qualitative research template analysis

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The Utility of Template Analysis in Qualitative Psychology Research

Joanna brooks.

a University of Huddersfield, Centre for Applied Psychological and Health Research , Institute for Research in Citizenship and Applied Human Sciences , Huddersfield, UK

Serena McCluskey

Emma turley.

b Manchester Metropolitan University , Department of Health Professions , Manchester, UK

Thematic analysis is widely used in qualitative psychology research, and in this article, we present a particular style of thematic analysis known as Template Analysis. We outline the technique and consider its epistemological position, then describe three case studies of research projects which employed Template Analysis to illustrate the diverse ways it can be used. Our first case study illustrates how the technique was employed in data analysis undertaken by a team of researchers in a large-scale qualitative research project. Our second example demonstrates how a qualitative study that set out to build on mainstream theory made use of the a priori themes (themes determined in advance of coding) permitted in Template Analysis. Our final case study shows how Template Analysis can be used from an interpretative phenomenological stance. We highlight the distinctive features of this style of thematic analysis, discuss the kind of research where it may be particularly appropriate, and consider possible limitations of the technique. We conclude that Template Analysis is a flexible form of thematic analysis with real utility in qualitative psychology research.

Introduction

Thematic analysis has for a long time held a rather uncertain place in qualitative psychology. On the one hand, it has often been treated rather dismissively as an approach that is rather simplistic and rather shallow. On the other, it is used extensively, both as an integral part of popular methodologies such as Interpretative Phenomenological Analysis (IPA) and Grounded Theory, and as a method in its own right. Braun and Clarke’s 2006 article in this journal played an important role in advocating the latter position, and provided guidelines for one particular style of analysis. However, as they acknowledge and as others have argued (e.g., King & Horrocks 2010 ), there exist multiple ways of doing thematic analysis. These alternatives tend not to be well-known to qualitative psychologists, as in many cases they have developed in other disciplines; examples include Matrix Analysis (Miles & Huberman 1994 ; Nadin & Cassell 2004 ) and Framework Analysis (Ritchie & Spencer 1994 ). It is our contention that broadening awareness of different ways to analyze data thematically can only be helpful to qualitative psychologists. In this article we present a style of thematic analysis known as Template Analysis, which has been widely used in organizational and management research, as well as across other disciplines, but is not prominent in qualitative psychology. We outline the technique and consider its epistemological position, before presenting three case studies of projects to illustrate the diverse ways in which it may usefully be employed. In the conclusion we highlight what we feel are the distinctive features of this style of thematic analysis, discuss the kind of research where it may be particularly appropriate, and consider possible limitations of the technique. This article is primarily intended for psychologists who may not be very familiar with this particular form of thematic analysis. Nonetheless, by demonstrating its use in different settings and from different methodological approaches, it offers potential new insights for those who already have some experience with Template Analysis.

What Is Template Analysis?

Template Analysis is a form of thematic analysis which emphasises the use of hierarchical coding but balances a relatively high degree of structure in the process of analysing textual data with the flexibility to adapt it to the needs of a particular study. Central to the technique is the development of a coding template, usually on the basis of a subset of data, which is then applied to further data, revised and refined. The approach is flexible regarding the style and the format of the template that is produced. Unlike some other thematic approaches to data coding, it does not suggest in advance a set sequence of coding levels. Rather, it encourages the analyst to develop themes more extensively where the richest data (in relation to the research question) are found. Equally, Template Analysis does not insist on an explicit distinction between descriptive and interpretive themes, nor on a particular position for each type of theme in the coding structure. The data involved in Template Analysis studies are usually interview transcripts (e.g., Goldschmidt et al. 2006 ; Lockett et al. 2012 ; Slade, Haywood & King 2009 ; Thompson et al. 2010 ) but may be any kind of textual data, including focus groups (e.g., Kirkby-Geddes, King & Bravington 2013 ; Brooks 2014 ), diary entries (e.g., Waddington & Fletcher 2005 ), and open-ended question responses on a written questionnaire (e.g., Dornan, Carroll & Parboosingh 2002 ; Kent 2000 ). The main procedural steps in carrying out Template Analysis are outlined below (these are described in more detail in King 2012 ).

Become familiar with the accounts to be analyzed. In a relatively small study, for example, ten or fewer hour-long interviews, it would be sensible to read through the data set in full at least once. In a larger study, the researcher may select a subset of the accounts (e.g., transcripts, diary entries, daily field notes) to start.
Carry out preliminary coding of the data. This is essentially the same process as used in most thematic approaches, where the researcher starts by highlighting anything in the text that might contribute toward his or her understanding. However, in Template Analysis, it is permissible (though not obligatory) to start with some a priori themes, identified in advance as likely to be helpful and relevant to the analysis. These are always tentative, and may be redefined or removed if they do not prove to be useful for the analysis at hand.
Organize the emerging themes into meaningful clusters, and begin to define how they relate to each other within and between these groupings. This will include hierarchical relationships, with narrower themes nested within broader ones. It may also include lateral relationships across clusters. Themes which permeate several distinct clusters are sometimes referred to as “integrative themes.” For example, in a study of the experience of diabetic renal disease, King et al. ( 2002 ) identified “stoicism” and “uncertainty” as integrative themes because these aspects of experience tended to infuse much of the discussion whatever the foreground issue.
Define an initial coding template. It is normal in Template Analysis to develop an initial version of the coding template on the basis of a subset of the data rather than carrying out preliminary coding and clustering on all accounts before defining the thematic structure. For example, in a study consisting of 20 face-to-face interviews, the researcher might carry out the steps described above on five of the interviews, and at that point draw together the initial template. The exact point at which it is appropriate to construct the initial template will vary from study to study and cannot be prescribed in advance—the researcher needs to be convinced that the subset selected captures a good cross-section of the issues and experiences covered in the data as a whole. This is usually facilitated by selecting initial accounts to analyse that are as varied as possible.
Apply the initial template to further data and modify as necessary. The researcher examines fresh data and where material of potential relevance to the study is identified, he or she considers whether any of the themes defined on the initial template can be used to represent it. Where existing themes do not readily “fit” the new data, modification of the template may be necessary. New themes may be inserted and existing themes redefined or even deleted if they seem redundant. Rather than reorganising the template after every new account examined, it is common to work through several accounts noting possible revisions and then construct a new version of the template. Thus in our hypothetical example at the previous stage, the researcher might take the initial template constructed from analysis of the first five interviews and apply it to another three transcripts, after which he or she would produce a revised version. This iterative process of trying out successive versions of the template, modifying and trying again can continue for as long as seems necessary to allow a rich and comprehensive representation of the researcher’s interpretation of the data. Of course, very often practical constraints of time and resource may limit the number of iterations possible, but the analysis should not leave any data of clear relevance to the study’s research question uncoded.
Finalize the template and apply it to the full data set. In some respects it should be said that there is never a “final” version of the template, in that continued engagement with the data can always suggest further refinements to coding. On a pragmatic basis, though, the researcher needs to decide when the template meets his or her needs for the project at hand, and considering the resources available. A good rule of thumb is that development of a template cannot be seen as sufficient if there remain substantial sections of data clearly relevant to the research question(s) that cannot been coded to it. It is always possible to revisit the template if further analysis is required; for example, a template that may work well to help the interpretation of data for an evaluation report might require considerable refinement to produce an analysis that informs an academic journal article (see Kirkby-Geddes, King & Bravington 2013 for an example).

Epistemological Position of Template Analysis

The epistemological position of thematic analysis can be problematic. It is not uncommon to see research articles at the review stage (and sometime in print) that claim to have used a specific methodology such as IPA or Grounded Theory, but where even a cursory examination shows that the basic theoretical underpinnings of such approaches have not been adhered to. Instead, the researchers have taken the thematic analysis procedures associated with a methodology and used them in a way that is not in keeping with the methodology as a whole. An example would be studies “using” IPA, where theoretical ideas from mainstream psychology (or elsewhere) have been used quite prominently in shaping the analysis. While the use of existing theory in, for example, the design of an IPA study need not conflict with the basic tenets of the approach (e.g., Brooks, King & Wearden 2013 ), IPA is principally focused on individual’s experience (Smith, Flowers & Larkin 2009 ) and codes are generated from the data using a “bottom up” approach. Approaching analysis with the intention of imposing existing theory or concepts on the data would thus be out of keeping with an IPA approach. Using methods such as IPA or grounded theory in a manner not in keeping with the approach often reflects a confusion between methodology and data analytical method, but may also be due to discomfort with a “mere” thematic analysis, devoid of inherent philosophical position. We would concur with Braun and Clarke’s ( 2006 ) position that thematic analysis methods can be applied across a range of theoretical and epistemological approaches: what is important is that researchers using thematic analysis methods are explicit and upfront about their particular epistemological assumptions.

Similarly, Template Analysis is not inextricably bound to any one epistemology; rather, it can be used in qualitative psychology research from a range of epistemological positions. The flexibility of the technique allows it to be adapted to the needs of a particular study and that study’s philosophical underpinning. Template Analysis can thus be used in research taking a similar realist position to mainstream quantitative psychology, concerned with “discovering” underlying causes of human action and particular human phenomena. When used in this way, one could expect to see the use of strong, well-defined a priori themes in analysis, and concerns with reliability and validity prioritised and addressed. In contrast, Template Analysis can also be used within what Madill, Jordan and Shirley ( 2000 ) have termed a “contextual constructivist” position (p. 9), a stance which assumes that there are always multiple interpretations to be made of any phenomenon, and that these depend upon the position of the researcher and the specific social context of the research. A researcher using Template Analysis from this position would be likely to take a bottom-up approach to template development, using a priori themes far more tentatively, if at all, in the development of their template. Additionally, and somewhere between these two approaches, Template Analysis can also be used in research adopting a “subtle realist” approach (e.g., Hammersley 1992 ), a position which acknowledges that a researcher’s perspective is inevitably influenced by his or her inability to truly stand outside one’s own position in the social world, but nonetheless retains a belief in phenomena that are independent of the researcher and knowable through the research process. Such an approach can thus make claims as to the validity of a representation arising from research while recognizing that other perspectives on the phenomenon are possible. The applicability of Template Analysis taking a social constructionist approach, with its radical relativist epistemology, is more questionable. Certainly, it is not suited to those social constructionist methodologies that are concerned with the fine detail of how language constructs social reality in interaction, such as discursive psychology and conversation analysis. However, some social constructionist work does seek to look at text in a broader manner and may use thematic types of analysis (e.g., Taylor & Ussher 2001 ; Budds, Locke & Burr 2013 ); there is no reason why Template Analysis should not be considered for this kind of analysis, though it would be crucial to be clear that themes were defined in terms of aspects of discourse rather than representations of personal experience.

How Template Analysis Relates to Other Forms of Thematic Analysis

We are sometimes asked how Template Analysis differs from, or is similar to, “thematic analysis.” This is not really a meaningful question because thematic analysis is not a particular approach in and of itself; rather, it is a broad category of approaches to qualitative analysis that seek to define themes within the data and organise those themes into some type of structure to aid interpretation. A more sensible question is therefore how Template Analysis relates to other forms of thematic analysis. One main distinction is between those thematic approaches that are incorporated within a specific methodology and its philosophical assumptions, such as Grounded Theory or IPA, and those that do not have such a commitment. Template Analysis clearly falls into the latter category, alongside such techniques as Braun and Clarke’s ( 2006 ) version of thematic analysis, Framework Analysis (Ritchie & Spencer 1994 ), and many others. We will summarize here some of the key similarities and differences between Template Analysis and these two approaches in particular.

Template Analysis shares with Braun and Clark’s approach flexibility and a focus on developing a hierarchical coding structure. They differ in three main ways. Firstly, in Braun and Clark development of themes and creation of a coding structure take place after initial coding of all the data. In Template Analysis it is normal to produce an initial version of the template on the basis of a sub-set of the data. Secondly, in Braun and Clark’s methods, defining themes is a late phase of the process. In Template Analysis the researchers often produce theme definitions at the initial template stage, to guide further coding and template development. Thirdly, while Braun and Clark do not specify limits to the number of levels of coding, studies using their approach typically have only one or two levels of sub-themes. Template Analysis commonly uses four or more levels to capture the richest and most detailed aspects of the data.

Template Analysis has much in common with Framework Analysis; they can be seen as having evolved in parallel to address many of the same needs. Both are examples of what Crabtree and Miller (1992) refer to as “codebook” approaches, where a coding structure is developed from a mixture of a priori interests and initial engagement with the data and then applied to the full data set. The most notable difference between them is that Template Analysis is more concerned with providing detailed guidance on the development of the coding structure than Framework Analysis, and less concerned with delineating techniques to aid in the interpretation of the data once fully coded. Studies using Framework Analysis do not typically show the depth of coding we see in Template Analysis, and while the need to modify the framework in the course of analysis is recognized (e.g., Gale et al. 2013 ), in general the iterative (re-) development of the coding structure is a much more central aspect in Template Analysis. Conversely, the emphasis in Framework Analysis on reducing the data through “charting” and identifying patterns by “mapping” is not an essential part of Template Analysis. These differences in emphasis to a large extent represent the different disciplinary backgrounds of the techniques and those involved in their evolution. Framework Analysis was developed by health services researchers specifically for use in health policy research contexts. Template Analysis has strong roots in organisational research and is probably used in a wider variety of research settings than Framework Analysis. It also tends to be employed rather more in experientially focused studies than is Framework Analysis, reflecting the input of experientially orientated psychologists in its development.

We feel that it is crucial that researchers are not precious about “their” ways of working with thematic analysis. For novice researchers, the detailed guidance provided by the likes of Braun and Clarke ( 2006 ), Richie and Spencer ( 1994 ), and the current authors can be very helpful in steering them through the rocks and shoals of qualitative analysis. For those with more experience, it is entirely appropriate that they should draw selectively on such sources to define an approach that suits the needs of their own study. This is one of the reasons we refer to Template Analysis as a “style” of thematic analysis; in the next section we offer examples to illustrate different ways in which this style can be put into practice.

Examples of Research Using Template Analysis

To illustrate how Template Analysis can be used in qualitative psychology research, we will now describe three rather different examples of research undertaken by the authors, all of which successfully used the technique. We focus on the analysis stage of each project to illustrate how Template Analysis may be used and adapted to meet the particular needs of a specific research project. At the end of each example, we have included a text box highlighting the main challenge in using Template Analysis in the particular case and how we addressed it. Beyond these examples, Template Analysis has been used in a wide range of other settings including education, clinical psychology and sports science (King 2012 provides more detail and references).

Case Study 1. Collaborative working in cancer care: An example of a large-scale project using a team analysis approach

Our first example is taken from a large scale three year research project, undertaken by the first and last author and colleagues (King, Melvin, Brooks, Wilde & Bravington 2013 ). The focus of this research, which was funded by Macmillan Cancer Support, was an exploration of how different health care professionals in the United Kingdom work together to provide supportive and palliative care to patients with cancer and other long-term chronic health conditions. The main research aims were: (i) to examine how specialist and generalist nurses work with each other and with other professionals, carers, and patients in providing supportive and palliative care to cancer patients; and (ii) to explore how these professional practices and relationships might differ in the care of long-term condition patients. The theoretical position of this research drew on symbolic interactionist views of the professions (e.g., Macdonald 1995 ) and constructivist psychological approaches to the person (e.g., Butt 1996 ). These traditions emphasize that professional roles and identities are not fixed aspects of social structures, but are defined through the ways in which individual professionals interact with the social world they inhabit. This does not mean that nurses (in the case of the present study) are free to construe roles and identities in whatever way they please; they are inevitably influenced by the views of professional bodies, their interactions with nursing and other professional colleagues, and of course the expectations of patients.

There were a number of implications for the research arising from this theoretical orientation. First, it suggested the need to examine the social context of particular groups of nurses in order to understand the circumstances that shape the way they understand their roles and identities. Second, it placed relationships and interactions at work at the center of our research agenda. Third, given the complexity of the ways in which people understand their roles and relationships, it necessitated a methodological approach that was flexible and able to examine nurses’ experiences in depth. Our methodology was therefore one that was centrally concerned with the way different groups of nurses involved in the supportive care of cancer and long-term condition patients worked with and understood each other’s—and their own—roles and professional identities.

The research utilized a novel interview tool, the Pictor technique (King Bravington, Brooks et al. 2013 ), to explore networks of care and support in health and social care. The technique requires the research participant to choose a case of collaborative working in which they are, or have been, involved (in interviews with lay people this means their own case as patient, or that of the person they care for). Participants are provided with a set of arrow-shaped Post It notes, and asked to label these to represent all those involved in a particular case, then lay the notes out on a large sheet of paper in a manner that helps them tell the story of their case. The Pictor chart produced by the participant serves as the basis for the researcher to explore and reflect upon the case with the participant. Figure 1 shows an example of a Pictor chart from this study. Our primary focus in this study was on nursing staff, in both the acute (secondary care) and community (primary care) sectors, but we also interviewed a wide range of other health and social care professionals, and some patients and carers.

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Example of a ‘Pictor’ chart.

Our total sample size numbered 79 participants, with most interviews lasting between 45 minutes and an hour and a quarter, making this a large scale project in qualitative research terms. Our research team comprised four academic psychologists and a nurse professional, and we adopted a team approach to data analysis and template development. A priori codes were defined through discussion, in the light of the stated aims of the project and through drawing on key issues emerging from previous research and policy literature in this area (including several of our own previous studies). For example, our a priori codes included broad themes identified as a priority by the research funder (e.g., issues around the concept of “survivorship”); themes determined by our research aims (e.g., comparisons between cancer and long-term conditions); and themes derived from our previous research findings (e.g., perceptions of a particular community nursing role, that of Community Matron; see King et al. 2010 ). An initial template ( Figure 2 ) was developed through group analysis of early interviews undertaken with participants from different professional groups. Over the next eight months, and in parallel with on-going data collection, the research team met at regular intervals to analyze further interviews, again using interview data from different participant groups. We have found this kind of collaborative working strategy valuable, as the process necessitates clear agreement and justification for the inclusion of each code, and a clear definition of its use.

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Case study 1 - initial template.

