data analysis for descriptive qualitative research

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Chapter 5: Qualitative descriptive research

Darshini Ayton

Learning outcomes

Upon completion of this chapter, you should be able to:

Identify the key terms and concepts used in qualitative descriptive research.
Discuss the advantages and disadvantages of qualitative descriptive research.

What is a qualitative descriptive study?

The key concept of the qualitative descriptive study is description.

Qualitative descriptive studies (also known as ‘exploratory studies’ and ‘qualitative description approaches’) are relatively new in the qualitative research landscape. They emerged predominantly in the field of nursing and midwifery over the past two decades. 1 The design of qualitative descriptive studies evolved as a means to define aspects of qualitative research that did not resemble qualitative research designs to date, despite including elements of those other study designs. 2

Qualitative descriptive studies describe phenomena rather than explain them. Phenomenological studies, ethnographic studies and those using grounded theory seek to explain a phenomenon. Qualitative descriptive studies aim to provide a comprehensive summary of events. The approach to this study design is journalistic, with the aim being to answer the questions who, what, where and how. 3

A qualitative descriptive study is an important and appropriate design for research questions that are focused on gaining insights about a poorly understood research area, rather than on a specific phenomenon. Since qualitative descriptive study design seeks to describe rather than explain, explanatory frameworks and theories are not required to explain or ‘ground’ a study and its results. 4 The researcher may decide that a framework or theory adds value to their interpretations, and in that case, it is perfectly acceptable to use them. However, the hallmark of genuine curiosity (naturalistic enquiry) is that the researcher does not know in advance what they will be observing or describing. 4 Because a phenomenon is being described, the qualitative descriptive analysis is more categorical and less conceptual than other methods. Qualitative content analysis is usually the main approach to data analysis in qualitative descriptive studies. 4 This has led to criticism of descriptive research being less sophisticated because less interpretation is required than with other qualitative study designs in which interpretation and explanation are key characteristics (e.g. phenomenology, grounded theory, case studies).

Diverse approaches to data collection can be utilised in qualitative description studies. However, most qualitative descriptive studies use semi-structured interviews (see Chapter 13) because they provide a reliable way to collect data. 3 The technique applied to data analysis is generally categorical and less conceptual when compared to other qualitative research designs (see Section 4). 2,3 Hence, this study design is well suited to research by practitioners, student researchers and policymakers. Its straightforward approach enables these studies to be conducted in shorter timeframes than other study designs. 3 Descriptive studies are common as the qualitative component in mixed-methods research ( see Chapter 11 ) and evaluations ( see Chapter 12 ), 1 because qualitative descriptive studies can provide information to help develop and refine questionnaires or interventions.

For example, in our research to develop a patient-reported outcome measure for people who had undergone a percutaneous coronary intervention (PCI), which is a common cardiac procedure to treat heart disease, we started by conducting a qualitative descriptive study. 5 This project was a large, mixed-methods study funded by a private health insurer. The entire research process needed to be straightforward and achievable within a year, as we had engaged an undergraduate student to undertake the research tasks. The aim of the qualitative component of the mixed-methods study was to identify and explore patients’ perceptions following PCI. We used inductive approaches to collect and analyse the data. The study was guided by the following domains for the development of patient-reported outcomes, according to US Food and Drug Administration (FDA) guidelines, which included:

Feeling: How the patient feels physically and psychologically after medical intervention
Function: The patient’s mobility and ability to maintain their regular routine
Evaluation: The patient’s overall perception of the success or failure of their procedure and their perception of what contributed to it. 5(p458)

We conducted focus groups and interviews, and asked participants three questions related to the FDA outcome domains:

From your perspective, what would be considered a successful outcome of the procedure?

Probing questions: Did the procedure meet your expectations? How do you define whether the procedure was successful?

How did you feel after the procedure?

Probing question: How did you feel one week after and how does that compare with how you feel now?

After your procedure, tell me about your ability to do your daily activities?

Prompt for activities including gardening, housework, personal care, work-related and family-related tasks.

Probing questions: Did you attend cardiac rehabilitation? Can you tell us about your experience of cardiac rehabilitation? What impact has medication had on your recovery?

What, if any, lifestyle changes have you made since your procedure? 5(p459)

Data collection was conducted with 32 participants. The themes were mapped to the FDA patient-reported outcome domains, with the results confirming previous research and also highlighting new areas for exploration in the development of a new patient-reported outcome measure. For example, participants reported a lack of confidence following PCI and the importance of patient and doctor communication. Women, in particular, reported that they wanted doctors to recognise how their experiences of cardiac symptoms were different to those of men.

The study described phenomena and resulted in the development of a patient-reported outcome measure that was tested and refined using a discrete-choice experiment survey, 6 a pilot of the measure in the Victorian Cardiac Outcomes Registry and a Rasch analysis to validate the measurement’s properties. 7

Advantages and disadvantages of qualitative descriptive studies

A qualitative descriptive study is an effective design for research by practitioners, policymakers and students, due to their relatively short timeframes and low costs. The researchers can remain close to the data and the events described, and this can enable the process of analysis to be relatively simple. Qualitative descriptive studies are also useful in mixed-methods research studies. Some of the advantages of qualitative descriptive studies have led to criticism of the design approach, due to a lack of engagement with theory and the lack of interpretation and explanation of the data. 2

Table 5.1. Examples of qualitative descriptive studies

Qualitative descriptive studies are gaining popularity in health and social care due to their utility, from a resource and time perspective, for research by practitioners, policymakers and researchers. Descriptive studies can be conducted as stand-alone studies or as part of larger, mixed-methods studies.

Bradshaw C, Atkinson S, Doody O. Employing a qualitative description approach in health care research. Glob Qual Nurs Res. 2017;4. doi:10.1177/2333393617742282
Lambert VA, Lambert CE. Qualitative descriptive research: an acceptable design. Pac Rim Int J Nurs Res Thail. 2012;16(4):255-256. Accessed June 6, 2023. https://he02.tci-thaijo.org/index.php/PRIJNR/article/download/5805/5064
Doyle L et al. An overview of the qualitative descriptive design within nursing research. J Res Nurs. 2020;25(5):443-455. doi:10.1177/174498711988023
Kim H, Sefcik JS, Bradway C. Characteristics of qualitative descriptive studies: a systematic review. Res Nurs Health. 2017;40(1):23-42. doi:10.1002/nur.21768
Ayton DR et al. Exploring patient-reported outcomes following percutaneous coronary intervention: a qualitative study. Health Expect. 2018;21(2):457-465. doi:10.1111/hex.1263
Barker AL et al. Symptoms and feelings valued by patients after a percutaneous coronary intervention: a discrete-choice experiment to inform development of a new patient-reported outcome. BMJ Open. 2018;8:e023141. doi:10.1136/bmjopen-2018-023141
Soh SE et al. What matters most to patients following percutaneous coronary interventions? a new patient-reported outcome measure developed using Rasch analysis. PLoS One. 2019;14(9):e0222185. doi:10.1371/journal.pone.0222185
Hiller RM et al. Coping and support-seeking in out-of-home care: a qualitative study of the views of young people in care in England. BMJ Open. 2021;11:e038461. doi:10.1136/bmjopen-2020-038461
Backman C, Cho-Young D. Engaging patients and informal caregivers to improve safety and facilitate person- and family-centered care during transitions from hospital to home – a qualitative descriptive study. Patient Prefer Adherence. 2019;13:617-626. doi:10.2147/PPA.S201054

Qualitative Research – a practical guide for health and social care researchers and practitioners Copyright © 2023 by Darshini Ayton is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

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data analysis for descriptive qualitative research

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

Interview transcripts
Surveys with open-ended questions
Contact center transcripts
Texts and documents
Audio and video recordings
Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

Gathering and collecting your qualitative data
Organizing and connecting into your qualitative data
Coding your qualitative data
Analyzing the qualitative data for insights
Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues. It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope, helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered. Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis. Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them. It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations. The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews.

G2.com reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.

The benefits of using computer-assisted qualitative data analysis software:

Assists in the organizing of your data
Opens you up to exploring different interpretations of your data analysis
Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:

Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably. For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
Keep repeating step 2, adding new codes and revising the code description as often as necessary. Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy. Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

Demographic
And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

Calculate overall NPS
Calculate NPS in the subset of responses that do not contain that theme
Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs. This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable. Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding. Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places. And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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29 Qualitative Data Analysis Strategies

Johnny Saldaña, School of Theatre and Film, Arizona State University

Published: 02 September 2020
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This chapter provides an overview of selected qualitative data analysis strategies with a particular focus on codes and coding. Preparatory strategies for a qualitative research study and data management are first outlined. Six coding methods are then profiled using comparable interview data: process coding, in vivo coding, descriptive coding, values coding, dramaturgical coding, and versus coding. Strategies for constructing themes and assertions from the data follow. Analytic memo writing is woven throughout as a method for generating additional analytic insight. Next, display and arts-based strategies are provided, followed by recommended qualitative data analytic software programs and a discussion on verifying the researcher’s analytic findings.

Qualitative Data Analysis Strategies

Anthropologist Clifford Geertz ( 1983 ) charmingly mused, “Life is just a bowl of strategies” (p. 25). Strategy , as I use it here, refers to a carefully considered plan or method to achieve a particular goal. The goal in this case is to develop a write-up of your analytic work with the qualitative data you have been given and collected as part of a study. The plans and methods you might employ to achieve that goal are what this article profiles.

Some may perceive strategy as an inappropriate, if not manipulative, word, suggesting formulaic or regimented approaches to inquiry. I assure you that is not my intent. My use of strategy is dramaturgical in nature: Strategies are actions that characters in plays take to overcome obstacles to achieve their objectives. Actors portraying these characters rely on action verbs to generate belief within themselves and to motivate them as they interpret their lines and move appropriately on stage.

What I offer is a qualitative researcher’s array of actions from which to draw to overcome the obstacles to thinking to achieve an analysis of your data. But unlike the prescripted text of a play in which the obstacles, strategies, and outcomes have been predetermined by the playwright, your work must be improvisational—acting, reacting, and interacting with data on a moment-by-moment basis to determine what obstacles stand in your way and thus what strategies you should take to reach your goals.

Another intriguing quote to keep in mind comes from research methodologist Robert E. Stake ( 1995 ), who posited, “Good research is not about good methods as much as it is about good thinking” (p. 19). In other words, strategies can take you only so far. You can have a box full of tools, but if you do not know how to use them well or use them creatively, the collection seems rather purposeless. One of the best ways we learn is by doing . So, pick up one or more of these strategies (in the form of verbs) and take analytic action with your data. Also keep in mind that these are discussed in the order in which they may typically occur, although humans think cyclically, iteratively, and reverberatively, and each research project has its unique contexts and needs. Be prepared for your mind to jump purposefully and/or idiosyncratically from one strategy to another throughout the study.

Qualitative Data Analysis Strategy: To Foresee

To foresee in qualitative data analysis (QDA) is to reflect beforehand on what forms of data you will most likely need and collect, which thus informs what types of data analytic strategies you anticipate using. Analysis, in a way, begins even before you collect data (Saldaña & Omasta, 2018 ). As you design your research study in your mind and on a text editing page, one strategy is to consider what types of data you may need to help inform and answer your central and related research questions. Interview transcripts, participant observation field notes, documents, artifacts, photographs, video recordings, and so on are not only forms of data but also foundations for how you may plan to analyze them. A participant interview, for example, suggests that you will transcribe all or relevant portions of the recording and use both the transcription and the recording itself as sources for data analysis. Any analytic memos (discussed later) you make about your impressions of the interview also become data to analyze. Even the computing software you plan to employ will be relevant to data analysis because it may help or hinder your efforts.

As your research design formulates, compose one to two paragraphs that outline how your QDA may proceed. This will necessitate that you have some background knowledge of the vast array of methods available to you. Thus, surveying the literature is vital preparatory work.

Qualitative Data Analysis Strategy: To Survey

To survey in QDA is to look for and consider the applicability of the QDA literature in your field that may provide useful guidance for your forthcoming data analytic work. General sources in QDA will provide a good starting point for acquainting you with the data analysis strategies available for the variety of methodologies or genres in qualitative inquiry (e.g., ethnography, phenomenology, case study, arts-based research, mixed methods). One of the most accessible (and humorous) is Galman’s ( 2013 ) The Good, the Bad, and the Data , and one of the most richly detailed is Frederick J. Wertz et al.’s ( 2011 ) Five Ways of Doing Qualitative Analysis . The author’s core texts for this chapter come from The Coding Manual for Qualitative Researchers (Saldaña, 2016 ) and Qualitative Research: Analyzing Life (Saldaña & Omasta, 2018 ).

If your study’s methodology or approach is grounded theory, for example, then a survey of methods works by authors such as Barney G. Glaser, Anselm L. Strauss, Juliet Corbin, and, in particular, the prolific Kathy Charmaz ( 2014 ) may be expected. But there has been a recent outpouring of additional book publications in grounded theory by Birks and Mills ( 2015 ), Bryant ( 2017 ), Bryant and Charmaz ( 2019 ), and Stern and Porr ( 2011 ), plus the legacy of thousands of articles and chapters across many disciplines that have addressed grounded theory in their studies.

Fields such as education, psychology, social work, healthcare, and others also have their own QDA methods literature in the form of texts and journals, as well as international conferences and workshops for members of the profession. It is important to have had some university coursework and/or mentorship in qualitative research to suitably prepare you for the intricacies of QDA, and you must acknowledge that the emergent nature of qualitative inquiry may require you to adopt analysis strategies that differ from what you originally planned.

Qualitative Data Analysis Strategy: To Collect

To collect in QDA is to receive the data given to you by participants and those data you actively gather to inform your study. Qualitative data analysis is concurrent with data collection and management. As interviews are transcribed, field notes are fleshed out, and documents are filed, the researcher uses opportunities to carefully read the corpus and make preliminary notations directly on the data documents by highlighting, bolding, italicizing, or noting in some way any particularly interesting or salient portions. As these data are initially reviewed, the researcher also composes supplemental analytic memos that include first impressions, reminders for follow-up, preliminary connections, and other thinking matters about the phenomena at work.

Some of the most common fieldwork tools you might use to collect data are notepads, pens and pencils; file folders for hard-copy documents; a laptop, tablet, or desktop with text editing software (Microsoft Word and Excel are most useful) and Internet access; and a digital camera and voice recorder (functions available on many electronic devices such as smartphones). Some fieldworkers may even employ a digital video camera to record social action, as long as participant permissions have been secured. But everything originates from the researcher. Your senses are immersed in the cultural milieu you study, taking in and holding onto relevant details, or significant trivia , as I call them. You become a human camera, zooming out to capture the broad landscape of your field site one day and then zooming in on a particularly interesting individual or phenomenon the next. Your analysis is only as good as the data you collect.

Fieldwork can be an overwhelming experience because so many details of social life are happening in front of you. Take a holistic approach to your entrée, but as you become more familiar with the setting and participants, actively focus on things that relate to your research topic and questions. Keep yourself open to the intriguing, surprising, and disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115), because these facets enrich your study by making you aware of the unexpected.

Qualitative Data Analysis Strategy: To Feel

To feel in QDA is to gain deep emotional insight into the social worlds you study and what it means to be human. Virtually everything we do has an accompanying emotion(s), and feelings are both reactions and stimuli for action. Others’ emotions clue you to their motives, values, attitudes, beliefs, worldviews, identities, and other subjective perceptions and interpretations. Acknowledge that emotional detachment is not possible in field research. Attunement to the emotional experiences of your participants plus sympathetic and empathetic responses to the actions around you are necessary in qualitative endeavors. Your own emotional responses during fieldwork are also data because they document the tacit and visceral. It is important during such analytic reflection to assess why your emotional reactions were as they were. But it is equally important not to let emotions alone steer the course of your study. A proper balance must be found between feelings and facts.

Qualitative Data Analysis Strategy: To Organize

To organize in QDA is to maintain an orderly repository of data for easy access and analysis. Even in the smallest of qualitative studies, a large amount of data will be collected across time. Prepare both a hard drive and hard-copy folders for digital data and paperwork, and back up all materials for security from loss. I recommend that each data unit (e.g., one interview transcript, one document, one day’s worth of field notes) have its own file, with subfolders specifying the data forms and research study logistics (e.g., interviews, field notes, documents, institutional review board correspondence, calendar).

For small-scale qualitative studies, I have found it quite useful to maintain one large master file with all participant and field site data copied and combined with the literature review and accompanying researcher analytic memos. This master file is used to cut and paste related passages together, deleting what seems unnecessary as the study proceeds and eventually transforming the document into the final report itself. Cosmetic devices such as font style, font size, rich text (italicizing, bolding, underlining, etc.), and color can help you distinguish between different data forms and highlight significant passages. For example, descriptive, narrative passages of field notes are logged in regular font. “Quotations, things spoken by participants, are logged in bold font.” Observer’s comments, such as the researcher’s subjective impressions or analytic jottings, are set in italics.

Qualitative Data Analysis Strategy: To Jot

To jot in QDA is to write occasional, brief notes about your thinking or reminders for follow-up. A jot is a phrase or brief sentence that will fit on a standard-size sticky note. As data are brought and documented together, take some initial time to review their contents and jot some notes about preliminary patterns, participant quotes that seem vivid, anomalies in the data, and so forth.

As you work on a project, keep something to write with or to voice record with you at all times to capture your fleeting thoughts. You will most likely find yourself thinking about your research when you are not working exclusively on the project, and a “mental jot” may occur to you as you ruminate on logistical or analytic matters. Document the thought in some way for later retrieval and elaboration as an analytic memo.

Qualitative Data Analysis Strategy: To Prioritize

To prioritize in QDA is to determine which data are most significant in your corpus and which tasks are most necessary. During fieldwork, massive amounts of data in various forms may be collected, and your mind can be easily overwhelmed by the magnitude of the quantity, its richness, and its management. Decisions will need to be made about the most pertinent data because they help answer your research questions or emerge as salient pieces of evidence. As a sweeping generalization, approximately one half to two thirds of what you collect may become unnecessary as you proceed toward the more formal stages of QDA.

To prioritize in QDA is also to determine what matters most in your assembly of codes, categories, patterns, themes, assertions, propositions, and concepts. Return to your research purpose and questions to keep you framed for what the focus should be.

Qualitative Data Analysis Strategy: To Analyze

To analyze in QDA is to observe and discern patterns within data and to construct meanings that seem to capture their essences and essentials. Just as there are a variety of genres, elements, and styles of qualitative research, so too are there a variety of methods available for QDA. Analytic choices are most often based on what methods will harmonize with your genre selection and conceptual framework, what will generate the most sufficient answers to your research questions, and what will best represent and present the project’s findings.

Analysis can range from the factual to the conceptual to the interpretive. Analysis can also range from a straightforward descriptive account to an emergently constructed grounded theory to an evocatively composed short story. A qualitative research project’s outcomes may range from rigorously achieved, insightful answers to open-ended, evocative questions; from rich descriptive detail to a bullet-point list of themes; and from third-person, objective reportage to first-person, emotion-laden poetry. Just as there are multiple destinations in qualitative research, there are multiple pathways and journeys along the way.

Analysis is accelerated as you take cognitive ownership of your data. By reading and rereading the corpus, you gain intimate familiarity with its contents and begin to notice significant details as well as make new connections and insights about their meanings. Patterns, categories, themes, and their interrelationships become more evident the more you know the subtleties of the database.

Since qualitative research’s design, fieldwork, and data collection are most often provisional, emergent, and evolutionary processes, you reflect on and analyze the data as you gather them and proceed through the project. If preplanned methods are not working, you change them to secure the data you need. There is generally a postfieldwork period when continued reflection and more systematic data analysis occur, concurrent with or followed by additional data collection, if needed, and the more formal write-up of the study, which is in itself an analytic act. Through field note writing, interview transcribing, analytic memo writing, and other documentation processes, you gain cognitive ownership of your data; and the intuitive, tacit, synthesizing capabilities of your brain begin sensing patterns, making connections, and seeing the bigger picture. The purpose and outcome of data analysis is to reveal to others through fresh insights what we have observed and discovered about the human condition. Fortunately, there are heuristics for reorganizing and reflecting on your qualitative data to help you achieve that goal.

