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Qualitative Data Analysis: What is it, Methods + Examples

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

What is Qualitative Data Analysis?

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods , and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

• Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
• Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
• Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
• Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
• Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

• Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
• Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
• Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

• Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
• Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
• Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
• Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

• Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
• Complexity: Analyzing huge data sets requires time and great attention to detail.
• Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

• Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
• Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
• Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

• Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
• Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
• Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

• Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
• Time-consuming: The study can be time-consuming, especially with large data sets.
• Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

• Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
• Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
• Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

• Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
• Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

• Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
• Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

• Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
• Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
• Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

• Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
• In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
• Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

• Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
• Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
• Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

• Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
• Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
• Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

• Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
• Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
• Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

• Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
• Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
• Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

• Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
• Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
• Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
• Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
• Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
• Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
• Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
• Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.

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Data Analysis in Qualitative Research

Ravindran, Vinitha 1,

1 College of Nursing, CMC, Vellore, Tamil Nadu, India

Address for correspondence: Dr. Vinitha Ravindran, College of Nursing, CMC, Vellore, Tamil Nadu, India. E-Mail: [email protected]

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.

Data analysis in qualitative research is an iterative and complex process. The focus of analysis is to bring out tacit meanings that people attach to their actions and responses related to a phenomenon. Although qualitative data analysis softwares are available, the researcher is the primary instrument who attempts to bring out these meanings by a deep engagement with the data and the individuals who share their stories. Although different approaches are suggested in different qualitative methods, the basic steps of content analysis that includes preparing the data, reading and reflection, coding, categorising and developing themes are integral to all approaches. The analysis process moves the researcher from describing the phenomenon to conceptualisation and abstraction of themes without losing the voice of the participants which are represented by the findings.

INTRODUCTION

Qualitative data analysis appears simple to those who have limited knowledge of qualitative research approach, but for the seasoned qualitative researcher, it is one of the most difficult tasks. According to Thorn,[ 1 ] it is the complex and elusive part of the qualitative research process. Many challenges that are inherent in the research approach makes the analysis process demanding. The first challenge is to convert the data from visual or auditory recording to textual data. As qualitative approach includes data generation through sharing of experiences, it becomes fundamentally necessary to record data rather than writing down accounts as the stories are shared. Essential data which may become apparent or uncovered when reflecting on audiotaped interviews may be missed or overlooked if interviews are not recorded.[ 2 ] Although field notes are written they often augment the experiences conveyed by participants rather than being the primary data source. Therefore, the researcher needs to spend effort and time to record as well as transcribe data to texts which can be analysed.

The second challenge is managing the quantum of textual data. One hour of interview may produce 20–40 pages of text. Even with fewer participants as generally is in qualitative research, the researcher may have many pages of data which need coding and analysing. Although software packages such as NVivo and Atlas-ti are available, they only help to organise, sort and categorise data and will not give meaning to the text.[ 3 ] The researcher has to read, reflect, compare and analyse data. The categories and themes have to be brought forth by the researcher. The third challenge is doing data generation and data analysis at the same time. Concurrent data generation and analysis is a predominant feature in qualitative research. An iterative or cyclic method of data collection and analysis is emphasised in qualitative approach. What it means is that as the researcher collects data, the analysis process is also initiated. The researcher does not wait to complete collecting data and then do analysis.[ 2 ] Iterative process enhances the researcher to focus on emerging concepts and categories in subsequent interviews and observations. It enables the researcher to address the gaps in the data and get information to saturate the gaps in subsequent contacts with earlier or new research participants. Sufficient time and resources are needed for sustaining the iterative process throughout the research process.

The above challenges are mentioned at the beginning of this article not to discourage the researchers but to emphasise the complexity of data analysis which has to be seriously considered by all researchers who are interested in doing qualitative research. In addition to the general challenges, data analysis in qualitative research also varies between different approaches and designs. There is also the possibility of flexibility and fluidity that enhances the researcher to choose different approaches to analysis, either one specific approach or a combination of approaches.[ 4 ] The framework for the analysis should, however, be made explicit at the beginning of the analysis. In qualitative research, the researcher is a bricoleur (weaver of stories) who is creating a bricolage.[ 5 ]

CHARACTERISTICS OF DATA ANALYSIS

In qualitative data analysis.

• Researcher attempts to understand the meaning behind actions and behaviours of participants
• Researcher becomes the instrument to generate data and ask analytical questions
• Emphasis is given to quality and depth of narration about a phenomenon rather than the number of study participants
• The context and a holistic view of the participants' experience are stressed
• The research is sensitive to what the influence he/she has on the interpretation of data
• Analytical themes are projected as findings rather than quantified variables.

Process of data analysis

Qualitative data analysis can be both deductive and inductive. The deductive process, in which there is an attempt to establish causal relationships, is although associated with quantitative research, can be applied also in qualitative research as a deductive explanatory process or deductive category application.[ 6 ] When the researcher's interest is on specific aspects of the phenomenon, and the research question is focused and not general, a deductive approach to analysis may be used. For example, in a study done by Manoranjitham et al .,[ 7 ] focus group discussions were conducted to identify perceptions of suicide in terms of causes, methods of attempting suicide, impact of suicide and availability of support as perceived by family and community members and health-care professionals. Focused questions were asked to elicit information on what people thought about the above aspects of suicide. The answers from participants in focus groups were coded under each question, which was considered as categories and the number of responders and the responses were elaborated under the said questions as perceived by the participants. Deductive process in qualitative data analysis allows the researcher to be at a descriptive level where the results are closer to participants accounts, rather than moving to a more interpretive or conceptual level. This process is often used when qualitative research is used as a part of the mixed methods approach or as a part of an elaborate research study.

In contrast, the inductive process which is the hallmark of qualitative data analysis involves asking questions of the in-depth and vast data that have been generated from different sources regarding a phenomenon.[ 2 , 4 ] The inductive process is applicable to all qualitative research in which the research question has been more explorative and overarching in terms of understanding the phenomenon in peoples' lives. For example, in Rempel et al .'s[ 8 ] study on parenting children with life-threatening congenital heart disease, the researchers explored the process of parenting children with a lethal heart condition. Volumes of data generated through individual interviews with parents and grandparents were inductively analysed to understand the 'facets of parenting' children with heart disease. Inductive analysis motivates and enhances researchers to rise above describing what the participants say about their experience to interpretive conceptualisation and abstraction. The process of deduction and induction in qualitative data analysis is depicted in Figure 1 .

GENERAL STEPS IN DATA ANALYSIS

Although different analytical processes are proposed by different researchers, there are generally four basic steps to qualitative data analysis. These steps are similar to what is generally known as qualitative content analysis.[ 4 , 9 ] In any qualitative approach, the analysis starts with the steps of content analysis. The content analysis ends generally at an interpretive descriptive level. Further analysis to raise data to abstraction may be needed in some approaches such as grounded theory.

Preparation of data

Reading and reflecting, coding, categorising and memoing.

• Developing themes/conceptual models or theory.

As already discussed, the inductive process in qualitative research begins when data collection starts. Each recorded data set from individual interviews, focus groups or conversations should be first transcribed and edited. The researcher may decide on units of data that can be analysed to further help in organising.[ 10 ] The units can be the whole interview from one individual or interview transcripts from one family or data from different individuals connected with in a case (as in case study). On some occasions, the unit may consist of all answers to one question or one aspect of the phenomenon. Many researchers may not form any such units at the beginning of the analysis which is also accepted. The essential aspect of the preparation is to ensure that participants' accounts are truly represented in transcribing. Researchers who have a large amount of content will need assistance in transcription. One hour of interview may take 4–6 h to transcribe.[ 2 ] An official transcriber will do a good job than a researcher who may spend a long time in transcribing volumes of data. However, the researcher has to edit the transcription by listening to the audiotaped version and include words and connotations that are missed to maintain accuracy in transcription.[ 11 ] Another important point to note is to transcribe and prepare the data as soon as interviews are completed. This facilitates the iterative process of data collection and analysis. All data, including field notes, should be organised with date, time and identification number or pseudonym for easy retrieval.[ 2 ] Assigning numbers or pseudonyms help to maintain the confidentiality of the participants.

Reading the data as a whole, and reflecting on what the participants are sharing gives an initial understanding of the narrative. The reflection may start at the time of the interview itself. However, reading and rereading the transcribed text from an interview gives an understanding of context, situations, events and actions related to the phenomenon of interest before the data can be analysed for concepts and themes.[ 12 ] Reading and reflection help the researcher to get immersed in the data, understand the perspectives of participants and decide on an analytical framework for further data analysis.[ 13 ] As texts are read, the researcher may jot down points or questions that are striking or unusual or does or does not support assumptions. Such reflective notes assist the researcher to decide on questions to be asked in further interviews or look for similarities or differences in interview texts from other participants. These initial reflections do not complete analysis; rather, it provides a platform for the analysis to develop. An example of initial reflections when analysing interviews from a study on home care of children with chronic illness is given below.

Reflections-family 1 interview

'This family has a lot of issues related to home care. Their conversation is a list of complaints about the system and the personnel. Even though it appears that help is being rendered for support of child at home, nothing seems to satisfy the parents. The conversation revolves around how they have not been given their due in terms of material and personnel support rather than about their sick child or the siblings.

After a while, it became tedious for me to read this transcript as I resent the complaints (which I should not do I suppose). I wonder how other families perceive home care.'

The initial reflections also help to understand our position as a researcher and the assumptions the researcher brings to the study. It helps us to be aware of one's own professional and personal prejudices which may influence the interpretation of data.

For analysis to progress further the researcher has to decide on an organised way of sorting and categorising data to come to an understanding about the phenomenon or the concepts embedded in the phenomenon. Researchers may choose to analyse only the manifest content in a descriptive qualitative study or may move further to look for latent content in an analytical-qualitative study.[ 4 ] The manifest content analysis includes looking for specific words or phrases used by the participants and accounting for how many have expressed the same or similar words/phrases in the data. It looks at what is obvious. Latent content analysis, on the other hand, involves coding and categorising to identify patterns and themes that are implicit in the data.

Coding is an essential first step in sorting and organising data.[ 4 ] Codes are labels given to phrases, expressions, behaviours, images and sentences as the researcher goes through the data.[ 13 ] It can be 'in vivo' codes or 'interpretive codes'. When participants' exact expressions itself are used as codes it is called ' in vivo ' codes.[ 14 ] If the researcher interprets the expression or behaviour of the participant depicted in the text, then it is called interpretive codes. In the grounded theory method, different levels of coding are suggested. The first level is called the 'open coding' that involves sifting through the initial data line by line and creating in vivo or interpretive codes. Questions such as what are this person saying or doing or what is happening here? will help in the initial coding of data. Initial coding may reveal gaps in the data or raise questions.[ 15 , 16 ] These gaps and questions will help the researcher to locate the sources from where further data are to be collected. The second level is known as 'focus or selective coding' will be used in subsequent interviews. Focused coding involves using the most frequent or most significant earlier codes to sift through large amounts of data. Focused codes are more directed, selective and conceptual and are employed to raise the sorting of data to an analytical level.[ 17 ] The first level of coding can be done manually or can be done using qualitative software packages. In other types of content analysis, the different levels of coding may not be followed instead the researcher engages in interpretive coding as the text is read. In a grounded theory study on parenting children with burn injury open codes such as scolded, accused, unwanted, guilt, nonsupport, difficult to care, terrible pain, blaming oneself and tired came up as the data were coded [ Table 1 ]. These codes gave the researcher an initial insight into the traumatic experiences that the parents undergo when caring for their burn-injured children. As texts were coded, the researcher attempted to understand further the struggles of parents in the successive interviews with other families.

Categorising

Categorising involves grouping similar codes together and formulating an understandable set within which related data can be clubbed. A category is 'a collection of similar data sorted into the same place' – the 'what', developed using content analysis and developing trajectories and relationships over time.[ 18 ] It is a group of content that shares commonality. Data can be categorised generally when the researcher realises that the same codes or codes that are relatively similar are emerging from the data. When categories are developed based on codes, they can be still at descriptive level or can be at an abstract level.[ 10 ] By developing categories a conceptual coding structure can be formulated. At this level, there is no need to continue line by line coding. Instead, the researcher uses the coding structure to sort data. In other words, parts of data that best fit the categories, and the codes are grouped appropriately from across the data sets. The grouping of data into categories is enabled by comparing and contrasting data from different sources or individuals.[ 19 ] As constant comparison continues[ 15 ] questions such as 'What is different between the accounts of two families? What are similar? Will help in grouping data into categories. As the researcher compares data, questions such as 'what if' may come up which will propel the researcher to return to participants to know more or even purposively include participants who will answer the question. The data under each category should be read again to ensure that they appropriately represent the category.[ 4 ] Qualitative software packages are very useful in sorting and organising data from this level. Any part of data which is not fitting into any category needs to be coded newly, and the new codes should be added. The emerging new codes may later fit into a category or form new categories. All data are thus accounted for during this phase of analysis.

As analysis and grouping of further data continue, the researcher may rearrange data within categories or come up with subcategories.[ 4 ] The researcher may also go from data to codes, to sub-categories which then can be abstracted into categories.[ 10 ] In the burn study, similar codes that were repeated in many transcripts were grouped together. Grouping these codes helped in developing subcategories such as physical trauma, emotional trauma, self-blame and shame. The sub-categories were then grouped to develop meaningful categories such as facing blame and enduring the burn [ Table 2 ]. Creating categories thus assists the researcher to move from describing phenomenon to interpretation and abstraction.

Memoing is 'the researcher's record of analysis, thoughts, interpretations, questions and directions for further data collection' (pp 110).[ 20 ] Memos are elaborations of thoughts on data, codes and categories that are written down.[ 17 ] Simply put, memoing is writing down the reflections and ideas that arise from the data as data analysis progresses. As data are coded, the researcher writes down his/her thoughts on the codes and their relationships as they occur. Memo-writing is an on-going process, and memos lead to abstraction and theorising for the write-up of ideas.[ 15 ] Initial or early memos help in exploring and filling out the initial qualitative codes. It helps the researcher to understand what the participants are saying or doing and what is happening. Advanced memos help in the emergence of categories and identify the beliefs and assumptions that support the categories. Memos also help in looking at the categories and the data from different vantage points.[ 21 ] One of the early memos from burn study is given as an example.

Extensive wound

24 June, 2010, 10 pm – After coding interview texts from three families.

'I am struck by the enormity of a burn injury. I realize that family members cannot do many things for the child at home after discharge of a severely burned child because the injury is so big that even some clinics and doctors who are not familiar with burn care cannot manage care. These children need continuous attention of the health care professionals. They need professional assistance with dressing. They need professional assistance with splints and gadgets, and therapies. The injury is extensive that it is difficult for family members to do many things on their own. It is very hard, very hard for the parents to take up a role of the caregiver for children with burns because it involves large wound which has not healed or is in the process of early healing and the child suffers severe pain. The post burn care is very different from caring for other children with chronic illness or congenital defects which most often does not involve pain. The child's suffering makes it easy for the parents to view them as vulnerable. Yet the parents do their best. They try to follow the Health Care Professionals advice, they try to go for follow-up, but it seems simply not enough. I think the parents are doing all that they can within the context of severe injury, lack of finances, lack of resources in home town, or blame and ridicule from neighbors and others…'

Stopping to memo helps the researcher to reflect on data, move towards developing themes and models and lay the ground for discussion of findings later. Memos need to include the time, date, place and context at which they were written.

Developing themes, conceptual models and theory

Developing themes involves the 'threading together of the underlying meaning' that run through all the categories. It is the interpretation of the latent content in the texts.[ 10 ] Theming involves integrating all the categories and explicating the relationship in the categories.[ 4 ] In coding and categorising the researcher is involved in deconstructing or dividing the data to understand the feelings, behaviours and actions. In the phase of theming, the researcher is trying to connect the deconstructed part by understanding the implicit meaning that connects the behaviour, actions and reactions related to a phenomenon. To identify theme, the grounded theorist asks: What is the core issue which the participants are dealing with? The phenomenologist will ask about the central essence or structure of the lived experience related to the phenomenon of interest. The ethnographer may look at the cultural themes that link the categories. The researcher generally comes up with one to three themes.[ 4 ] Too many categories or themes may indicate that the analysis is prematurely closed and implies the need for the researcher to further interpret and conceptualise the data.[ 4 ] In the study on parenting children with burn injury, the researcher came up with the theme of 'Double Trauma' which explicated the experiences of parents living the burn with their children and also enduring the blame within the context of both the hospital and home [ Table 2 ].

In phenomenology and ethnography, the analysis may end with identifying themes. In other approaches, such as grounded theory and interpretive description, the analysis may progress further to developing theory or conceptual models. Identifying the core category/variable from the coding activity, memos and constant comparisons are the first step in moving towards theory development in grounded theory.[ 15 ] The core category is the main theme that the researcher identifies in the data. The next step in grounded theory is to identify the basic social process (BSP). The BSP evolves from understanding how participants are dealing with the core issue. In real-world situations, individuals develop their own strategies and process to deal with the core issue in any situation. Identifying this process is the stepping stone to theory development in grounded theory. In the example of burn study, the theme 'Double Trauma' was the core category and parenting in the burn study involved a dual process of 'embracing the survival' and 'enduring the blame'.[ 22 , 23 ] A conceptual model was developed based on these processes.

PITFALLS IN QUALITATIVE ANALYSIS

Large data sets for analysis.

As already explained, the amount of data text or field notes from observations and other sources in qualitative research can become overwhelming if data analysis is not initiated concurrently with data collection/generation. Coding large data text is tedious and takes much of the researcher's time. Postponing analysis to the end of data collection also prevents the researcher from becoming focused in subsequent interviews and filling gaps in data in further data collection. Therefore, deferring data analysis should be avoided.

Premature closure

Researcher should not hasten to conclude analysis with developing categories or themes. This may lead to 'premature closure' of the research and the danger that the participants' experiences are misunderstood or incompletely understood.[ 15 ] Qualitative data analysis involves in-depth interaction with the data and understanding the nuances in the experiences and the meanings behind actions. The researcher continues to generate data until all the categories are saturated, which means that the categories are mutually exclusive and can be explained from all aspects or angle.[ 21 ] In the burn study, although the table in this article appears simple, the codes and categories were developed from larger data sets representing multiple participant interviews and field notes. The category 'facing blame' was brought forth with parents' accounts of experiencing blame in almost all the families in one or multiple ways: from family members, health-care professionals, strangers and the child itself. The researcher needs to be reflexive and iteratively do data generation and analysis until there is no new information forthcoming in the data. Inferring conclusions too soon which is otherwise known as 'inferential leaps', will prevent the researcher from getting the whole picture of the phenomenon.[ 2 ]

Interpretation of meanings

During the analysis process as the researcher interprets and conceptualises the participants' experiences, he/she delves into the tacit meanings of actions and feelings expressed by participants or observed in various situations. The researcher endeavours to keep the interpretations as close to the participants' accounts as possible. However, it should be understood that the meanings are co-constructed by both the participant and researcher by collaborative effort which is also a hallmark of qualitative research.[ 2 ] In the process of co-construction, researcher should be cautious to not lose the voice of the participants. Discussion with peer at all steps of analysis or checks on codes and categories by others in the research team may help to avoid this problem.

Qualitative data analysis is a complex process that demands much of reading, thinking and reflection on the part of researcher. It is time-consuming as the researcher has to be constantly engaged with the texts to tease out the hidden meanings. Beyond the differences in data analysis in different qualitative methods, coding, categorising and developing themes are the essential phases of data analysis in most methods. Researchers should avoid premature conclusions and ensure that the findings are comprehensively represented by participants' accounts. Qualitative data analysis is an iterative process.

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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.

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, and review analysis is a great place to start. 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.

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.

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 ( conversational analytics ), 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.

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 .

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 .

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:

The visualization could look like this:

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 .

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.

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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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

• Content analysis
• Narrative analysis
• Discourse analysis
• Thematic analysis
• Grounded theory (GT)
• Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

• Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
• Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
• Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

• First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
• Then we looked at narrative analysis , which is about analysing how stories are told.
• Next up was discourse analysis – which is about analysing conversations and interactions.
• Then we moved on to thematic analysis – which is about identifying themes and patterns.
• From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
• And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

87 Comments

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Great to hear that. Good luck with your qualitative data analysis, Pramod!

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Precise explanation of method.

Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

You explained it in very simple language, everyone can understand it. Thanks so much.

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Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify – https://onlinelibrary.wiley.com/doi/10.1111/nhs.12048

This is my first time to come across a well explained data analysis. so helpful.

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Hi Derek, What other theories/methods would you recommend when the data is a whole speech?

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Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?

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What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Informative video, explained in a clear and simple way. Kudos

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.

This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

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very educative

Nicely written especially for novice academic researchers like me! Thank you.

choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?

that was very helpful for me. because these details are so important to my research. thank you very much

I learnt a lot. Thank you

Relevant and Informative, thanks !

Well-planned and organized, thanks much! 🙂

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Clear explanation on qualitative and how about Case study

This was helpful. Thank you

This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

This was helpful thanks .

Very helpful…. clear and written in an easily understandable manner. Thank you.

This was so helpful as it was easy to understand. I’m a new to research thank you so much.

so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?

Thank you for the great content, I have learnt a lot. So helpful

precise and clear presentation with simple language and thank you for that.

very informative content, thank you.

