what is grounded theory research in qualitative research

Grounded Theory In Qualitative Research: A Practical Guide

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Grounded theory is a useful approach when you want to develop a new theory based on real-world data Instead of starting with a pre-existing theory, grounded theory lets the data guide the development of your theory.

What Is Grounded Theory?

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967.

• Data shapes the theory:  Instead of trying to prove an existing theory, you let the data guide your findings.
• No guessing games:  You don’t start with assumptions or try to confirm your own biases.
• Data collection and analysis happen together:  You analyze information as you gather it, which helps you decide what data to collect next.

It is important to note that grounded theory is an inductive approach where a theory is developed from collected real-world data rather than trying to prove or disprove a hypothesis like in a deductive scientific approach

You gather information, look for patterns, and use those patterns to develop an explanation.

It is a way to understand why people do things and how those actions create patterns. Imagine you’re trying to figure out why your friends love a certain video game.

Instead of asking an adult, you observe your friends while they’re playing, listen to them talk about it, and maybe even play a little yourself. By studying their actions and words, you’re using grounded theory to build an understanding of their behavior.

This qualitative method of research focuses on real-life experiences and observations, letting theories emerge naturally from the data collected, like piecing together a puzzle without knowing the final image.

When should you use grounded theory? 

Grounded theory research is useful for beginning researchers, particularly graduate students, because it offers a clear and flexible framework for conducting a study on a new topic.

Grounded theory works best when existing theories are either insufficient or nonexistent for the topic at hand.

Since grounded theory is a continuously evolving process, researchers collect and analyze data until theoretical saturation is reached or no new insights can be gained.

What is the final product of a GT study?

The final product of a grounded theory (GT) study is an integrated and comprehensive grounded theory that explains a process or scheme associated with a phenomenon.

The quality of a GT study is judged on whether it produces this middle-range theory

Middle-range theories are sort of like explanations that focus on a specific part of society or a particular event. They don’t try to explain everything in the world. Instead, they zero in on things happening in certain groups, cultures, or situations.

Think of it like this: a grand theory is like trying to understand all of weather at once, but a middle-range theory is like focusing on how hurricanes form.

Here are a few examples of what middle-range theories might try to explain:

• How people deal with feeling anxious in social situations.
• How people act and interact at work.
• How teachers handle students who are misbehaving in class.

Core Components of Grounded Theory

This terminology reflects the iterative, inductive, and comparative nature of grounded theory, which distinguishes it from other research approaches.

• Theoretical Sampling: The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study. The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.
• Theoretical Sensitivity:  Researchers need to be aware of their preconceptions going into a study and understand how those preconceptions could influence the research. However, it is not possible to completely separate a researcher’s history and experience from the construction of a theory.
• Coding: Coding is the process of analyzing  qualitative data  (usually text) by assigning labels (codes) to chunks of data that capture their essence or meaning. It allows you to condense, organize and interpret your data.
• Core Category:  The core category encapsulates and explains the grounded theory as a whole. Researchers identify a core category to focus on during the later stages of their research.
• Memos: Researchers use memos to record their thoughts and ideas about the data, explore relationships between codes and categories, and document the development of the emerging grounded theory. Memos support the development of theory by tracking emerging themes and patterns.
• Theoretical Saturation:  This term refers to the point in a grounded theory study when collecting additional data does not yield any new theoretical insights. The researcher continues the process of collecting and analyzing data until theoretical saturation is reached.
• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data.

Barney Glaser and Anselm Strauss first introduced grounded theory in 1967 in their book, The Discovery of Grounded Theory .

Their aim was to create a research method that prioritized real-world data to understand social behavior.

However, their approaches diverged over time, leading to two distinct versions: Glaserian and Straussian grounded theory.

The different versions of grounded theory diverge in their approaches to  coding , theory construction, and the use of literature.

All versions of grounded theory share the goal of generating a  middle-range theory  that explains a social process or phenomenon.

They also emphasize the importance of  theoretical sampling ,  constant comparative analysis , and  theoretical saturation  in developing a robust theory

Glaserian Grounded Theory

Glaserian grounded theory emphasizes the  emergence of theory from data  and discourages the use of pre-existing literature.

Glaser believed that adopting a specific philosophical or disciplinary perspective reduces the broader potential of grounded theory.

For Glaser, prior understandings should be based on the general problem area and reading very wide to alert or sensitize one to a wide range of possibilities.

It prioritizes  parsimony ,  scope , and  modifiability  in the resulting theory

Straussian Grounded Theory

Strauss and Corbin (1990) focused on developing the analytic techniques and providing guidance to novice researchers.

Straussian grounded theory utilizes a more structured approach to coding and analysis and acknowledges the role of the literature in shaping research.

It acknowledges the role of  deduction  and  validation  in addition to induction.

Strauss and Corbin also emphasize the use of  unstructured interview questions  to encourage participants to speak freely

Critics of this approach believe it produced a rigidity never intended for grounded theory.

Constructivist Grounded Theory

This version, primarily associated with Charmaz, recognizes that knowledge is situated, partial, provisional, and socially constructed. It emphasizes abstract and conceptual understandings rather than explanations.

Kathy Charmaz expanded on original versions of GT, emphasizing the researcher’s role in interpreting findings

Constructivist grounded theory acknowledges the researcher’s influence on the research process and the co-creation of knowledge with participants

Situational Analysis

Developed by Clarke, this version builds upon Straussian and Constructivist grounded theory and incorporates  postmodern ,  poststructuralist , and  posthumanist  perspectives.

Situational analysis incorporates postmodern perspectives and considers the role of nonhuman actors

It introduces the method of  mapping  to analyze complex situations and emphasizes both  human and nonhuman elements .

• Discover New Insights:  Grounded theory lets you uncover new theories based on what your data reveals, not just on pre-existing ideas.
• Data-Driven Results:  Your conclusions are firmly rooted in the data you’ve gathered, ensuring they reflect reality. This close relationship between data and findings is a key factor in establishing trustworthiness.
• Avoids Bias:  Because gathering data and analyzing it are closely intertwined, researchers are truly observing what emerges from data, and are less likely to let their preconceptions color the findings.
• Streamlined data gathering and analysis:  Analyzing and collecting data go hand in hand. Data is collected, analyzed, and as you gain insight from analysis, you continue gathering more data.
• Synthesize Findings : By applying grounded theory to a qualitative metasynthesis , researchers can move beyond a simple aggregation of findings and generate a higher-level understanding of the phenomena being studied.

Limitations

• Time-Consuming:  Analyzing qualitative data can be like searching for a needle in a haystack; it requires careful examination and can be quite time-consuming, especially without software assistance6.
• Potential for Bias:  Despite safeguards, researchers may unintentionally influence their analysis due to personal experiences.
• Data Quality:  The success of grounded theory hinges on complete and accurate data; poor quality can lead to faulty conclusions.

Practical Steps

Grounded theory can be conducted by individual researchers or research teams. If working in a team, it’s important to communicate regularly and ensure everyone is using the same coding system.

Grounded theory research is typically an iterative process. This means that researchers may move back and forth between these steps as they collect and analyze data.

Instead of doing everything in order, you repeat the steps over and over.

This cycle keeps going, which is why grounded theory is called a circular process.

Continue to gather and analyze data until no new insights or properties related to your categories emerge. This saturation point signals that the theory is comprehensive and well-substantiated by the data.

Theoretical sampling, collecting sufficient and rich data, and theoretical saturation help the grounded theorist to avoid a lack of “groundedness,” incomplete findings, and “premature closure.

1. Planning and Philosophical Considerations

Begin by considering the phenomenon you want to study and assess the current knowledge surrounding it.

However, refrain from detailing the specific aspects you seek to uncover about the phenomenon to prevent pre-existing assumptions from skewing the research.

• Discern a personal philosophical position.  Before beginning a research study, it is important to consider your philosophical stance and how you view the world, including the nature of reality and the relationship between the researcher and the participant. This will inform the methodological choices made throughout the study.
• Investigate methodological possibilities.  Explore different research methods that align with both the philosophical stance and research goals of the study.
• Plan the study.  Determine the research question, how to collect data, and from whom to collect data.
• Conduct a literature review.  The literature review is an ongoing process throughout the study. It is important to avoid duplicating existing research and to consider previous studies, concepts, and interpretations that relate to the emerging codes and categories in the developing grounded theory.

2. Recruit participants using theoretical sampling

Initially, select participants who are readily available ( convenience sampling ) or those recommended by existing participants ( snowball sampling ).

As the analysis progresses, transition to  theoretical sampling , involving the deliberate selection of participants and data sources to refine your emerging theory.

This method is used to refine and develop a grounded theory. The researcher uses theoretical sampling to choose new participants or data sources based on the emerging findings of their study.

This could mean recruiting participants who can shed light on gaps in your understanding uncovered during the initial data analysis.

Theoretical sampling guides further data collection by identifying participants or data sources that can provide insights into gaps in the emerging theory

The goal is to gather data that will help to further develop and refine the emerging categories and theoretical concepts.

Theoretical sampling starts early in a GT study and generally requires the researcher to make amendments to their ethics approvals to accommodate new participant groups.

3. Collect Data

The researcher might use interviews, focus groups, observations, or a combination of methods to collect qualitative data.

• Observations : Watching and recording phenomena as they occur. Can be participant (researcher actively involved) or non-participant (researcher tries not to influence behaviors), and covert (participants unaware) or overt (participants aware).
• Interviews : One-on-one conversations to understand participants’ experiences. Can be structured (predetermined questions), informal (casual conversations), or semi-structured (flexible structure to explore emerging issues).
• Focus groups : Dynamic discussions with 4-10 participants sharing characteristics, moderated by the researcher using a topic guide.
• Ethnography : Studying a group’s behaviors and social interactions in their environment through observations, field notes, and interviews. Researchers immerse themselves in the community or organization for an in-depth understanding.

4. Begin open coding as soon as data collection starts

Open coding   is the first stage of coding in grounded theory, where you carefully examine and label segments of your data to identify initial concepts and ideas.

This process involves scrutinizing the data and creating codes grounded in the data itself.

The initial codes stay close to the data, aiming to capture and summarize critically and analytically what is happening in the data

To begin open coding, read through your data, such as interview transcripts, to gain a comprehensive understanding of what is being conveyed.

As you encounter segments of data that represent a distinct idea, concept, or action, you assign a code to that segment. These codes act as descriptive labels summarizing the meaning of the data segment.

For instance, if you were analyzing interview data about experiences with a new medication, a segment of data might describe a participant’s difficulty sleeping after taking the medication. This segment could be labeled with the code “trouble sleeping”

Open coding is a crucial step in grounded theory because it allows you to break down the data into manageable units and begin to see patterns and themes emerge.

As you continue coding, you constantly compare different segments of data to refine your understanding of existing codes and identify new ones.

For instance, excerpts describing difficulties with sleep might be grouped under the code “trouble sleeping”.

This iterative process of comparing data and refining codes helps ensure the codes accurately reflect the data.

Open coding is about staying close to the data, using in vivo terms or gerunds to maintain a sense of action and process

5. Reflect on thoughts and contradictions by writing grounded theory memos during analysis

During open coding, it’s crucial to engage in memo writing. Memos serve as your “notes to self”, allowing you to reflect on the coding process, note emerging patterns, and ask analytical questions about the data.

Document your thoughts, questions, and insights in memos throughout the research process.

These memos serve multiple purposes: tracing your thought process, promoting reflexivity (self-reflection), facilitating collaboration if working in a team, and supporting theory development.

Early memos tend to be shorter and less conceptual, often serving as “preparatory” notes. Later memos become more analytical and conceptual as the research progresses.

Memo Writing

• Reflexivity and Recognizing Assumptions:  Researchers should acknowledge the influence of their own experiences and assumptions on the research process. Articulating these assumptions, perhaps through memos, can enhance the transparency and trustworthiness of the study.
• Write memos throughout the research process.  Memo writing should occur throughout the entire research process, beginning with initial coding.67 Memos help make sense of the data and transition between coding phases.8
• Ask analytic questions in early memos.  Memos should include questions, reflections, and notes to explore in subsequent data collection and analysis.8
• Refine memos throughout the process.  Early memos will be shorter and less conceptual, but will become longer and more developed in later stages of the research process.7 Later memos should begin to develop provisional categories.

6. Group codes into categories using axial coding

Axial coding is the process of identifying connections between codes, grouping them together into categories to reveal relationships within the data.

Axial coding seeks to find the axes that connect various codes together.

For example, in research on school bullying, focused codes such as “Doubting oneself, getting low self-confidence, starting to agree with bullies” and “Getting lower self-confidence; blaming oneself” could be grouped together into a broader category representing the impact of bullying on self-perception.

Similarly, codes such as “Being left by friends” and “Avoiding school; feeling lonely and isolated” could be grouped into a category related to the social consequences of bullying.

These categories then become part of the emerging grounded theory, explaining the multifaceted aspects of the phenomenon.

Qualitative data analysis software often represents these categories as nested codes, visually demonstrating the hierarchy and interconnectedness of the concepts.

This hierarchical structure helps researchers organize their data, identify patterns, and develop a more nuanced understanding of the relationships between different aspects of the phenomenon being studied.

This process of axial coding is crucial for moving beyond descriptive accounts of the data towards a more theoretically rich and explanatory grounded theory.

7. Define the core category using selective coding

During  selective coding , the final development stage of grounded theory analysis, a researcher focuses on developing a detailed and integrated theory by selecting a  core category  and connecting it to other categories developed during earlier coding stages.

The core category is the central concept that links together the various categories and subcategories identified in the data and forms the foundation of the emergent grounded theory.

This core category will encapsulate the main theme of your grounded theory, that encompasses and elucidates the overarching process or phenomenon under investigation.

This phase involves a concentrated effort to refine and integrate categories, ensuring they align with the core category and contribute to the overall explanatory power of the theory.

The theory should comprehensively describe the process or scheme related to the phenomenon being studied.

For example, in a study on school bullying, if the core category is “victimization journey,” the researcher would selectively code data related to different stages of this journey, the factors contributing to each stage, and the consequences of experiencing these stages.

This might involve analyzing how victims initially attribute blame, their coping mechanisms, and the long-term impact of bullying on their self-perception.

Continue collecting data and analyzing until you reach theoretical saturation

Selective coding focuses on developing and saturating this core category, leading to a cohesive and integrated theory.

Through selective coding, researchers aim to achieve theoretical saturation, meaning no new properties or insights emerge from further data analysis.

This signifies that the core category and its related categories are well-defined, and the connections between them are thoroughly explored.

This rigorous process strengthens the trustworthiness of the findings by ensuring the theory is comprehensive and grounded in a rich dataset.

It’s important to note that while a grounded theory seeks to provide a comprehensive explanation, it remains grounded in the data.

The theory’s scope is limited to the specific phenomenon and context studied, and the researcher acknowledges that new data or perspectives might lead to modifications or refinements of the theory

• Constant Comparative Analysis:  This method involves the systematic comparison of data points, codes, and categories as they emerge from the research process. Researchers use constant comparison to identify patterns and connections in their data. There are different methods for comparing excerpts from interviews, for example, a researcher can compare excerpts from the same person, or excerpts from different people. This process is ongoing and iterative, and it continues until the researcher has developed a comprehensive and well-supported grounded theory.
• Continue until reaching theoretical saturation : Continue to gather and analyze data until no new insights or properties related to your categories. This saturation point signals that the theory is comprehensive and well-substantiated by the data.

8. Theoretical coding and model development

Theoretical coding is a process in grounded theory where researchers use advanced abstractions, often from existing theories, to explain the relationships found in their data. 

Theoretical coding often occurs later in the research process and involves using existing theories to explain the connections between codes and categories.

This process helps to strengthen the explanatory power of the grounded theory. Theoretical coding should not be confused with simply describing the data; instead, it aims to explain the phenomenon being studied, distinguishing grounded theory from purely descriptive research.

Using the developed codes, categories, and core category, create a model illustrating the process or phenomenon.