Qualitative Data Analysis Strategy: To Pattern

To pattern in QDA is to detect similarities within and regularities among the data you have collected. The natural world is filled with patterns because we, as humans, have constructed them as such. Stars in the night sky are not just a random assembly; our ancestors pieced them together to form constellations like the Big Dipper. A collection of flowers growing wild in a field has a pattern, as does an individual flower’s patterns of leaves and petals. Look at the physical objects humans have created and notice how pattern oriented we are in our construction, organization, and decoration. Look around you in your environment and notice how many patterns are evident on your clothing, in a room, and on most objects themselves. Even our sometimes mundane daily and long-term human actions are reproduced patterns in the form of routines, rituals, rules, roles, and relationships (Saldaña & Omasta, 2018 ).

This human propensity for pattern-making follows us into QDA. From the vast array of interview transcripts, field notes, documents, and other forms of data, there is this instinctive, hardwired need to bring order to the collection—not just to reorganize it but to look for and construct patterns out of it. The discernment of patterns is one of the first steps in the data analytic process, and the methods described next are recommended ways to construct them.

Qualitative Data Analysis Strategy: To Code

To code in QDA is to assign a truncated, symbolic meaning to each datum for purposes of qualitative analysis—primarily patterning and categorizing. Coding is a heuristic—a method of discovery—to the meanings of individual sections of data. These codes function as a way of patterning, classifying, and later reorganizing them into emergent categories for further analysis. Different types of codes exist for different types of research genres and qualitative data analytic approaches, but this chapter will focus on only a few selected methods. First, a code can be defined as follows:

A code in qualitative data analysis is most often a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data. The data can consist of interview transcripts, participant observation field notes, journals, documents, open-ended survey responses, drawings, artifacts, photographs, video, Internet sites, e-mail correspondence, academic and fictional literature, and so on. The portion of data coded … can range in magnitude from a single word to a full paragraph, an entire page of text or a stream of moving images.… Just as a title represents and captures a book or film or poem’s primary content and essence, so does a code represent and capture a datum’s primary content and essence. (Saldaña, 2016 , p. 4)

One helpful precoding task is to divide or parse long selections of field note or interview transcript data into shorter stanzas . Stanza division unitizes or “chunks” the corpus into more manageable paragraph-like units for coding assignments and analysis. The transcript sample that follows illustrates one possible way of inserting line breaks between self-standing passages of interview text for easier readability.

Process Coding

As a first coding example, the following interview excerpt about an employed, single, lower middle-class adult male’s spending habits during a difficult economic period in the United States is coded in the right-hand margin in capital letters. The superscript numbers match the beginning of the datum unit with its corresponding code. This method is called process coding (Charmaz, 2014 ), and it uses gerunds (“-ing” words) exclusively to represent action suggested by the data. Processes can consist of observable human actions (e.g., BUYING BARGAINS), mental or internal processes (e.g., THINKING TWICE), and more conceptual ideas (e.g., APPRECIATING WHAT YOU’VE GOT). Notice that the interviewer’s (I) portions are not coded, just the participant’s (P). A code is applied each time the subtopic of the interview shifts—even within a stanza—and the same codes can (and should) be used more than once if the subtopics are similar. The central research question driving this qualitative study is, “In what ways are middle-class Americans influenced and affected by an economic recession?”

Different researchers analyzing this same piece of data may develop completely different codes, depending on their personal lenses, filters, and angles. The previous codes are only one person’s interpretation of what is happening in the data, not a definitive list. The process codes have transformed the raw data units into new symbolic representations for analysis. A listing of the codes applied to this interview transcript, in the order they appear, reads:

BUYING BARGAINS

QUESTIONING A PURCHASE

THINKING TWICE

STOCKING UP

REFUSING SACRIFICE

PRIORITIZING

FINDING ALTERNATIVES

LIVING CHEAPLY

NOTICING CHANGES

STAYING INFORMED

MAINTAINING HEALTH

PICKING UP THE TAB

APPRECIATING WHAT YOU’VE GOT

Coding the data is the first step in this approach to QDA, and categorization is just one of the next possible steps.

Qualitative Data Analysis Strategy: To Categorize

To categorize in QDA is to cluster similar or comparable codes into groups for pattern construction and further analysis. Humans categorize things in innumerable ways. Think of an average apartment or house’s layout. The rooms of a dwelling have been constructed or categorized by their builders and occupants according to function. A kitchen is designated as an area to store and prepare food and to store the cooking and dining materials, such as pots, pans, and utensils. A bedroom is designated for sleeping, a closet for clothing storage, a bathroom for bodily functions and hygiene, and so on. Each room is like a category in which related and relevant patterns of human action occur. There are exceptions now and then, such as eating breakfast in bed rather than in a dining area or living in a small studio apartment in which most possessions are contained within one large room (but nonetheless are most often organized and clustered into subcategories according to function and optimal use of space).

The point is that the patterns of social action we designate into categories during QDA are not perfectly bounded. Category construction is our best attempt to cluster the most seemingly alike things into the most seemingly appropriate groups. Categorizing is reorganizing and reordering the vast array of data from a study because it is from these smaller, larger, and meaning-rich units that we can better grasp the particular features of each one and the categories’ possible interrelationships with one another.

One analytic strategy with a list of codes is to classify them into similar clusters. The same codes share the same category, but it is also possible that a single code can merit its own group if you feel it is unique enough. After the codes have been classified, a category label is applied to each grouping. Sometimes a code can also double as a category name if you feel it best summarizes the totality of the cluster. Like coding, categorizing is an interpretive act, because there can be different ways of separating and collecting codes that seem to belong together. The cut-and-paste functions of text editing software are most useful for exploring which codes share something in common.

Below is my categorization of the 15 codes generated from the interview transcript presented earlier. Like the gerunds for process codes, the categories have also been labeled as “-ing” words to connote action. And there was no particular reason why 15 codes resulted in three categories—there could have been less or even more, but this is how the array came together after my reflections on which codes seemed to belong together. The category labels are ways of answering why they belong together. For at-a-glance differentiation, I place codes in CAPITAL LETTERS and categories in upper- and lowercase Bold Font :

Category 1: Thinking Strategically

Category 2: Spending Strategically

Category 3: Living Strategically

Notice that the three category labels share a common word: strategically . Where did this word come from? It came from analytic reflection on the original data, the codes, and the process of categorizing the codes and generating their category labels. It was the analyst’s choice based on the interpretation of what primary action was happening. Your categories generated from your coded data do not need to share a common word or phrase, but I find that this technique, when appropriate, helps build a sense of unity to the initial analytic scheme.

The three categories— Thinking Strategically, Spending Strategically , and Living Strategically —are then reflected on for how they might interact and interplay. This is where the next major facet of data analysis, analytic memos, enters the scheme. But a necessary section on the basic principles of interrelationship and analytic reasoning must precede that discussion.

Qualitative Data Analysis Strategy: To Interrelate

To interrelate in QDA is to propose connections within, between, and among the constituent elements of analyzed data. One task of QDA is to explore the ways our patterns and categories interact and interplay. I use these terms to suggest the qualitative equivalent of statistical correlation, but interaction and interplay are much more than a simple relationship. They imply interrelationship . Interaction refers to reverberative connections—for example, how one or more categories might influence and affect the others, how categories operate concurrently, or whether there is some kind of domino effect to them. Interplay refers to the structural and processual nature of categories—for example, whether some type of sequential order, hierarchy, or taxonomy exists; whether any overlaps occur; whether there is superordinate and subordinate arrangement; and what types of organizational frameworks or networks might exist among them. The positivist construct of cause and effect becomes influences and affects in QDA.

There can even be patterns of patterns and categories of categories if your mind thinks conceptually and abstractly enough. Our minds can intricately connect multiple phenomena, but only if the data and their analyses support the constructions. We can speculate about interaction and interplay all we want, but it is only through a more systematic investigation of the data—in other words, good thinking—that we can plausibly establish any possible interrelationships.

Qualitative Data Analysis Strategy: To Reason

To reason in QDA is to think in ways that lead to summative findings, causal probabilities, and evaluative conclusions. Unlike quantitative research, with its statistical formulas and established hypothesis-testing protocols, qualitative research has no standardized methods of data analysis. Rest assured, there are recommended guidelines from the field’s scholars and a legacy of analysis strategies from which to draw. But the primary heuristics (or methods of discovery) you apply during a study are retroductive, inductive, substructive, abductive , and deductive reasoning.

Retroduction is historic reconstruction, working backward to figure out how the current conditions came to exist. Induction is what we experientially explore and infer to be transferable from the particular to the general, based on an examination of the evidence and an accumulation of knowledge. Substruction takes things apart to more carefully examine the constituent elements of the whole. Abduction is surmising from a range of possibilities that which is most likely, those explanatory hunches of plausibility based on clues. Deduction is what we generally draw and conclude from established facts and evidence.

It is not always necessary to know the names of these five ways of reasoning as you proceed through analysis. In fact, you will more than likely reverberate quickly from one to another depending on the task at hand. But what is important to remember about reasoning is:

to examine the evidence carefully and make reasonable inferences;

to base your conclusions primarily on the participants’ experiences, not just your own;

not to take the obvious for granted, because sometimes the expected will not happen;

your hunches can be right and, at other times, quite wrong; and

to logically yet imaginatively think about what is going on and how it all comes together.

Futurists and inventors propose three questions when they think about creating new visions for the world: What is possible (induction)? What is plausible (abduction)? What is preferable (deduction)? These same three questions might be posed as you proceed through QDA and particularly through analytic memo writing, which is substructive and retroductive reflection on your analytic work thus far.

Qualitative Data Analysis Strategy: To Memo

To memo in QDA is to reflect in writing on the nuances, inferences, meanings, and transfer of coded and categorized data plus your analytic processes. Like field note writing, perspectives vary among practitioners as to the methods for documenting the researcher’s analytic insights and subjective experiences. Some advise that such reflections should be included in field notes as relevant to the data. Others advise that a separate researcher’s journal should be maintained for recording these impressions. And still others advise that these thoughts be documented as separate analytic memos. I prescribe the latter as a method because it is generated by and directly connected to the data themselves.

An analytic memo is a “think piece” of reflective free writing, a narrative that sets in words your interpretations of the data. Coding and categorizing are heuristics to detect some of the possible patterns and interrelationships at work within the corpus, and an analytic memo further articulates your retroductive, inductive, substructive, abductive, and deductive thinking processes on what things may mean. Though the metaphor is a bit flawed and limiting, think of codes and their consequent categories as separate jigsaw puzzle pieces and their integration into an analytic memo as the trial assembly of the complete picture.

What follows is an example of an analytic memo based on the earlier process coded and categorized interview transcript. It is intended not as the final write-up for a publication, but as an open-ended reflection on the phenomena and processes suggested by the data and their analysis thus far. As the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the final report. Note how the memo is dated and given a title for future and further categorization, how participant quotes are occasionally included for evidentiary support, and how the category names are bolded and the codes kept in capital letters to show how they integrate or weave into the thinking:

April 14, 2017 EMERGENT CATEGORIES: A STRATEGIC AMALGAM There’s a popular saying: “Smart is the new rich.” This participant is Thinking Strategically about his spending through such tactics as THINKING TWICE and QUESTIONING A PURCHASE before he decides to invest in a product. There’s a heightened awareness of both immediate trends and forthcoming economic bad news that positively affects his Spending Strategically . However, he seems unaware that there are even more ways of LIVING CHEAPLY by FINDING ALTERNATIVES. He dines at all-you-can-eat restaurants as a way of STOCKING UP on meals, but doesn’t state that he could bring lunch from home to work, possibly saving even more money. One of his “bad habits” is cigarettes, which he refuses to give up; but he doesn’t seem to realize that by quitting smoking he could save even more money, not to mention possible health care costs. He balks at the idea of paying $2.00 for a soft drink, but doesn’t mind paying $6.00–$7.00 for a pack of cigarettes. Penny-wise and pound-foolish. Addictions skew priorities. Living Strategically , for this participant during “scary times,” appears to be a combination of PRIORITIZING those things which cannot be helped, such as pet care and personal dental care; REFUSING SACRIFICE for maintaining personal creature-comforts; and FINDING ALTERNATIVES to high costs and excessive spending. Living Strategically is an amalgam of thinking and action-oriented strategies.

There are several recommended topics for analytic memo writing throughout the qualitative study. Memos are opportunities to reflect on and write about:

A descriptive summary of the data;

How the researcher personally relates to the participants and/or the phenomenon;

The participants’ actions, reactions, and interactions;

The participants’ routines, rituals, rules, roles, and relationships;

What is surprising, intriguing, or disturbing (Sunstein & Chiseri-Strater, 2012 , p. 115);

Code choices and their operational definitions;

Emergent patterns, categories, themes, concepts, assertions, and propositions;

The possible networks and processes (links, connections, overlaps, flows) among the codes, patterns, categories, themes, concepts, assertions, and propositions;

An emergent or related existent theory;

Any problems with the study;

Any personal or ethical dilemmas with the study;

Future directions for the study;

The analytic memos generated thus far (i.e., metamemos);

Tentative answers to the study’s research questions; and

The final report for the study. (adapted from Saldaña & Omasta, 2018 , p. 54)

Since writing is analysis, analytic memos expand on the inferential meanings of the truncated codes, categories, and patterns as a transitional stage into a more coherent narrative with hopefully rich social insight.

Qualitative Data Analysis Strategy: To Code—A Different Way

The first example of coding illustrated process coding, a way of exploring general social action among humans. But sometimes a researcher works with an individual case study in which the language is unique or with someone the researcher wishes to honor by maintaining the authenticity of his or her speech in the analysis. These reasons suggest that a more participant-centered form of coding may be more appropriate.

In Vivo Coding

A second frequently applied method of coding is called in vivo coding. The root meaning of in vivo is “in that which is alive”; it refers to a code based on the actual language used by the participant (Strauss, 1987 ). The words or phrases in the data record you select as codes are those that seem to stand out as significant or summative of what is being said.

Using the same transcript of the male participant living in difficult economic times, in vivo codes are listed in the right-hand column. I recommend that in vivo codes be placed in quotation marks as a way of designating that the code is extracted directly from the data record. Note that instead of 15 codes generated from process coding, the total number of in vivo codes is 30. This is not to suggest that there should be specific numbers or ranges of codes used for particular methods. In vivo codes, however, tend to be applied more frequently to data. Again, the interviewer’s questions and prompts are not coded, just the participant’s responses:

The 30 in vivo codes are then extracted from the transcript and could be listed in the order they appear, but this time they are placed in alphabetical order as a heuristic to prepare them for analytic action and reflection:

“ALL-YOU-CAN-EAT”

“ANOTHER DING IN MY WALLET”

“BAD HABITS”

“CHEAP AND FILLING”

“COUPLE OF THOUSAND”

“DON’T REALLY NEED”

“HAVEN’T CHANGED MY HABITS”

“HIGH MAINTENANCE”

“INSURANCE IS JUST WORTHLESS”

“IT ALL ADDS UP”

“LIVED KIND OF CHEAP”

“NOT A BIG SPENDER”

“NOT AS BAD OFF”

“NOT PUTTING AS MUCH INTO SAVINGS”

“PICK UP THE TAB”

“SCARY TIMES”

“SKYROCKETED”

“SPENDING MORE”

“THE LITTLE THINGS”

“THINK TWICE”

“TWO-FOR-ONE”

Even though no systematic categorization has been conducted with the codes thus far, an analytic memo of first impressions can still be composed:

March 19, 2017 CODE CHOICES: THE EVERYDAY LANGUAGE OF ECONOMICS After eyeballing the in vivo codes list, I noticed that variants of “CHEAP” appear most often. I recall a running joke between me and a friend of mine when we were shopping for sales. We’d say, “We’re not ‘cheap,’ we’re frugal .” There’s no formal economic or business language in this transcript—no terms such as “recession” or “downsizing”—just the everyday language of one person trying to cope during “SCARY TIMES” with “ANOTHER DING IN MY WALLET.” The participant notes that he’s always “LIVED KIND OF CHEAP” and is “NOT A BIG SPENDER” and, due to his employment, “NOT AS BAD OFF” as others in the country. Yet even with his middle class status, he’s still feeling the monetary pinch, dining at inexpensive “ALL-YOU-CAN-EAT” restaurants and worried about the rising price of peanut butter, observing that he’s “NOT PUTTING AS MUCH INTO SAVINGS” as he used to. Of all the codes, “ANOTHER DING IN MY WALLET” stands out to me, particularly because on the audio recording he sounded bitter and frustrated. It seems that he’s so concerned about “THE LITTLE THINGS” because of high veterinary and dental charges. The only way to cope with a “COUPLE OF THOUSAND” dollars worth of medical expenses is to find ways of trimming the excess in everyday facets of living: “IT ALL ADDS UP.”

Like process coding, in vivo codes could be clustered into similar categories, but another simple data analytic strategy is also possible.

Qualitative Data Analysis Strategy: To Outline

To outline in QDA is to hierarchically, processually, and/or temporally assemble such things as codes, categories, themes, assertions, propositions, and concepts into a coherent, text-based display. Traditional outlining formats and content provide not only templates for writing a report but also templates for analytic organization. This principle can be found in several computer-assisted qualitative data analysis software (CAQDAS) programs through their use of such functions as “hierarchies,” “trees,” and “nodes,” for example. Basic outlining is simply a way of arranging primary, secondary, and subsecondary items into a patterned display. For example, an organized listing of things in a home might consist of the following:

Large appliances

Refrigerator

Stove-top oven

Microwave oven

Small appliances

Coffee maker

Dining room

In QDA, outlining may include descriptive nouns or topics but, depending on the study, it may also involve processes or phenomena in extended passages, such as in vivo codes or themes.

The complexity of what we learn in the field can be overwhelming, and outlining is a way of organizing and ordering that complexity so that it does not become complicated. The cut-and-paste and tab functions of a text editing page enable you to arrange and rearrange the salient items from your preliminary coded analytic work into a more streamlined flow. By no means do I suggest that the intricate messiness of life can always be organized into neatly formatted arrangements, but outlining is an analytic act that stimulates deep reflection on both the interconnectedness and the interrelationships of what we study. As an example, here are the 30 in vivo codes generated from the initial transcript analysis, arranged in such a way as to construct five major categories:

Now that the codes have been rearranged into an outline format, an analytic memo is composed to expand on the rationale and constructed meanings in progress:

March 19, 2017 NETWORKS: EMERGENT CATEGORIES The five major categories I constructed from the in vivo codes are: “SCARY TIMES,” “PRIORTY,” “ANOTHER DING IN MY WALLET,” “THE LITTLE THINGS,” and “LIVED KIND OF CHEAP.” One of the things that hit me today was that the reason he may be pinching pennies on smaller purchases is that he cannot control the larger ones he has to deal with. Perhaps the only way we can cope with or seem to have some sense of agency over major expenses is to cut back on the smaller ones that we can control. $1,000 for a dental bill? Skip lunch for a few days a week. Insulin medication to buy for a pet? Don’t buy a soft drink from a vending machine. Using this reasoning, let me try to interrelate and weave the categories together as they relate to this particular participant: During these scary economic times, he prioritizes his spending because there seems to be just one ding after another to his wallet. A general lifestyle of living cheaply and keeping an eye out for how to save money on the little things compensates for those major expenses beyond his control.

Qualitative Data Analysis Strategy: To Code—In Even More Ways

The process and in vivo coding examples thus far have demonstrated only two specific methods of 33 documented approaches (Saldaña, 2016 ). Which one(s) you choose for your analysis depends on such factors as your conceptual framework, the genre of qualitative research for your project, the types of data you collect, and so on. The following sections present four additional approaches available for coding qualitative data that you may find useful as starting points.

Descriptive Coding

Descriptive codes are primarily nouns that simply summarize the topic of a datum. This coding approach is particularly useful when you have different types of data gathered for one study, such as interview transcripts, field notes, open-ended survey responses, documents, and visual materials such as photographs. Descriptive codes not only help categorize but also index the data corpus’s basic contents for further analytic work. An example of an interview portion coded descriptively, taken from the participant living in tough economic times, follows to illustrate how the same data can be coded in multiple ways:

For initial analysis, descriptive codes are clustered into similar categories to detect such patterns as frequency (i.e., categories with the largest number of codes) and interrelationship (i.e., categories that seem to connect in some way). Keep in mind that descriptive coding should be used sparingly with interview transcript data because other coding methods will reveal richer participant dynamics.

