personally formulated and commonly agreed goals . Person‐centred care represents a shift from solely medically oriented goals as it includes the patient's personal goals in shared care planning (Ekman et al., 2011 ; Jansson et al., 2018 ). These personal goals are life‐world goals based on the everyday world shared with others. The life‐world includes family life, culture, and social life; but excludes organised or institution‐driven aspects (Barry et al., 2001 ). A qualitative interview study with researchers working with the gPCC found that healthcare professionals often heard patients speak more about life‐world goals than biomedical goals (Britten et al., 2017 ). Life‐world goals included, for example, activities like picking mushrooms in the forest, digging a potato patch, or walking the dog, as well as personal goals such as having a job or a partner (Britten et al., 2017 ).

The third gPCC routine safeguards the partnership by documenting the narrative, the resources and the agreed goals in a shared care plan (Ekman et al., 2011 ). Care planning should be based on patients' own personally formulated goals and resources and needs to be discussed and, if necessary, revised continuously (Britten et al., 2020 ). Indeed, Berntsen et al. ( 2015 ) argue that patients have the moral and legal right to have their life‐world goals placed at the top of the ‘goal hierarchy’. Goal documentation should be adapted to changes in the patient's goals over time and across different care settings, for example, when moving from hospital to outpatient care. Reviewing and adjusting patient goals support continuity of care. Goal documentation can also enable a discussion of care on a ‘new level’ that actively includes the patient's expertise and resources (Wolf et al., 2017 ). Arguably, the role of documentation is essential to person‐centred care.

Yet research shows that person‐centred documentation is a substantial challenge because patient records are legal records firmly embedded in healthcare structures and rooted in biomedical traditions. Current patient records comprise patient diagnosis, treatment and care planning, delivery and outcomes (Blair & Smith, 2012 ). Their primary purpose is to ensure communication between healthcare professionals rather than patient–healthcare professional communication.

Although person‐centredness is a quality criterion in documentation (Jefferies et al., 2010 ), integrating person‐centred care aspects such as goal‐setting challenges current medical and nursing documentation (Britten et al., 2017 ; Dellenborg et al., 2019 ; Heckemann et al., 2020 ; Sefcik et al., 2020 ). Existing documentation systems and structures often fail to prompt and support person‐centred documentation (Broderick & Coffey, 2012 ; Gyllensten et al., 2020 ). Structured, template‐style documentation that focuses on the medical problem contributes to improving patient care (Björvell et al., 2003 ) because it facilitates clinical auditing and evaluation (Saranto & Kinnunen, 2009 ). However, the dialogical or narrative elicitation (Ekman et al., 2011 ) that is essential in person‐centred care is often less structured. Person‐centred care elicitation includes the patient's personal experience and exceeds the focus on medical problems. As a result, person‐centred care documentation is often fragmented, poorly developed, and lacking in various settings, including, for example, older people out of hospital settings, ageing migrant communities or coronary care (Ebrahimi et al., 2021 ; Moore et al., 2017 ).

Moreover, nursing documentation often fails to go beyond the descriptions of the routine aspects of care (Frank‐Stromborg & Christensen, 2001 ). Patients' psychosocial concerns and the details of the clinical communication are often lacking (Broderick & Coffey, 2012 ). In a similar vein, medical documentation in shared patient records lacked person‐centred content, and physicians often used terminology and abbreviations that were inaccessible to patients (Heckemann et al., 2020 ). This is particularly problematic in collaborative goal setting and its documentation, which are essential in person‐centred care (Ekman et al., 2011 ; Jansson et al., 2018 ). Person‐centred goals combine medical and patients' personal goals and resources, documented in plain language. Continued collaborative goal setting is important for patients with chronic conditions yet to date an under‐researched area that requires further development (Vermunt et al., 2017 ). To our knowledge, there are no previous studies examining goal documentation across the patient trajectory, yet the continuity of care is essential, particularly in chronic illness. This study broadens the knowledge base about longitudinal goal‐setting documentation through a secondary analysis of nursing care plans. Registered Nurses (RNs) who had received person‐centred care training developed the care plans within a randomised controlled trial. The trial assessed the effects of a person‐centred care intervention in patients with acute coronary syndrome (ACS) (myocardial infarction or angina pectoris), which is most common in older patients (Fors et al., 2015 ). The analysis enabled us to investigate longitudinal, person‐centred goals and resource documentation in a clinical trial.

2.1. Study context

The data for this secondary analysis derives from a clinical trial conducted by researchers affiliated with the Gothenburg Centre for Person‐Centred Care (Fors et al., 2015 ). The trial enrolled 199 participants with ACS treated at two coronary care units at Sahlgrenska University Hospital between June 2011 and February 2014. The intervention group comprised 89 patients who received a person‐centred care plan in addition to usual care. The care plan was co‐created during their hospital stay (T1), updated in outpatient care (4 weeks after discharge) (T2) and in primary care (8 weeks after discharge) (T3).

‘Usual’ care for acute heart disease in Sweden follows the Socialstyrelsens (National Board of Health and Welfare) evidence‐based guidelines for cardiac care (Socialstyrelsen, 2018a ). In addition to pharmacological and medical measures, the guidance includes recommendations for changing health‐related risk factors. Patient education through specially trained nurses (‘heart school’) is vital to ensure that the patient follows the treatment, undergoes regular weight checks, and participates in decisions about medical treatment (Socialstyrelsen, 2018a ).

Registered nurses and patients developed the first person‐centred care plan (T1) within 24 h after admission to the hospital ward. At T2 and T3, RNs and physicians trained in person‐centred interviewing reviewed the initial care plan and updated it in collaboration with the patient. The care plans included information on (a) medical/health and personal (life‐world) patient goals, (b) how to achieve these goals, (c) patients’ resources and (d) support needs (see Figure ​ Figure1 1 ).

Person‐centred care plan

The person‐centred training comprised lectures, seminars and workshops about the theory and practice of the Gothenburg framework for Person‐centred Care (gPCC). In addition, healthcare professionals learned about formulating and executing gPCC plans. In addition, RNs participated in four 3‐h sessions with case examples and tutoring to ensure they adhered to the gPCC approach during the intervention (Fors et al., 2015 ).

2.2. Sampling and analysis

The intervention arm of the RCT included 89 patients; however, care plans for five patients were missing from the data set. Therefore, our study sample included 84 patients, with three care plans per patient from inpatient, outpatient and primary care visits, giving a total of 252 care plans.

DL and BH transcribed the care plans into excel spreadsheets and repeatedly read them to familiarise themselves with the content. All authors met three times to discuss and review a deductive coding framework. The coding framework comprised two of the key aspects of the gPCC: resources and goals (Table ​ (Table1 1 ).

Coding frame for goals and resources

We built on the definition and operalisation of the concept ‘life‐world’ by Barry et al. ( 2001 ) to define codes for medical/health and life‐world goals and resources. Medical/health goals were biomedical and not necessarily connected to the patient's life‐world.

Based on the coding framework, we conducted a deductive quantitative content analysis. The codes were treated as categorical variables and each entry was assigned value (medical/health = 1, life‐world = 2 and missing = 0). Data were imported into SPSS for a descriptive analysis of goals and to examine shifts over time. BH and DL discussed and chose examples to illustrate these changes.