Here is some advice for novice researchers on how to apply theoretical coding:

• Begin with data analysis:  Don’t start with a pre-determined theory. Instead, allow the theory to emerge from your data through careful analysis and coding.
• Use existing theories as a guide:  While the theory should primarily emerge from your data, you can use existing theories from any discipline to help explain the connections you are seeing between your categories. This demonstrates how your research builds on established knowledge.
• Use Glaser’s coding families:  Consider applying Glaser’s (1978) coding families in the later stages of analysis as a simple way to begin theoretical coding. Remember that your analysis should guide which theoretical codes are most appropriate.
• Keep it simple:  Theoretical coding doesn’t need to be overly complex.   Focus on finding an existing theory that effectively explains the relationships you have identified in your data.
• Be transparent:  Clearly articulate the existing theory you are using and how it explains the connections between your categories.
• Theoretical coding is an iterative process : Remain open to revising your chosen theoretical codes as your analysis deepens and your grounded theory evolves.

9. Write your grounded theory

Present your findings in a clear and accessible manner, ensuring the theory is rooted in the data and explains the relationships between the identified concepts and categories.

The end product of this process is a well-defined, integrated grounded theory that explains a process or scheme related to the phenomenon studied.

• Develop a dissemination plan : Determine how to share the research findings with others.
• Evaluate and implement : Reflect on the research process and quality of findings, then share findings with relevant audiences in service of making a difference in the world

Reading List

Grounded Theory Review : This is an international journal that publishes articles on grounded theory.

• Birks, M., & Mills, J. (2015).  Grounded theory: A practical guide . Sage.
• Corbin, J., & Strauss, A. (1990). Grounded theory research: Procedures, canons, and evaluative criteria. Qualitative Sociology, 13, 3-21.
• Charmaz, K. (2006). Constructing Grounded Theory: A practical guide through Qualitative Analysis. Thousand Oaks, California: Sage.
• Clarke, A. E. (2003). Situational analyses: Grounded theory mapping after the postmodern turn .  Symbolic interaction ,  26 (4), 553-576.
• Glaser, B. G. (1978).  Theoretical sensitivity . University of California.
• Glaser, B. G. (2005).  The grounded theory perspective III: Theoretical coding . Sociology Press.
• Glaser, B. G., & Holton, J. (2004, May). Remodeling grounded theory. In  Forum qualitative sozialforschung/forum: qualitative social research  (Vol. 5, No. 2).
• Charmaz, K. (2012). The power and potential of grounded theory.  Medical sociology online ,  6 (3), 2-15.
• Glaser, B., & Strauss, A. (1965). Awareness of dying. New Brunswick. NJ: Aldine. This was the first published grounded theory study
• Glaser, B., & Strauss, A. (2017).  Discovery of grounded theory: Strategies for qualitative research . Routledge.
• Pidgeon, N., & Henwood, K. (1997). Using grounded theory in psychological research. In N. Hayes (Ed.), Doing qualitative analysis in psychology Press/Erlbaum (UK) Taylor & Francis.

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Grounded theory research: A design framework for novice researchers

Ylona chun tie.

1 Nursing and Midwifery, College of Healthcare Sciences, James Cook University, Townsville, QLD, Australia

Melanie Birks

Karen francis.

2 College of Health and Medicine, University of Tasmania, Australia, Hobart, TAS, Australia

Background:

Grounded theory is a well-known methodology employed in many research studies. Qualitative and quantitative data generation techniques can be used in a grounded theory study. Grounded theory sets out to discover or construct theory from data, systematically obtained and analysed using comparative analysis. While grounded theory is inherently flexible, it is a complex methodology. Thus, novice researchers strive to understand the discourse and the practical application of grounded theory concepts and processes.

The aim of this article is to provide a contemporary research framework suitable to inform a grounded theory study.

This article provides an overview of grounded theory illustrated through a graphic representation of the processes and methods employed in conducting research using this methodology. The framework is presented as a diagrammatic representation of a research design and acts as a visual guide for the novice grounded theory researcher.

Discussion:

As grounded theory is not a linear process, the framework illustrates the interplay between the essential grounded theory methods and iterative and comparative actions involved. Each of the essential methods and processes that underpin grounded theory are defined in this article.

Conclusion:

Rather than an engagement in philosophical discussion or a debate of the different genres that can be used in grounded theory, this article illustrates how a framework for a research study design can be used to guide and inform the novice nurse researcher undertaking a study using grounded theory. Research findings and recommendations can contribute to policy or knowledge development, service provision and can reform thinking to initiate change in the substantive area of inquiry.

Introduction

The aim of all research is to advance, refine and expand a body of knowledge, establish facts and/or reach new conclusions using systematic inquiry and disciplined methods. 1 The research design is the plan or strategy researchers use to answer the research question, which is underpinned by philosophy, methodology and methods. 2 Birks 3 defines philosophy as ‘a view of the world encompassing the questions and mechanisms for finding answers that inform that view’ (p. 18). Researchers reflect their philosophical beliefs and interpretations of the world prior to commencing research. Methodology is the research design that shapes the selection of, and use of, particular data generation and analysis methods to answer the research question. 4 While a distinction between positivist research and interpretivist research occurs at the paradigm level, each methodology has explicit criteria for the collection, analysis and interpretation of data. 2 Grounded theory (GT) is a structured, yet flexible methodology. This methodology is appropriate when little is known about a phenomenon; the aim being to produce or construct an explanatory theory that uncovers a process inherent to the substantive area of inquiry. 5 – 7 One of the defining characteristics of GT is that it aims to generate theory that is grounded in the data. The following section provides an overview of GT – the history, main genres and essential methods and processes employed in the conduct of a GT study. This summary provides a foundation for a framework to demonstrate the interplay between the methods and processes inherent in a GT study as presented in the sections that follow.

Glaser and Strauss are recognised as the founders of grounded theory. Strauss was conversant in symbolic interactionism and Glaser in descriptive statistics. 8 – 10 Glaser and Strauss originally worked together in a study examining the experience of terminally ill patients who had differing knowledge of their health status. Some of these suspected they were dying and tried to confirm or disconfirm their suspicions. Others tried to understand by interpreting treatment by care providers and family members. Glaser and Strauss examined how the patients dealt with the knowledge they were dying and the reactions of healthcare staff caring for these patients. Throughout this collaboration, Glaser and Strauss questioned the appropriateness of using a scientific method of verification for this study. During this investigation, they developed the constant comparative method, a key element of grounded theory, while generating a theory of dying first described in Awareness of Dying (1965). The constant comparative method is deemed an original way of organising and analysing qualitative data.

Glaser and Strauss subsequently went on to write The Discovery of Grounded Theory: Strategies for Qualitative Research (1967). This seminal work explained how theory could be generated from data inductively. This process challenged the traditional method of testing or refining theory through deductive testing. Grounded theory provided an outlook that questioned the view of the time that quantitative methodology is the only valid, unbiased way to determine truths about the world. 11 Glaser and Strauss 5 challenged the belief that qualitative research lacked rigour and detailed the method of comparative analysis that enables the generation of theory. After publishing The Discovery of Grounded Theory , Strauss and Glaser went on to write independently, expressing divergent viewpoints in the application of grounded theory methods.

Glaser produced his book Theoretical Sensitivity (1978) and Strauss went on to publish Qualitative Analysis for Social Scientists (1987). Strauss and Corbin’s 12 publication Basics of Qualitative Research: Grounded Theory Procedures and Techniques resulted in a rebuttal by Glaser 13 over their application of grounded theory methods. However, philosophical perspectives have changed since Glaser’s positivist version and Strauss and Corbin’s post-positivism stance. 14 Grounded theory has since seen the emergence of additional philosophical perspectives that have influenced a change in methodological development over time. 15

Subsequent generations of grounded theorists have positioned themselves along a philosophical continuum, from Strauss and Corbin’s 12 theoretical perspective of symbolic interactionism, through to Charmaz’s 16 constructivist perspective. However, understanding how to position oneself philosophically can challenge novice researchers. Birks and Mills 6 provide a contemporary understanding of GT in their book Grounded theory: A Practical Guide. These Australian researchers have written in a way that appeals to the novice researcher. It is the contemporary writing, the way Birks and Mills present a non-partisan approach to GT that support the novice researcher to understand the philosophical and methodological concepts integral in conducting research. The development of GT is important to understand prior to selecting an approach that aligns with the researcher’s philosophical position and the purpose of the research study. As the research progresses, seminal texts are referred back to time and again as understanding of concepts increases, much like the iterative processes inherent in the conduct of a GT study.

Genres: traditional, evolved and constructivist grounded theory

Grounded theory has several distinct methodological genres: traditional GT associated with Glaser; evolved GT associated with Strauss, Corbin and Clarke; and constructivist GT associated with Charmaz. 6 , 17 Each variant is an extension and development of the original GT by Glaser and Strauss. The first of these genres is known as traditional or classic GT. Glaser 18 acknowledged that the goal of traditional GT is to generate a conceptual theory that accounts for a pattern of behaviour that is relevant and problematic for those involved. The second genre, evolved GT, is founded on symbolic interactionism and stems from work associated with Strauss, Corbin and Clarke. Symbolic interactionism is a sociological perspective that relies on the symbolic meaning people ascribe to the processes of social interaction. Symbolic interactionism addresses the subjective meaning people place on objects, behaviours or events based on what they believe is true. 19 , 20 Constructivist GT, the third genre developed and explicated by Charmaz, a symbolic interactionist, has its roots in constructivism. 8 , 16 Constructivist GT’s methodological underpinnings focus on how participants’ construct meaning in relation to the area of inquiry. 16 A constructivist co-constructs experience and meanings with participants. 21 While there are commonalities across all genres of GT, there are factors that distinguish differences between the approaches including the philosophical position of the researcher; the use of literature; and the approach to coding, analysis and theory development. Following on from Glaser and Strauss, several versions of GT have ensued.

Grounded theory represents both a method of inquiry and a resultant product of that inquiry. 7 , 22 Glaser and Holton 23 define GT as ‘a set of integrated conceptual hypotheses systematically generated to produce an inductive theory about a substantive area’ (p. 43). Strauss and Corbin 24 define GT as ‘theory that was derived from data, systematically gathered and analysed through the research process’ (p. 12). The researcher ‘begins with an area of study and allows the theory to emerge from the data’ (p. 12). Charmaz 16 defines GT as ‘a method of conducting qualitative research that focuses on creating conceptual frameworks or theories through building inductive analysis from the data’ (p. 187). However, Birks and Mills 6 refer to GT as a process by which theory is generated from the analysis of data. Theory is not discovered; rather, theory is constructed by the researcher who views the world through their own particular lens.

Research process

Before commencing any research study, the researcher must have a solid understanding of the research process. A well-developed outline of the study and an understanding of the important considerations in designing and undertaking a GT study are essential if the goals of the research are to be achieved. While it is important to have an understanding of how a methodology has developed, in order to move forward with research, a novice can align with a grounded theorist and follow an approach to GT. Using a framework to inform a research design can be a useful modus operandi.

The following section provides insight into the process of undertaking a GT research study. Figure 1 is a framework that summarises the interplay and movement between methods and processes that underpin the generation of a GT. As can be seen from this framework, and as detailed in the discussion that follows, the process of doing a GT research study is not linear, rather it is iterative and recursive.

Research design framework: summary of the interplay between the essential grounded theory methods and processes.

Grounded theory research involves the meticulous application of specific methods and processes. Methods are ‘systematic modes, procedures or tools used for collection and analysis of data’. 25 While GT studies can commence with a variety of sampling techniques, many commence with purposive sampling, followed by concurrent data generation and/or collection and data analysis, through various stages of coding, undertaken in conjunction with constant comparative analysis, theoretical sampling and memoing. Theoretical sampling is employed until theoretical saturation is reached. These methods and processes create an unfolding, iterative system of actions and interactions inherent in GT. 6 , 16 The methods interconnect and inform the recurrent elements in the research process as shown by the directional flow of the arrows and the encompassing brackets in Figure 1 . The framework denotes the process is both iterative and dynamic and is not one directional. Grounded theory methods are discussed in the following section.

Purposive sampling

As presented in Figure 1 , initial purposive sampling directs the collection and/or generation of data. Researchers purposively select participants and/or data sources that can answer the research question. 5 , 7 , 16 , 21 Concurrent data generation and/or data collection and analysis is fundamental to GT research design. 6 The researcher collects, codes and analyses this initial data before further data collection/generation is undertaken. Purposeful sampling provides the initial data that the researcher analyses. As will be discussed, theoretical sampling then commences from the codes and categories developed from the first data set. Theoretical sampling is used to identify and follow clues from the analysis, fill gaps, clarify uncertainties, check hunches and test interpretations as the study progresses.

Constant comparative analysis

Constant comparative analysis is an analytical process used in GT for coding and category development. This process commences with the first data generated or collected and pervades the research process as presented in Figure 1 . Incidents are identified in the data and coded. 6 The initial stage of analysis compares incident to incident in each code. Initial codes are then compared to other codes. Codes are then collapsed into categories. This process means the researcher will compare incidents in a category with previous incidents, in both the same and different categories. 5 Future codes are compared and categories are compared with other categories. New data is then compared with data obtained earlier during the analysis phases. This iterative process involves inductive and deductive thinking. 16 Inductive, deductive and abductive reasoning can also be used in data analysis. 26

Constant comparative analysis generates increasingly more abstract concepts and theories through inductive processes. 16 In addition, abduction, defined as ‘a form of reasoning that begins with an examination of the data and the formation of a number of hypotheses that are then proved or disproved during the process of analysis … aids inductive conceptualization’. 6 Theoretical sampling coupled with constant comparative analysis raises the conceptual levels of data analysis and directs ongoing data collection or generation. 6

The constant comparative technique is used to find consistencies and differences, with the aim of continually refining concepts and theoretically relevant categories. This continual comparative iterative process that encompasses GT research sets it apart from a purely descriptive analysis. 8

Memo writing is an analytic process considered essential ‘in ensuring quality in grounded theory’. 6 Stern 27 offers the analogy that if data are the building blocks of the developing theory, then memos are the ‘mortar’ (p. 119). Memos are the storehouse of ideas generated and documented through interacting with data. 28 Thus, memos are reflective interpretive pieces that build a historic audit trail to document ideas, events and the thought processes inherent in the research process and developing thinking of the analyst. 6 Memos provide detailed records of the researchers’ thoughts, feelings and intuitive contemplations. 6

Lempert 29 considers memo writing crucial as memos prompt researchers to analyse and code data and develop codes into categories early in the coding process. Memos detail why and how decisions made related to sampling, coding, collapsing of codes, making of new codes, separating codes, producing a category and identifying relationships abstracted to a higher level of analysis. 6 Thus, memos are informal analytic notes about the data and the theoretical connections between categories. 23 Memoing is an ongoing activity that builds intellectual assets, fosters analytic momentum and informs the GT findings. 6 , 10

Generating/collecting data

A hallmark of GT is concurrent data generation/collection and analysis. In GT, researchers may utilise both qualitative and quantitative data as espoused by Glaser’s dictum; ‘all is data’. 30 While interviews are a common method of generating data, data sources can include focus groups, questionnaires, surveys, transcripts, letters, government reports, documents, grey literature, music, artefacts, videos, blogs and memos. 9 Elicited data are produced by participants in response to, or directed by, the researcher whereas extant data includes data that is already available such as documents and published literature. 6 , 31 While this is one interpretation of how elicited data are generated, other approaches to grounded theory recognise the agency of participants in the co-construction of data with the researcher. The relationship the researcher has with the data, how it is generated and collected, will determine the value it contributes to the development of the final GT. 6 The significance of this relationship extends into data analysis conducted by the researcher through the various stages of coding.

Coding is an analytical process used to identify concepts, similarities and conceptual reoccurrences in data. Coding is the pivotal link between collecting or generating data and developing a theory that explains the data. Charmaz 10 posits,

codes rely on interaction between researchers and their data. Codes consist of short labels that we construct as we interact with the data. Something kinaesthetic occurs when we are coding; we are mentally and physically active in the process. (p. 5)

In GT, coding can be categorised into iterative phases. Traditional, evolved and constructivist GT genres use different terminology to explain each coding phase ( Table 1 ).

Comparison of coding terminology in traditional, evolved and constructivist grounded theory.