Values Coding

Values coding identifies the values, attitudes, and beliefs of a participant, as shared by the individual and/or interpreted by the analyst. This coding method infers the “heart and mind” of an individual or group’s worldview as to what is important, perceived as true, maintained as opinion, and felt strongly. The three constructs are coded separately but are part of a complex interconnected system.

Briefly, a value (V) is what we attribute as important, be it a person, thing, or idea. An attitude (A) is the evaluative way we think and feel about ourselves, others, things, or ideas. A belief (B) is what we think and feel as true or necessary, formed from our “personal knowledge, experiences, opinions, prejudices, morals, and other interpretive perceptions of the social world” (Saldaña, 2016 , p. 132). Values coding explores intrapersonal, interpersonal, and cultural constructs, or ethos . It is an admittedly slippery task to code this way because it is sometimes difficult to discern what is a value, attitude, or belief since they are intricately interrelated. But the depth you can potentially obtain is rich. An example of values coding follows:

For analysis, categorize the codes for each of the three different constructs together (i.e., all values in one group, attitudes in a second group, and beliefs in a third group). Analytic memo writing about the patterns and possible interrelationships may reveal a more detailed and intricate worldview of the participant.

Dramaturgical Coding

Dramaturgical coding perceives life as performance and its participants as characters in a social drama. Codes are assigned to the data (i.e., a “play script”) that analyze the characters in action, reaction, and interaction. Dramaturgical coding of participants examines their objectives (OBJ) or wants, needs, and motives; the conflicts (CON) or obstacles they face as they try to achieve their objectives; the tactics (TAC) or strategies they employ to reach their objectives; their attitudes (ATT) toward others and their given circumstances; the particular emotions (EMO) they experience throughout; and their subtexts (SUB), or underlying and unspoken thoughts. The following is an example of dramaturgically coded data:

Not included in this particular interview excerpt are the emotions the participant may have experienced or talked about. His later line, “that’s another ding in my wallet,” would have been coded EMO: BITTER. A reader may not have inferred that specific emotion from seeing the line in print. But the interviewer, present during the event and listening carefully to the audio recording during transcription, noted that feeling in his tone of voice.

For analysis, group similar codes together (e.g., all objectives in one group, all conflicts in another group, all tactics in a third group) or string together chains of how participants deal with their circumstances to overcome their obstacles through tactics:

OBJ: SAVING MEAL MONEY → TAC: SKIPPING MEALS + COUPONS

Dramaturgical coding is particularly useful as preliminary work for narrative inquiry story development or arts-based research representations such as performance ethnography. The method explores how the individuals or groups manage problem solving in their daily lives.

Versus Coding

Versus (VS) coding identifies the conflicts, struggles, and power issues observed in social action, reaction, and interaction as an X VS Y code, such as MEN VS WOMEN, CONSERVATIVES VS LIBERALS, FAITH VS LOGIC, and so on. Conflicts are rarely this dichotomous; they are typically nuanced and much more complex. But humans tend to perceive these struggles with an US VS THEM mindset. The codes can range from the observable to the conceptual and can be applied to data that show humans in tension with others, themselves, or ideologies.

What follows are examples of versus codes applied to the case study participant’s descriptions of his major medical expenses:

As an initial analytic tactic, group the versus codes into one of three categories: the Stakeholders , their Perceptions and/or Actions , and the Issues at stake. Examine how the three interrelate and identify the central ideological conflict at work as an X VS Y category. Analytic memos and the final write-up can detail the nuances of the issues.

Remember that what has been profiled in this section is a broad brushstroke description of just a few basic coding processes, several of which can be compatibly mixed and matched within a single analysis (see Saldaña’s 2016 The Coding Manual for Qualitative Researchers for a complete discussion). Certainly with additional data, more in-depth analysis can occur, but coding is only one approach to extracting and constructing preliminary meanings from the data corpus. What now follows are additional methods for qualitative analysis.

Qualitative Data Analysis Strategy: To Theme

To theme in QDA is to construct summative, phenomenological meanings from data through extended passages of text. Unlike codes, which are most often single words or short phrases that symbolically represent a datum, themes are extended phrases or sentences that summarize the manifest (apparent) and latent (underlying) meanings of data (Auerbach & Silverstein, 2003 ; Boyatzis, 1998 ). Themes, intended to represent the essences and essentials of humans’ lived experiences, can also be categorized or listed in superordinate and subordinate outline formats as an analytic tactic.

Below is the interview transcript example used in the previous coding sections. (Hopefully you are not too fatigued at this point with the transcript, but it is important to know how inquiry with the same data set can be approached in several different ways.) During the investigation of the ways middle-class Americans are influenced and affected by an economic recession, the researcher noticed that participants’ stories exhibited facets of what he labeled economic intelligence , or EI (based on the formerly developed theories of Howard Gardner’s multiple intelligences and Daniel Goleman’s emotional intelligence). Notice how theming interprets what is happening through the use of two distinct phrases—ECONOMIC INTELLIGENCE IS (i.e., manifest or apparent meanings) and ECONOMIC INTELLIGENCE MEANS (i.e., latent or underlying meanings):

Unlike the 15 process codes and 30 in vivo codes in the previous examples, there are now 14 themes to work with. They could be listed in the order they appear, but one initial heuristic is to group them separately by “is” and “means” statements to detect any possible patterns (discussed later):

EI IS TAKING ADVANTAGE OF UNEXPECTED OPPORTUNITY

EI IS BUYING CHEAP

EI IS SAVING A FEW DOLLARS NOW AND THEN

EI IS SETTING PRIORITIES

EI IS FINDING CHEAPER FORMS OF ENTERTAINMENT

EI IS NOTICING PERSONAL AND NATIONAL ECONOMIC TRENDS

EI IS TAKING CARE OF ONE’S OWN HEALTH

EI MEANS THINKING BEFORE YOU ACT

EI MEANS SACRIFICE

EI MEANS KNOWING YOUR FLAWS

EI MEANS LIVING AN INEXPENSIVE LIFESTYLE

EI MEANS YOU CANNOT CONTROL EVERYTHING

EI MEANS KNOWING YOUR LUCK

There are several ways to categorize the themes as preparation for analytic memo writing. The first is to arrange them in outline format with superordinate and subordinate levels, based on how the themes seem to take organizational shape and structure. Simply cutting and pasting the themes in multiple arrangements on a text editing page eventually develops a sense of order to them. For example:

A second approach is to categorize the themes into similar clusters and to develop different category labels or theoretical constructs . A theoretical construct is an abstraction that transforms the central phenomenon’s themes into broader applications but can still use “is” and “means” as prompts to capture the bigger picture at work:

Theoretical Construct 1: EI Means Knowing the Unfortunate Present

Supporting Themes:

Theoretical Construct 2: EI Is Cultivating a Small Fortune

Theoretical Construct 3: EI Means a Fortunate Future

What follows is an analytic memo generated from the cut-and-paste arrangement of themes into “is” and “means” statements, into an outline, and into theoretical constructs:

March 19, 2017 EMERGENT THEMES: FORTUNE/FORTUNATELY/UNFORTUNATELY I first reorganized the themes by listing them in two groups: “is” and “means.” The “is” statements seemed to contain positive actions and constructive strategies for economic intelligence. The “means” statements held primarily a sense of caution and restriction with a touch of negativity thrown in. The first outline with two major themes, LIVING AN INEXPENSIVE LIFESTYLE and YOU CANNOT CONTROL EVERYTHING also had this same tone. This reminded me of the old children’s picture book, Fortunately/Unfortunately , and the themes of “fortune” as a motif for the three theoretical constructs came to mind. Knowing the Unfortunate Present means knowing what’s (most) important and what’s (mostly) uncontrollable in one’s personal economic life. Cultivating a Small Fortune consists of those small money-saving actions that, over time, become part of one’s lifestyle. A Fortunate Future consists of heightened awareness of trends and opportunities at micro and macro levels, with the understanding that health matters can idiosyncratically affect one’s fortune. These three constructs comprise this particular individual’s EI—economic intelligence.

Again, keep in mind that the examples for coding and theming were from one small interview transcript excerpt. The number of codes and their categorization would increase, given a longer interview and/or multiple interviews to analyze. But the same basic principles apply: codes and themes relegated into patterned and categorized forms are heuristics—stimuli for good thinking through the analytic memo-writing process on how everything plausibly interrelates. Methodologists vary in the number of recommended final categories that result from analysis, ranging anywhere from three to seven, with traditional grounded theorists prescribing one central or core category from coded work.

Qualitative Data Analysis Strategy: To Assert

To assert in QDA is to put forward statements that summarize particular fieldwork and analytic observations that the researcher believes credibly represent and transcend the experiences. Educational anthropologist Frederick Erickson ( 1986 ) wrote a significant and influential chapter on qualitative methods that outlined heuristics for assertion development . Assertions are declarative statements of summative synthesis, supported by confirming evidence from the data and revised when disconfirming evidence or discrepant cases require modification of the assertions. These summative statements are generated from an interpretive review of the data corpus and then supported and illustrated through narrative vignettes—reconstructed stories from field notes, interview transcripts, or other data sources that provide a vivid profile as part of the evidentiary warrant.

Coding or theming data can certainly precede assertion development as a way of gaining intimate familiarity with the data, but Erickson’s ( 1986 ) methods are a more admittedly intuitive yet systematic heuristic for analysis. Erickson promotes analytic induction and exploration of and inferences about the data, based on an examination of the evidence and an accumulation of knowledge. The goal is not to look for “proof” to support the assertions, but to look for plausibility of inference-laden observations about the local and particular social world under investigation.

Assertion development is the writing of general statements, plus subordinate yet related ones called subassertions and a major statement called a key assertion that represents the totality of the data. One also looks for key linkages between them, meaning that the key assertion links to its related assertions, which then link to their respective subassertions. Subassertions can include particulars about any discrepant related cases or specify components of their parent assertions.

Excerpts from the interview transcript of our case study will be used to illustrate assertion development at work. By now, you should be quite familiar with the contents, so I will proceed directly to the analytic example. First, there is a series of thematically related statements the participant makes:

“Buy one package of chicken, get the second one free. Now that was a bargain. And I got some.”

“With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.”

“I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

Assertions can be categorized into low-level and high-level inferences . Low-level inferences address and summarize what is happening within the particulars of the case or field site—the micro . High-level inferences extend beyond the particulars to speculate on what it means in the more general social scheme of things—the meso or macro . A reasonable low-level assertion about the three statements above collectively might read, The participant finds several small ways to save money during a difficult economic period . A high-level inference that transcends the case to the meso level might read, Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending.

Assertions are instantiated (i.e., supported) by concrete instances of action or participant testimony, whose patterns lead to more general description outside the specific field site. The author’s interpretive commentary can be interspersed throughout the report, but the assertions should be supported with the evidentiary warrant . A few assertions and subassertions based on the case interview transcript might read as follows (and notice how high-level assertions serve as the paragraphs’ topic sentences):

Selected businesses provide alternatives and opportunities to buy products and services at reduced rates during a recession to maintain consumer spending. Restaurants, for example, need to find ways during difficult economic periods when potential customers may be opting to eat inexpensively at home rather than spending more money by dining out. Special offers can motivate cash-strapped clientele to patronize restaurants more frequently. An adult male dealing with such major expenses as underinsured dental care offers: “With Sweet Tomatoes I get those coupons for a few bucks off for lunch, so that really helps.” The film and video industries also seem to be suffering from a double-whammy during the current recession: less consumer spending on higher-priced entertainment, resulting in a reduced rate of movie theater attendance (recently 39 percent of the American population, according to a CNN report); coupled with a media technology and business revolution that provides consumers less costly alternatives through video rentals and Internet viewing: “I don’t go to movies anymore. I rent DVDs from Netflix or Redbox or watch movies online—so much cheaper than paying over ten or twelve bucks for a movie ticket.”

To clarify terminology, any assertion that has an if–then or predictive structure to it is called a proposition since it proposes a conditional event. For example, this assertion is also a proposition: “Special offers can motivate cash-strapped clientele to patronize restaurants more frequently.” Propositions are the building blocks of hypothesis testing in the field and for later theory construction. Research not only documents human action but also can sometimes formulate statements that predict it. This provides a transferable and generalizable quality in our findings to other comparable settings and contexts. And to clarify terminology further, all propositions are assertions, but not all assertions are propositions.

Particularizability —the search for specific and unique dimensions of action at a site and/or the specific and unique perspectives of an individual participant—is not intended to filter out trivial excess but to magnify the salient characteristics of local meaning. Although generalizable knowledge is difficult to formulate in qualitative inquiry since each naturalistic setting will contain its own unique set of social and cultural conditions, there will be some aspects of social action that are plausibly universal or “generic” across settings and perhaps even across time.

To work toward this, Erickson advocates that the interpretive researcher look for “concrete universals” by studying actions at a particular site in detail and then comparing those actions to actions at other sites that have also been studied in detail. The exhibit or display of these generalizable features is to provide a synoptic representation, or a view of the whole. What the researcher attempts to uncover is what is both particular and general at the site of interest, preferably from the perspective of the participants. It is from the detailed analysis of actions at a specific site that these universals can be concretely discerned, rather than abstractly constructed as in grounded theory.

In sum, assertion development is a qualitative data analytic strategy that relies on the researcher’s intense review of interview transcripts, field notes, documents, and other data to inductively formulate, with reasonable certainty, composite statements that credibly summarize and interpret participant actions and meanings and their possible representation of and transfer into broader social contexts and issues.

Qualitative Data Analysis Strategy: To Display

To display in QDA is to visually present the processes and dynamics of human or conceptual action represented in the data. Qualitative researchers use not only language but also illustrations to both analyze and display the phenomena and processes at work in the data. Tables, charts, matrices, flow diagrams, and other models and graphics help both you and your readers cognitively and conceptually grasp the essence and essentials of your findings. As you have seen thus far, even simple outlining of codes, categories, and themes is one visual tactic for organizing the scope of the data. Rich text, font, and format features such as italicizing, bolding, capitalizing, indenting, and bullet pointing provide simple emphasis to selected words and phrases within the longer narrative.

Think display was a phrase coined by methodologists Miles and Huberman ( 1994 ) to encourage the researcher to think visually as data were collected and analyzed. The magnitude of text can be essentialized into graphics for at-a-glance review. Bins in various shapes and lines of various thicknesses, along with arrows suggesting pathways and direction, render the study a portrait of action. Bins can include the names of codes, categories, concepts, processes, key participants, and/or groups.

As a simple example, Figure 29.1 illustrates the three categories’ interrelationship derived from process coding. It displays what could be the apex of this interaction, LIVING STRATEGICALLY, and its connections to THINKING STRATEGICALLY, which influences and affects SPENDING STRATEGICALLY.

Three categories’ interrelationship derived from process coding.

Figure 29.2 represents a slightly more complex (if not playful) model, based on the five major in vivo codes/categories generated from analysis. The graphic is used as a way of initially exploring the interrelationship and flow from one category to another. The use of different font styles, font sizes, and line and arrow thicknesses is intended to suggest the visual qualities of the participant’s language and his dilemmas—a way of heightening in vivo coding even further.

In vivo categories in rich text display

Accompanying graphics are not always necessary for a qualitative report. They can be very helpful for the researcher during the analytic stage as a heuristic for exploring how major ideas interrelate, but illustrations are generally included in published work when they will help supplement and clarify complex processes for readers. Photographs of the field setting or the participants (and only with their written permission) also provide evidentiary reality to the write-up and help your readers get a sense of being there.

Qualitative Data Analysis Strategy: To Narrate

To narrate in QDA is to create an evocative literary representation and presentation of the data in the form of creative nonfiction. All research reports are stories of one kind or another. But there is yet another approach to QDA that intentionally documents the research experience as story, in its traditional literary sense. Narrative inquiry serves to plot and story-line the participant’s experiences into what might be initially perceived as a fictional short story or novel. But the story is carefully crafted and creatively written to provide readers with an almost omniscient perspective about the participants’ worldview. The transformation of the corpus from database to creative nonfiction ranges from systematic transcript analysis to open-ended literary composition. The narrative, however, should be solidly grounded in and emerge from the data as a plausible rendering of social life.

The following is a narrative vignette based on interview transcript selections from the participant living through tough economic times:

Jack stood in front of the soft drink vending machine at work and looked almost worriedly at the selections. With both hands in his pants pockets, his fingers jingled the few coins he had inside them as he contemplated whether he could afford the purchase. Two dollars for a twenty-ounce bottle of Diet Coke. Two dollars. “I can practically get a two-liter bottle for that same price at the grocery store,” he thought. Then Jack remembered the upcoming dental surgery he needed—that would cost one thousand dollars—and the bottle of insulin and syringes he needed to buy for his diabetic, high maintenance cat—almost two hundred dollars. He sighed, took his hands out of his pockets, and walked away from the vending machine. He was skipping lunch that day anyway so he could stock up on dinner later at the cheap-but-filling all-you-can-eat Chinese buffet. He could get his Diet Coke there.

Narrative inquiry representations, like literature, vary in tone, style, and point of view. The common goal, however, is to create an evocative portrait of participants through the aesthetic power of literary form. A story does not always have to have a moral explicitly stated by its author. The reader reflects on personal meanings derived from the piece and how the specific tale relates to one’s self and the social world.

Qualitative Data Analysis Strategy: To Poeticize

To poeticize in QDA is to create an evocative literary representation and presentation of the data in poetic form. One approach to analyzing or documenting analytic findings is to strategically truncate interview transcripts, field notes, and other pertinent data into poetic structures. Like coding, poetic constructions capture the essence and essentials of data in a creative, evocative way. The elegance of the format attests to the power of carefully chosen language to represent and convey complex human experience.

In vivo codes (codes based on the actual words used by participants themselves) can provide imagery, symbols, and metaphors for rich category, theme, concept, and assertion development, in addition to evocative content for arts-based interpretations of the data. Poetic inquiry takes note of what words and phrases seem to stand out from the data corpus as rich material for reinterpretation. Using some of the participant’s own language from the interview transcript illustrated previously, a poetic reconstruction or “found poetry” might read as follows:

Scary Times Scary times … spending more (another ding in my wallet) a couple of thousand (another ding in my wallet) insurance is just worthless (another ding in my wallet) pick up the tab (another ding in my wallet) not putting as much into savings (another ding in my wallet) It all adds up. Think twice: don’t really need skip Think twice, think cheap: coupons bargains two-for-one free Think twice, think cheaper: stock up all-you-can-eat (cheap—and filling) It all adds up.

Anna Deavere Smith, a verbatim theatre performer, attests that people speak in forms of “organic poetry” in everyday life. Thus, in vivo codes can provide core material for poetic representation and presentation of lived experiences, potentially transforming the routine and mundane into the epic. Some researchers also find the genre of poetry to be the most effective way to compose original work that reflects their own fieldwork experiences and autoethnographic stories.

Qualitative Data Analysis Strategy: To Compute

To compute in QDA is to employ specialized software programs for qualitative data management and analysis. The acronym for computer-assisted qualitative data analysis software is CAQDAS. There are diverse opinions among practitioners in the field about the utility of such specialized programs for qualitative data management and analysis. The software, unlike statistical computation, does not actually analyze data for you at higher conceptual levels. These CAQDAS software packages serve primarily as a repository for your data (both textual and visual) that enables you to code them, and they can perform such functions as calculating the number of times a particular word or phrase appears in the data corpus (a particularly useful function for content analysis) and can display selected facets after coding, such as possible interrelationships. Basic software such as Microsoft Word and Excel provides utilities that can store and, with some preformatting and strategic entry, organize qualitative data to enable the researcher’s analytic review. The following Internet addresses are listed to help in exploring selected CAQDAS packages and obtaining demonstration/trial software; video tutorials are available on the companies’ websites and on YouTube:

ATLAS.ti: http://www.atlasti.com

Dedoose: http://www.dedoose.com

HyperRESEARCH: http://www.researchware.com

MAXQDA: http://www.maxqda.com

NVivo: http://www.qsrinternational.com

QDA Miner: http://www.provalisresearch.com

Quirkos: http://www.quirkos.com

Transana: http://www.transana.com

V-Note: http://www.v-note.org

Some qualitative researchers attest that the software is indispensable for qualitative data management, especially for large-scale studies. Others feel that the learning curve of most CAQDAS programs is too lengthy to be of pragmatic value, especially for small-scale studies. From my own experience, if you have an aptitude for picking up quickly on the scripts and syntax of software programs, explore one or more of the packages listed. If you are a novice to qualitative research, though, I recommend working manually or “by hand” for your first project so you can focus exclusively on the data and not on the software.