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!

Brilliant Delivery. You made a complex subject seem so easy. Well done.

Beautifully explained.

Thanks a lot

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

content analysis can be qualitative research?

THANK YOU VERY MUCH.

Thank you very much for such a wonderful content

do you have any material on Data collection

What a powerful explanation of the QDA methods. Thank you.

Great explanation both written and Video. i have been using of it on a day to day working of my thesis project in accounting and finance. Thank you very much for your support.

very helpful, thank you so much

The tutorial is useful. I benefited a lot.

This is an eye opener for me and very informative, I have used some of your guidance notes on my Thesis, I wonder if you can assist with your 1. name of your book, year of publication, topic etc., this is for citing in my Bibliography,

I certainly hope to hear from you

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Data Analysis Techniques for Qualitative Study

• First Online: 27 October 2022

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• Heather Douglas 4  

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This chapter describes how to analyse qualitative data to produce authentic findings. Analysis of qualitative data must be a slow and careful process. This chapter will only discuss how to analyse text since this is the most common form of data in qualitative studies. Text is written language, such as field notes, transcribed interviews and focus group discussions, reports, organizational records, journal articles, or website/social media information. Qualitative data include photos and videos, objects and artefacts, and actions and sounds. More complex data include events, organizational processes, or interactions between people. This chapter will discuss the interview and similar written data since novice researchers often generate these data: however, the analysis process is similar for all textual data. The analysis process described in this chapter is based on a simple grounded theory approach Thomas (Am J Eval 27:237–246, 2006). Thomas’s approach is simpler for novices than that described by Glaser (Emergence versus forcing: Basics of grounded theory analysis. Sociology Press, 1992) or Strauss and Corbin (Basics of qualitative research: Grounded theory procedures and techniques. Sage, 1990), but it must be done correctly to produce trustworthy findings.

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Grounded Theory Methodology: Principles and Practices

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Glaser, B. G. (1992). Emergence vs forcing: Basics of grounded theory analysis . Sociology Press.

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Douglas, H. (2022). Data Analysis Techniques for Qualitative Study. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_30

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Data analysis in qualitative research, theertha raj, august 30, 2024.

While numbers tell us "what" and "how much," qualitative data reveals the crucial "why" and "how." But let's face it - turning mountains of text, images, and observations into meaningful insights can be daunting.

This guide dives deep into the art and science of how to analyze qualitative data. We'll explore cutting-edge techniques, free qualitative data analysis software, and strategies to make your analysis more rigorous and insightful. Expect practical, actionable advice on qualitative data analysis methods, whether you're a seasoned researcher looking to refine your skills or a team leader aiming to extract more value from your qualitative data.

What is qualitative data?

Qualitative data is non-numerical information that describes qualities or characteristics. It includes text, images, audio, and video. 

This data type captures complex human experiences, behaviors, and opinions that numbers alone can't express.

A qualitative data example can include interview transcripts, open-ended survey responses, field notes from observations, social media posts and customer reviews

Importance of qualitative data

Qualitative data is vital for several reasons:

• It provides a deep, nuanced understanding of complex phenomena.
• It captures the 'why' behind behaviors and opinions.
• It allows for unexpected discoveries and new research directions.
• It puts people's experiences and perspectives at the forefront.
• It enhances quantitative findings with depth and detail.

What is data analysis in qualitative research?

Data analysis in qualitative research is the process of examining and interpreting non-numerical data to uncover patterns, themes, and insights. It aims to make sense of rich, detailed information gathered through methods like interviews, focus groups, or observations.

This analysis moves beyond simple description. It seeks to understand the underlying meanings, contexts, and relationships within the data. The goal is to create a coherent narrative that answers research questions and generates new knowledge.

How is qualitative data analysis different from quantitative data analysis?

Qualitative and quantitative data analyses differ in several key ways:

• Data type: Qualitative analysis uses non-numerical data (text, images), while quantitative analysis uses numerical data.
• Approach: Qualitative analysis is inductive and exploratory. Quantitative analysis is deductive and confirmatory.
• Sample size: Qualitative studies often use smaller samples. Quantitative studies typically need larger samples for statistical validity.
• Depth vs. breadth: Qualitative analysis provides in-depth insights about a few cases. Quantitative analysis offers broader insights across many cases.
• Subjectivity: Qualitative analysis involves more subjective interpretation. Quantitative analysis aims for objective, statistical measures.

What are the 3 main components of qualitative data analysis?

The three main components of qualitative data analysis are:

• Data reduction: Simplifying and focusing the raw data through coding and categorization.
• Data display: Organizing the reduced data into visual formats like matrices, charts, or networks.
• Conclusion drawing/verification: Interpreting the displayed data and verifying the conclusions.

These components aren't linear steps. Instead, they form an iterative process where researchers move back and forth between them throughout the analysis.

How do you write a qualitative analysis?

Step 1: organize your data.

Start with bringing all your qualitative research data in one place. A repository can be of immense help here. Transcribe interviews , compile field notes, and gather all relevant materials.

Immerse yourself in the data. Read through everything multiple times.

Step 2: Code & identify themes

Identify and label key concepts, themes, or patterns. Group related codes into broader themes or categories. Try to connect themes to tell a coherent story that answers your research questions.

Pick out direct quotes from your data to illustrate key points.

Step 3: Interpret and reflect

Explain what your results mean in the context of your research and existing literature.

Als discuss, identify and try to eliminate potential biases or limitations in your analysis. 

Summarize main insights and their implications.

What are the 5 qualitative data analysis methods?

Thematic Analysis Identifying, analyzing, and reporting patterns (themes) within data.

Content Analysis Systematically categorizing and counting the occurrence of specific elements in text.

Grounded Theory Developing theory from data through iterative coding and analysis.

Discourse Analysis Examining language use and meaning in social contexts.

Narrative Analysis Interpreting stories and personal accounts to understand experiences and meanings.

Each method suits different research goals and data types. Researchers often combine methods for comprehensive analysis.

What are the 4 data collection methods in qualitative research?

When it comes to collecting qualitative data, researchers primarily rely on four methods.

• Interviews : One-on-one conversations to gather in-depth information.
• Focus Groups : Group discussions to explore collective opinions and experiences.
• Observations : Watching and recording behaviors in natural settings.
• Document Analysis : Examining existing texts, images, or artifacts.

Researchers often use multiple methods to gain a comprehensive understanding of their topic.

How is qualitative data analysis measured?

Unlike quantitative data, qualitative data analysis isn't measured in traditional numerical terms. Instead, its quality is evaluated based on several criteria. 

Trustworthiness is key, encompassing the credibility, transferability, dependability, and confirmability of the findings. The rigor of the analysis - the thoroughness and care taken in data collection and analysis - is another crucial factor. 

Transparency in documenting the analysis process and decision-making is essential, as is reflexivity - acknowledging and examining the researcher's own biases and influences. 

Employing techniques like member checking and triangulation all contribute to the strength of qualitative analysis.

Benefits of qualitative data analysis

The benefits of qualitative data analysis are numerous. It uncovers rich, nuanced understanding of complex phenomena and allows for unexpected discoveries and new research directions. 

By capturing the 'why' behind behaviors and opinions, qualitative data analysis methods provide crucial context. 

Qualitative analysis can also lead to new theoretical frameworks or hypotheses and enhances quantitative findings with depth and detail. It's particularly adept at capturing cultural nuances that might be missed in quantitative studies.

Challenges of Qualitative Data Analysis

Researchers face several challenges when conducting qualitative data analysis. 

Managing and making sense of large volumes of rich, complex data can lead to data overload. Maintaining consistent coding across large datasets or between multiple coders can be difficult. 

There's a delicate balance to strike between providing enough context and maintaining focus on analysis. Recognizing and mitigating researcher biases in data interpretation is an ongoing challenge. 

The learning curve for qualitative data analysis software can be steep and time-consuming. Ethical considerations, particularly around protecting participant anonymity while presenting rich, detailed data, require careful navigation. Integrating different types of data from various sources can be complex. Time management is crucial, as researchers must balance the depth of analysis with project timelines and resources. Finally, communicating complex qualitative insights in clear, compelling ways can be challenging.

Best Software to Analyze Qualitative Data

G2 rating: 4.6/5

Pricing: Starts at $30 monthly.

Looppanel is an AI-powered research assistant and repository platform that can make it 5x faster to get to insights, by automating all the manual, tedious parts of your job. 

Here’s how Looppanel’s features can help with qualitative data analysis:

• Automatic Transcription: Quickly turn speech into accurate text; it works across 8 languages and even heavy accents, with over 90% accuracy.
• AI Note-Taking: The research assistant can join you on calls and take notes, as well as automatically sort your notes based on your interview questions.
• Automatic Tagging: Easily tag and organize your data with free AI tools.
• Insight Generation: Create shareable insights that fit right into your other tools.
• Repository Search: Run Google-like searches within your projects and calls to find a data snippet/quote in seconds
• Smart Summary: Ask the AI a question on your research, and it will give you an answer, using extracts from your data as citations.

Looppanel’s focus on automating research tasks makes it perfect for researchers who want to save time and work smarter.

G2 rating: 4.7/5

Pricing: Free version available, with the Plus version costing $20 monthly.

ChatGPT, developed by OpenAI, offers a range of capabilities for qualitative data analysis including:

• Document analysis : It can easily extract and analyze text from various file formats.
• Summarization : GPT can condense lengthy documents into concise summaries.
• Advanced Data Analysis (ADA) : For paid users, Chat-GPT offers quantitative analysis of data documents.
• Sentiment analysis: Although not Chat-GPT’s specialty, it can still perform basic sentiment analysis on text data.

ChatGPT's versatility makes it valuable for researchers who need quick insights from diverse text sources.

How to use ChatGPT for qualitative data analysis

ChatGPT can be a handy sidekick in your qualitative analysis, if you do the following:

• Use it to summarize long documents or transcripts
• Ask it to identify key themes in your data
• Use it for basic sentiment analysis
• Have it generate potential codes based on your research questions
• Use it to brainstorm interpretations of your findings

G2 rating: 4.7/5 Pricing: Custom

Atlas.ti is a powerful platform built for detailed qualitative and mixed-methods research, offering a lot of capabilities for running both quantitative and qualitative research.

It’s key data analysis features include:

• Multi-format Support: Analyze text, PDFs, images, audio, video, and geo data all within one platform.
• AI-Powered Coding: Uses AI to suggest codes and summarize documents.
• Collaboration Tools: Ideal for teams working on complex research projects.
• Data Visualization: Create network views and other visualizations to showcase relationships in your data.

G2 rating: 4.1/5 Pricing: Custom

NVivo is another powerful platform for qualitative and mixed-methods research. It’s analysis features include:

• Data Import and Organization: Easily manage different data types, including text, audio, and video.
• AI-Powered Coding: Speeds up the coding process with machine learning.
• Visualization Tools: Create charts, graphs, and diagrams to represent your findings.
• Collaboration Features: Suitable for team-based research projects.

NVivo combines AI capabilities with traditional qualitative analysis tools, making it versatile for various research needs.

Can Excel do qualitative data analysis?

Excel can be a handy tool for qualitative data analysis, especially if you're just starting out or working on a smaller project. While it's not specialized qualitative data analysis software, you can use it to organize your data, maybe putting different themes in different columns. It's good for basic coding, where you label bits of text with keywords. You can use its filter feature to focus on specific themes. Excel can also create simple charts to visualize your findings. But for bigger or more complex projects, you might want to look into software designed specifically for qualitative data analysis. These tools often have more advanced features that can save you time and help you dig deeper into your data.

How do you show qualitative analysis?

Showing qualitative data analysis is about telling the story of your data. In qualitative data analysis methods, we use quotes from interviews or documents to back up our points. Create charts or mind maps to show how different ideas connect, which is a common practice in data analysis in qualitative research. Group your findings into themes that make sense. Then, write it all up in a way that flows, explaining what you found and why it matters.

What is the best way to analyze qualitative data?

There's no one-size-fits-all approach to how to analyze qualitative data, but there are some tried-and-true steps. 

Start by getting your data in order. Then, read through it a few times to get familiar with it. As you go, start marking important bits with codes - this is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes - how do they connect? 

Finally, think about what it all means in the bigger picture of your research. Remember, it's okay to go back and forth between these steps as you dig deeper into your data. Qualitative data analysis software can be a big help in this process, especially for managing large amounts of data.

In qualitative methods of test analysis, what do test developers do to generate data?

Test developers in qualitative research might sit down with people for in-depth chats or run group discussions, which are key qualitative data analysis methods. They often use surveys with open-ended questions that let people express themselves freely. Sometimes, they'll observe people in their natural environment, taking notes on what they see. They might also dig into existing documents or artifacts that relate to their topic. The goal is to gather rich, detailed information that helps them understand the full picture, which is crucial in data analysis in qualitative research.

Which is not a purpose of reflexivity during qualitative data analysis?

Reflexivity in qualitative data analysis isn't about proving you're completely objective. That's not the goal. Instead, it's about being honest about who you are as a researcher. It's recognizing that your own experiences and views might influence how you see the data. By being upfront about this, you actually make your research more trustworthy. It's also a way to dig deeper into your data, seeing things you might have missed at first glance. This self-awareness is a crucial part of qualitative data analysis methods.

What is a qualitative data analysis example?

A simple example is analyzing customer feedback for a new product. You might collect feedback, read through responses, create codes like "ease of use" or "design," and group similar codes into themes. You'd then identify patterns and support findings with specific quotes. This process helps transform raw feedback into actionable insights.

How to analyze qualitative data from a survey?

First, gather all your responses in one place. Read through them to get a feel for what people are saying. Then, start labeling responses with codes - short descriptions of what each bit is about. This coding process is a fundamental qualitative data analysis method. Group similar codes into bigger themes. Look for patterns in these themes. Are certain ideas coming up a lot? Do different groups of people have different views? Use actual quotes from your survey to back up what you're seeing. Think about how your findings relate to your original research questions. 

Which one is better, NVivo or Atlas.ti?

NVivo is known for being user-friendly and great for team projects. Atlas.ti shines when it comes to visual mapping of concepts and handling geographic data. Both can handle a variety of data types and have powerful tools for qualitative data analysis. The best way to decide is to try out both if you can. 

While these are powerful tools, the core of qualitative data analysis still relies on your analytical skills and understanding of qualitative data analysis methods.

Do I need to use NVivo for qualitative data analysis?

You don't necessarily need NVivo for qualitative data analysis, but it can definitely make your life easier, especially for bigger projects. Think of it like using a power tool versus a hand tool - you can get the job done either way, but the power tool might save you time and effort. For smaller projects or if you're just starting out, you might be fine with simpler tools or even free qualitative data analysis software. But if you're dealing with lots of data, or if you need to collaborate with a team, or if you want to do more complex analysis, then specialized qualitative data analysis software like NVivo can be a big help. It's all about finding the right tool for your specific research needs and the qualitative data analysis methods you're using.

Here’s a guide that can help you decide.

How to use NVivo for qualitative data analysis

First, you import all your data - interviews, documents, videos, whatever you've got. Then you start creating "nodes," which are like folders for different themes or ideas in your data. As you read through your material, you highlight bits that relate to these themes and file them under the right nodes. NVivo lets you easily search through all this organized data, find connections between different themes, and even create visual maps of how everything relates.

How much does NVivo cost?

NVivo's pricing isn't one-size-fits-all. They offer different plans for individuals, teams, and large organizations, but they don't publish their prices openly. Contact the team here for a custom quote.

What are the four steps of qualitative data analysis?

While qualitative data analysis is often iterative, it generally follows these four main steps:

1. Data Collection: Gathering raw data through interviews, observations, or documents.

2. Data Preparation: Organizing and transcribing the collected data.

3. Data Coding: Identifying and labeling important concepts or themes in the data.

4. Interpretation: Drawing meaning from the coded data and developing insights.

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Qualitative Data Analysis

23 Presenting the Results of Qualitative Analysis

Mikaila Mariel Lemonik Arthur

Qualitative research is not finished just because you have determined the main findings or conclusions of your study. Indeed, disseminating the results is an essential part of the research process. By sharing your results with others, whether in written form as scholarly paper or an applied report or in some alternative format like an oral presentation, an infographic, or a video, you ensure that your findings become part of the ongoing conversation of scholarship in your field, forming part of the foundation for future researchers. This chapter provides an introduction to writing about qualitative research findings. It will outline how writing continues to contribute to the analysis process, what concerns researchers should keep in mind as they draft their presentations of findings, and how best to organize qualitative research writing

As you move through the research process, it is essential to keep yourself organized. Organizing your data, memos, and notes aids both the analytical and the writing processes. Whether you use electronic or physical, real-world filing and organizational systems, these systems help make sense of the mountains of data you have and assure you focus your attention on the themes and ideas you have determined are important (Warren and Karner 2015). Be sure that you have kept detailed notes on all of the decisions you have made and procedures you have followed in carrying out research design, data collection, and analysis, as these will guide your ultimate write-up.

First and foremost, researchers should keep in mind that writing is in fact a form of thinking. Writing is an excellent way to discover ideas and arguments and to further develop an analysis. As you write, more ideas will occur to you, things that were previously confusing will start to make sense, and arguments will take a clear shape rather than being amorphous and poorly-organized. However, writing-as-thinking cannot be the final version that you share with others. Good-quality writing does not display the workings of your thought process. It is reorganized and revised (more on that later) to present the data and arguments important in a particular piece. And revision is totally normal! No one expects the first draft of a piece of writing to be ready for prime time. So write rough drafts and memos and notes to yourself and use them to think, and then revise them until the piece is the way you want it to be for sharing.

Bergin (2018) lays out a set of key concerns for appropriate writing about research. First, present your results accurately, without exaggerating or misrepresenting. It is very easy to overstate your findings by accident if you are enthusiastic about what you have found, so it is important to take care and use appropriate cautions about the limitations of the research. You also need to work to ensure that you communicate your findings in a way people can understand, using clear and appropriate language that is adjusted to the level of those you are communicating with. And you must be clear and transparent about the methodological strategies employed in the research. Remember, the goal is, as much as possible, to describe your research in a way that would permit others to replicate the study. There are a variety of other concerns and decision points that qualitative researchers must keep in mind, including the extent to which to include quantification in their presentation of results, ethics, considerations of audience and voice, and how to bring the richness of qualitative data to life.

Quantification, as you have learned, refers to the process of turning data into numbers. It can indeed be very useful to count and tabulate quantitative data drawn from qualitative research. For instance, if you were doing a study of dual-earner households and wanted to know how many had an equal division of household labor and how many did not, you might want to count those numbers up and include them as part of the final write-up. However, researchers need to take care when they are writing about quantified qualitative data. Qualitative data is not as generalizable as quantitative data, so quantification can be very misleading. Thus, qualitative researchers should strive to use raw numbers instead of the percentages that are more appropriate for quantitative research. Writing, for instance, “15 of the 20 people I interviewed prefer pancakes to waffles” is a simple description of the data; writing “75% of people prefer pancakes” suggests a generalizable claim that is not likely supported by the data. Note that mixing numbers with qualitative data is really a type of mixed-methods approach. Mixed-methods approaches are good, but sometimes they seduce researchers into focusing on the persuasive power of numbers and tables rather than capitalizing on the inherent richness of their qualitative data.

A variety of issues of scholarly ethics and research integrity are raised by the writing process. Some of these are unique to qualitative research, while others are more universal concerns for all academic and professional writing. For example, it is essential to avoid plagiarism and misuse of sources. All quotations that appear in a text must be properly cited, whether with in-text and bibliographic citations to the source or with an attribution to the research participant (or the participant’s pseudonym or description in order to protect confidentiality) who said those words. Where writers will paraphrase a text or a participant’s words, they need to make sure that the paraphrase they develop accurately reflects the meaning of the original words. Thus, some scholars suggest that participants should have the opportunity to read (or to have read to them, if they cannot read the text themselves) all sections of the text in which they, their words, or their ideas are presented to ensure accuracy and enable participants to maintain control over their lives.

Audience and Voice

When writing, researchers must consider their audience(s) and the effects they want their writing to have on these audiences. The designated audience will dictate the voice used in the writing, or the individual style and personality of a piece of text. Keep in mind that the potential audience for qualitative research is often much more diverse than that for quantitative research because of the accessibility of the data and the extent to which the writing can be accessible and interesting. Yet individual pieces of writing are typically pitched to a more specific subset of the audience.

Let us consider one potential research study, an ethnography involving participant-observation of the same children both when they are at daycare facility and when they are at home with their families to try to understand how daycare might impact behavior and social development. The findings of this study might be of interest to a wide variety of potential audiences: academic peers, whether at your own academic institution, in your broader discipline, or multidisciplinary; people responsible for creating laws and policies; practitioners who run or teach at day care centers; and the general public, including both people who are interested in child development more generally and those who are themselves parents making decisions about child care for their own children. And the way you write for each of these audiences will be somewhat different. Take a moment and think through what some of these differences might look like.