Adapted from Birks and Mills. 6

Coding terminology in evolved GT refers to open (a procedure for developing categories of information), axial (an advanced procedure for interconnecting the categories) and selective coding (procedure for building a storyline from core codes that connects the categories), producing a discursive set of theoretical propositions. 6 , 12 , 32 Constructivist grounded theorists refer to initial, focused and theoretical coding. 9 Birks and Mills 6 use the terms initial, intermediate and advanced coding that link to low, medium and high-level conceptual analysis and development. The coding terms devised by Birks and Mills 6 were used for Figure 1 ; however, these can be altered to reflect the coding terminology used in the respective GT genres selected by the researcher.

Initial coding

Initial coding of data is the preliminary step in GT data analysis. 6 , 9 The purpose of initial coding is to start the process of fracturing the data to compare incident to incident and to look for similarities and differences in beginning patterns in the data. In initial coding, the researcher inductively generates as many codes as possible from early data. 16 Important words or groups of words are identified and labelled. In GT, codes identify social and psychological processes and actions as opposed to themes. Charmaz 16 emphasises keeping codes as similar to the data as possible and advocates embedding actions in the codes in an iterative coding process. Saldaña 33 agrees that codes that denote action, which he calls process codes, can be used interchangeably with gerunds (verbs ending in ing ). In vivo codes are often verbatim quotes from the participants’ words and are often used as the labels to capture the participant’s words as representative of a broader concept or process in the data. 6 Table 1 reflects variation in the terminology of codes used by grounded theorists.

Initial coding categorises and assigns meaning to the data, comparing incident-to-incident, labelling beginning patterns and beginning to look for comparisons between the codes. During initial coding, it is important to ask ‘what is this data a study of’. 18 What does the data assume, ‘suggest’ or ‘pronounce’ and ‘from whose point of view’ does this data come, whom does it represent or whose thoughts are they?. 16 What collectively might it represent? The process of documenting reactions, emotions and related actions enables researchers to explore, challenge and intensify their sensitivity to the data. 34 Early coding assists the researcher to identify the direction for further data gathering. After initial analysis, theoretical sampling is employed to direct collection of additional data that will inform the ‘developing theory’. 9 Initial coding advances into intermediate coding once categories begin to develop.

Theoretical sampling

The purpose of theoretical sampling is to allow the researcher to follow leads in the data by sampling new participants or material that provides relevant information. As depicted in Figure 1 , theoretical sampling is central to GT design, aids the evolving theory 5 , 7 , 16 and ensures the final developed theory is grounded in the data. 9 Theoretical sampling in GT is for the development of a theoretical category, as opposed to sampling for population representation. 10 Novice researchers need to acknowledge this difference if they are to achieve congruence within the methodology. Birks and Mills 6 define theoretical sampling as ‘the process of identifying and pursuing clues that arise during analysis in a grounded theory study’ (p. 68). During this process, additional information is sought to saturate categories under development. The analysis identifies relationships, highlights gaps in the existing data set and may reveal insight into what is not yet known. The exemplars in Box 1 highlight how theoretical sampling led to the inclusion of further data.

Examples of theoretical sampling.

Thus, theoretical sampling is used to focus and generate data to feed the iterative process of continual comparative analysis of the data. 6

Intermediate coding

Intermediate coding, identifying a core category, theoretical data saturation, constant comparative analysis, theoretical sensitivity and memoing occur in the next phase of the GT process. 6 Intermediate coding builds on the initial coding phase. Where initial coding fractures the data, intermediate coding begins to transform basic data into more abstract concepts allowing the theory to emerge from the data. During this analytic stage, a process of reviewing categories and identifying which ones, if any, can be subsumed beneath other categories occurs and the properties or dimension of the developed categories are refined. Properties refer to the characteristics that are common to all the concepts in the category and dimensions are the variations of a property. 37

At this stage, a core category starts to become evident as developed categories form around a core concept; relationships are identified between categories and the analysis is refined. Birks and Mills 6 affirm that diagramming can aid analysis in the intermediate coding phase. Grounded theorists interact closely with the data during this phase, continually reassessing meaning to ascertain ‘what is really going on’ in the data. 30 Theoretical saturation ensues when new data analysis does not provide additional material to existing theoretical categories, and the categories are sufficiently explained. 6

Advanced coding

Birks and Mills 6 described advanced coding as the ‘techniques used to facilitate integration of the final grounded theory’ (p. 177). These authors promote storyline technique (described in the following section) and theoretical coding as strategies for advancing analysis and theoretical integration. Advanced coding is essential to produce a theory that is grounded in the data and has explanatory power. 6 During the advanced coding phase, concepts that reach the stage of categories will be abstract, representing stories of many, reduced into highly conceptual terms. The findings are presented as a set of interrelated concepts as opposed to presenting themes. 28 Explanatory statements detail the relationships between categories and the central core category. 28

Storyline is a tool that can be used for theoretical integration. Birks and Mills 6 define storyline as ‘a strategy for facilitating integration, construction, formulation, and presentation of research findings through the production of a coherent grounded theory’ (p. 180). Storyline technique is first proposed with limited attention in Basics of Qualitative Research by Strauss and Corbin 12 and further developed by Birks et al. 38 as a tool for theoretical integration. The storyline is the conceptualisation of the core category. 6 This procedure builds a story that connects the categories and produces a discursive set of theoretical propositions. 24 Birks and Mills 6 contend that storyline can be ‘used to produce a comprehensive rendering of your grounded theory’ (p. 118). Birks et al. 38 had earlier concluded, ‘storyline enhances the development, presentation and comprehension of the outcomes of grounded theory research’ (p. 405). Once the storyline is developed, the GT is finalised using theoretical codes that ‘provide a framework for enhancing the explanatory power of the storyline and its potential as theory’. 6 Thus, storyline is the explication of the theory.

Theoretical coding occurs as the final culminating stage towards achieving a GT. 39 , 40 The purpose of theoretical coding is to integrate the substantive theory. 41 Saldaña 40 states, ‘theoretical coding integrates and synthesises the categories derived from coding and analysis to now create a theory’ (p. 224). Initial coding fractures the data while theoretical codes ‘weave the fractured story back together again into an organized whole theory’. 18 Advanced coding that integrates extant theory adds further explanatory power to the findings. 6 The examples in Box 2 describe the use of storyline as a technique.

Writing the storyline.

Theoretical sensitivity

As presented in Figure 1 , theoretical sensitivity encompasses the entire research process. Glaser and Strauss 5 initially described the term theoretical sensitivity in The Discovery of Grounded Theory. Theoretical sensitivity is the ability to know when you identify a data segment that is important to your theory. While Strauss and Corbin 12 describe theoretical sensitivity as the insight into what is meaningful and of significance in the data for theory development, Birks and Mills 6 define theoretical sensitivity as ‘the ability to recognise and extract from the data elements that have relevance for the emerging theory’ (p. 181). Conducting GT research requires a balance between keeping an open mind and the ability to identify elements of theoretical significance during data generation and/or collection and data analysis. 6

Several analytic tools and techniques can be used to enhance theoretical sensitivity and increase the grounded theorist’s sensitivity to theoretical constructs in the data. 28 Birks and Mills 6 state, ‘as a grounded theorist becomes immersed in the data, their level of theoretical sensitivity to analytic possibilities will increase’ (p. 12). Developing sensitivity as a grounded theorist and the application of theoretical sensitivity throughout the research process allows the analytical focus to be directed towards theory development and ultimately result in an integrated and abstract GT. 6 The example in Box 3 highlights how analytic tools are employed to increase theoretical sensitivity.

Theoretical sensitivity.

The grounded theory

The meticulous application of essential GT methods refines the analysis resulting in the generation of an integrated, comprehensive GT that explains a process relating to a particular phenomenon. 6 The results of a GT study are communicated as a set of concepts, related to each other in an interrelated whole, and expressed in the production of a substantive theory. 5 , 7 , 16 A substantive theory is a theoretical interpretation or explanation of a studied phenomenon 6 , 17 Thus, the hallmark of grounded theory is the generation of theory ‘abstracted from, or grounded in, data generated and collected by the researcher’. 6 However, to ensure quality in research requires the application of rigour throughout the research process.

Quality and rigour

The quality of a grounded theory can be related to three distinct areas underpinned by (1) the researcher’s expertise, knowledge and research skills; (2) methodological congruence with the research question; and (3) procedural precision in the use of methods. 6 Methodological congruence is substantiated when the philosophical position of the researcher is congruent with the research question and the methodological approach selected. 6 Data collection or generation and analytical conceptualisation need to be rigorous throughout the research process to secure excellence in the final grounded theory. 44

Procedural precision requires careful attention to maintaining a detailed audit trail, data management strategies and demonstrable procedural logic recorded using memos. 6 Organisation and management of research data, memos and literature can be assisted using software programs such as NVivo. An audit trail of decision-making, changes in the direction of the research and the rationale for decisions made are essential to ensure rigour in the final grounded theory. 6

This article offers a framework to assist novice researchers visualise the iterative processes that underpin a GT study. The fundamental process and methods used to generate an integrated grounded theory have been described. Novice researchers can adapt the framework presented to inform and guide the design of a GT study. This framework provides a useful guide to visualise the interplay between the methods and processes inherent in conducting GT. Research conducted ethically and with meticulous attention to process will ensure quality research outcomes that have relevance at the practice level.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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

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Your complete guide to grounded theory research.

11 min read If you have an area of interest, but no hypothesis yet, try grounded theory research. You conduct data collection and analysis, forming a theory based on facts. Read our ultimate guide for everything you need to know.

What is grounded theory in research?

Grounded theory is a systematic qualitative research method that collects empirical data first, and then creates a theory ‘grounded’ in the results.

The constant comparative method was developed by Glaser and Strauss, described in their book, Awareness of Dying (1965). They are seen as the founders of classic grounded theory.

Research teams use grounded theory to analyze social processes and relationships.

Because of the important role of data, there are key stages like data collection and data analysis that need to happen in order for the resulting data to be useful.

The grounded research results are compared to strengthen the validity of the findings to arrive at stronger defined theories. Once the data analysis cannot continue to refine the new theories down, a final theory is confirmed.

Grounded research is different from experimental research or scientific inquiry as it does not need a hypothesis theory at the start to verify. Instead, the evolving theory is based on facts and evidence discovered during each stage.Also, grounded research also doesn’t have a preconceived understanding of events or happenings before the qualitative research commences.

Free eBook: Qualitative research design handbook

When should you use grounded theory research?

Grounded theory research is useful for businesses when a researcher wants to look into a topic that has existing theory or no current research available. This means that the qualitative research results will be unique and can open the doors to the social phenomena being investigated.

In addition, businesses can use this qualitative research as the primary evidence needed to understand whether it’s worth placing investment into a new line of product or services, if the research identifies key themes and concepts that point to a solvable commercial problem.

Grounded theory methodology

There are several stages in the grounded theory process:

1. Data planning

The researcher decides what area they’re interested in.

They may create a guide to what they will be collecting during the grounded theory methodology. They will refer to this guide when they want to check the suitability of the qualitative data, as they collect it, to avoid preconceived ideas of what they know impacting the research.

A researcher can set up a grounded theory coding framework to identify the correct data. Coding is associating words, or labels, that are useful to the social phenomena that is being investigated. So, when the researcher sees these words, they assign the data to that category or theme.

In this stage, you’ll also want to create your open-ended initial research questions. Here are the main differences between open and closed-ended questions:

These will need to be adapted as the research goes on and more tangents and areas to explore are discovered. To help you create your questions, ask yourself:

• What are you trying to explain?
• What experiences do you need to ask about?
• Who will you ask and why?

2. Data collection and analysis

Data analysis happens at the same time as data collection. In grounded theory analysis, this is also known as constant comparative analysis, or theoretical sampling.

The researcher collects qualitative data by asking open-ended questions in interviews and surveys, studying historical or archival data, or observing participants and interpreting what is seen. This collected data is transferred into transcripts.

The categories or themes are compared and further refined by data, until there are only a few strong categories or themes remaining. Here is where coding occurs, and there are different levels of coding as the categories or themes are refined down:

• Data collection (Initial coding stage): Read through the data line by line
• Open coding stage: Read through the transcript data several times, breaking down the qualitative research data into excerpts, and make summaries of the concept or theme.
• Axial coding stage: Read through and compare further data collection to summarize concepts or themes to look for similarities and differences. Make defined summaries that help shape an emerging theory.
• Selective coding stage: Use the defined summaries to identify a strong core concept or theme.

During analysis, the researcher will apply theoretical sensitivity to the collected data they uncover, so that the meaning of nuances in what they see can be fully understood.

This coding process repeats until the researcher has reached theoretical saturation. In grounded theory analysis, this is where all data has been researched and there are no more possible categories or themes to explore.

3. Data analysis is turned into a final theory

The researcher takes the core categories and themes that they have gathered and integrates them into one central idea (a new theory) using selective code. This final grounded theory concludes the research.

The new theory should be a few simple sentences that describe the research, indicating what was and was not covered in it.

An example of using grounded theory in business

One example of how grounded theory may be used in business is to support HR teams by analyzing data to explore reasons why people leave a company.

For example, a company with a high attrition rate that has not done any research on this area before may choose grounded theory to understand key reasons why people choose to leave.

Researchers may start looking at the quantitative data around departures over the year and look for patterns. Coupled with this, they may conduct qualitative data research through employee engagement surveys , interview panels for current employees, and exit interviews with leaving employees.

From this information, they may start coding transcripts to find similarities and differences (coding) picking up on general themes and concepts. For example, a group of excepts like:

• “The hours I worked were far too long and I hated traveling home in the dark”
• “My manager didn’t appreciate the work I was doing, especially when I worked late”
• There are no good night bus routes home that I could take safely”

Using open coding, a researcher could compare excerpts and suggest the themes of managerial issues, a culture of long hours and lack of traveling routes at night.

With more samples and information, through axial coding, stronger themes of lack of recognition and having too much work (which led people to working late), could be drawn out from the summaries of the concepts and themes.

This could lead to a selective coding conclusion that people left because they were ‘overworked and under-appreciated’.

With this information, a grounded theory can help HR teams look at what teams do day to day, exploring ways to spread workloads or reduce them. Also, there could be training supplied to management and employees to engage professional development conversations better.

 Advantages of grounded theory

• No need for hypothesis – Researchers don’t need to know the details about the topic they want to investigate in advance, as the grounded theory methodology will bring up the information.
• Lots of flexibility – Researchers can take the topic in whichever direction they think is best, based on what the data is telling them. This means that exploration avenues that may be off-limits in traditional experimental research can be included.
• Multiple stages improve conclusion – Having a series of coding stages that refine the data into clear and strong concepts or themes means that the grounded theory will be more useful, relevant and defined.
• Data-first – Grounded theory relies on data analysis in the first instance, so the conclusion is based on information that has strong data behind it. This could be seen as having more validity.

Disadvantages of grounded theory

• Theoretical sensitivity dulled – If a researcher does not know enough about the topic being investigated, then their theoretical sensitivity about what data means may be lower and information may be missed if it is not coded properly.
• Large topics take time – There is a significant time resource required by the researcher to properly conduct research, evaluate the results and compare and analyze each excerpt. If the research process finds more avenues for investigation, for example, when excerpts contradict each other, then the researcher is required to spend more time doing qualitative inquiry.
• Bias in interpreting qualitative data – As the researcher is responsible for interpreting the qualitative data results, and putting their own observations into text, there can be researcher bias that would skew the data and possibly impact the final grounded theory.
• Qualitative research is harder to analyze than quantitative data – unlike numerical factual data from quantitative sources, qualitative data is harder to analyze as researchers will need to look at the words used, the sentiment and what is being said.
• Not repeatable – while the grounded theory can present a fact-based hypothesis, the actual data analysis from the research process cannot be repeated easily as opinions, beliefs and people may change over time. This may impact the validity of the grounded theory result.

What tools will help with grounded theory?

Evaluating qualitative research can be tough when there are several analytics platforms to manage and lots of subjective data sources to compare. Some tools are already part of the office toolset, like video conferencing tools and excel spreadsheets.

However, most tools are not purpose-built for research, so researchers will be manually collecting and managing these files – in the worst case scenario, by pen and paper!

Use a best-in-breed management technology solution to collect all qualitative research and manage it in an organized way without large time resources or additional training required.

Qualtrics provides a number of qualitative research analysis tools, like Text iQ , powered by Qualtrics iQ, provides powerful machine learning and native language processing to help you discover patterns and trends in text.