Decisions about when a template is “good enough” are unique to each particular project and will inevitably face pragmatic external constraints. In this project, at version 8 of our template, and after group analysis of 25 interviews, all research team members were in agreement that the template covered all sections of text thus far encountered adequately and was likely to require no more than minimal modifications. The remaining interview transcripts were analysed individually by team members. Our group sessions of data analysis meant that all members of the research team had a good understanding of the template, which was, given the size and complexity of the final version, a great advantage when coding at these later stages. Coding was undertaken using the qualitative research software NVivo, which allowed for the coding of any data which appeared to be important but which was not accounted for in the template to be coded under a “free node.” Any such additions were reviewed at our on-going regular research team meetings, and agreements reached as to whether and where to make revisions to the template.

In our final version of the template we had four top level themes: (1) what affects collaborative working, (2) condition-specific involvement, (3) survivorship, and (4) current National Health Service (NHS) re-organization. Given that there are fewer top level themes than in our initial version ( Figure 2 ), it might appear that the template has been shortened and simplified. Not so: in fact, with closer examination of the data, and continual discussion amongst the team, the coding has significantly increased in depth, representing increased discrimination and clarity in our thinking about the data. Figure 3 shows the first top level theme (What affects collaborative working?) for the final version of our template. Depth, rather than breadth, of coding allows fine distinctions to be made in key areas: having too many top level themes may make it hard for a researcher to draw together the analysis as a whole. Figure 2 shows the entire initial template, including top level themes and all lower level coding. In contrast, Figure 3 shows just the first top level theme and the lower level coding associated with this one theme from our final template. Our final version template was a nine A4 page document, in comparison to our version 1 template, which, in contrast, covered just one and a half sides of A4.

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Case study 1 - version 8 template (top theme 1 only shown).

Findings from this research particularly highlighted the role of relational issues in collaborative working both between generalist and specialist nurses, and between different professionals from health and social care sectors. Rather than seeing relational issues as one among a number of important factors, our work suggests that relationships and relating are the core of collaborative working, and that as such, all those concerned with this phenomenon—researchers, practitioners, and policy makers—should view collaborative working through a relational lens and think carefully about the impact on relational aspects of collaborative working in the design and implementation of any change in services. We also drew attention in our findings to the striking difference between nursing services available to patients in the community depending on their particular illness condition, suggesting that there was seemingly a gap in support for cancer survivors within community-based services. Full findings are reported in King, Melvin, et al. ( 2013 ). The key challenge for using Template Analysis in this study, and our solutions to it, are shown in Figure 4 .

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Case Study 2. Patient and significant other beliefs about back pain and work participation: An example of a study using existing theory in template development

Our second example used Template Analysis in a rather different way to meet the needs of the particular research project. The focus of this study (undertaken by the first, second, and last authors and colleagues) (McCluskey et al. 2011 ) was an exploration of work participation outcomes in patients with chronic low back pain, a leading cause of work disability in the United Kingdom (Health and Safety Executive 2007 ).

An emerging body of research has suggested that beliefs about illness (illness perceptions) are important influences on clinical and work outcomes for those with back pain (e.g., Foster et al. 2008 ; Main, Foster & Buchbinder 2010 ; Hoving et al. 2010 ; Giri et al. 2009 ). One theoretical model which has been widely established as a useful framework through which to explore illness perceptions is the Common- Sense model of self-regulation of health and illness (the CSM; e.g., Leventhal, Nerenz & Steele 1984 ). According to the CSM, illness perceptions (also known as illness beliefs, illness cognitions or illness representations) are patients’ own implicit, common-sense beliefs about their illness, and guide the way an individual responds to and manages their condition. Illness perceptions are categorised by the CSM into five core dimensions: illness identity (including symptoms and label), perceived cause, expectations about timeline (how long the illness is expected to last), consequences of the illness and beliefs about curability and control (Leventhal, Meyer & Nerenz 1980 ; Leventhal et al. 1984 ). The CSM holds that cognitive and emotional representations of illness exist in parallel. The two types of representations are proposed to result in differing behaviors and coping procedures with cognitive representations leading to problem-based coping and emotional representations to emotion-focused coping procedures (Moss-Morris et al. 2002). Illness perceptions have been acknowledged to determine coping style, treatment compliance, and emotional impact in a wide range of physical and mental health conditions (for a review see Hagger & Orbell 2003 ).

Most of the research which has undertaken using the CSM in the context of back pain has used quantitative measures to elicit individuals’ illness beliefs (e.g., Foster et al. 2008 ). In response to calls in the literature for more qualitative research to provide better insight into psychosocial obstacles to recovery and work participation for those with back pain (Nicholas 2010 ; Wynn & Money 2009 ), we undertook an exploratory interview study using the CSM to ask patients about their back pain in relation to work participation outcomes (see McCluskey et al. 2011 ). A novel aspect to this research was our inclusion of those close to patients—their “significant others”—to allow for an exploration of the wider social influences on work participation for those with back pain. Despite the body of empirical evidence documenting the role that significant others have on individual pain outcomes (e.g., Boothby et al. 2004 ; Leonard, Cano & Johansen 2006 ; Stroud et al. 2006 ), they are rarely included in work participation research. We interviewed five dyads (five patients and their significant others) about the patients’ back pain. A well validated quantitative measure of illness perceptions (the revised Illness Perceptions Questionnaire [IPQ-R]; Moss-Morris et al. 2002) was used as a guide to construct a semi-structured interview schedule, and we also used the scales which make up the IPQ-R as a priori themes to organise our initial template.

The IPQ-R provides a quantitative self-report assessment of the components delineated in the CSM, and additionally includes an assessment of emotional responses to illness (“emotional representations”) and an assessment of the extent to which individuals believe they have a clear understanding of their condition (“illness coherence”). The timeline dimension is divided into two subscales: acute/ chronic and cyclical (assessing whether or not patients believe their condition to be of a cyclical nature). The cure/ control dimension is also divided into two subscales: personal control (measuring the degree to which respondents believe they have the ability to control their condition themselves) and treatment control (measuring the degree to which respondents believe treatment is effective in controlling their condition). Our aim in this study was to build on existing mainstream theory, and we used the IPQ-R subscales as clear, strong a priori themes with which to design our initial coding template. We thus began our analysis using nine a priori themes (illness identity; beliefs about causality; beliefs about timeline (acute/chronic); beliefs about timeline (cyclical); beliefs about consequences of back pain; beliefs about personal control of back pain; beliefs about treatment control of back pain; emotional responses to condition; illness coherence). Our final coding template was comprised of six themes derived from the IPQ-R constructs (illness identity; beliefs about causality; expectations about timeline; perceived consequences of back pain; control, management and treatment of back pain; emotional responses to illness). Two additional higher level themes also emerged in our final template: (1) Patient identity—“claimant as genuine” and (2) role of significant others—“influence of/ impact on significant others.” The final template is shown in Figure 5 .

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Case study 2 - final version template.

Patient identity emerged as an important aspect for both patient and significant other respondents. Findings from this research suggest that there is a danger that patients who feel unable to stay in or return to their previous employment may adopt a very limiting “disabled” identity as a protection from socio-cultural scepticism about their condition, and derogatory rhetoric about “benefits scroungers.” Such a strategy for defending the self may lead to a vicious circle whereby the patient focuses on what s/he cannot do, restricts activity further, and exacerbates the condition making it even less likely they will be able to return to work (McCluskey et al. 2011 ; Brooks et al. 2013 ). In the CSM framework, “illness identity” pertains to the specific symptoms associated by a patient with an illness, ideas about the label given to an illness, and beliefs about its nature. Our “patient identity” theme highlighted the ways in which an individual’s self-perception may additionally play an important role in their formulation of a model for symptoms, and their subsequent behavioural responses. Equally as important, such beliefs were shared and sometimes further reinforced by significant others. Our analysis supported the CSM as a useful framework to explore beliefs about illness, but was additionally able to incorporate some of the ways in which other factors and wider social circumstances, may impact on work outcomes for patients with chronic back pain. The key challenge for using Template Analysis in this study, and our solutions to it, are shown in Figure 6 .

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Case Study 3. The erotic experience of BDSM participation: An example of a study using Template Analysis from an interpretive phenomenological stance

Our final example is taken from the third author’s research (Turley 2012 ). Broadly, this study aimed to investigate the lived experience of practitioners of consensual bondage, discipline, dominance & submission, and sadism & masochism (BDSM). Using a variety of innovative techniques to explore these experiences, the project successfully combined mixed phenomenological methods across two stages of empirical work. Template Analysis, when used within a broadly phenomenological perspective, has some clear similarities with IPA (e.g., Smith et al. 2009 ), another methodology widely used in qualitative research in psychology. There are two key features which differentiate between the approaches. Firstly, the idiographic focus of IPA and its detailed case-by-case analysis of individual transcripts maintaining the individual as a unit of analysis in their own right, mean that IPA studies are usually based on small samples. Template Analysis studies often (though not always) have rather more participants, and focus more on across case rather than within case analysis. Secondly, in IPA, codes are generated from the data rather than using pre-existing knowledge or theory that might be applied to the data set. In Template Analysis, the use of a priori codes allows the researcher to define some themes in advance of the analysis process.

In the first stage of this work, a descriptive phenomenological approach was used to explore the experiences of a sample of practitioners of consensual BDSM about their experiences (Turley, King & Butt 2011 ). A description of what was specifically erotic about BDSM participation was found to be noticeably absent from participants’ accounts. The second stage of this work, which employed Template Analysis to elucidate the specific constituents of BDSM that held erotic significance for participants, will be the focus of discussion in the present article. In this stage of the empirical work, semi-structured interviews were conducted with nine participants, four of whom had taken part in the first stage of the project while five were new recruitments to the research. The detail and depth of data collected produced a rich and complex final template, reflecting the experiences of practitioners of consensual BDSM.

Procedurally, this research differs from the other examples presented in this article in a number of ways. As is often the case when using Template Analysis, a set of a priori themes were selected as a focus for the initial template. However, rather than drawing on current theory or existing literature, salient findings from the first stage of this work were used to inform the selection of pertinent a priori themes. This was in part due to the paucity of previous research literature addressing eroticism and BDSM. Importantly, it was also a reflection of the phenomenological stance underpinning this work, and ensured analysis was grounded firmly in participants’ own accounts rather than presuppositions about the topic. Recognising that a phenomenological analysis requires an open attitude towards data, the researcher opted for a few broad a priori themes so as to avoid overlooking new and important aspects of the data that did not explicitly relate to those themes elicited from earlier work. This allowed for iterative redefinition of the themes, which were composed of the following: (1) the relationships between those involved in the BDSM scenes, (2) anticipation, (3) fantasy, and (4) authenticity. The author’s intention was to remain sensitive to these thematic areas, whilst simultaneously maintaining an open phenomenological stance. The interpretive phenomenological approach used in this research does not subscribe to the epochē, the philosophical notion that one can completely suspend presuppositions and judgements of a topic under study, viewing the phenomenon as if for the first time (Husserl 1931 ). The author used the technique of bracketing and self-reflection, endeavoring to remain open to participants’ accounts and making a determined effort to step outside of any personal and taken-for-granted views about the participants, their experiences and BDSM more generally. Through a process of critical self-reflection, enabling the author to identify, clarify, and reflect on her own perceptions of the research participants and the details of their experiences, the author was also able to critically examine how these perceptions might influence the analysis.

A further procedural difference between this study and other examples of work using Template Analysis relates to the development of the initial template (shown in Figure 7 ). It is common practice in studies utilizing Template Analysis to code a subset of data and to use this coding in the development of an initial template. At first, the intention was to use the original four participants’ transcripts to develop the template. However, the author became increasingly concerned that using only this data would make it more difficult to approach the remaining transcripts with a truly open phenomenological attitude. She therefore carried out preliminary coding of the entire data set, resulting in an initial template which is rather more comprehensive than might usually be expected (see Figure 7 ).

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Case study 3 - initial template.

The initial template was used to code each interview transcript. The template was developed through the coding process to represent new emergent themes to segments of text, and modified so that existing themes were edited to include new material. A system using different colored self-adhesive notes was useful here, making it easier to move and reorganize themes in order to modify and develop the template. As reflected on above, the initial template in this study was more comprehensive than is often the case in Template Analysis. One potential danger in producing a very detailed initial template is that researchers may, further on in the analysis process, be unwilling to modify or alter the template structure. The ability to easily add, remove and alter the position of the adhesive notes representing themes in this analysis helped overcome any potential reluctance to make any significant modifications and made it easy to reorganise or reclassify themes when required. Each time a change was made to the template, the previous coding was adjusted to incorporate this. The final template is illustrated in Figure 8 , and demonstrates the extent of the modifications made through this process. Some themes from the initial template ( Figure 7 ) have been deleted completely, whilst others have been modified by broadening or narrowing a theme, or through re-classification of the theme’s hierarchical level. For example, the initial template contains the higher-order theme “temporality”; through development and modification of the template, this theme eventually became a lower order theme encapsulated within the higher order theme of “the qualities of BDSM participation,” which had itself been modified from its initial incarnation of “participants” understanding of their BDSM experience. The theme of “fantasy and reality” was reclassified from a higher order theme and became subsumed into the higher-order theme of “co-creation of fantasy world” as a lower order theme. Modifications to the templates were made on the basis of how best to capture and encompass all of the important elements of participants’ experiences of BDSM, and there were five versions of the developing template between the initial and final template. Through the various modifications of the template, and in line with her phenomenological approach to this work, the author endeavored to maintain an open attitude of discovery toward emerging themes in the data, ensuring that the structure of the template did not become fixed too early in the process of coding and analysis.

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Case study 3 – final template.

Findings from both this and the first stage of this research reveal the complexity of BDSM, illustrated by subtle variations in the erotic scripts of participants. The co-creation of fantasy and the notion of authenticity were fundamental to the experience, which along with a sense of care, trust, and partnership, were vital in order to achieve the erotic atmosphere—the latter concepts appearing contrary to the kinds of sexual activity involved. While traditionally conceptualized as pathological by researchers taking an external perspective, this work successfully employed a phenomenological approach to contribute to an increasing body of work researching BDSM from a nonpathologizing perspective. The key challenge for using Template Analysis in this study, and our solutions to it, are shown in Figure 9 .

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Conclusions

In this concluding section, we highlight what we feel are the main distinctive features of Template Analysis, and discuss how these may be advantageous in qualitative psychological research. We also reflect on possible limitations of the technique.

As exemplified by the three very different case studies presented in this article, Template Analysis is a highly flexible approach that can be readily modified for the needs of a particular study. The method can be adapted to fit different research topics and the available resources of a particular study. Additionally, Template Analysis can be used from a range of different epistemological and methodological positions. Compared with some other methods of qualitative data analysis, Template Analysis may offer a more flexible technique with fewer specified procedures, allowing researchers to tailor the approach to the requirements of their own project. Template Analysis may offer a suitable alternative to qualitative psychology researchers who find that other methods of analysis come with prescriptions and procedures which are difficult to reconcile with features of their own study.

A particular feature of Template Analysis is its use of a priori themes, allowing researchers to define some themes in advance of the analysis process. The case studies presented in this article demonstrate how a priori themes can be usefully employed in different ways in diverse areas of research: ensuring focus on key areas potentially relevant to a study, building on existing theory, and developing ideas in linked pieces of research. Although not a requirement of the method, the use of a priori themes can be particularly advantageous in qualitative psychology research with particular applied concerns which need to be incorporated into the analysis. A priori themes are equally subject to redefinition or removal as any other theme should they prove ineffective at characterising the data. However, the selective and judicious use of a priori themes can allow researchers to capture important theoretical concepts or perspectives that have informed the design and aims of a study, or to address practical concerns such as evaluation criteria that a research project has been designed to address.

There are potential limitations to the approach which should be acknowledged. The focus in Template Analysis is typically on across case rather than within case analysis, the result of which is unavoidably some loss of holistic understanding in relation to individual accounts. This is a limitation of any thematic approach to qualitative data analysis, and a problem which manifests more evidently in studies employing larger sample sizes. While recognizing this as a principally inescapable limitation, our response to this in our own work has often been to combine cross-case analysis with the more idiographic—the sagacious use of individual case summaries to illustrate a line of reasoning can successfully achieve this.

While the flexibility of Template Analysis is one of the method’s acknowledged strengths, the tractable process of developing an initial template and of the template structure itself may feel less secure for relatively inexperienced qualitative researchers than the kind of clear progression described in other forms of thematic analysis which explicitly advocate moving from the descriptive to the interpretive and finally toward overarching themes. Without such explicit instruction to keep initial coding purely descriptive, there is a danger that researchers can rush too far in the direction of abstraction in interpretation. However, in our extensive experience of teaching Template Analysis to novice researchers, we have not encountered many who have found the method’s flexible approach to coding structure problematic. A more prevalent difficulty we have observed in novice researchers using Template Analysis is the danger of losing sight of the original research project aims, and focusing on the constructed template as an end product, rather than a means to an end. It is essential to remember that template development is intended as a way of making sense of data, and not the purpose of the analysis in and of itself.

Overall though, we would suggest that Template Analysis offers a clear, systematic, and yet flexible approach to data analysis in qualitative psychology research. The flexibility of the coding structure in Template Analysis allows researchers to explore the richest aspects of data in real depth. The principles of the method are easily grasped, and the discipline of producing a template forces the researcher to take a systematic and well-structured approach to data handling. The use of an initial template followed by the iterative process of coding means that the method is often less time-consuming than other approaches to qualitative data analysis. Iterative use of the template encourages careful consideration of how themes are defined and how they relate to one another. This approach lends itself well to group or team analysis, and working in this way ensures a careful focus on elaborate coding structures as the team collaboratively define meanings and structure. Template analysis can additionally handle larger data sets rather more comfortably than some other methods of qualitative data analysis although it can also be used with small sample sizes and has been used in the analysis of a single autobiographical case (King 2008 ).

A common feature of qualitative psychological research is the extensive and often complex data it produces. How a researcher or research team move from this mass of data to produce an understanding of their research participants’ experiences depends upon their choice of data analysis technique. There is no one “best” method of data analysis in qualitative psychology research, with choices regarding analysis determined by broader theoretical assumptions underpinning the work and the research question itself. Nonetheless, we hope that in this article we have demonstrated that Template Analysis is, in our experience, one method which can have real utility in diverse areas of qualitative psychology research settings.