If you are writing to academic audiences, using specialized academic language and working within the typical constraints of scholarly genres, as will be discussed below, can be an important part of convincing others that your work is legitimate and should be taken seriously. Your writing will be formal. Even if you are writing for students and faculty you already know—your classmates, for instance—you are often asked to imitate the style of academic writing that is used in publications, as this is part of learning to become part of the scholarly conversation. When speaking to academic audiences outside your discipline, you may need to be more careful about jargon and specialized language, as disciplines do not always share the same key terms. For instance, in sociology, scholars use the term diffusion to refer to the way new ideas or practices spread from organization to organization. In the field of international relations, scholars often used the term cascade to refer to the way ideas or practices spread from nation to nation. These terms are describing what is fundamentally the same concept, but they are different terms—and a scholar from one field might have no idea what a scholar from a different field is talking about! Therefore, while the formality and academic structure of the text would stay the same, a writer with a multidisciplinary audience might need to pay more attention to defining their terms in the body of the text.

It is not only other academic scholars who expect to see formal writing. Policymakers tend to expect formality when ideas are presented to them, as well. However, the content and style of the writing will be different. Much less academic jargon should be used, and the most important findings and policy implications should be emphasized right from the start rather than initially focusing on prior literature and theoretical models as you might for an academic audience. Long discussions of research methods should also be minimized. Similarly, when you write for practitioners, the findings and implications for practice should be highlighted. The reading level of the text will vary depending on the typical background of the practitioners to whom you are writing—you can make very different assumptions about the general knowledge and reading abilities of a group of hospital medical directors with MDs than you can about a group of case workers who have a post-high-school certificate. Consider the primary language of your audience as well. The fact that someone can get by in spoken English does not mean they have the vocabulary or English reading skills to digest a complex report. But the fact that someone’s vocabulary is limited says little about their intellectual abilities, so try your best to convey the important complexity of the ideas and findings from your research without dumbing them down—even if you must limit your vocabulary usage.

When writing for the general public, you will want to move even further towards emphasizing key findings and policy implications, but you also want to draw on the most interesting aspects of your data. General readers will read sociological texts that are rich with ethnographic or other kinds of detail—it is almost like reality television on a page! And this is a contrast to busy policymakers and practitioners, who probably want to learn the main findings as quickly as possible so they can go about their busy lives. But also keep in mind that there is a wide variation in reading levels. Journalists at publications pegged to the general public are often advised to write at about a tenth-grade reading level, which would leave most of the specialized terminology we develop in our research fields out of reach. If you want to be accessible to even more people, your vocabulary must be even more limited. The excellent exercise of trying to write using the 1,000 most common English words, available at the Up-Goer Five website ( https://www.splasho.com/upgoer5/ ) does a good job of illustrating this challenge (Sanderson n.d.).

Another element of voice is whether to write in the first person. While many students are instructed to avoid the use of the first person in academic writing, this advice needs to be taken with a grain of salt. There are indeed many contexts in which the first person is best avoided, at least as long as writers can find ways to build strong, comprehensible sentences without its use, including most quantitative research writing. However, if the alternative to using the first person is crafting a sentence like “it is proposed that the researcher will conduct interviews,” it is preferable to write “I propose to conduct interviews.” In qualitative research, in fact, the use of the first person is far more common. This is because the researcher is central to the research project. Qualitative researchers can themselves be understood as research instruments, and thus eliminating the use of the first person in writing is in a sense eliminating information about the conduct of the researchers themselves.

But the question really extends beyond the issue of first-person or third-person. Qualitative researchers have choices about how and whether to foreground themselves in their writing, not just in terms of using the first person, but also in terms of whether to emphasize their own subjectivity and reflexivity, their impressions and ideas, and their role in the setting. In contrast, conventional quantitative research in the positivist tradition really tries to eliminate the author from the study—which indeed is exactly why typical quantitative research avoids the use of the first person. Keep in mind that emphasizing researchers’ roles and reflexivity and using the first person does not mean crafting articles that provide overwhelming detail about the author’s thoughts and practices. Readers do not need to hear, and should not be told, which database you used to search for journal articles, how many hours you spent transcribing, or whether the research process was stressful—save these things for the memos you write to yourself. Rather, readers need to hear how you interacted with research participants, how your standpoint may have shaped the findings, and what analytical procedures you carried out.

Making Data Come Alive

One of the most important parts of writing about qualitative research is presenting the data in a way that makes its richness and value accessible to readers. As the discussion of analysis in the prior chapter suggests, there are a variety of ways to do this. Researchers may select key quotes or images to illustrate points, write up specific case studies that exemplify their argument, or develop vignettes (little stories) that illustrate ideas and themes, all drawing directly on the research data. Researchers can also write more lengthy summaries, narratives, and thick descriptions.

Nearly all qualitative work includes quotes from research participants or documents to some extent, though ethnographic work may focus more on thick description than on relaying participants’ own words. When quotes are presented, they must be explained and interpreted—they cannot stand on their own. This is one of the ways in which qualitative research can be distinguished from journalism. Journalism presents what happened, but social science needs to present the “why,” and the why is best explained by the researcher.

So how do authors go about integrating quotes into their written work? Julie Posselt (2017), a sociologist who studies graduate education, provides a set of instructions. First of all, authors need to remain focused on the core questions of their research, and avoid getting distracted by quotes that are interesting or attention-grabbing but not so relevant to the research question. Selecting the right quotes, those that illustrate the ideas and arguments of the paper, is an important part of the writing process. Second, not all quotes should be the same length (just like not all sentences or paragraphs in a paper should be the same length). Include some quotes that are just phrases, others that are a sentence or so, and others that are longer. We call longer quotes, generally those more than about three lines long, block quotes , and they are typically indented on both sides to set them off from the surrounding text. For all quotes, be sure to summarize what the quote should be telling or showing the reader, connect this quote to other quotes that are similar or different, and provide transitions in the discussion to move from quote to quote and from topic to topic. Especially for longer quotes, it is helpful to do some of this writing before the quote to preview what is coming and other writing after the quote to make clear what readers should have come to understand. Remember, it is always the author’s job to interpret the data. Presenting excerpts of the data, like quotes, in a form the reader can access does not minimize the importance of this job. Be sure that you are explaining the meaning of the data you present.

A few more notes about writing with quotes: avoid patchwriting, whether in your literature review or the section of your paper in which quotes from respondents are presented. Patchwriting is a writing practice wherein the author lightly paraphrases original texts but stays so close to those texts that there is little the author has added. Sometimes, this even takes the form of presenting a series of quotes, properly documented, with nothing much in the way of text generated by the author. A patchwriting approach does not build the scholarly conversation forward, as it does not represent any kind of new contribution on the part of the author. It is of course fine to paraphrase quotes, as long as the meaning is not changed. But if you use direct quotes, do not edit the text of the quotes unless how you edit them does not change the meaning and you have made clear through the use of ellipses (…) and brackets ([])what kinds of edits have been made. For example, consider this exchange from Matthew Desmond’s (2012:1317) research on evictions:

The thing was, I wasn’t never gonna let Crystal come and stay with me from the get go. I just told her that to throw her off. And she wasn’t fittin’ to come stay with me with no money…No. Nope. You might as well stay in that shelter.

A paraphrase of this exchange might read “She said that she was going to let Crystal stay with her if Crystal did not have any money.” Paraphrases like that are fine. What is not fine is rewording the statement but treating it like a quote, for instance writing:

The thing was, I was not going to let Crystal come and stay with me from beginning. I just told her that to throw her off. And it was not proper for her to come stay with me without any money…No. Nope. You might as well stay in that shelter.

But as you can see, the change in language and style removes some of the distinct meaning of the original quote. Instead, writers should leave as much of the original language as possible. If some text in the middle of the quote needs to be removed, as in this example, ellipses are used to show that this has occurred. And if a word needs to be added to clarify, it is placed in square brackets to show that it was not part of the original quote.

Data can also be presented through the use of data displays like tables, charts, graphs, diagrams, and infographics created for publication or presentation, as well as through the use of visual material collected during the research process. Note that if visuals are used, the author must have the legal right to use them. Photographs or diagrams created by the author themselves—or by research participants who have signed consent forms for their work to be used, are fine. But photographs, and sometimes even excerpts from archival documents, may be owned by others from whom researchers must get permission in order to use them.

A large percentage of qualitative research does not include any data displays or visualizations. Therefore, researchers should carefully consider whether the use of data displays will help the reader understand the data. One of the most common types of data displays used by qualitative researchers are simple tables. These might include tables summarizing key data about cases included in the study; tables laying out the characteristics of different taxonomic elements or types developed as part of the analysis; tables counting the incidence of various elements; and 2×2 tables (two columns and two rows) illuminating a theory. Basic network or process diagrams are also commonly included. If data displays are used, it is essential that researchers include context and analysis alongside data displays rather than letting them stand by themselves, and it is preferable to continue to present excerpts and examples from the data rather than just relying on summaries in the tables.

If you will be using graphs, infographics, or other data visualizations, it is important that you attend to making them useful and accurate (Bergin 2018). Think about the viewer or user as your audience and ensure the data visualizations will be comprehensible. You may need to include more detail or labels than you might think. Ensure that data visualizations are laid out and labeled clearly and that you make visual choices that enhance viewers’ ability to understand the points you intend to communicate using the visual in question. Finally, given the ease with which it is possible to design visuals that are deceptive or misleading, it is essential to make ethical and responsible choices in the construction of visualization so that viewers will interpret them in accurate ways.

The Genre of Research Writing

As discussed above, the style and format in which results are presented depends on the audience they are intended for. These differences in styles and format are part of the genre of writing. Genre is a term referring to the rules of a specific form of creative or productive work. Thus, the academic journal article—and student papers based on this form—is one genre. A report or policy paper is another. The discussion below will focus on the academic journal article, but note that reports and policy papers follow somewhat different formats. They might begin with an executive summary of one or a few pages, include minimal background, focus on key findings, and conclude with policy implications, shifting methods and details about the data to an appendix. But both academic journal articles and policy papers share some things in common, for instance the necessity for clear writing, a well-organized structure, and the use of headings.

So what factors make up the genre of the academic journal article in sociology? While there is some flexibility, particularly for ethnographic work, academic journal articles tend to follow a fairly standard format. They begin with a “title page” that includes the article title (often witty and involving scholarly inside jokes, but more importantly clearly describing the content of the article); the authors’ names and institutional affiliations, an abstract , and sometimes keywords designed to help others find the article in databases. An abstract is a short summary of the article that appears both at the very beginning of the article and in search databases. Abstracts are designed to aid readers by giving them the opportunity to learn enough about an article that they can determine whether it is worth their time to read the complete text. They are written about the article, and thus not in the first person, and clearly summarize the research question, methodological approach, main findings, and often the implications of the research.

After the abstract comes an “introduction” of a page or two that details the research question, why it matters, and what approach the paper will take. This is followed by a literature review of about a quarter to a third the length of the entire paper. The literature review is often divided, with headings, into topical subsections, and is designed to provide a clear, thorough overview of the prior research literature on which a paper has built—including prior literature the new paper contradicts. At the end of the literature review it should be made clear what researchers know about the research topic and question, what they do not know, and what this new paper aims to do to address what is not known.

The next major section of the paper is the section that describes research design, data collection, and data analysis, often referred to as “research methods” or “methodology.” This section is an essential part of any written or oral presentation of your research. Here, you tell your readers or listeners “how you collected and interpreted your data” (Taylor, Bogdan, and DeVault 2016:215). Taylor, Bogdan, and DeVault suggest that the discussion of your research methods include the following:

• The particular approach to data collection used in the study;
• Any theoretical perspective(s) that shaped your data collection and analytical approach;
• When the study occurred, over how long, and where (concealing identifiable details as needed);
• A description of the setting and participants, including sampling and selection criteria (if an interview-based study, the number of participants should be clearly stated);
• The researcher’s perspective in carrying out the study, including relevant elements of their identity and standpoint, as well as their role (if any) in research settings; and
• The approach to analyzing the data.

After the methods section comes a section, variously titled but often called “data,” that takes readers through the analysis. This section is where the thick description narrative; the quotes, broken up by theme or topic, with their interpretation; the discussions of case studies; most data displays (other than perhaps those outlining a theoretical model or summarizing descriptive data about cases); and other similar material appears. The idea of the data section is to give readers the ability to see the data for themselves and to understand how this data supports the ultimate conclusions. Note that all tables and figures included in formal publications should be titled and numbered.

At the end of the paper come one or two summary sections, often called “discussion” and/or “conclusion.” If there is a separate discussion section, it will focus on exploring the overall themes and findings of the paper. The conclusion clearly and succinctly summarizes the findings and conclusions of the paper, the limitations of the research and analysis, any suggestions for future research building on the paper or addressing these limitations, and implications, be they for scholarship and theory or policy and practice.

After the end of the textual material in the paper comes the bibliography, typically called “works cited” or “references.” The references should appear in a consistent citation style—in sociology, we often use the American Sociological Association format (American Sociological Association 2019), but other formats may be used depending on where the piece will eventually be published. Care should be taken to ensure that in-text citations also reflect the chosen citation style. In some papers, there may be an appendix containing supplemental information such as a list of interview questions or an additional data visualization.

Note that when researchers give presentations to scholarly audiences, the presentations typically follow a format similar to that of scholarly papers, though given time limitations they are compressed. Abstracts and works cited are often not part of the presentation, though in-text citations are still used. The literature review presented will be shortened to only focus on the most important aspects of the prior literature, and only key examples from the discussion of data will be included. For long or complex papers, sometimes only one of several findings is the focus of the presentation. Of course, presentations for other audiences may be constructed differently, with greater attention to interesting elements of the data and findings as well as implications and less to the literature review and methods.

Concluding Your Work

After you have written a complete draft of the paper, be sure you take the time to revise and edit your work. There are several important strategies for revision. First, put your work away for a little while. Even waiting a day to revise is better than nothing, but it is best, if possible, to take much more time away from the text. This helps you forget what your writing looks like and makes it easier to find errors, mistakes, and omissions. Second, show your work to others. Ask them to read your work and critique it, pointing out places where the argument is weak, where you may have overlooked alternative explanations, where the writing could be improved, and what else you need to work on. Finally, read your work out loud to yourself (or, if you really need an audience, try reading to some stuffed animals). Reading out loud helps you catch wrong words, tricky sentences, and many other issues. But as important as revision is, try to avoid perfectionism in writing (Warren and Karner 2015). Writing can always be improved, no matter how much time you spend on it. Those improvements, however, have diminishing returns, and at some point the writing process needs to conclude so the writing can be shared with the world.

Of course, the main goal of writing up the results of a research project is to share with others. Thus, researchers should be considering how they intend to disseminate their results. What conferences might be appropriate? Where can the paper be submitted? Note that if you are an undergraduate student, there are a wide variety of journals that accept and publish research conducted by undergraduates. Some publish across disciplines, while others are specific to disciplines. Other work, such as reports, may be best disseminated by publication online on relevant organizational websites.

After a project is completed, be sure to take some time to organize your research materials and archive them for longer-term storage. Some Institutional Review Board (IRB) protocols require that original data, such as interview recordings, transcripts, and field notes, be preserved for a specific number of years in a protected (locked for paper or password-protected for digital) form and then destroyed, so be sure that your plans adhere to the IRB requirements. Be sure you keep any materials that might be relevant for future related research or for answering questions people may ask later about your project.

And then what? Well, then it is time to move on to your next research project. Research is a long-term endeavor, not a one-time-only activity. We build our skills and our expertise as we continue to pursue research. So keep at it.

• Find a short article that uses qualitative methods. The sociological magazine Contexts is a good place to find such pieces. Write an abstract of the article.
• Choose a sociological journal article on a topic you are interested in that uses some form of qualitative methods and is at least 20 pages long. Rewrite the article as a five-page research summary accessible to non-scholarly audiences.
• Choose a concept or idea you have learned in this course and write an explanation of it using the Up-Goer Five Text Editor ( https://www.splasho.com/upgoer5/ ), a website that restricts your writing to the 1,000 most common English words. What was this experience like? What did it teach you about communicating with people who have a more limited English-language vocabulary—and what did it teach you about the utility of having access to complex academic language?
• Select five or more sociological journal articles that all use the same basic type of qualitative methods (interviewing, ethnography, documents, or visual sociology). Using what you have learned about coding, code the methods sections of each article, and use your coding to figure out what is common in how such articles discuss their research design, data collection, and analysis methods.
• Return to an exercise you completed earlier in this course and revise your work. What did you change? How did revising impact the final product?
• Find a quote from the transcript of an interview, a social media post, or elsewhere that has not yet been interpreted or explained. Write a paragraph that includes the quote along with an explanation of its sociological meaning or significance.

The style or personality of a piece of writing, including such elements as tone, word choice, syntax, and rhythm.

A quotation, usually one of some length, which is set off from the main text by being indented on both sides rather than being placed in quotation marks.

A classification of written or artistic work based on form, content, and style.

A short summary of a text written from the perspective of a reader rather than from the perspective of an author.

Social Data Analysis Copyright © 2021 by Mikaila Mariel Lemonik Arthur is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Article contents

Qualitative data analysis and the use of theory.

• Carol Grbich Carol Grbich Flinders University
• https://doi.org/10.1093/acrefore/9780190264093.013.554
• Published online: 23 May 2019

The role of theory in qualitative data analysis is continually shifting and offers researchers many choices. The dynamic and inclusive nature of qualitative research has encouraged the entry of a number of interested disciplines into the field. These discipline groups have introduced new theoretical practices that have influenced and diversified methodological approaches. To add to these, broader shifts in chronological theoretical orientations in qualitative research can be seen in the four waves of paradigmatic change; the first wave showed a developing concern with the limitations of researcher objectivity, and empirical observation of evidence based data, leading to the second wave with its focus on realities - mutually constructed by researcher and researched, participant subjectivity, and the remedying of societal inequalities and mal-distributed power. The third wave was prompted by the advent of Postmodernism and Post- structuralism with their emphasis on chaos, complexity, intertextuality and multiple realities; and most recently the fourth wave brought a focus on visual images, performance, both an active researcher and an interactive audience, and the crossing of the theoretical divide between social science and classical physics. The methods and methodological changes, which have evolved from these paradigm shifts, can be seen to have followed a similar pattern of change. The researcher now has multiple paradigms, co-methodologies, diverse methods and a variety of theoretical choices, to consider. This continuum of change has shifted the field of qualitative research dramatically from limited choices to multiple options, requiring clarification of researcher decisions and transparency of process. However, there still remains the difficult question of the role that theory will now play in such a high level of complex design and critical researcher reflexivity.

• qualitative research
• data analysis
• methodologies

Theory and Qualitative Data Analysis

Researchers new to qualitative research, and particularly those coming from the quantitative tradition, have often expressed frustration at the need for what appears to be an additional and perhaps unnecessary process—that of the theoretical interpretation of their carefully designed, collected, and analyzed data. The justifications for this process have tended to fall into one of two areas: the need to lift data to a broader interpretation beyond the Monty Pythonesque “this is my theory and it’s my very own,” to illumination of findings from another perspective—by placing the data in its relevant discipline field for comparison with previous theoretical data interpretations, while possibly adding something original to the field.

“Theory” is broadly seen as a set of assumptions or propositions, developed from observation or investigation of perceived realties, that attempt to provide an explanation of relationships or phenomena. The framing of data via theoretical imposition can occur at different levels. At the lowest level, various concepts such as “role,” “power,” “socialization,” “evaluation,” or “learning styles” refer to limited aspects of social organization and are usually applied to a specific group of people.

At a more complex level, theories of the Middle Range, identified by Robert Merton to link theory and practice, are used to build theory from empirical data. These tend to be discipline specific and incorporate concepts plus variables such as “gender,” “race,” or “class.” Concepts and variables are then combined into meaningful statements, which can be applied to more diverse social groups. For example, in education an investigation of student performance could emphasize such concepts as “safety,” “zero bullying,” “communication,” and “tolerance,” with variables such as “race” and “gender” to lead to a statement that good microsystems and a focus on individual needs are necessary for optimal student performance.

The third and most complex level uses the established or grand theories such as those of Sigmund Freud’s stages of children’s development, Jean Piaget’s theory of cognitive development, or Urie Bronfenbrenner’s ecological systems, which have been widely accepted as meaningful across a number of disciplines and provide abstract explanations of the uniformity of aspects of social organization, social behavior, and social change.

The trend in qualitative research regarding the application of chosen levels of theory has been generally either toward theory direction/verification or theory generation, although the two are often intertwined. In the first, a relevant existing theory is chosen early and acts as a point of critical comparison for the data to be collected. This approach requires the researcher to think theoretically as s/he designs the study, collects data, and collates it into analytical groupings. The danger of theory direction is that an over focus on a chosen theoretical orientation may limit what the researcher can access or “see” in the data, but on the upside, this approach can also enable the generation of new theoretical aspects, as it is rare that findings will fall precisely within the implications of existing statements. Theory generation is a much looser approach and involves either one or a range of relevant levels of theory being identified at any point in the research process, and from which, in conjunction with data findings, some new combination or distillation can enhance interpretation.