Qualitative Data Analysis Strategy: To Verify

To verify in QDA is to administer an audit of “quality control” to your analysis. After your data analysis and the development of key findings, you may be thinking to yourself, “Did I get it right?” “Did I learn anything new?” Reliability and validity are terms and constructs of the positivist quantitative paradigm that refer to the replicability and accuracy of measures. But in the qualitative paradigm, other constructs are more appropriate.

Credibility and trustworthiness (Lincoln & Guba, 1985 ) are two factors to consider when collecting and analyzing the data and presenting your findings. In our qualitative research projects, we must present a convincing story to our audiences that we “got it right” methodologically. In other words, the amount of time we spent in the field, the number of participants we interviewed, the analytic methods we used, the thinking processes evident to reach our conclusions, and so on should be “just right” to assure the reader that we have conducted our jobs soundly. But remember that we can never conclusively prove something; we can only, at best, convincingly suggest. Research is an act of persuasion.

Credibility in a qualitative research report can be established in several ways. First, citing the key writers of related works in your literature review is essential. Seasoned researchers will sometimes assess whether a novice has “done her homework” by reviewing the bibliography or references. You need not list everything that seminal writers have published about a topic, but their names should appear at least once as evidence that you know the field’s key figures and their work.

Credibility can also be established by specifying the particular data analysis methods you employed (e.g., “Interview transcripts were taken through two cycles of process coding, resulting in three primary categories”), through corroboration of data analysis with the participants themselves (e.g., “I asked my participants to read and respond to a draft of this report for their confirmation of accuracy and recommendations for revision”), or through your description of how data and findings were substantiated (e.g., “Data sources included interview transcripts, participant observation field notes, and participant response journals to gather multiple perspectives about the phenomenon”).

Data scientist W. Edwards Deming is attributed with offering this cautionary advice about making a convincing argument: “Without data, you’re just another person with an opinion.” Thus, researchers can also support their findings with relevant, specific evidence by quoting participants directly and/or including field note excerpts from the data corpus. These serve both as illustrative examples for readers and to present more credible testimony of what happened in the field.

Trustworthiness, or providing credibility to the writing, is when we inform the reader of our research processes. Some make the case by stating the duration of fieldwork (e.g., “Forty-five clock hours were spent in the field”; “The study extended over a 10-month period”). Others put forth the amounts of data they gathered (e.g., “Sixteen individuals were interviewed”; “My field notes totaled 157 pages”). Sometimes trustworthiness is established when we are up front or confessional with the analytic or ethical dilemmas we encountered (e.g., “It was difficult to watch the participant’s teaching effectiveness erode during fieldwork”; “Analysis was stalled until I recoded the entire data corpus with a new perspective”).

The bottom line is that credibility and trustworthiness are matters of researcher honesty and integrity . Anyone can write that he worked ethically, rigorously, and reflexively, but only the writer will ever know the truth. There is no shame if something goes wrong with your research. In fact, it is more than likely the rule, not the exception. Work and write transparently to achieve credibility and trustworthiness with your readers.

The length of this chapter does not enable me to expand on other QDA strategies such as to conceptualize, theorize, and write. Yet there are even more subtle thinking strategies to employ throughout the research enterprise, such as to synthesize, problematize, and create. Each researcher has his or her own ways of working, and deep reflexivity (another strategy) on your own methodology and methods as a qualitative inquirer throughout fieldwork and writing provides you with metacognitive awareness of data analysis processes and possibilities.

Data analysis is one of the most elusive practices in qualitative research, perhaps because it is a backstage, behind-the-scenes, in-your-head enterprise. It is not that there are no models to follow. It is just that each project is contextual and case specific. The unique data you collect from your unique research design must be approached with your unique analytic signature. It truly is a learning-by-doing process, so accept that and leave yourself open to discovery and insight as you carefully scrutinize the data corpus for patterns, categories, themes, concepts, assertions, propositions, and possibly new theories through strategic analysis.

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Glob Qual Nurs Res
v.4; Jan-Dec 2017

Employing a Qualitative Description Approach in Health Care Research

Carmel bradshaw.

1 University of Limerick, Limerick, Ireland

Sandra Atkinson

A qualitative description design is particularly relevant where information is required directly from those experiencing the phenomenon under investigation and where time and resources are limited. Nurses and midwives often have clinical questions suitable to a qualitative approach but little time to develop an exhaustive comprehension of qualitative methodological approaches. Qualitative description research is sometimes considered a less sophisticated approach for epistemological reasons. Another challenge when considering qualitative description design is differentiating qualitative description from other qualitative approaches. This article provides a systematic and robust journey through the philosophical, ontological, and epistemological perspectives, which evidences the purpose of qualitative description research. Methods and rigor issues underpinning qualitative description research are also appraised to provide the researcher with a systematic approach to conduct research utilizing this approach. The key attributes and value of qualitative description research in the health care professions will be highlighted with the aim of extending its usage.

Introduction

There is a myriad of qualitative approaches to research. Yet, the researcher may be confronted with a question or a topic that belongs within the qualitative paradigm but does not correspond neatly with approaches that are well documented and clearly delineated. Within the literature, various terms have been used to describe research that does not fit within a traditional qualitative approach. Thorne, Kirkham, and MacDonald-Emes (1997) define “interpretive description” as a “noncategorical” qualitative research approach (p. 169). Merriam (1998) refers to this type of research as “basic or generic qualitative research” (p. 20) and Sandelowski (2000 , p. 335, 2010) explores what she calls “basic or fundamental qualitative description.” Exploratory research is the umbrella term used by Brink and Wood (2001) to describe all description qualitative research and suggest it “is a Level 1 research endeavor” (p. 85), and Savin-Baden and Howell Major (2013) refer to a pragmatic qualitative approach. This interchangeable use of terms creates ambiguity and confusion in relation to qualitative description research as a methodology in its own right. Reference to “interpretive” as described by Thorne et al. (1997) can cause confusion with phenomenology, for example, and Savin-Baden and Howell Major’s (2013) use of a “pragmatic qualitative approach” might suggest that if all else fails, the researcher should adopt a pragmatic approach.

A clear identification of qualitative description research is required, one that best captures what it does to aid researchers in determining which approach best suits the question or phenomenon which has been identified for exploration. Qualitative description research studies are those that represent the characteristics of qualitative research rather than focusing on culture as does ethnography, the lived experience as in phenomenology or the building of theory as with grounded theory. Qualitative description research studies are those that seek to discover and understand a phenomenon, a process, or the perspectives and worldviews of the people involved ( Caelli, Ray, & Mill, 2003 ; Merriam, 1998 ). As a methodology, qualitative description research studies have gained popularity in recent years within nursing and midwifery, and Polit and Beck (2014) identified they accounted for more than half of qualitative studies. The use of a qualitative description approach is particularly relevant where information is required directly from those experiencing the phenomenon under investigation, where time and resources are limited and perhaps as part of a mixed methods approach ( Neergaard, Oleson, Anderson, & Sondergaard, 2009 ).

Philosophical Assumptions

Philosophical perspectives dictate what constitutes knowledge and how phenomena should be studied ( Weaver & Olson, 2006 ), thus assisting researchers to refine and specify the types of evidence necessary, how it should be collected, and how it should be interpreted and used. Qualitative description research lies within the naturalistic approach, which creates an understanding of a phenomenon through accessing the meanings participants ascribe to them. The study of phenomena in their natural context is central, along with the acceptance that researchers cannot evade affecting the phenomenon under investigation. A value neutral position can never be adopted by the naturalistic researcher and their philosophy is central to the phenomena under investigation ( Parahoo, 2014 ). There can be no reality without understanding language and acknowledging the researcher’s preconceptions, and only through subjective interpretation can this reality be truly uncovered. The philosophical assumptions identified by the authors of this article are identified in Table 1 . These can guide the researcher in their ontology and epistemology assumptions, which directs subsequent methodology providing a framework to accomplish a study using a qualitative description approach.

Philosophical Underpinnings of Qualitative Description Approach.

Source. Developed by the authors.

Sullivan-Bolyai, Bova, and Harper (2005) also make a compelling argument for the use of qualitative description in health care research because of its ability to provide clear information on how to improve practice. In addition, other qualitative approaches may not be appropriate for the issue requiring exploration or investigation. Furthermore, the findings emanating from such studies can often create a platform for more extensive and focused work on the topic. The misconception that qualitative description research is less theoretical or methodologically sound is unmerited as evidenced by Sandelowski (2000 , 2010 ), Sullivan-Bolyai et al. (2005) , and Neergaard et al. (2009) . This article addresses the philosophical, ontological, epistemological methods and rigor underpinning qualitative description methodology and aims to provide the researcher with a systematic approach to conducting research utilizing a qualitative description design.

Ontological Assumptions

Ontology is the study of being ( Crotty, 1998 ) and is concerned with what constitutes reality, what the real world is, and what can be known about it ( Denzin & Lincoln, 2011 ). The ontological position of naturalistic research is relativism, which holds the view that reality is subjective and varies from person to person ( Parahoo, 2014 ) and this is evident in the reporting of findings from qualitative description research. Realities are influenced by senses and emerge when consciousness engages with objects, which already have meaning for the individual ( Crotty, 1998 , p. 43). What follows is that there are many realities, and no one reality can exist as individuals ascribe their own interpretation and meaning to the phenomenon. In addition, the use of language actively shapes and molds our reality ( Frowe, 2001 ). Thus, reality is constructed through the interaction between language and aspects of an independent world where people’s description of a phenomenon can be seen as either a proxy or literal description or a combination of both. Qualitative description research strives for in-depth understanding but with emphasis first on literal description ( Sandelowski, 2010 ) and then on the understanding of human phenomena through analysis and interpretation of meaning people ascribe to events.

Epistemological Assumptions

Epistemological assumptions relate to how knowledge can be created, developed, and communicated, in other words, what it means to know and involves asking what is the nature of the relationship between the would-be knower and what can be known ( Denzin & Lincoln, 2011 ). The epistemological position of qualitative research is subjectivism, which is based on real-world phenomena; the world does not exist independently of our knowledge of it ( Grix, 2004 ). Subjectivism accepts the reality of all objects, relies entirely on an individual’s subjective awareness of it, and stresses the role and contribution the researcher plays, and this is congruent with the qualitative description approach to research.

The qualitative description approach accepts that many interpretations of reality exist and that what is offered is a subjective interpretation strengthened and supported by reference to verbatim quotations from participants. Knowledge of reality from a naturalistic perspective as is the case in qualitative description research is socially constructed not only by the participants obviously but also by the researchers, and it is therefore recognized that an objective reality cannot be discovered or replicated by others.

Methodological Assumptions

Methodological assumptions consider how researchers approach finding out what they believe can be known ( Denzin & Lincoln, 2011 ), finding the best fit to the phenomena under investigation in a pragmatic manner. Within qualitative description, the outcome is to describe the phenomenon literally as a starting point and its methodological orientation may be drawn from a range of theorists, for example, Sandelowski (2000) . Qualitative description design then moves beyond the literal description of the data and attempts to interpret the findings without moving too far from that literal description. Stating one’s theoretical orientation will help readers understand how research methods are decided, for example, data collection, data analysis, interpretation, findings presentation, and rigor. Within the qualitative description approach, the phenomenon of interest is explored with participants in a particular situation and from a particular conceptual framework ( Parse, 2001 ) with the research question related to the meaning of the experience. The participants are a purposive or purposeful sample who have the requisite knowledge and experience of the phenomena being researched. The interactions of a given social unit are investigated and the “participant group is selected from the population the researcher wishes to engage in the study” ( Parse, 2001 , p. 59). The descriptions obtained from participants are then analyzed and synthesized from the perspective of the chosen framework. Researchers aiming to use a qualitative description approach need to address from the outset (as indeed do all researchers regardless of approach) their theoretical positioning, congruence between methodology and methods, strategies to establish rigor, and the analytic lens through which data analysis is conducted.

The goal of qualitative description research is not “discovery” as is the case in grounded theory, not to “explain” or “seeking to understand” as with ethnography, not to “explore a process” as is a case study or “describe the experiences” as is expected in phenomenology ( Doody & Bailey, 2016 ). Qualitative description research seeks instead to provide a rich description of the experience depicted in easily understood language ( Sullivan-Bolyai et al., 2005 ). The researcher seeks to discover and understand a phenomenon, a process, or the perspectives and worldviews of the people involved ( Caelli et al., 2003 ). A qualitative description approach, therefore, offers the opportunity to gather rich descriptions about a phenomenon which little may be known about. Within the process, the researcher strives to stay close to the “surface of the data and events” ( Sandelowski, 2000 , p. 336), where the experience is described from the viewpoint of the participants ( Sullivan-Bolyai et al., 2005 ).

The goal of the researcher is to provide an account of the “experiences, events and process that most people (researchers and participants) would agree are accurate” ( Sullivan-Bolyai et al., 2005 , p. 128). The focus on producing rich description about the phenomenon from those who have the experience offers a unique opportunity to gain inside or emic knowledge and learn how they see their world.

Two main elements constant with qualitative description studies in health care research are learning from the participants and their descriptions, and second, using this knowledge to influence interventions ( Sullivan-Bolyai et al., 2005 ). Therefore, a fundamental qualitative description design is valuable in its own right. Qualitative description studies are typically directed toward discovering the who, what, where, and why of events or experiences ( Neergaard et al., 2009 ). A qualitative descriptive approach does not require the researcher to move as far from the data and does not require a highly abstract rendering of data compared with other qualitative designs ( Lambert & Lambert, 2012 ) but of course does result in some interpretation. The findings from these studies can often be of special relevance to practitioners and policy makers ( Sandelowski, 2000 ).

Methods Assumptions

Methods refer to the tools, techniques, or procedures used to gather and interpret evidence. Researchers employing a qualitative description approach must clearly articulate their disciplinary connection, what brought them to the question, and the assumptions they make about the topic of interest. The tools used to collect and analyze the data must be congruent with the philosophical, epistemological, and ontological assumptions underpinning the research ( van Manen, 1998 ). In their results, researchers must demonstrate congruence between the questions posed and the approach employed. Some methods have their origins in a particular methodology, for example, constant comparative methods as in grounded theory ( Glaser & Strauss, 1967 ). However, a variety of methods can be utilized in qualitative description research as long as they are congruent with the research question and the purpose of the research, and contribute to the rigor of the research. In research methods researchers can address: ethics, sampling, collecting and analyzing rich data ( Polit & Beck, 2014 ; Sandelowski, 2000 ); and extensive interaction with participants ( Streubert & Carpenter, 2011 ). A flexible plan of inquiry that is responsive to real-world contexts ( Patterson & Morin, 2012 ), naturalistic study methods ( Holloway, 2005 ; Sandelowski, 2000 ), and rigor can also be included in research methods.

Sampling and Sample Size

It is essential that the sampling techniques selected within a research study are reflective of the research design and research question. The sampling process best able to achieve this within qualitative studies and in particular qualitative description designs is a nonprobability technique of convenience or purposive sampling ( Parahoo, 2014 ). Convenience sampling allows the researcher to select participants who are readily accessible or available. Likewise, purposive sampling avails of accessible participants, but it provides the additional advantage of facilitating the selection of participants whose qualities or experiences are required for the study.

The size of the sample has generated discussion among qualitative researchers. Qualitative samples tend to be small because of the emphasis on intensive contact with participants and the findings are not expected to be generalizable. The principle of “data saturation” has become an accepted standard to determine sample size within qualitative designs. However, the difficulties and challenges regarding the concept of “data saturation” have recently been debated ( Fusch & Ness, 2015 ; Malterud, Siersma, & Guassora, 2015 ). The concept originated from “theoretical saturation,” an element of constant comparative method, which is a specific component of grounded theory methodology ( Glaser & Strauss, 1967 ). However, in other qualitative research designs, the concept of “data saturation” has a number of definitions and is rarely made explicit within research studies ( O’Reilly & Parker, 2013 ). Data saturation can be considered to apply to the point where no new information emerges from the study participants during data collection ( Coyne, 1997 ), when the ability to obtain new information has been attained and when additional coding is no longer feasible ( Guest, Bunce, & Johnson, 2006 ) or when enough information is gathered to replicate the study ( Walker, 2012 ). However, data saturation is often referred to in a pragmatic manner to signal the end of data collection. The concept of data saturation is also contested within other qualitative research designs such as phenomenology, and in particular, hermeneutic phenomenology ( Ironside, 2006 ) and Interpretative Phenomenological Analysis ( Smith, Flowers, & Larkin, 2009 ). These research designs stress the uniqueness of each individual’s experience (mirroring the philosophy of qualitative description design) and therefore argue that data saturation can never truly be reached ( Ironside, 2006 ). LoBiondo-Wood and Haber (2014) concur and suggest that there is no fixed rule to establish the most appropriate sample size in qualitative research, instead a number of factors should be considered. These include careful consideration of the research design, sampling procedure, and the relative frequency of the phenomena being researched. Therefore, according to Fawcett and Garity (2009) , an adequate sample size is one that sufficiently answers the research question, the goal being to obtain cases deemed rich in information. Therefore, consideration can be given to include tentative sample sizes in any proposal delineating a qualitative description approach. It is evident that regardless of the strategies engaged in sampling and subsequently sample size, all research studies are required to defend their sampling strategies and provide clarity as to how sample size was determined to meet the objectives of the study.

Cluett and Bluff (2006) emphasize a researcher’s responsibility to address ethical principles relevant to their study to demonstrate “ professional, legal and social accountability ” (p. 199). There are a number of ethical principles that a researcher must address prior to and throughout the research process to safe guard the participant and uphold the integrity of the study. In particular, participants’ confidentiality and anonymity can be compromised as data collection methods, for example, face-to-face interviews, which are more intimate, are often used in qualitative description designs due to the open-ended nature of data collection. The more information researchers give when constructing a rich description, the greater the danger of participant identification. Researchers may have to mask contextualization to some extent to protect participants’ identities, while still ensuring that what is reported is verbatim or as near to the meaning literally described by the participant ( Doody & Noonan, 2016 ). Study participants must be viewed as autonomous agents with the right to voluntarily accept or decline to participate in any study and to cease participation at any stage without prejudice. To uphold the principle of nonmaleficence, the researcher must pay close attention to the possible psychological consequences of participating in a study, particularly in qualitative research ( Savin-Baden & Howell Major, 2013 ). According to Lowes and Gill (2006) , interviews have the potential to evoke emotions and unexpected feelings. Therefore, preparation prior to data collection is advised to consider any potential consequence and arrange an appropriate referral system if required ( Atkinson & Mannix McNamara, 2016 ) and should be integral in the research design.

Participants are susceptible to researchers imposing their own subjective interpretations that represent participant’s understandings ( Danby & Farrell, 2004 ), although this is less of an issue in qualitative description design where the focus is primarily on rich description of the data and then on interpretation. Subjective interpretation raises issues of who owns the data, how will data be used, and how much control over the findings do participants have? Even though participants are given a voice, it is usually the researcher who decides on the direction that the research takes, the final interpretation of the data, and which information is reported. However, this does not contradict qualitative researchers’ focus on the veracity of the data; it is in fact fundamental to qualitative research to describe the individuals’ experiences. Researchers, therefore, have a responsibility to keep as near to the participants’ meaning as possible by using their own words and with a degree of interpretation that is consistent with the research question and the data collected.

Data Collection

Data collection involves the use of data to understand and explain the phenomenon. The primary sources of data collection in qualitative description research are often semistructured in-depth interviews, but other methods are not discounted ( Stanley, 2015 ). Data collection methods in qualitative description designs can include interviews, focus groups, observation, or document review ( Colorafi & Evans, 2016 ). However, the use of interviews enables the researcher to explore issues with participants through encouraging depth and rigor, which facilitates emergence of new concepts/issues ( Doody & Noonan, 2013 ; Fetterman, 1998 ) and contributes to the “richness of data” required in qualitative description designs.