2.3. Ethics

The Regional Ethical Review Board (DNr 275‐11) approved the original RCT study. The application included the option to conduct secondary analyses of the documentation material. The study conformed to the principles of the Declaration of Helsinki. When extracting the data, we assigned anonymous Case‐IDs to all patients. Identifying information such as name or social security number were excluded from the dataset.

The results are presented in two sections as follows: goals and resources. We provide a quantitative summary of the data over three time points followed by qualitative data extracts that illustrate the nature of the goals or resources.

3.1. Section one: Goals

The RNs had written the care plans. There was no indication of whether patients confirmed the stated goals as reflective of their own narrative, wishes and needs. The goals were documented in plain language, with little use of medical jargon. Still, there were abbreviations such as VC (vårdcentral = Primary Healthcare Centre) or FaR (fysisk aktivitet på recept = Physical activity on prescription). These abbreviations are standard and widely used in Swedish healthcare.

The descriptive analysis of the longitudinal data showed that the reporting of goals decreased over time. At T1, the RNs had recorded either life‐world or medical/health goals in most care plans ( n  = 84). At T3, the number of missing goals had increased ( n  = 12). Still, most care plans contained goals ( n  = 72). The focus of the reported goals shifted with fewer life‐world goals over time ( n  = 56 at T1; n  = 43 at T2; n  = 32 at T3) and more medical/health goals ( n  = 28 at T1; n  = 30 at T2; n  = 40 at T3) (Figure ​ (Figure2 2 ).

Number of documented goals, T1‐T3

Many medical/health goals were in line with evidence‐based guidelines and concerned health factors such as smoking, weight loss, physical activity and stress (Socialstyrelsen, 2018a , 2018b ). In contrast, life‐world goals concerned areas such as the patients' social role or family and ethical or spiritual issues (Schellinger et al., 2018 ), and thus, extended beyond medical/health, guideline‐driven goals. However, medical/health goals may represent life‐world goals when connected to the patients' social life or role.

In the following paragraphs, we provide examples of documentation of both kinds of goals for older persons.

3.1.1. Life‐world goals

The excerpt below illustrates the integration of life‐world and medical/health goals. This patient wished to resume his social role as a bookbinder and return to his previous hobbies. The medical/health goal (physical exercise) was linked to the patient's life‐world goals: returning to cycling. Across all time points, the focus in care planning remained on life‐world goals (Table ​ (Table2 2 ).

Life‐world goals of a male patient, 61 years old

This example illustrates the maintenance of life‐world goals and medical/health goals across all time points (Table ​ (Table3 3 ).

Life‐world and health goals of a male patient, 74 years old

This example shows that medical/health and life‐world perspectives can exist parallel and are seemingly disconnected throughout this patient's care trajectory.

The focus on life‐world goals was not maintained in all care plans (Figure ​ (Figure2). 2 ). The example below illustrates how the focus on life‐world goals vanished over time.

3.1.2. From life‐world to medical/health goals

At T1, we found a mix of life‐world (writing a book, living a long life, travelling with the son) and medical/health goals (being more physically active). At T2, the documentation became scant, and the focus shifted towards medical/health goals. The care plan stated that goals were ‘as previously’, and there was no follow‐up on the progress towards the previously stated life‐world goals. Instead, the health goal—to increase physical activity—was revisited (Table ​ (Table4 4 ).

Changing goals of a male patient, 74 years old

The shift towards medical/health goals became more evident at T3, where the sole documented goal concerns a return to previous physical strength.

3.1.3. Medical/health goals

Some care plans did not include a life‐world goal but featured medical/health goals from T1 to T3. The example below illustrates this focus on medical/health goals (weight reduction and physical fitness). In these cases, the RNs possibly paid less attention to life‐world goals, or the patient had no desire to share their life‐world goals (Table ​ (Table5 5 ).

Health goals of a male patient, 72 years old

3.2. Section two: Resources

The RNs documented patient resources that supported the agreed goals. About half of the reported resources had a connection to the patient's life‐world at T1 ( n  = 48). However, there was a shift towards reporting fewer life‐world resources ( n  = 48 at T1; n  = 28 at T2; n  = 23 at T3) and more medical/health resources ( n  = 36 at T1; n  = 39 at T2; n  = 46 at T3) from T1 to T3 (Figure ​ (Figure3 3 ).

Number of documented resources, T1‐T3

We found that many of the documented resources were medical/health resources concerning the patient's efforts and abilities to achieve their goals. These included physical activities and abilities (e.g. living healthy) and mental abilities or states (e.g. a positive attitude towards recovery). Life‐world resources pertained to the patient's social support network (for example, a general sense of feeling supported by a spouse or friends). Some care plans reported a lack of physical or mental resources, preventing goal achievement. These included physical barriers such as pain and fatigue or negative /unresourceful mental states such as feelings of depression, anxiety and stress.

The following paragraph provides examples of patients' life‐world and medical/health resources.

3.2.1. Life‐world resources

The example below illustrates the link between the life‐world and health resources, which we commonly found in our analysis. The documented resources depict a patient with a positive attitude (with ‘no depressive thoughts’) and a tight social network including children and grandchildren. This link between life‐world and health resources is seen at T1 and T2, while the care plan at T3 only includes ‘being positive’ as a health resource (Table ​ (Table6 6 ).

Life‐world and health resources of a male patient, 72 years old

The following example includes more details about the patient's social network. The patient has comprehensive support and previous experience of giving and receiving support in difficult times (Table ​ (Table7 7 ).

Life‐world resources of a female patient, 64 years old

3.2.2. Medical/health resources

Some care plans featured medical/health resources from T1 through to T3 (Table ​ (Table8 8 ).

Health resources of female patient, 70 years old

Many patients wish to get better and return to life before a disruptive event, such as getting ill (Bury, 1982 ). However, the example above does not connect to the patient's life‐world, and the resources are more ‘standalone’.

4. DISCUSSION

This article offers two important insights about life‐world and medical/health goals and resource documentation in person‐centred care plans for older people with acute coronary syndrome. First, the analysis illustrates the overlaps and differences between life‐world and medical/health goal and resource reporting. Second, it demonstrates that documented goals and resources change over time.

Our analysis showed that many medical/health goals aligned with evidence‐based guidelines (Socialstyrelsen, 2018a , 2018b ). However, these goals do not necessarily reflect the person's own wishes and preferences, and RNs may have steered patients towards these practical guideline‐based goals. This is reflective of two issues. First, our findings confirm a lack of knowledge regarding collaborative goal setting (Vermunt et al., 2017 ) and documentation, even in staff trained in person‐centred care. Second, healthcare professionals prioritise medical/health goal setting because it is a legal obligation. Healthcare professionals who fail to set medical/health goals may be liable to legal prosecution (Berntsen et al., 2015 ). Accordingly, it is currently in the healthcare professional's self‐interest to comply with legal‐professional requirements rather than prioritise person‐centred principles (Berntsen et al., 2015 ). This might explain RNs' tendency to uphold stable and familiar routines rather than test newly acquired practices (Naldemirci et al., 2017 ).