The question of whether a well-designed study should negate the need for theoretical interpretation has been minimally debated. Mehdi and Mansor ( 2010 ) identified three trends in the literature on this topic: that theory in qualitative research relates to integrated methodology and epistemology; that theory is a separate and additional element to any methodological underpinnings; and that theory has no solid relationship with qualitative research. No clear agreement on any of these is evident. Overall, there appears to be general acceptance that the process of using theory, albeit etically (imposed) or emically (integrated), enhances outcomes, and moves research away from being a-theoretical or unilluminated by other ideas. However, regarding praxis, a closer look at the issue of the use of theory and data may be in order. Theoretical interpretation, as currently practiced, has limits. To begin with, the playing field is not level. In the grounded theory tradition, Glaser and Strauss ( 1967 ) were initially clear that in order to prevent undue influence on design and interpretation, the researcher should avoid reviewing the literature on a topic until after some data collection and analysis had been undertaken. The presumption that most researchers would already be well versed in theory/ies and would have a broad spectrum to draw on in order to facilitate the constant comparative process from which data-based concepts could be generated was found to be incorrect. Glaser ( 1978 ) suggested this lack could be improved at the conceptual level via personal and professional reflexivity.

This issue became even more of a problem with the advent of practice-led disciplines such as education and health into the field of qualitative research. These groups had not been widely exposed to the theories of the traditional social sciences such as sociology, psychology, and philosophy, although in education they would have been familiar with John Dewey’s concept of “pragmatism” linking learning with hands-on activity, and were more used to developing and using models of practice for comparison with current realities. By the mid- 20th century , Education was more established in research and had moved toward the use of middle range theories and the late 20th-century grand theorists: Michel Foucault, with his emphasis on power and knowledge control, and Jurgen Habermas, with his focus on pragmatism, communication, and knowledge management.

In addition to addictive identification with particular levels of theory and discipline-preferred theories and methods, activity across qualitative research seems to fall between two extremes. At one end it involves separate processes of data collection and analysis before searching for a theoretical framework within which to discuss the findings—often choosing a framework that has gained traction in a specific discipline. This “best/most acceptable fit” approach often adds little to the relevant field beyond repetition and appears somewhat forced. At the other extreme there are those who weave methods, methodologies, data, and theory throughout the whole research process, actively critiquing and modifying it as they go, usually with the outcome of creating some new direction for both theory and practice. The majority of qualitative research practice, however, tends to fall somewhere between these two.

The final aspect of framing data lies in the impact of researchers themselves, and the early- 21st-century emphasis is on exposing relevant personal frames, particularly those of culture, gender, socioeconomic class, life experiences such as education, work, and socialization, and the researcher’s own values and beliefs. The twin purposes of this exposure are to create researcher awareness and encourage accountability for their impact on the data, as well as allowing the reader to assess the value of research outcomes in terms of potential researcher bias or prejudice. This critical reflexivity is supposed to be undertaken at all stages of the research but it is not always clear that it has occurred.

Paradigms: From Interactionism to Performativity

It appears that there are potentially five sources of theory: that which is generally available and can be sourced from different disciplines; that which is imbedded in the chosen paradigm/s; that which underpins particular methodologies; that which the researcher brings, and that which the researched incorporate within their stories. Of these, the paradigm/s chosen are probably the most influential in terms of researcher position and design. The variety of the sets of assumptions, beliefs, and researcher practices that comprise the theoretical paradigms, perspectives, or broad world views available to researchers, and within which they are expected to locate their individual position and their research approach, has shifted dramatically since the 1930s. The changes have been distinct and identifiable, with their roots located in the societal shifts prompted by political, social, and economic change.

The First Wave

The Positivist paradigm dominated research, largely unquestioned, prior to the early 20th century . It emphasized the distancing of the researcher from his/her subjects; researcher objectivity; a focus on objective, cause–effect, evidence-based data derived from empirical observation of external realities; experimental quantitative methods involving testing hypotheses; and the provision of finite answers and unassailable future predictions. From the 1930s, concerns about the limitations of findings and the veracity of research outcomes, together with improved communication and exposure to the worldviews of other cultures, led to the advent of the realist/post-positivist paradigm. Post-positivism, or critical realism, recognized that certainty in proving the truth of a hypothesis was unachievable and that outcomes were probably limited to falsification (Popper, 1963 ), that true objectivity was unattainable and that the researcher was most likely to impact on or to contaminate data, that both qualitative and quantitative approaches were valuable, and that methodological pluralism was desirable.

The Second Wave

Alongside the worldwide political shifts toward “people power” in the 1960s and 1970s, two other paradigms emerged. The first, the Interpretivist/Constructivist, focused on the social situations in which we as humans develop and how our construction of knowledge occurs through interactions with others in these contexts. This paradigm also emphasized the gaining of an understanding of the subjective views or experiences of the participants being researched, and recognized the impact of the researcher on researcher–researched mutually constructed realities. Here, theory generation is the preferred outcome to explain the what, how, and why of the findings. This usually involves the development of a conceptual model, forged from both the data gained and from the application/integration of relevant theory, to provide explanations for and interpretations of findings, together with a new perspective for the field/discipline.

The second paradigm, termed the Critical/Emancipatory, focused on locating, critiquing, and changing inequalities in society. The identification of the location of systemic power discrepancies or systematic power misuse in situations involving gender, sexuality, class, and race is expected to be followed by moves to right any oppression discovered. Here, the use of theory has been focused more on predetermined concept application for “fit.” This is because the very strong notion of problematic societal structures and power inappropriately wielded have been the dominant underpinnings.

In both the Interpretive and Critical paradigms, researcher position shifted from the elevated and distant position of positivism, to one of becoming equal with those being researched, and the notion of researcher framing emerged to cover this shift and help us—the readers—to “see” (and judge) the researcher and her/his processes of data management more clearly.

The Third Wave

In the 1980s, the next wave of paradigmatic options—postmodernism and poststructuralism—emerged. Postmodernism, with its overarching cultural implications, and poststructuralism, with its focus on language, severely challenged the construction, limitations, and claims to veracity of all knowledge and in particular the use of theory derived from siloed disciplines and confined research methods. Regardless of whether the postmodern/poststructural label is attached to grounded theory, ethnography, phenomenology, action, or evaluative designs, one general aspect that prevails is a focus on language. Language has become viewed as dubious, with notions of “slippage”—the multiple meanings of individual words, and “difference”—the difference and deferral of textual meaning (Derrida, 1970 , 1972 ), adding complexity. Double coding, irony, and juxtaposition are encouraged to further identify meaning, and to uncover aspects of social organization and behavior that have been previously marginalized or made invisible by existing discourses and discursive practices. Texts are seen as complex constructions, and intertextuality is favored, resulting in multiply constructed texts. The world is viewed as chaotic and unknowable; individuals are no longer seen as two dimensional—they are viewed as multifaceted with multiple realities. Complex “truths” are perceived as limited by time and context, requiring multiple data sets and many voices to illuminate them, and small-scale focused local research is seen as desirable. The role of researcher also changed: the politics of position and self-reflexivity dominate and the researcher needs to clearly expose past influences and formerly hidden aspects of his/her life. S/he inhabits the position of an offstage or decentered facilitator, presenting data for the reader to judge.

Theory is used mainly at the conceptual level with no particular approach being privileged. The researcher has become a “bricoleur” (Levi-Strauss, 1962 ) or handyman, using whatever methods or theories that are within reach, to adapt, craft, and meld technological skills with mythical intellectual reflection in order to create unique perspectives on the topic. Transitional interpretations dominate, awaiting further challenges and deconstruction by the next researcher in the field.

The need for multifaceted data sets in the 1990s led inevitably to a search for other research structures, and mixed and multiple methods have become topical. In crossing the divide between qualitative and quantitative approaches, the former initially developed its own sub-paradigms: pragmatist (complimentary communication and shared meanings) and transformative/emancipatory (inequalities in race, class, gender, and disability, to be righted). An increasing focus on multiple methods led to the advent of dialectics (multiple paradigm use) and critical realism (the acceptance of divergent results) (Shannon-Baker, 2016 ). The dilemmas of theory use raised by these changes include whether to segregate data sets and try to explain disparate outcomes in terms of diversity using different theories; whether to integrate them through a homogeneous “smoothing” process—one theory fits all, in order to promote a singular interpretation; or whether to let the strongest paradigm—in terms of data—dominate the theoretical findings.

The Fourth Wave

During the early 21st century , as the third wave was becoming firmly established, the Performative paradigm emerged. The incorporation of fine art–based courses into universities has challenged the prescribed rules of the doctoral thesis, initially resulting in a debate—with echoes of Glaser and Strauss—as to whether theory, if used initially, is too directive, thereby potentially contaminating the performance, or whether theory application should be an outcome to enhance performances, or even whether academic guidelines regarding theory use need to be changed to accommodate these disciplines (Bolt, 2004 ; Freeman, 2010 ; Riley & Hunter, 2009 ). Performativity is seen in terms of “effect,” a notion derived from John Austin’s ( 1962 ) assertion that words and speech utterances do not just act as descriptors of content, they have social force and impact on reality. Following this, a productive work is seen as capable of transforming reality (Bolt, 2016 ). The issue most heard here is the problem of how to judge this form of research when traditional guidelines of dependability, transformability, and trustworthiness appear to be irrelevant. Barbara Bolt suggests that drawing on Austin’s ( 1962 ) terms “locutionary” (semantic meaning), “illocutionary” (force), and “perlocutionary” (effect achieved on receivers), together with the mapping of these effects in material, effective, and discursive domains, may be useful, despite the fact that mapping transformation may be difficult to track in the short term.

During the second decade of the 21st century , however, discussions relating to the use of theory have increased dramatically in academic performative research and a variety of theoreticians are now cited apart from John Austin. These include Maurice Merleu-Ponty ( 1945 and the spatiality of lived events; Jacques Derrida ( 1982 ) on iterability, simultaneous sameness, and difference; Giles Deleuze and Felix Guatarri ( 1987 ) on rituals of material objects and transformative potential; Jean-Francois Lyotard ( 1988 ) on plurality of micro narratives, “affect,” and its silent disruption of discourse; and Bruno Latour ( 2005 ) with regard to actor network theory—where theory is used to engage with rather than to explain the world in a reflective political manner.

In performative doctoral theses, qualitative theory and methods are being creatively challenged. For example, from the discipline of theater and performance Lee Miller and Joanne/Bob Whalley ( 2010 ) disrupt the notion of usual spaces for sincere events by taking their six-hour-long performance Partly Cloudy, Chance of Rain , involving a public reaffirmation of their marriage vows, out of the usual habitats to a service station on a highway. The performance involves a choir, a band, a pianist, 20 performers dressed as brides and grooms, photographers, a TV crew, an Anglican priest, plus 50 guests. The theories applied to this event include an exploration of Marc Auge’s ( 1992 ) conception of the “non-place”; Mikhail Bakhtin’s ( 1992 ) concepts of “dialogism” (many voices) together with “heteroglossia” (juxtaposition of many voices in a dialogue); and Ludwig Wittgenstein’s ( 1953 ) discussion of the “duck rabbit”—once the rabbit is seen (participatory experience) the duck (audience) is always infected by its presence. This couple further challenged the guidelines of traditional doctoral theses by successfully negotiating two doctoral awards for a joint piece of research

A more formal example of a doctoral thesis (Reik, 2014 ) using traditional qualitative approaches has examined at school level the clash of paradigms of performative creative styles of teaching with the neoliberalist focus on testing, curriculum standardization, and student outcomes.

Leah Mercer ( 2012 ), an academic in performative studies, used the performative paradigm in her doctoral thesis to challenge and breach not only the methodological but also the theoretical silos of the quantitative–qualitative divide. The physics project is an original work using live performances of personal storytelling with video and web streaming to depict the memories, preoccupations, and the formative relationship of two women, an Australian and an American, living in contemporary mediatized society. Using scientific theory, Mercer explores personal identity by reframing the principles of contemporary physics (quantum mechanics and uncertainty principle) as aesthetic principles (uncertainty and light) with the physics of space (self), time (memory), light (inspiration), and complementarity (the reconciliation of opposites) to illuminate these experiences.

The performative paradigm has also shifted the focus on the reader, developed in postmodernism, to a broader group—an active audience. Multi-methods have been increased to include symbolic imagery, in particular visual images, as well as sound and live action. The researcher’s role here is often that of performer within a cultural frame, creating and investigating multiple realities and providing the link between the text/script and the audience/public. Theory is either minimized to the level of concepts or used to break through the silos of different disciplines to integrate and reconcile aspects from long-lasting theoretical divides.

In these chronological lines of paradigm shifts, changes in researcher position and changes in the application of theory can clearly be seen. The researcher has moved out of the shadows and into the mainstream; her/his role has shifted from an authoritarian collector and presenter of finite “truths” to a creator and often performer of multiple and disparate data images for the audience to respond to. Theory options have shifted from direction and generation within existing perspectives to creative amalgamations of concepts from disciplines previously rarely combined.

Methodologies: From Anthropology to Fine Arts

It would be a simple matter if all the researcher had to contend with was siting oneself in a particular paradigm/s. Unfortunately, not only have paradigms shifted in terms of researcher position and theoretical usage but so also have methodological choices and research design. One of the most popular methodologies, ethnography, with its roots in classical anthropology and its fieldwork-based observations of action and interaction in cultural contexts, can illustrate the process of methodological change following paradigm shift. If a researcher indicates that he/she has undertaken an ethnographic study, the reader will be most likely to query “which form?”: classical?, critical?, auto?, visual?, ethno drama?, cyber/net?, or performative? The following examples from this methodology should indicate how paradigm shifts have resulted in increasing complexity of design, methods, and interpretive options.

In c lassical ethnography the greatest borrowing is from traditional anthropology in terms of process and tools, and this can be seen with the inclusion of initial time spent in the setting to learn the language of the culture and to generally “bathe” oneself in the environment, often with minimal data collection. This process is supposed to help increase researcher understanding of the culture and minimize the problem of “othering” (treating as a different species/alien). Then a fairly lengthy amount of time is usually spent in the cultural setting either as an observer or as a participant observer to collect as much data as is relevant to answer the research question. This is followed by a return to post-check whether the findings previously gathered have stood the test of time. The analytical toolkit can involve domain analysis, freelists, pilesorts, triads and taxonomies, frame and social network, and event analysis. Truncated mini-ethnographies became more common as time became an issue, but these can still involve years of managing descriptive data, often collected by several participating researchers as seen in Douglas, Rasmussen, and Flanagan’s ( 1977 ) study of the culture of a nudist beach. Shorter versions undertaken by one researcher, for example Sohn ( 2015 ), have explored strategies of teacher and student learning in a science classroom. Theoretical interpretation can be by conceptual application for testing, such as Margaret Mead’s ( 1931 ) testing of the concept of “adolescence”—derived from American culture—in Samoan culture, or, more generally, by concept generation. The latter can be seen in David Rozenhan’s ( 1973 ) investigation of the experience of a group of researcher pseudo-patients admitted to hospitals for the mentally ill in the United States. The main concepts generated were labeling, powerlessness, and depersonalization.

De-colonial ethnography recognizes the “othering” frames of colonial and postcolonial research and takes a position that past colonial supremacy over Third World countries persists in political, economic, educational, and social constructions. Decolonizing requires a critical examination of language, attitudes, and research methods. Kakal Battacharya ( 2016 ) has exposed the micro-discourses of the continuing manifestation of colonial power in a parallel narrative written by a South Asian woman and a white American male. Concepts of colonialism and patriarchy, displayed through the discourses exposed, provide a theoretical critique.

Within critical ethnography , with its focus on power location and alleviation of oppression, Dale Spender ( 1980 ) used structured and timed observations of the styles, quality, and quantity of interaction between staff and students in a range of English classrooms. The theory-directive methodological frames of feminism and gender inequality were applied to identify and expose the lesser time and lesser quality of interaction that teachers had with female students in comparison with that assigned to male students. Widespread distribution of these results alerted education authorities and led to change, in some environments, toward introducing single-sex classrooms for certain topics. This was seen as progress toward alleviating oppressive behaviors. This approach has produced many excellent educational studies, including Peter Willis ( 1977 ) on the preparation of working-class kids for working-class jobs; Michele Fine ( 1991 ) on African American and Latino students who dropped out of a New York high school; Angela Valenzuela ( 1999 ) on emigrant and other under-achievers in American schools; Lisa Patel ( 2013 ) on inclusion and exclusion of immigrants into education; and Jean Anyon ( 1981 ) on social stratification of identical curriculum knowledge in different classrooms

A less concept-driven and more descriptive approach to critical ethnography was emphasized by Phil Carspecken’s hermeneutic approach ( 1996 ), which triggered a move toward data-generated theoretical concepts that could then be used to challenge mainstream theoretical positions.

Post-critical ethnography emphasizes power and ideology and the social practices that contribute to oppression, in particular objectivity, positionality, representation and reflexivity, and critical insufficiency or “antipower.”

Responsibility is shifted to the researcher for the world they create and critique when they interpret their research contexts (Noblit, Flores, & Murillo, 2004 ).

Autoethnography emerged from the postmodern paradigm, with its search for different “truths” and different relationships with readers, and prompted an emphasis on personal experience and documentation of the self in a particular cultural context (Ellis, 2004 ). In order to achieve this, the researcher has to inhabit the dual positions of being the focus of activities, feelings, and emotions experienced in the setting while at the same time being positioned distantly—observing and recording the behaviors of the self in that culture. Well-developed skills of critical reflexivity are required. The rejection of the power-laden discourses/grand theories of the past and the emphasis on transitional explanations has resulted in minimal theorizing and an emphasis on data display, the reader, and the reader’s response. Open presentations of data can be seen in the form of narrative storytelling, or re-presentations in the form of fiction, dramatic performances, and poetry. Carolyn Ellis ( 2004 ) has argued that “story is theory and theory is story” and our “making sense of stories” involves contributing to a broader understanding of human existence. Application/generation of concepts may also occur, and the term “Critical Autoethnography” has been used (Hughes & Pennington, 2017 ), particularly where experiences of race, class, or gender inequality are being experienced. Jennifer Potter ( 2015 ) used the concept “whiteness of silence” to introduce a critical race element into her autoethnographic experience of black–white racial hatred experiences within a university class on African American communication in which she was a student.

Visual ethnography uses a variety of tools, including photography, sketches, movies, social media, the Web and virtual reality, body art, clothing, painting, and sculpture, to demonstrate and track culture. This approach has been available for some time both as a methodology in its own right and as a method of data collection. An example of this approach, which mixes classical and visual ethnography, is Philippe Bourgois and Jeff Schonberg’s 12-year study of two dozen homeless heroin injectors and crack smokers living under a freeway overpass in San Francisco ( 2009 ). Their data comprised extensive black and white photos, dialogue, taped conversations, and fieldwork observation notes. The themes of violence, race relations, family trauma, power relations, and suffering were theoretically interpreted through reworked notions of “power” that incorporated Pierre Bourdieu’s ( 1977 , 1999 ) concepts of “symbolic violence”—linking observed practices to social domination, and “habitus”—an individual’s personal disposition comprising unique feelings and actions grounded in biography and history; Karl Marx’s “lumpen” from “lumpenproletariat” ( 1848 ), the residual class—the vagrants and beggars together with criminal elements that lie beneath the labor force; and Michel Foucault’s “biopower” ( 1978 , 2008 )—the techniques of subjugation used by the state on the population, and “governmentality” ( 1991 )—where individuals are disciplined through institutions and the “knowledge–power” nexus. The ideas of these three theorists were used to create and weave a theory of “lumpen abuse” to interpret the lives of the participants.

Ethno Drama involves transforming the results from an ethnographic study into a performance to be shared, for example the educational experiences of children and youth (Gabriel & Lester, 2013 ). The performance medium can vary from a film (Woo, 2008 ), an article presented in dramatic form (Carter, 2014 ), or more usually a play script to be staged for an audience in a theater (Ethno Theater). One of the main purposes is to provide a hearing space for voices that have been marginalized or previously silenced. These voices and their contexts can be presented by research participants, actors, or the research team, and are often directed at professionals from the field. Audience-based meetings to devise recommendations for further action may follow a performance. Because of the focus on inequality, critical theory has been the major theoretical orientation for this approach. The structure of the presentation invites audiences to identify situations of oppression, in the hope that this will inform them sufficiently to enable modification of their own practices or to be part of the development of recommendations for future change.

Lesnick and Humphrie ( 2018 ) explored the views of identity of LGBTQ+ youth between 14 and 24 years of age via interviews and online questionnaires, the transcriptions of which were woven into a script that was performed by actors presenting stories not congruent with their own racial/gender scripts in order to challenge audience expectations and labels. The research group encouraged the schools where they performed to structure discussion groups to follow the school-located performances. The scripts and discussions revealed and were lightly interpreted through concepts of homelessness, racism, and “oppression Olympics”—the way oppressed people sometimes view one another in competition rather than in solidarity. These issues were found to be relevant to both school and online communities. Support for these young people was discovered to be mostly from virtual sources, being provided by dialogues within Facebook groups.