According to Fetterman (1998) , interviews take the researcher into the “heart of the phenomenon classifying and organising an individual’s perception of reality” (p. 40). Sandelowski (2000) suggests that a semistructured and open-ended interview guide be used to avoid limiting responses and to encourage participants to express themselves freely. Similarly, Sullivan-Bolyai et al. (2005) suggest the development or use of a framework to guide and focus interview questions, reflecting the relevant published literature as suggested by Miles, Huberman, and Saldana (2014) . This framework may provide general or specific direction about topics to be addressed in interviews. Regardless of which template is used, it is important to ensure the focus remains on the original phenomenon of interest.

Data Analysis

Qualitative data analysis predominantly consists of content or thematic analyses, which are often erroneously used interchangeably ( Miles et al., 2014 ).There are many similarities in the above approaches including searching for patterns and themes ( Vaismoradi, Turunen, & Bondas, 2013 ) and both can be used with good effect in the analysis of data from qualitative description studies. However, as noted by Vaismoradi et al. (2013) , quantification of the data is more likely with content analysis which may fit better with the “straight description” of the data ( Sandelowski, 2000 ) associated with qualitative descriptive designs. Nevertheless, use of a named framework for data analysis ( Braun & Clarke, 2006 ; Burnard, 2011 ; Elo & Kyngas, 2008 ), which is carefully described, is vital to demonstrate the rigor of the study. Transcribing the interviews and listening to the voices of the participants repeatedly enables the transcriptions to come alive during the analysis in the quest for themes and subthemes, regardless of which framework for analysis is used. A large number of themes may be identified initially, but after further analysis and focusing on the purpose of the study, a smaller number of themes will stand out to capture the experience. These are described as “straight descriptions” of the data arranged in a way that “fits the data” ( Sandelowski, 2000 ), a decision that can be verified by the participants through the member checks procedure (as a means of augmenting rigor) if agreed previously or desired. The various subthemes can then be captured by identifying similar or dissonant patterns within the themes. Data can be organized in tables to create a visual and contextual interpretation. However, although this process may appear linear, the analysis follows a circular movement and there may be several iterations made before establishing themes and subthemes emanating from the data. This repeated reading, reviewing, and refining of themes and subthemes while keeping in mind the whole text demonstrate how the iterative process includes comparisons on all types of data ( Ayres, Kavanaugh, & Knafl, 2003 ). During this process, the researchers follow the data as concepts emerge, and stays open and close to what the data said and how it was said, creating an inductive process within the world of the data. Creswell (2014) calls this process “The Data Analysis Spiral.” Although emphasis is placed on description, analysis of qualitative description data by its very nature will involve some degree of interpretation ( Sandelowski, 2010 ).

Adopting a flexible design such as qualitative description enables data collection and analysis to be an iterative process by responding to participant’s responses to questions and simultaneously adapting the analytical process as new insights emerge as the study progresses ( Patterson & Morin, 2012 ). The advantage of a qualitative description approach is that data analysis is more likely to remain true to participants’ accounts and contribute to ensuring the researchers’ own interpretations are transparent ( Clancy, 2013 ; Sandelowski, 2000 ).

The demonstration of quality regarding the research process and subsequently the data collected is essential for all approaches to research. However, qualitative research cannot be judged using the same criteria as the scientific paradigm. It is generally acknowledged that procedures to assess rigor within quantitative studies (validity and reliability) are inappropriate for qualitative research ( Creswell, 2014 ). This does not suggest that qualitative researchers are unconcerned with data quality. It is in fact fundamental to qualitative research to demonstrate the truth of an individual’s experience and to ensure that the researcher presents a truthful representation of the participants’ voice and experience.

To demonstrate the quality of the data, qualitative researchers are concerned with issues of trustworthiness, which include principles of credibility, dependability, confirmability, and transferability. These principles were first introduced and developed in the 1980s by Lincoln and Guba (1985) to facilitate description of rigor within qualitative research. However, debate continues regarding the appropriateness or effectiveness of these concepts to demonstrate rigor in qualitative research. Morse, Barrett, Maynan, Olson, and Spiers (2002) are opponents of these concepts and argue that the terms reliability and validity remain the most appropriate criteria for attaining rigor in qualitative studies. These authors’ main criticisms are that the elements advocated to demonstrate trustworthiness are focused at the end of a study and are therefore evaluative in nature rather than identifiable or explicit during the research process. This, according to Morse et al. (2002) , results in the continuing view that qualitative research is unscientific or less rigorous than quantitative research. However, Ryan-Nicholls and Will (2009) refute these claims. These authors stress the importance of acknowledging the epistemological positions of each research approach and argue the necessity of utilizing a process that best demonstrate rigor in qualitative research. Consequently, the four principles identified by Lincoln and Guba (1985) remain an important framework for all qualitative researchers to demonstrate the quality of their research and can be readily applied to qualitative description research. The authors of this article identify means to support these four criteria in Table 2 specific to qualitative description and note the importance of demonstrating rigor from the inception of the research and throughout the research process to address the concerns of Morse et al. (2002) .

Demonstrating Rigor in Qualitative Description Research.

Quality indicators for qualitative description research must reflect the philosophical underpinning of the research design and the research question. Finlay (2006) presents possible methods to engage in and demonstrate quality or trustworthiness within qualitative research. These include, for example, providing a detailed audit trail to defend decisions made during the research process, evidence of prolonged engagement with the narrative data and including the participants’ voice/narrative within the findings to demonstrate the quality of the research findings ( Finlay, 2006 ). In addition, the practice of reflexivity is an essential component to incorporate into and engage within the research process to demonstrate trustworthiness ( Finlay, 2006 ; Kingdon, 2005 ). Reflexivity is vital to augment the critical appraisal of the researcher in an analysis of the intersubjective dynamics between researcher and the participants. Reflexivity requires critical self-reflection of the ways in which researchers’ social background, assumptions, positioning, and behavior affect the research process ( Finlay, 2006 ; McCabe & Holmes, 2009 ) which are often a factor when nurses and midwives are researching their practice areas. Therefore, the researcher is implicit in safeguarding the integrity of the study by demonstrating the study’s trustworthiness.

Qualitative description research designs have been predominately used in nursing and midwifery research to provide direct descriptions of phenomena ( Sandelowski, 2000 ). There is a clear alignment of qualitative description research with the philosophies and principles, which underpin both nursing and midwifery, including understanding and supporting the person, their family, and society as it explores meaning and/or how people make sense of the world and promoting person-centered/women-centered care. Qualitative description research provides a vehicle for the voices of those experiencing the phenomena of interest and can transform nursing and midwifery practice and indeed health care services generally by developing effective, culturally sensitive interventions, and make policy recommendations among those that are the focus of the research ( Sullivan-Bolyai et al., 2005 ) and influence health care provision.

Qualitative description studies will have overtones of other qualitative methods, which is acceptable as noted by Law (2004) . These overtones need to be acknowledged and described explicitly while recognizing that the research approach remains qualitative description and should be appropriately named ( Sandelowski, 2000 ). A qualitative description approach needs to be the design of choice when a description of a phenomenon is desired, with a focus on the Who, What, Where, and Why of the experience ( Neergaard et al., 2009 ). Researchers can confidently name their research design as qualitative description, and reference to description does not exclude the fact that an exercise of thought, practice of analysis, activity of reflection, and interpretation occurs.

This article provides the researcher with theoretical underpinning of a qualitative description approach, including the philosophical, ontological, and epistemological perspectives, which are the foundations of qualitative description research. In addition, key issues which are integral to the development of a research design, for example, methods, data collection, and data analysis are discussed in relation to qualitative description methodology. The key attributes and value of qualitative description research in the health care professions have been delineated with the aim of acting as a resource for researchers and extending the use of qualitative description in research.

Author Biographies

Carmel Bradshaw is a doctoral student, midwife and nurse lecturing in the University of Limerick. Research interests include clinical assessment, midwifery education, midwifery practice and research methods and methodology.

Sandra Atkinson is a midwife, nurse and lecturer in midwifery at the University of Limerick. Research interests include women’s health, transcultural health and community midwifery practice.

Owen Doody is a registered intellectual disability nurse working as a lecturer at the University of Limerick who teaches and publishes on nursing, nurse education, intellectual disability practice and supporting people with intellectual disability and their families.

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors received no financial support for the research, authorship, and/or publication of this article.

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Exploring Phenomena: A Brief Guide to Conducting Descriptive Qualitative Research

This article summarizes descriptive qualitative research, a method used to explore and understand the characteristics and qualities of a phenomenon. The article explains key features of the method, such as the importance of detailed descriptions, open-ended questions, and context and meaning.

It also comprehensively discusses data collection and analysis techniques, including interviews, observations, and thematic analysis. I highlight communication of research findings, along with potential limitations and biases of the method.

Key features of the descriptive qualitative research.

Descriptive qualitative research is a method of research that is focused on understanding a phenomenon by examining its characteristics and qualities. We use this type of research when we want to explore a topic that has not been studied in depth before, or when we want to gain a better understanding of a previously studied topic but using a different perspective and gain valuable insights in the process.

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Descriptive qualitative research is a type of qualitative research that explores the characteristics of a phenomenon, rather than explaining the underlying causes or mechanisms.

It involves the collection and data analysis in the form of words , images , or other non-numerical forms of information.

Goal of descriptive qualitative research

The goal of descriptive qualitative research is to provide a rich and detailed account of the phenomenon under study. Doing so allows us to develop further research questions. The activity will also help inform policy or practice.

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Applicability of descriptive qualitative research

Researchers in various fields can use descriptive qualitative research, including social sciences, education, psychology, health sciences, and business.

In social sciences, for example, descriptive qualitative research can be used to explore social, cultural, or political issues, and to understand the perspectives and experiences of marginalized or underrepresented groups.

In education, descriptive qualitative research can be used to explore teaching and learning processes, student experiences, and educational practices.

In health sciences, descriptive qualitative research can be used to explore patients’ experiences with illness, healthcare providers’ experiences, and health policies.

Data Collection Methods Used in Descriptive Qualitative Research

The data collection methods used in descriptive qualitative research can vary. Typically, the method involves an observation or interaction with the phenomenon being studied.

Examples include personal interview of individuals who have experience or knowledge of the phenomenon studied, focus group discussion , observing the phenomenon in its natural setting, document analysis or other forms of data collection that apply to the phenomenon.

Strengths of the Descriptive Qualitative Method

Flexible research method.

One of the key strengths of descriptive qualitative research is its flexibility. Flexibility means that the method can be used in a wide range of settings. It can be adapted to suit the needs of the researcher and the specific research question being investigated.

Few and easily obtained resources

Descriptive qualitative research can be conducted using relatively few resources, easily accessible, and can often be completed more quickly than other types of research. These resources include the following:

research participants,
the researcher,
data collection tools like interviews, focus group discussions, observations, or document analysis;
recording equipment, particularly audio or video recorders;
transcription software for easier and faster transcription; and
data analysis software like nVivo or ATLAS to facilitate analysis.

Despite these simple requirements, however, researchers must ensure that ethical considerations are adequately complied with (e.g. informed consent, confidentiality, privacy concerns, and data storage).

Compared to quantitative research, descriptive qualitative research can be time-consuming and resource intensive if the aim is to have a thorough and effective research outcome.

Captures the complexity and richness of a phenomenon

Another strength of descriptive qualitative research is its ability to capture the complexity and richness of a phenomenon.

Because this type of research is focused on the exploration of the characteristics and qualities of a phenomenon, it allows researchers to capture a wide range of information about the phenomenon, including its context, history, and cultural significance.

Limitations of Descriptive Qualitative Research

Can be time consuming, potential for researcher bias.

Because descriptive qualitative research often involves the interpretation of data, researchers may inadvertently introduce their own biases into the analysis. One researcher’s perspective may vary from another researcher’s viewpoint in studying the same phenomenon.

The researcher’s bias can be minimized through careful data collection and analysis techniques, but it is important for researchers to be aware of their own biases and to mitigate their impact on the research.

Does not provide the same level of generalizability as quantitative research methods

Another limitation of descriptive qualitative research is that it may not provide the same level of generalizability as quantitative research methods.

Because we often focus descriptive qualitative research on a specific phenomenon or context, it may not be possible to generalize the findings to other contexts or populations.

However, this does not mean that the findings are not valuable or informative. Descriptive qualitative research can still be an important tool for understanding specific phenomena and contexts.

Steps in Conducting Descriptive Qualitative Research

In order to conduct descriptive qualitative research, researchers typically follow a series of steps. I list them in the following section.

Step 1. Identify the research question or topic of interest

The first step is to identify the research question or topic of interest. Knowledge of the research agenda of an organization or institution where the researcher belongs will be most helpful.

The question should focus on exploring the characteristics and qualities of a phenomenon, rather than explaining its underlying causes or mechanisms.

Step 2. Determine the data collection method or methods to use

The next step is to determine the data collection methods that will be used. This may involve interviewing, observations, or analyzing documents or other forms of data. There should be a one-to-one correspondence between the research questions and the method to use. Thus, preparing a matrix to match the research question, method, and other parts of the research paper will facilitate and ensure that the research objectives are met.

The data collection methods should be chosen based on their ability to provide rich and detailed information about the phenomenon under study.

Step 3. Analyze the data collected

Once the data has been collected, the next step is to analyze it. Analysis may involve coding the data into categories or themes, or using other analytical techniques to identify patterns and relationships within the data.

The goal of the analysis is to develop a rich and detailed understanding of the phenomenon under study. Doing so allows researchers to develop further research questions or inform policy or practice.

Step 4. Disseminate the findings

Finally, the results of the descriptive qualitative research should be communicated to others. This may involve writing a report, presenting the findings at a conference, or publishing the research in a peer-reviewed journal . Other researchers can build on the findings.

In communicating the results, it is important to provide a clear and detailed account of the phenomenon under study and to contextualize the findings within the broader literature on the topic.

Usefulness of the Qualitative Descriptive Research

In conclusion, descriptive qualitative research is a valuable tool for exploring the characteristics and qualities of a phenomenon. It allows researchers to capture the complexity and richness of a phenomenon and provides a detailed understanding of its context, history, and cultural significance.

While there are some limitations to descriptive qualitative research, it can still be an important method for understanding specific phenomena and contexts.

Researchers can use a variety of data collection and analysis techniques to conduct descriptive qualitative research.

Qualitative researchers using qualitative research methods should communicate their findings to others in a clear and detailed manner.

As with any research method, it is important for researchers to approach descriptive qualitative research with a critical eye and to be aware of the potential biases and limitations of the method.

By following careful research procedures and communicating their findings clearly, descriptive qualitative researchers can make valuable contributions to our understanding of a wide range of phenomena.

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Two questions for great research topics, the role of internet technology in enhancing research skills, social media and data collection, about the author, patrick regoniel.

Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

Open access
Published: 17 April 2024

Sustaining the nursing workforce - exploring enabling and motivating factors for the retention of returning nurses: a qualitative descriptive design

Kumiko Yamamoto 1 ,
Katsumi Nasu 1 ,
Yoko Nakayoshi 1 &
Miyuki Takase 1

BMC Nursing volume 23 , Article number: 248 ( 2024 ) Cite this article

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The nursing shortage represents a persistent and urgent challenge within the healthcare industry. One of the most cost-effective and time-efficient solutions to address this issue is the recruitment of inactive nurses to rejoin the nursing workforce, while simultaneously ensuring the long-term sustainability of their careers following their return to work. The aim of this study is to explore the factors that facilitate the retention of nurses who have returned to work, from their perspective.

To achieve this aim, a qualitative descriptive design was employed. A total of 15 registered nurses who had not practiced nursing for a minimum of three years prior to their return to work, and had been working as nurses for at least three months following their return, were selected from seven healthcare institutions using convenience sampling. Face-to-face or online semi-structured interviews were conducted, and qualitative inductive analysis was employed to analyze the collected data.

The analysis revealed five key themes, two of which were related to the enabling factors making it possible for the nurses to continue their work, while the remaining three pertained to the motivating factors driving the pursuit of professional careers. The two themes associated with enabling factors were identified as “Conditions and support that sustain work-life balance” and “A workplace that acknowledges my career, and encourages my growth as an experienced nurse”. The three themes related to motivating factors were entitled “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and my family’s life”.

Conclusions

Returning nurses constitute a valuable asset for healthcare institutions. To effectively retain these nurses, it is crucial to implement multi-dimensional approaches that enable and motivate them to sustain and enrich their professional and personal lives while continuing their work in the nursing field.

Peer Review reports

Nurses constitute a vital cornerstone of the healthcare system, assuming a foundational role in providing patient care, and notably representing over half of the entire healthcare workforce [ 1 ]. The global nurse population was estimated at 27.9 million in 2018 [ 1 ], and there was a notable growth of 4.7 million nurses between 2013 and 2018 [ 1 ]. Simultaneously, the WHO [ 1 ] reported a deficit of 5.9 million nurses in 2018, with the shortfall in the number of nurses expected to reach 10.6 million by 2030 [ 2 ]. This trend is primarily driven by the mounting demand for nursing services stemming from population aging dynamics. Moreover, the aging composition of the nursing workforce exacerbates the existing shortage of nurses. Currently, 17% of the global nursing population is aged 55 years or over [ 2 ], and projections indicate that within the upcoming decade, approximately 4.7 million nurses are expected to retire [ 3 ]. This means that an estimated annual influx of 47,000 new nurses is required just to sustain the current nursing workforce. Failure to meet this demand will probably intensify the nursing shortage at an accelerating pace. There is an immediate need for cost-effective measures aimed at mitigating the shortage of nurses.

Numerous policies have been implemented on a global scale to address the persistent shortage of nursing professionals. These policy measures encompass creating new registered nurses through education; facilitating re-entry into the nursing workforce for currently inactive registered nurses, and recruiting nurses from other countries [ 4 ]. Among the aforementioned strategies, one particularly promising approach to overcoming the nursing shortage involves the recruitment of inactive nurses, which has been implemented in many countries [ 4 , 5 ]. The reintegration of inactive nurses into the labor force is advantageous in terms of cost and time, as it obviates the need to invest social capital and years of resources in educating and nurturing new nursing students. Countries have implemented Return to Practice Programs designed for inactive nurses, each varying in educational content and duration [e.g., 6 , 7 ], and these initiatives have demonstrated notable success in augmenting the nursing labor force [ 8 , 9 , 10 , 11 ].

The reintegration of these nurses into the labor force holds significant importance in addressing the nursing shortage in Japan in particular. Japan is currently facing the challenge of a super-aging population, with 29.0% of its total population being 65 years and older [ 12 ]. This demographic shift has imposed increasing demands on nursing professionals, as older people often experience multiple chronic illnesses that result in physical and cognitive decline [ 13 ], necessitating substantial medical support and assistance in daily activities. In response to this demand, the Japanese government has actively pursued strategies to increase enrollment in nursing schools, reduce attrition rates, promote the retention of currently practicing nurses, and encourage inactive nurses to return to nursing practice [ 14 ]. However, the declining birth rate in Japan has led to a decrease in the number of students enrolling in nursing schools since 2018 [ 15 ]. Although the improvement in the workplace environment has contributed to a reduction in the turnover rate of full-time nursing personnel from 11.0% in 2013 to 10.6% in 2021, which is slightly lower than the average turnover rate across all occupations (i.e., 11.3%) [ 16 ], this alone cannot address the issue of the nursing shortage. Consequently, an inevitable imbalance between demand and supply persists. The Ministry of Health, Labor, and Welfare in Japan [ 14 ] projected a demand for 1.88–2.02 million nurses by 2025, when the baby boomer generation reaches 75 years old or older, while the projected supply would be 1.75–1.82 million nurses, resulting in a shortage of 60,000 to 250,000 nurses. Therefore, the recruitment of inactive nurses has emerged as a pivotal measure to rectify this imbalance promptly.

Available statistics show that there is an estimated population of approximately 700,000–860,000 inactive nurses in Japan [ 17 ], the United States [ 18 ] and Germany [ 19 ]. Several studies have demonstrated that a significant proportion of surveyed inactive nurses, ranging from 43 to 85%, expressed a desire to return to nursing practice [ 20 , 21 ]. The motivations behind their return or desire to return to nursing practice encompass factors such as no longer having childcare responsibilities [ 22 ], a yearning for nursing practice [ 22 ], seeking a renewed purpose in life after completing child-rearing [ 23 ], financial incentives [ 10 , 22 , 23 ], and a desire to update skills and knowledge in acute care nursing [ 24 ]. Similarly, a more recent study conducted in Taiwan reported that incentives for returning to practice included the improvement of the nurse staffing level, and the provision of a safer working environment and re-entry preparation programs [ 20 ].