This article reports a secondary analysis of care plans from a randomised controlled trial. Since RNs trained in person‐centred care wrote the care plans, they may not reflect routine clinical practice settings. Notwithstanding the controlled nature of the setting, our analysis demonstrated that the documentation of life‐world goals decreased over time, and the documentation of medical/health goals increased. RCT studies are designed to show the effect of an intervention between selected study groups. The controlled nature of the study context and the selected study population often precludes the direct transfer of RCT results to real‐world settings. Previous research shows that patients recruited to clinical trials usually have better outcome measures than non‐participants. Research shows that adherence to the study protocol (regardless if an intervention or placebo) is independently associated with outcomes (Granger et al., 2005 ; Rogers et al., 2021 ). This, combined with the attention of carefully trained RNs, might suggest that in non‐trial settings, the analysis of care plans might reveal similar results, if not greater attrition of life‐world goals. This assumption supports previous research that underscores the difficulty in sustaining person‐centred care over time (Ekman et al., 2021 ; Naldemirci et al., 2017 ).

Life‐world goals may relate to patients' social, cultural or personal lives. Therefore, they are more difficult to describe as they extend beyond the realm of the more clearly defined medical/health goals. However, the boundaries are fluid: medical/health goals may become life‐world goals if they are aligned with the patient's wishes, needs or preferences. This alignment is critical: Berntsen et al. ( 2015 ), who propose a goal hierarchy, argue that goals can be aligned by prioritising the patient's health rather than medical goals. Our analysis shows this type of aligned goal setting that places health goals at the top of the ‘goal hierarchy’. For example, the evidence‐based recommendation to pursue regular physical exercise becomes a life‐world goal if the patient can connect it with a favourite pastime, such as cycling. Still, our analysis also demonstrates that medical/health and life‐world goals do not always overlap. This may point towards a need to discuss goal hierarchies in person‐centred care training.

Our analysis shows that the reporting of resources followed the same pattern as the reporting of the goals. From T1 to T3, fewer life‐world resources were recorded, while medical/health resource reporting increased. The biomedical paradigm regards the patient in isolation, while person‐centred care sees the patient as an individual in a social context of relationships with family, friends and colleagues (Dewing, 2008 ; Kitwood, 1997 ). However, life‐world resources such as a supportive partner or a strong social network can play an important role in achieving desired goals.

We can only speculate why goal and resource reporting shifted over time. The care plans were our sole data source, and we did not have access to additional data about the communication between patients and the RNs. However, the shifts may point to a changing tone in the communication, and they may not necessarily be related to fading person‐centred care and life‐world orientation. Previous research shows that patients tend to be more receptive to clinicians' guidance in setting goals once a sense of ease and trust is established (Edvardsson et al., 2005 ; Wolf et al., 2017 ); hence, medicalised language and content may occur. In contrast, Broderick and Coffey ( 2012 ) saw an increase in person‐centred documentation over time. This may be due to different settings. Our study analysed patient records from different settings (inpatient, outpatient clinic and primary care clinic), while Broderick and Coffey ( 2012 ) examined care records from a long‐term setting. Long‐term care offers patients and staff the time and space to get to know each other as ‘persons’; this might be more difficult on a care trajectory that includes several settings.

In our analysis of the care plans, we did not know whether the life‐world goals and resources were recorded in the patients' own words or whether the RNs interpreted the patients' narratives. Nursing documentation may feature the patient's voice, the nurses' view of the patient's thoughts or situation, or the mutual view of the relationship (Laitinen et al., 2010 ). Our sample does not enable us to draw conclusions about the perception of the relationship between patients and RNs. The relationship and discussion of goals and resources may have been more person‐centred than the notes reveal. Patients may not be used to or encouraged to talk about life‐world goals, as healthcare professionals struggle to elicit these (Wolf et al., 2017 ). Likewise, patient‐related factors can hamper life‐world goal elicitation. Person‐centred care encourages healthcare professionals to see patients as persons, to talk to them like partners, rather than talking about or above them (Britten et al., 2017 ). However, patients may have been socialised into taking a passive role or may not be familiar with collaborative goal setting (Wolf et al., 2017 ). Other patients may prefer to leave the care decisions to clinicians (Chewning et al., 2012 ) or agree with clinicians to avoid challenging their authority or make undue demands on their time (Joseph‐Williams et al., 2014 ).

In a previous study (Naldemirci et al., 2017 ), we underscored that narrative‐inducing questions did not immediately lead to the 2021 of life‐world goals because many patients were not used to telling stories and setting goals. Patients tended to articulate generic goals like ‘being healthy’. We also observed that asking follow‐up questions enabled RNs to identify specific life‐world goals more easily. Follow‐up questions could be, for example: ‘ Ok, what would you like to do when you are healthy again?’ ‘ Would you like to be able to spend more time in your garden when you go home?’ (Naldemirci et al., 2017 , 2020 ).

However, the concept of ‘resources’ is even less well‐established than ‘goals’ in healthcare. ‘Resources’ are a distinctive aspect of person‐centred care, as the patient is regarded as a ‘capable person’ (Ekman et al., 2011 ). To date, patients' resources are rarely considered in clinical communication (Naldemirci et al., 2020 ) and what exactly could and should be considered a patient resource is not defined in previous literature.

Our analysis shows that even under the controlled conditions of an RCT testing person‐centred care, the alignment of goals with resources was unclear and inconsistent. This emphasises the value of asking follow‐up questions such as ‘What can help you achieve this goal?’ to help patients identify the resources they need. Our findings indicate that the elicitation of goals and resources should be more strongly emphasised in person‐centred care training.

Ekman et al. ( 2021 :3) propose that future research in person‐centred care should ‘describe and evaluate different forms of health plans, including those recorded and written only by patients and relatives'. This is one of the first papers to make a secondary analysis of the content of care plans over time (an exception is Jansson et al., 2018 ). Whatever their limitations, care plans are the basis for ongoing care. As such, they should reflect the perspectives of both healthcare professionals and patients alike (Heckemann et al., 2020 ).

4.1. Limitations and recommendations

This study builds on an analysis of care plans generated within an RCT and written by RNs trained in person‐centred care. Therefore, the documentation may not be representative of usual care as it was part of a clinical trial pathway. We were unable to interview RNs and patients to triangulate our results. Moreover, we could not explore whether the life‐world goals and resources we identified were mutually agreed upon or merely based on the RNs' interpretation of the patient narrative. In particular, the study highlights that further research is needed to achieve continued person‐centred care documentation along a care pathway that involves different care levels.

This study raises questions regarding person‐centred care documentation, such as what prevents healthcare professionals from focusing on the life‐world of their patients, or, at a more abstract level, to which degree the concept of person‐centredness is compatible within the current framework of medical treatment. Future qualitative studies should address these questions. Despite its limitations, our analysis adds much insight as few studies address the actual content of person‐centred care plans.

5. CONCLUSION

This paper reports a secondary analysis of care plans for older persons from a randomised controlled trial evaluating a person‐centred care intervention in patients with acute coronary syndrome. Nurses with training in the theory and practice of person‐centred care had written the care plans. We found both overlaps and differences between life‐world and medical/health goals and resources. We also demonstrated that documented goals and resources change over time. However, we need to know more about whether the goals and resources documented in care plans accurately reflect the wishes of older patients and if similar results can be found in real‐world data. Placing life‐world goals at the top of the ‘goal hierarchy’ will enable alignment with medical/health goals, as this will tap into people's motivation and increase chances that supportive behaviours will be adopted during the recovery after a cardiac event.