Cyber/net or/virtual ethnographies involve the study of online communities within particular cultures. Problems which have emerged from the practice of this approach include; discovery of the researcher lurking without permission on sites, gaining prior permission which often disturbs the threads of interaction, gaining permission post–data collection but having many furious people decline participation, the “facelessness” of individuals who may have uncheckable multiple personas, and trying to make sense of very disparate data in incomplete and non-chronological order.. There has been acceptance that online and offline situations can influence each other. Dibbell ( 1993 ) demonstrated that online sexual violence toward another user’s avatar in a text-based “living room” reduced the violated person to tears as she posted pleas for the violator to be removed from the site. Theoretical interpretation at the conceptual level is common; Michel Foucault’s concept of heterotopia ( 1967 , 1984 ) was used to explain such spatio-temporal prisons as online rooms. Heterotropic spaces are seen as having the capacity to reflect and distort real and imagined experiences.

Poststructural ethnography tracks the instability of concepts both culturally and linguistically. This can be demonstrated in the deconstruction of language in education (Lather, 2001 ), particularly the contradictions and paradoxes of sexism, gender, and racism both in texts and in the classroom. These discourses are implicated in relations of power that are dynamic and within which resistance can be observed. Poststructuralism accepts that texts are multiple, as are the personas of those who created them, and that talk such as that which occurs in a classroom can be linked with knowledge control. Walter Humes ( 2000 ) discovered that the educational management discourses of “community,” “leadership,” and “participation” could be disguised by such terms as “learning communities” and “transformational leadership.” He analyzed the results with a conceptual framework derived from management theory and policy studies and linked the findings with political power.

Performative ethnography , from the post-postmodern paradigm, integrates the performances of art and theater with the focus on culture of ethnography (Denzin, 2003 ). A collaborative performance ethnography (van Katwyk & Seko, 2017 ) used a poem re-presenting themes from a previous research study on youth self-harming to form the basis of the creation of a performative dance piece. This process enabled the researcher participants to explore less dominant ways of knowing through co-learning and through the discovery of self-vulnerability. The research was driven by a social justice-derived concern that Foucault’s notion of “sovereignty” was being implemented through a web of relations that commodified and limited knowledge, and sanctioned the exploitation of individuals and communities.

This exploration of the diversity in ethnographic methods, methodologies, and interpretive strategies would be repeated in a similar trek through the interpretive, critical, postmodern, and post-postmodern approaches currently available for undertaking the various versions of grounded theory, phenomenology, feminist research, evaluation, action, or performative research.

Implications of Changes for the Researcher

The onus is now less on finding the “right” (or most familiar in a field) research approaches and following them meticulously, and much more on researchers making their own individual decisions as to which aspects of which methodologies, methods and theoretical explanations will best answer their research question. Ideally this should not be constrained by the state of the discipline they are part of; it should be equally as easy for a fine arts researcher to carry out a classical ethnography with a detailed theoretical interpretation derived from a grand theorist/s as it would be for a researcher in law to undertake a performative study with the minimum of conceptual insights and the maximum of visual and theoretical performances. Unfortunately, the reality is that trends within disciplines dictate publication access, thereby reinforcing the prevailing boundaries of knowledge.

However, the current diversity of choice has indeed shifted the field of qualitative research dramatically away from the position it was in several decades ago. The moves toward visual and performative displays may challenge certain disciplines but these approaches have now become well entrenched in others, and in qualitative research publishing. The creativity of the performative paradigm in daring to scale the siloed and well-protected boundaries of science in order to combine theoretical physics with the theories of social science, and to re-present data in a variety of newer ways from fiction to poetry to researcher performances, is exciting.

Given that theoretical as well as methodological and methods’ domains are now wide open to researchers to pick and choose from, two important aspects—justification and transparency of process—have become essential elements in the process of convincing the reader.

Justification incorporates the why of decision-making. Why was the research question chosen? Why was the particular paradigm, or paradigms, chosen best for the question? Why were the methodology and methods chosen most appropriate for both the paradigm/s and research question/s? And why were the concepts used the most appropriate and illuminating for the study?

Transparency of process not only requires that the researcher clarifies who they are in the field with relation to the research question and the participants chosen, but demands an assessment of what impact their background and personal and professional frames have had on research decisions at all stages from topic choice to theoretical analysis. Problems faced in the research process and how they were managed or overcome also requires exposition as does the chronology of decisions made and changed at all points of the research process.

Now to the issue of theory and the question of “where to?” This brief walk through the paradigmatic, methodological, and theoretical changes has demonstrated a significant move from the use of confined paradigms with limited methodological options to the availability of multiple paradigms, co-methodologies, and methods of many shades, for the researcher to select among Regarding theory use, there has been a clear move away from grand and middle range theories toward the application of individual concepts drawn from a variety of established and minor theoreticians and disciplines, which can be amalgamated into transitory explanations. The examples of theoretical interpretation presented in this article, in my view, very considerably extend, frame, and often shed new light on the themes that have been drawn out via analytical processes. Well-argued theory at any level is a great enhancer, lifting data to heights of illumination and comparison, but it could equally be argued that in the presence of critical researcher reflexivity, complex, layered, longitudinal, and well-justified design, meticulous analysis, and monitored audience response, it may no longer be essential.

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Forms of data analysis in qualitative research: methods explained.

Home » Forms of data analysis in qualitative research: Methods explained

Qualitative Analysis Methods serve as essential tools in the exploration of human experiences and perceptions, providing rich insights that quantitative data often overlook. In qualitative research, the emphasis lies not solely on numbers but on understanding the deeper meanings behind participants' responses. This approach allows researchers to capture the nuances of human behavior, making it invaluable in fields like social sciences, psychology, and market research.

Many researchers face challenges in analyzing qualitative data due to its complex nature. Traditional analytical methods may fall short, particularly when dealing with vast amounts of unstructured data. Effective qualitative analysis requires an adaptable framework that encourages critical thinking and creativity. By mastering various qualitative analysis methods, researchers can transform raw data into actionable insights that can drive informed decision-making and strategy.

Popular Qualitative Analysis Methods

Qualitative analysis methods are essential for transforming raw data into meaningful insights. Researchers employ various approaches to unravel intricate patterns and themes within qualitative data. Popular qualitative analysis methods include thematic analysis, grounded theory, and discourse analysis, among others.

Thematic analysis is a widely used method that identifies and interprets patterns across qualitative data. It allows researchers to extract important themes that arise from interviews, focus groups, or open-ended survey responses. Grounded theory, on the other hand, emphasizes generating theories through the systematic gathering and analysis of data, ensuring that findings are grounded in the participants' perspectives. Lastly, discourse analysis focuses on the language and communication used in interactions, providing insights into the social context and power dynamics present in conversations. Understanding these methods equips researchers with the tools needed to analyze qualitative data effectively, leading to richer interpretations and actionable insights.

Thematic Analysis in Qualitative Research

Thematic analysis is a pivotal method in qualitative research, enabling researchers to systematically identify and analyze themes within data. This analytical approach allows for a deeper understanding of patterns and experiences expressed in qualitative data, such as interviews or focus groups. By focusing on recurring themes, researchers can extract significant insights that reflect participants' viewpoints and social contexts.

To conduct effective thematic analysis, researchers often follow a structured process. First, they familiarize themselves with the data through careful reading and re-reading. Next, they generate initial codes by highlighting important features related to the research question. Following this, they search for themes by collating codes into groups, then review themes for coherence and relevance. Finally, they define and name each theme, providing a comprehensive report that illustrates the findings clearly.

This approach serves as a robust qualitative analysis method, offering vital insights into complex behavioral and emotional dynamics. Its versatility allows it to be adapted across various fields, making it essential for researchers seeking depth in their qualitative inquiries.

Content Analysis in Qualitative Studies

Content analysis is a systematic approach used in qualitative studies to interpret and understand textual data. Researchers explore documents, interviews, and various forms of media to identify patterns, themes, and meaningful insights. This method can reveal underlying meanings and categorize data, making it an essential tool in qualitative analysis methods.

To conduct effective content analysis, researchers generally follow these key steps. First, they define the research question clearly to guide their analysis. Next, they select relevant texts or media that will provide rich data. Third, they create coding schemes that categorize the data, helping to organize findings. Finally, researchers interpret the results, drawing conclusions that aid in understanding the broader context of the data. By engaging in content analysis, scholars and practitioners can make informed decisions grounded in well-analyzed qualitative data.

Advanced Qualitative Analysis Methods

Advanced qualitative analysis methods signify a shift towards more sophisticated approaches in understanding qualitative data. These methods enable researchers to interpret complex patterns and derive deeper insights from extensive narrative data. Regardless of the specific qualitative analysis methods employed, their common goal is to enhance the understanding of human behavior and societal trends.

A few advanced qualitative analysis methods include thematic analysis, grounded theory, and content analysis. Thematic analysis focuses on identifying themes across qualitative data, offering a framework to interpret information systematically. Grounded theory is an iterative process that develops theories based on data collected, allowing for a rich understanding of contexts over time. Content analysis systematically categorizes verbal or textual data, providing quantifiable insights along with qualitative depth. By applying these methods effectively, researchers can create a more comprehensive narrative that informs decision-making and strategy development in various fields.

Grounded Theory Approach

The Grounded Theory Approach is a systematic methodology used in qualitative analysis methods that allows researchers to generate theories based on collected data. This technique considers data collection, coding, and analysis as interrelated processes, helping researchers identify patterns and develop insights that emerge from the data. Instead of testing a pre-existing theory, this method builds theories from the ground up, reflecting the realities of the participants' experiences.

In this context, the approach involves several key steps. First, researchers should begin with open coding, which involves breaking down data into distinct parts to identify themes. Next, they use axial coding to connect these themes, exploring how they interact. Finally, selective coding helps researchers consolidate their findings into a coherent theory. By employing this iterative process, the Grounded Theory Approach facilitates a deep understanding of complex phenomena in qualitative research.

Phenomenological Analysis

Phenomenological analysis focuses on understanding the essence of individuals' lived experiences. This qualitative analysis method emphasizes the subjective perception of experiences, allowing researchers to capture deep insights that quantitative methods often overlook. By exploring how individuals interpret their experiences, phenomenological analysis provides a nuanced understanding of complex phenomena.

To conduct phenomenological analysis effectively, researchers typically follow several key steps. First, they immerse themselves in the data by engaging with participants' narratives through interviews or open-ended responses. Next, they identify significant statements that highlight essential themes within the shared experiences. After categorizing these themes, researchers interpret the findings to reveal underlying meanings relevant to the study's objectives. This structured approach enables a rich exploration of human experiences, enhancing the validity and depth of qualitative analysis methods used in research.

Conclusion on Forms of Qualitative Analysis Methods

Qualitative Analysis Methods provide a rich understanding of human experiences and social phenomena. They emphasize depth over breadth, allowing researchers to explore nuanced insights that quantitative methods may overlook. Through various strategies, such as interviews and focus groups, these methods glean valuable data from participants, shedding light on their emotions, behaviors, and perspectives.

In conclusion, employing qualitative analysis enhances the breadth of data interpretation, unveiling complex themes and patterns. As researchers continue to utilize these methods, they reveal that human-centric insights are critical for informed decision-making. Ultimately, embracing qualitative analysis fosters a deeper connection with the subject, enriching both research outcomes and practical applications.

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• v.74(8); 2010 Oct 11

Presenting and Evaluating Qualitative Research

The purpose of this paper is to help authors to think about ways to present qualitative research papers in the American Journal of Pharmaceutical Education . It also discusses methods for reviewers to assess the rigour, quality, and usefulness of qualitative research. Examples of different ways to present data from interviews, observations, and focus groups are included. The paper concludes with guidance for publishing qualitative research and a checklist for authors and reviewers.

INTRODUCTION

Policy and practice decisions, including those in education, increasingly are informed by findings from qualitative as well as quantitative research. Qualitative research is useful to policymakers because it often describes the settings in which policies will be implemented. Qualitative research is also useful to both pharmacy practitioners and pharmacy academics who are involved in researching educational issues in both universities and practice and in developing teaching and learning.

Qualitative research involves the collection, analysis, and interpretation of data that are not easily reduced to numbers. These data relate to the social world and the concepts and behaviors of people within it. Qualitative research can be found in all social sciences and in the applied fields that derive from them, for example, research in health services, nursing, and pharmacy. 1 It looks at X in terms of how X varies in different circumstances rather than how big is X or how many Xs are there? 2 Textbooks often subdivide research into qualitative and quantitative approaches, furthering the common assumption that there are fundamental differences between the 2 approaches. With pharmacy educators who have been trained in the natural and clinical sciences, there is often a tendency to embrace quantitative research, perhaps due to familiarity. A growing consensus is emerging that sees both qualitative and quantitative approaches as useful to answering research questions and understanding the world. Increasingly mixed methods research is being carried out where the researcher explicitly combines the quantitative and qualitative aspects of the study. 3 , 4

Like healthcare, education involves complex human interactions that can rarely be studied or explained in simple terms. Complex educational situations demand complex understanding; thus, the scope of educational research can be extended by the use of qualitative methods. Qualitative research can sometimes provide a better understanding of the nature of educational problems and thus add to insights into teaching and learning in a number of contexts. For example, at the University of Nottingham, we conducted in-depth interviews with pharmacists to determine their perceptions of continuing professional development and who had influenced their learning. We also have used a case study approach using observation of practice and in-depth interviews to explore physiotherapists' views of influences on their leaning in practice. We have conducted in-depth interviews with a variety of stakeholders in Malawi, Africa, to explore the issues surrounding pharmacy academic capacity building. A colleague has interviewed and conducted focus groups with students to explore cultural issues as part of a joint Nottingham-Malaysia pharmacy degree program. Another colleague has interviewed pharmacists and patients regarding their expectations before and after clinic appointments and then observed pharmacist-patient communication in clinics and assessed it using the Calgary Cambridge model in order to develop recommendations for communication skills training. 5 We have also performed documentary analysis on curriculum data to compare pharmacist and nurse supplementary prescribing courses in the United Kingdom.

It is important to choose the most appropriate methods for what is being investigated. Qualitative research is not appropriate to answer every research question and researchers need to think carefully about their objectives. Do they wish to study a particular phenomenon in depth (eg, students' perceptions of studying in a different culture)? Or are they more interested in making standardized comparisons and accounting for variance (eg, examining differences in examination grades after changing the way the content of a module is taught). Clearly a quantitative approach would be more appropriate in the last example. As with any research project, a clear research objective has to be identified to know which methods should be applied.

Types of qualitative data include:

• Audio recordings and transcripts from in-depth or semi-structured interviews
• Structured interview questionnaires containing substantial open comments including a substantial number of responses to open comment items.
• Audio recordings and transcripts from focus group sessions.
• Field notes (notes taken by the researcher while in the field [setting] being studied)
• Video recordings (eg, lecture delivery, class assignments, laboratory performance)
• Case study notes
• Documents (reports, meeting minutes, e-mails)
• Diaries, video diaries
• Observation notes
• Press clippings
• Photographs

RIGOUR IN QUALITATIVE RESEARCH

Qualitative research is often criticized as biased, small scale, anecdotal, and/or lacking rigor; however, when it is carried out properly it is unbiased, in depth, valid, reliable, credible and rigorous. In qualitative research, there needs to be a way of assessing the “extent to which claims are supported by convincing evidence.” 1 Although the terms reliability and validity traditionally have been associated with quantitative research, increasingly they are being seen as important concepts in qualitative research as well. Examining the data for reliability and validity assesses both the objectivity and credibility of the research. Validity relates to the honesty and genuineness of the research data, while reliability relates to the reproducibility and stability of the data.

The validity of research findings refers to the extent to which the findings are an accurate representation of the phenomena they are intended to represent. The reliability of a study refers to the reproducibility of the findings. Validity can be substantiated by a number of techniques including triangulation use of contradictory evidence, respondent validation, and constant comparison. Triangulation is using 2 or more methods to study the same phenomenon. Contradictory evidence, often known as deviant cases, must be sought out, examined, and accounted for in the analysis to ensure that researcher bias does not interfere with or alter their perception of the data and any insights offered. Respondent validation, which is allowing participants to read through the data and analyses and provide feedback on the researchers' interpretations of their responses, provides researchers with a method of checking for inconsistencies, challenges the researchers' assumptions, and provides them with an opportunity to re-analyze their data. The use of constant comparison means that one piece of data (for example, an interview) is compared with previous data and not considered on its own, enabling researchers to treat the data as a whole rather than fragmenting it. Constant comparison also enables the researcher to identify emerging/unanticipated themes within the research project.

STRENGTHS AND LIMITATIONS OF QUALITATIVE RESEARCH

Qualitative researchers have been criticized for overusing interviews and focus groups at the expense of other methods such as ethnography, observation, documentary analysis, case studies, and conversational analysis. Qualitative research has numerous strengths when properly conducted.

Strengths of Qualitative Research

• Issues can be examined in detail and in depth.
• Interviews are not restricted to specific questions and can be guided/redirected by the researcher in real time.
• The research framework and direction can be quickly revised as new information emerges.
• The data based on human experience that is obtained is powerful and sometimes more compelling than quantitative data.
• Subtleties and complexities about the research subjects and/or topic are discovered that are often missed by more positivistic enquiries.
• Data usually are collected from a few cases or individuals so findings cannot be generalized to a larger population. Findings can however be transferable to another setting.

Limitations of Qualitative Research

• Research quality is heavily dependent on the individual skills of the researcher and more easily influenced by the researcher's personal biases and idiosyncrasies.
• Rigor is more difficult to maintain, assess, and demonstrate.
• The volume of data makes analysis and interpretation time consuming.
• It is sometimes not as well understood and accepted as quantitative research within the scientific community
• The researcher's presence during data gathering, which is often unavoidable in qualitative research, can affect the subjects' responses.
• Issues of anonymity and confidentiality can present problems when presenting findings
• Findings can be more difficult and time consuming to characterize in a visual way.

PRESENTATION OF QUALITATIVE RESEARCH FINDINGS

The following extracts are examples of how qualitative data might be presented:

Data From an Interview.

The following is an example of how to present and discuss a quote from an interview.

The researcher should select quotes that are poignant and/or most representative of the research findings. Including large portions of an interview in a research paper is not necessary and often tedious for the reader. The setting and speakers should be established in the text at the end of the quote.

The student describes how he had used deep learning in a dispensing module. He was able to draw on learning from a previous module, “I found that while using the e learning programme I was able to apply the knowledge and skills that I had gained in last year's diseases and goals of treatment module.” (interviewee 22, male)

This is an excerpt from an article on curriculum reform that used interviews 5 :

The first question was, “Without the accreditation mandate, how much of this curriculum reform would have been attempted?” According to respondents, accreditation played a significant role in prompting the broad-based curricular change, and their comments revealed a nuanced view. Most indicated that the change would likely have occurred even without the mandate from the accreditation process: “It reflects where the profession wants to be … training a professional who wants to take on more responsibility.” However, they also commented that “if it were not mandated, it could have been a very difficult road.” Or it “would have happened, but much later.” The change would more likely have been incremental, “evolutionary,” or far more limited in its scope. “Accreditation tipped the balance” was the way one person phrased it. “Nobody got serious until the accrediting body said it would no longer accredit programs that did not change.”

Data From Observations

The following example is some data taken from observation of pharmacist patient consultations using the Calgary Cambridge guide. 6 , 7 The data are first presented and a discussion follows:

Pharmacist: We will soon be starting a stop smoking clinic. Patient: Is the interview over now? Pharmacist: No this is part of it. (Laughs) You can't tell me to bog off (sic) yet. (pause) We will be starting a stop smoking service here, Patient: Yes. Pharmacist: with one-to-one and we will be able to help you or try to help you. If you want it. In this example, the pharmacist has picked up from the patient's reaction to the stop smoking clinic that she is not receptive to advice about giving up smoking at this time; in fact she would rather end the consultation. The pharmacist draws on his prior relationship with the patient and makes use of a joke to lighten the tone. He feels his message is important enough to persevere but he presents the information in a succinct and non-pressurised way. His final comment of “If you want it” is important as this makes it clear that he is not putting any pressure on the patient to take up this offer. This extract shows that some patient cues were picked up, and appropriately dealt with, but this was not the case in all examples.

Data From Focus Groups

This excerpt from a study involving 11 focus groups illustrates how findings are presented using representative quotes from focus group participants. 8

Those pharmacists who were initially familiar with CPD endorsed the model for their peers, and suggested it had made a meaningful difference in the way they viewed their own practice. In virtually all focus groups sessions, pharmacists familiar with and supportive of the CPD paradigm had worked in collaborative practice environments such as hospital pharmacy practice. For these pharmacists, the major advantage of CPD was the linking of workplace learning with continuous education. One pharmacist stated, “It's amazing how much I have to learn every day, when I work as a pharmacist. With [the learning portfolio] it helps to show how much learning we all do, every day. It's kind of satisfying to look it over and see how much you accomplish.” Within many of the learning portfolio-sharing sessions, debates emerged regarding the true value of traditional continuing education and its outcome in changing an individual's practice. While participants appreciated the opportunity for social and professional networking inherent in some forms of traditional CE, most eventually conceded that the academic value of most CE programming was limited by the lack of a systematic process for following-up and implementing new learning in the workplace. “Well it's nice to go to these [continuing education] events, but really, I don't know how useful they are. You go, you sit, you listen, but then, well I at least forget.”

The following is an extract from a focus group (conducted by the author) with first-year pharmacy students about community placements. It illustrates how focus groups provide a chance for participants to discuss issues on which they might disagree.