Biographies

Joanna Brooks is a Senior Research Fellow in the Centre for Applied Psychological and Health Research at the University of Huddersfield, and a committee member and honorary treasurer of the Qualitative Methods in Psychology section of the British Psychological Society. Her primary research interests focus on applied research topics in health and education settings, usually around chronic health conditions. Jo has a special interest in issues relating to significant others such as family members and close peers, and in using qualitative research methodologies.

Serena McCluskey is a Senior Research Fellow in the Centre for Applied Psychological & Health Research at the University of Huddersfield. She has considerable experience researching the psychosocial influences on health and illness, and her primary interests are focused around work, health and wellbeing. Serena is currently developing an area of research exploring the role of the family in sickness absence and work disability.

Emma Turley is a senior lecturer in psychology at Manchester Metropolitan University. Emma is interested in gender, sexualities and erotic minorities, particularly BDSM, and the ways that these are understood and experienced from a non-pathologising perspective. Other specialist areas of interest include qualitative research methods, especially phenomenological psychology and experiential research, and the use of innovative data collection techniques.

Nigel King is Professor in Applied Psychology, Director of the Centre for Applied Psychological and Health Research and Co-Director of the Institute for Research in Citizenship and Applied Human Sciences at the University of Huddersfield. He is a committee member of the Qualitative Methods in Psychology section of the British Psychological Society. He has a long standing interest in the use of qualitative methods in “real world” research, especially in community health and social care settings, and he is well-known for his publications in this field and his development of innovative research methods. His research interests include professional identities and interprofessional relations in health and social care, psychological aspects of contact with nature, and ethics in qualitative research.

Funding Statement

The “Unpicking the Threads” study presented in Case Study 1 was funded by a grant from Macmillan Cancer Support. The research presented in Case Study 2 was supported by grants from BackCare, NHS Blackburn with Darwen, and the Bupa Foundation.

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Template Analysis

Welcome to the template analysis website.

This website, designed by Professor Nigel King , provides a resource for those using an approach to qualitative data analysis known as ‘template analysis’ (TA). Whether you are new to this kind of analysis or a veteran qualitative researcher, it is hoped the website will be of use to you. This resource has been designed to be of relevance to researchers from any social scientific discipline. Much of the content uses examples from organizational and health psychology, but by contributing to the resource's Facebook page you can explore template analysis in relation to your own discipline.

Professor Nigel King

Applied Psychology, Department of Behavioural Sciences

'I am a Professor in Applied Psychology in the Department of Behavioural Sciences at the University of Huddersfield. Having written widely on innovation and change in organisations, my recent empirical work has focused on primary healthcare settings. However, my interests and publications also explore the experience of chronic illness, professional identities in health and social care, and paranormal beliefs and experiences. I am particularly interested in phenomenology and its implications for psychology. My long-standing interest in qualitative approaches in psychology draws together these diverse strands.'

Introduction

Professor Nigel King has taught and written about qualitative methods in psychology and allied disciplines for over 10 years, and has used them extensively in his own research. Whilst there is a large quantity of literature on thematic approaches in general, there is relatively little specifically on the use of the template style, the main sources being literature by Crabtree and Miller and Nigel.

This website has been developed to help answer queries on template analysis and to create a space for researchers to discuss and explore the use of template analysis in relation to their work. This material shows how template analysis can not only be used to introduce people to qualitative data analysis, but also to carry out more sophisticated and complex research.

Visit the FAQs section for further information.

What is template analysis?

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The Utility of Template Analysis in Qualitative Psychology Research

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1 University of Huddersfield, Centre for Applied Psychological and Health Research, Institute for Research in Citizenship and Applied Human Sciences , Huddersfield , UK.
2 Manchester Metropolitan University, Department of Health Professions , Manchester , UK.
PMID: 27499705
PMCID: PMC4960514
DOI: 10.1080/14780887.2014.955224

Keywords: Pictor technique; Template Analysis; a priori themes; applied research; group data analysis; qualitative research; thematic analysis.

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DOI: 10.1080/14780887.2014.955224
Corpus ID: 6249522

The Utility of Template Analysis in Qualitative Psychology Research

J. Brooks , S. McCluskey , +1 author N. King
Published in Qualitative Research in… 2 September 2014

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Celebrations amongst challenges: considering the past, present and future of the qualitative methods in psychology section of the british psychology society, template analysis in business and management research, qualitative research methodology: a neo-empiricist perspective, criteria for qualitative methods in human reliability analysis, lived experiences of a community: merging interpretive phenomenology and community-based participatory research, what are interviews for a qualitative study of employment interview goals and design, using template and matrix analysis, explaining “explaining” : a phenomenological study of meaning making, analyzing the effects of incivility beyond workplaces, one size fits all what counts as quality practice in (reflexive) thematic analysis, 58 references, objectivity and reliability in qualitative analysis: realist, contextualist and radical constructionist epistemologies., interpretative phenomenological analysis: theory, method and research, using the framework method for the analysis of qualitative data in multi-disciplinary health research, gossip and emotion in nursing and health-care organizations., 'it started when i barked once when i was licking his boots' : a phenomenological study of the experience of bondage, discipline, dominance & submission, and sadism & masochism (bdsm), making sense of s&m: a discourse analytic account, what will hatch a constructivist autobiographical account of writing poetry, a meta-analytic review of the common-sense model of illness representations, using a semi-structured interview to explore imagery experienced during social anxiety for clients with a diagnosis of psychosis: an exploratory study conducted within an early intervention for psychosis service., the influence of 'significant others' on persistent back pain and work participation: a qualitative exploration of illness perceptions.

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Practical thematic...

Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

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on behalf of the Coproduction Laboratory
1 Dartmouth Health, Lebanon, NH, USA
2 Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
3 Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
4 Jönköping Academy for Improvement of Health and Welfare, School of Health and Welfare, Jönköping University, Jönköping, Sweden
5 Highland Park, NJ, USA
6 Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
Correspondence to: C H Saunders catherine.hylas.saunders{at}dartmouth.edu
Accepted 26 April 2023

Qualitative research methods explore and provide deep contextual understanding of real world issues, including people’s beliefs, perspectives, and experiences. Whether through analysis of interviews, focus groups, structured observation, or multimedia data, qualitative methods offer unique insights in applied health services research that other approaches cannot deliver. However, many clinicians and researchers hesitate to use these methods, or might not use them effectively, which can leave relevant areas of inquiry inadequately explored. Thematic analysis is one of the most common and flexible methods to examine qualitative data collected in health services research. This article offers practical thematic analysis as a step-by-step approach to qualitative analysis for health services researchers, with a focus on accessibility for patients, care partners, clinicians, and others new to thematic analysis. Along with detailed instructions covering three steps of reading, coding, and theming, the article includes additional novel and practical guidance on how to draft effective codes, conduct a thematic analysis session, and develop meaningful themes. This approach aims to improve consistency and rigor in thematic analysis, while also making this method more accessible for multidisciplinary research teams.

Through qualitative methods, researchers can provide deep contextual understanding of real world issues, and generate new knowledge to inform hypotheses, theories, research, and clinical care. Approaches to data collection are varied, including interviews, focus groups, structured observation, and analysis of multimedia data, with qualitative research questions aimed at understanding the how and why of human experience. 1 2 Qualitative methods produce unique insights in applied health services research that other approaches cannot deliver. In particular, researchers acknowledge that thematic analysis is a flexible and powerful method of systematically generating robust qualitative research findings by identifying, analysing, and reporting patterns (themes) within data. 3 4 5 6 Although qualitative methods are increasingly valued for answering clinical research questions, many researchers are unsure how to apply them or consider them too time consuming to be useful in responding to practical challenges 7 or pressing situations such as public health emergencies. 8 Consequently, researchers might hesitate to use them, or use them improperly. 9 10 11

Although much has been written about how to perform thematic analysis, practical guidance for non-specialists is sparse. 3 5 6 12 13 In the multidisciplinary field of health services research, qualitative data analysis can confound experienced researchers and novices alike, which can stoke concerns about rigor, particularly for those more familiar with quantitative approaches. 14 Since qualitative methods are an area of specialisation, support from experts is beneficial. However, because non-specialist perspectives can enhance data interpretation and enrich findings, there is a case for making thematic analysis easier, more rapid, and more efficient, 8 particularly for patients, care partners, clinicians, and other stakeholders. A practical guide to thematic analysis might encourage those on the ground to use these methods in their work, unearthing insights that would otherwise remain undiscovered.

Given the need for more accessible qualitative analysis approaches, we present a simple, rigorous, and efficient three step guide for practical thematic analysis. We include new guidance on the mechanics of thematic analysis, including developing codes, constructing meaningful themes, and hosting a thematic analysis session. We also discuss common pitfalls in thematic analysis and how to avoid them.

Summary points

Qualitative methods are increasingly valued in applied health services research, but multidisciplinary research teams often lack accessible step-by-step guidance and might struggle to use these approaches

A newly developed approach, practical thematic analysis, uses three simple steps: reading, coding, and theming

Based on Braun and Clarke’s reflexive thematic analysis, our streamlined yet rigorous approach is designed for multidisciplinary health services research teams, including patients, care partners, and clinicians

This article also provides companion materials including a slide presentation for teaching practical thematic analysis to research teams, a sample thematic analysis session agenda, a theme coproduction template for use during the session, and guidance on using standardised reporting criteria for qualitative research

In their seminal work, Braun and Clarke developed a six phase approach to reflexive thematic analysis. 4 12 We built on their method to develop practical thematic analysis ( box 1 , fig 1 ), which is a simplified and instructive approach that retains the substantive elements of their six phases. Braun and Clarke’s phase 1 (familiarising yourself with the dataset) is represented in our first step of reading. Phase 2 (coding) remains as our second step of coding. Phases 3 (generating initial themes), 4 (developing and reviewing themes), and 5 (refining, defining, and naming themes) are represented in our third step of theming. Phase 6 (writing up) also occurs during this third step of theming, but after a thematic analysis session. 4 12

Key features and applications of practical thematic analysis

Step 1: reading.

All manuscript authors read the data

All manuscript authors write summary memos

Step 2: Coding

Coders perform both data management and early data analysis

Codes are complete thoughts or sentences, not categories

Step 3: Theming

Researchers host a thematic analysis session and share different perspectives

Themes are complete thoughts or sentences, not categories

Applications

For use by practicing clinicians, patients and care partners, students, interdisciplinary teams, and those new to qualitative research

When important insights from healthcare professionals are inaccessible because they do not have qualitative methods training

When time and resources are limited

Steps in practical thematic analysis

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We present linear steps, but as qualitative research is usually iterative, so too is thematic analysis. 15 Qualitative researchers circle back to earlier work to check whether their interpretations still make sense in the light of additional insights, adapting as necessary. While we focus here on the practical application of thematic analysis in health services research, we recognise our approach exists in the context of the broader literature on thematic analysis and the theoretical underpinnings of qualitative methods as a whole. For a more detailed discussion of these theoretical points, as well as other methods widely used in health services research, we recommend reviewing the sources outlined in supplemental material 1. A strong and nuanced understanding of the context and underlying principles of thematic analysis will allow for higher quality research. 16

Practical thematic analysis is a highly flexible approach that can draw out valuable findings and generate new hypotheses, including in cases with a lack of previous research to build on. The approach can also be used with a variety of data, such as transcripts from interviews or focus groups, patient encounter transcripts, professional publications, observational field notes, and online activity logs. Importantly, successful practical thematic analysis is predicated on having high quality data collected with rigorous methods. We do not describe qualitative research design or data collection here. 11 17

In supplemental material 1, we summarise the foundational methods, concepts, and terminology in qualitative research. Along with our guide below, we include a companion slide presentation for teaching practical thematic analysis to research teams in supplemental material 2. We provide a theme coproduction template for teams to use during thematic analysis sessions in supplemental material 3. Our method aligns with the major qualitative reporting frameworks, including the Consolidated Criteria for Reporting Qualitative Research (COREQ). 18 We indicate the corresponding step in practical thematic analysis for each COREQ item in supplemental material 4.

Familiarisation and memoing

We encourage all manuscript authors to review the full dataset (eg, interview transcripts) to familiarise themselves with it. This task is most critical for those who will later be engaged in the coding and theming steps. Although time consuming, it is the best way to involve team members in the intellectual work of data interpretation, so that they can contribute to the analysis and contextualise the results. If this task is not feasible given time limitations or large quantities of data, the data can be divided across team members. In this case, each piece of data should be read by at least two individuals who ideally represent different professional roles or perspectives.

We recommend that researchers reflect on the data and independently write memos, defined as brief notes on thoughts and questions that arise during reading, and a summary of their impressions of the dataset. 2 19 Memoing is an opportunity to gain insights from varying perspectives, particularly from patients, care partners, clinicians, and others. It also gives researchers the opportunity to begin to scope which elements of and concepts in the dataset are relevant to the research question.

Data saturation

The concept of data saturation ( box 2 ) is a foundation of qualitative research. It is defined as the point in analysis at which new data tend to be redundant of data already collected. 21 Qualitative researchers are expected to report their approach to data saturation. 18 Because thematic analysis is iterative, the team should discuss saturation throughout the entire process, beginning with data collection and continuing through all steps of the analysis. 22 During step 1 (reading), team members might discuss data saturation in the context of summary memos. Conversations about saturation continue during step 2 (coding), with confirmation that saturation has been achieved during step 3 (theming). As a rule of thumb, researchers can often achieve saturation in 9-17 interviews or 4-8 focus groups, but this will vary depending on the specific characteristics of the study. 23

Data saturation in context

Braun and Clarke discourage the use of data saturation to determine sample size (eg, number of interviews), because it assumes that there is an objective truth to be captured in the data (sometimes known as a positivist perspective). 20 Qualitative researchers often try to avoid positivist approaches, arguing that there is no one true way of seeing the world, and will instead aim to gather multiple perspectives. 5 Although this theoretical debate with qualitative methods is important, we recognise that a priori estimates of saturation are often needed, particularly for investigators newer to qualitative research who might want a more pragmatic and applied approach. In addition, saturation based, sample size estimation can be particularly helpful in grant proposals. However, researchers should still follow a priori sample size estimation with a discussion to confirm saturation has been achieved.

Definition of coding

We describe codes as labels for concepts in the data that are directly relevant to the study objective. Historically, the purpose of coding was to distil the large amount of data collected into conceptually similar buckets so that researchers could review it in aggregate and identify key themes. 5 24 We advocate for a more analytical approach than is typical with thematic analysis. With our method, coding is both the foundation for and the beginning of thematic analysis—that is, early data analysis, management, and reduction occur simultaneously rather than as different steps. This approach moves the team more efficiently towards being able to describe themes.

Building the coding team

Coders are the research team members who directly assign codes to the data, reading all material and systematically labelling relevant data with appropriate codes. Ideally, at least two researchers would code every discrete data document, such as one interview transcript. 25 If this task is not possible, individual coders can each code a subset of the data that is carefully selected for key characteristics (sometimes known as purposive selection). 26 When using this approach, we recommend that at least 10% of data be coded by two or more coders to ensure consistency in codebook application. We also recommend coding teams of no more than four to five people, for practical reasons concerning maintaining consistency.

Clinicians, patients, and care partners bring unique perspectives to coding and enrich the analytical process. 27 Therefore, we recommend choosing coders with a mix of relevant experiences so that they can challenge and contextualise each other’s interpretations based on their own perspectives and opinions ( box 3 ). We recommend including both coders who collected the data and those who are naive to it, if possible, given their different perspectives. We also recommend all coders review the summary memos from the reading step so that key concepts identified by those not involved in coding can be integrated into the analytical process. In practice, this review means coding the memos themselves and discussing them during the code development process. This approach ensures that the team considers a diversity of perspectives.

Coding teams in context

The recommendation to use multiple coders is a departure from Braun and Clarke. 28 29 When the views, experiences, and training of each coder (sometimes known as positionality) 30 are carefully considered, having multiple coders can enhance interpretation and enrich findings. When these perspectives are combined in a team setting, researchers can create shared meaning from the data. Along with the practical consideration of distributing the workload, 31 inclusion of these multiple perspectives increases the overall quality of the analysis by mitigating the impact of any one coder’s perspective. 30

Coding tools

Qualitative analysis software facilitates coding and managing large datasets but does not perform the analytical work. The researchers must perform the analysis themselves. Most programs support queries and collaborative coding by multiple users. 32 Important factors to consider when choosing software can include accessibility, cost, interoperability, the look and feel of code reports, and the ease of colour coding and merging codes. Coders can also use low tech solutions, including highlighters, word processors, or spreadsheets.

Drafting effective codes

To draft effective codes, we recommend that the coders review each document line by line. 33 As they progress, they can assign codes to segments of data representing passages of interest. 34 Coders can also assign multiple codes to the same passage. Consensus among coders on what constitutes a minimum or maximum amount of text for assigning a code is helpful. As a general rule, meaningful segments of text for coding are shorter than one paragraph, but longer than a few words. Coders should keep the study objective in mind when determining which data are relevant ( box 4 ).

Code types in context

Similar to Braun and Clarke’s approach, practical thematic analysis does not specify whether codes are based on what is evident from the data (sometimes known as semantic) or whether they are based on what can be inferred at a deeper level from the data (sometimes known as latent). 4 12 35 It also does not specify whether they are derived from the data (sometimes known as inductive) or determined ahead of time (sometimes known as deductive). 11 35 Instead, it should be noted that health services researchers conducting qualitative studies often adopt all these approaches to coding (sometimes known as hybrid analysis). 3

In practical thematic analysis, codes should be more descriptive than general categorical labels that simply group data with shared characteristics. At a minimum, codes should form a complete (or full) thought. An easy way to conceptualise full thought codes is as complete sentences with subjects and verbs ( table 1 ), although full sentence coding is not always necessary. With full thought codes, researchers think about the data more deeply and capture this insight in the codes. This coding facilitates the entire analytical process and is especially valuable when moving from codes to broader themes. Experienced qualitative researchers often intuitively use full thought or sentence codes, but this practice has not been explicitly articulated as a path to higher quality coding elsewhere in the literature. 6

Example transcript with codes used in practical thematic analysis 36

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Depending on the nature of the data, codes might either fall into flat categories or be arranged hierarchically. Flat categories are most common when the data deal with topics on the same conceptual level. In other words, one topic is not a subset of another topic. By contrast, hierarchical codes are more appropriate for concepts that naturally fall above or below each other. Hierarchical coding can also be a useful form of data management and might be necessary when working with a large or complex dataset. 5 Codes grouped into these categories can also make it easier to naturally transition into generating themes from the initial codes. 5 These decisions between flat versus hierarchical coding are part of the work of the coding team. In both cases, coders should ensure that their code structures are guided by their research questions.