However, it should be noted that despite the expressed intentions, many inactive nurses have faced challenges in returning to practice as well as in sustaining their employment [ 25 ]. These challenges related to returning to work include difficulties in balancing work with childcare and household responsibilities, anxiety arising from a perceived lack of competency, concern about heavy work responsibilities, and fears of committing medical errors [ 15 ]. Consequently, previous research findings have indicated that only 57–69% of nurses who completed the Return to Practice Program were able to successfully re-enter the nursing workforce [ 26 ]. These challenges persist even after returning to work, as reported in subsequent studies [ 27 , 28 , 29 ], exacerbated by the absence of family-friendly working conditions, inadequate on-the-job training opportunities, and insufficient ongoing education and mental support to overcome anxiety and regain confidence [ 30 ]. As a consequence, nurses who have returned to work experience a sense of guilt toward both their colleagues and patients for perceived inadequacies in care provision, as well as feelings of guilt toward their families due to the sacrifices necessitated by their work obligations [ 31 ], all of which contribute to higher attrition rates among returners. In fact, the findings from a small-scale survey conducted in Japan revealed that 25% of nurses who participated in refresher programs and returned to work were unable to sustain their employment [ 32 ]. This retention rate is significantly higher compared to the turnover rates observed among newly graduated nurses (7.8%) and nurses with prior experience (17.7%) [ 16 ].

While it is crucial to address the barriers encountered by nurses who wish to return to practice and have successfully done so, it is equally imperative to ensure the long-term sustainability of their careers following their return to work. However, the factors that contribute to the retention of these returners have not been thoroughly investigated. For instance, Barriball et al. [ 33 ] and Elwin [ 27 ] investigated the experiences of nurses returning to practice, although their focus was primarily on the experiences within the Return to Practice Program, rather than the process of returning to the workplace itself. Conversely, Durand and Randhawa [ 34 ], Hammer and Craig [ 23 ] and Costantini, et al. [ 35 ] explored the experiences of nurses returning to work; however, they did not focus on the specific factors that facilitate retention. In fact, only a limited number of studies have endeavored to identify factors that facilitate the retention of inactive nurses. The key findings facilitating their retention were preceptors fulfilling their learning needs [ 28 , 31 ], support on nursing units [ 31 ], flexible working atmosphere [ 28 ], and re-building a new family life [ 28 ] or re-negotiation with both work and home life [ 36 ]. Nevertheless, these studies are based on a relatively small sample of five to eight nurses who have returned to practice, thus leaving the possibility that some factors remain undiscovered. A comprehensive understanding of the factors that not only prompt nurses to leave their positions but also motivate them to remain is crucial for the development of strategies that ensure a sufficient nursing workforce and the provision of high-quality nursing care in countries grappling with nursing shortages.

Therefore, the aim of this study is to explore the factors that facilitate the retention of nurses who have returned to work, from their perspectives.

Methodology

This study employed a qualitative descriptive design [ 37 ]. The qualitative descriptive approach produces “findings closer to the data as given, or data-near” [ 38 , p. 78], without commitment to any theoretical views and without being bounded by preconceptions [ 38 ]. As such, this approach provides straightforward and comprehensive descriptive summaries of participants’ experiences and perceptions [ 39 , 40 ], thus it is suitable for areas where little is known about the topic under investigation [ 39 ]. We applied this approach to investigate the factors that contributed to the retention of these returners.

Participants

The participants were selected from seven healthcare institutions located in the southwestern region of Japan, and using convenience sampling and snowball sampling. The participants comprised re-entry nurses employed in five community hospitals and two long-term care facilities situated across metropolitan, urban, and rural areas of Japan with populations ranging from 0.4 million to 2.7 million. Inclusion criteria for the nurses were that they (1) had not practiced nursing for a minimum of three years prior to returning to work (based on the Japanese childcare policy allowing a maximum three-year leave), (2) had been working as nurses for a minimum of three months after returning to work, and (3) were able to participate in interviews conducted in Japanese. Exclusion criteria included: (1) working as nursing managers after returning to work, and (2) being without prior experience of working in Japanese healthcare institutions (i.e., those who only had overseas experience). Participants were recruited until saturation was reached, i.e., no further new information emerged during the interviews. A total of 15 participants were recruited as a result.

Data collection

The research team approached the Directors of Nursing and obtained permission to recruit potential participants. Written statements were distributed to the potential participants to explain the purpose and methods of the study.

Semi-structured interviews (see Table 1 for the interview guide) were conducted face-to-face or online, between November 2021 and July 2022. The interview guide was developed based on the research purpose and the review of existing literature. The first author conducted all interviews because her 16-year career hiatus from nursing for child-rearing would help her establish a mutually respectful relationship with the participants and foster an environment free from intimidation. These conditions are crucial for eliciting participants’ genuine sentiments. Throughout the interviews, the author demonstrated respect and empathy toward the participants by openly sharing her own feelings. Additionally, she skilfully guided the discussions to extract the participants’ experiences, concurrently undergoing a process of reintegration in tandem with them. Conversely, the dynamic between the interviewer and participants could be impacted by the assumptions and biases inherent in the interviewer’s background. To mitigate this potential influence, data analysis was performed independently by two researchers (refer to the Data Analysis section).

The interviews were conducted in private rooms, and all sessions were audio-recorded. Nonverbal data, such as the participants’ posture during the interviews, were recorded in an observation notebook. Each participant underwent a single interview session and received a book voucher valued at ¥2500 as a token of appreciation. The interviews lasted between 18 and 49 min (Mean = 39.2 min). Audio-recorded data were transcribed verbatim.

Data analysis

Qualitative inductive analysis [ 41 ] was conducted. Verbatim transcripts were thoroughly reviewed to develop an overall understanding of the participants’ statements. Meaningful words and paragraphs related to the factors that had facilitated the retention of these re-entry nurses were extracted, and codes were assigned to represent the symbolic meanings of the data segments (first-cycle coding). Subsequently, the codes were compared and contrasted to group them into categories based on their similarities in meaning. These categories were further integrated into themes that captured the essence of the factors facilitating the retention of nurses who returned to the nursing workforce (second cycle coding). The first-cycle coding was conducted by the first author (KY) by utilizing her understandings of the participants’ context and their experiences. In the second cycle of coding, the first (KY)and second (KN) authors independently categorized the codes, and the congruencies or discrepancies between them were discussed among all the research team members (KY, KN, YN, and MT), who possessed nursing backgrounds and qualitative research experience. Discussion continued until consensus was reached among all the research members. NVivo12 (QSR International, Melbourne, Australia) software was used for data management.

The trustworthiness of the study

Ensuring credibility, confirmability, transferability and dependability contributes to the trustworthiness of the study [ 42 ]. To enhance the credibility, we applied method triangulation. The interviewer (i.e., the first author) took notes on the participants’ facial expressions and eye movements during the interview, which were included in the analysis along with the verbatim transcripts of the interview data. During the analysis process, the first author repeatedly read the transcripts and observational notes to code the data. For confirmability, two researchers independently categorized the codes, and discussions among the research team took place repeatedly to ensure the elimination of any preconceptions or biases. Any disagreements that arose during this process were resolved through discussions among the research team. To enhance the transferability of the findings, participants were recruited from diverse practice areas and various regions. Furthermore, detailed information was provided regarding the participants’ characteristics and their practicing contexts. In addition, the dependability of the findings was assured by providing detailed descriptions of the data collection and analysis process.

Ethical considerations

This study was approved by the Review Board of Yasuda Women’s University (approval number: 210007), and ethical approval was waived by the participating institutions. This study was conducted in accordance with the Declaration of Helsinki. The participants were fully informed about the study’s purpose, methods, potential risks, and benefits of participation as well as their right to decline participation or withdraw from the study. Written informed consent was obtained from each participant before the data collection. The interview schedule and location were prioritized according to the preferences of the participants, as many were balancing work and childcare responsibilities. Participants were assured that they could refrain from answering any questions that made them feel uncomfortable. Additionally, they were informed that they could end the interview session at any time if they experienced emotional distress. The collected data were securely stored in a locked cabinet, and pseudonyms were used to maintain the participants’ anonymity and protect their privacy.

All 15 eligible participants were female. The reasons cited for leaving employment were childbirth/child-rearing in 11 cases, caring for older family members in three cases, and pursuing a postgraduate degree in one case. The range of length of clinical experience before leaving employment was 3–20 (Mean = 8.2, SD = 4.2) years, that of career breaks was 3–19 (Mean = 6.6, SD = 4.0) years, and that of work after returning was 7 months to 8 years (Mean = 2.6 years, SD = 1.7 years). During the period of data collection, only two participants worked full-time, and 13 worked part-time. The areas of practice encompassed outpatient departments in hospitals ( n = 8), hospital wards ( n = 4), and long-term care facilities ( n = 3) (see Table 2 ).

The data analysis revealed five themes that facilitated the continuation of work for these participants. These themes include “Conditions and support that sustain work-life balance,” “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and family’s life.” The first two themes represent conditions that enabled the participants to continue their work. Thus, these conditions are referred to as “enablers”. The latter three themes describe factors that motivated the participants to pursue their professional careers. Thus, these factors are referred to as “motivators”.

Theme 1: conditions and support that sustain work-life balance

The participants identified support systems at home, in the workplace, and within society as prerequisites for maintaining a work-life balance, essential for sustaining their employment. This theme encompasses crucial elements that allow nurses to balance their work and family responsibilities, such as work conditions that consider their family circumstances, and support from family and friends. The theme consists of three categories: “Work (i.e., hours and location) and childcare conditions that meet my preferences,” “A family-friendly work environment,” and “Instrumental and emotional support from family and friends.”

Most participants juggled work, household, and childcare responsibilities. Therefore, effectively managing childcare duties while fulfilling work roles became a priority in their lives. Access to childcare facilities was deemed a basic requirement for them to work, as well as conditions such as workplaces located close to their homes and offering flexible working hours to address child-related matters promptly.

“When I was contemplating returning to work, one requirement was that I should be able to look after my two children, so it was important for me that all the conditions related to my children were in place, such as time restrictions and being able to go home immediately if something happens to them.” (ID 10) .

The participants also emphasized the need for a family-friendly work environment, where colleagues and supervisors understood their family circumstances and provided support in balancing work and family duties.

“When I returned to work, I wondered if I would be allowed to take a sudden leave if my child was ill. And they told me, ‘We take turns (taking a leave) so you can do it now, it’s fine,’ as well as ‘We can’t do it for you (take care of your child) but we can do the work in your place.’ Here at my current workplace, we can say such things to each other.” (ID 06) .

Given that most participants were engaged in multiple tasks both at home and work, they experienced physical and mental fatigue and strain. However, they managed to overcome these challenges by receiving instrumental and emotional support from their families and friends. Examples of such assistance included husbands and children sharing household chores and friends providing emotional support during conflicts arising from the intersection of family and work responsibilities.

“Regarding my husband, yes. When I started working, I was no longer a full-time housewife. But I’ve been working alongside him, and he’s been supporting me a lot, such as by taking the kids to school and picking them up after, things like that.” (ID 13) .

Ensuring the effective management of household responsibilities, particularly childcare, was a fundamental prerequisite for the participants to continue their employment. Consequently, the provision of “Conditions and support that sustain work-life balance” acted as an enabler, facilitating their continued engagement in work by sustaining their personal lives.

Theme 2: a workplace that acknowledges my career, and encourages my growth as an experienced nurse

The participants asserted that receiving support to cultivate their professional competencies within their work environment facilitated their transition through a process of reorientation. The participants were returners who had prior nursing experience and possessed a certain level of nursing competence required for professional practice. Initially apprehensive about their competence level, they desired recognition and appreciation for their previous experience and expertise from their supervisors and colleagues. They also expressed a preference for on-the-job refresher training that helped them regain necessary knowledge and skills. This training differed from that provided to newly graduated nurses. This theme represents the importance of receiving educational support to function as a nurse and opportunities for further growth, both of which facilitated the continuation of their work. The theme comprises three categories: “Supervisors and colleagues who appreciate and accept me,” “Support for myself as both a beginner and someone with experience,” and “Comprehensive manual and training.”

The participants emphasized the significance of being recognized and accepted by their colleagues and supervisors. The acknowledgment of their efforts by supervisors and the understanding of their hard work by colleagues served as encouragement to sustain their work. Furthermore, perceiving themselves as individuals who were relied upon by others and striving to meet those expectations facilitated their professional growth and their desire to contribute to the workplace.

“One thing is that um, I also discussed this with the Head Nurse, regarding training, that maybe we should improve the training even more, and the Head Nurse feels the same way, and so, she said I can go ahead and think about a program or something. When I’m entrusted with making these kinds of decisions, the work becomes fulfilling.” (ID 09) .

The participants also expressed the importance of receiving support from their colleagues as newcomers while appreciating their prior experience. The participants were often perceived as fully capable individuals and were assigned a workload equivalent to that of experienced nurses. However, the participants stressed the need for support from their colleagues during the initial phase of readjustment to their duties. Simultaneously, they sought appropriate levels of support while valuing their previous work experience and expertise. They felt reassured when their supervisors or colleagues offered support, recognizing them as both a beginner but also as someone with experience.

“From the day after I started working, I had my own room, and on that day, someone from the day shift always made it a point to talk to me and support me, and it felt like fate. I thought if I were being supported this much, I should do the same, and well, everyone in the ward helped me understand the patients within the week, so much that I thought I already remember them. I felt that I should make an effort to do so, since they supported me so much.” (ID 06) .

Additionally, they desired to receive training and manuals tailored to their skill set, enabling them to effectively perform their roles as staff members.

“Although it was only 3 years, I did have a work gap, so I was thinking that my skills and knowledge might be obsolete and that I might have forgotten some things, but this hospital has a very detailed manual.” (ID 06) .

Acceptance and support from both managers and colleagues, coupled with access to on-the-job training and manuals, emerged as crucial resources enabling participants to realign with their work responsibilities, especially in cases where they lacked up-to-date knowledge and skills. Additionally, feeling valued and trusted by colleagues played a pivotal role in bolstering their confidence, an essential attribute for navigating through challenging periods. Consequently, the provision of “A workplace that acknowledges my career, and encourages my growth as an experienced nurse” served as the pivotal enabler that sustained their professional life though continued commitment to their careers.

Theme 3: pride in reconnecting with and contributing to society

The participants described working as nurses as giving them a sense of pride and of being valuable to society, which motivated them to continue their work. Prior to returning to work, the participants experienced social isolation due to their engagement in various household responsibilities. However, returning to the nursing profession allows the participants to reclaim their roles as active members of society and regain confidence in their contribution to society. The theme comprises three categories: “Desire to contribute as a nurse,” “Expansion of relationships resulting from stepping out of the home,” and “My children feeling proud of me for being an active nurse.”

The participants maintained a strong sense of pride in their profession and were motivated by the desire to contribute to society as nurses, utilizing their nursing qualifications. As the demand for nurses increased during the COVID-19 pandemic, their determination to support patients as nurses grew even stronger. They also expressed a desire to share their expertise with younger nurses and provide guidance to other inactive nurses who were considering returning to work.

“Nurses are needed in situations like COVID-19, and I had gone through the trouble of getting my license, and all that.” (ID 03) . “Well, I’d like to be in a position where people feel they can ask me and maybe find a bit of a solution. I work with the mindset that someone a bit older, like me, should take a role of listening to and giving advice to younger colleagues.” (ID 8) .

Moreover, returning to work reaffirmed their sense of belonging to society not only as mothers but also as nurses. When they were solely focused on child-rearing, their social interactions were limited to those associated with their children. However, by returning to work and establishing their own place in the workplace, their social connections expanded beyond the confines of their homes. The opportunity to reconnect with broader society and experience personal freedom outside of their domestic responsibilities served as a motivation for the participants to continue their work.

“It definitely connects me to society. Until now, my connections with society were through my child. I think I couldn’t have had that without my child, and now it feels like I have a separate community of my own. I feel like that.” (ID 08) .

Furthermore, their pride in being nurses was reinforced by the admiration of their children, who proudly spoke of their mothers’ profession, especially during the challenging times of the pandemic. This alleviated any guilt associated with not having enough time to devote to their children and not fulfilling their maternal roles to the same extent as before. On the contrary, their professional engagement enhanced their self-esteem as proud mothers to their children.

“When I think of these moments, it makes me really happy. Like those moments when I feel that my children have become interested in me (omitted). For example, when they say things like, ‘Nurses are really cool,’ or ‘My mom works in a hospital.’ They’ve even written about me in their diaries.” (ID 01) .

Reclaiming a sense of pride and expanding their professional network through contributions to society represented profoundly fulfilling experiences for the participants. These experiences not only brought them joy in their work but also transcended the mere facilitation of work continuation. Consequently, “Pride in reconnecting with and contributing to society” operated as a potent motivator, driving their commitment to pursue their professional careers and advance, thus enriching their professional life.

Theme 4: cultivating confidence through incremental professional development and future envisioning

The participants were motivated to continue their work by their passion for professional growth and self-actualization. The participants engaged in introspection regarding their journey from the moment they returned to work up until the present. Despite encountering challenging circumstances, they swiftly reacquired previously possessed skills and knowledge, thus restoring their self-assurance in the practice of nursing. This newfound confidence propelled them to envision their future career paths. The following three categories encompass this overarching theme: “Confidence arising from successfully surmounting challenges upon restarting,” “Realization that I have finally made my comeback as a nurse,” and “Personal aspirations for the future.”

According to the participants, they encountered arduous situations upon re-entering the workforce, as they were frequently required to perform tasks that exceeded their current skill sets. Irrespective of their absence from work, their colleagues often regarded them as seasoned nurses. Struggling to fulfill assigned responsibilities, they engaged in negotiations with colleagues and supervisors, asserting their capabilities and limitations. These challenging experiences facilitated the recovery and enhancement of the necessary skills and knowledge, bolstering their confidence, and motivating them to persevere in their work.

“After returning to work, for about half a year, I struggled for a while before getting used to it again. It took me more than six months to understand why I was struggling. But when I got used to the working life, I was able to gain self-confidence.” (ID 04) .

Through introspection and self-comparison between the time of restarting and the present, the participants recognized their continuous development as nursing professionals, observing their ability to provide a sufficient level of patient care.

“In the sense that my intuition has returned, um, it was definitely the fact that before I started working, all I had was anxiety, but when I was actually able to perform my work by myself again, I think that was when I became confident.” (ID 10) .

This developmental process stimulated their anticipation of future career prospects. Some participants expressed aspirations to acquire advanced qualifications and pursue managerial positions, thus making career advancement their future objective.

“There was definitely something different about me, internally, before and after returning to work. It seems like I was lively, like I was going to set my goals, and that I was doing my best. There was a sense of certainty (omitted) and I was able to find what I wanted to do, too.” (ID 11) .

The successful completion of the readjustment journey played a pivotal role in bolstering the participants’ confidence, and encouraged them to envisage future professional goals. The process of “Cultivating confidence through incremental professional development and future envisioning” emerged as a critical motivating factor (i.e., motivator), propelling the participants towards continued professional growth, and thereafter enriching their professional life.

Theme 5. Enrichment of my own and my family’s life

The participants perceived added value when their own lives and their families were enriched by their work, which encouraged them to continue their jobs. The participants acknowledged the positive transformations in their physical and emotional well-being, as well as in the lives of their families, following their return to work. They perceived an overall improvement in their daily lives. This theme encompasses three categories: “A healthy mind and body attained by adding variety to life,” “Positive influence on family dynamics,” and “Income that enriches my life.”

The participants said that resuming employment contributed to a well-rounded lifestyle and positively impacted their physical and mental health. Specifically, those who were responsible for raising children noted that having time away from their children reduced feelings of irritability and enabled them to engage with their children in a more compassionate and nurturing manner upon returning home from work.

“I feel like my day has become balanced. I do feel a little sad that I’m spending a lot more time away from my children (omitted). I make up for it when I see them, and I think I’ve become a little less irritable.” (ID 10) .

Furthermore, having a job established a consistent rhythm to their lives and facilitated physical fitness, thus promoting a balanced existence. They also perceived the involvement of others in caring for their children as an opportunity for their children to interact with a broader network of individuals, fostering their growth and healthy development. Moreover, the up-to-date medical knowledge gained through their work served to safeguard the health of their families.