6. IMPLICATIONS FOR PRACTICE

Life‐world and medical/health goals differ, both require consideration in person‐centred care planning. Patients’ resources that support their recovery and goal attainment are important, but not sufficiently considered in person‐centred care.

AUTHOR CONTRIBUTIONS

Birgit Heckemann: Conceptualisation, methodology, data curation, investigation, formal analysis, project administration, supervision, visualisation, writing (original draft preparation; review and editing) (shared lead). Doris Lydahl: Conceptualisation, methodology, data curation, investigation, formal analysis, project administration, supervision, writing (original draft preparation; review and editing) (shared lead). Axel Wolf: Conceptualisation, methodology, formal analysis, validation, writing (original draft preparation; review and editing). Helen Lloyd: Conceptualisation, methodology, formal analysis, validation, writing (original draft preparation; review and editing). Nicky Britten: Conceptualisation, methodology, formal analysis, validation, writing (original draft preparation; review and editing), Writing (original draft preparation; review and editing). Öncel Naldemirci: Conceptualisation, methodology, formal analysis, validation, writing (original draft preparation; review and editing).

CONFLICT OF INTEREST

The authors have no conflict of interest to declare.

ACKNOWLEDGEMENTS

The authors would like to acknowledge the University of Gothenburg Centre for Person‐centred Care for funding this project. We thank Analisa Avila, ELS, of Edanz Group ( https://en‐author‐services.edanzgroup.com/ac ) for editing a draft of this manuscript.

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• Published: 21 April 2022

Documenting research software in engineering science

• Sibylle Hermann 1 , 2 &
• Jörg Fehr 1  

Scientific Reports volume  12 , Article number:  6567 ( 2022 ) Cite this article

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• Engineering
• Mechanical engineering

The reuse of research software needs good documentation, however, the documentation in particular is often criticized. Especially in non-IT specific disciplines, the lack of documentation is attributed to the lack of training, the lack of time or missing rewards. This article addresses the hypothesis that scientists do document but do not know exactly what they need to document, why, and for whom. In order to evaluate the actual documentation practice of research software, we examined existing recommendations, and we evaluated their implementation in everyday practice using a concrete example from the engineering sciences and compared the findings with best practice examples. To get a broad overview of what documentation of research software entailed, we defined categories and used them to conduct the research. Our results show that the big picture of what documentation of research software means is missing. Recommendations do not consider the important role of researchers, who write research software, whose documentation takes mainly place in their research articles. Moreover, we show that research software always has a history that influences the documentation.

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A dataset describing data discovery and reuse practices in research

Introduction.

Documentation of research software 1 in engineering science is inadequate 2 . Nevertheless, researchers–particularly within the FAIR (Findable, Accessible, Interoperable, Reusable) movement–state that documentation of research software as a major prerequisite for reuse 3 . Although, research data and software play a central role in the Cluster of Excellence “Data-Integrated Simulation Science (SimTech)” 4 , documentation is also lacking here.

But why is research software documented poorly? And what does good documentation actually imply? Previous approaches provide rather explanatory models why documentation is not done; they explain the missing documentation with lack of time 5 or insufficient training 6 . But is this really the case? Is it even clearly defined what documentation entails? Until now, incentives and rewards are missing for well documented research software. But, the scientific environment is changing, Gil et al. 7 observe a shift in the scientific environment in different areas: scientific publishing, scientists, public interest and funding. In recent years, these developments are gaining momentum with initiatives like EOSC (European Open Science Cloud) 8 and NFDI (National Research Data Infrastructure, Germany) 9 . Moreover, research funding agencies demand reusability; the guidelines for good scientific practices, for example, require documentation of research software explicitly 10 . Surprisingly, it remains rather unclear what good documentation of research software involves. We illustrate that there are recommendations on how to document (research) software. But are the recommendations actually applied?

The hypothesis of this paper is, that it is still unclear what good documentation actually involves. The approach intends to examine how documentation takes place in everyday work in a research environment in engineering science within the Cluster of Excellence, SimTech. We also examine if and how given recommendations are implemented. We defined categories to represent different documentation purposes. Based on these categories, we examined three different aspects:

Given recommendations on how research software should be documented.

An actual documentation workflow of a specific research software project from engineering science within SimTech.

Given documentation of two best practice examples within SimTech.

Previous approaches have been concerned with reasons why research software is poorly documented, but not with what good documentation actually entails. Also, it has not been investigated what and how documentation must be implemented in order to be perceived as good. It’s the intention of this approach to show what is missing and give an overview on who has to document for whom what, where, when, and how.

We want to investigate how research software is documented in a field where scientists usually don’t have a computer science background. Due to the highly disciplinary nature of research software, we focused on our discipline, Engineering Science. We conducted a multi-case study with a case-based approach 11 . To see how the documentation is implemented, one specific research software was chosen: Neweul-M 2 12 , a research software that has been developed over years in an institute by engineers without formal software development training. Neweul-M 2 continues to be actively developed and is often used to address specific research questions. We cross-case synthesis with two other research software’s documentation habits, to compare the gained insights. We selected these best practice examples because they received funding from the DFG (German Research Foundation) sustainable software funding call to improve their documentation 13 . In contrast to Neweul-M 2 , both best practice examples are open source. DuMu x 14 is a research software from engineering sciences, which is also programmed by Research Software Developers with an engineering background. The other example preCICE 15 is a research software developed from more experienced Research Software Developers working in an informatics institute; their users are mainly engineers. The central rival hypotheses we considered are the lack of time to document and the lack of training of researchers in software engineering 2 , 6 .

Two main research questions structure the investigation.

Research questions

What are the recommendations for documenting research software? Which rules and best practices exist? Do the given recommendations cover the defined categories?

What is the practice of documenting research software? How is research software documented in the daily life of researchers? Which workflows are implemented? What are the obstacles to document research software?

Data collection

For collecting data, we choose different sources of evidence:

Documentation : An evaluation of literature was conducted to assess what recommendations are given (RQ1). Furthermore, the three research software documentation were evaluated (RQ2).

Participant observation : Both authors are familiar with Neweul-M 2 , one author from a new Research Software Developer perspective, and the other from years of experience. One author is part of the project from the sustainable software funding call and has thus witnessed the discussions about the possibilities for improvement and shortcomings of the documentation of DuMu x (RQ2).

Direct observations : The concepts, thoughts, and insights were further discussed with the old and later the new Research Software Engineer from Neweul-M 2 and with DuMu x and preCICE Researcher Software Engineers (RQ2).

Interviews : The two best practice examples were evaluated with semi-structured interviews (RQ1, RQ2).

Data analysis

Our first idea was to evaluate the research software using given recommendations from the literature. As we soon noticed, the recommendations for research software do not give a complete picture of what documentation should actually contain. Therefore, we switched to an inductive strategy and formed categories that we consider necessary from everyday work with research software, supplemented with categories from literature and internet resources like blogs and wikis. Moreover, we decided to include the best practice examples to answer the research questions. We defined four documentation categories for research software, intending to picture possible documentation forms. Based on the defined categories, we evaluated the recommendations given for, Neweul-M 2 , DuMu x , and preCICE. In the following, we introduce the categories, followed by the recommendations and conclude with the analyzed research software examples.