Developing the codebook

A codebook is a shared document that lists code labels and comprehensive descriptions for each code, as well as examples observed within the data. Good code descriptions are precise and specific so that coders can consistently assign the same codes to relevant data or articulate why another coder would do so. Codebook development is iterative and involves input from the entire coding team. However, as those closest to the data, coders must resist undue influence, real or perceived, from other team members with conflicting opinions—it is important to mitigate the risk that more senior researchers, like principal investigators, exert undue influence on the coders’ perspectives.

In practical thematic analysis, coders begin codebook development by independently coding a small portion of the data, such as two to three transcripts or other units of analysis. Coders then individually produce their initial codebooks. This task will require them to reflect on, organise, and clarify codes. The coders then meet to reconcile the draft codebooks, which can often be difficult, as some coders tend to lump several concepts together while others will split them into more specific codes. Discussing disagreements and negotiating consensus are necessary parts of early data analysis. Once the codebook is relatively stable, we recommend soliciting input on the codes from all manuscript authors. Yet, coders must ultimately be empowered to finalise the details so that they are comfortable working with the codebook across a large quantity of data.

Assigning codes to the data

After developing the codebook, coders will use it to assign codes to the remaining data. While the codebook’s overall structure should remain constant, coders might continue to add codes corresponding to any new concepts observed in the data. If new codes are added, coders should review the data they have already coded and determine whether the new codes apply. Qualitative data analysis software can be useful for editing or merging codes.

We recommend that coders periodically compare their code occurrences ( box 5 ), with more frequent check-ins if substantial disagreements occur. In the event of large discrepancies in the codes assigned, coders should revise the codebook to ensure that code descriptions are sufficiently clear and comprehensive to support coding alignment going forward. Because coding is an iterative process, the team can adjust the codebook as needed. 5 28 29

Quantitative coding in context

Researchers should generally avoid reporting code counts in thematic analysis. However, counts can be a useful proxy in maintaining alignment between coders on key concepts. 26 In practice, therefore, researchers should make sure that all coders working on the same piece of data assign the same codes with a similar pattern and that their memoing and overall assessment of the data are aligned. 37 However, the frequency of a code alone is not an indicator of its importance. It is more important that coders agree on the most salient points in the data; reviewing and discussing summary memos can be helpful here. 5

Researchers might disagree on whether or not to calculate and report inter-rater reliability. We note that quantitative tests for agreement, such as kappa statistics or intraclass correlation coefficients, can be distracting and might not provide meaningful results in qualitative analyses. Similarly, Braun and Clarke argue that expecting perfect alignment on coding is inconsistent with the goal of co-constructing meaning. 28 29 Overall consensus on codes’ salience and contributions to themes is the most important factor.

Definition of themes

Themes are meta-constructs that rise above codes and unite the dataset ( box 6 , fig 2 ). They should be clearly evident, repeated throughout the dataset, and relevant to the research questions. 38 While codes are often explicit descriptions of the content in the dataset, themes are usually more conceptual and knit the codes together. 39 Some researchers hypothesise that theme development is loosely described in the literature because qualitative researchers simply intuit themes during the analytical process. 39 In practical thematic analysis, we offer a concrete process that should make developing meaningful themes straightforward.

Themes in context

According to Braun and Clarke, a theme “captures something important about the data in relation to the research question and represents some level of patterned response or meaning within the data set.” 4 Similarly, Braun and Clarke advise against themes as domain summaries. While different approaches can draw out themes from codes, the process begins by identifying patterns. 28 35 Like Braun and Clarke and others, we recommend that researchers consider the salience of certain themes, their prevalence in the dataset, and their keyness (ie, how relevant the themes are to the overarching research questions). 4 12 34

Use of themes in practical thematic analysis

Constructing meaningful themes

After coding all the data, each coder should independently reflect on the team’s summary memos (step 1), the codebook (step 2), and the coded data itself to develop draft themes (step 3). It can be illuminating for coders to review all excerpts associated with each code, so that they derive themes directly from the data. Researchers should remain focused on the research question during this step, so that themes have a clear relation with the overall project aim. Use of qualitative analysis software will make it easy to view each segment of data tagged with each code. Themes might neatly correspond to groups of codes. Or—more likely—they will unite codes and data in unexpected ways. A whiteboard or presentation slides might be helpful to organise, craft, and revise themes. We also provide a template for coproducing themes (supplemental material 3). As with codebook justification, team members will ideally produce individual drafts of the themes that they have identified in the data. They can then discuss these with the group and reach alignment or consensus on the final themes.

The team should ensure that all themes are salient, meaning that they are: supported by the data, relevant to the study objectives, and important. Similar to codes, themes are framed as complete thoughts or sentences, not categories. While codes and themes might appear to be similar to each other, the key distinction is that the themes represent a broader concept. Table 2 shows examples of codes and their corresponding themes from a previously published project that used practical thematic analysis. 36 Identifying three to four key themes that comprise a broader overarching theme is a useful approach. Themes can also have subthemes, if appropriate. 40 41 42 43 44

Example codes with themes in practical thematic analysis 36

Thematic analysis session

After each coder has independently produced draft themes, a carefully selected subset of the manuscript team meets for a thematic analysis session ( table 3 ). The purpose of this session is to discuss and reach alignment or consensus on the final themes. We recommend a session of three to five hours, either in-person or virtually.

Example agenda of thematic analysis session

The composition of the thematic analysis session team is important, as each person’s perspectives will shape the results. This group is usually a small subset of the broader research team, with three to seven individuals. We recommend that primary and senior authors work together to include people with diverse experiences related to the research topic. They should aim for a range of personalities and professional identities, particularly those of clinicians, trainees, patients, and care partners. At a minimum, all coders and primary and senior authors should participate in the thematic analysis session.

The session begins with each coder presenting their draft themes with supporting quotes from the data. 5 Through respectful and collaborative deliberation, the group will develop a shared set of final themes.

One team member facilitates the session. A firm, confident, and consistent facilitation style with good listening skills is critical. For practical reasons, this person is not usually one of the primary coders. Hierarchies in teams cannot be entirely flattened, but acknowledging them and appointing an external facilitator can reduce their impact. The facilitator can ensure that all voices are heard. For example, they might ask for perspectives from patient partners or more junior researchers, and follow up on comments from senior researchers to say, “We have heard your perspective and it is important; we want to make sure all perspectives in the room are equally considered.” Or, “I hear [senior person] is offering [x] idea, I’d like to hear other perspectives in the room.” The role of the facilitator is critical in the thematic analysis session. The facilitator might also privately discuss with more senior researchers, such as principal investigators and senior authors, the importance of being aware of their influence over others and respecting and eliciting the perspectives of more junior researchers, such as patients, care partners, and students.

To our knowledge, this discrete thematic analysis session is a novel contribution of practical thematic analysis. It helps efficiently incorporate diverse perspectives using the session agenda and theme coproduction template (supplemental material 3) and makes the process of constructing themes transparent to the entire research team.

Writing the report

We recommend beginning the results narrative with a summary of all relevant themes emerging from the analysis, followed by a subheading for each theme. Each subsection begins with a brief description of the theme and is illustrated with relevant quotes, which are contextualised and explained. The write-up should not simply be a list, but should contain meaningful analysis and insight from the researchers, including descriptions of how different stakeholders might have experienced a particular situation differently or unexpectedly.

In addition to weaving quotes into the results narrative, quotes can be presented in a table. This strategy is a particularly helpful when submitting to clinical journals with tight word count limitations. Quote tables might also be effective in illustrating areas of agreement and disagreement across stakeholder groups, with columns representing different groups and rows representing each theme or subtheme. Quotes should include an anonymous label for each participant and any relevant characteristics, such as role or gender. The aim is to produce rich descriptions. 5 We recommend against repeating quotations across multiple themes in the report, so as to avoid confusion. The template for coproducing themes (supplemental material 3) allows documentation of quotes supporting each theme, which might also be useful during report writing.

Visual illustrations such as a thematic map or figure of the findings can help communicate themes efficiently. 4 36 42 44 If a figure is not possible, a simple list can suffice. 36 Both must clearly present the main themes with subthemes. Thematic figures can facilitate confirmation that the researchers’ interpretations reflect the study populations’ perspectives (sometimes known as member checking), because authors can invite discussions about the figure and descriptions of findings and supporting quotes. 46 This process can enhance the validity of the results. 46

In supplemental material 4, we provide additional guidance on reporting thematic analysis consistent with COREQ. 18 Commonly used in health services research, COREQ outlines a standardised list of items to be included in qualitative research reports ( box 7 ).

Reporting in context

We note that use of COREQ or any other reporting guidelines does not in itself produce high quality work and should not be used as a substitute for general methodological rigor. Rather, researchers must consider rigor throughout the entire research process. As the issue of how to conceptualise and achieve rigorous qualitative research continues to be debated, 47 48 we encourage researchers to explicitly discuss how they have looked at methodological rigor in their reports. Specifically, we point researchers to Braun and Clarke’s 2021 tool for evaluating thematic analysis manuscripts for publication (“Twenty questions to guide assessment of TA [thematic analysis] research quality”). 16

Avoiding common pitfalls

Awareness of common mistakes can help researchers avoid improper use of qualitative methods. Improper use can, for example, prevent researchers from developing meaningful themes and can risk drawing inappropriate conclusions from the data. Braun and Clarke also warn of poor quality in qualitative research, noting that “coherence and integrity of published research does not always hold.” 16

Weak themes

An important distinction between high and low quality themes is that high quality themes are descriptive and complete thoughts. As such, they often contain subjects and verbs, and can be expressed as full sentences ( table 2 ). Themes that are simply descriptive categories or topics could fail to impart meaningful knowledge beyond categorisation. 16 49 50

Researchers will often move from coding directly to writing up themes, without performing the work of theming or hosting a thematic analysis session. Skipping concerted theming often results in themes that look more like categories than unifying threads across the data.

Unfocused analysis

Because data collection for qualitative research is often semi-structured (eg, interviews, focus groups), not all data will be directly relevant to the research question at hand. To avoid unfocused analysis and a correspondingly unfocused manuscript, we recommend that all team members keep the research objective in front of them at every stage, from reading to coding to theming. During the thematic analysis session, we recommend that the research question be written on a whiteboard so that all team members can refer back to it, and so that the facilitator can ensure that conversations about themes occur in the context of this question. Consistently focusing on the research question can help to ensure that the final report directly answers it, as opposed to the many other interesting insights that might emerge during the qualitative research process. Such insights can be picked up in a secondary analysis if desired.

Inappropriate quantification

Presenting findings quantitatively (eg, “We found 18 instances of participants mentioning safety concerns about the vaccines”) is generally undesirable in practical thematic analysis reporting. 51 Descriptive terms are more appropriate (eg, “participants had substantial concerns about the vaccines,” or “several participants were concerned about this”). This descriptive presentation is critical because qualitative data might not be consistently elicited across participants, meaning that some individuals might share certain information while others do not, simply based on how conversations evolve. Additionally, qualitative research does not aim to draw inferences outside its specific sample. Emphasising numbers in thematic analysis can lead to readers incorrectly generalising the findings. Although peer reviewers unfamiliar with thematic analysis often request this type of quantification, practitioners of practical thematic analysis can confidently defend their decision to avoid it. If quantification is methodologically important, we recommend simultaneously conducting a survey or incorporating standardised interview techniques into the interview guide. 11

Neglecting group dynamics

Researchers should concertedly consider group dynamics in the research team. Particular attention should be paid to power relations and the personality of team members, which can include aspects such as who most often speaks, who defines concepts, and who resolves disagreements that might arise within the group. 52

The perspectives of patient and care partners are particularly important to cultivate. Ideally, patient partners are meaningfully embedded in studies from start to finish, not just for practical thematic analysis. 53 Meaningful engagement can build trust, which makes it easier for patient partners to ask questions, request clarification, and share their perspectives. Professional team members should actively encourage patient partners by emphasising that their expertise is critically important and valued. Noting when a patient partner might be best positioned to offer their perspective can be particularly powerful.

Insufficient time allocation

Researchers must allocate enough time to complete thematic analysis. Working with qualitative data takes time, especially because it is often not a linear process. As the strength of thematic analysis lies in its ability to make use of the rich details and complexities of the data, we recommend careful planning for the time required to read and code each document.

Estimating the necessary time can be challenging. For step 1 (reading), researchers can roughly calculate the time required based on the time needed to read and reflect on one piece of data. For step 2 (coding), the total amount of time needed can be extrapolated from the time needed to code one document during codebook development. We also recommend three to five hours for the thematic analysis session itself, although coders will need to independently develop their draft themes beforehand. Although the time required for practical thematic analysis is variable, teams should be able to estimate their own required effort with these guidelines.

Practical thematic analysis builds on the foundational work of Braun and Clarke. 4 16 We have reframed their six phase process into three condensed steps of reading, coding, and theming. While we have maintained important elements of Braun and Clarke’s reflexive thematic analysis, we believe that practical thematic analysis is conceptually simpler and easier to teach to less experienced researchers and non-researcher stakeholders. For teams with different levels of familiarity with qualitative methods, this approach presents a clear roadmap to the reading, coding, and theming of qualitative data. Our practical thematic analysis approach promotes efficient learning by doing—experiential learning. 12 29 Practical thematic analysis avoids the risk of relying on complex descriptions of methods and theory and places more emphasis on obtaining meaningful insights from those close to real world clinical environments. Although practical thematic analysis can be used to perform intensive theory based analyses, it lends itself more readily to accelerated, pragmatic approaches.

Strengths and limitations

Our approach is designed to smooth the qualitative analysis process and yield high quality themes. Yet, researchers should note that poorly performed analyses will still produce low quality results. Practical thematic analysis is a qualitative analytical approach; it does not look at study design, data collection, or other important elements of qualitative research. It also might not be the right choice for every qualitative research project. We recommend it for applied health services research questions, where diverse perspectives and simplicity might be valuable.

We also urge researchers to improve internal validity through triangulation methods, such as member checking (supplemental material 1). 46 Member checking could include soliciting input on high level themes, theme definitions, and quotations from participants. This approach might increase rigor.

Implications

We hope that by providing clear and simple instructions for practical thematic analysis, a broader range of researchers will be more inclined to use these methods. Increased transparency and familiarity with qualitative approaches can enhance researchers’ ability to both interpret qualitative studies and offer up new findings themselves. In addition, it can have usefulness in training and reporting. A major strength of this approach is to facilitate meaningful inclusion of patient and care partner perspectives, because their lived experiences can be particularly valuable in data interpretation and the resulting findings. 11 30 As clinicians are especially pressed for time, they might also appreciate a practical set of instructions that can be immediately used to leverage their insights and access to patients and clinical settings, and increase the impact of qualitative research through timely results. 8

Practical thematic analysis is a simplified approach to performing thematic analysis in health services research, a field where the experiences of patients, care partners, and clinicians are of inherent interest. We hope that it will be accessible to those individuals new to qualitative methods, including patients, care partners, clinicians, and other health services researchers. We intend to empower multidisciplinary research teams to explore unanswered questions and make new, important, and rigorous contributions to our understanding of important clinical and health systems research.

Acknowledgments

All members of the Coproduction Laboratory provided input that shaped this manuscript during laboratory meetings. We acknowledge advice from Elizabeth Carpenter-Song, an expert in qualitative methods.

Coproduction Laboratory group contributors: Stephanie C Acquilano ( http://orcid.org/0000-0002-1215-5531 ), Julie Doherty ( http://orcid.org/0000-0002-5279-6536 ), Rachel C Forcino ( http://orcid.org/0000-0001-9938-4830 ), Tina Foster ( http://orcid.org/0000-0001-6239-4031 ), Megan Holthoff, Christopher R Jacobs ( http://orcid.org/0000-0001-5324-8657 ), Lisa C Johnson ( http://orcid.org/0000-0001-7448-4931 ), Elaine T Kiriakopoulos, Kathryn Kirkland ( http://orcid.org/0000-0002-9851-926X ), Meredith A MacMartin ( http://orcid.org/0000-0002-6614-6091 ), Emily A Morgan, Eugene Nelson, Elizabeth O’Donnell, Brant Oliver ( http://orcid.org/0000-0002-7399-622X ), Danielle Schubbe ( http://orcid.org/0000-0002-9858-1805 ), Gabrielle Stevens ( http://orcid.org/0000-0001-9001-178X ), Rachael P Thomeer ( http://orcid.org/0000-0002-5974-3840 ).

Contributors: Practical thematic analysis, an approach designed for multidisciplinary health services teams new to qualitative research, was based on CHS’s experiences teaching thematic analysis to clinical teams and students. We have drawn heavily from qualitative methods literature. CHS is the guarantor of the article. CHS, AS, CvP, AMK, JRK, and JAP contributed to drafting the manuscript. AS, JG, CMM, JAP, and RWY provided feedback on their experiences using practical thematic analysis. CvP, LCL, SLB, AVC, GE, and JKL advised on qualitative methods in health services research, given extensive experience. All authors meaningfully edited the manuscript content, including AVC and RKS. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: This manuscript did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interests: All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

Provenance and peer review: Not commissioned; externally peer reviewed.

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Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

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

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

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

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

What (exactly) is qualitative data analysis?

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

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

So, if it’s not numbers, what is it?

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

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

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

qualitative data analysis vs quantitative data analysis

So, qualitative analysis is easier than quantitative, right?

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

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

Need a helping hand?

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

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods

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

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

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

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

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

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

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

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

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

QDA Method #2: Narrative Analysis

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

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

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

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

QDA Method #3: Discourse Analysis

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

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

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

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

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

QDA Method #4: Thematic Analysis

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

Let’s take a look at an example.

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

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

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

Thematic analysis takes bodies of data and groups them according to similarities (themes), which help us make sense of the content.