Interviewer: So you are saying that you would prefer health related placements? Student 1: Not exactly so long as I could be developing my communication skill. Student 2: Yes but I still think the more health related the placement is the more I'll gain from it. Student 3: I disagree because other people related skills are useful and you may learn those from taking part in a community project like building a garden. Interviewer: So would you prefer a mixture of health and non health related community placements?

GUIDANCE FOR PUBLISHING QUALITATIVE RESEARCH

Qualitative research is becoming increasingly accepted and published in pharmacy and medical journals. Some journals and publishers have guidelines for presenting qualitative research, for example, the British Medical Journal 9 and Biomedcentral . 10 Medical Education published a useful series of articles on qualitative research. 11 Some of the important issues that should be considered by authors, reviewers and editors when publishing qualitative research are discussed below.

Introduction.

A good introduction provides a brief overview of the manuscript, including the research question and a statement justifying the research question and the reasons for using qualitative research methods. This section also should provide background information, including relevant literature from pharmacy, medicine, and other health professions, as well as literature from the field of education that addresses similar issues. Any specific educational or research terminology used in the manuscript should be defined in the introduction.

The methods section should clearly state and justify why the particular method, for example, face to face semistructured interviews, was chosen. The method should be outlined and illustrated with examples such as the interview questions, focusing exercises, observation criteria, etc. The criteria for selecting the study participants should then be explained and justified. The way in which the participants were recruited and by whom also must be stated. A brief explanation/description should be included of those who were invited to participate but chose not to. It is important to consider “fair dealing,” ie, whether the research design explicitly incorporates a wide range of different perspectives so that the viewpoint of 1 group is never presented as if it represents the sole truth about any situation. The process by which ethical and or research/institutional governance approval was obtained should be described and cited.

The study sample and the research setting should be described. Sampling differs between qualitative and quantitative studies. In quantitative survey studies, it is important to select probability samples so that statistics can be used to provide generalizations to the population from which the sample was drawn. Qualitative research necessitates having a small sample because of the detailed and intensive work required for the study. So sample sizes are not calculated using mathematical rules and probability statistics are not applied. Instead qualitative researchers should describe their sample in terms of characteristics and relevance to the wider population. Purposive sampling is common in qualitative research. Particular individuals are chosen with characteristics relevant to the study who are thought will be most informative. Purposive sampling also may be used to produce maximum variation within a sample. Participants being chosen based for example, on year of study, gender, place of work, etc. Representative samples also may be used, for example, 20 students from each of 6 schools of pharmacy. Convenience samples involve the researcher choosing those who are either most accessible or most willing to take part. This may be fine for exploratory studies; however, this form of sampling may be biased and unrepresentative of the population in question. Theoretical sampling uses insights gained from previous research to inform sample selection for a new study. The method for gaining informed consent from the participants should be described, as well as how anonymity and confidentiality of subjects were guaranteed. The method of recording, eg, audio or video recording, should be noted, along with procedures used for transcribing the data.

Data Analysis.

A description of how the data were analyzed also should be included. Was computer-aided qualitative data analysis software such as NVivo (QSR International, Cambridge, MA) used? Arrival at “data saturation” or the end of data collection should then be described and justified. A good rule when considering how much information to include is that readers should have been given enough information to be able to carry out similar research themselves.

One of the strengths of qualitative research is the recognition that data must always be understood in relation to the context of their production. 1 The analytical approach taken should be described in detail and theoretically justified in light of the research question. If the analysis was repeated by more than 1 researcher to ensure reliability or trustworthiness, this should be stated and methods of resolving any disagreements clearly described. Some researchers ask participants to check the data. If this was done, it should be fully discussed in the paper.

An adequate account of how the findings were produced should be included A description of how the themes and concepts were derived from the data also should be included. Was an inductive or deductive process used? The analysis should not be limited to just those issues that the researcher thinks are important, anticipated themes, but also consider issues that participants raised, ie, emergent themes. Qualitative researchers must be open regarding the data analysis and provide evidence of their thinking, for example, were alternative explanations for the data considered and dismissed, and if so, why were they dismissed? It also is important to present outlying or negative/deviant cases that did not fit with the central interpretation.

The interpretation should usually be grounded in interviewees or respondents' contributions and may be semi-quantified, if this is possible or appropriate, for example, “Half of the respondents said …” “The majority said …” “Three said…” Readers should be presented with data that enable them to “see what the researcher is talking about.” 1 Sufficient data should be presented to allow the reader to clearly see the relationship between the data and the interpretation of the data. Qualitative data conventionally are presented by using illustrative quotes. Quotes are “raw data” and should be compiled and analyzed, not just listed. There should be an explanation of how the quotes were chosen and how they are labeled. For example, have pseudonyms been given to each respondent or are the respondents identified using codes, and if so, how? It is important for the reader to be able to see that a range of participants have contributed to the data and that not all the quotes are drawn from 1 or 2 individuals. There is a tendency for authors to overuse quotes and for papers to be dominated by a series of long quotes with little analysis or discussion. This should be avoided.

Participants do not always state the truth and may say what they think the interviewer wishes to hear. A good qualitative researcher should not only examine what people say but also consider how they structured their responses and how they talked about the subject being discussed, for example, the person's emotions, tone, nonverbal communication, etc. If the research was triangulated with other qualitative or quantitative data, this should be discussed.

Discussion.

The findings should be presented in the context of any similar previous research and or theories. A discussion of the existing literature and how this present research contributes to the area should be included. A consideration must also be made about how transferrable the research would be to other settings. Any particular strengths and limitations of the research also should be discussed. It is common practice to include some discussion within the results section of qualitative research and follow with a concluding discussion.

The author also should reflect on their own influence on the data, including a consideration of how the researcher(s) may have introduced bias to the results. The researcher should critically examine their own influence on the design and development of the research, as well as on data collection and interpretation of the data, eg, were they an experienced teacher who researched teaching methods? If so, they should discuss how this might have influenced their interpretation of the results.

Conclusion.

The conclusion should summarize the main findings from the study and emphasize what the study adds to knowledge in the area being studied. Mays and Pope suggest the researcher ask the following 3 questions to determine whether the conclusions of a qualitative study are valid 12 : How well does this analysis explain why people behave in the way they do? How comprehensible would this explanation be to a thoughtful participant in the setting? How well does the explanation cohere with what we already know?

CHECKLIST FOR QUALITATIVE PAPERS

This paper establishes criteria for judging the quality of qualitative research. It provides guidance for authors and reviewers to prepare and review qualitative research papers for the American Journal of Pharmaceutical Education . A checklist is provided in Appendix 1 to assist both authors and reviewers of qualitative data.

ACKNOWLEDGEMENTS

Thank you to the 3 reviewers whose ideas helped me to shape this paper.

Appendix 1. Checklist for authors and reviewers of qualitative research.

Introduction

• □ Research question is clearly stated.
• □ Research question is justified and related to the existing knowledge base (empirical research, theory, policy).
• □ Any specific research or educational terminology used later in manuscript is defined.
• □ The process by which ethical and or research/institutional governance approval was obtained is described and cited.
• □ Reason for choosing particular research method is stated.
• □ Criteria for selecting study participants are explained and justified.
• □ Recruitment methods are explicitly stated.
• □ Details of who chose not to participate and why are given.
• □ Study sample and research setting used are described.
• □ Method for gaining informed consent from the participants is described.
• □ Maintenance/Preservation of subject anonymity and confidentiality is described.
• □ Method of recording data (eg, audio or video recording) and procedures for transcribing data are described.
• □ Methods are outlined and examples given (eg, interview guide).
• □ Decision to stop data collection is described and justified.
• □ Data analysis and verification are described, including by whom they were performed.
• □ Methods for identifying/extrapolating themes and concepts from the data are discussed.
• □ Sufficient data are presented to allow a reader to assess whether or not the interpretation is supported by the data.
• □ Outlying or negative/deviant cases that do not fit with the central interpretation are presented.
• □ Transferability of research findings to other settings is discussed.
• □ Findings are presented in the context of any similar previous research and social theories.
• □ Discussion often is incorporated into the results in qualitative papers.
• □ A discussion of the existing literature and how this present research contributes to the area is included.
• □ Any particular strengths and limitations of the research are discussed.
• □ Reflection of the influence of the researcher(s) on the data, including a consideration of how the researcher(s) may have introduced bias to the results is included.

Conclusions

• □ The conclusion states the main finings of the study and emphasizes what the study adds to knowledge in the subject area.

• Open access
• Published: 02 September 2024

Understanding how and why travel mode changes: analysis of longitudinal qualitative interviews

• Kate Garrott   ORCID: orcid.org/0009-0009-8714-5408 1 , 2 ,
• Louise Foley 1 ,
• David Ogilvie 1 &
• Jenna Panter 1  

International Journal of Behavioral Nutrition and Physical Activity volume  21 , Article number:  96 ( 2024 ) Cite this article

21 Altmetric

Metrics details

Encouraging alternatives to the car such as walking, cycling or public transport is a key cross-sector policy priority to promote population and planetary health. Individual travel choices are shaped by individual and environmental contexts, and changes in these contexts – triggered by key events – can translate to changes in travel mode. Understanding how and why these changes happen can help uncover more generalisable findings to inform future intervention research. This study aimed to identify the mechanisms and contexts facilitating changes in travel mode.

Prospective longitudinal qualitative cohort study utilising semi-structured interviews at baseline (in 2021), three- and six-month follow up. Participants were residents in a new town in Cambridgeshire, UK, where design principles to promote walking, cycling and public transport were used at the planning stage. At each interview, we followed a topic guide asking participants about previous and current travel patterns and future intentions. All interviews were audio recorded and transcribed. Data analysis used the framework approach based on realist evaluation principles identifying the context and mechanisms described by participants as leading to travel behaviour change.

We conducted 42 interviews with 16 participants and identified six mechanisms for changes in travel mode. These entailed increasing or reducing access, reliability and financial cost, improving convenience, increasing confidence and raising awareness. Participants described that these led to changes in travel mode in contexts where their existing travel mode had been disrupted, particularly in terms of reducing access or reliability or increasing cost, and where there were suitable alternative travel modes for their journey. Experiences of the new travel mode played a role in future travel intentions.

Implications

Applying realist evaluation principles to identify common mechanisms for changes in travel mode has the potential to inform future intervention strategies. Future interventions using mechanisms that reduce access to, reduce reliability of, or increase the financial cost of car use may facilitate modal shift to walking, cycling and public transport when implemented in contexts where alternative travel modes are available and acceptable.

Introduction

Replacing car use with active travel and public transport could facilitate multiple favourable outcomes for public and planetary health [ 1 , 2 ] including increasing physical activity [ 3 ], reducing sedentary behaviour [ 4 ] and improving air quality [ 5 ]. Despite these benefits, the private car remains the dominant transport mode in many countries [ 6 , 7 ], and reducing car use is a shared cross-sectoral policy priority [ 5 ].

Individual travel choices are shaped by both individual and environmental context, and changes to these contexts can explain periods of stability or change in individual mobility [ 8 ]. Key events may change the context within which people make their travel choices, and may therefore lead to changes in the travel mode selected. Within the literature on mobility geographies, the term ‘key events’ embraces both life events (e.g. having a child, moving house or acquiring a driving licence) and exogenous interventions (e.g. the introduction of a new bus timetable) that change contexts and may consequently influence travel patterns [ 9 ].

Exogenous interventions may be introduced that change travel patterns, either as their deliberate intention or as an unintended consequence. Deliberate interventions are typically implemented by actors such as national or local governments or transport service providers, and may target individual behaviour or wider economic, physical and social conditions. These interventions take place within a dynamic wider social context in which life events [ 8 , 10 ] or other changes in the environment may also affect travel choices. Some research has focused on estimating the effect of specific types of key events on travel behaviour, such as residential relocation or the introduction of cycle lanes. While this has provided insights useful for policy and practice, findings have been mixed across travel modes [ 8 ], population subgroups [ 11 ] and locations, and estimating the effects of these changes without also considering the mechanisms involved may limit our understanding of how change occurs and therefore of the transferability of the findings [ 12 ].

Understanding the mechanisms by which key events bring about changes in travel mode is central to the approach of realist evaluation, which aims to understand what works, in what circumstances and for whom and is often used to evaluate or synthesise evidence about complex interventions [ 13 ]. Within realist evaluation, the term mechanisms denotes explanatory accounts that include the resources provided by an intervention and the reasoning and responses of participants to the intervention [ 14 ]. Furthermore, this approach explicitly considers that differences in contexts (the conditions of the setting in which an event occurs) or in the characteristics of individuals exposed to events may lead to different outcomes [ 12 ]. Identifying the contexts, mechanisms and outcomes can help build an explanation of how interventions work to bring about their effects that is both more detailed and more generalisable [ 15 ]. Applying this approach beyond intervention evaluation to understand mechanisms of changes in travel behaviour arising from key events has potential to uncover useful insights that can be applied within interventions.

There is limited research explicitly using realist approaches to explore the underlying processes of how a broader range of key events (i.e. not limited to ‘interventions’) may change the context for travel behaviour and thereby bring about changes in travel mode. One longitudinal qualitative study explored changes in cycling over three years in Cycling Demonstration Towns (CDT) in England, where there were major investments in cycling infrastructure, training and marketing [ 16 ]. This study identified triggers arising from key events that led to changes in cycling behaviour, while personal history, intrinsic motivations and the existing environment were contextual aspects that influenced behaviour. The authors provide illustrative examples to explain the reasoning for cycling behaviour change [ 17 ]. A young cyclist getting their first job, cites financial (cycling is cheaper than the bus), safety (there is somewhere safe to keep the bike) and availability (no car available) reasoning as forming the mechanism for a change in travel patterns.

We hypothesise that elaborating this type of analysis to describe and synthesise mechanisms across participants exposed to a variety of key events may be helpful in understanding the potential of a broader range of policy strategies to promote alternatives to the car. In this exploratory study, we tested a novel approach using longitudinal qualitative interviews to explore changes in travel mode arising from a variety of key events. We applied principles of realist evaluation to understanding the mechanisms and contexts that facilitate these changes, with a view to informing more generalisable principles for intervention design. Our specific objectives were to identify common mechanisms arising from key events that lead to changes in context, and to understand the contextual conditions that facilitate changes in travel mode.

Study design

This was a prospective longitudinal qualitative cohort study nested within a randomised controlled trial (RCT) to assess the feasibility of financial incentives for promoting alternatives to the car, implemented in Northstowe, a new town in Cambridgeshire, United Kingdom [ 18 ]. Eligible individuals for the RCT were those aged over 16 years and living in a household in Northstowe that had not claimed financial incentives, meaning they were either unaware of the incentives or had previously chosen not to claim them. The RCT was conducted while covid-19 was circulating (October 2021- July 2022), which may have influenced participants’ travel patterns and their attitudes towards different travel modes. A subset of participants from the RCT was invited to complete remote semi-structured interviews at baseline and at three and six months post-baseline. Participants who completed at least one follow-up interview were eligible for this analysis. No additional incentives were provided to participate in the semi-structured interviews.

All participants gave written informed consent at the beginning of each interview. The School of Humanities and Social Sciences Research Ethics Committee, University of Cambridge provided ethical approval (HVS/2019/2778).

Northstowe is a new town currently under construction eight miles north of Cambridge, United Kingdom. At the time of the study, 908 of the projected 10,000 new homes were occupied. The town received Healthy New Town funding [ 19 ] and is intended to provide a sustainable environment promoting health and wellbeing. During the study, all residents lived within one mile of a bus stop, providing a service to Cambridge City Centre within 20 min. At the time of the study, available public facilities were two schools, open spaces, children’s play parks and outdoor leisure facilities but no shops or other amenities.

Sample recruitment and representativeness

From RCT participants, we purposively selected a sample of participants for invitation to interview based on treatment group allocation, age, car ownership and baseline travel patterns in order to understand a broad range of perspectives and experiences. There were no additional eligibility criteria to participate in the interviews. Interested participants responded and completed an online consent form before arranging an appropriate time for the interview. For those who completed baseline interviews, we repeated this process at three and six months post baseline.

Data collection

We conducted semi-structured interviews following a flexibly applied topic guide (Supplementary material A ). The topic guide covered impressions of living in Northstowe, experiences of the environment, current travel patterns and intentions to change. Open-ended questions were used to minimise the risk of social desirability bias. Three and six month interviews also discussed changes in travel since the previous interview. The interviews also explored the use of financial incentives, which were the subject of the RCT. KG conducted all interviews between November 2021 and July 2022 via video call or telephone due to the continued circulation of covid-19 and wrote detailed field notes. Each interview lasted between 18 and 70 min, and was audio recorded and transcribed verbatim by a third party.

Data analysis

All transcripts were imported to NVivo software to facilitate and manage the coding process. We analysed the transcripts using a framework approach [ 20 ] based on pre-defined realist evaluation principles [ 21 ]. We coded key events, contexts, mechanisms and outcomes as defined in Table  1 . We linked these codes to maintain individual configurations and wrote contextual descriptions for each participant. We did not code any data related to the use of financial incentives (the intervention being tested in the parent RCT). While only one participant reported a direct effect of incentives on travel mode, we cannot rule out that the intervention worked in more implicit ways, for example by signalling approval of alternative travel modes. We compared the emerging codes and developed an analytical framework which was refined throughout the coding process, until a final analytical framework was applied to all transcripts.

We charted the data, maintaining the linked individual configurations. Each row in our data table represented a unique configuration and each column represented a participant, with its heading containing the individual’s contextual description. Where a configuration was reported by a participant, we summarised the verbatim quotes within the applicable cell, retaining the original feel of the transcript. We analysed the matrix to identify mechanisms for changes in travel and to describe the contexts that facilitated or inhibited these changes.

Participants

Figure  1 displays participant recruitment in the context of the RCT. Of the 99 participants in the RCT, 20 of the invited subset completed baseline interviews. 16 participants completed at least one follow-up and were eligible for this analysis. Ten participants completed three interviews and six participants completed two interviews, making a total of 42 interviews included in this analysis (Baseline, n  = 16; 3-month, n  = 15; 6-month, n  = 11). Table  2 presents demographic details of interview participants collected at baseline.

Participant flow diagram

Summary of common mechanisms identified

We identified six common mechanisms that facilitated modal shift in response to a diverse range of key events. We take each of these mechanisms in turn and describe the key events that led to a change in contexts and the individual and environmental contexts that influenced whether a change in travel mode change occurred. Table  3 describes the generalisable configurations identified. Using an illustrative example, when a bus cancellation (key event) occurs in a context where (i) an individual had planned to use the bus to commute, (ii) they are not able to work from home and are required to be at work on time (for example, a teacher) and (iii) they have access to a car, this leads to a modal shift from bus to car (outcome). The reasoning (mechanism) is that access to the planned transport mode has been removed and the individual is unable to reach the destination on time, leading to a deliberation process and the selection of an alternative mode.

Changing access

Reduced access.

When access to a transport mode was reduced or removed and the planned mode was unable to transport participants to their destination, modal shift was dependent on available alternative modes and characteristics of the planned journey. The key event that reduced access to the existing travel mode triggered modal shift in contexts where the journey was necessary and there was an acceptable alternative mode available. For example, ‘ Yesterday I was not allowed on the bus going into work in the morning and I wasn’t the only one , there were about 15 or 20 other people who couldn’t get on the bus because it was full , and where they’ve changed the timetable , the next bus isn’t for another half an hour after that , so I wouldn’t have got to work on time , so I had to drive (P0811)’. We observed examples of events that reduced access to the bus through acute cancellations, occurring due to timetable changes, driver strikes and adverse weather; and that reduced access to the car when ‘ my car was having its service done (P0371)’ , participants had been drinking alcohol, or car parking was unavailable at the destination.

In contexts where travel was required but there was no acceptable alternative mode available, participants implemented alternative strategies which included staying overnight at the destination, changing travel route or time, or taking annual leave because it was not possible to get to the workplace: ‘ I used to be a 9 to 5.30 person. Since using the [local bus service] I’ve changed to being an 8 to 4.30 person just to avoid the rush hour traffic (P3291)’. This was evident among people without access to a car who used the bus. Where there was no alternative mode available and the journey was not necessary, participants simply did not travel or, where flexibility allowed, worked from home.

Increasing access

Some key events led to increased access to alternative travel modes. These included new jobs or colleagues moving to Northstowe. Modal shift occurred among participants who had new jobs when the new workplace was accessible by an alternative travel mode, generating a new stable context with potential to facilitate longer-term modal shift. In some cases, however, the alternative travel modes described would not have been feasible for everyone (for example, not everyone can realistically run to work). For others, meanwhile, both the original and new work locations were accessible only by car, and despite exploring alternative options no modal shift happened for those participants.

Changing reliability

Reducing reliability.