Domain Research software can belong to different domains 16 : private , shared and open . Usually, research software is developed in the private domain with one main Research Software Developer. The shared domain varies from a few users at an institute to many users all over the world, nevertheless the research software is unavailable to the broader public. Published research software in the open domain is accessible for everyone. Where open can have two different meanings: only the source code is available open source or the software is developed openly. The domains may change over time and require more documentation, as more people need to understand the research software.

Role As we noticed, it is essential who documents for whom; we differentiate between three roles: Research Software Engineer (RSE) , Research Software Developer (RSD) and user . One person can have multiple roles, multiple people can share the role and the role of a person can change. As the roles in classical software engineering are conceptualized 17 , we defined the roles from our perspective—which is biased from our education as engineers and work in an interdisciplinary research cluster. When we speak about engineers, we think of the classic engineering fields such as mechanical engineering, civil engineering or chemical engineering. We explicitly neglect software engineers—due to their formal education in software development and maintenance, which is mostly missing in the other fields.

* Research Software Engineers are responsible (i) for the infrastructure and maintenance of the software, (ii) they give the rules of how research software should be written, (iii) but are often not part of the active feature development. The problems of funding, education and missing credit of Research Software Engineers are discussed in the RSE movement 18 .

* Users are (i) research software users who want to do either computer-aided engineering or computer-based experiments without writing code.

* Research Software Developers are (i) the link between Research Software Engineers and users , (ii) they develop new features, mainly to answer–with the research software–specific new research questions (iii) in engineering without education in software development and are often less experienced than Research Software Engineers. They typically need a specific answer for their research question, for which they need to implement a specific new or missing feature in existing research software. A typical example in Neweul-M2: A RSD implemented the calculation of reaction forces. This new feature can be reused for other research questions from other researchers and, therefore, need to be documented. RSDs are an essential part of the documentation process; they mainly know their developed features but are usually not deeply involved in the maintenance and documentation process.

Purpose The purposes describe the content of the documentation: why , what and how . The documentation of the problem should describe why the research software or a feature is written–similar to describing the research question, i.e. the RSE Manuscript/Dev docs row from Table 3 . The feature’s documentation should describe what is needed to be done to solve the research question. How the feature is implemented should be documented in a technical documentation, i.e. the Help/Handbook/User Docs row from Table 3 .

Type The type describes the characteristics of the documentation 19 : problem , product and technology . The three above introduced categories can be expressed in different types of documentation: The documentation of the problem can involve how the problem is implemented and why a solution was preferred. The product documentation contains the list of all features provided by the software and how they work together. The technical documentation should help the RSD and RSE to understand the code, how the research software is engineered and how to build over the existing source code. It should contain different schemas about the used model, the logical, and physical architecture. The types are intended to be umbrella terms for different forms of documentation types. For example, code comments are a form of technical documentation or tutorials as a type of product description.

We assume that each of these categories requires a different type of documentation. In each domain, researchers can document purposes in their role as RSE or RSD for different roles. The combination leads to several types of content, which then appear in a variety of forms. For example, a RSD can describe why they solved what and how in a problem description such as an article. Or a RSE describes how to solve a problem for the user in a product description as a how-to-guide.

Different aspects of documentation.

Recommendations

Aspects of good research software, and its documentation, has also been addressed in various recommendations. In order to find recommendations for documenting (research) software, we conducted a literature review in Web of Science using the terms “research software” and “documentation” or “reusability”. Most articles refer to the whole process of developing research software, and not only to documentation. Often just one small paragraph is dedicated to documentation. The selection of the articles was limited to those that include rules or best practices for documenting research software in at least one paragraph. Ten articles with interdisciplinary and different disciplinary focus were found. As described above, the evaluation of the recommendations did not provide a complete picture of what the documentation in our opinion should contain. Therefore, we also analysed the recommendations according to the categories we defined (Fig.  1 ).

Analysis of research software

Neweul-M 2 Neweul-M 2 is a software package that allows the dynamic analysis of mechanical systems in calculating multibody systems with symbolical equations 20 . The first version of Neweul was written in FORTRAN with an own symbolic formula manipulator engine in the mid 1970s and was rewritten in 2003 using MATLAB. The new version is called Neweul-M 2 . In Kurz et al. 12 , the history and changes are documented until the year 2010 (for further information see Table 1 ). Neweul-M 2 is used from:

external people (user)

PhD students (RSD and user)

students (user)

The source code is developed and administrated by PhD students within the developing institute, they aim for a degree in mechanical engineering and usually don’t have a formal software development education. One experienced RSD is the RSE, a new colleague is briefed as RSE from the previous one.

For the external people and students, a content-obscure (P-code) version of Neweul-M 2 is provided. One part of the documentation is in an integrated help within MATLAB. The help includes a product description, tutorials and examples and a function reference, automatically generated from the code. For PhD students from the developing institute, the full source code is accessible. The source code is managed via a Git repository hosted at an institutional GitLab instance. Bug fixes and support are the responsibility of the RSE. Another part of the documentation is done in a local wiki with information on how to get started and how to document with coding guidelines, tests and checklists. Decisions and discussions are documented via GitLab. For the RSE, an additional document gives information on how everything is organized. PhD students, who use Neweul-M 2 for their research, develop new features in Neweul-M 2 that they need for their research. They document these features mainly in publications.

DuMu x The research code for the free and open-source simulator is written in C++ and is based on DUNE (Distributed and Unified Numerics Environment). DuMu x stands for “DUNE for Multi-{Phase, Component, Scale, Physics, ...} flow and transport in porous media 21 . The main intention is to provide a sustainable and consistent framework for implementing and applying of porous media model concepts and constitutive relations (for further information see Table 1 ). All documentation is linked on the Website. The documentation consists of a collection of documented code examples within the institute’s publicly accessible GitLab instance, a manual in PDF format, code documentation within Doxygen, a reference to the most important publications and a wiki that is still under construction. The software is written by PhD candidates in civil engineering with a predominantly engineering background, who have taught themselves to program.

preCICE The research software preCICE 22 is an open-source coupling library for partitioned multi-physics simulations, including fluid-structure interaction and conjugate heat transfer simulations. The research is about methods how two systems can be coupled (for further information see Table 1 ). In preCICE the research question is not solved with the software, rather the research results are provided to users of the software. For this reason, there are only RSEs and users. The software is written by PhD candidates with different backgrounds, who are aiming for a degree in computer science. The documentation is bundled in a website. By using GitHub pages, pull requests can be made to all the documentation. The website is divided in quick start, docs, tutorials, community and blog. The section docs start with a user documentation with fundamentals, installation, configuration, tooling and provided adapters. The API is described in the category “couple your code”. Followed by a developer documentation, with a link to the source code documentation in Doxygen, and a description of coding conventions, tooling, workflow and testing. Even a description, how the documentation is build, exists. For the users–in addition to the conventional documentation–a community page gives insights on workshops, other contributors and publications. Furthermore, there is a blog where there is also the possibility to ask questions.