QDA Method #5: Grounded theory (GT)

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

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

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

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

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

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

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

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

Grounded theory is used to create a new theory (or theories) by using the data at hand, as opposed to existing theories and frameworks.

QDA Method #6: Interpretive Phenomenological Analysis (IPA)

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

Let’s just stick with IPA, okay?

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

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

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

IPA can help you understand the personal experiences of a person or group concerning a major life event, an experience or a situation.

How to choose the right analysis method

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

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

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

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

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

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

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

No single analysis method is perfect, so it can often make sense to adopt more than one method (this is called triangulation).

Let’s recap on QDA methods…

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

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

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

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

Psst... there’s more!

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

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86 Comments

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

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Precise explanation of method.

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

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

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

Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands

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

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

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

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

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

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

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

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Clear explanation on qualitative and how about Case study

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

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

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

content analysis can be qualitative research?

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do you have any material on Data collection

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

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

very helpful, thank you so much

The tutorial is useful. I benefited a lot.

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Methodology

What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

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

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

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

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

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

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

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

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

Qualitative research approaches
Approach	What does it involve?
Grounded theory	Researchers collect rich data on a topic of interest and develop theories .
	Researchers immerse themselves in groups or organizations to understand their cultures.
Action research	Researchers and participants collaboratively link theory to practice to drive social change.
Phenomenological research	Researchers investigate a phenomenon or event by describing and interpreting participants’ lived experiences.
Narrative research	Researchers examine how stories are told to understand how participants perceive and make sense of their experiences.

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

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

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

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

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

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

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

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

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

Qualitative data analysis
Approach	When to use	Example
	To describe and categorize common words, phrases, and ideas in qualitative data.	A market researcher could perform content analysis to find out what kind of language is used in descriptions of therapeutic apps.
	To identify and interpret patterns and themes in qualitative data.	A psychologist could apply thematic analysis to travel blogs to explore how tourism shapes self-identity.
	To examine the content, structure, and design of texts.	A media researcher could use textual analysis to understand how news coverage of celebrities has changed in the past decade.
	To study communication and how language is used to achieve effects in specific contexts.	A political scientist could use discourse analysis to study how politicians generate trust in election campaigns.

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

Flexibility

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

Natural settings

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

Meaningful insights

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

Generation of new ideas

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

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

Unreliability

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

Subjectivity

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

Limited generalizability

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

Labor-intensive

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

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

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

Research bias

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

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

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

There are five common approaches to qualitative research :

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

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

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

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

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

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Helen Noble 1 ,
Joanna Smith 2
1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
2 Department of Health Sciences , University of Huddersfield , Huddersfield , UK
Correspondence to : Dr Helen Noble School of Nursing and Midwifery, Queen's University Belfast, Medical Biology Centre, 97 Lisburn Road, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2013-101603

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The aim of this paper is to equip readers with an understanding of the principles of qualitative data analysis and offer a practical example of how analysis might be undertaken in an interview-based study.

What is qualitative data analysis?

What are the approaches in undertaking qualitative data analysis.

Although qualitative data analysis is inductive and focuses on meaning, approaches in analysing data are diverse with different purposes and ontological (concerned with the nature of being) and epistemological (knowledge and understanding) underpinnings. 2 Identifying an appropriate approach in analysing qualitative data analysis to meet the aim of a study can be challenging. One way to understand qualitative data analysis is to consider the processes involved. 3 Approaches can be divided into four broad groups: quasistatistical approaches such as content analysis; the use of frameworks or matrices such as a framework approach and thematic analysis; interpretative approaches that include interpretative phenomenological analysis and grounded theory; and sociolinguistic approaches such as discourse analysis and conversation analysis. However, there are commonalities across approaches. Data analysis is an interactive process, where data are systematically searched and analysed in order to provide an illuminating description of phenomena; for example, the experience of carers supporting dying patients with renal disease 4 or student nurses’ experiences following assignment referral. 5 Data analysis is an iterative or recurring process, essential to the creativity of the analysis, development of ideas, clarifying meaning and the reworking of concepts as new insights ‘emerge’ or are identified in the data.

Do you need data software packages when analysing qualitative data?

Qualitative data software packages are not a prerequisite for undertaking qualitative analysis but a range of programmes are available that can assist the qualitative researcher. Software programmes vary in design and application but can be divided into text retrievers, code and retrieve packages and theory builders. 6 NVivo and NUD*IST are widely used because they have sophisticated code and retrieve functions and modelling capabilities, which speed up the process of managing large data sets and data retrieval. Repetitions within data can be quantified and memos and hyperlinks attached to data. Analytical processes can be mapped and tracked and linkages across data visualised leading to theory development. 6 Disadvantages of using qualitative data software packages include the complexity of the software and some programmes are not compatible with standard text format. Extensive coding and categorising can result in data becoming unmanageable and researchers may find visualising data on screen inhibits conceptualisation of the data.

How do you begin analysing qualitative data?

Despite the diversity of qualitative methods, the subsequent analysis is based on a common set of principles and for interview data includes: transcribing the interviews; immersing oneself within the data to gain detailed insights into the phenomena being explored; developing a data coding system; and linking codes or units of data to form overarching themes/concepts, which may lead to the development of theory. 2 Identifying recurring and significant themes, whereby data are methodically searched to identify patterns in order to provide an illuminating description of a phenomenon, is a central skill in undertaking qualitative data analysis. Table 1 contains an extract of data taken from a research study which included interviews with carers of people with end-stage renal disease managed without dialysis. The extract is taken from a carer who is trying to understand why her mother was not offered dialysis. The first stage of data analysis involves the process of initial coding, whereby each line of the data is considered to identify keywords or phrases; these are sometimes known as in vivo codes (highlighted) because they retain participants’ words.

View inline

Data extract containing units of data and line-by-line coding

When transcripts have been broken down into manageable sections, the researcher sorts and sifts them, searching for types, classes, sequences, processes, patterns or wholes. The next stage of data analysis involves bringing similar categories together into broader themes. Table 2 provides an example of the early development of codes and categories and how these link to form broad initial themes.

Development of initial themes from descriptive codes

Table 3 presents an example of further category development leading to final themes which link to an overarching concept.

Development of final themes and overarching concept

How do qualitative researchers ensure data analysis procedures are transparent and robust?

In congruence with quantitative researchers, ensuring qualitative studies are methodologically robust is essential. Qualitative researchers need to be explicit in describing how and why they undertook the research. However, qualitative research is criticised for lacking transparency in relation to the analytical processes employed, which hinders the ability of the reader to critically appraise study findings. 7 In the three tables presented the progress from units of data to coding to theme development is illustrated. ‘Not involved in treatment decisions’ appears in each table and informs one of the final themes. Documenting the movement from units of data to final themes allows for transparency of data analysis. Although other researchers may interpret the data differently, appreciating and understanding how the themes were developed is an essential part of demonstrating the robustness of the findings. Qualitative researchers must demonstrate rigour, associated with openness, relevance to practice and congruence of the methodological approch. 2 In summary qualitative research is complex in that it produces large amounts of data and analysis is time consuming and complex. High-quality data analysis requires a researcher with expertise, vision and veracity.

Cheater F ,
Robshaw M ,
McLafferty E ,
Maggs-Rapport F

Competing interests None.

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Template Analysis in Business and Management Research

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Nigel King 4 ,
Joanna Brooks 5 &
Saloomeh Tabari 6

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Thematic methods of data analysis are widely used in qualitative organizational research. In this chapter, we will introduce you to Template Analysis (King and Brooks, Template Analysis for Business and Management Students . London: Sage, 2017), a particular style of thematic analysis that has been widely used in organizational and management research as well as in many other disciplines. We will begin by explaining how thematic approaches to data analysis are commonly used in qualitative organizational research, before moving on to present Template Analysis as an approach with particular utility in this field. We will then present a case study example to illustrate how Template Analysis is used by qualitative organizational researchers. In our conclusion, we will consider the overall strengths and weaknesses of the method and reflect on further developments which may extend the use of Template Analysis as a flexible form of thematic analysis with wide utility in qualitative organizational research.

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King, N., Brooks, J., Tabari, S. (2018). Template Analysis in Business and Management Research. In: Ciesielska, M., Jemielniak, D. (eds) Qualitative Methodologies in Organization Studies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65442-3_8

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Thematic analysis in qualitative research.

11 min read Your guide to thematic analysis, a form of qualitative research data analysis used to identify patterns in text, video and audio data.

What is thematic analysis?

Thematic analysis is used to analyse qualitative data – that is, data relating to opinions, thoughts, feelings and other descriptive information. It’s become increasingly popular in social sciences research, as it allows researchers to look at a data set containing multiple qualitative sources and pull out the broad themes running through the entire data set.

That data might consist of articles, diaries, blog posts, interview transcripts, academic research, web pages, social media and even audio and video files. They are put through data analysis as a group, with researchers seeking to identify patterns running through the corpus as a whole.

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Thematic analysis steps

Image source: https://www.nngroup.com/articles/thematic-analysis/

While there are many types of thematic analysis, the thematic analysis process can be generalised into six steps. Thematic analysis involves initial analysis, coding data, identifying themes and reporting on the findings.

Familiarisation – During the first stage of thematic analysis, the research teams or researchers become familiar with the dataset. This may involve reading and re-reading, and even transcribing the data. Researchers may note down initial thoughts about the potential themes they perceive in the data, which can be the starting point for assigning initial codes.
Coding – Codes in thematic analysis are the method researchers use to identify the ideas and topics in their data and refer to them quickly and easily. Codes can be assigned to snippets of text data or clips from videos and audio files. Depending on the type of thematic analysis used, this can be done with a systematic and rigorous approach, or in a more intuitive manner.
Identifying theme – Themes are the overarching ideas and subject areas within the corpus of research data. Researchers can identify themes by collating together the results of the coding process, generating themes that tie together the identified codes into groups according to their meaning or subject matter.
Reviewing themes – Once the themes have been defined, the researchers check back to see how well the themes support the coded data extracts. At this stage they may start to organise the themes into a map, or early theoretical framework.
Defining and naming themes – As researchers spend more time reviewing the themes, they begin to define them more precisely, giving them names. Themes are different from codes, because they capture patterns in the data rather than just topics, and they relate directly to the research question.
Writing up – At this stage, researchers begin to develop the final report, which offers a comprehensive summary of the codes and themes, extracts from the original data that illustrate the findings, and any other data relevant to the analysis. The final report may include a literature review citing other previous research and the observations that helped frame the research question. It can also suggest areas for future research the themes support, and which have come to light during the research process.

Another step which precedes all of these is data collection. Common to almost all forms of qualitative analysis, data collection means bringing together the materials that will be part of the data set, either by finding secondary data or generating first-party data through interviews, surveys and other qualitative methods.

Types of thematic analysis

There are various thematic analysis approaches currently in use. For the most part, they can be viewed as a continuum between two different ideologies. Reflexive thematic analysis (RTA) sits at one end of the continuum of thematic analysis methods. At the other end is code reliability analysis.

Code reliability analysis emphasises the importance of the codes given to themes in the research data being as accurate as possible. It takes a technical or pragmatic view, and places value on codes being replicable between different researchers during the coding process. Codes are based on domain summaries, which often link back to the questions in a structured research interview.

Researchers using a code reliability approach may use a codebook. A codebook is a detailed list of codes and their definitions, with exclusions and examples of how the codes should be applied.

Reflexive thematic analysis was developed by Braun & Clarke in 2006 for use in the psychology field. In contrast to code reliability analysis, it isn’t concerned with consistent codes that are agreed between researchers. Instead, it acknowledges and finds value in each researcher’s interpretation of the thematic content and how it influences the coding process. The codes they assign are specific to them and exist within a unique context that is made up of:

The data set
The assumptions made during the setup of the analysis process
The researcher’s skills and resources

This doesn’t mean that reflexive thematic analysis should be unintelligible to anyone other than the researcher. It means that the researcher’s personal subjectivity and uniqueness is made part of the process, and is expected to have an influence on the findings. Reflexive thematic analysis is a flexible method, and initial codes may change during the process as the researcher’s understanding evolves.

Reflexive thematic analysis is an inductive approach to qualitative research. With an inductive approach, the final analysis is based entirely on the data set itself, rather than from any preconceived themes or structures from the research team.

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Thematic analysis vs other qualitative research methods

Thematic analysis sits within a whole range of qualitative analysis methods which can be applied to social sciences, psychology and market research data.

Thematic analysis vs comparative analysis – Comparative analysis and thematic analysis are closely related, since they both look at relationships between multiple data sources. Comparative analysis is a form of qualitative research that works with a smaller number of data sources. It focuses on causal relationships between events and outcomes in different cases, rather than on defining themes.
Thematic analysis vs discourse analysis – Unlike discourse analysis, which is a type of qualitative research that focuses on spoken or written conversational language, thematic analysis is much more broad in scope, covering many kinds of qualitative data.
Thematic analysis vs narrative analysis – Narrative analysis works with stories – it aims to keep information in a narrative structure, rather than allowing it to be fragmented, and often to study the stories from participants’ lives. Thematic analysis can break narratives up as it allocates codes to different parts of a data source, meaning that the narrative context might be lost and even that researchers might miss nuanced data.
Thematic analysis vs content analysis – Both content analysis and thematic analysis use data coding and themes to find patterns in data. However, thematic analysis is always qualitative, but researchers agree there can be quantitative and qualitative content analysis, with numerical approaches to the frequency of codes in content analysis data.

Thematic analysis advantages and disadvantages

Like any kind of qualitative analysis, thematic analysis has strengths and weaknesses. Whether it’s right for you and your research project will depend on your priorities and preferences.

Thematic analysis advantages

Easy to learn – Whether done manually or assisted by technology, the thematic analysis process is easy to understand and conduct, without the need for advanced statistical knowledge
Flexible – Thematic analysis allows qualitative researchers flexibility throughout the process, particularly if they opt for reflexive thematic analysis
Broadly applicable – Thematic analysis can be used to address a wide range of research questions.

Thematic analysis – the cons

As well as the benefits, there are some disadvantages thematic analysis brings up.

Broad scope – In identifying patterns on a broad scale, researchers may become overwhelmed with the volume of potential themes, and miss outlier topics and more nuanced data that is important to the research question.
Themes or codes? – It can be difficult for novice researchers to feel confident about the difference between themes and codes
Language barriers – Thematic analysis relies on language-based codes that may be difficult to apply in multilingual data sets, especially if the researcher and / or research team only speaks one language.

How can you use thematic analysis for business research?

Thematic analysis, and other forms of qualitative research, are highly valuable to businesses who want to develop a deeper understanding of the people they serve, as well as the people they employ. Thematic analysis can help your business get to the ‘why’ behind the numerical information you get from quantitative research.

An easy way to think about the interplay between qualitative data and quantitative data is to consider product reviews. These typically include quantitative data in the form of scores (like ratings of up to 5 stars) plus the explanation of the score written in a customer’s own words. The word part is the qualitative data. The scores can tell you what is happening – lots of 3 star reviews indicate there’s some room for improvement for example – but you need the addition of the qualitative data, the review itself, to find out what’s going on.

Qualitative data is rich in information but hard to process manually. To do qualitative research at scale, you need methods like thematic analysis to get to the essence of what people think and feel without having to read and remember every single comment.

Qualitative analysis is one of the ways businesses are borrowing from the world of academic research, notably social sciences, statistical data analysis and psychology, to gain an advantage in their markets.

Analysing themes across video, text, audio and more

Carrying out thematic analysis manually may be time-consuming and painstaking work, even with a large research team. Fortunately, machine learning and other technologies are now being applied to data analysis of all kinds, including thematic analysis, taking the manual work out of some of the more laborious thematic analysis steps.

The latest iterations of machine learning tools are able not only to analyse text data, but to perform efficient analysis of video and audio files, matching the qualitative coding and even helping build out the thematic map, while respecting the researcher’s theoretical commitments and research design.

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Open access
Published: 17 June 2024

Adapting the serious illness conversation guide for unhoused older adults: a rapid qualitative study

Abigail Latimer 1 ,
Natalie D. Pope 2 ,
Chin-Yen Lin 3 ,
JungHee Kang 1 ,
Olivia Sasdi 1 ,
Jia-Rong Wu 1 ,
Debra K. Moser 1 &
Terry Lennie 1

BMC Palliative Care volume 23 , Article number: 153 ( 2024 ) Cite this article

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Older adults experiencing homelessness (OAEH) age quickly and die earlier than their housed counterparts. Illness-related decisions are best guided by patients’ values, but healthcare and homelessness service providers need support in facilitating these discussions. The Serious Illness Conversation Guide (SICG) is a communication tool to guide discussions but has not yet been adapted for OAEH.

We aimed to adapt the SICG for use with OAEH by nurses, social workers, and other homelessness service providers. We conducted semi-structured interviews with homelessness service providers and cognitive interviews with OAEH using the SICG. Service providers included nurses, social workers, or others working in homeless settings. OAEH were at least 50 years old and diagnosed with a serious illness. Interviews were conducted and audio recorded in shelters, transitional housing, a hospital, public spaces, and over Zoom. The research team reviewed transcripts, identifying common themes across transcripts and applying analytic notetaking. We summarized transcripts from each participant group, applying rapid qualitative analysis. For OAEH, data that referenced proposed adaptations or feedback about the SICG tool were grouped into two domains: “SICG interpretation” and “SICG feedback”. For providers, we used domains from the Toolkit of Adaptation Approaches: “collaborative working”, “team”, “endorsement”, “materials”, “messages”, and “delivery”. Summaries were grouped into matrices to help visualize themes to inform adaptations. The adapted guide was then reviewed by expert palliative care clinicians for further refinement.

The final sample included 11 OAEH (45% Black, 61 ± 7 years old) and 10 providers (80% White, 8.9 ± years practice). Adaptation themes included changing words and phrases to (1) increase transparency about the purpose of the conversation, (2) promote OAEH autonomy and empowerment, (3) align with nurses’ and social workers’ scope of practice regarding facilitating diagnostic and prognostic awareness, and (4) be sensitive to the realities of fragmented healthcare. Responses also revealed training and implementation considerations.

Conclusions

The adapted SICG is a promising clinical tool to aid in the delivery of serious illness conversations with OAEH. Future research should use this updated guide for implementation planning. Additional adaptations may be dependent on specific settings where the SICG will be delivered.