This also provides you with research process tools:

• Sentiment analysis — a technique to help identify the underlying sentiment (say positive, neutral, and/or negative) in qualitative research text responses
• Topic detection/categorisation — The solution makes it easy to add new qualitative research codes and group by theme. Easily group or bucket of similar themes that can be relevant for the business and the industry (eg. ‘Food quality’, ‘Staff efficiency’ or ‘Product availability’)

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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An Overview of Grounded Theory in Qualitative Research

Using grounded theory, you can examine a specific process or phenomenon and develop new theories derived from the collected real-world data and their analysis.

Grounded theory research is an inductive approach in which a theory is developed based on data. This is the opposite of the traditional hypothesis-deductive research approaches where hypotheses are formulated and are then tried to be proved or disproved.

In grounded theory, the process of collecting data, and developing theory is a continuous one and should be incorporated in the research design. The process of collecting and analyzing data is repeated until theoretical saturation is reached or no new insights will be gained from additional data.

In Situational Analysis Extending Grounded Theory with Dr. Adele Clarke, Clarke discusses situational analysis as an extension of grounded theory for analyzing qualitative data including interview, ethnographic, historical, visual, and/or other discursive materials. Clarke describes how it is especially useful for multi-site research, feminist, and critical inquiry. To dive deeper into the messy complexities in data and understand relations among the elements constitutive of the situation, watch Clarke’s webinar Situational Analysis Extending Grounded Theory.

>> View Webinar: Situational Analysis Extending Grounded Theory

What is Grounded Theory Approach in Research?

The grounded theory approach is a qualitative research methodology that attempts to unravel the meanings of people's interactions, social actions, and experiences. In other words, these explanations are grounded in the participants' own interpretations or explanations.

In 1967, Barney Glaser and Anselm Strauss published the book, The Discovery Of Grounded Theory which introduced this method. Many disciplines have since used grounded theory, including anthropology, sociology, economics, psychology, and public health.

Qualitative research using grounded theory was regarded as being groundbreaking upon its introduction. By using the inductive methodology, data (such as interviews and observations, and on rare occasions, historical data, archival data, and more) could be analyzed as they are being collected. They sought to move away from the dominant practice in the 1950s and 60s of starting with a theoretical framework which needed to be verified. They turned that practice on its head by starting with the data to develop theory.

Grounded theory has the following salient features:

Begins with data- Researchers using the grounded theory approach typically start with a case study by observing an individual or group in action. Through an analysis of cases, researchers formulate a tentative definition of their concept. An explanation for the construct is later crafted based on this case analysis.

A personal approach- In this method, researchers study participants as they go about their daily activities, observe them interacting with others, conduct individual or group interviews, and ask participants specific questions about their observations, daily lives, experiences, or other sources relevant to the study.

The application of grounded theory qualitative research is a dynamic and flexible way to answer questions that can't be addressed by other research methods.

What is Grounded Theory in Research?

A grounded theory is often used in cases where there is no existing theory that explains the phenomenon being studied. It is also possible to use it if there is an existing theory, but it is potentially incomplete because the information wasn’t gathered from the group you intend to research.

Check out ScienceDirect's page for more examples on how grounded theory can be applied .

What are the Advantages of Grounded Theory?

Grounded theory offers various advantages.

Results reflect real-world settings

By using grounded theory, one can develop theories that are based on observations and interviews with real subjects in real situations. This results in findings that more closely reflect reality. In contrast, other types of research take place in less natural settings, such as focus groups and lab settings.

Excellent for discovering new things

The premise of grounded theory is that you discover new theories by inductive means. In other words, you don't assume anything about the outcome and aren't concerned about validating or describing it. Instead, you use the data you collect to inform your analysis and your theoretical construct, resulting in new insights.

Streamlined data gathering and analysis

Analyzing and collecting data go hand in hand. Data is collected, analyzed, and as you gain insight from analysis, you continue gathering more data. In this way, your data collection will be adequate to explain the results of your analysis.

Findings are tightly connected to the data

In grounded theory, the outcome is determined primarily by collected data, so findings are tightly tied to those data. It contrasts with other research methods that are primarily constructed through external frameworks or theories that are so far removed from the data.

Protection from confirmation bias

Because gathering data and analyzing it are closely intertwined, researchers are truly observing what emerges from data. By having a buffer, you avoid confirming preconceived notions about the topic.

Provides analysis strategies

An important aspect of grounded theory is that it provides specific strategies for analysis. Grounded theory may be characterized as an open-ended method, but its analysis strategies keep you organized and analytical throughout the research process.

Disadvantages of Grounded Theory

In addition to the multiple advantages of grounded theory listed above, there are a few disadvantages of grounded theory, and qualitative methods in general, that are important to consider.

Grounded theory is often a time-consuming process that involves collecting data from multiple sources, analyzing the data for patterns and themes, and then finally coding the data – all steps that can take significant time if not using qualitative data analysis software like NVivo.

Additional disadvantages in grounded theory include a researcher’s own biases and assumptions which may impact their data analysis and the quality of their data – whether it’s low quality or simply incomplete.

How to Use NVivo for Grounded Theory

If you’re ready to start using grounded theory, using tools like NVivo can help!

With NVivo, you can analyze interviews (and occasionally survey) data by visually exploring datasets with the Detail View feature. This ability lets you limit the amount of data you’re viewing and filter to help identify patterns in your data.

Additionally, NVivo can help with transcribing, making connections between themes and participants, and keeping your interview data organized. Learn more about how to use NVivo for interview data in Thematic Analysis of Interview Data: 6 Ways NVivo Can Help .

Learn more about how to use NVivo for grounded theory in this paper Using NVivo to Facilitate the Development of a Grounded Theory Project: An Account of a Worked Example and the video below.

Learn more about how to use NVivo for grounded theory >>

Start transforming your qualitative research by requesting a free demo of NVivo today!

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What is grounded theory in simple terms?

Core components of grounded theory, role of the researcher in grounded theory, constructivist grounded theory, what tools will help with grounded theory.

• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative data analysis software

Grounded theory

Among the various approaches to qualitative data analysis , grounded theory is among those that stay very close to the data to develop a theory. Grounded theory analysis is essential, particularly in research inquiries where there is little or no existing theory, to guide the organization of knowledge from data collection .

Let's look at the topic of grounded theory methodology by exploring its rationale, its potential for theory development, the steps employed in grounded theory procedures, and how ATLAS.ti can facilitate grounded theory methods.

Barney G. Glaser and Anselm L. Strauss are credited with developing grounded theory as a widely-used research methodology in qualitative analysis in the social sciences. Over the decades, other researchers, such as Kathy Charmaz, have further developed grounded theory approaches. Glaser and Strauss broke new ground in qualitative inquiry through grounded theory by arguing against any notions that science had maximized its potential for developing new theory. As a result, the primary purpose of grounded theory is to construct theories grounded in systematically gathered and analyzed data rather than beginning with a preconceived theory. The ultimate goal is to generate a theory that offers an explanation of the research question that stems from what emerges from the data.

What is the main point of grounded theory?

At its core, grounded theory is about the discovery of new concepts and relationships. Rather than starting research with a theory and then testing it, grounded theory researchers begin with an area of study and allow what is relevant in that area to emerge. The methodology was developed as a response to the traditional approach of having a hypothesis before conducting research, which can lead to forcing the data to meet preconceived notions. The main point of grounded theory is to cultivate an understanding of social phenomena from the perspective of those experiencing them.

When should you use grounded theory research?

Grounded theory research is appropriate when there is little prior information or established theory about a phenomenon. It is most suitable for investigations of processes, actions, and interactions. Grounded theory can be particularly useful in exploratory studies where the aim is to identify key issues, explore them in detail, and construct a model or theory that can be used to understand the phenomenon from a new perspective.

You might choose to use grounded theory when you want to learn about people's experiences, their perceptions of these experiences, and the actions they take as a result. The inductive nature of grounded theory makes it suitable for studying social processes over time, understanding the changes and development of a phenomenon, and gaining in-depth insights from the perspective of those directly involved.

Benefits of using grounded theory

One of the key advantages of using grounded theory is that it promotes the emergence of new theories and deepens our understanding of the social world around us. Here are some of the key benefits:

• Flexibility: Grounded theory is adaptive and can be adjusted during the research process, providing a degree of flexibility not often seen in other research methods.
• Inductive nature: Because it is inductive , grounded theory is well suited for discovering how individuals interpret their experiences and the world around them.
• Rich, detailed insights: Grounded theory's focus on the exploration of phenomena can lead to rich, detailed insights and in-depth understanding.
• Practical outcomes: Grounded theory generates theory and provides practical implications that can inform policy, intervention, or program development.

Limitations of grounded theory

While grounded theory offers many advantages, it is essential to be aware of its limitations:

• Time-consuming: The process of data collection and analysis can be time-consuming due to the method's iterative nature.
• Complexity: The method can be complex to apply correctly due to its abstract concepts and the various stages of coding and analysis.
• Requires skill and experience: Successful implementation of grounded theory requires strong analytical skills and experience in qualitative research.
• Subjectivity: While subjectivity can be a strength in understanding the experiences of others, it can also be a limitation if biases are not properly acknowledged and managed.

Grounded theory, as a research methodology , consists of several core components that guide the research process, from data collection to the development of a final theoretical framework . These components are interrelated, each influencing and shaping the others in a dynamic, iterative process. The core components of grounded theory include theoretical sensitivity, theoretical sampling, coding and analysis , theoretical saturation, and theoretical integration.

Theoretical sensitivity

Theoretical sensitivity refers to a researcher's ability to understand and define phenomena in terms of their underlying patterns or structures. It's an acquired skill that grows with experience, through exposure to literature, professional experiences, and personal experiences. It's about being sensitive to the nuances and complexities of the data , understanding the subtle cues or messages, and being able to pull these together to form a coherent understanding. Theoretical sensitivity can be developed in many ways. Reading and engaging with relevant literature , attending workshops or seminars, conducting preliminary interviews or observations , or even through casual conversations related to the research topic, can help to increase a researcher's theoretical sensitivity. It is about having a sense of what is important in the data, what to pay attention to, and what can be given less importance.

Theoretical sampling

Theoretical sampling is the process of data collection driven by the emerging theory. Instead of having a predefined sample at the start of the research, grounded theorists allow their theoretical ideas to guide them in selecting new data sources to explore. This iterative process means that data collection and analysis occur simultaneously, and both are influenced by the emerging theory. Theoretical sampling can be quite challenging for new researchers as it requires a level of flexibility and openness that is not typically found in more structured research designs. The researcher needs to be comfortable with uncertainty and willing to follow the data wherever it may lead.

Coding and analysis

Coding and analysis are key processes in grounded theory, consisting of several stages. The first step is open coding , where the researcher examines the data in a detailed and line-by-line manner to identify initial concepts. The focus is on breaking down the data into discrete parts and closely examining them for their underlying meaning. The next stage is axial coding , where the researcher begins to assemble the data in new ways after the initial breakdown during open coding. The aim is to identify relationships between the initial codes and to group them into more abstract categories. The final stage is selective coding , where the researcher integrates and refines the categories to form a cohesive theoretical framework . Unlike in thematic analysis where the goal is often simply to outline the dimensions of a phenomenon, the focus here is on developing a unifying theory around which all other categories are related.

Theoretical saturation

Theoretical saturation is a critical concept in grounded theory. It refers to the point at which no new insights or concepts are being found in the data, indicating that the categories are well-developed and that further data collection is unnecessary. Saturation doesn't mean that every single aspect of the data has been explored but rather that the categories within the theory are robust and comprehensive. The concept of saturation is tied closely to the idea of theoretical sampling. As the theory begins to take shape, the researcher focuses their data collection on areas that will help to further develop or refine their emerging categories.

Theoretical integration

Theoretical integration is the final stage in grounded theory. It involves pulling together all the categories that have been developed, linking them together, and integrating them into a cohesive and coherent theory. Integration also involves a process of validation, where the researcher returns to their data and checks that their theory fits and explains the data. At this stage, it's important that the researcher is able to explain their theory clearly and convincingly, showing how it offers a new and insightful understanding of the phenomenon they have studied.

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Detailed steps in grounded theory research

The research process in grounded theory consists of a series of interconnected and iterative steps. Each step is part of a holistic process designed to allow a theory to emerge from the data inductively. The process of constant comparison, also known as the constant comparative method or constant comparative analysis, is central to this process. Here, we'll walk through these steps in detail.

Data collection

The first step in grounded theory research is data collection. Data can come from various sources such as interviews , observations , documents, or any other source relevant to the research question . The form of data collection can vary greatly, and the selection depends largely on the nature of the research question and the context of the study. It's important to note that in grounded theory, data collection is an iterative process and continues throughout the entire research process. Initial data collection informs the early stages of analysis and the emerging theory, which then guides further data collection. This back-and-forth between data collection and analysis is a distinguishing feature of grounded theory.

Open coding

After some data has been collected, the process of open coding begins. This is the first step in the constant comparative method. During open coding, the researcher carefully reads and re-reads the data, breaking it down into discrete incidents or ideas. Each of these incidents is then given a code - a word or short phrase that represents the essence of that piece of data. Open coding is a line-by-line analysis, which means that every line of the data is scrutinized and potentially given a code. It's during this process that the researcher starts to see categories and properties emerge from the data.

The open coding process is also where constant comparison begins. As each piece of data is coded, it's compared to other data coded in the same way. This comparison process allows the researcher to refine the definitions of codes and begin to see patterns and relationships.

Axial coding

The next step in the grounded theory process is axial coding . This stage of the constant comparative method involves taking the initial categories developed during open coding and beginning to see how they relate to each other.

During axial coding, the researcher is constantly comparing data within a category, as well as comparing categories to each other. This process allows for more abstract thinking about the data. It helps to identify central phenomena, contexts, conditions, strategies, and consequences - elements that help to give a structure to the emerging theory.

Selective coding

Selective coding is the final stage of constant comparative analysis. At this point, the researcher has a clear idea of the main categories and how they relate to each other. The goal of selective coding is to integrate these categories around a central, core category. This core category represents the main theme or process that the theory explains.

During selective coding, the researcher is still using constant comparison, but the focus is now on making sure that all categories are connected to the core category and that all categories are well-developed. This stage of the research process ends when theoretical saturation is reached - when no new data appears to add to the understanding of the core category.

Writing the theory

The final step in grounded theory research is to write up the theory. This is an important part of the process because it's where the researcher takes the abstract ideas that have been developed and turns them into a concrete, coherent theory.

Writing the theory involves clearly defining the core category, explaining how other categories relate to the core, and demonstrating how the theory explains the process or phenomena under study. The result is a well-integrated set of theoretical concepts that can offer new insights into the research question.

The researcher plays a critical role in grounded theory. They are not a passive observer but an active participant in the research process. From data collection to analysis to theory formation, the researcher's perspectives, experiences, and interpretive skills significantly shape the research process and outcomes. This section discusses the role of the researcher in grounded theory, including aspects of objectivity and subjectivity , as well as the importance of reflective practice.

Objectivity and subjectivity in research

In grounded theory research, the objectivity and subjectivity of the researcher are both significant considerations. Objectivity refers to the ability to conduct research in a neutral, unbiased manner. On the other hand, subjectivity acknowledges the researcher's personal experiences, backgrounds, and perspectives that they bring to the study.

In grounded theory, researchers aim for a balance between these two. While striving for objectivity helps foster the study's credibility, it's also important to recognize and consider the subjectivity of the researcher. It's this subjectivity that allows the researcher to interpret the data , relate to the participants, and understand the phenomenon in depth. Researchers should be transparent about their assumptions, biases, and preconceptions. Acknowledging these factors not only aids reflexivity but also contributes to the credibility and trustworthiness of the research.

Importance of reflective practice

Reflective practice is a cornerstone of grounded theory methodology. It involves the researcher critically reflecting on their own role in the research process and the impact they may have on the data collection , analysis , and theory formation. Through reflective practice, researchers become more aware of their own assumptions and perspectives and can better understand how these elements might influence their research.