The ongoing reduced access led participants to reason that they were unable to rely on those transport modes for consistent journey times that enabled them to arrive at the destination on time. Within this study, the ongoing bus cancellations and overcrowding was sparked from wider contextual changes influencing the transport infrastructure. Participants reported that Britain’s exit from the European Union, led to HGV driver shortages, resulting in a shift of bus drivers to HGV driving leading to an acute shortage of bus drivers and a reduced bus timetable. Once again, modal shift occurred in contexts where reduced reliability affected the existing travel mode and within the context of journeys that were time dependent, for example, travelling to an airport or repeated journeys such as the commute: ‘ On a number of occasions either the bus hasn’t turned up because it’s been cancelled , or I’ve not been allowed on because it’s been too full … I think the last interview was close to the point at which I just stopped using the bus altogether because I couldn’t rely on it (P0811)’. The repeating nature of journeys exposed individuals to the ongoing unreliability which they viewed as disruptive. It was notable that tolerance towards reduced reliability was variable and affected by weather and sustainability values. For those making journeys infrequently or leisure time journeys that were not time dependent, unreliability did not seem appear to trigger modal shift.

Faced with deterioration of the reliability of the bus service, participants who had no alternative travel mode were unable to immediately switch mode. However, the consequences of unreliable bus travel affected their ability to arrive at work on time: ‘ I usually reach five to ten minutes late … despite all the running I do (P1291)’. This triggered participants to seek alternative travel modes, such as purchasing bikes and learning to drive for those who were physically and financially capable: ‘ My experience for the past two months have kind of like proven that the busway’s not very reliable with regards to time. So I would still gear towards having at least a car for the household , just for the situations when I have to get somewhere on time (P1281).’ One participant’s coping mechanism was to change to a job not in line with their career goals in order to cope with the unreliability of the bus service.

Improving reliability

Restoration of the bus timetable resulted in a more reliable bus service which reversed the reasoning described above: ‘ it is now my intention to try and use the bus again going forwards and from that one experience I had it does seem to have made things a little better (P0811)’. In the case of the bus, improving reliability triggered a modal shift among participants whose travel preference was the bus and who were aware of the improvements. We noted a difference in timing returning to bus travel dependent on sustainability motivations, whereby participants with stronger motivations were more eager to restore bus travel.

Changing financial cost

Increasing financial cost.

We found that modal shift occurred when participants were faced with rising travel costs, resulting in this study from rising petrol costs linked to global fuel prices, coupled with increased frequency of office working following lifting of covid-19 restrictions. Participants who changed travel mode reasoned that the cost of their current travel mode was becoming intolerable and sought cheaper alternatives. Modal shift occurred in the context of commuting journeys which were required and where cheaper and acceptable alternatives were available. For example, one participant initiated a car share with a colleague who lived nearby, sacrificing the convenience of being a sole car user to reduce journey costs.

In a context in which individuals believed they were already travelling in the cheapest way, no modal shift occurred, but as one participant noted ‘ I’m so glad I’m car sharing because it’s like £10 more expensive to fill up my car (P1191)’. Furthermore discretionary journeys, for example to eat out at restaurants, did not appear to be subject to modal shift. Instead, participants chose not to make these journeys.

Reducing financial cost

We identified one event that reduced the financial cost of travel: ‘ They’ve [bus company] introduced some flexible fares that mean it’s very economical , it works out at £2.94 a day for bus travel , which is definitely cheaper than petrol (P0811)’. When this occurred simultaneously with increased commuting frequency described above, the cost difference was amplified and the participant reasoned that bus travel was the cheapest mode and modal shift occurred from the car to the bus. In this context, the participant was actively seeking alternative travel modes due to the cost of using the car. We saw no instances of modal shift due to the reduced bus fares among participants who were unable to use the bus for commuting. Among participants already using the bus, the reduced cost was welcome and while no modal shift occurred, the reduced cost may have contributed to maintaining that travel mode.

Changing convenience

Convenience to meet changing demands of dependents.

We noted instances where the key event led to changes in the convenience required of travel due to the demands of dependents. Where this required greater convenience (e.g. the acquisition of a new pet) and an alternative mode provided greater convenience, modal shift occurred. For example, shifting from car-sharing to sole car use enhanced the flexibility to leave work early, or switching from walking to car use decreased the journey time. In contrast, when the key event reduced the need for convenient travel – because a participant’s child was starting school near home, and no longer travelled to with her to the workplace nursery – intentions were formed to travel by bus.

Increasing confidence

We found that prompts to encourage individuals to try alternative travel modes, including suggestions from friends – ‘ She [friend] said , do you want to walk or shall we cycle? So I said , no , let’s cycle (P2591)’ – or workplace initiatives, resulted in a modal shift towards the bicycle. This shift happened among nervous cyclists when there was social support in place and cycling equipment was available in the wider environment, via spare bicycles or local cycle hire schemes. The social support increased participant confidence that they were capable of cycling. When similar prompts were experienced by nervous cyclists without social support, modal shift did not occur and the participant did not trial cycling, despite the availability of cycling equipment in the local environment.

Raising awareness

Raising awareness of pro-environmental behaviour.

Finally, we observed modal shift from the car to walking or cycling for journeys to complete short errands (e.g. posting letters, visiting doctors) due to an enhanced awareness of the environmental impacts of car use and the health benefits of active travel. ‘ I don’t use the car quite so much. I’m either not going anywhere or I’m walking … so I do try and not use it . .because it’s better for me to just to walk everywhere (P2691)’. These participants reasoned that their current car use did not align with their view on environmental impact, and therefore changed mode. Both participants had sufficient time to complete these journeys, and the proximity of the errands to their homes enabled these journeys to be completed via walking or cycling.

Common principles

Across the six mechanisms, we identified three notable commonalities. Firstly, when key events disrupted existing travel modes, they often led to changes in travel modes because participants were required to consider alternative travel modes. For example, two participants reported changing their travel mode when their car was in the garage, reflecting a disruption in their current patterns of car use for commuting or errands. In contrast, when events acted on travel modes not currently used by participants they had little influence on their travel choices. For example, deterioration of the bus service was not reported to influence car users’ choice of travel mode. Secondly, we identified the availability of alternative modes and the journey characteristics as important contextual influences. Changes in travel mode were only observed where there was a suitable alternative mode available. In the absence of a suitable alternative participants continued their current travel patterns, although we did observe them forming intentions to increase the availability of alternative modes, by obtaining a driving license or purchasing a bike. We identified the characteristics of the journey as an important context and participants’ travel choice was different dependent on whether a journey was discretionary, necessary or time-dependent. Thirdly, we observed that the experience of the new travel mode played an important role and positive or negative experiences of the alternative mode informed future travel intentions. For example, one participant described a pleasant experience of cycling after commuting as part of a workplace sustainability day, citing the sunset and social aspect as enjoyable aspects that prompted intentions to continue with this mode.

Summary of findings

We identified six mechanisms, potentially generalisable as intervention strategies, that explained the process of how changes in travel mode occurred: changing access, reliability, financial cost, convenience, confidence and awareness. For some mechanisms we found evidence that they could operate in either a positive or a negative direction, for example increasing and reducing access. We found that these mechanisms were triggered by a diverse set of key events, and we identified the features that may be generalisable to apply as intervention strategies. Firstly, key events acting on existing travel modes appeared to have a greater influence than those acting on potential alternative modes. Secondly, modal shift was only observed among participants who had a suitable and acceptable alternative for the journey, and for whom the journey was not discretionary. Individuals without alternative travel modes available may have to implement coping strategies, for example starting work at a different time or taking annual leave if their existing transport mode is disrupted. Thirdly, the experience of the alternative travel mode appeared to influence future intentions.

Comparison to previous literature

Similar to the findings from this study, previous reviews of interventions have identified that financial mechanisms appear to be effective for reducing driving behaviour [ 22 ], and mechanisms involving accessibility, awareness and experience appear to be effective for increasing active travel [ 22 , 23 ]. The fact that we identified these mechanisms suggests commonalities in the mechanisms of travel behaviour change, regardless of whether the event originates from deliberate interventions or from other sources. Previous research has identified additional mechanisms relating to aesthetics, safety, skills and space [ 22 ]. These may be more likely to originate from interventions or environmental changes with a deliberate intention to change travel behaviour, and are unlikely to have occurred in Northstowe during the six months of our study. We have previously identified that the same event might have different mechanisms depending on its context, and that within some contexts these mechanisms may not be triggered at all [ 23 ]. Those principles are reinforced by the current study, in which we identified that individuals responded differently to the same events depending on characteristics of the journey and the availability of alternative travel modes. This analysis therefore contributes to an emerging field of research focusing on understanding the process of how travel behaviour change occurs.

Many of the concepts identified in this analysis are consistent with other conceptualisations or framings. We identified mechanisms operating in both positive (reducing cost or increasing confidence) and negative directions (reducing access, increasing cost or reducing reliability), described elsewhere as ‘carrots’ or ‘sticks’ respectively [ 24 ]. We found no evidence of a negative direction for the mechanisms of experience, awareness and convenience in this study, but it is plausible, for example, a change in bus route may reduce the convenience of the bus. A previous review found that interventions using carrot and stick strategies in combination, or sticks alone, were more effective than interventions using only carrot strategies [ 22 ]. For example, workplace travel plans including parking restrictions and charging (sticks) plus off-site parking provision and improved bus services (carrots) were more effective than strategies to encourage alternatives to driving (carrot) alone [ 25 ]. Our study gleans additional insight, suggesting that ‘sticks’ often disrupted existing travel modes. We found that key events that operate as ‘carrots’ often act on travel modes not currently used, and may therefore be more influential among those predisposed to walking and cycling but insufficient to spur broader modal shift among those without such dispositions [ 24 ]. The habit discontinuity hypothesis supports these insights, whereby disruptions to a stable context lead to active deliberation about travel mode [ 26 ]. Research in this domain suggests that strong habitual tendencies attenuate information acquisition whereby those with strong travel habits are less likely to notice and acquire information about alternative travel modes [ 26 ], potentially explaining why few participants noted key events that affected alternative travel modes. Furthermore, interventions that require individuals to use a high levels of personal resources (or agency) in order to benefit from the intervention are hypothesised to be less effective and equitable, compared to those with low demands [ 27 ]. Exposure to key events that impact on existing travel modes require individuals to use few or no personal resources compared to those operating on alternative travel modes [ 28 ], suggesting that they are more likely to trigger deliberation. Taken together, this suggests that an event disrupting existing travel behaviour, and acceptable alternatives may be required to ensure such policies are equitable.

Implementing more disruptive ‘stick’ approaches poses challenges for public and political acceptability [ 29 ], due to the perceived political risk that has potential to instigate policy conflict or public backlash against future policies [ 24 ]. However, implementing these alongside positive and supportive ‘carrot’ approaches that provide acceptable alternatives to facilitate travel behaviour change has the potential to enhance their acceptability. The different disciplinary theories described above describes commonalities and synergies, and bringing them together contributes to ‘ holistic sense-making’ and strengthens the generalisability of these findings across behaviours, contexts and populations which can inform future interventions [ 15 ].

Implications for intervention research

Understanding how changes in travel mode occur in response to a variety of events can help to inform intervention strategies. Key events that act on existing travel modes, particularly those that operate to deter their use, appear to influence modal shift. Applying this principle to interventions suggests strategies to reduce access, reduce reliability and increase financial cost of car use to facilitate modal shift towards active travel and public transport. These mechanisms could be triggered by interventions such as road user charging, pedestrian zones or speed restrictions. However, a cautious approach is required because interventions of this kind are often less publicly and politically acceptable [ 29 ], and ensuring that walking, cycling and public transport infrastructure provides and accessible, convenient and pleasant alternative is likely to be a pre-requisite to enhance their effectiveness, equity and acceptability [ 30 ]. Without this, we found that individuals are likely to continue using their existing transport mode with barriers in place by implementing coping strategies, such as changing jobs, taking annual leave, changing working hours or being late to work. This may have important implications for some population groups and these should be identified to assess the equity impacts of interventions and to explore potential unintended consequences, for example on wellbeing or employment.

We also found that key events acting on transport modes not currently used by participants were described as leading to travel behaviour change among participants already considering those travel modes, suggesting that this approach may have limited success for attracting new walkers, cyclists and public transport users. Furthermore, we observed several instances of one-off travel behaviour change and found that these presented opportunities to form opinions on alternative travel modes. In order to capitalise on such chance opportunities, efforts should be made to ensure that experiences are pleasant, reliable and accessible to increase the likelihood of future modal shift [ 31 ].

Strengths and limitations

A strength of the study is its longitudinal design to assess temporal travel behaviour trends. The follow-up period was sufficient to report on intentions for travel behaviour change and allowed sufficient time for changes to occur, while being short enough to be unlikely to impact recall. We purposively sampled participants according to their car ownership and travel patterns to understand experiences from a range of participants with differing initial travel patterns. This analysis is based on interviews collected as part of an RCT exploring financial incentives to reduce car use, and therefore participants may have self-selected to participate in a travel related study. Further selection bias is possible whereby participants in the semi-structured interviews may have been more interested in environmental and health issues. Despite little evidence that these incentives affected travel behaviour [ 15 ], participation in the study may have subliminally primed individuals to consider or change their travel behaviour. Furthermore, our reliance on information provided by participants may have not have identified additional mechanisms that operate via less deliberative process of reasoning. Participants in this study reflect a relatively affluent population [ 18 ], and it is unclear whether key events would trigger the same functions in different population groups, or whether the availability of alternative travel modes differs by population group.

In this study we applied realist evaluation principles to understanding travel behaviour change. We identified six common mechanisms that facilitate travel behaviour change: increasing or reducing access, reliability and financial cost, improving convenience and confidence, and raising awareness. We observed changes in travel behaviour when these mechanisms disrupted an existing travel mode and when an alternative travel mode was available. This study contributes to understanding how key events change travel behaviour and provides evidence about what contexts appear supportive of change. The mechanisms identified here could form targets for intervention strategies and could be generalisable to a range of other settings.

Data availability

The datasets analysed during the current study are not publicly available as it is not possible to fully anonymise the interview transcripts.

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Acknowledgements

The authors would like to thank local authority partners for facilitating the research. Specifically, we would like to than Clare Gibbons, Jon London and Mihaela Stan (South Cambridgeshire District Council), Prajina Baisyet (Smart Journeys) and Christine Sprowell (Living Sport). We would also like to thank the Data Management Team at the MRC Epidemiology Unit, University of Cambridge, particularly Susie Boatman and Anna Melachrou. For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.

This work was supported by the National Institute for Health and Care Research (NIHR) School for Public Health Research is a partnership between the Universities of Sheffield; Bristol; Cambridge; Imperial; and University College London; the London School for Hygiene and Tropical Medicine(LSHTM); LiLaC–a collaboration between the Universities of Liverpool and Lancaster; and Fuse–The Centre for Translational Research in Public Health, a collaboration between Newcastle, Durham, Northumbria, Sunderland and Teesside Universities. This study is funded by the National Institute for Health Research (NIHR) School for Public Health Research (Grant Reference Number: SPHR-PROG-WSBT-CS2). JP and DO are supported by the Medical Research Council (MRC; Unit Programme number MC_UU_12015/6 & MC_UU_00006/7). LF was funded by the National Institute for Health Research (NIHR) (NIHR 133205) using UK aid from the UK Government to support global health research. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

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Garrott, K., Foley, L., Ogilvie, D. et al. Understanding how and why travel mode changes: analysis of longitudinal qualitative interviews. Int J Behav Nutr Phys Act 21 , 96 (2024). https://doi.org/10.1186/s12966-024-01647-x

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BMC Palliative Care volume  23 , Article number:  221 ( 2024 ) Cite this article

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Deprescribing has been defined as the planned process of reducing or stopping medications that may no longer be beneficial or are causing harm, with the goal of reducing medication burden while improving patient quality of life. At present, little is known about the specific challenges of decision-making to support deprescribing for patients who are accessing palliative care. By exploring the perspectives of healthcare professionals, this qualitative study aimed to address this gap, and explore the challenges of, and potential solutions to, making decisions about deprescribing in a palliative care context.

Semi-structured interviews were conducted with healthcare professionals in-person or via video call, between August 2022 – January 2023. Perspectives on approaches to deprescribing in palliative care; when and how they might deprescribe; and the role of carers and family members within this process were discussed. Interviews were audio-recorded and transcribed verbatim. Reflexive thematic analysis enabled the development of themes. QSR NVivo (Version 12) facilitated data management. Ethical approval was obtained from the NHS Health Research Authority (ref 305394).

Twenty healthcare professionals were interviewed, including: medical consultants, nurses, specialist pharmacists, and general practitioners (GPs). Participants described the importance of deprescribing decision-making, and that it should be a considered, proactive, and planned process. Three themes were developed from the data, which centred on: (1) professional attitudes, competency and responsibility towards deprescribing; (2) changing the culture of deprescribing; and (3) involving the patient and family/caregivers in deprescribing decision-making.

Conclusions

This study sought to explore the perspectives of healthcare professionals with responsibility for making deprescribing decisions with people accessing palliative care services. A range of healthcare professionals identified the importance of supporting decision-making in deprescribing, so it becomes a proactive process within a patient’s care journey, rather than a reactive consequence. Future work should explore how healthcare professionals, patients and their family can be supported in the shared decision-making processes of deprescribing.

Trial registration

Ethical approval was obtained from the NHS Health Research Authority (ref 305394).

Peer Review reports

Deprescribing has been defined as the planned process, under the supervision of healthcare professionals, of reducing or stopping medications that may no longer be beneficial or are causing harm, with the goal of reducing medication burden while improving patient quality of life [ 1 , 2 , 3 ]. On a global level, there is a growing recognition of the need for deprescribing [ 4 ], with the World Health Organisation (WHO) including it as a component of safe medication management, stating the process should be ‘as robust as that of prescribing’ [ 5 ]. In a palliative care context, where polypharmacy is common and medication burden is high [ 6 , 7 ], deprescribing is an increasingly important care consideration. Indeed, there is growing evidence that deprescribing can be a safe and effective way to improve outcomes for patients using potentially inappropriate medication [ 8 , 9 , 10 ]. For example, studies concerning the deprescribing of anti-hypertensives, diuretics, benzodiazepines, antipsychotics and statins have demonstrated physical and cognitive benefits, and no significant harm, when these medications are reduced or stopped [ 11 , 12 , 13 , 14 ]. Studies have also shown that deprescribing of medications for patients with life limiting illness can improve quality of life [ 15 ], as well as reducing the risk of developing adverse drug events [ 16 ]. Whilst evidence has demonstrated that some interventions can be used to support deprescribing, practical challenges still remain; these can influence a decision to stop or reduce medication - especially in a palliative care context. For example: how to involve a multi-disciplinary team of healthcare professionals, patients and their family members when making shared decisions about possible deprescribing [ 3 ]. A recent systematic review explored these challenges further and highlighted the importance of involving all stakeholders in the deprescribing decision-making process to ensure a joint decision is made between the patient and healthcare professional [ 17 ]. Whilst several studies have focused on the broader issues of taking steps towards deprescribing in a palliative care context [ 18 , 19 , 20 , 21 ], at present, little is known about the specific challenges of decision-making that support deprescribing to occur. By exploring the perspectives of healthcare professionals with responsibility for prescribing medication, this qualitative study aimed to address this gap, explore the challenges of, and potential solutions to, making decisions about deprescribing in a palliative care context.

Recruitment and sampling

The consolidated criteria for reporting qualitative research (COREQ) checklist was followed (see Item 1 , Supplementary File) [ 22 ]. Inclusion criteria comprised: healthcare professionals (such as nurses, pharmacists and medical doctors) with experience of prescribing or reviewing medication for patients in receipt of palliative care. To be included in the study, healthcare professionals had to practice in the UK. Recruitment was facilitated by (i) two hospital and two hospice research sites in the North East of England, United Kingdom (UK), (ii) professional palliative care networks of the research team and (iii) social media. All interested participants were emailed a participant information sheet and consent form detailing the purpose and aims of the research. Those who expressed an interest and gave their informed written consent, were enrolled in the study. There was no relationship established between the researcher and participants prior to study commencement or recruitment. Convenience sampling was used to recruit participants from a variety of job roles and responsibilities, as well as providing generalist or specialist palliative care. Practitioner age and time qualified were also considered within the sampling framework.

Semi-structured interviews

In-depth, semi-structured interviews were conducted by one researcher (ZB, a female postdoctoral researcher with experience of qualitative research) between August 2022 and January 2023. Interviews were conducted remotely ( via Zoom) or in-person (face-to-face) depending on participant preference. The semi-structured interview topic guide (see Item 2 , Supplementary File for the complete interview guide) was developed based on three pilot interviews (not included in the final participant numbers) and was informed by findings from previous studies conducted by the research team [ 23 , 24 ], as well as the lived-experiences of patient champions involved in this study (AB and RB). Areas explored included: participants’ perspectives on, and approaches to, deprescribing; when and how they might deprescribe; the role of carers and family members within this process; and exploration of gaps around deprescribing decision-making.

Data analysis

All semi-structured interviews were audio-recorded to enable data analysis. The audio files were encrypted and transcribed verbatim by an external transcription company; audio files were uploaded to an encrypted, password-protected site and immediately following confirmation of accurate transcription were deleted. All interview data were anonymised at the point of transcription and all transcripts were checked for accuracy and correctness by ZB and CLR (a researcher with expertise in qualitative research). Participants did not provide comment on the transcripts, nor feedback on results.