Peer Review reports

In the United States of America, in 2023, nearly one in four adults over the age of 55 has experienced homelessness without shelter, and as the population of older adults increases, the number of older adults experiencing homelessness (OAEH) is expected to triple by 2030 [ 1 ]. While the impact of COVID-19 and economic recession is still unknown, many OAEH are experiencing homelessness for the first time in late adulthood [ 1 , 2 , 3 ]. What is considered “older age” for unhoused populations is unclear, and collecting accurate and timely data on this population can be challenging. However, aging OAEH are more likely to have multiple chronic and life-threatening illnesses sooner, leading to geriatric syndromes in their 50s and 60s [ 4 , 5 ]. Contending with poor health and multiple chronic conditions [ 6 ], the life expectancy of OAEH ranges from 64 to 70 years, considerably less than that of the general population (around 77 years) [ 7 ]. Higher rates of mental illness, substance use disorders [ 8 ], and victimization [ 9 ] in OAEHs further worsen quality of life.

Kelley and Bollens-Lund [ 10 ] emphasize that populations with multimorbidity (three or more conditions) represent a subpopulation of unhoused or housing insecure patients with serious illness healthcare utilization, functional impairment, and overall high care needs. Recently, a count of unhoused hospitalized patients in a single night, over half of whom were age 55 and older and had multimorbidities, was estimated to be 20-fold higher than in the community setting [ 11 ]. In addition to high rates of multimorbidity for unhoused populations [ 6 ], homelessness complicates all aspects of health status [ 12 ]. This happens in a variety of ways including homelessness fomented by OAEH physical and mental illnesses (e.g., functional status decline resulting in loss of income or housing), homelessness causing or exacerbating illnesses (e.g., skin disorders, trauma, and malnutrition), and homelessness complicating healthcare treatment (e.g., medication access or proper storage, repeat hospitalizations without outpatient management, and no health insurance) [ 12 ]. For example, limited income, food insecurity, and lack of health insurance and assistive devices (e.g., eyeglasses, hearing aids) all worsen health outcomes [ 13 ]. Furthermore, OAEH face barriers to accessing healthcare that result in poor healthcare transitions and continuity [ 14 ], which are required when managing multiple chronic physical (e.g., hypertension, heart failure, and diabetes) and mental health conditions (e.g., depression, anxiety, and post-traumatic) [ 4 , 6 ]. While access to health insurance coverage is helpful, the inability to afford co-pays or access to transportation prevents OAEH from having routine clinical care [ 15 ], leaving them to navigate life-threatening illnesses on their own. A life-threatening or serious illness is considered “a health condition that carries an elevated risk of mortality and either negatively impacts a person’s daily function or quality of life, or excessively strains their caregivers.” 10(S-8) Currently, there is no agreed upon definition of what constitutes a serious illness for OAEH. However, the harsh environmental conditions OAEH endure while managing, sometimes otherwise treatable illnesses, contribute to the seriousness of their conditions.

Serious illness conversations for unhoused older adults

Unhoused persons with serious illnesses could benefit from conversations to help them make decisions about their health conditions. Facilitating serious illness conversations reflects a process of understanding what matters most to patients and includes dialogue related to the patient’s knowledge of their health condition and clinical recommendations, prognosis, values, worries, hopes, and goals [ 16 , 17 ]. These conversations can improve care by reducing the use of life-sustaining therapies at the end of life, increasing awareness of end of life wishes [ 17 ] and lowering anxiety and depression [ 18 ]. Provider perceptions that death or dying is not a priority for unhoused persons is a barrier to discussing the experience of living with a life-threatening illness [ 19 ]. However, unhoused people want to talk about their health, especially when thinking about their end-of-life experience or discussing future medical treatment preferences [ 19 , 20 , 21 , 22 , 23 , 24 ]. These discussions in unhoused populations have been effective at completing advance directives, naming surrogates, and giving OAEH an opportunity to discuss fears or concerns related to their serious illness [ 25 ]. While beneficial, without training and support to facilitate discussions, providers may feel ill-equipped to facilitate these discussions [ 17 ].

The Serious Illness Conversation Guide (SICG) is an existing tool that offers a script with question prompts, mapping the flow of a serious illness conversation for providers. The purpose of the guide is to facilitate a shared understanding between patient and provider about what is most important regarding the patient’s health and quality of life [ 26 ]. It is a tool designed to support professionals who lack serious illness communication training in having serious illness conversations and does not require extensive training or skills. The SICG was initially developed for nurse practitioners and physicians in oncology settings [ 18 ], and has been adapted for various settings and populations that include emergency departments with social workers [ 27 ], dementia care [ 28 ], indigenous populations [ 29 ], and telehealth delivery for older adults [ 30 ]. However, the guide has not yet been adapted to consider the needs of multiple professions in non-healthcare settings or OAEH.

Guidance and tools are needed to assist nurses, social workers, and other providers who care for OAEH in facilitating serious illness conversations and recognizing and acknowledging the unique needs of this population. Social workers in homeless settings bear witness to tragic and traumatic stories associated with housing instability, oftentimes with little support from supervisors or management, yet they want to reduce clients’ distress [ 31 ]. Nurses are also integral in improving health for unhoused populations [ 32 ]. A lack of compassionate person-centered care and attention to the unique needs of unhoused people persists [ 33 ]. Therefore, our aim in this study was to adapt the SICG for use with OAEH.

Data collection

To adapt the SICG for use with OAEH, we conducted a multi-phase, iterative qualitative study in which we elicited feedback from three groups: OAEH, homelessness service providers, and palliative care providers (as content experts). Data were collected from February through December 2023 in two cities (220,000 and 630,000 population size) in the southeastern United States. We first conducted individual semi-structured interviews with provider experts in homelessness service settings (homelessness service providers) while concurrently completing cognitive individual interviews with OAEH. Analysis occurred during data collection, so once interviews were completed, we modified the SICG based on emergent findings. Lastly, we conducted interviews with expert palliative care providers to make further refinements to the revised (or adapted) version of the SICG. Palliative care aims to alleviate psychological, physical, and spiritual distress for people with life-threatening illnesses [ 34 ]. One of the core domains of palliative care is to facilitate medical decision-making and serious illness conversations, making them ideal providers to help further refine the SICG. Interview guides were developed for this study and can be viewed in supplementary files. Our framework to guide adaptations came from Davidson and colleagues’ (2013) Tool Kit of Adaptation Approaches [ 35 ]. In their study, they conducted a systematic review of approaches used to adapt interventions, along with 26 interviews from experts in adapting behavior change interventions. They present six typologies of approaches that researchers can consider when adapting interventions, clarifying that not all approaches will be used. These approaches included collaborative working, team, endorsement, materials, messages, and delivery. We focused on a few most relevant to adapting the guide: collaborative working (i.e., what is appropriate and effective with target group), messaging (i.e., population preferences, resources, and norms), and delivery (i.e., preferred communication methods, socioeconomic barriers, and environment). Our process for adapting the SICG is outlined below. Informed consent was obtained by all participants prior to participating in this study. This study was approved by the University of Kentucky Institutional Review Board (IRB # 83,381).

Reflexive processes

The research team was intentionally multidisciplinary and comprised of social workers and nurses with clinical experience in homelessness, healthcare, and hospice and palliative care. The primary investigator (AL) of this research is a licensed clinical social worker with over a decade of experience in hospice and palliative care and trained in qualitative research methods. AL and the study’s co-investigator (NP), who has extensive training in qualitative methods and research experience with OAEH, conducted most of the interviews. To promote perspective taking, research team member OS, a registered nurse and doctoral of nursing practice student, also assisted with interviews. Additional team members with varying experiences in nursing, homelessness and palliative care, and the Serious Illness Conversation Guide assisted with transcriptions and templated summaries. The research team met weekly during the study’s 12-month duration to discuss data collection and analysis, keeping a detailed audit trail and memoing reflections. These discussions also allowed team members to reflect on the interview content and their reactions as interviewers and analysts. Palliative care research can be emotionally heavy. Weekly team meetings helped to attend to the well-being of research team members, which promoted their ability to connect with the data [ 36 ], collaborate, and work compassionately on this project.

Interviews with homelessness service providers

We identified and recruited social work, nursing, and social service providers working with OAEH in homelessness service settings in one state within the southeastern US. Settings included a homeless shelter, a transitional supportive housing site, and a community-based hospital. We conducted two separate interviews with each provider participant using a semi-structured interview format. The first interview focused on understanding existing processes and context to consider when adapting the SICG. The second interview explored providers’ perceptions on adaptations needed to the content of the SICG and implementation at their site. The first interview topics were: (1) individual, organizational, or community-level facilitators and barriers when caring for OAEH and having serious illness conversations, (2) descriptions of any tools or training received related to having serious illness conversations, and (3) examples of when serious illness communication did or did not occur and perceived patient outcomes. The second interview focused on feedback regarding the SICG (e.g., messaging, delivery, collaborative working) and anticipated needs or implementation barriers to using the SICG at their site. Exploring existing processes for facilitating values-based serious illness conversations and reviewing the tool was intended to provide insight and direction into needed adaptations to the SICG and inform training and implementation needs. Interviews were held on site at providers’ service locations, in quiet public places (e.g., coffee shops), or via Zoom. Interviews were audio-recorded and professionally transcribed verbatim.

Cognitive interviews with older adults experiencing homelessness

Concurrent with our individual interviews with homelessness service providers, we also separately conducted individual cognitive interviews with OAEH. We worked closely with recruitment site clinicians for referrals and to ensure capacity to consent and participate. recruited OAEH who: (1) were homeless adults aged 50 to 90, (2) had a serious illness, and (3) possessed the capacity to engage in the informed consent process. We operationalized “homelessness” consistent with the Health Resources and Services Administration which considers individuals homeless if they lack housing, reside in public or private facility providing shelter, reside in temporary or permanent housing or other housing programs for homeless [ 37 ]. We utilized the serious illness definition as described by Kelley and colleagues (2018) and given the impact of housing, healthcare, and food insecurity on the condition and life expectancy of OAEH, we included a broad range of illnesses such as liver disease, chronic lung disease, diabetes, heart conditions or cardiovascular disease, chronic kidney disease, or cancer.

Our cognitive interviewing process involved individual interviews exploring how OAEH “understand, mentally process, and respond” to the SICG [ 38 ]. Although cognitive interviewing is a standard in instrument development, its use among unhoused people or those with cognitive impairments has been limited. However, the method can still be effective and is an important approach for this population [ 39 , 40 ]. In our interviews with OAEH, we adapted “think aloud” procedures to explain or describe more about how they answered the questions [ 41 ]. The think-aloud procedure is one by which participants will verbalize their thoughts about what the questions mean, explaining how they arrived at their answer, any difficulties answering, and other pertinent information they may want to provide [ 41 ]. Additional open-ended questions explored participants’ thoughts, feelings, and perspectives about the SICG, and the context of how it might be delivered. Interviews were conducted by a research member trained in having sensitive and difficult conversations with older and seriously ill adults. Interviews were held on sites where OAEH were currently sheltered; they were audio-recorded and professionally transcribed verbatim.

Interviews with palliative care providers

Following interviews with homelessness service providers and OAEH (and adaptations made to the SICG), we conducted individual interviews with providers trained in palliative care and currently/ formerly practicing palliative care with OAEH. Interviews included open-ended questions about participants’ roles, scope of practice, and experience in delivering palliative care and facilitating serious illness conversations with OAEH. Participating providers were asked to give feedback and recommendations on how to further modify the adapted SICG. Each interview informed the next where participants were asked to comment on modifications from previous interviews. Interviews were audio-recorded and held either on-site in private settings (e.g., offices) or via Zoom.

Data analyses

We conducted a rapid qualitative analysis, which is a team-based iterative approach to understanding and exploring complex phenomena from “insiders’ perspectives” and applying knowledge to real-world activities and situations [ 42 ]. Rapid qualitative analysis was ideal for this project as it is designed to be used in time-sensitive projects providing timely results and allowing for a “big picture view” of collected data [ 43 , 44 , 45 , 46 , 47 ]. Employing rapid qualitative analysis enabled us to disseminate findings quickly to community partners who facilitated recruitment and expressed a desire in addressing serious illness care for unhoused older adults. Furthermore, findings are comparable with traditional approaches (e.g., thematic analysis), take less time, and are less cost intensive [ 46 , 47 ]. Methods used in rapid qualitative analysis vary and have been applied in various contexts, particularly in healthcare, to adapt and implement interventions [ 48 , 49 , 50 ]. We used two methods to condense the data and identify themes [ 51 ], to inform SICG adaptations: templated summaries and matrices analysis. These are described below.

Templated summaries

Templated summaries were used as a data reduction technique to promote accessibility and understanding of the data [ 52 , 53 ]. Templates were organized by a priori categories, or key topics, derived from interview questions. Themes emerged by reviewing transcripts and identifying phrases that reflect surface meanings requiring little to no interpretation by the researcher [ 52 , 53 ]. We approached our templated summaries like Keniston et al. (2022) using Microsoft Word [ 49 ]. Summaries for OAEH reflected key data excerpts related to the SICG’s central domains. In contrast, summaries for homelessness service providers reflected Davidson’s and colleagues’ (2013) Tool Kit of Adaptation Approaches [ 35 ] guiding possible areas that may need adaptation (collaborative working, team, endorsement, materials, messages, and delivery). Data segments from the OAEH captured (1) how they interpreted specific prompts and questions in the SICG and (2) feedback they had on modifications to the SICG. The research team (AL, NP, OS) conceptualized OAEH interpretations of the SICG, based on how they answered the questions and responses from the “think aloud” procedures. Data segments from the homelessness service providers that spoke to their feedback on SICG adaptations were also extracted.

Prior to starting the summaries, the research team worked together to clarify and define each domain of the transcript summaries. This ensured we had a shared understanding of each area (e.g., materials, endorsement, collaborative working) [ 53 ]. Each transcript was assigned to a member of the research team; they read and completed an individual summary, pulling data that related to interpretation and feedback on the SICG. Templated summaries were reviewed by AL and NP, doctorly prepared qualitative researchers, to ensure accuracy and consistency with the agreed-upon categories, summaries, and level of interpretation used to reflect the data [ 52 , 53 ].

Matrices analysis

Once individual summaries were created, we synthesized this information into three data matrices. Matrices column headings reflected category names used in templated summaries, while row headings were assigned to each participant. Reviewing data segments via a grid enabled the researcher team to compare similarities and differences across transcripts [ 52 , 53 , 54 ]. Using matrices allows the researcher to draw conclusions with “immediate, precise, and accessible reference to specific differences of opinion among participant groups” 53(p858) The organization of the matrix cells increases the trustworthiness of the data, improving the ability to derive meaning from the data in an organized way [ 54 ]. One data matrix focused on SICG interpretation (i.e., how each OAEH interpreted questions of the SICG). The second and third matrix focused on SICG adaptation and collated feedback from the OAEH and homelessness service provider interviews. Lead research team members (AL and NP) reviewed matrices independently, noting patterns and emerging themes. The two team members then met to establish consensus on themes that would inform adaptations. All team members then reviewed themes to establish agreement, a process also taken by Schexnayder et al. (2023) [ 55 ].

Adaptations

The research team adapted the SICG through an iterative process that involved reviewing the data matrices, reading/ reviewing the SICG, and discussing potential changes. We conferenced regularly to discuss questions and issues that came up regarding adapting the guide. Team members worked through places of agreement and difference in our understanding of how OAEH and expert feedback translated into SICG adaptations. A preliminary adapted guide was created for interviews with palliative care providers. To make further refinements, researchers AL and NP made real-time adjustments to the guide during interviews and met collaboratively after every two to three interviews to reach consensus on real-time adaptations that were consistent with adaptations suggested by OAEH and homelessness service providers.

The team included two researchers from social work and two from nursing. Colleagues from the same discipline can often “share the same blind spots” (p501) [ 56 ] during analysis (e.g., defining and refining codes); investigator and interdisciplinary triangulation throughout all aspects of the study contributed to the rigor of this project [ 57 ]. During all phases of this project (study conceptualization, data collection, adaptation, analysis), we maintained a shared audit trail to document our process, record analytic decisions, and engage with data reflectively [ 58 , 59 ].

Overall, adaptation themes revealed a need (1) for increased transparency about the purpose and intent of the conversation, (2) to promote OAEH autonomy and empowerment, (3) to align with nurses’ and social workers’ scope of practice regarding facilitating diagnostic and prognostic awareness, and (4) to be sensitive to the realities of fragmented healthcare. Training and implementation considerations also emerged. See Table 1 for data segments from matrices and representative quotes. See Additional files for the adapted guide, The Serious Illness Conversation Guide for Unhoused or Housing Vulnerable Older Adults.

Homelessness service provider interviews

We interviewed 10 providers two times who worked directly with the homeless population across various settings, including the hospital ( n = 4), ambulatory clinic ( n = 1), non-profit organization ( n = 3), and emergency shelter with recovery-based programming ( n = 2). Most ( n = 9) of the providers were female and most were non-Hispanic White ( n = 8); one woman identified as Black non-Hispanic, and one woman identified as biracial. Participants held a variety of job titles (e.g., intake worker, Veterans Affairs coordinator, RN) and represented multiple disciplines (e.g., social work, nursing). Of note, four of the provider participants had previously worked with the unhoused clients/ patients prior to their current employment, with almost nine years of experience on average. See Table 2 for provider characteristics. The first interview ranged from 50 to 94 min, averaging 64 min; the second interview ranged from 15 to 76 min, averaging 38 min.

Many of the same themes in OAEH interviews emerged in our conversations with homeless provider participants. Specifically, providers echoed OAEH participants’ need for more transparency to promote trust and avert paranoia, given the sensitive nature of the questions. Exploring information preferences by asking the question, “How much information about what might be ahead with your health?” felt vague and prompted responses that would be too medically focused for providers’ comfort level. Providers suggested this as an opportunity to elicit information that would help inform referrals or connections to resources, they could make for clients. Based on homelessness service provider interviews, we added language such as, “to make sure I share information that is helpful” and “I’d like you to have the information and support you need” to distinguish support they were able to provide from providing medical advice or suggestions related to improving physical health of OAEH.