Validity analysis

We have deliberately chosen projects in which we can gain a more in-depth insight. These projects are in the engineering field to establish comparability of the training background of RSEs, RSDs and users. The interview partners are based on personal relationships and recommendations, which could lead to a specific research bias due to the small sample size and the personal connection. More extensive studies based on the research hypothesis of this study should be conducted in the future, e.g. at research software conferences. In order to validate our conclusions, we presented and discussed our results and methods with the RSEs and RSDs of the three software projects. We also presented a poster at the internal SimTech conference to receive feedback on our method and conclusions from other researchers. The feedback received confirmed our approach. The poster and other material is published in the case study database 23 . The selection of the case studies initially limits the generalizability of the results. However, the feedback received confirmed that our approach could be transferable to other research software projects. For example, PhD students at the conference confirmed that our approach is similar to their experiences with research software. The generalizability of the findings obtained in this study will be tested in another larger interview study with more extensive surveys in the future.

We present three main observations from the recommendations and the documentation of three research software examples, based on the categories that are presented in the method section.

Observation 1: A big picture is missing

A big picture is missing on what documentation of research software should contain and how it should be done. The examined recommendations focus only on specific aspects of research software documentation (see Fig.  1 ).

Observation 1.1: Problem and decision are undocumented

The recommendations rarely mention that the why should be documented and seldom take the underlying problem into account (see Table 2 ). They refer–according to our categories–mainly to the technology of the research software. Their focus is on techniques (like version control systems and programs that generate a documentation out of comments in the code) not on content. Lee 6 state for example to “use automated documentation tools” and that “the best type of documentation is documentation that writes itself”, but do not explain what have to be the content of the automated documentation. Looking into the practice, all three examples use these automated documentation tools.

In Neweul-M 2 (see Table 3 ) the function reference within the help is created with a given template, including: short description, syntax, long description, parameters, examples and references. In DuMu x (see Table 3 ) the modules are documented with Doxygen 24 . In comparison to Neweul-M 2 the description involves the underlying concept, mostly explaining the formula behind the code. In preCICE (see Table 3 ) as well, Doxygen is used with a generic documentation template: the parameters and a one line description is needed and an optional elaborate description. Here, the description do not contain the underlying concept. These tools are intended to document the code not the decisions and problems: RSDs document the problem and decision in research articles or thesis, which are not linked to the documentation; they implement new features to solve a specific task for a thesis; and they describe the problem only in the thesis referring to a specific version of the software. Eventually, the description of the problem and the feature differ from the software solution. Once the feature is included in the main branch, the dependencies are further maintained.

Observation 1.2: Shared and private domain are neglected

The recommendations focus on the open domain (see Table 4 ). Especially, the documentation for the shared domain is rarely mentioned. Neweul-M 2 is a research software from the shared domain, the different documentation types live in different domains (see Table 5 ). RSDs document mainly for their successors at the own institute. But the knowledge is not only transported by documentation: students of the institute learn about the software in their lectures and later on from their supervisor and fellow students or colleagues. In the best practice examples, the documentation is openly available.

Observation 2: Research software has a history

In engineering science, PhD candidates usually stay round about six years, become experts in a very specific field and then leave. Successors–interested in the same topic–often do not have the chance to talk to them. So the experts omit feedback on their documentation and are ignorant of which questions they have to address in their documentation.

Quality of research software documentation over time. The research software undergoes different phases, from first implementation to continuous application. During these phases, the quality of the documentation varies accordingly. In particular, the change of RSE involves risks, but can also lead to improvements.

Figure 2 describes the observed effect in Neweul-M 2 , showing the quality of the research software documentation over the different phases of research software development. The quality of the documentation behaves similarly to Kondratiev waves 31 : prosperity, recession, depression, and improvement. In the case of Neweul-M 2 one researcher developed the research software to solve a specific problem in the initiation phase. In the maturation phase, other researchers adopt the research software and more people get involved in the project. At the beginning, the documentation was good (enough) for the people who use the software (prosperity). Eventually, the initial RSD left, and new researchers added new features and modified the code. The quality of the documentation decreased (recession) in the saturation phase because modifications were not documented (depression); until a point was reached where the research software needed a refactoring. A new documentation is needed, usually written with a new tool (improvement). But the old documentation is still used because some aspects are important in there: different documentations exist for different roles in different places with different up-to-dateness. The descriptions of the implemented features in the articles referring to the research software before the refactoring are now more difficult or even impossible to reproduce. A new researcher inherits this history. Comparing this conceptual model with the other research software projects confirms the principle progression; the cycles are more or less pronounced depending on the dynamics of the research software and happen more or less rapidly. New RSEs do not necessarily contribute to the documentation quality. The funding received made it possible to solve many problems in the documentation, which were mainly pointed out by external RSDs and users. They both best practice examples benefit of being open source, receive more feedback from users outside the institute, and spend more effort and money to improve the documentation. So the effect of unconscious knowledge can be minimized.

Observation 2.1: Unconscious knowledge

In Neweul-M 2 new users and RSDs are inducted to the software with the help of an more experienced RSD or the RSE. The knowledge of experienced RSDs is often unconscious, that means that they are not aware of their own knowledge and therefore do not explain important steps to the new RSD 32 . The effect shows up for example in the description of the workflow. It is described in the help theoretically but not concretely how and where information is stored and called. Relevant information about what is written in which files are presupposed. Usually, the RSE or experienced RSDs compensates this divergence. For example, where and how storing files is explained in a lecture about Neweul-M 2 , but this information is totally missing in the documentation. In the best practice examples the problem is less pronounced, because users from outside ask questions and draw attention to the problem, and especially in preCICE there is a workflow to update the help, according to the asked questions. Both projects have improved user-friendliness of the documentation through third-party funding. The recommendations do not address this problem.

Observation 2.2: Missing consistency

In Neweul-M 2 the documentation lives in different places: An integrated MATLAB help, mainly intended for users; an internal Wiki with more information, mainly intended for RSDs; and an internal document for the RSE with storage locations, workflows and pieces of the history. Not all the documentation is updated with changes in the code. RSDs usually archive their documentation with their thesis in a zip-file. This kind of documentation often refers to an obsolete version, inherited from the history. Moreover, outdated dependencies, which are not documented, invalidate the function or a lot of effort must be spent to fix the dependencies. Looking at the best practice examples: one of the first issues was to have all in one place. Through feedback from external users, the best practice examples are more consistent. Moreover, they spend money and effort to meet these challenges. In DuMu x all the documentation is linked on the homepage, an overview where to find which information is missing. In preCICE the documentation is directly on the homepage, with an overview where to find what. Additionally, there is even meta information about the documentation itself. The recommendations give hints about documentation tools, which can be used–how to structure this information is mentioned in 30 .

Observation 3: Research software has different purposes

Researchers write research software for different purposes. Therefore, the focus of the documentation can differ as well. The purpose of Neweul-M 2 is to implement a physical model to evaluate the same effects that occur in or beyond experiments. Engineers use already developed algorithms to solve their research question using research software. Here, in addition to documentation for the users, documentation for the RSDs is also necessary, because the scientists at the institute need to understand the results of their predecessors and want to be able to adapt them to their own needs.

The purpose of DuMu x is similar. However, there is a larger community outside the own institute, which uses the software and develops it accordingly.

The purpose of preCICE is to implement new algorithms and to show that these algorithms work. In preCICE the role of RSDs is not specifically taken into account. Users do couple their software with the help of preCICE but do not contribute to the code with features. So the documentation is mainly intended and improved for scientific users (see Table 6 ).