Reflective practice takes place throughout the research process. During data collection, researchers might reflect on their interactions with participants, considering how their questions, demeanor, or reactions might influence the responses. During data analysis, reflective practice helps researchers understand how their preconceptions and interpretations shape the coding and emerging theory. In grounded theory, reflective practice is not a linear step but a continuous process that loops back and forth throughout the research. It's through this reflective practice that researchers can build a comprehensive and nuanced understanding of the phenomenon under study.

Role of the researcher in data collection and analysis

In grounded theory, the researcher is considered the primary tool of data collection and analysis. This is different from quantitative research , where data collection tools are often standardized questionnaires or tests.

As the primary tool of data collection, the researcher is involved in interviewing participants, observing behavior, and gathering documents or other artifacts. The researcher must be skilled in establishing rapport with participants, asking insightful questions, and carefully observing and noting details.

In terms of data analysis , the researcher's intellectual capacity, intuition, and creativity play a crucial role. The process of coding data , recognizing patterns, developing categories , and forming an overarching theory heavily relies on the researcher's analytical skills. Moreover, their ability to critically reflect on their own role and influence in the research process is vital to ensure the study's trustworthiness.

When carefully considering where they stand in any qualitative study, especially in a grounded theory study, the researcher should carefully reflect on their thinking and methods. Reflexivity is a process where researchers continuously evaluate and reflect upon their entire research process and their role within it. Researchers need to be conscious of their potential influence on the research and actively work to verify their conclusions. Maintaining a research diary, where thoughts, ideas, and reflections can be recorded throughout the study, is a common strategy used to promote reflexivity.

Grounded theory has evolved since its original inception by sociologists Barney Glaser and Anselm Strauss in the 1960s. One significant development is constructivist grounded theory, an approach that emphasizes the interpretive aspects of knowledge creation. This approach, most notably propagated by Kathy Charmaz, views research as a co-construction of knowledge between the researcher and the participants. Let's examine the foundations of constructivist grounded theory and the associated constructivist grounded theory methods.

Foundation of constructivist grounded theory

Constructivist grounded theory stems from the philosophical perspective of constructivism, which asserts that reality is socially constructed and subjective. Constructivists believe that people construct their own understanding of the world based on their experiences and interactions. Applying this viewpoint to grounded theory, constructivist grounded theorists argue that researchers and participants co-construct the data and the ensuing analysis. Hence, the researcher is not an objective observer but an active participant in the research process, contributing their interpretations and perspectives .

Key characteristics of constructivist grounded theory

There are several key characteristics that differentiate constructivist grounded theory from its traditional counterpart. These include the emphasis on researcher-participant interaction, the recognition of multiple realities, the focus on interpretive understanding, and the flexible use of grounded theory methods.

• Emphasis on researcher-participant interaction: Constructivist grounded theory acknowledges that data doesn't exist in a vacuum. It's produced through interactions between the researcher and the participant. These interactions are dynamic, context-dependent, and mutually influential, contributing to the co-construction of knowledge.
• Recognition of multiple realities: In line with constructivist philosophy, this approach recognizes the existence of multiple realities. Each participant and the researcher have their unique interpretation of reality, informed by their experiences, values, and social contexts.
• Focus on interpretive understanding: Constructivist grounded theory prioritizes interpretive understanding. Rather than seeking an objective truth, this approach aims to understand how individuals interpret and make sense of their experiences.
• Flexible use of grounded theory methods: While constructivist grounded theory maintains the core grounded theory methods, such as coding and theoretical sampling, it's more flexible in its use. The emphasis is on using these methods as tools to facilitate understanding rather than rigid steps to be followed.

Constructivist grounded theory methods

The process of conducting a constructivist approach to grounded theory study largely mirrors the steps of traditional grounded theory, albeit with a greater emphasis on reflexivity and the interpretive role of the researcher.

Data collection in constructivist grounded theory often involves in-depth interviews , observations , and document analysis , with the researcher actively engaging with the participants to co-construct the data. During the analysis, the researcher remains reflexive about their interpretations and assumptions, constantly checking them against the data .

Coding in constructivist grounded theory still involves open, axial, and selective coding, but the process is more flexible and intuitive. The researcher uses their insights and perspectives to guide the coding process, constantly comparing the data and remaining open to multiple interpretations.

The ultimate goal of constructivist grounded theory is to generate an interpretive theory that makes sense of the participants' experiences and actions. This theory is not seen as a concrete truth but a context-dependent, co-constructed interpretation of the phenomenon under study.

While this section has focused on the philosophical and methodological dimensions of grounded theory research, it's also important to think about what tools might be useful for researchers involved in conducting a grounded theory study. Previously in this guide, we have explored how ATLAS.ti can aid you in coding your data . That said, there are additional tools in qualitative data analysis and especially in ATLAS.ti that can facilitate the grounded theory coding and analysis process.

The axial coding stage of grounded theory, which deals with theoretical development, shifts the focus of data analysis from coding discrete instances of data to drawing connections between those codes. The researcher is responsible for identifying relationships between discrete phenomena that might have otherwise been thought of as unrelated to each other. Without such relationships, there would be no foundation for developing a novel theory relevant to the social world. Moreover, the sorting of knowledge and information cannot be done, nor can scientific knowledge be easily retrieved and understood, without visualizing these networks of social phenomena.

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Grounded Theory Methodology: Principles and Practices

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• Linda Liska Belgrave 2 &
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Since Barney Glaser and Anselm Strauss’ (The discovery of grounded theory: strategies for qualitative research. New York: Adline De Gruyter, 1967) publication of their groundbreaking book, The Discovery of Grounded Theory , grounded theory methodology (GTM) has been an integral part of health social science. GTM allows for the systematic collection and analysis of qualitative data to inductively develop middle-range theories to make sense of people’s actions and experiences in the social world. Since its introduction, grounded theorists working from diverse research paradigms have expanded the methodology and developed alternative approaches to GTM. As a result, GTM permeates multiple disciplines and offers a wide diversity of variants in its application. The availability of many options can, at times, lead to confusion and misconceptions, particularly among novice users of the methodology. Consequently, in this book chapter, we aim to acquaint readers with this qualitative methodology. More specifically, we sort through five major developments in GTM and review key elements, from data collection through writing. Finally, we review published research reflecting these methods, to illustrate their application. We also note the value of GTM for elucidating components of culture that might otherwise remain hidden.

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Qualitative study design: Grounded theory

• Qualitative study design
• Phenomenology

Grounded theory

• Ethnography
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• Case Studies
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Theory development.

Grounded theory proposes that careful observation of the social world can lead to the construction of theory (Rice & Ezzy, 1999). It is iterative and evolving, aiming to construct new theory from collected data that accounts for those data. It is also known as the “grounded theory method”, although the terms have become interchangeable (Bryant & Charmaz, 2007).

Grounded theory characteristics include:

• Data collection and analysis occurring simultaneously, with one informing the other.
• Data grouped into concepts, categories and themes.
• A data collection process influenced by the simultaneous development of those concepts, categories and themes.

Notably, data collection is cyclical and reflective. This is different from the more linear processes occurring in other methodologies.

Theoretical sampling is a key aspect of the sampling stage of grounded theory. Recruitment continues until the sample finally represents all aspects that make up the theory the data represent (Starks & Brown Trinidad, 2007). Participants are recruited based on their different experiences of a phenomenon.

Researchers collect participant data using these methods:

• Examination of documents
• Focus groups and interviews

Focus groups and interviews are typically being more practical in health research than observation (Starks & Brown Trinidad, 2007).

After the initial phase of data collection, researchers repeat the following cycle of steps:

Researchers’ developing understanding of the concepts, categories and relationships informs their actions at each step. These elements result in a theoretical framework explaining the data. 

This cycle reflects two crucial components of grounded theory:

• The process of coding, sorting and organising data. This aims to increasingly move towards more abstract terms in order to develop a related theory for the data
• The principle of constant comparison. This refers to the process of noting issues of interest in data and comparing them to other examples to identify similarities and differences.
• Widely used across a wide range of disciplines (Bryant & Charmaz, 2007).  
• Facilitates theory construction and the construction of fresh concepts. It also avoids assuming structures are stable (Charmaz, 2017). 
• Useful for when researchers wish to explain a process, not to test an existing theory. 

Limitations

• Inherently not useful for the application of received theory. 
• Not useful for testing hypotheses. 
• Analysis of data involves elements of researcher’s own subjective judgement.

Example questions

• How do perioperative nurses foster a culture of safety and risk aversion? 
• What is the impact of hand nerve disorders on a person’s function, activity and participation? 
• What are the barriers to health care access for a refugee population? 

Example studies

Attree, M. (2001). Patients' and relatives' experiences and perspectives of 'Good' and 'Not so Good' quality care . J Adv Nurs , 33(4), 456-466. doi: 10.1046/j.1365-2648.2001.01689.x 

Lingard, L., Reznick, R., Espin, S., Regehr, G., & DeVito, I. (2002). Team communications in the operating room: talk patterns, sites of tension, and implications for novices . Acad Med , 77(3), 232-237. doi: 10.1097/00001888-200203000-00013 

Pettersson, S., Ekstrom, M. P., & Berg, C. M. (2013). Practices of weight regulation among elite athletes in combat sports: a matter of mental advantage? J Athl Train , 48(1), 99-108. doi: 10.4085/1062-6050-48.1.04 

Bryant, A., & Charmaz, K. (2007). The SAGE handbook of grounded theory : SAGE Publications Ltd.

Charmaz, K. (2017). An introduction to grounded theory : SAGE Publications Ltd. 

Lingard, L., Albert, M., & Levinson, W. (2008). Grounded theory, mixed methods, and action research . BMJ , 337, a567. doi: 10.1136/bmj.39602.690162.47 

Rice, P. L., & Ezzy, D. (1999). Qualitative research methods: a health focus . South Melbourne, Australia: Oxford University Press. 

Starks, H., & Brown Trinidad, S. (2007). Choose Your Method: A Comparison of Phenomenology, Discourse Analysis, and Grounded Theory . Qualitative Health Research , 17(10), 1372-1380. doi: 10.1177/1049732307307031   

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10 Grounded Theory Examples (Qualitative Research Method)

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Grounded theory is a qualitative research method that involves the construction of theory from data rather than testing theories through data (Birks & Mills, 2015).

In other words, a grounded theory analysis doesn’t start with a hypothesis or theoretical framework, but instead generates a theory during the data analysis process .

This method has garnered a notable amount of attention since its inception in the 1960s by Barney Glaser and Anselm Strauss (Corbin & Strauss, 2015). 

Grounded Theory Definition and Overview

A central feature of grounded theory is the continuous interplay between data collection and analysis (Bringer, Johnston, & Brackenridge, 2016).

Grounded theorists start with the data, coding and considering each piece of collected information (for instance, behaviors collected during a psychological study).

As more information is collected, the researcher can reflect upon the data in an ongoing cycle where data informs an ever-growing and evolving theory (Mills, Bonner & Francis, 2017).

As such, the researcher isn’t tied to testing a hypothesis, but instead, can allow surprising and intriguing insights to emerge from the data itself.

Applications of grounded theory are widespread within the field of social sciences . The method has been utilized to provide insight into complex social phenomena such as nursing, education, and business management (Atkinson, 2015).

Grounded theory offers a sound methodology to unearth the complexities of social phenomena that aren’t well-understood in existing theories (McGhee, Marland & Atkinson, 2017).

While the methods of grounded theory can be labor-intensive and time-consuming, the rich, robust theories this approach produces make it a valuable tool in many researchers’ repertoires.

Real-Life Grounded Theory Examples

Title: A grounded theory analysis of older adults and information technology

Citation: Weatherall, J. W. A. (2000). A grounded theory analysis of older adults and information technology. Educational Gerontology , 26 (4), 371-386.

Description: This study employed a grounded theory approach to investigate older adults’ use of information technology (IT). Six participants from a senior senior were interviewed about their experiences and opinions regarding computer technology. Consistent with a grounded theory angle, there was no hypothesis to be tested. Rather, themes emerged out of the analysis process. From this, the findings revealed that the participants recognized the importance of IT in modern life, which motivated them to explore its potential. Positive attitudes towards IT were developed and reinforced through direct experience and personal ownership of technology.

Title: A taxonomy of dignity: a grounded theory study

Citation: Jacobson, N. (2009). A taxonomy of dignity: a grounded theory study. BMC International health and human rights , 9 (1), 1-9.

Description: This study aims to develop a taxonomy of dignity by letting the data create the taxonomic categories, rather than imposing the categories upon the analysis. The theory emerged from the textual and thematic analysis of 64 interviews conducted with individuals marginalized by health or social status , as well as those providing services to such populations and professionals working in health and human rights. This approach identified two main forms of dignity that emerged out of the data: “ human dignity ” and “social dignity”.

Title: A grounded theory of the development of noble youth purpose

Citation: Bronk, K. C. (2012). A grounded theory of the development of noble youth purpose. Journal of Adolescent Research , 27 (1), 78-109.

Description: This study explores the development of noble youth purpose over time using a grounded theory approach. Something notable about this study was that it returned to collect additional data two additional times, demonstrating how grounded theory can be an interactive process. The researchers conducted three waves of interviews with nine adolescents who demonstrated strong commitments to various noble purposes. The findings revealed that commitments grew slowly but steadily in response to positive feedback, with mentors and like-minded peers playing a crucial role in supporting noble purposes.

Title: A grounded theory of the flow experiences of Web users

Citation: Pace, S. (2004). A grounded theory of the flow experiences of Web users. International journal of human-computer studies , 60 (3), 327-363.

Description: This study attempted to understand the flow experiences of web users engaged in information-seeking activities, systematically gathering and analyzing data from semi-structured in-depth interviews with web users. By avoiding preconceptions and reviewing the literature only after the theory had emerged, the study aimed to develop a theory based on the data rather than testing preconceived ideas. The study identified key elements of flow experiences, such as the balance between challenges and skills, clear goals and feedback, concentration, a sense of control, a distorted sense of time, and the autotelic experience.

Title: Victimising of school bullying: a grounded theory

Citation: Thornberg, R., Halldin, K., Bolmsjö, N., & Petersson, A. (2013). Victimising of school bullying: A grounded theory. Research Papers in Education , 28 (3), 309-329.

Description: This study aimed to investigate the experiences of individuals who had been victims of school bullying and understand the effects of these experiences, using a grounded theory approach. Through iterative coding of interviews, the researchers identify themes from the data without a pre-conceived idea or hypothesis that they aim to test. The open-minded coding of the data led to the identification of a four-phase process in victimizing: initial attacks, double victimizing, bullying exit, and after-effects of bullying. The study highlighted the social processes involved in victimizing, including external victimizing through stigmatization and social exclusion, as well as internal victimizing through self-isolation, self-doubt, and lingering psychosocial issues.

Hypothetical Grounded Theory Examples

Suggested Title: “Understanding Interprofessional Collaboration in Emergency Medical Services”

Suggested Data Analysis Method: Coding and constant comparative analysis

How to Do It: This hypothetical study might begin with conducting in-depth interviews and field observations within several emergency medical teams to collect detailed narratives and behaviors. Multiple rounds of coding and categorizing would be carried out on this raw data, consistently comparing new information with existing categories. As the categories saturate, relationships among them would be identified, with these relationships forming the basis of a new theory bettering our understanding of collaboration in emergency settings. This iterative process of data collection, analysis, and theory development, continually refined based on fresh insights, upholds the essence of a grounded theory approach.

Suggested Title: “The Role of Social Media in Political Engagement Among Young Adults”

Suggested Data Analysis Method: Open, axial, and selective coding

Explanation: The study would start by collecting interaction data on various social media platforms, focusing on political discussions engaged in by young adults. Through open, axial, and selective coding, the data would be broken down, compared, and conceptualized. New insights and patterns would gradually form the basis of a theory explaining the role of social media in shaping political engagement, with continuous refinement informed by the gathered data. This process embodies the recursive essence of the grounded theory approach.

Suggested Title: “Transforming Workplace Cultures: An Exploration of Remote Work Trends”

Suggested Data Analysis Method: Constant comparative analysis

Explanation: The theoretical study could leverage survey data and in-depth interviews of employees and bosses engaging in remote work to understand the shifts in workplace culture. Coding and constant comparative analysis would enable the identification of core categories and relationships among them. Sustainability and resilience through remote ways of working would be emergent themes. This constant back-and-forth interplay between data collection, analysis, and theory formation aligns strongly with a grounded theory approach.