Scope of practice concerns guided revisions to the sharing prognosis section of the SICG. Homelessness service providers desired this section to focus on facilitating and comprehending the patient’s understanding of their illness and how much information they had. Practically, most homelessness service providers would not know about the patient’s condition; if they did, they did not feel it was their responsibility to share that information. For example, the nurse and social work participants in community hospitals relayed that it was the attending physician’s responsibility. Therefore, this section was changed to elicit what the patient is “most worried about with their illness” and share a general concern that their “health might get worse” and acknowledge they could get “sicker or injured”. We wanted to include language to reinforce transparency and intent by adding the statement, “to know what’s most important to you if that happens”.

Provider participants felt it important to promote OAEH autonomy and person-centered care throughout the guide by removing “recommendation” language. They also did not feel it appropriate to offer reassurance for continuity of care or that they would receive the best care as they recognized patients’ care experiences were often fragmented. So, we omitted the language at the end of the guide suggesting they will “receive the best care possible” and replaced it with actionable steps that the facilitator would take next.

All provider participants advocated for the needs of OAEH by offering general feedback about the delivery of the conversation. Participants reinforced the need for facilitators to ensure receptiveness, emotional safety, and trust before starting and throughout the conversation. However, some of the suggestions made by provider participants contradicted what the OAEH participants said. For example, several provider participants expressed concern about having serious illness conversations with OAEH, citing concerns that it may be too emotionally difficult for them, or they would not engage willingly. However, this was not the case with our sample of OAEH who expressed a desire to have these conversations. Nevertheless, homelessness service providers also expressed concerns that addressing too many of the emotional aspects of this conversation would be outside of their scope of practice. Therefore, we replaced the facilitator prompt to “validate and explore emotions” with a prompt to “pause and allow silence” with specific language. This finding and other feedback prompted key implementation and training considerations that would need to be considered before facilitating conversations using this guide in any homelessness service setting.

Interviews with older adults experiencing homelessness

We interviewed 11 OAEH, mostly men ( n = 9) between the ages of 53 and 72 ( M = 61). The sample included six white participants and five Black participants. Most were of non-Hispanic descent ( n = 10). Participants had a range of self-reported illnesses and comorbidities based on past medical diagnoses. These included poor cardiovascular health (e.g., heart failure, hypertension), diabetes, chronic kidney disease, severe or infected wounds, small bowel syndrome, lung disease, and human immunodeficiency virus. All participants reported mental health problems, including depression ( n = 5), anxiety ( n = 1), depression and anxiety ( n = 1), attention-deficit hyperactivity disorder ( n = 1), post-traumatic stress disorder ( n = 1), bipolar disorder ( n = 1), and other non-specified diagnoses ( n = 1). See Table 3 for characteristics.

Cognitive interviews with OAEH lasted between 53 and 97 min (averaging 70 min); participants answered questions from the SICG and gave direct feedback about their thoughts, feelings, and suggestions regarding the delivery and messaging of the guide’s content. Overall, participants voiced appreciation for having the opportunity to discuss what was important to them regarding their health-related goals, values, and preferences. Changes were made based on participants’ feedback and interpretation of the SICG questions. During the set up and share portion of the guide, participants requested transparency regarding the intent of the conversation. Also, to improve transparency and trust, we included language clarifying the role and discipline of the facilitator and their relationship with healthcare providers. Given that many conversations using the SICG will take place in non-healthcare settings, participants expressed a desire to know what type of healthcare experience the facilitator has (if any) or their relationship with healthcare providers.

None of the OAEH participants wanted to know the prognosis related to “time”; however, they did want direct and compassionate communication about the facilitator’s concerns for their health. Participants’ interpretation of the questions provided insight into how to rephrase questions to elicit responses congruent with the question’s intent. For example, the purpose of the original question, “What would you be willing to go through for the possibility of gaining more time?”, is to explore limit setting regarding invasive treatments (e.g., code status, use of mechanical ventilation) that may go against patients’ values or preferences. However, most participants either did not understand the question or responded with vague or contradictory answers that provided limited detail.

Older unhoused adults endorsed exploratory questions related to their worries, strengths, and activities they enjoy; these questions seemed easy to answer. However, when asked about “the people closest to them”, many found this question to trigger negative feelings (e.g., guilt, shame) because of estranged relationships with relatives and loved ones they were previously close to. Therefore, we removed labeling the relationship with others and asked more neutrally about whether they have “talked about” their worries or what is important to them to “other people”. We then added a follow-up question to identify who they have spoken to as possible health surrogates or collaborators in their care. Closing the conversation also required more clarity. When asked about recommendations, participants typically requested medical information regarding what they needed to do to take care of or improve their health. We revised this question to “is it okay if I share what may be helpful?” to allow more flexibility for providers to provide information aligned with their scope of practice and setting.

Overall, participants requested the conversation be delivered with compassion and respect to ensure they are spoken with and not at . Participants described past experiences with medical and non-medical providers that influenced their perception and trust of the facilitator. Remaining positive was an aspect of their life that all participants relayed was of critical importance. The difficulty of the conversation did not deter them from having it, but participants did convey that compassion and respect were important aspects to remember when speaking with them. Focusing on the negative or not prioritizing communication that fostered hope was considered scary for their mental health and attitude, given the daily stressors and realities that come with having insecure housing.

Palliative care provider interviews

Following preliminary adaptations to the SICG, we interviewed nine providers (two nurse practitioners and seven social workers) with training and past or current experience providing palliative care to OAEH. Their experience reflected work done across the United States in the Southeast ( n = 6), West ( n = 1), Southwest ( n = 1), and Northeast ( n = 1) across a variety of settings, including inpatient palliative teams within academic medical centers and community hospitals ( n = 6), a palliative care mobile unit ( n = 1), an emergency department in an acute care hospital ( n = 1), and home palliative and hospice care ( n = 1). Participants had key roles on their respective interdisciplinary teams and settings by engaging unhoused older adults in serious illness conversations, facilitating resources, and coordinating care. See Table 4 for characteristics. Experience in palliative care ranged from 1 year to 30 years, averaging nearly 10 years. Interviews lasted from 36 min to 59 min, averaging 47 min.

Palliative experts were key in further modifying the SICG to reflect core tenets of serious illness conversations. Their knowledge of and skills in advance care planning, goals of care discussions, delivering serious news, and discussing prognosis ensured the spirit of the guide remained intact. For example, experts reinforced the use of phrases and skills such as “I wish, I worry” statements, exploring patients’ understanding of their illness, and seeking permission throughout the guide. Moreover, they aligned these palliative care conversation skills with their knowledge and experience working with OAEH. Sharing worry about the reality that many OAEH may experience acute illness or injury in addition to their chronic and life-threatening illnesses was included in their sharing of concern about their prognosis and increasing transparency about the need for this conversation. Adaptations included language changes that mirror the target populations and decrease power differentials between providers and OAEH. Experts also offered guidance to keep the conversation focused on OAEH’s health, rather than other concerns they may have. The addition of this question, “what are you most worried about with your illness?” was one adaptation made to keep the conversation focused on health. Palliative providers acknowledged that many OAEH have worries that they may bring to the homelessness service provider; without the context of a hospital admission or direct healthcare service provider to guide their thinking, the focus of the conversation may get lost. Additionally, OAEH may have many co-occurring conditions to contend with. This allows the OAEH to identify the illness of most importance/ concern to them and provides insight for the homelessness service provider on what their client may be managing.

Additional takeaways

The following sections outline some central findings that are important to consider when having serious illness conversations with OAEH.

All providers interviewed ( n = 19) suggested aspects that would need to be incorporated into training prior to using the adapted SICG. Homelessness service providers emphasized the need to develop trust and rapport with each OAEH and to recognize the impact of their emotional and mental status on their ability to participate fully in the conversation. For example, many providers discussed the impact of trauma and the need for this conversation to be facilitated in line with trauma-informed care practices. Incorporating those aspects into the SICG training would be needed. How the patient’s symptoms are impacting their life may or may not be a routine part of their role, so training homelessness service providers on how to address this within the flow of the conversation would be helpful. Also, none of the homelessness service providers identified grief and loss training as part of their current roles. While this may be beyond the scope of the training provided before using the guide, this feedback was identified as pertinent to the general care of OAEH and providers, as they care for a population with high mortality. Palliative providers also discussed ways to incorporate serious illness conversation skills into the training portion of the guide. They suggested homelessness service providers may need additional training on what to do if the OAEH declines to answer questions, how to use silence therapeutically, and how to normalize having the conversation.

Implementation

Homelessness service providers identified several areas that will need careful implementation mapping and additional adaptation to use the guide appropriately in these settings. Overall, there were considerable differences among providers in the frequency of contacts and time spent with patients based on their setting. For example, there were instances where OAEH were seen repeatedly at an emergency shelter location, but often they were only seen once. Comparatively, in the transitional supportive housing space, OAEH may stay in a space for weeks or months with repeat contact with the social work provider. Providers working at community hospitals would often see OAEH repeatedly but described external pressure to discharge them quickly, thus impacting their ability to engage in lengthier conversations outside the scope of discharge planning. Questions were also raised regarding how long the conversation would take. In addition to having the time available, many homelessness service providers struggled to imagine the timing of and appropriate space to have the conversation. Homelessness service providers expressed concern about whether it would fit within their intake process and wondered whether they were the right person to have the conversation.

Homelessness service providers also questioned the process that would happen after having the conversation. Providers wondered how the information gleaned during the conversation would be used since there is no shared electronic health record system between healthcare and homelessness services. While implementation strategies would address this concern, we removed recommendation language and replaced it with actionable steps (e.g., contacting OAEH provider, completing advance directive). This change puts homelessness service providers in an advocacy and facilitation role to bridge care between homeless and healthcare settings while also reaffirming their commitment to OAEH. Despite the concerns, all homelessness service providers acknowledged the utility and importance of the conversation guide. They were receptive to training and to a tool to help them have a serious illness conversation.

This study is a first step to having nurses, social workers, and other providers use The Serious Illness Conversation Guide for Unhoused or Housing Vulnerable Older Adults in their practice. Our study used an iterative data collection and analytical process of engaging OAEH and homeless and palliative providers to inform adaptations. Adaptations included modifications to increase transparency and sensitivity to the social and emotional aspects of being unhoused. Interviews also reflected the need for implementation planning and training for homelessness service providers before using the SICG in these settings. Limits in scope of practice prompted changes to how OAEH conditions were discussed, while leveraging provider skillsets in facilitation and advocacy. Prognosis adaptations reflect a new tool created by Ariadne Labs (2024) [ 26 ], “The Role of Social Workers”. The impact of fragmented and siloed services permeated throughout interviews, both in how OAEH discussed the care they currently received and how providers imagined care they could provide.

Our sample of OAEH was willing and receptive to serious illness conversations and expressed a desire to talk about it with providers. Unhoused people often have previous encounters and experiences with death [ 21 , 60 ] and have unique fears and worries that they want to be acknowledged [ 19 ]. Responding to emotions and speaking with sensitivity and compassion was a priority in our sample and is mirrored in previous research with unhoused populations [ 19 , 20 , 23 ]. Past studies have also shown unhoused people want serious illness communication to be delivered with respect, acceptance, and without judgment [ 19 ]. Our participants echoed the need to include language that empowered OAEH and decreased power differentials between patient and provider. While we limited our OAEH to 50 years and older, unhoused adults seem to have chronic illness even younger than “older adult” age; [ 61 ] thus, these conversations may need to occur with unhoused persons in their 40s. Overall, using a tool to guide a conversation with OAEH was well received by all participants in our study and emphasizes a needed area of focus in both practice and research.

As stated previously, our approach focused on a few areas offered by Davidson et al. (2013) as guidance for intervention adaptations (i.e., collaborative working, messaging, delivery). Our interviews with the target population (OAEH, homeless and palliative care providers) reflect “exploratory work with the target population…or community leaders” [ 35 ] to learn what might be effective and appropriate. The providers we spoke with discussed training needs for utilizing the SICG in non-medical service provision for OAEH. While the homelessness service providers felt ill-equipped to have difficult conversations with clients about serious illness, this is a common experience for many providers when caring for individuals with serious illnesses [ 62 , 63 ]. Studies suggest that communication training can improve providers’ comfort and self- perceived skills in having serious illness conversations [ 64 , 65 ]. However, more research is needed to test whether this training improves clinical outcomes as training intervention outcomes are inconsistent [ 66 ]. Implementing and evaluating training for homelessness service providers is a needed.

Revisions to the SICG for use with OAEH also reflected messaging adaptations [ 35 ]. Individuals experiencing chronic homelessness often have a limited social network with fewer family members and supportive friends, and more ties to individuals in crisis [ 67 , 68 ]. Original SICG wording such as “how much does your family know about your…wishes” was adapted to remove labeling the relationship with others. Instead, we suggest asking more neutrally about whether they have “talked about” their worries or what is important to them to “other people”. These changes, as well as scope of practice revisions, are examples of messaging adaptations that consider issues unique to the context of OAEH and their non-medical service providers.

Lastly, some adaptations made for The Serious Illness Conversation Guide for Unhoused or Housing Vulnerable Older Adults were related to the delivery of the intervention. Some examples of delivery adaptations include considering the target population’s referred method of communication and addressing potential barriers to participation [ 35 ]. In our study, both OAEH and homelessness service providers acknowledged possible emotional barriers that might hinder a fruitful conversation about serious illness. These included a desire for OAEH to maintain positivity, to have direct messaging (to deter paranoia), and for providers to use a person-centered approach. While these considerations are important for serious illness conversations with all patient populations, they are particularly crucial for OAEH. For instance, unhoused adults can experience limited autonomy because of shelter rules and regulations. The connection between trauma and chronic homelessness is also well-established; most people who are homeless have experienced trauma throughout their lifetime [ 68 , 69 , 70 , 71 ] and homelessness itself is traumatic [ 72 ]. While some of our adaptations (e.g., increasing transparency) might encourage psychological safety, we suggest practitioners also couple SICG with a trauma informed approach [ 73 ].

Our methods present a systematic and timely approach to adapting a communication tool. Like with Adair et al. (2012) [ 39 ], using cognitive interviews with unhoused adults proved to be a successful approach to modifying the content and language throughout the guide. Skilled interviewers who took the time to clarify and help participants understand the question were helpful to ensure meaningful responses from the OAEH. In addition to the interviews, using rapid qualitative analysis did not require experience or extensive training from our research team. We completed the study in under 12 months and gathered the data needed to make adaptations, reflecting a timely process. Trustworthiness was upheld by employing multiple strategies to identify patterns in the data, including building consensus via regular team meetings, documenting our process using an audit trail, and using templated summaries and data matrices. Our data has less bias because we did not rely on researchers’ interpretation of the transcripts but on summaries of exactly what the participants said [ 52 ].

Limitations

There are additional considerations about our study. First is the nature of the sample. All OAEH participants and homelessness service providers are from one state within the US. While many OAEH shared about life outside the region, most were born and raised there. The sample size of each participant group was small; however, we sought a range of experiences within the group. Yet, aspects of the sample, such as recruitment location, limited the diversity of the sample. The size of the cities where recruitment took place is lower to upper medium density cities and are surrounded by rural counties. Rural homelessness is substantially under-researched but, given geographical barriers, is characterized by unsafe or lack of housing options, jobs, transportation, and healthcare access [ 74 , 75 ]. The study focused on adapting the guide for those providers who are limited in prognostic delivery, but further adaptations incorporating perspectives of other health and homelessness professionals, such as physicians and chaplains, would be beneficial.

Second, we did not conduct additional modifications after analysis and revising the guide. This study would have been strengthened by gathering perspectives and feedback from participants with the final adapted guide. Interviews with palliative care providers were conducted to offer an iterative approach to adaptations, gradually incorporating modifications with each subsequent interview and building upon the interviews gathered by OAEH and homelessness service providers. Additionally, we were able to use our team’s extensive experience facilitated direction in the adaptation process. All participants were encouraged to contact the research team in between interviews or at the conclusion of their final interview if they had any additional ideas or feedback. We encourage clinicians and researchers to continue to refine the guide and disseminate their methods and results. Next steps for the development of this tool include feasibility and implementation studies, as well as determining the impact training has for homelessness service providers.

Ultimately, improved housing stability and case management will improve healthcare for OAEH; [ 76 ] however, while policy and practice initiatives are developed to address these needs, equipping homelessness service providers with tools to promote serious illness conversations is a promising strategy to improve serious illness care. While training and implementation mapping are needed before initiating this tool in any homelessness service setting, The Serious Illness Conversation Guide for Unhoused or Housing Vulnerable Older Adults paper illustrate a promising first step towards addressing service gaps between healthcare and homelessness services for vulnerable OAEH.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

Older Adults Experiencing Homelessness

Serious Illness Conversation Guide

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Acknowledgements

We appreciate the support in data preparation for this manuscript by Michael A. Light. MSW, MPH, LICSW, LMP, APHSW-C, Co-director of the University of Washington Palliative Care Training Center and Clinical Social Worker with the Harborview Medical Center Homeless Palliative Care Program, Hilda Okeyo, BSN, RN doctoral student with the University of Kentucky College of Nursing, and Amanda Murphy, RN, Clinical Implementation Specialist with Ariadne Labs Serious Illness Care Program.

This research is funded by The Rita and Alex Hillman Foundation, Grant No. A-2773.

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Published: 22 June 2024

Creep level qualitative evaluating of crushed rock based on uncertainty measurement theory and hierarchical analysis

Shiwei Wu 1 ,
Qi Mou 2 , 3 , 4 &
Tao Yang 1

Scientific Reports volume 14 , Article number: 14365 ( 2024 ) Cite this article

Metrics details

Civil engineering
Computational methods

A large number of tectonically mixed rock belts and complex tectonic zones are distributed in the southwestern part of China. In these areas, high geostress and tectonic stresses have caused some underground rock layers to be crushed and broken, eventually forming crushed rock zones. Which may undergo creep deformation under long-term loads. The manuscript is based on a typical crushed rock in the southwestern China. Firstly, the factors affecting creep deformation were analysed, and the response law of each influencing factor to rock creep is demonstrated. Then, the theory of uncorroborated measures and hierarchical analysis were used to systematically correlate the factors influencing creep. Thereby, a creep level qualitative evaluating model of crushed rock is established. Finally, this model was used to qualitatively evaluate the creep level of the crushed rock in the study area. It is concluded that the creep level qualitative evaluating of this crushed rock is rated as Class II, which is characterised by a low creep level and small creep deformations (0–10 mm). The research results can provide a reference for the creep analysis of crushed rock and provide a basis for the safe construction of engineering slopes.