Following reflexive thematic analysis processes, as defined by Braun and Clarke [ 25 , 26 ], the principle of constant comparison guided an iterative process of data collection and analysis. Reflexive thematic analysis was performed by two researchers (CLR and AR-B, a researcher with expertise in conducting qualitative research) to analyse the interview data. Close and detailed reading of the transcripts allowed the researchers to familiarise themselves with the data. Initial descriptive codes were identified in a systematic manner across the data; these were then sorted into common coding patterns, which enabled the development of analytic themes from the data. The themes were reviewed, refined and named once coherent and distinctive. Two authors (AR-B and CLR) performed the data analysis through discussion and, if agreement was not reached, by consensus with the other members of the research team (AT, AH and CE, with expertise in palliative care and qualitative research). Post-interview field notes enhanced this reflective process. NVivo (version 12) software was used to facilitate data management. The research team were in agreement that data sufficiency and information power [ 27 , 28 ] occurred after 18 semi-structured interviews and thus, study recruitment stopped following interview number 20; recurring similarity within participant responses, with no new concepts discussed, guided this decision. To ensure confidentiality when using direct participant quotes, non-identifiable pseudonyms are used throughout the research (e.g., Participant 1 and Participant 2 etc.)

Ethical approval

This study was granted ethical approval by the UK National Health Service (NHS) Health Research Authority (reference 305394, date approved: 08.04.2022, South Birmingham REC). Research governance was followed in accordance with Newcastle University and NHS Trust research policies at study sites.

Participant demographics

Twenty participants were recruited and interviewed for this study (participant demographics are described in Table  1 ). Of the twenty participants interviewed; 7 described their job role as a medical consultant working within various settings in the UK (including palliative medicine and respiratory medicine), 6 participants were nurses providing palliative and end-of-life care to patients across primary or secondary care settings, 4 were specialist pharmacists working across a range of disciplines, including frailty and heart failure, and 3 participants were general practitioners (GPs). Nineteen participants self-reported their ethnicity as White British and one participant identified as British Asian. Twelve of the 20 participants were aged between 40 and 50 years and the mean time qualified in their respective roles was 17 years (SD ± 8.5 years). Five interviews were conducted using video software (Zoom ® , n  = 5), while the rest were carried out in-person ( n  = 15). Interviews ranged from 27 to 62 min. There were no refusals to partake, participant dropouts or repeat interviews.

From the interviews, many participants described the importance of deprescribing decision-making, and that it should be a considered, proactive, and planned process. To achieve this, three themes and subsequent sub-themes were developed from the data (Fig.  1 ); these focused on: (1) professional attitudes, competency and responsibility towards deprescribing; (2) changing the culture of deprescribing and (3) involving the patient and family/caregivers in deprescribing decision-making. Note the use of arrows in the figure to visually represent the interplay between the themes, and their sub-themes, as part of the bigger picture of achieving proactive deprescribing.

Themes developed to support the delivery of deprescribing as a proactive process, rather than a reactive consequence for patients in receipt of palliative care

Theme 1: Professional attitudes, competency and responsibility towards deprescribing

Taking ownership of the deprescribing decision.

Participants across all healthcare professional roles shared beliefs that deprescribing was potentially a positive process; however, a number of challenges and concerns were raised, which seemed to influence whether deprescribing was undertaken, or not. The first of these related to taking ownership of the process, and ultimately, the decision-making. A number of participants described a lack of clarity around “whose job is it to do deprescribing?” (Participant 15). In turn, participants discussed feeling “like I don’t want to be treading on other people’s toes” by making decisions about stopping medicines, particularly when patients are receiving care from “a big multidisciplinary team” (Participant 10).

“One of the big barriers I see is ownership… There’s a sense that “I don’t want to touch the rheumatology drugs” or “they’re under the heart failure team so I don’t want to touch any of that” … having some sense professionally , as a cohesive profession about who does it – I think we need to get to that place” (Participant 15 , Medical Consultant).

In efforts to take responsibility for deprescribing practices, participants acknowledged that certain healthcare settings may be better suited to make decisions about deprescribing. Two participants proposed making use of multidisciplinary team meetings as an approach to deprescribing decision-making in collaboration with members of the wider care team. One participant stated: “I would always do (deprescribing) in collaboration with , I guess what we’d call , the parent team” , indicating the connection that may be required between specialist palliative care services and other clinical specialities (Participant 12). While another participant highlighted “the beauty of palliative care specialities (for having) an easily accessible multidisciplinary team with a doctor , a specialist nurse , a physio , an OT (occupational therapist) … you can sit round the table and make those decisions and offer advice” (Participant 13). Several participants also emphasised general practice or “care offered by teams based in the community” as preferable settings to make decisions for deprescribing (Participant 1). For example, one participant working within secondary care shared a preference for community-based healthcare professionals to take ownership of deprescribing processes as they can “go into somebody’s home , where they (the patient) will feel more comfortable… in an environment where they feel safe so that open communication can be a bit easier” (Participant 1). The same participant indicated this preference for community-based ownership compared with conversations occurring “if they (the patient) were on a ward setting” (Participant 1). This was echoed by a Nurse Specialist who suggested deprescribing decision making “needs to be done in a calmer environment when you’ve got the time to have those discussions” (Participant 12).

Recognising the challenges associated with deprescribing decisions

Participants recognised the challenges that healthcare professionals face when making decisions around deprescribing medicines for patients in receipt of palliative care. One such challenge related to healthcare professionals’ fear and uncertainty about the repercussions of stopping medications. For example, the hesitancy “of stopping medications because it is not always clear what the outcome of that is going to be” for their patients (Participant 2).

“I think they’re scared of stopping an aspirin and then someone having a stroke… or stopping a NOAC (non-vitamin K oral anticoagulant) and their AF (atrial fibrillation) being uncontrolled and causing a stroke. I think they’re scared of the consequences” (Participant 13 , Specialist Pharmacist).

Several participants suggested that this fear may relate to a lack of formal education on deprescribing for healthcare professionals during their training years with one participant stating “F1 (foundation year 1) doctors are scared of doing deprescribing because it feels like a big responsibility , and that’s scary” (Participant 13). Other participants noted caution amongst medical healthcare professionals when it came to deprescribing, and postulated that causes of this could be due to a lack of exposure in training and the lack of normalisation of the process as part of their scope of practice. One participant stated that “if they (medics) learnt about it as undergraduates , like anything , the more you do it , the easier it becomes and the more confident you feel in doing it” (Participant 3) and another suggested “it will take a little time to become a normal thing for prescribers to do more confidently” (Participant 13).

Theme 2: changing the culture of deprescribing

Establishing the culture of deprescribing, by starting from the start.

Healthcare professionals shared views around better establishing the culture of deprescribing within clinical practice. They highlighted perspectives around the importance of “starting to think that way from the start” (Participant 11) at the point of prescribing a medication. Participants felt that this change to prescribing culture may also prompt patients to normalise “medicines aren’t always needed to be a forever thing” (Participant 14).

“When you prescribe something… historically people are just like , I’ve written it up and here you go and off you go. But I think maybe just having a real explanation of it… even saying there might come a point where you might want to think about stopping it” (Participant 14 , General Practitioner).

A key aspect within establishing a culture of deprescribing centred on participant’s views of the ‘best times’ to have conversations around stopping or reducing medication. One participant discussed a common trend seen in their clinical experience where conversations often happen too late in a patient’s clinical trajectory. They described feeling “ like it ends up being , if a patient is just really unwell… it’s probably then when [deprescribing] really starts being thought about” (Participant 14). Another participant working in palliative care, echoed these views and stated “by the time I’ve got involved as a palliative care nurse , it can be too late for some of these conversations. It would be nice if somebody would at least start them earlier on” (Participant 12). Despite recognising the importance of this, the ‘optimal’ timing of these conversations was not identified by participants; instead, they placed emphasis that having conversations around deprescribing should be guided according to a person’s individualised care needs. One proposed timeframe was at the point of “ an annual medication review for everyone in respect of what you’re diagnosing and treating” (Participant 12) and then “continually , as and when” thereafter (Participant 11).

Educating and empowering patients

Alongside the need for healthcare professionals to begin establishing the culture of deprescribing, it was also recognised that patient education and empowerment to make decisions around deprescribing was vital. Participants viewed deprescribing as a process ideally underpinned by shared decision-making, which should be “collaborative… we should say (to the patient) “look , in a medical opinion , we could do this , but what do you think?” I think should be a proper open discussion” (Participant 3). In order to achieve this collaboration, participants recognised the need to “give patients information in a way that they a) want and b) understand” so that they feel valued and “feel a true part of the conversation” (Participant 13). One palliative medicine consultant acknowledged the importance of patient empowerment when it came to changing the culture around deprescribing; they viewed medicines education and psychological support as vital tools to achieve supportive deprescribing and understood the worry that may accompany people having to think - and make decisions - about potentially stopping medications.

“It’s probably the first time they’ve been asked , “What does this drug really mean to you? Would it worry you if you had to stop it? What do you think it’s doing for you?” Those are quite big conversations.” (Participant 15 , Medical Consultant) .

Theme 3: involving patients (and others) in deprescribing decision-making

Acknowledging the priorities of patients, alongside others involved in their care.

Healthcare professionals recognised the need to involve patients closely in conversations and decisions around deprescribing, particularly taking into consideration their priorities when it comes to medication. A number of participants reflected on the importance of individualisation and tailoring deprescribing conversations to each specific person, rather than adopting a ‘one size fits all’ approach. One participant reflected on focusing on an individual’s preferences about taking medications, recognising that there may be discrepancies between their own clinical judgement about the necessity of a medication and the desire of a person to take it; they felt it important to voice both sides of the conversation and help the patient make an informed choice about deprescribing. From this, the importance of establishing the priorities of the patient was considered essential in deprescribing decision-making.

“Focusing on what we know what is important for that person… if somebody turned around to me and said , “I’m terrified of dying of a stroke , is it alright if I keep taking the statins?” the answer would be “absolutely yes” , and saying , “Well , what are your priorities in your remaining weeks , months , years of life , and what would you like us to focus on as healthcare professionals?” (Participant 4 , Medical Consultant).

Taking time to understand what influences a patient to take their medications was recognised by participants. It appeared important to appreciate that some patients may not want to make any changes with their medication at the point of an initial deprescribing conversation taking place; it was recognised that this may change over time, with fluctuations in their symptoms, disease trajectory, prognosis or even shifts in a person’s preferences or priorities around their care.

Whilst it was important to have a patient voice present in those discussions, healthcare professionals also recognised the importance of acknowledging priorities of others involved in their care, such as family, friends and caregivers. Healthcare professionals acknowledged the day-to-day input that family and carers have with their loved one’s medication, and reported it was vital to have their input into overall deprescribing decisions. One participant discussed the importance of “pre-planning” discussions about deprescribing with relatives, and having these conversations at an early enough stage “when there’s less tightened emotions to give them (relative) a chance to think and input into decisions about medicines , at a pace that is suitable for all parties” (Participant 3).

Participants reflected on the challenges that can sometimes present when making decisions about deprescribing if medication priorities differ between the patient and family member(s). One participant shared an example “where the patient wants to carry on with some medication but the family thought “I don’t think you need that – it’s making you too drowsy”” (Participant 14). They went on to further postulate the implications that continuation of a medication may have for the carer “or family member who feels like “well because you’re more drowsy , I’ve got to come and help you with this and that”” (Participant 14). Another participant discussed the commonality of conflicts between patients and family members when it concerns the trajectory of life-limiting illnesses. Specifically, if a carer or relative “hasn’t accepted that the person is dying , and me coming in and talking about stopping all these meds they’ve been on for years – that’s often the thing that really brings it home to them” (Participant 8).

Reflections on language used to establish shared conversations around deprescribing

Another participant reflected on the influence that language can have when establishing and shaping shared conversations with patients around deprescribing. They reflected on common phrasing used when prescribing and commencing new medicines, believing that often “the message we give as professionals is “once you’re on it , you’re on it forever”. I get why we do that for compliance , concordance … but maybe we need a bit of “this will be reviewed annually and we might change it” phrasing built in” (Participant 15). In doing so, it was deemed helpful to establish and share expectations between the prescriber and the patient relating to the intention and duration of each medication, whilst also involving patient perspectives in the ongoing review process.

Another participant shared an example where language used when starting a medication was a barrier to deprescribing in a specialist palliative care setting. They reflected on how often it is communicated by prescribers: ”never stop this medicine” or “this medicine is so important” that (when discussing deprescribing with one patient) … he just kind of thought as soon as he stopped it , he would die” (Participant 17). Reflections in language were also noted by other participants, specifically when describing successful deprescribing episodes as consultations that are framed in a way to minimise (or avoid) patient fear and/or concern. They recommended using shared language that aligned with “trialling without” a particular medication, as opposed to “stopping it” (Participant 5), so that the patient felt they could have input as an equal partner in the deprescribing decision-making process.

“It’s the framing of how you’re doing it – “we’ll put this to one side. I’m not stopping it , we’ll put it to one side. If you feel any different , you can put the medicine straight back up” (Participant 5 , Medical Consultant).

Another participant picked up on the influence of language when discussing deprescribing medication for patients with life-limiting illnesses. In particular, the importance of gently introducing deprescribing concepts that “explain we’re not giving up on them” and “emphasising that we’re on a journey together and they’re not being left high and dry” (Participant 9).

A further participant reflected on the importance of language and terms used to establish a relationship of trust between the healthcare professional and the patient, prior to making a deprescribing decision. For example, to help establish such a relationship they flatten the hierarchy between the patient and themselves, in a bid to “ feel that they (patients) can ask things to me that maybe they otherwise won’t” (Participant 10). In doing so, the participant felt the dynamic of the consultation was one of shared decision-making, rather than a “traditional consultation” with a paternalistic approach. Other participants alluded to the power and value of forming relationships between the patient and the prescriber where “trust needs to be at the centre … if they don’t know they can trust you , they might not be comfortable (with deprescribing decisions) … until you’ve built up that rapport” (Participant 1).

By exploring the perspectives of healthcare professionals, this study builds on previous evidence by exploring the challenges of, and potential solutions to, making decisions about deprescribing in a palliative care context. This study collated the perspectives from a range of healthcare professional groups working with responsibility for prescribing medication in a palliative care context – a specialist patient cohort, which has previously been under-reported in the deprescribing literature, as well as healthcare research more broadly. A consistent finding across all interviews was that the decision-making to underpin deprescribing approaches should be considered, planned, and done proactively as part of ongoing clinical care, rather than as a separate reactive process as a consequence of a patient’s illness or development of an adverse drug reaction. This was a unique finding from this work, which encompassed elements of professional responsibility and attitudes, alongside a need to change the culture in which prescribing and deprescribing are recognised as similar entities when it comes to safe and effective medicines use. Another unique learning point centres on the use of shared language within deprescribing conversations; framing decision-making consultations with terminology that reflects shared, equal conversations may prove more inclusive and balanced. As well, patient empowerment and person-centred approaches were deemed essential components of deprescribing decision-making; whereby the priorities of patients and others (such as family members or carers) are recognised and included as part of the deprescribing process.

Echoing previous studies in the wider deprescribing literature [ 29 , 30 , 31 ], healthcare professionals interviewed as part of this study acknowledged the value of forming and establishing trust with patients prior to deprescribing conversations taking place. Indeed, given the emotions that accompany a life-limiting illness, once a positive patient-healthcare professional relationship was formed, participants perceived it was easier to broach the subject of deprescribing. It is not clear from this research if patients perceive all healthcare professionals as the same, but findings in the wider literature suggest that older adults have greater trust in certain healthcare professionals, such as medical doctors, compared to others, like pharmacists [ 30 , 32 , 33 , 34 ]. Further building on elements of trust within deprescribing practices, participants discussed the underpinning uncertainty or fear that is experienced from a healthcare professional standpoint when a decision is made whether to deprescribe. Specifically, participants reported a lack of education built within their professional undergraduate and/or postgraduate training about how to approach deprescribing. In keeping with this study, these findings have been reported by prescribers working in a range of healthcare specialities including mental health [ 35 ], geriatric medicine [ 36 , 37 , 38 ] and wider primary care services [ 39 , 40 ]. In recognition of the inconsistent and non-standardised implementation of deprescribing within undergraduate education, a curricular framework for approaches to deprescribing has been proposed [ 41 ]. In this, recommendations were made aiming to improve the previously reported low prescriber self-efficacy and self-confidence when it comes to deprescribing [ 42 , 43 , 44 , 45 , 46 , 47 ] – something which future research may seek to evaluate, specifically within specialist patient cohorts like people accessing palliative care services.

Findings in this study echoed the value of building and maintaining interdisciplinary relationships between healthcare professional groups when approaching deprescribing as part of a multi-disciplinary team [ 38 , 48 , 49 ]. As well as this, the significance of shared collaborative conversations between clinicians, patients, and family members or carers was also echoed [ 17 ]. Much focus within previous studies has rightly placed patient preferences of deprescribing at the centre [ 50 , 51 , 52 ], however, future research may wish to further explore the dynamics and interplay between the preferences of others involved in a person’s care. Considering the role that carers and family members play in supporting people in receipt of palliative care [ 53 , 54 , 55 , 56 ], a greater understanding of shared patient-relative-clinician triad discussions about deprescribing would be useful to explore.

Another important study finding was the importance of the language used to have shared deprescribing conversations and the language used by healthcare professionals. Studies in wider healthcare literature have previously alluded to the complexity of conversations about end-of-life care and life-limiting illness [ 57 ], as well as the challenges of initiating discussions about medicines during periods of clinical deterioration or changes in disease trajectory [ 58 ]. Findings from this work show the importance of aligning language with shared expectations and shared decisions between the prescriber and the patient, specifically relating to the intended duration and rationale for each prescribed medication. A recent study by Green et al. [ 59 ] recommended suggestions for patient-preferred language that clinicians could use when communicating deprescribing decisions amongst older adults; findings from this and other studies [ 60 , 61 ] could be transferable to this work, in particular recognition of framing deprescribing decisions around an individual’s priorities and goals [ 59 ].

Whilst we believe our results are robust and have important implications for the way in which healthcare professionals approach deprescribing decision-making, we do acknowledge that the majority of our sample was limited to practice within the North East of England thus, the experience of our participants was that of, predominantly, White British females. Including the voices of healthcare professionals from other ethnic groups may have brought previously unheard considerations to the forefront, which may impact or influence deprescribing processes in minoritised groups – given the growing diversity of the patient population within the UK, this warrants further investigation in a bid to ensure cultural competence is embedded within deprescribing decision-making.

There remains a need to develop interventions to promote deprescribing decision-making for patients accessing palliative care services. In particular, there are still gaps in knowledge concerning the needs and challenges of patients and their family members or carers in this context, especially when it comes to the prescribing and deprescribing of medications as an overall component of care. Future research approaches should seek to further explore the steps within shared decision-making of deprescribing to understand how future interventions could best support this process. To achieve this, co-design methodology could be used to collectively combine the views of all people involved in the deprescribing process, including people with lived experience of receiving palliative care, their family members or carers, as well as healthcare professionals. By exploring the perspectives of healthcare professionals with responsibility for prescribing medication, this qualitative study addressed the first steps of this process and could be used to support future co-design work in this area.

This study sought to further explore the perspectives of healthcare professionals with responsibility for making deprescribing decisions with people accessing palliative care services. A diverse range of healthcare professionals identified the importance of supporting deprescribing decision-making so it becomes a proactive process, using shared language, rather than a reactive consequence, within a person’s care journey. The identified themes included professional attitudes, competency and responsibility towards deprescribing decision-making; changing the culture of deprescribing; and, the importance of involving patients and their family/caregivers in deprescribing decision-making. Future work should explore how healthcare professionals, patients and their family can best be supported in the shared decision-making processes of deprescribing.

Data availability

Data is provided within the manuscript or supplementary information files.

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Acknowledgements

Acknowledgement is given to the healthcare professionals who were participants in this study – without their support, the study would not have been possible. Also, thanks are given to the wider PONDER team - Dr. Lisa Baker, Dr. Ellie Bond, and Dr. Rachel Quibell.

This project is funded by the National Institute for Health and Care Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number Award ID: NIHR202283). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. In addition, authors AT, AH and BH are part-funded by the NIHR Newcastle Patient Safety Research Collaboration (PSRC).

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Contributions

Z.B. led on the day-to-day running of the project and data collection. A.T. oversaw the running of this project as Principal Investigator and provided project management expertise. A.R-B. and C.L.R. led on data interpretation and analysis and writing of this manuscript. C.E. provided methodological expertise on data collection, interpretation, and analysis. J.J., J.E., B.H., A.H. provided topic input, interpretation and analysis. J.E. supported the recruitment of participants. A.B. and R.B. contributed in their appointment as patient champions with lived-experience and ensured appropriateness and sensitivity throughout the entire research process. All authors read, provided comments on, and approved the final manuscript.

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Correspondence to Adam Todd .

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Robinson-Barella, A., Richardson, C.L., Bayley, Z. et al. “ Starting to think that way from the start” : approaching deprescribing decision-making for people accessing palliative care - a qualitative exploration of healthcare professionals views. BMC Palliat Care 23 , 221 (2024). https://doi.org/10.1186/s12904-024-01523-2

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Received : 12 January 2024

Accepted : 16 July 2024

Published : 06 September 2024

DOI : https://doi.org/10.1186/s12904-024-01523-2

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