Suggested Title: “Persistence Amidst Challenges: A Grounded Theory Approach to Understanding Resilience in Urban Educators”

Suggested Data Analysis Method: Iterative Coding

How to Do It: This study would involve collecting data via interviews from educators in urban school systems. Through iterative coding, data would be constantly analyzed, compared, and categorized to derive meaningful theories about resilience. The researcher would constantly return to the data, refining the developing theory with every successive interaction. This procedure organically incorporates the grounded theory approach’s characteristic iterative nature.

Suggested Title: “Coping Strategies of Patients with Chronic Pain: A Grounded Theory Study”

Suggested Data Analysis Method: Line-by-line inductive coding

How to Do It: The study might initiate with in-depth interviews of patients who’ve experienced chronic pain. Line-by-line coding, followed by memoing, helps to immerse oneself in the data, utilizing a grounded theory approach to map out the relationships between categories and their properties. New rounds of interviews would supplement and refine the emergent theory further. The subsequent theory would then be a detailed, data-grounded exploration of how patients cope with chronic pain.

Grounded theory is an innovative way to gather qualitative data that can help introduce new thoughts, theories, and ideas into academic literature. While it has its strength in allowing the “data to do the talking”, it also has some key limitations – namely, often, it leads to results that have already been found in the academic literature. Studies that try to build upon current knowledge by testing new hypotheses are, in general, more laser-focused on ensuring we push current knowledge forward. Nevertheless, a grounded theory approach is very useful in many circumstances, revealing important new information that may not be generated through other approaches. So, overall, this methodology has great value for qualitative researchers, and can be extremely useful, especially when exploring specific case study projects . I also find it to synthesize well with action research projects .

Atkinson, P. (2015). Grounded theory and the constant comparative method: Valid qualitative research strategies for educators. Journal of Emerging Trends in Educational Research and Policy Studies, 6 (1), 83-86.

Birks, M., & Mills, J. (2015). Grounded theory: A practical guide . London: Sage.

Bringer, J. D., Johnston, L. H., & Brackenridge, C. H. (2016). Using computer-assisted qualitative data analysis software to develop a grounded theory project. Field Methods, 18 (3), 245-266.

Corbin, J., & Strauss, A. (2015). Basics of qualitative research: Techniques and procedures for developing grounded theory . Sage publications.

McGhee, G., Marland, G. R., & Atkinson, J. (2017). Grounded theory research: Literature reviewing and reflexivity. Journal of Advanced Nursing, 29 (3), 654-663.

Mills, J., Bonner, A., & Francis, K. (2017). Adopting a Constructivist Approach to Grounded Theory: Implications for Research Design. International Journal of Nursing Practice, 13 (2), 81-89.

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What is Grounded Theory in Qualitative Research?

• April 15, 2022

You may have come across the term “ grounded theory ” in qualitative and quantitative research. Typically, grounded theory is discussed in academic research, though as market researchers, we find that we often use this framework when developing studies. In this post, we’ll try to break grounded theory down for market research usage and help provide an understanding of how impactful this mode of framing research can be for B2B and B2C studies.

First off, what is grounded theory – in real-speak?

Let’s explain grounded theory in non-academic jargon to make this simple to digest:

Use grounded theory methods when you’re not sure what you’re looking for in a study or there is no clear theory as to why certain behaviors or patterns are occurring.

Or to make it even more clear:

Use grounded theory methods when you don’t know what you don’t know.

In typical research methods (both quantitative and qualitative), teams come together with a clear hypothesis about what they’re studying.

For example, “When travelers are booking flights online, they will go for the best prices and flight times.”

That’s a clear hypothesis, likely based on previous data and studies. The research team may be tasked with investigating this hypothesis further and adding more details to it – or even disproving it, to uncover whether there are other factors at play in how people choose and book airline flights. To test the hypothesis, the research team would design a user experience study, where they observe how people book flights online (with screensharing), while asking them questions as the traveler goes through the process. This will help gather essential data that can be analyzed, thematically, to further prove or disprove the initial hypothesis.

However, that’s not what grounded theory would do, because in this case we just described, the hypothesis was set from the beginning.

What if, however, the researchers instead had a situation such as this: A product team wants to understand how people react to working from home exclusively during the pandemic so that they can develop software tools for remote teams.

In this example, the team doesn’t have a clear hypothesis to work from. For this specific case, the study question was posed in early 2020, when working from home for entire teams was new. The pandemic situation was unprecedented in the modern tech age, so the development team wasn’t sure exactly what hypothesis question to pose – or, to put it more simply – they didn’t know what they were looking for exactly, but they did know that there were likely software tools they could develop that could be helpful for remote teams.

This is a perfect example of when to apply grounded theory research. Let’s explore this example further, through the lens of grounded theory.

How to set up a grounded theory study

When you’re not sure exactly what you’re looking for, using grounded theory methods helps you explore themes, in an iterative research style.

So let’s go back to our remote-team software example.

Because the research team wasn’t sure exactly how people were adapting to at-home work, they first assembled a small sample to study. Using mobile ethnographies , the team had a sample of at-home workers record their daily work patterns. They were asked more general questions about highs and lows, efficiencies, and inefficiencies, and where they were feeling frustrated or lost by not having in-person collaboration. They also explored “workarounds” that teams were doing to stay productive.

Once they received the data back and did follow-up in-depth interviews with the participants, the team then sorted the themes into “codes.” Codes essentially sum up patterns in the data that are reoccurring. For example “ workers are less efficient when brainstorming new creative ideas” was a code that came out of the initial round.

From the initial round, some ideas started to take place and patterns emerged. The research team realized they needed to expand their participant pool to also include in-house designers, and not just product managers. The research team then devised a second round of research, also using mobile ethnographies and in-depth interviews, but this time with in-house designers and product managers.

After this second round, even more themes and codes emerged, and the product design team felt like they were getting closer to specific issues that they could develop software to address.

But they needed more data.

After analyzing the second round, the research team decided to hone in on a specific topic: in this case, how to improve brainstorming and enhance the creative process for remote teams. So they developed a third round of research, and they pulled in creative design teams, product managers, and upper-level managers.

The questions the researchers posed in this third round were now quite specific, and they designed exercises around remote creative brainstorming (also using mobile ethnographies and in-depth interviews). This round was especially illuminating because they now were much closer to proving and disproving new hypotheses that had emerged from the initial research rounds.

After analyzing the third round, the product team felt ready to design software prototypes that would address some of the issues they found in the exploratory research phases. In short: They had come up with a hypothesis, which was “Remote teams are struggling to collaborate creatively using their current software.” Now they had a hypothesis (a problem statement) and a mission for their software design work.

Let’s now break down that case study to uncover the steps of grounded theory research

We just took you through a real-world example of using grounded theory research methods to uncover patterns and arrive at a hypothesis. Grounded theory, as you can see from this example, is the opposite of typical research projects, where teams know what they’re looking for, so they recruit participants, design specific questions and exercises, and then spend the bulk of the research proving or disproving the hypothesis they’re testing.

In grounded theory, it’s exploratory, from the very beginning. Teams start with some initial ideas, recruit samples to test, and from the early tests, start to see patterns. They then may have to shift and recruit different personas and start to ask questions specific to the themes from the first round of research. In each subsequent round of research, the team uncovers ideas and then tests and poses hypotheses based on what they’re learning.

Grounded theory is a great method for specific types of research issues

Grounded theory is best applied when research teams come into a problem with uncertainty about the full landscape and situation. Because it requires multiple rounds of research, it’s more costly and time-consuming than studies where the hypothesis and testing is clear, from the very beginning. However, hopefully as the example we used illustrated, it’s a fantastic method to generate new product ideas. The key is to have an open mind and be able to first cast a wide net of ideas, before narrowing down on emerging themes to test.

What is grounded theory? Finding insights in qualitative research

Let’s role-play for a minute

Imagine with me—it’s your first day on the job as a UX designer in a completely new industry. You have your shiny new computer, your LinkedIn is updated, and you’re ready to shake things up.

"Welcome aboard," says the friendly product manager, "Hope you’re ready to roll up your sleeves and disrupt the industry. Let me know if you have any questions before we get started. Oh, and by the way, we have more than 20 engineers waiting for your designs for this upcoming sprint, but no rush..."

Chances are if you’re reading this you’ve experienced a similar situation before. As UX designers, our job is to bridge the needs of our users and the business to create the best experience for both. But how do we do that when we’re starting from scratch and know very little about the domain we’re entering? Who are our users? What are their problems? What are their goals?

Abraham Lincoln says it best:

If I only had an hour to chop down a tree, I would spend the first 45 minutes sharpening my axe.

In this case, UX Research is our axe and the tree is our user experience—the better we understand our users and their problems, the easier it will be to address and anticipate their needs. With this, every great product starts with excellent research.

With UX, there are dozens of research methods for discovery, testing, and validation—but what’s the right methodology (i.e., evaluative, generative, explorative, quantitative, qualitative, etc.) to drive our research method selection? In our case, when you’re in a new domain building out foundational research or looking to gather large amounts of raw data from several data sources, one methodology always sticks out —Grounded Theory.

I stumbled across GT nearly four years ago when I was running a research discovery effort for a large fortune 100 client that needed to “start over.” Their previous research had gone stale and they were losing touch with their users and what problems to solve. I figured GT, with its simple bottom-up approach to data-driven theory generation , would be the perfect methodology for the project.

Hopefully, in this article, you’ll see how Grounded Theory can help you build data-driven theories about your users, their environment, and the phenomena (an observable fact or event) as a whole to inform your customer understandings and design decisions moving forward.

.css-1nrevy2{position:relative;display:inline-block;} What is grounded theory?

'The discovery of theory from data systematically obtained from social research' ( Glaser and Strauss 1967 )

The purpose of GT is to generate theories that emerge from or are ”grounded” in data . It is used to uncover learnings around processes, social relationships, and behaviors of groups.

For example, I work in the insurance industry. When I first entered the industry, I figured price was the driving component behind a business owner’s purchasing behavior. However, after several rounds of interviews, I learned the relationship with the insurance agent is orders of magnitude more important, since the business owner needs to have “peace of mind” that their business is insured appropriately. They wanted a “trusted advisor”, not just an affordable insurance policy. 

With GT, we’re tasked with the goal of finding patterns and categories that might emerge from the data rather than making assumptions (“insurance buyers just want the cheapest policy”). The theory needs to be “grounded” in the data, hence “Grounded Theory”. 

So then, what is considered “data”? Really anything that comes out of a research method (quantitative or qualitative) should be considered data when developing your theories. Primarily, you’ll want your theories steeped in first-party data (from your interviews, feedback sessions, etc.) but feel free to harden your theories with even third-party data like whitepapers or academic resources.

When to use grounded theory

When you need to familiarize yourself with a new domain or topic and you want to be close to the data

When there is a lack of existing theories or research available

If you’re looking to create theories or insights with a mixed-methods approach (qual/quant)

When collecting a large amount of data

Benefits of grounded theory

Great for new projects where discovery and exploratory methods are needed

Produces large amounts of data

Provides a “fresh” perspective on deep and rich data surrounding a given research topic

Helps reduce confirmation bias since everything is rooted in data

Once a researcher/designer has a nuanced level of understanding on a topic, they’re enabled to think divergently and creatively

GT is very flexible and the methods employed can change as the research study progresses

If adopted across the organization, GT supports a structured approach to data analysis

Observations/findings are easily traceable and tightly connected to the source data

Limitations of grounded theory

The process of GT is extremely time consuming and can be difficult to do consistently as more and more data comes in

The methods for data collection and analysis take skill and proper training to perform

With large amounts of data in hand, it can pose problems to manage and analyze consistently

Each research topic has no guaranteed start/end date

Difficulty recruiting for ongoing research

Grounded theory approach

When it comes to GT, a researcher does not just begin with a theory and set out to prove it. Rather, a researcher begins with an area of study and allows relevant data to emerge.

As with most research efforts, GT starts off with a research question—this question helps define the scope (who to talk to and what to ask about) and strategy (what methods to use) around the research topic.

Grounded theory steps

Data Collection

In grounded theory, data collection is exactly the same as traditional qualitative research methods and typically begins with a research question.

After a research question/area of interest has been identified, it’s important to begin your research by taking a few steps back and starting with very broad concepts, more general in your thinking.

Additionally, if you’re familiar with the topic at hand it’s equally important to remove biases and assumptions. To do this effectively, try to adjust your worldview and remove any existing theories you might have so you can evaluate the data with a fresh perspective and allow the world to teach you the words, phrases, and idioms while you’re studying and observing so you can ask the “dumb questions” (“what does that mean? Can you explain…?, etc.”).

Remember, “all is data” so get creative with what data you collect and how.

Sample list of data collection methods

Recorded in-depth interviews

Observational methods

Focus groups

In-app feedback tools

Customer advisory boards

Product analytics

Note-taking

Coding and Tagging (analysis)

Coding (and tagging) can simply be defined as the process of breaking down your data and organizing them by codes (or labels) so you can identify themes and causal relationships between disparate points. 

By its nature, the process of data collection and analysis in grounded theory is quite flexible and can often be done simultaneously. This type of back and forth is called the “zig-zag approach” to collection and analysis where the researcher is continuously refining their “concepts” and “categories” (we’ll define these in a second) on the subject until there is diminishing returns or “theoretical saturation” (when new data comes in and does not lead to refinement of thinking or new ideas). 

When in the data collection phase, thematic analysis tends to go hand-in-hand with grounded theory and is one of my personal favorites when analyzing large amounts of data and setting up a proper UX repository (especially in Dovetail)! 

**End Aside**

When analyzing your data, we need to somehow deconstruct it so we can reassemble it into actionable information or findings—we do this by evaluating our data and organizing our findings into concepts and categories.

“Concepts” are your low-level codes, or put simply, the first layer of data you highlight/ tag that you found important. I once asked my mentor—“what should I code?” and she said, “code and tag anything that you might find interesting and that you might want to share with me if we bumped into each other in the hallway...”. 

It’s quite simple. Anything that you find interesting is meaningful—tag it! 

That’s “concepts” in GT.

Now, “categories” are clustered concepts that have similar ideas (and can come from several data sources). Typically, these are written in the form of insights or themes in my experience.

A key method that’s used in grounded theory is called the “constant comparative” method, which means any findings (concepts, categories, theories, etc.) are constantly compared against each other as new data emerges in order to further refine your interpretations and theories.

Revising data collection methods through theoretical sampling

As with any process of analysis, it’s important to look back and course-correct. How are things going? Are we learning anything new? Are we surprised by the new data coming in or are we receiving similar answers? Are the existing categories and concepts abundantly supported?

In grounded theory, Glaser and Strauss call this process “theoretical sampling”, and it's a way for researchers to identify a category that might need further research and inquiry. 

Glaser and Strauss on theoretical sampling:

“The process of data collection for generating theory whereby the analyst jointly collects, codes, and analyzes his data and decides what data to collect next and where to find them” — Glaser and Strauss (1967)

As you review your concepts and categories over time, your existing research should help direct (or redirect) you as to where to go and what data to collect next.

But how do researchers know when to stop collecting data? 

Strauss calls this moment “theoretical saturation.”

“Theoretical saturation: The point in category development at which no new properties, dimensions, or relationships emerge during analysis”

So when the concepts and categories are theoretically saturated and no new concepts are emerging from the data!

Theory Building

Lastly and most importantly, in order to identify a theory that has emerged from the data, you have to have a deep and nuanced understanding of it—Glaser and Strauss call this “theoretical sensitivity.” 

Theoretical sensitivity simply means that through the ongoing process of data collection, analysis, and even more collection, researchers become more familiar with the data and are able to unearth insights and evaluate relationships between concepts and categories that lead to relevant theories (or insights). 

In grounded theory, most of these theories come from identified categories which are also referred to as “core categories.”

In my experience, writing theories are not as actionable for our needs in the user experience space, therefore, I’ve found that writing your learnings as one of the following can be helpful:

Insight statement

Problem statement

Job-To-Be-Done

Wrapping Up

Wrangling research data and developing theories surrounding your topic can be overwhelming and difficult. As a researcher who also designs, I’m all for making things easier.