“Because I want to know about cutting-edge technology. You know, if I quit this job, it will affect my life directly, because it’s a job that involves the body after all. I think it’s always gonna be useful (in my life).” (ID 13) .

By earning their own income, they were able to provide economic security to their families, which was closely linked to their mental well-being.

“Before I was reinstated, we were living on my husband’s salary alone. I felt bad about it, but now we have some financial leeway, so that definitely was a benefit for me.” (ID 11) .

Resuming employment engendered an ‘Enrichment of my own and my family’s life,’ demonstrated by enhancements in physical and mental well-being, the wholesome development of children, and economic incentives. Consequently, this theme illustrates the enrichment of the participants’ personal lives as a result of having fulfilling professional lives, and emerged as an additional motivator.

This study explored factors contributing to the retention of nurses re-entering the workforce after a career break, resulting in the identification of five themes. The first two, “Conditions and support that sustain work-life balance” and “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” were identified as enablers, sustaining the participants’ continued engagement in work. The next three themes, “Pride in reconnecting with and contributing to society,” “Cultivating confidence through incremental professional development and future envisioning,” and “Enrichment of my own and family’s life,” served as motivators, propelling them toward a professional career.

The concept of enablers and motivators parallels Herzberg’s Two-Factor Theory of Motivation [ 43 ], where hygiene factors, including salary and work conditions, are essential but their absence leads to dissatisfaction, while motivation factors, like achievement and recognition, promote job satisfaction and enhanced performance [ 43 ]. Similarly, enablers such as family-friendly work conditions, peer support, and on-the-job training played pivotal roles in the participants’ job continuity, and their absence could result in dissatisfaction or job exit. Likewise, motivators such as pride and confidence yielded personal fulfillment, motivating participants to pursue their professional goals. However, distinctions arise. While the Two-Factor Theory focuses on work components, our study contends that healthcare institutions must address both professional and personal factors for nurse retention. This is critical, particularly for returning nurses, often with caregiving responsibilities, necessitating a balance between sustaining and enriching their professional and personal lives. Another distinction lies in the relationship between the enablers and motivators. According to the Two-Factor Theory, hygiene and motivation factors exist independently, while motivators do not exist without the presence of enablers. For example, without adequate support for nurses to achieve work-life balance, they are unable to enhance their own or their family’s quality of life. Similarly, lacking encouragement in professional development, nurses are unable to cultivate pride or confidence, or envision their future. These relationships are depicted in Fig. 1 . The subsequent sections provide a detailed explanation of each of these factors.

Framework for the sustainability of career for returners

The first theme, “Conditions and support that sustain work-life balance,” functions as an enabler that sustains nurses’ personal life. Nurses are prominent double-duty caregivers, tending to family and patients [ 44 ]. The majority of our participants had children, reflecting the fact that in Japan, 55–66% of nurses are parents [ 16 , 45 ]. Therefore, balancing family and work is crucial, regardless of career breaks. Specifically, nurses who temporarily left their employment due to childcare responsibilities had various reasons such as the absence of available childcare support. Especially in Japan, women often prioritize their childcare responsibilities over work commitments, or may feel societal pressure to remain at home and care for their children [ 46 ]. These cultural practices and norms could potentially elucidate their career hiatus. Therefore, family-friendly working conditions (e.g., flexible hours, location, childcare support) are vital for returning and sustaining work. This finding is consistent with previous studies indicating that workplace flexibility, which helps alleviate childcare concerns, is crucial for enabling nurses to sustain their work [ 28 , 30 , 35 , 36 ]. Furthermore, nurses who juggle dual caregiving roles often experience fatigue and stress [ 44 ]. Therefore, receiving instrumental and emotional support from their spouses is essential for maintaining a healthy work-life balance. In fact, recent studies have highlighted that support from their families enables nurses to effectively manage the demands of both their family and work spheres, facilitating their re-entry into professional practice [ 28 , 35 ]. The successful sharing of household responsibilities and childcare is indispensable for returners who aspire to continue their professional work, particularly those with young children.

The second theme, “A workplace that acknowledges my career, and encourages my growth as an experienced nurse,” serves as an enabler that sustains the professional practice of returners. This finding is also in line with previous studies that have highlighted the significance of a supportive work environment in aiding individuals to manage their jobs and regain confidence [ 28 , 35 ]. Although returners are often perceived as experienced nurses capable of functioning independently, the literature indicates that they encounter significant challenges in reacquiring their previous knowledge and skills, while also adapting to the rapidly advancing field of medical technology [ 21 , 33 , 35 ]. Reintegrating into the nursing workforce is arduous, and returners often experience anxiety and confidence issues [ 27 , 31 ]. This was also evident among our participants. Consequently, receiving appropriate initial training and access to manuals are critical factors enabling returners to fulfill their duties and sustain their professional work [ 30 ]. On the other hand, the majority of the participants had achieved an expert nurse level, possessing more than five years of previous clinical experience [ 47 ], thus they desired recognition and acceptance of this. The need for acceptance and respect was also identified in previous studies on returning nurses [ 27 , 30 ]. Appreciating their skills, efforts, and contributions while identifying areas for professional development represents the ideal “just-right preceptorship” for returners. Organizational support of this nature promotes work engagement [ 48 ], thus sustaining their professional practice.

While the existing literature commonly highlights the enablers necessary for nurses to return to work and continue their professional roles, previous studies have overlooked the motivating factors that drive them to work. Merely creating a sustainable environment for their return is insufficient. Internal drivers are essential to maintain their motivation to work, especially during challenging times. The following three themes describe the motivators that encourage nurses to pursue their professional careers, thus enriching their professional life.

“Pride in reconnecting with and contributing to society” stimulates nurses’ work motivation and enriches their professional lives. Previous studies have demonstrated that returning to work helps them regain self-esteem through their contribution to society, increasing pride as valuable society members [ 35 , 36 ]. This study contributed new knowledge by highlighting how this sense of pride motivates returning nurses to pursue their professional careers. Nurses who had previously been inactive cited the desire to utilize their qualifications and contribute to the welfare of society as the main reason for returning to work [ 16 ]. They took pride in being nurses and were eager to apply their professional knowledge and skills, supported by their abundant clinical experience. This aligns with previous studies emphasizing their high levels of clinical and leadership skills [ 20 , 28 ] and the enthusiasm exhibited by returners [ 30 ]. While initially struggling to adjust, their experience enables them to quickly adapt [ 33 ]. Once they regain competence, they contribute to healthcare and society by providing competent nursing care, educating colleagues, and serving as successful examples for potential returners. These experiences may instill a career calling characterized by self-actualization, personal fulfillment, and passion for their work [ 49 ], which promote job satisfaction [ 50 ] and engagement [ 51 ]. Returning to work also allows them to establish their societal position and expand their network, which is limited when solely fulfilling household responsibilities. According to the Self-Determination Theory [ 52 ], relating to others by engaging in employment outside the home not only alleviates isolation but also enhances their motivation. Additionally, contributing to society as valued members of the healthcare profession enhances their self-esteem [ 36 ] and allows them to cultivate a professional identity. If their children or significant others take pride in the nursing profession, their identification with nursing becomes stronger. During the COVID-19 pandemic, nurses were portrayed as heroes combating the crisis, which enhanced their professional identity and the pride their families had in them. Professional identity is known to enhance individual motivation to remain in the profession [ 53 , 54 ]. Therefore, reconnecting with and making contributions to society enrich nurses’ professional lives.

“Cultivating confidence through incremental professional development and future envisioning” represents another motivator that enriches the professional lives of returners. Previous studies have shown the struggles and challenges that returning nurses faced in their journey towards reintegration, and in reaffirming their identity as nursing professionals [ 28 , 31 , 35 ]. When restarting their careers, returning nurses often experience anxiety due to changes within healthcare institutions, such as the introduction of new medical equipment and technology, shifts in insurance policies, increased demands for high-level physical assessment skills, and the expanded scope of responsibilities they now carry [ 55 ]. Nevertheless, the participants in this study successfully overcame numerous challenges and navigated the journey of reintegration. This experience of triumph and the acquisition of new knowledge and skills enabled them to regain the confidence they had in their previous career. Reflecting on their hard work and learning trajectory also instilled a sense of professional growth. Possessing confidence and a sense of self-worth has enhanced their self-efficacy, which, in turn, has promoted affective organizational commitment [ 56 ] and work engagement [ 57 ]. Furthermore, a successful reintegration fulfills their need for competence, thereby bolstering their motivation [ 52 ]. In addition. their learning achievements foster expectations for their future career goals. Having a clear goal enhances their professional development and further enriches their professional life. This study contributes new insights by demonstrating that perceiving their own professional development and embracing future goals motivates them to continue their work.

The final theme, “Enrichment of my own and family’s life,” highlights the reciprocity between personal and professional aspects for returners. Returning to work enables a balanced lifestyle, which improves mental and physical health and reduces strain and fatigue for double-duty caregivers. Employment also provides financial stability and enriches personal life, aligning with the previous findings [ 35 ]. Financial incentives are often cited as reasons for nurses to consider returning [ 23 , 33 ]. While extrinsic, these incentives improve individuals’ quality of life, enriching their minds and energizing their work. Furthermore, work positively influences family dynamics, countering feelings of guilt at leaving children, often portrayed as a negative consequence of returning to work [ 31 ]. The participants in this study recognized the benefits, such as positive effects on their children’s healthy development, and how it led to an improved relationship with their children. Another study also observes a positive reciprocal relationship between work and family [ 35 ]. The theory of work-family enrichment asserts that " experiences in one role improve the quality of life in the other role” [ 58 ]. Work enriches personal life, while fulfillment in personal life motivates job continuation. Positive family experiences also enhance work performance [ 59 ]. Enrichment of personal life forms the foundation for individual professional life, and vice versa. This study reveals a new insight: returning to work can yield positive outcomes for nurses’ own lives and those of their families, particularly concerning child development.

Implications for nursing management

The findings of this study suggest that in order to retain returners in the current nursing force, it is imperative to maximize both the enablers and motivators that contribute to the sustainability and enrichment of their personal and professional lives. In order to maximize the enablers, the establishment of a family-friendly environment is crucial. Nurse managers should strive to comprehend the personal and professional lifestyles that returners desire and should provide support accordingly. Furthermore, the formation of a mutual support group among returners can facilitate the exchange of experiences and encouragement, as well as make it possible to accommodate shift changes when family-related issues arise. The provision of adequate training is also of paramount importance. Unlike new graduate nurses, returning nurses possess diverse nursing skills and experience, necessitating a comprehensive evaluation by managers and colleagues to determine their competencies, while simultaneously providing them with the necessary knowledge and skills required for current clinical practice.

To enhance motivators, nursing managers should actively encourage returners to revive their professional pride and sense of fulfillment as nurses. One effective approach involves providing positive and constructive feedback on their contributions to the well-being of patients, thereby bolstering their pride. Additionally, managers need to assist returners in regaining their confidence and should support their progress toward achieving personal goals. Encouraging self-reflection on their clinical experiences can serve as a powerful means to help them realize the extent of their growth and subsequently enhance their confidence [ 31 ]. Assisting them in setting future professional goals represents another important strategy. Finally, managers should help returners recognize the positive changes that have occurred in their family dynamics as a result of their return to work. Engaging in discussions about personal life with managers or other returners may prove beneficial in this regard.

Limitations

Efforts were made to enhance the transferability of the findings, by recruiting a heterogeneous sample of returning nurses, considering factors such as the duration of their career breaks, the length of clinical experience after returning, their employment status, and their area of practice. However, it cannot be assured that our sample is truly representative of Japanese returning nurses due to the relatively limited number of participants in this study. To enhance the transferability of the results, future studies should aim to replicate this research by encompassing diverse characteristics of returning nurses from various geographical locations. This approach would facilitate the aggregation of findings and the formulation of more robust programs designed to promote the retention of re-entering nurses.

The nursing shortage is a persistent issue that is anticipated to worsen in the foreseeable future. The available solutions to alleviate this problem are limited, and a cost-effective approach involves incentivizing inactive nurses to rejoin the nursing workforce [ 60 ]. Returning nurses constitute a valuable asset for hospitals, as they possess a renewed professional commitment and can quickly regain nursing competence. Furthermore, their diverse experience in various clinical areas and organizations has the potential to introduce innovative clinical and managerial solutions within the current healthcare setting, thereby enhancing clinical outcomes and improving patient satisfaction. Therefore, it is imperative to implement multi-dimensional approaches aimed at retaining and harnessing the potential of these valuable human resources.

Data availability

The data are not publicly available because they contain information that could compromise the privacy of the research participants.

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Acknowledgements

The authors would like to thank the participants for participating in the study and for sharing their experiences.

This work was supported by JSPS KAKENHI Grant Number 22K10697.　The funder had no role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.

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KY designed this study under the supervision of MT. KY performed the data collection and the initial data analysis. KY, KN, YN and MT contributed to the data analysis. KY, KN and MT wrote the manuscript. All co-authors reviewed the manuscript and approved the final manuscript for publication.

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This study was approved by the Review Board of Yasuda Women’s University (approval number: 210007). This study was conducted in accordance with the Declaration of Helsinki. The participants were fully informed about the study’s purpose, methods, potential risks, and benefits of participation as well as their right to decline participation or withdraw from the study. Written informed consent was obtained from each participant before the data collection. The collected data were securely stored in a locked cabinet, and pseudonyms were used throughout the paper to maintain the participants’ anonymity and protect their privacy.

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Yamamoto, K., Nasu, K., Nakayoshi, Y. et al. Sustaining the nursing workforce - exploring enabling and motivating factors for the retention of returning nurses: a qualitative descriptive design. BMC Nurs 23 , 248 (2024). https://doi.org/10.1186/s12912-024-01900-5

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Effectiveness of Psychosocial Skills Training and Community Mental Health Services: A Qualitative Research

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Published: 22 April 2024

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Halil İbrahim Bilkay ORCID: orcid.org/0000-0002-8231-960X 1 ,
Burak Şirin ORCID: orcid.org/0000-0002-8485-5756 2 &
Nermin Gürhan ORCID: orcid.org/0000-0002-3472-7115 2

This study employs a phenomenological approach to investigate the experiences of individuals who access services at a community mental health center (CHMC) in Türkiye The aim of this study is to comprehend the experiences of individuals who participate in psychosocial skills training at the CHMC. Thematic analysis of data from sixteen in-depth interviews revealed three main themes and eight sub-themes. Functionality theme emphasizes the positive impact of CHMC services and training on daily life and social functioning. Effective Factors theme encompasses the elements that improve the effectiveness of CHMC services. Participants have provided suggestions for the content of the training under the theme of Recommendations. Study results show that CHMC services and psychosocial skills training benefit individuals' daily lives and functioning, but that opportunities for improvement exist. It is crucial to incorporate participant feedback, and further research should be conducted to investigate the effectiveness of these services in this area.

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Published: 20 April 2024

“I am in favour of organ donation, but I feel you should opt-in”—qualitative analysis of the #options 2020 survey free-text responses from NHS staff toward opt-out organ donation legislation in England

Natalie L. Clark 1 ,
Dorothy Coe 2 ,
Natasha Newell 3 ,
Mark N. A. Jones 4 ,
Matthew Robb 4 ,
David Reaich 1 &
Caroline Wroe 2

BMC Medical Ethics volume 25 , Article number: 47 ( 2024 ) Cite this article

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In May 2020, England moved to an opt-out organ donation system, meaning adults are presumed to be an organ donor unless within an excluded group or have opted-out. This change aims to improve organ donation rates following brain or circulatory death. Healthcare staff in the UK are supportive of organ donation, however, both healthcare staff and the public have raised concerns and ethical issues regarding the change. The #options survey was completed by NHS organisations with the aim of understanding awareness and support of the change. This paper analyses the free-text responses from the survey.

The #options survey was registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992] 14 February 2020, and was completed between July and December 2020 across NHS organisations in the North-East and North Cumbria, and North Thames. The survey contained 16 questions of which three were free-text, covering reasons against, additional information required and family discussions. The responses to these questions were thematically analysed.

The #options survey received 5789 responses from NHS staff with 1404 individuals leaving 1657 free-text responses for analysis. The family discussion question elicited the largest number of responses (66%), followed by those against the legislation (19%), and those requiring more information (15%). Analysis revealed six main themes with 22 sub-themes.

Conclusions

The overall #options survey indicated NHS staff are supportive of the legislative change. Analysis of the free-text responses indicates that the views of the NHS staff who are against the change reflect the reasons, misconceptions, and misunderstandings of the public. Additional concerns included the rationale for the change, informed decision making, easy access to information and information regarding organ donation processes. Educational materials and interventions need to be developed for NHS staff to address the concepts of autonomy and consent, organ donation processes, and promote family conversations. Wider public awareness campaigns should continue to promote the positives and refute the negatives thus reducing misconceptions and misunderstandings.

Trial registration

National Institute of Health Research (NIHR) [IRAS 275992].

Peer Review reports

In England May 2020, Max and Kiera’s Law, also known as the Organ Donation (Deemed Consent) Bill, came into effect [ 1 , 2 ]. This means adults in England are now presumed to have agreed to deceased organ donation unless they are within an excluded group, have actively recorded their decision to opt-out of organ donation on the organ donor register (ODR), or nominated an individual to make the decision on their behalf [ 1 , 2 ]. The rationale for the legislative change is to improve the organ donation rates and reduce the shortage of organs available to donate following brain or circulatory death within the UK [ 2 , 3 , 4 ]. This is particularly important considering the growing number of patients awaiting a transplant. Almost 7000 patients were waiting in the UK at the end of March 2023 [ 5 ]. Wales was the first to make the legislative change in December 2015, followed by Scotland in March 2021 and lastly Northern Ireland in June 2023 [ 2 ]. Following the change in Wales, consent rates had increased from 58% in 2015/16 to 77% in 2018/19 [ 6 ], suggesting the opt-out system can significantly increase consent, though it further suggests that it might take a few years to fully appreciate the impact [ 7 , 8 ]. Spain, for example, has had an opt-out legislation since 1979 with increases in organ donation seen 10 years later [ 9 ].

Research, however, has raised concerns from both the public and healthcare staff regarding the move to an opt-out system. These concerns predominantly relate to a loss of freedom and individual choice [ 9 , 10 ], as well as an increased perception of state ownership of organs [ 10 , 11 , 12 ] after death. Healthcare staff additionally fear of a loss of trust and a damaged relationship with their patients [ 9 , 11 ]. These concerns are frequently linked to emotional and attitudinal barriers towards organ donation, understanding and acceptance [ 9 ]. Four often referenced barriers include (1) jinx factor: superstitious beliefs [ 13 , 14 , 15 ]; (2) ick factor: feelings of disgust related to donating [ 13 , 14 , 15 ]; (3) bodily integrity: body must remain intact [ 13 , 14 , 15 ]; (4) medical mistrust: believing doctors will not save the life of someone on the ODR [ 13 , 14 , 15 ]. The latter barrier is mostly reported by the general public in countries with opt-out systems [ 13 , 14 , 16 ] although medical mistrust does feature as a barrier across all organ donation systems. In addition, it is a reported barrier healthcare staff believe will occur in the UK under an opt-out system [ 9 , 16 ].

Deceased donation from ethnic minority groups is low in the UK, with family consent being a predominant barrier in these groups. Consent rates are 35% for ethnic minority eligible donors compared to 65% for white eligible donors [ 5 ]. The reasons for declining commonly relate to being uncertain of the person’s wishes and believing it was against their religious/cultural beliefs. Healthcare staff, particularly in the intensive care setting, have expressed a lack of confidence in communication and supporting ethnic minority groups because of language barriers and differing religious/cultural beliefs to their own [ 17 ]. However, one study has highlighted that generally all religious groups are in favour of organ donation with respect to certain rules and processes. Therefore, increasing knowledge amongst healthcare staff of differing religious beliefs would improve communication and help to sensitively support families during this difficult time [ 18 , 19 ]. However, individually and combined, the attitudinal barriers, concerns towards an opt-out system, and lack of understanding about ethnic minority groups, can have a significant impact within a soft opt-out system whereby the family are still approached about donation and can veto if they wish [ 11 , 12 , 20 ].