Observation 3.1: Research Software Developers are neglected

RSDs are often neglected as authors and as audience. As authors of the documentation, the recommendations consider mainly RSEs; the audience are RSEs and users (see Table 7 ). The documentation requirements for RSDs are unclear. While in practice some requirements are given, the documentation reality often differs. In Neweul-M 2 the RSE has formalized the documentation of and for the RSDs through a given template. RSDs document directly in the code, which is automatically transferred to the help. The description often remains very short and are sometimes insufficient to be understood by others. No feedback from the successors is given about the quality of the documentation, because the Research Software Developers leave the institute and no longer notice possible problems (see Table 6 ).

In DuMu x RSDs have as well a guideline how and what they have to document. Experience shows that instead of following the guidelines, RSDs tend to keep the effort as small as possible and do not describe as expected, especially if the requirement is considered unnecessary (see Table 6 ).

The results undoubtedly show that research software is documented. We found out that a literature review could not answer RQ1. One hypothesis why we were not able to answer RQ1 from a literature review is Observation 1: The big picture is missing. Who documents what for whom in which domain and for what purpose. The primary hypothesis was that researchers document their software, but that this is not perceived as sufficiently documented. The data collected should provide information about who documents how and why the documentation is not sufficient. The already described rival hypotheses of lack of time and training seemed to be insufficient due to the existing documentation and software knowledge. Our study shows that not necessarily the motivation or missing skills lead to the opinion that software is not documented; rather, research software is not documented as expected.

Often the main problem is that documentation is seen as an event and not a process. Observation 2 shows that the RSEs do not necessarily contribute to documentation quality. Possible reasons for this are different perceptions among the RSEs about what good documentation is, and that old documentation is not discarded. However, funding can improve documentation because they can transform the event character of documentation into a process. The path dependency described in the results as well as the missing consistency and missing framework can be mitigated by setting uniform standards . This will always be a balancing act between freedom of research and predefined framework. However, the movement in given structures allows a more efficient work. Also, writing and documenting are not in itself the actual research work, but only the framework in which the research takes place. Researchers have other goals in writing and documenting code than professional software developers. Software developers aim to achieve the objectives defined in the product requirements: Specifying these requirements can be seen as a part of the documentation. Research software, on the other hand, is a means to an end and is not documented in product requirements. Researchers aim to answer research questions with the help of software 33 . Consequently, one part of the documentation happens in research articles, doctoral, master and bachelor theses. Those can be seen as delayed product requirements . Nevertheless, this kind of documentation is focused on the research question and not on the research software. Moreover, research papers discuss scientific results based on research software; but the research software behind the results is quickly outdated and developed further. Above all, the precise implementation of physic into code in the research software is not specified 34 , but particularly this point is essential for reusing the research software. Besides, articles document research results, not software. Sometimes the material is also not accessible or difficult to find . Certainly the lack of time to document is critical, but at a later stage much more time needs to be invested to support the users 30 and to understand the research software as a RSD. Some papers argue with the lack of training of researchers in software engineering 2 , 6 . But even in professional software development, documentation is neglected. Ludewig and Lichter 35 see two reasons for this neglection: Firstly, documentation is not necessarily learned even in software developer training and secondly, although it is said that documentation is important, other aspects usually have priority. Websites like “write the docs” 36 and The blog “I’d Rather be Writing” 37 gives advice for technical writers how to document code. Some approaches can certainly be adopted, though not everything can be transferred one-to-one to research software. Initiatives like “Better Scientific Software” 38 and the “Software Sustainability Institute” 39 draw attention to the problem and provide assistance. Although these sites are certainly helpful, you need to know them. They give only possible assistance and are not in themselves a standard. Nevertheless, a generally applicable standardization of documenting research software is difficult to find. Existing standards from software engineering 40 are complex, in parts not relevant for research software and difficult to access. Even if a standardization will be helpful to share results openly, it needs a clear guideline to document results for oneself and in a group. Therefore, all three examined research software have some forms of standardized templates, testing strategies and checklists provided by the RSE. However, compliance must also be checked, which in turn costs time. Especially, in engineering science several points add to the described difficulties:

use of other funding possibilities

existence of confidentiality reasons

fear of sabotaging the business model

modification of existing software, which is unclear how to document

But working together with industry demands good documented results–independent from publishing the software. Moreover, other RSDs need the documentation in order to understand the work from their predecessors. In Neweul-M 2 RSDs contribute to code and documentation. They have to understand the code, and they have to develop new features, which have to be documented. This part of the documentation can not be written but be controlled by a RSE. RSDs depend on the documentation of their predecessors and the existing structure. This experience happens as well in DuMu x . It can also be discussed whether some problems described could be avoided by making the software open source. There are good reasons, such as confidentiality obligations and also often a business model, that make these steps undesirable. Nevertheless, from our point of view, some investigated methods can be transferred to the shared domain.

All in all, it can be said that it should be clear who documents what and where. Hence, adopting best practices and principles from technical documentation and professional software development can help to improve the documentation of research software. Nevertheless, the study shows that all three case studies struggle with similar problems in the documentation and in part also decided on similar solution strategies, making transferability to other research software projects conceivable. Future research should explore how principles from the best practices examples can be transferred into the shared domain. A possible standardization of content would certainly be helpful here, but this cannot be solved by the individual scientist. The national and international initiatives certainly contribute to improving the situation here. One limitation of the current research is that the findings are not evaluated with more examples. This obstacle can be overcome in evaluating more software documentations. It can be expected that other research software in engineering science has similar problems. Moreover, there is a personal bias when trying to solve the problem with a given documentation. The experience showed that it was totally clear for the Research Software Engineer of the help where they can find the information and how the documentation is structured. But for inexperienced users, it is not obvious, they have to ask the Research Software Engineer. The effort to write documentation should be taken into account. Will the benefit exceed the effort that must be used for documentation? This can be an area for future research. As long as people are there who can help, it is just inefficient but not impossible to solve the given task without a sufficient documentation. But in the current discussion about FAIR, research software documentation plays an important role. The pay-off for Research Software Developers is may be marginal at the moment, but the importance is increasing. Good documentation pays off in the long run.

We discovered that researchers are often not aware for whom and why they document. A big picture what documentation for research software means is missing. The new approach in this paper is to define for what purpose and what appearance the documentation is intended and who has to document what for whom depending on the domain. The paper shows that even in recommendations, the objective of the documentation of research software is unclear. Until now the focus lies on Research Software Engineers and user, the researcher who develops features to an existing research software is here brought into focus. While essentially only the open domain has been considered so far, a substantial part of research software does not take place publicly in the first place; here, too, documentation is needed in order to ensure sustainable research.

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Acknowledgements

Funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SimTech). We also want to thank the Research Software Engineers of the three examined research software examples Bernd Flemisch, Georg Schneider and Benjamin Uekermann for their support and helpful inputs.

Open Access funding enabled and organized by Projekt DEAL.

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S.H. studied the research software documentation, developed the methodology, and wrote the article. J.F. contributed to the writing process through valuable discussion and feedback, as well as his own experience in documenting research software. All authors read and approved the final manuscript.

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Correspondence to Sibylle Hermann .