Use GT to organize your data and consistently groom it for structured, deliberate, and insightful theories/learnings. The consistency, clearly defined and organized concepts and categories will help you over the long run! Additionally, as a design manager (who’s currently hiring wink wink ) it’s important to have a consistent process among our UX teams when it comes to data collection and analysis, so when we scale our research efforts, we’re not left with a dumping ground of raw data, rather clearly defined piles of dynamite insights!

So the next time you’re poised with a discovery project or enter a new job in a new industry, give Grounded Theory a try and watch the learnings pile up!

This article covers the very basics of grounded theory, if you’re interested in learning more please check out my full course on UX research and a few of my favorite books . You can also reach out to me any time via LinkedIn .

Written by Brennan Martin , User Experience Lead, Manager, Acrisure. Brennan is a UX designer and researcher with over eight years of experience. He’s worked on various mobile and web applications ranging from enterprise B2B to B2B2C and B2C experiences. He’s known for his strong sense of storytelling and his fearless advocacy for his users and their needs. Brennan’s pathfinding in uncertain situations is well tested and highly structured—with his years of experience leading UX strategy, design thinking workshops and driving Atomic UX Research best practices in large organizations, he seeks to deliver high quality insights with measurable impact. When he’s not out in the field researching or testing new designs, you can find him trying out new restaurants with his wife, hiking the amazing national parks all over the US, or mentoring budding designers though one of his UX research courses.

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Qualitative Research for Intervention Development and Evaluation:

• Conducting Research

Qualitative Research

October 2022

• Slides (PDF, 1MB)

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This webinar describes how qualitative evaluation can make a vital contribution to every stage of developing and optimizing an intervention.

This program does not offer CE credit.

Lucy Yardley, PhD

University of Bristol and University of Southampton, UK

More in this series

Presents a handful of key strategies to maintain our mental well-being when trying to help others do the same.

October 2022 On Demand Webinar

Emphasizes the basics of classic grounded theory and shows how the original tenets of the method guide the procedures.

Provides practical guidance to help researchers carry out IPA studies which take advantage of the strengths and potential for flexibility within the approach.

September 2022 On Demand Webinar

Reviews four different strategies for integrating qualitative and quantitative data or results that invite a more instrumental role for a qualitative inquiry in contributing analytical insight.

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The Importance of Qualitative Data Analysis in Research: A Comprehensive Guide

August 29th, 2024

Qualitative data analysis, in essence, is the systematic examination of non-numerical information to uncover patterns, themes, and insights.

This process is crucial in various fields, from product development to business process improvement.

Key Highlights

• Defining qualitative data analysis and its importance
• Comparing qualitative and quantitative research methods
• Exploring key approaches: thematic, grounded theory, content analysis
• Understanding the qualitative data analysis process
• Reviewing CAQDAS tools for efficient analysis
• Ensuring rigor through triangulation and member checking
• Addressing challenges and ethical considerations
• Examining future trends in qualitative research

Introduction to Qualitative Data Analysis

Qualitative data analysis is a sophisticated process of examining non-numerical information to extract meaningful insights.

It’s not just about reading through text; it’s about diving deep into the nuances of human experiences, opinions, and behaviors.

This analytical approach is crucial in various fields, from product development to process improvement , and even in understanding complex social phenomena.

Importance of Qualitative Research Methods

The importance of qualitative research methods cannot be overstated. In my work with companies like 3M , Dell , and Intel , I’ve seen how qualitative analysis can uncover insights that numbers alone simply can’t reveal.

These methods allow us to understand the ‘why’ behind the ‘what’, providing context and depth to our understanding of complex issues.

Whether it’s improving a manufacturing process or developing a new product, qualitative research methods offer a rich, nuanced perspective that’s invaluable for informed decision-making.

Comparing Qualitative vs Quantitative Analysis

While both qualitative and quantitative analyses are essential tools in a researcher’s arsenal, they serve different purposes.

Quantitative analysis, which I’ve extensively used in Six Sigma projects, deals with numerical data and statistical methods.

It’s excellent for measuring, ranking, and categorizing phenomena. On the other hand, qualitative analysis focuses on the rich, contextual data that can’t be easily quantified.

It’s about understanding meanings, experiences, and perspectives.

Key Approaches in Qualitative Data Analysis

Explore essential techniques like thematic analysis, grounded theory, content analysis, and discourse analysis.

Understand how each approach offers unique insights into qualitative data interpretation and theory building.

Thematic Analysis Techniques

Thematic analysis is a cornerstone of qualitative data analysis. It involves identifying patterns or themes within qualitative data.

In my workshops on Statistical Thinking and Business Process Charting , I often emphasize the power of thematic analysis in uncovering underlying patterns in complex datasets.

This approach is particularly useful when dealing with interview transcripts or open-ended survey responses.

The key is to immerse yourself in the data, coding it systematically, and then stepping back to see the broader themes emerge.

Grounded Theory Methodology

Grounded theory is another powerful approach in qualitative data analysis. Unlike methods that start with a hypothesis, grounded theory allows theories to emerge from the data itself.

I’ve found this particularly useful in projects where we’re exploring new territory without preconceived notions.

It’s a systematic yet flexible approach that can lead to fresh insights and innovative solutions.

The iterative nature of grounded theory, with its constant comparison of data, aligns well with the continuous improvement philosophy of Six Sigma .

Content Analysis Strategies

Content analysis is a versatile method that can be both qualitative and quantitative.

In my experience working with diverse industries, content analysis has been invaluable in making sense of large volumes of textual data.

Whether it’s analyzing customer feedback or reviewing technical documentation, content analysis provides a structured way to categorize and quantify qualitative information.

The key is to develop a robust coding framework that captures the essence of your research questions.

Discourse Analysis Approaches

Discourse analysis takes a deeper look at language use and communication practices.

It’s not just about what is said, but how it’s said and in what context. In my work on improving communication processes within organizations , discourse analysis has been a powerful tool.

It helps uncover underlying assumptions, power dynamics, and cultural nuances that might otherwise go unnoticed.

This approach is particularly useful when dealing with complex organizational issues or when trying to understand stakeholder perspectives in depth.

The Qualitative Data Analysis Process

Navigate through data collection, coding techniques, theme development, and interpretation. Learn how to transform raw qualitative data into meaningful insights through systematic analysis.

Data collection methods (interviews, focus groups, observation)

The foundation of any good qualitative analysis lies in robust data collection. In my experience, a mix of methods often yields the best results.

In-depth interviews provide individual perspectives, focus groups offer insights into group dynamics, and observation allows us to see behaviors in their natural context.

When working on process improvement projects , I often combine these methods to get a comprehensive view of the situation.

The key is to align your data collection methods with your research questions and the nature of the information you’re seeking.

Qualitative Data Coding Techniques

Coding is the heart of qualitative data analysis. It’s the process of labeling and organizing your qualitative data to identify different themes and the relationships between them.

In my workshops, I emphasize the importance of developing a clear, consistent coding system.

This might involve open coding to identify initial concepts, axial coding to make connections between categories, and selective coding to integrate and refine the theory.

The goal is to transform raw data into meaningful, analyzable units.

Developing Themes and Patterns

Once your data is coded, the next step is to look for overarching themes and patterns. This is where the analytical magic happens.

It’s about stepping back from the details and seeing the bigger picture. In my work with companies like Motorola and HP, I’ve found that visual tools like mind maps or thematic networks can be incredibly helpful in this process.

They allow you to see connections and hierarchies within your data that might not be immediately apparent in text form.

Data Interpretation and Theory Building

The final step in the qualitative data analysis process is interpretation and theory building.

This is where you bring together your themes and patterns to construct a coherent narrative or theory that answers your research questions.

It’s crucial to remain grounded in your data while also being open to new insights. In my experience, the best interpretations often challenge our initial assumptions and lead to innovative solutions.

Tools and Software for Qualitative Analysis

Discover the power of CAQDAS in streamlining qualitative data analysis workflows. Explore popular tools like NVivo, ATLAS.ti, and MAXQDA for efficient data management and analysis .

Overview of CAQDAS (Computer Assisted Qualitative Data Analysis Software)

Computer Assisted Qualitative Data Analysis Software (CAQDAS) has revolutionized the way we approach qualitative analysis.

These tools streamline the coding process, help manage large datasets, and offer sophisticated visualization options.

As someone who’s seen the evolution of these tools over the past two decades, I can attest to their transformative power.

They allow researchers to handle much larger datasets and perform more complex analyses than ever before.

Popular Tools: NVivo, ATLAS.ti, MAXQDA

Among the most popular CAQDAS tools are NVivo, ATLAS.ti, and MAXQDA.

Each has its strengths, and the choice often depends on your specific needs and preferences. NVivo , for instance, offers robust coding capabilities and is excellent for managing multimedia data.

ATLAS.ti is known for its intuitive interface and powerful network view feature. MAXQDA stands out for its mixed methods capabilities, blending qualitative and quantitative approaches seamlessly.

Ensuring Rigor in Qualitative Data Analysis

Implement strategies like data triangulation, member checking, and audit trails to enhance credibility. Understand the importance of reflexivity in maintaining objectivity throughout the research process.

Data triangulation methods

Ensuring rigor in qualitative analysis is crucial for producing trustworthy results.

Data triangulation is a powerful method for enhancing the credibility of your findings. It involves using multiple data sources, methods, or investigators to corroborate your results.

In my Six Sigma projects, I often employ methodological triangulation, combining interviews, observations, and document analysis to get a comprehensive view of a process or problem.

Member Checking for Validity

Member checking is another important technique for ensuring the validity of your qualitative analysis.

This involves taking your findings back to your participants to confirm that they accurately represent their experiences and perspectives.

In my work with various organizations, I’ve found that this not only enhances the credibility of the research but also often leads to new insights as participants reflect on the findings.

Creating an Audit Trail

An audit trail is essential for demonstrating the rigor of your qualitative analysis.

It’s a detailed record of your research process, including your raw data, analysis notes, and the evolution of your coding scheme.

Practicing Reflexivity

Reflexivity is about acknowledging and critically examining your own role in the research process. As researchers, we bring our own biases and assumptions to our work.

Practicing reflexivity involves constantly questioning these assumptions and considering how they might be influencing our analysis.

Challenges and Best Practices in Qualitative Data Analysis

Address common hurdles such as data saturation , researcher bias, and ethical considerations. Learn best practices for conducting rigorous and ethical qualitative research in various contexts.

Dealing with data saturation

One of the challenges in qualitative research is knowing when you’ve reached data saturation – the point at which new data no longer brings new insights.

In my experience, this requires a balance of systematic analysis and intuition. It’s important to continuously review and compare your data as you collect it.

In projects I’ve led, we often use data matrices or summary tables to track emerging themes and identify when we’re no longer seeing new patterns emerge.

Overcoming Researcher Bias

Researcher bias is an ever-present challenge in qualitative analysis. Our own experiences and preconceptions can inadvertently influence how we interpret data.

To overcome this, I advocate for a combination of strategies. Regular peer debriefing sessions , where you discuss your analysis with colleagues, can help uncover blind spots.

Additionally, actively seeking out negative cases or contradictory evidence can help challenge your assumptions and lead to more robust findings.

Ethical Considerations in Qualitative Research

Ethical considerations are paramount in qualitative research, given the often personal and sensitive nature of the data.

Protecting participant confidentiality, ensuring informed consent, and being transparent about the research process are all crucial.

In my work across various industries and cultures, I’ve learned the importance of being sensitive to cultural differences and power dynamics.

It’s also vital to consider the potential impact of your research on participants and communities.

Ethical qualitative research is not just about following guidelines, but about constantly reflecting on the implications of your work.

The Future of Qualitative Data Analysis

As we look to the future of qualitative data analysis, several exciting trends are emerging.

The increasing use of artificial intelligence and machine learning in qualitative analysis tools promises to revolutionize how we handle large datasets.

We’re also seeing a growing interest in visual and sensory methods of data collection and analysis, expanding our understanding of qualitative data beyond text.

In conclusion, mastering qualitative data analysis is an ongoing journey. It requires a combination of rigorous methods, creative thinking, and ethical awareness.

As we move forward, the field will undoubtedly continue to evolve, but its fundamental importance in research and decision-making will remain constant.

For those willing to dive deep into the complexities of qualitative data, the rewards in terms of insights and understanding are immense.

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Speaker 1: Hey guys, welcome to Grad Coach TV, where we demystify and simplify the oftentimes confusing world of academic research. My name's David, and today I'm chatting to one of our trusted coaches, Alexandra, about five common mistakes students make during their qualitative research analysis. This discussion is based on one of the many, many articles over at the Grad Coach blog. So, if you'd like to learn more about qualitative research analysis, head over to gradcoach.com forward slash blog. Also, if you're looking for a helping hand with your dissertation, thesis or research project, be sure to check out our one-on-one private coaching service, where we hold your hand throughout the research journey, step-by-step. For more information and to book a free consultation, head over to gradcoach.com. Hey, Alexandra, welcome back to the CoachCast. It's really great to have you back on board.

Speaker 2: Hey, David. always a pleasure to be here and happy to talk with you today. So today we are talking about

Speaker 1: five common mistakes students make about qualitative research analysis, and let us just dive into it. The first one that comes up quite frequently is a lack of alignment between the analysis and the golden thread. Alexandra, what am I getting at with this? Yes, so this idea

Speaker 2: of the golden thread, you will hear it in all walks of research, whether it is quantitative, mixed methods and qualitative so really what you want to do and consider for this golden thread are these three fundamental we'll call them puzzle pieces of the research aims the research objectives and the research questions so these are kind of the foundation of your qualitative research study and so how you consider these and you know what you're trying to do and answer and how you're going to do it will then help you determine what methodology you should choose that would be the most appropriate or suitable to answer those questions and this is not particularly easy because there are several different kinds of qualitative methodologies out there but it can have some some positive outcomes or some negative consequences depending on which methodology you choose to answer those aims objectives and questions of your golden thread so that's

Speaker 1: really helpful alexandra maybe you can give us an example or two of where there's alignment or a

Speaker 2: lack of alignment sure so two of the most common methodologies in qualitative research that we see at grad coach or elsewhere are case studies versus grounded theory and so the first thing to keep in mind with any study is that your the methodology that you choose should be the most suitable one to answer those golden thread notions of the aims objectives research questions not the other way around and so for example with the case study the case study should be used if in your golden thread ideas of the aims objectives and research questions you already have some sort of working knowledge of a group or an event and so you're using this case study methodology because it will appropriately answer those foundational aspects of the golden thread on the other hand let's say your research aims or objectives or questions are about something that you really have limited knowledge about or there's scarce research out there and you're wanting to kind of build up a framework or a theory in that case using a methodology like grounded theory would be more suitable. So you can see there with those two examples of case study versus grounded theory, these two methodology should be applied to answer different golden thread foundational aspects.