Introduction

In the southwest of China lies the world's highest average altitude plateau, the Tibetan Plateau. The Tibetan Plateau was formed as a result of strong tectonic movements between plates, and this process of movement is not done all at once, but continues over a long period of time to this day and beyond. The maximum horizontal stress azimuthal rosette for each region of China is shown in Fig. 1 . It can also be seen from the figure that the maximum horizontal geostress in the Qinghai-Tibet Lot is 64 MPa and the minimum horizontal geostress is 38 MPa (buried depth of 2000 m), which is significantly higher than that of other regions in China 1 .

Horizontal stress values and orientation characteristics by region in China.

Macroscopically analyse, tectonic movements have resulted in the existence of a large number of tectonic fracture zones in the south-western part of China, and formation of numerous mountains, rivers and canyons. Microscopic analysis, the development of geological tectonics and the high altitude of the mountains in the region are very likely to cause the rock inside the mountains to be in a high stress and complexity environment, resulting in the development of structural planes in the rock and the rock are more fragmented. Resulting in rock development of structural planes and fragmentation.

The paper takes the crushed rock in southwest China as the research object, the geological environment in this region is extremely complex, under the action of long-term high stress environment, and so on, the crushed rock inside the mountain is susceptible to creep deformation. The paper firstly analyses the main influencing factors and influencing mechanisms of rock creep. Then, the theory of computation of the unconfirmed measurement model and the hierarchical analysis method were used to establish a creep level qualitative evaluation model of the crushed rock. Finally, this model was used to qualitatively evaluate the creep level of the crushed rock in the southwestern China. The research results can provide a reference for the creep analysis of crushed rock and provide a basis for the safe construction of engineering slopes.

Analysis of factors influencing the creep behaviour of rock

The effect of lithology on rock creep.

The influence of lithology on rock creep is mainly manifested in the fact that different rocks have different strength characteristics. In order to study the effect of different lithology on the creep characteristics of rock, Wu Zhiyong 2 designed a creep test programme for different types of rock mass under the same conditions of stress, temperature, and so on. Creep tests were carried out on sandstone, mudstone, and mixed sand-mudstone respectively, as a means of investigating the response degree to creep of rocks with different softness and strengths. The test results are shown in Fig. 2 . As can be seen from Fig. 2 , the creep deformation size of the three rock types: sandstone < mixed sand-mudstone < mudstone. It can be seen that, all other things being equal, a rock with softer lithology has a more pronounced creep phenomenon and greater creep deformation compared to a rock with harder lithology.

Creep test curves of sandstone, mudstone and mixed sand and mudstone (axial strains).

The effect of rock structure on rock creep

The effect of rock structure on rock creep is manifested in two main ways. Firstly, the degree of fragmentation of the rock. For the more complete rock, the less distribution of structural planes and cracks, therefore, its resistance to creep deformation will be stronger, the less likely to creep. On the contrary, for the more broken rock, the more distribution of structural planes and cracks, significant creep deformation of the rock occurs under the long-term external forces. The code of Practice for Geotechnical Investigation classifies rocks with varying degrees of fragmentation into five categories 3 (see Table 1 for details).

Secondly, the properties of the structural planes of the rock also have a great influence on rock creep. For rocks with rigid structural planes (structural planes with high friction coefficient, mostly without fillers, small openings), their properties are relatively stable and not susceptible to creep. Nevertheless, for rocks with weak structural planes (structural planes with low friction coefficient, mostly clay filled, wide openings), their ability to withstand creep is weak and they are susceptible to creep deformation 4 .

The effect of stress environments on rock creep

The effect of the stress environments on rock creep is mainly reflected in both axial pressure and confining pressure. Firstly, for the axial pressure aspect, Griggs designed a scheme for creep experiments on solnhofen limestone specimens using different axial pressure at 500 MPa confining pressure, the results are shown in Fig. 3 . It can be found from the figure, when the confining pressure is certain, the axial pressure is slightly greater than the peripheral pressure (such as 650 MPa), three stages of typical creep deformation can then be seen. When the axial pressure is small, only the first two creep stages can occur. It can be seen that, all other things being equal, the higher the axial pressure, the more intense the creep occurs, and conversely, the lower the axial pressure, the weaker the creep phenomenon of the rock 5 .

Creep curves of solnhofen limestone under different axial pressures.

In order to study the effect of different confining pressures on the creep characteristics of the rock, Wei Yao 6 designed a creep deformation test of the rock with different confining pressures under the same conditions of lithology, temperature, and so on. The test results are shown in Fig. 4 , from the figure, it can be found that under the same conditions, the higher the confining pressure, the rock can show lower creep deformation characteristics (Load factor = 0.6 means that the axial stress remains unchanged during loading and the size is 0.6 times the peak axial strength in conventional triaxial compression).

Creep curves of sandstones at different confining pressures.

The effect of temperature on rock creep

In order to study the effect of different temperatures on the creep characteristics of the rock, Wei Yao 6 designed a creep deformation test of the rock with different temperatures under the same conditions of lithology, stress, and so on. The test results are shown in Fig. 5 , from the figure, it can be found that under the same conditions, the higher the temperature, the more rapid the development of the creep of the rock specimen, the macroscopic manifestation of creep deformation is larger; Meanwhile the lower the temperature, the rock specimen creep development is slower, the macroscopic manifestation of creep deformation is smaller (Load factor = 0.3 means that the axial stress remains unchanged during loading and the size is 0.3 times the peak axial strength in conventional triaxial compression) 7 .

Creep curves of sandstone at different temperatures.

The effect of humidity on rock creep

Griggs conducted uniaxial creep tests by immersing snowflake gypsum in different solutions as a means of investigating the pattern of humidity effects on rock creep, and the results are shown in Fig. 6 . As can be seen from the figure, the creep curve of the snowflake gypsum changed very significantly after immersing it in the solution compared to the dry condition, and the creep deformation increased greatly. Therefore, it can be seen that under the same conditions of stress and temperature, and so on, the creep deformation of the rock in solution is greater than that under dry conditions.

Creep curves of snowflake gypsum at different humidity.

For rock in natural slopes, the influence of the creeping behaviour of the rock is mainly due to natural environmental effects, such as lithology, rock structure, axial pressure, confining pressure, temperature, humidity, and so on. As for the rock in the engineered slopes, in addition to considering the action of the natural environment, but also to consider the impact of construction, such as excavation, filling, vehicle loads and other operations will cause changes in the stress environment of the rock, which will lead to changes in the creep behaviour. Therefore, the study of rock creep should be carried out depending on the overall environment to which the rock is subjected.

The manuscript summarises a large number of theoretical studies and test results on rock creep at home and abroad, and analyses the factors affecting the creep behaviour of rock, including lithology, rock structure, axial pressure, peripheral pressure, temperature, humidity, construction and so on 8 . The response law between rock creep and various influencing factors is explored too. However, in the case of real engineering can not exist only a single factor, the creep of the rock is bound to be affected by the coupling of various factors, only a single factor is obviously unreasonable. So it is necessary to find a way to carry out a multi-factor analysis of the creep of the rock.

Evaluation methods and steps

Determining the computational matrix based on the theory of uncorroborated measures.

Suppose that the object A to be evaluated has n impact factors 9 , 10 a 1 , a 2 , …, a n , then the object can be written as A = {a 1 , a 2 , …, a n }. And for each impact factor a i (i = 1, 2, …, n) there are P evaluation levels e 1 , e 2 , …, e p , and all have e 1 > e 2 > ⋯ > e p , noting that P = {e 1 , e 2 , …, e p } 11 , 12 .

Firstly, each impact factor of the evaluation object is rated according to the enterprise scoring method or inductive method 13 , 14 and \({\sum }_{j=1}^{p}{a}_{ij}=100\) . Where a ij denotes the observed value of indicator a i at the j-th evaluation level e j (j = 1, 2, …, p), Normalising the resulting observations to obtain u ij = a ij /100 denotes the uncorroborated measure of influence factor a i 15 , 16 , count u ij = {a i1 , a i2 , …, a ip } (i = 1, 2, …, n), u ij is then the uncorroborated measurement matrix as shown in Eq. ( 1 ).

Determining the indicator weights based on hierarchical analysis

Each impact factor will not have the same degree of importance in relation to the object 17 , 18 , so the degree of impact and importance will be determined by weighting it. Hierarchical analysis is suitable for evaluating and analysing research objects affected by multiple factors 19 , 20 , and it is systematic and the calculations are clear, so it makes sense to use it to determine the weights of the impact factors 21 , 22 . The calculation steps of the hierarchical analysis method are shown in Fig. 7 (b ij is the importance of B i to B j with respect to the element A n in the previous level).

AHP weight calculation step diagram.

Comprehensive metrics evaluation

Let u k = u (a i ∈ e k ) be the degree to which each impact factor of the evaluation object at the k-th evaluation level, then the following equations can be obtained:

Obviously, 0 ≤ u k ≤ 1 and \({\sum }_{k=1}^{p}{\mu }_{k}=1\) 。

Creep level qualitative evaluating of crushed rock

Engineering geological data of the research object.

This paper takes the crushed rock in a region in south-west China as the research object, the geological environment of this region is extremely complex, under the action of long-term high stress environment, the crushed rock inside the mountain is easy to occur creep deformation. According to the results of the ground investigation report, the study area is located in a tectonically mixed rock belt, and the area is mostly characterised by high and steep slopes 23 . The manuscript selected a typical high steep engineering slope in the region as the target 24 , according to the results of the rock coring in the deep layer of the slope, selected the crushed representative rock as the object of study, the relevant engineering geological data as shown in Fig. 8 .

Engineering geological data of crushed rock.

Establishing the qualitative evaluation model for the creep level of crushed rock

The paper has already carried out analysis of the 8 factors affecting the creep level of crushed rock, including rock quality, rock integrity, structural surface properties, axial pressure, confining pressure, temperature, humidity, construction. Among these, rock quality, rock integrity and structural plane properties characterise the strength of the rock itself, classifying them as strength factors; The axial pressure and confining pressure characterise the stress environment in which the rock is located and are classified as stress factors; Temperature, humidity, and construction are the external environments in which the rock mass is exposed, classifying them as external factors. Eventually, a creep level qualitative evaluation model of the crushed rock is formed with the creep level qualitative evaluation of the crushed rock as the target layer, the strength factor, stress factor, and external factor as the normative layer, and the eight specific influencing factors as the index layer. The model built is shown in Fig. 9 .

Creep level qualitative evaluation model of the crushed rock.

Determining the weights

The AHP method was used to determine the weights of the indicators in the creep level qualitative evaluation model. Based on the processing of the ground investigation report and other related information of the research object, combined with the results of expert scoring, the comparison matrices A, B 1 , B 2 , B 3 is constructed as shown below.

From the above comparison matrices, the weights of the normative layer layer to the target layer and each indicator layer to the normative layer can be calculated, and then checking the consistency of calculations. The results of the weighting calculations are shown in Table 2 .

After obtaining the weights of the indicators at each level, the matrices were then tested for consistency. Firstly, the value of CI is calculated according to Eq. ( 4 ), and then it is compared with the random consistency index RI, which takes the values shown in Table 3 .

Only when the random consensus ratio CR = CI/RI < 0.1 can the judgement matrix be established; Otherwise, the judgement matrix will have to be adjusted. The matrix consistency tests of the target layer to the criterion layer are as follows:

Matrix consistency test of the target layer against the normative layer:

Matrix consistency test of the normative layer against the index layer:

From the above calculation results, it can be seen that each judgement matrix has passed the consistency test, so the constructed judgement matrix is valid and reasonable, and then the calculation can be continued to derive the weight vector:

Determining the unknown measurement matrix

In order to ensure the accuracy of the grading of each impact factor and to reduce errors in all aspects, the grading should be divided into as few grades as possible to reduce the influence of subjective factors. With reference to the domestic and foreign crushed rock creep theory researches, the creep level is divided into 4 levels. Respectively, Grade I: the creep level is weak, creep phenomenon is not evident; Grade II: the creep level is low, the deformation is small (0–10 mm); Grade III: the creep level is medium, the creep deformation is slightly large (1–5 cm); Grade IV: the creep level is high, the creep deformation is large (more than 5 cm). The division standard is shown in Table 4 .

By collating the measured data of the crushed rock in the study area; Surveying 100 people, including management, employees and experts responsible for the project area; Summarising the mean scoring table. The scoring results were obtained as shown in Table 5 .

According to Eq. ( 1 ) Uncertainty Measurement Matrix Calculation Method can get the Uncertainty Measurement Matrix, as shown in Eq. ( 5 ).

Combined with the results of the weighting calculations, the final composite measure evaluation vector is calculated:

According to the calculation theory of the unconfirmed measure model, the maximum value in the final derived comprehensive measure evaluation vector μ is 0.360, and its corresponding qualitative evaluation grade of the creep level is Class II, indicating that the creep level of the crushed rock within the mountain in the region is low, and the amount of creep deformation is small. And from the calculation process, it can be seen that, among the eight indicators affecting the creep level of the research object, the results leading to the low creep level should be the result of the combined coupling of four influencing factors: rock quality, rock integrity, axial pressure, and confining pressure, other factors have a low effect on creep.

Firstly, it can be found from the previous engineering geological data (core sampling diagram) that the crushed rock is extremely fragmented and low rock integrity, which will make it susceptible to creep even lead to a large creep deformation. However, the crushed rock properties are good (the rock has a gravity of 26 kn/m 3 and an internal friction angle of 40°, which indicates that the strength of the crushed rock is high). It is assumed that its ability to resist creep deformation is strong. In addition, the stratigraphical sections show that the maximum burial depths of the crushed rock is up to 300 m, as a result, the crushed rock is in a good stress environment in the stratigraphy. Which means that the crushed rock is stabilised, and has a high resistance to creep deformation caused by other factors (such as construction, temperature and humidity, et al.)

Therefore, under the effect of mutual coupling of various factors, the final result is that the creep level of the crushed rock in the study area is low, and the amount of deformation is small (the creep level of the crushed rock is evaluated to be class II).

Taking the crushed rock in Southwest China as a research object, the paper systematically analysed eight factors affecting the creep of the rock. Then based on the theory of unconfirmed measurement and hierarchical analysis, a model was established to qualitatively evaluate the creep level of the crushed rock. Finally, the established model was used to qualitatively evaluate the creep level of the crushed rock in the study area. It is concluded that the creep level of the crushed rock is evaluated at Grade II, which is characterised by a low creep level of the rock and a small amount of deformation (0–10 mm). The research results can provide a reference for the creep analysis of crushed rock and provide a basis for the safe construction of engineering slopes.

Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

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Sichuan Provincial Transportation Science and Technology Project “Research and Demonstration of Green Comprehensive Utilisation and Ecological Restoration Technology of Tunnel Slag in High-speed Tunnels Along the River” (2018-ZL-15).

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Tao Yang and Shiwei Wu responsible for proposing ideas; Qi Mou responsible for providing engineering geological information; Shiwei Wu processed the data and wrote the main manuscript text.

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Wu, S., Mou, Q. & Yang, T. Creep level qualitative evaluating of crushed rock based on uncertainty measurement theory and hierarchical analysis. Sci Rep 14 , 14365 (2024). https://doi.org/10.1038/s41598-024-65222-x

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DOI : https://doi.org/10.1038/s41598-024-65222-x

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Research Title:	Comprehensive Research Report on Innovative Technologies in 2055
Author(s):
Date:	January 1, 2055

I. Executive Summary

This research report explores the advancements and anticipated trends in innovative technologies by the year 2055. Our findings provide a detailed analysis of emerging technologies, their potential impact on various industries, and future projections.

II. Introduction

A. purpose of the report.

The purpose of this report is to investigate and present insights into the innovative technologies that are expected to shape the future. It aims to offer a comprehensive understanding of these technologies' development and potential applications.

B. Scope and Objectives

Identify key innovative technologies.

Analyze the impact of these technologies on different sectors.

Provide future projections and recommendations.

III. Methodology

A. research design.

Our research employs a mixed-method approach, combining quantitative data analysis with qualitative insights from industry experts.

B. Data Collection

Data was collected from various primary and secondary sources, including academic journals, industry reports, and expert interviews.

IV. Findings and Analysis

A. technology trends.

The following table highlights the emerging innovative technologies and their potential implications:

Technology	Potential Application	Impact
Artificial Intelligence	Healthcare, Finance, Manufacturing	High
Quantum Computing	Cryptography, Drug Discovery	Medium
Biotechnology	Genetic Engineering, Agriculture	High

B. Sectoral Impact

Healthcare: Improved diagnostics and personalized treatment through AI.

Finance: Enhanced risk management and fraud detection using machine learning.

Manufacturing: Increased efficiency through automation and advanced materials.

V. Discussion

The integration of innovative technologies poses both opportunities and challenges. This section discusses the potential risks and benefits, as well as ethical considerations associated with these advancements.

VI. Conclusion and Recommendations

A. conclusion.

In conclusion, the innovative technologies of 2055 are set to transform industries and enhance human capabilities. Stakeholders must prepare for these changes to leverage their full potential.

B. Recommendations

Invest in research and development to stay ahead of technological advancements.

Prioritize ethical considerations and regulatory compliance.

Foster collaboration between industries to drive innovation.

VII. References

All sources referenced in this report are available upon request from [Your Company Name] .

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The Utility of Template Analysis in Qualitative Psychology Research

Serena McCluskey

Introduction

What Is Template Analysis?

Epistemological Position of Template Analysis

How Template Analysis Relates to Other Forms of Thematic Analysis

Examples of Research Using Template Analysis

Case Study 1. Collaborative working in cancer care: An example of a large-scale project using a team analysis approach

Case Study 2. Patient and significant other beliefs about back pain and work participation: An example of a study using existing theory in template development

Case Study 3. The erotic experience of BDSM participation: An example of a study using Template Analysis from an interpretive phenomenological stance

Conclusions

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Template Analysis

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Practical thematic analysis: a guide for multidisciplinary health services research teams engaging in qualitative analysis

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