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Hermann, S., Fehr, J. Documenting research software in engineering science. Sci Rep 12 , 6567 (2022). https://doi.org/10.1038/s41598-022-10376-9

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Published : 21 April 2022

DOI : https://doi.org/10.1038/s41598-022-10376-9

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User & Design Research

Document research.

Document research method refers to the analysis of documents that contains information about the scenario or event under consideration. It is used to investigate, categorize and analyze physical sources, most commonly written documents, in the social, public or digital world. This research method is just as good as and sometimes even more cost effective than the surveys, in-depth interviews or other observation based methods such as ethnography.

Quick details: Document Research

Structure: Structured

Preparation: Documents to be researched

Deliverables: Research notes, Documentation, Insights

More about Document Research

A document is defined as written text. Documents can be files, statistical data, records of official or unofficial nature providing an account of an event, images, other written material that can be accessed in a social, public or digital context. For example, institutional memoranda and reports, census publications, government pronouncements and proceedings, diaries and other written, visual and pictorial sources in different forms and so on are socially, publically or digitally accessible either openly or on request.

Document research is not a standalone method; it is usually used in conjunction with other design methods. Document research is employed when the researcher has questions to which they seek answers. It is not as helpful in an open inquiry to determine patterns as the number of documents that would need to be investigated can be huge, turning the researcher’s task into a never-ending exercise. Therefore, This research method is often used only as a supplement to the other research methods.

Along with surveys and ethnography, document research is one of the three major types of social research and arguably has been the most widely used of the three, to study needs, behavior and expectations of user groups. The analysis of the documents in document research is either quantitative or qualitative analysis (or both). The key issues surrounding types of documents and our ability to use them as reliable sources of evidence on the user groups must be considered by all who use documents in their research.

Advantages of Document Research

1. availability.

Document research method uses documents that are public or can be accessed on request if private.

2. Time & Cost effective

As the phenomenon being investigated is clearly defined before the method is exercised, the research is focused and closed. This saves a lot of time for the researcher and also costs that would have been incurred if an expert was consulted for the study.

3. Unbiased Collection Process

As the collection process doesn’t involve direct interaction of the researcher with the user groups or author of the documents, the chances of introducing bias stays low. Again, if the document is of the statistical record type, then the data being collected is based on facts that can be verified and cross-checked for errors.

4. Researcher Presence

The researcher is not required to be present at the time of data collection.

Disadvantages of Document Research

1. limited by available data.

As the data or documents that are available on the phenomenon being investigated as the primary resources for the study, the findings will be based on only the data that is documented on the subject.

2. Errors in written material

If there are errors in the documents being referred, these errors will render erroneous findings as well .

3. Out of context

If the documents studied are out of context, they will not contribute meaning to the study or will not lead to an insightful findings.

4. Preparation before analysis

The preparation required before document analysis is performed is a task in itself. The efforts are usually directed toward recruiting the researchers, identifying sources, shortlisting the material to be researched and analyzed, among others.

Think Design's recommendation

More often than not, any design project starts with some kind of Document Research or the other. It is predominantly a secondary research method; however, the researcher/ designer is using the documented material for his own understanding of the context. 

Do not use document research as a stand-alone method and do not proceed with your design assuming that the documents you studied have provided you with all the answers. This method compliments other methods and is usually a good starting point.

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• Heatmap Analysis
• Social Network Mapping
• Time Lapse Video
• Trend Analysis
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Research Documentation

When publishing research , it is important to make documentation available so that readers can understand the details of the research design that the work reports. This includes all of the technical details and decisions that could influence how the findings are read or understood. Usually, this will involve producing a document along the lines of a methodological note or appendix. That document will describe how a given study was designed and how the design was carried out. The level of detail is in such a document should be relatively high. This page will describe some common approaches to compiling this kind of material and retaining the needed information in an organized fashion throughout the life of a research project.

• Research documentation provides the context to understanding the results of a given research output.
• There is no standard form for this documentation, and its location and format will depend on the type of research output produced.
• For academic materials, this documentation often takes the form of a structured methodological appendix.
• For policy outputs or online products, it may be appropriate to include an informative README webpage or document.
• The most important process for preparing this documentation will be retaining and organizing the needed information throughout the life of the project, so that the team will not have to search through communications or data archives for small details at publication time.

What to include in research documentation

Research documentation should include all the information that is needed to understand the underlying design for the research output. This can include descriptions of:

• Populations of interest that informed the study
• Methods of sampling or other sources of data about selecting the units of observation that were actually included in the study
• Power calculations and pre-analysis plans
• Field work, including data collection or experimental manipulation, such as study protocols and monitoring or quality assurance information
• Data collection tools such as survey instruments, search keywords, and instructions or code for API requests or database queries
• Statistical approaches such as definitions of key constructed indicators, corrections or adjustments to data, and precise definitions of estimators and estimation procedures
• Data completeness, including non-observed units or quantities that were planned or "tracking" information

All of the research documentation taken together should broadly allow a reader to understand how information was gathered, what it represents, what kind of information and data files to expect, and how to relate that information to the results of the research. Research documentation is not a complete guide to data, however; it does not need to provide the level of detail or instructions that would enable a reader to approach different research questions using the same data.

Documentation will take different forms depending on the information included. Much of it will be written narrative rather than, for example, formal datasets . Understanding research documentation should not require the user to have any special software or to undertake any analytical tasks themselves. Relevant datasets (such as tracking of units of observation over time) might be included alongside the documentation, but the documentation should summarize in narrative form all the information from that dataset that is likely to affect the interpretation of the research.

Structuring research documentation as a publication appendix

If you are preparing documentation to accompany the publication of an academic output such as a working paper or journal article, the most common form of research documentation is a structured supplemental appendix. Check the journal's publication process for details. Some publishers allow unlimited supplementary materials to be included in a format such as an author-created document. These materials may or may not be included under the peer review of the main manuscript and might only be intended to provide context for readers and reviewers. In this case you should provide complete information in that material. Other publishers expect all supplementary materials to be read and reviewed as part of the publication process. In this case you should provide the minimum additional detail required to understand the research here (since much of the appendix will likely be taken up by supplementary results rather than documentation), and consider other methods for releasing complete documentation, such as self-publication on OSF or Zenodo.

Since there is unlimited space and you may have a large amount of material to include in a documentation appendix, organization is essential. It is appropriate to have several appendices that cover different aspects of the research. For example, Appendix A may include information about the study population and data, such as the total number of units available for observation , the number selected or included for observation, the number successfully included, and descriptive statistics about subgroups, strata, clusters, or other units relevant to the research. It could be accompanied by a tracking dataset with full information about the process. Appendix B might include information about an intended experimental manipulation in one section, and information about implementation, take-up, and fidelity in a second section. It could be accompanied by a dataset with key indicators. Appendix C might include data collection protocols and definitions of constructed variables and comparisons with alternative definitions, and be accompanied by data collection instruments and illustrative figures. Each appendix should included relevant references. Supplementary exhibits should be numbered to correspond with the appendix they pertain to. More granular appendices are generally preferable so that referencing and numbering remains relatively uncomplicated.

There have been many attempts to standardized some of these elements, such as the STROBE and CONSORT reporting checklists . Journals will let you know if they expect these exact templates to be followed. Even if they are not required, such templates can still be used directly or to provide inspiration or structure for the materials you might want to include.