Speaker 1: That is really helpful, Alexandra. And I know it can seem a little bit overwhelming to think about getting this alignment right. In cases like this, do not necessarily just rely on your own judgment. It can be really helpful to get a friend or someone from your cohort just to take a look through and read of what you are working on. They will be able to help you identify where there is a lack of alignment. For instance, if you ask them to sort of give you the elevator pitch back of what you set out to do, and it is not lining up with your thinking, then maybe it is a good point to sort of identify where those lack of alignments are, and use that to help you sort of address that. But try and do this earlier rather than later. It's definitely going to make your life easier. So our second mistake is making use of a transcription program software without checking the transcripts. Alexandra, why is this such a problem? Yes. So first of all, you know, there

Speaker 2: are programs, an increasing number out there that are cost effective, mostly free, and for the most part accurate things like zoom transcription software otter ai atlas ti and these certainly have a lot of benefits for convenience sake and cost effectiveness however um that's not to say that these programs are perfect because with a lot of ai and other kinds of automated software it does lose that human element that can miss some of the more nuanced or minute pieces of information that are important. So for instance, in my own dissertation research, I had about 100 participants who all verbally reacted to a stimulus. And half of my participants were doing this in English and the other half in French. And each of these were about 30 minutes long, each participant 30 minutes now with qualitative research you know you have to have something to analyze and it's difficult to do that directly from the audio files so what you have to do is transcribe these from audio to text and so i was going through and i was doing these manually myself from about participant 80 i was beyond exhausted and so i decided to use one of these outside services or programs to kind of expedite this, kind of help me. And of course it was convenient. However, when I got the transcripts back, I noticed as I was going through the first few of them, some errors to content, to spelling, different words were showing up where other words had been said actually in the audio files. And as I was going along through the rest of them, I noticed that pretty much all 20 or so of these outside transcribed files had errors. So I ended up having to go back myself regardless and going through them again and fixing them. So this is all to kind of say that even though these programs can be very convenient and cost effective, there are some drawbacks. most of that has to do with kind of content, the words that they miss, spelling, punctuation, grammar, et cetera, et cetera. And you'll oftentimes definitely actually still have to go in and check these for quality and accuracy. This is why it's very important to kind of think about, even though these programs might be convenient, they're never going to replace kind of that human element of being able to really read and understand what's going on, make sure that it matches what was said in the audio files. And so one of the things that you can do if it's not yourself, you should check it yourself, but even go beyond that and ask someone else to check these transcripts for accuracy. Because either if you've used an outside service or program, or if you've done all the transcriptions by yourself, sometimes we miss things. Having someone else, an outside person, an actual person look at these and kind of make sure that they're accurate will not only help you catch potential errors, but in doing so, it kind of promotes the credibility of the transcripts because they're accurate, they're clear, they're actually what was said in the audio files and so sometimes what might be happen if you don't do this having that like human element it can diminish the credibility of the rest of your transcripts if they are accurate because the reader or your marker might say well this one was not accurate so maybe there's some flaws in the other ones as well but beyond that I mean other than the marking your transcriptions this is your this is really your raw data in qualitative analysis and so if you have errors or missing information in your transcripts that were there in the audio files this makes the coding and analysis flawed this puts things in misalignment and as such there's kind of a domino effect of repercussions that can happen if these things aren't transcribed

Speaker 1: accurately. I think that in the same way that in quantitative research your actual data is key to your analysis, it is the same for qualitative. So we really want to make sure we are doing due diligence to assess the quality of the work. That is not to say you cannot use services to help out. It will depend on your type of research as well. For instance, from a business perspective, you might be less interested in the specific nuance of how someone presented an idea compared to a language study. So in cases like that, there is a bit of a cost benefit to consider, but regardless of whether you are using a service or not, getting a second run through of it can be super helpful. And there are a range of services out there that you can use, both in terms of software or human run services. If you are interested in it, we even do it here at Grad Coach. So do take a look for the link down below. So our third mistake that frequently comes up is not specifying what type of coding you are doing in advance of actually jumping into the analysis. Alexandra, why do we need to be aware of what coding type we are using so early in the process?

Speaker 2: This goes back to the idea of making sure that all steps of your research align with the previous one and are justifiable in terms of it makes sense. There's a reason why you're doing what you're doing in the order that you're doing it. And coding is no exception to this. So the reason why coding is so important in qualitative research is that qualitative research is inherently kind of subjective. There is this inherent human interpretation that can happen. And so one of the reasons why it is so important to do coding appropriately is to kind of add the systematicity and the academic rigor to your research that is inherently not there. And so to kind of ensure this increased objectivity of something that is inherently subjective, doing this coding, you need to consider which kind of coding will be the most appropriate to answer your research goals that you've outlined prior, going back to that notion of the golden thread. And coding inherently kind of falls into two camps. There is inductive coding and deductive coding. So on the one hand, inductive coding is an approach where you are going into your data analysis and you are kind of, you're letting the themes and the codes emerge from the data. You don't have any preconceived notions, no existing ideas of what to expect. You're really letting the data, whether it comes from interviews or focus groups, you're letting the data from those transcripts emerge into these codes. And this is best for studies such as grounded theory approaches where you don't really have any idea of what to expect or anticipate. And you're really kind of trying to explore what is out there. You're letting these codes emerge directly from the data. On the other hand, deductive coding is another coding approach where you are actually, you have some ideas about what is out there, what you're looking for, what you hope your final findings to be. And for this coding approach, it's top down where prior to even collect the data, the interviews, focus groups what have you you have developed an initial set of codes into a code book whether you've put this in say Microsoft Excel or Microsoft Word or Google Sheets etc and you have kind of looked through the existing literature on your research topic and seen what what are the potential codes out there what are the themes you're looking for And then once you have collected your data and transcribed it, you're assigning pieces of that data to those codes that you've already created in advance. And you are not looking for new codes to emerge like you did in inductive. So all codes should go into something from your codebook.

Speaker 1: I think deductive coding is most commonly used where you have a theoretical framework that you're working within or a field that is really, really well researched. There, you're not going to be starting something new. Similarly, it's also become really popular to use a mixed approach of inductive and deductive. This is primarily starting deductively with a codebook and using that codebook to lead your coding and then develop further from that with an inductive approach. It is worth noting this is a fairly new way to go about coding, and so it is important that if you are choosing to go this way, that you can justify why it is appropriate and why it is useful relative to that golden thread, those research aims, objectives, and questions. Because you You don't want to be overcomplicating things or stepping too far out of your comfort zone just because it's novel. Rather, make sure it is what you need to do, where you need to do it.

Speaker 2: That's great advice, because sometimes as graduate students, we have this urge to do something novel or do it a different way. And that should not be your motivation or your justification to do something. So even though this this kind of new way is developing and coming and becoming increasingly popular, that doesn't mean that it's right for your study. So how you know it's right for your study is going back to that notion of the golden thread. And this idea extends even beyond inductive and deductive coding, because those are kind of your your starting idea of how you're going to code. Beyond that, there are additional specific approaches that you will use for your initial or your first set of coding versus your second set of coding. As an aside here, you should absolutely do more than one round of coding. Again, this will increase the systematicity, the rigor, and kind of the credibility, so to speak, of your data analysis. and so there are many different specific coding approaches but some of the the most common ones we'll name here are starting with your open coding and so for this one this kind of approach it's very loose it's very tentative as indicated from its name it's open and so this is more suitable when you're starting out other common approaches are things like in vivo coding and so with in vivo coding, this is actually using the participants own words in your analysis, not putting your interpretation of what they said or suggesting what they meant, but actually letting the participants own words do the talking, so to speak. And so this is typically most suitable to things where you're really interested in the perspectives or points of view or experiences of your participants and then the last one we'll mention but there are still plenty more is structural coding and so we use structural coding specifically well not specifically but commonly in cases where you say have conducted an interview or focus group discussion and you want to use those questions that you posed in the interview or the focus group kind of as headings all of the codes that go under one specific column for instance should be related to one specific question that was asked in the data collection and so this is really best if you are kind of looking for specific answers or codes or themes in response to one of your interview questions so or focus group questions so again there are still plenty more out there but these are some of the more common coding approaches.

Speaker 1: That's really helpful, Alexandra. And it can feel a little overwhelming that there are so many options to choose from. Don't worry, there are a ton of resources out there. Definitely take a look at any of your methodological textbooks from a qualitative perspective. You can take a look at methodology papers that have been published, YouTube tutorials, blog posts, you name it, it's out there. We even have some videos and some content about coding as well on the Grad Coach blog. Links to that will be down in the description below. But importantly, when you are considering these coding decisions, it is important to realize again what you are using them for. So look for that alignment, make sure it is on track, and then it will flow much smoother going forward as well. So our fourth common mistake is students downplay the importance of organization during both coding and analysis. How important is organization, Alexandra? It is so important. The reason why

Speaker 2: this is so important is that oftentimes we kind of assume that qualitative research and qualitative data cannot be structured. Of course, it's not as black and white or objective as quantitative research. And so what you need to do as a qualitative research is to kind of apply a framework that yourself that will promote this kind of objectivity, systematicity. And part of this relies on organization. And organization is important not only for the coding, but also the analysis. So part of the difficulty, but the importance of organizing is that sometimes the codes that you end up with after you've transcribed and done your, let's say, initial round of coding, you can end up with very high numbers of codes. For instance, I've seen some where it's upwards of 1000 codes. And so this number is very overwhelming, very large. and some of the ways to tackle this large amount of codes is one to make sure that you're organizing all of your codes in a spreadsheet of sorts whether it's excel or google sheets having them all in one place will then further facilitate you doing additional rounds of coding which we recommended previously and in doing so having these additional rounds of coding on your codes that are organized in one place, it will help you kind of whittle down these codes to the point where you have the codes that you need. There's none that are kind of superfluous or repeated, but it's very important to keep these organized in one place and to go through multiple rounds of coding. And this will make your life a whole lot easier and make sure that you have only the

Speaker 1: codes that you need and can justify. I think that's super helpful. It's also worth emphasizing that coding and organization it's a back and forth you're going to be moving from one to the next and back again and that's a good thing to do it enriches your analysis but it also allows your organization to inform your coding and your coding to inform your organizational structure and through that iterative process you're really going to develop the analysis so don't think I've coded it once, I'm done and dusted. Sorry to say it's a multiple approach. In terms of organization helping analysis, Alexandra, why is it also important to keep a track in that Google document

Speaker 2: or sheet of all your codes? Yeah, so this goes back to that notion we've repeated several times of the golden thread. So if you think of dominoes, for instance, you need to have your dominoes set up in such a way that if you knock one down, the rest go down. We can think of that, our qualitative research in such a way. And so if in the coding stage, everything has aligned with that golden thread and we move on to the analysis, the analysis will be further aligned with the coding, the transcription, the data collection, going back to the research questions, aims and objectives. And so having our codes organized in a sheet will then allow us to start to analyze our codes in a way that we can see themes and patterns emerging that are aligned with the codes, which will then add this rigor and systematicity of your study by having analysis that you know is based on very organized, solid foundations of your coding and your transcription. And so through this analysis, if we have our analysis organized, we can keep track of our patterns, our themes, and then going beyond that, actually, when we get to the point where we're writing our findings chapter, we have this set organization that will then kind of allow us to know how we're going to present these results because everything has been organized and justified up to that point.

Speaker 1: I think that's really helpful. It's also worth noting that having your codebook organized can be really helpful in sort of preventing you from getting stuck with your analysis or feeling like you're unsure of how to code because, you know, things are feeling uncertain. If you have an Excel sheet that you've developed before you start your coding process, you have it organized by the different rounds and you start bringing it from a large number of codes to the specific codes you are going to be using, that organization really helps make that process move forward. And it can be kind of cathartic to really work through that process, get it from a hundred transcripts of 30 minutes each down to some key findings. So our fifth and final mistake that we're covering today is not considering your researcher influence on your analysis. Alexandra, how do we affect our analysis and why is this something that we need to even think about?

Speaker 2: Yeah, so this kind of just goes back to the innate nature of qualitative research. It relies a lot on interpretation. It is subjective. It's not inherently black and white, such as quantitative research. And so the ways that this is kind of mitigated is through things like positionality and reflexivity. So these two concepts are becoming much more prominent and required in qualitative dissertations and theses. And so what these essentially mean is that you have your positionality, which are the underlying kind of beliefs, judgments, opinions, perceptions, all of those things that kind of make you you, the human elements. And so the way that you think about things might be different than the way someone else thinks about them. And so why we need to state our positionality in qualitative research is that it can impact our interpretation of the data, which then impacts the findings. And so, for example, in an example study where someone is exploring the perceptions of the tech industry of men versus women, a researcher who kind of identifies as a feminist versus one who identifies as more conservative or traditional, they might have underlying beliefs or assumptions about gender when it comes to the workplace or just in general. and so acknowledging that that you have these kind of underlying preferences or perspectives what have you it's important to acknowledge that because like i said it can have consequences for your analysis and your findings taking this a step further typically now we also have to to talk about our reflexivity in qualitative research and so essentially what this refers to is how our positionality affects our kind of interpretation so whereas positionality has to do more with the underlying assumptions reflexivity is taking those underlying assumptions and acknowledging how they might actually impact our interpretation and our findings and so the reason oftentimes why these are required now in qualitative studies is that this idea of you know validity and reliability we don't really use those in qualitative research we use more of these ideas of trustworthiness and that connects to our positionality and our reflexivity this reflexivity how it can impact you know it can impact the coding of your data the themes that you pull from the coding how you interpret it how you present it so in my example of the researcher who has more feminist underlying beliefs versus more traditional conservatives even if they're exploring the same phenomenon they can have vastly different interpretations and so acknowledging your positionality and indicating with your reflexivity how it might impact those steps of the research analysis can lend more credibility and more kind of trustworthiness to your your

Speaker 1: findings and ultimately your study. So that's really helpful to think about these aspects because we do need to consider how our positionality and our reflexivity might affect how we proceed with our analysis. There are potential opportunities for bias and if we're engaging in these behaviors we are able to a mitigate them during the analysis and in cases where you cannot mitigate it you can at least acknowledge it so other researchers can interpret that going forward but bias goes a little bit beyond just your positionality and reflexivity so Alexandra what other biases can come up because of research effect yes this idea of bias so going

Speaker 2: further beyond positionality and reflexivity it can be very easy to have biased interpretations and there are a few ways this can manifest so for instance spending too much time presenting the the findings from one particular participant in your study and neglecting those of the others and so one reason why this might happen is either you as the researcher totally agree personally with their perspective or even totally disagree and you want to to present that in um in some for some sort of reason um so it's very important to kind of mitigate that bias by presenting a balanced approach of all participants on the other hand there's also things like spending a lot of time presenting on one particular theme that emerged from your qualitative analysis and you know, kind of avoiding or neglecting the other ones. So this can happen where you found a theme that emerged from your analysis that was particularly interesting to you, whether it was novel, whether it confirmed what you thought, or even aligned with your personal beliefs. It's very important to make sure that you are giving enough attention to all the different themes that have emerged. And a third common bias that we see is that sometimes it can be easy to make claims or assumptions such as this means that or people should do this. So for instance, in my example of the tech industry and gender norms, making claims in your writing such as women in the tech industry felt that, or the way that the women in the tech industry talked means that, or the tech industry should do that. So making those kinds of grand sweeping claims that your qualitative findings mean some sort of big, big thing. We really have to try to avoid that in qualitative writing, despite it being tempting, especially if it aligns with our personal perspective. So, those are some common biases we see.

Speaker 1: I think that is super helpful to think through, particularly because biases are inherent to us. So, it is important to take that step back, to think about how you might interpret, interact with things, and then engage with that. One way to really go back to this is take a look at the data. We do not want to be making statements or assumptions that do not have support in the data. that is just gonna undermine your argument and your position as the researcher. So wherever possible, if you don't have data to support it, maybe consider not including it. If you do have data to support it, maybe just confirm with a second opinion, your supervisor or someone else, just to make sure that there's not bias coming in. But I think the most important part here is to think about the fact that we do have biases. And so as long as we're considering this, we're doing our due diligence as researchers.

Speaker 2: Yeah, and so one of the ways that you can also make sure that you are kind of following what you said you were going to do from the get-go is not to step out of your codes and your themes that you've established. The reason why this might be tempting to do, again, is going back to that fact that maybe you found something super interesting to you and you want to present it. What I would caution you towards is making sure that any findings that you're presenting fit or align with what your objectives, aims, and research questions were. Another reason why this might happen is because the dissertation or the thesis is such a long process, sometimes we can kind of get away from our original intent of our study. And so presenting these things that are outside of our codes or our themes, we think we can get away with but in reality this kind of minimizes the the rigor of of your findings and so even though you might find something very interesting like you said David be really careful make sure that you're still kind of staying within your codes within your themes and following that golden thread that you've been establishing throughout yeah you've

Speaker 1: probably heard it so much today but golden thread is key we want to make sure that we're maintaining alignment with our research. It is only going to improve the impact. So Alexandra, thank you so much for joining us today. It has been really great. There are some great insights here and thank you again for joining us on the CoachCasts. Always a pleasure, David. Thanks so much for

Speaker 2: having me and letting me kind of chat about these qualitative foibles.

Speaker 1: Alright, so that pretty much wraps up this episode of Grad Coach TV. Remember, if you are looking for more information about qualitative research analysis, be sure to check out our blog at gradcoach.com forward slash blog. There you can also get access to our free dissertation and thesis writing mini course that'll give you all the information you need to get started with your research journey. Also, if you're looking for a helping hand with your dissertation, thesis or research project, be sure to check out our one-on-one private coaching service where you can work with one of our friendly coaches, just like Alexandra. For more information and to book a free consultation, head over to gradcoach.com.