The #options survey [ 21 ] was completed online by healthcare staff from National Health Service (NHS) organisations in North-East and North Cumbria (NENC) and North Thames. The aim was to gain an understanding of the awareness and support to the change in legislation. The findings of the survey suggested that NHS staff are more aware, supportive, and proactive about organ donation than the general public, including NHS staff from religious and ethnic minority groups. However, there were still a number who express direct opposition to the change in legislation due to personal choice, views surrounding autonomy, misconceptions or lack of information. This paper will focus on the qualitative analysis of free-text responses to three questions included in the #options survey. It aims to explore the reasons for being against the legislation, what additional information they require to make a decision, and why had they not discussed their organ donation decision with their family. It will further explore a subset analysis of place of work, ethnicity, and misconceptions. The findings will aid suggestions for future educational and engagement work.

Design, sample and setting

The #options survey was approved as a clinical research study through the integrated research application system (IRAS) and registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992]. The survey was based on a previously used public survey [ 22 ] and peer reviewed by NHS Blood and Transplant (NHSBT). The free-text responses used in #options were an addition to the closed questions used in both the #options and the public survey. Due to the COVID-19 pandemic, the start of the survey was delayed by 4 months, opening for responses between July to December 2020. All NHS organisations in the NENC and North Thames were invited to take part. Those that accepted invitations were supplied with a communication package to distribute to their staff. All respondents voluntarily confirmed their agreement to participate in the survey at the beginning. The COnsolidated criteria for REporting Qualitative research (COREQ) checklist was used to guide analysis and reporting of findings [ 23 ], see Supplementary material 1 .

Data collection and analysis

The survey contained 16 questions, including a brief description of the change in legislation. The questions consisted of demographic details (age, sex, ethnicity, religion), place of work, and if the respondent had contact with or worked in an area offering support to donors and recipients. Three of the questions filtered to a free-text response, see Supplementary material 2 . These responses were transferred to Microsoft Excel to be cleaned and thematically analysed by DC. Thematic analysis was chosen to facilitate identification of groups and patterns within large datasets [ 24 ]. Each response was read multiple times to promote familiarity and initially coded. Following coding, they were reviewed to allow areas of interest to form and derive themes and sub-themes. Additional subsets were identified and analysed to better reflect and contrast views. This included, at the request of NHSBT, the theme of ‘misconceptions’. The themes were reviewed within the team (DC, CW, NK, NC, MJ) and shared with NHSBT. Any disagreements were discussed and agreed within the team.

Overall, the #options survey received 5789 responses from NHS staff. The COVID-19 pandemic further impacted on NHS organisations from North Thames to participate, resulting in respondents predominantly being from NENC (86%). Of the respondents, 1404 individuals (24%) left 1657 free-text responses for analysis. The family discussion question elicited the largest number of responses, accounting for 66% of the responses ( n = 1088), followed by against the legislation at 19% ( n = 316) and more information needed at 15% ( n = 253). The responses to the against legislation question provided the richest data as they contained the most information. Across the three questions, there were six main themes and 22 sub-themes, see Table 1 . The large number of free-text responses illustrate the multifaceted nature of individuals views with many quotes containing overlap between themes and sub-themes.

Respondent characteristics

In comparison to the whole #options survey respondents, the free-text response group contained proportionally more males (21% vs 27%), less females (78% vs 72%), and marginally more 18–24year-olds (7% vs 8%), respectively. There were 5% more 55 + year olds in the free-text group, however all other age groups were between 2–3% lower when compared to the whole group. Additionally, the free-text group were more ethnically diverse than the whole group (6.9% vs 15.4%), with all named religions also having a higher representation (3.9% vs 7.3%), respectively.

Question one: I am against the legislation – Can you help us understand why you are against this legislation?

Of the three questions, this elicited the largest number of responses from males ( n = 94, 30%), those aged over 55 years ( n = 103, 33%), and ethnic minority responders ( n = 79, 25%). Subset analysis of place of employment indicates 27% were from the transplant centre ( n = 84), 8% were from the mental health trust ( n = 26), and 4% from the ambulance trust ( n = 14). Thematic analysis uncovered four main themes and 12 sub-themes from the responses, with the predominant theme being a perceived loss of autonomy.

Theme one: loss of autonomy

Respondents’ reasons for a loss of autonomy were categorised into four sub-themes. Firstly, calling into question the nature of informed consent and secondly, peoples’ awareness of the legislative change. One respondent stated individuals need to be “fully aware and informed” [R2943] in order to have consented to organ donation. However, one respondent stated that they believe individuals have “not [been] informed well” [R930] and thus “if people are not aware of it, how are they making a choice on what happens to their organs” [R1166] . It was suggested that awareness of the change may have “been overshadowed by COVID” [R4119] .

Furthermore, there was concerns regarding the means to record an opt-out decision, specifically to those that are “not tech savvy” [R167] , “homeless” [R5721] , “vulnerable” [R4553] , and “elderly” [R2155] . Therefore, removing that individual’s right to record their decision due to being at a disadvantage.

Finally, respondents expressed concerns of a move to an authoritarian model of State ownership of organs. This elicited strong, negative reactions from individuals under the belief the State would own and “harvest” a person’s organs under a deemed consent approach, with some removing themselves as a donor consequently, “I am furious that the Government has decided that my organs are theirs to assign. It is MY gift to give, not theirs. I have now removed myself as a long-standing organ donor.” [R593] .

Theme two: consequences

Following respondents stating their reason for being against the legislative change, they discussed further what they believed to be the consequences of an opt-out legislation, with a focus on trust. Respondents cited a lack of trust towards the system, “I have no Trust in the UK government” [R5374] , with some surprisingly citing a lack of trust towards healthcare professionals, “Don’t trust doctors in regard to organ donation” [R3010], as well as a fear of eroding trust with the general public, “This brings the NHS Organ Donation directly into dispute with the public.” [R1237]. Respondents additionally believed the legislative change would lead to an increase in mistakes i.e., organ’s being removed against a person’s wishes by presuming, “not convinced that errors won't be made in my notifying my objection and that this won't be dealt with or handed over correctly” [R3018]. Finally, it is believed this change would also lead to, “additional upset” [R587], for already grieving families.

Theme three: legislation

Respondents were additionally against the legislation itself as they believed it lacked an evidence-base to prove it is successful at increasing the numbers of organs donated. As well as this, respondents perceived the legislation as one that removed the donor’s choice as to which organs they want to donate, some with a religious attribute “I don't mind donating but would like choice of what I like to i.e., not my cornea as for after life I want to see where I am going.” [R5274].

Theme four: religion and culture

Religion and culture was another common theme with sub-themes relating to maintaining bodily integrity following death and the lack of clarity around the definition of brain death. Many others stated that organ donation is against their religion or, were “unsure whether organ donation is permissible” [R1067].

Question two: I need more information to decide—What information would you like to help you decide?

This question elicited the most responses from females ( n = 188, 74%), those aged over 55 years ( n = 80, 32%), with 19% being from ethnic minority groups ( n = 49). Subset analysis of place of employment indicates 18% were from the transplant centre ( n = 46), 8% were from the mental health trust ( n = 18), and 9% from the ambulance trust ( n = 23). Thematic analysis uncovered a main theme of “everything” . There were many responses that did not specify what information was required, but indicated that more general information on organ donation was required, within this there were five sub-themes.

Sub-themes:

The first sub-theme identified a request for information around the influence a family has on the decision to donate and the information that will be provided to families. This included providing “emotional wellbeing” [R162] support, and information on whether families can “appeal against the decision” [R539] or “be consulted” [R923] following their loved one’s death. This was mainly requested by those employed by transplant centres.

The second request was for information on the “process involved after death for organ retrieval” [R171] , predominantly by ethnic minority groups and those employed by the mental health trusts, with specific requests on confirming eligibility. Other examples of requested information on the process and pathway included “how the organs will be used” [R1086] , “what will be donated” [R1629] , and “who benefits from them” [R3730] .

The third request was information regarding the publicity strategy to raise awareness of the legislative change. Many of the respondents stated they did not think there was enough “coverage in the media” [R3668]. Additional considerations of public dissemination were to ensure it was an “ easy read update” [R137 3 ] , specifically for “the elderly or those with poor understanding of English who may struggled with the process” [R1676] .

The fourth request was information around the systems in place to record a decision. There were additional requests for the opt-out processes if someone was within the excluded group and “what safeguards are in place” [R3777], as well as what if individuals change their mind and the ease of recording this new decision.

Finally, and similarly to the first question, the fifth request was information for an evidence-base. Respondents stated that they, “would like to know the reasons behind this change” [R3965] , believing that if they had a greater understanding then this might increase their support towards the legislative change.

Question three: Have you discussed your decision with a family member? If no, can you help us understand what has stopped you discussing this with your family?

The free-text responses to analyse were from those who responded “No” to, “Have you discussed your decision with a family member?”. This received 1430 responses with females ( n = 1025, 27%) predominantly answering “No”. However, not everyone left a free-text response, leaving 1088 comments for analysis. These were predominantly made by those aged over 55 years ( n = 268, 24%), with 5% being from ethnic minority groups ( n = 49). Subset analysis of the 1088 responses regarding place of employment indicated 14% were from the transplant centre ( n = 147), 7% were from the mental health trust ( n = 78), and 9% from the ambulance trust ( n = 96). The analysis uncovered a main theme of priority and relevance made up of five sub-themes.

The first sub-theme identified one reason to be that it was their “individual decision” [R3] and there would be “nothing to be gained” [R248] from a discussion. Some respondents stated that despite a lack of discussion, their family members would assume their wishes in relation to organ donation and support these, “I imagine they are all of the same mindset” [R4470]. However, some stated their reasons to be because they “don’t have a family” [R1127] to discuss this with or have “young ones whose understanding is limited about organ donation” [R356] . Positively, there were several respondents who suggested the question had acted as a prompt to speak to their family.

Another reason stated by respondents was that they found the topic to be too difficult to discuss due to “recent bereavements” [R444], “current environment” [R441] , and “a reluctance to address death” [R4486] . As evident in the latter quote, many respondents viewed discussions around death and dying as a “taboo subject” [R3285] , increasing the avoidance of having such conversations.

Finally, the fifth reason was that several respondents “had not made any decision yet” [R2478] . One respondent wanted to ensure they had reviewed all available information before deciding and having a well-informed discussion with them.

Misconceptions

A further subset analysis of responses coded as misconceptions was reviewed at the request of NHSBT, with interest in whether these occurred from healthcare staff working with donors and recipients. Misconceptions were identified across the three questions, with misconceptions accounting for 24% of the responses to the against the legislation question. Responses used emotive, powerful words with suggestions of State ownership of organs, abuse of the system to procure organs, change in treatment of donors to hasten death, religious and cultural objections, and recipient worthiness.

I worked in organ retrieval theatre during my career and I was uncomfortable with the way the operations were performed during this period. Although the 'brain dead' tests had been completed prior to the operation the vital signs of the patient often reflected that the patient was responding to painful stimuli. Sometimes the patient was not given the usual analgesia that is often given during routine operations. This made me rethink organ donation therefore I am uncomfortable with this. I always carried a donation card prior to my experience but subsequently would not wish to donate. This may be a personal feeling but that is my experience. [R660].

I think that this is a choice that should be left to individuals and families to make. After many years in nursing lots of it spent with transplant patients not all recipients embrace a 'healthy lifestyle' post-transplant with many going back to old lifestyle choices which made a transplant necessary in the first place. [R867].

Additional comments suggested certain medical conditions and advancing age precludes donation and that the ability to choose which organs to donate had been removed.

Most of them will be of no use as I have had a heart attack, I smoke and have Type 2 diabetes. [R595]

Further analysis indicated that 27% ( n = 24) of these comments were made by individuals who worked with or in an area that supported donors and recipients.

In summary, this qualitative paper has evidenced that the ability to make an autonomous informed decision is foremost in the respondent’s thoughts regarding an opt-out system. This has been commonly cited as a reason throughout the literature by those against an opt-out system [ 9 , 10 , 25 , 26 ]. The loss of that ability was the primary reason for respondents being against the change in legislation with the notion that the decision is a personal choice cited as a reason for lack of discussion with family members. Respondents stated that the ability to make autonomous decisions needs to be adequately supported by evidence-based information that is accessible to all. If the latter is unavailable, they expressed concern for negative consequences. This includes an increase in the perceived belief of the potential for mistakes and abuse of the system, as well as family distress and loss of trust in the donation system and the staff who work in it, as supported by previous literature [ 9 , 11 ].

Our findings further coincide with that of previous literature, highlighting views suggesting that the opt-out system is a move towards an authoritarian system, illustrating the commercialisation of organs, and a system that is open to abuse and mistakes [ 10 , 11 , 12 , 27 , 28 , 29 ]. Healthcare staff require reassurance that the population, specifically the hard-to-reach groups like the elderly and homeless, have access to information and systems in order to be able to make an informed decision [ 30 , 31 ]. Whilst the findings from the overall #options survey demonstrated awareness is higher in NHS staff, there was a significant narrative in the free-text response regarding a lack of awareness and a concern the general public must also lack the same awareness of the system change. Some responses also reflected medical mistrust concerns of the general public [ 13 , 14 , 16 ] as well as expressing a fear of losing trust with the public [ 9 , 11 , 16 ], as found within previous work. Additional research articles raising awareness of the opt-out system in England suggest that despite publicising the change with carefully crafted positive messaging, negative views and attitudes are likely to influence interpretation leading to an increase in misinformation [ 28 ]. Targeted, evidence-based interventions and campaigns that address misinformation, particularly in sub-groups like ethnic minorities, is likely to provide reassurance to NHS staff and the general public, as well as providing reliable resources of information [ 28 ].

Respondents also requested more detailed information about the process of organ donation. The disparity of information and the knowledge of the processes of donation includes eligibility criteria, perceived religious and cultural exclusions, practical processes of brain and circulatory death, and subsequent organ retrieval. As well as, most importantly, more information on the care provided to the donor before and after the donation procedure. The gap of available factual knowledge is instead filled by misconceptions and misunderstandings which is perpetuated until new information and knowledge is acquired. It may also be attributed to the increased awareness of ethical and regulatory processes. These attitudes and views illustrate the complexity of opinions associated with religion, culture, medical mistrust, and ignorance of the donation processes [ 11 , 15 , 32 ]. There is evidently a need for healthcare staff to display openness and transparency about the processes of organ donation and how this is completed, particularly with the donor’s family. It further reinforces the need to increase the knowledge of differing religious and cultural beliefs to support conversations with families [ 18 , 19 ].

Both healthcare staff and the public would benefit from educational materials and interventions to address attitudes towards organ donation [ 19 , 28 , 33 ]. This would assist in correcting misconceptions and misunderstandings held by NHS staff, specifically those who support and work with organ donors and recipients. Previous work illustrates support for donation being higher in intensivists, recommending educational programmes to increase awareness across all healthcare staff [ 34 ]. The quantitative and qualitative findings of the #options survey would support this recommendation, adding that interventions need to be delivered by those working within organ donation and transplantation. This would build on the community work being conducted by NHSBT, hopefully leading NHS staff to become transplant ambassadors within their local communities.

A further finding was that of confusion and misunderstanding surrounding the role of the family, a finding also supported by the literature [ 11 ]. It was suggested that family distress would be heightened, and families would override the premise of opt-out. Literature also supports this could be further impacted if the family holds negative attitudes towards organ donation [ 20 ]. The uncertainty of the donors’ wishes was the most common reason for refusing from ethnic minority groups [ 35 ], further highlighting the need for family discussions. Without this, families feel they are left with no prior indication so they opt-out as a precaution. Making an opt-in decision known can aid the grieving process as the family takes comfort in knowing they are fulfilling the donors wishes [ 26 ] and reduces the likelihood of refusal due to uncertainty about their wishes [ 36 ]. The ambiguity around the role of the family, coupled with not explicitly stating a choice via the organ donor register or discussions with family can make it problematic for next of kin and NHS staff.

Limitations

It is acknowledged that the findings of this study could have been influenced by the COVID-19 pandemic beyond the changes to the research delivery plan including a shift in critical care priorities, initial increase of false information circulating social media, delayed specialist nurse training, and removal of planned public campaigns [ 37 , 38 ]. The degree of the impact is unknown and supports the view that ongoing research into healthcare staff attitudes is required. Additionally, the survey did not collect job titles and is therefore limited to combining all healthcare staff responses. It is understood not all staff, such as those working in mental health, would know in depth details of organ donation and legislation, but it is expected that their level of knowledge would be greater than that of the general public.

The quantitative analysis [ 21 ] of the #options survey showed that overall NHS staff are well informed and more supportive of the change in legislation when compared to the general public. This qualitative analysis of the free-text responses provides a greater insight into the views of the healthcare staff who against the change. The reasons given reflect the known misconceptions and misunderstandings held by the general public and evidenced within the literature [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. There are further concerns about the rationale for the change, the nature of the informed decision making, ease of access to information including information regarding organ donation processes. We therefore propose that educational materials and interventions for NHS staff are developed to address the concepts of autonomy and consent, are transparent about organ donation processes, and address the need for conversations with family. Regarding the wider public awareness campaigns, there is a continued need to promote the positives and refute the negatives to fill the knowledge gap with evidence-based information [ 39 ] and reduce misconceptions and misunderstandings.

Availability of data and materials

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Coronavirus Disease 2019

Integrated research application system

North-East and North Cumbria

National Health Service

National Health Service Blood and Transplant

National Institute of Health Research

Organ donor register

United Kingdom

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Acknowledgements

With thanks to the NHSBT legislation implementation team for peer review of the questionnaire and the Kantar population survey data.

Funding for the project was gained from the Northern Counties Kidney Research Fund. Grant number 16.01.

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NC, DC, and CW were responsible for the drafting and revising of the manuscript. NN, MJ, MR, DR, and CW were responsible for the design of the study. DC completed the qualitative analysis. NC, DC, NN, MJ, MR, DR, and CW read and approved the final manuscript.

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Correspondence to Caroline Wroe .

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The research was carried out in accordance with the Declaration of Helsinki. The study was reviewed and approved by a Health Research Authority (HRA) and Health and Care Research Wales (HCRW) [REC reference: 20/HRA/0150] via the integrated research application system (IRAS) and registered as a National Institute of Health Research (NIHR) portfolio trial [IRAS 275992]. Informed Consent was obtained from all the participants and/or their legal guardians.

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Clark, N.L., Coe, D., Newell, N. et al. “I am in favour of organ donation, but I feel you should opt-in”—qualitative analysis of the #options 2020 survey free-text responses from NHS staff toward opt-out organ donation legislation in England. BMC Med Ethics 25 , 47 (2024). https://doi.org/10.1186/s12910-024-01048-6

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Factors promoting and hindering resilience in youth with inflammatory bowel disease: A descriptive qualitative study

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1 Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China.
2 Nursing Department, Peking Union Medical College Hospital, Beijing, China.
3 Department of Internal Medicine, Peking Union Medical College Hospital, Beijing, China.
PMID: 38629398
PMCID: PMC11022225
DOI: 10.1002/nop2.2150

Aim: To explore factors promoting and hindering resilience in youth with inflammatory bowel disease (IBD) based on Kumpfer's resilience framework.

Design: A descriptive qualitative study design with an interpretative approach was used.

Methods: Participants consisted of 10 youths with IBD from a tertiary hospital in Beijing (China) recruited using the purposive sampling method. Data were collected by semi-structured interviews from December 2020 to March 2021. The directed content analysis was performed for data analysis.

Results: Both promoting factors and hindering factors could be divided into personal factors and environmental factors. Thirteen themes were identified. The promoting factors included acceptance of illness, strict self-management, previous treatment experience, life goals, family support, medical support and peer encouragement. Stigma, lack of communication, negative cognition, societal incomprehension, economic pressure and academic and employment pressure were hindering factors.

Conclusion: Health care professionals need to develop greater awareness of factors, stemming from both the individual and the outside world, that hinder or promote resilience in order to aid young patients with IBD. Building targeted nursing measures to excavate the internal positive quality of patients, provide external support and promote the development of resilience.

Keywords: inflammatory bowel disease; nursing; qualitative study; resilience; youth.

Health Personnel
Inflammatory Bowel Diseases* / therapy
Qualitative Research
Resilience, Psychological*

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3332021005/Fundamental Research Funds for the Central Universities

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Chapter 5: Qualitative descriptive research

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