two granddaughters when I get the chance!! I enjoy most
music except for Rap! I keep fit by jogging, walking, and bicycling(at least three times a week). I have travelled to many places and RVD the South-West U.S., but I would now like to find that special travel partner to do more travel to warm and interesting countries. I now feel it’s time to meet a nice, kind, honest woman who has some of the same interests as I do; to share the happy times, quiet times and adventures together.
Profile No. | Data Item | Initial Codes |
---|---|---|
2 | I enjoy photography, lapidary & seeking collectables in the form of classic movies & 33 1/3, 45 & 78 RPM recordings from the 1920s, ’30s & ’40s. I am retired & looking forward to travelling to Canada, the USA, the UK & Europe, China. I am unique since I do not judge a book by its cover. I accept people for who they are. I will not demand or request perfection from anyone until I am perfect, so I guess that means everyone is safe. My musical tastes range from Classical, big band era, early jazz, classic ’50s & 60’s rock & roll & country since its inception. | HobbiesFuture plans Travel Unique Values Humour Music |
At this stage, you have to make the themes. These themes should be categorised based on the codes. All the codes which have previously been generated should be turned into themes. Moreover, with the help of the codes, some themes and sub-themes can also be created. This process is usually done with the help of visuals so that a reader can take an in-depth look at first glance itself.
Now you have to take an in-depth look at all the awarded themes again. You have to check whether all the given themes are organised properly or not. It would help if you were careful and focused because you have to note down the symmetry here. If you find that all the themes are not coherent, you can revise them. You can also reshape the data so that there will be symmetry between the themes and dataset here.
For better understanding, a mind-mapping example is given here:
You need to review the themes after coding them. At this stage, you are allowed to play with your themes in a more detailed manner. You have to convert the bigger themes into smaller themes here. If you want to combine some similar themes into a single theme, then you can do it. This step involves two steps for better fragmentation.
You need to observe the coded data separately so that you can have a precise view. If you find that the themes which are given are following the dataset, it’s okay. Otherwise, you may have to rearrange the data again to coherence in the coded data.
Here you have to take into consideration all the corpus data again. It would help if you found how themes are arranged here. It would help if you used the visuals to check out the relationship between them. Suppose all the things are not done accordingly, so you should check out the previous steps for a refined process. Otherwise, you can move to the next step. However, make sure that all the themes are satisfactory and you are not confused.
When all the two steps are completed, you need to make a more précised mind map. An example following the previous cases has been given below:
Now you have to define all the themes which you have given to your data set. You can recheck them carefully if you feel that some of them can fit into one concept, you can keep them, and eliminate the other irrelevant themes. Because it should be precise and clear, there should not be any ambiguity. Now you have to think about the main idea and check out that all the given themes are parallel to your main idea or not. This can change the concept for you.
The given names should be so that it can give any reader a clear idea about your findings. However, it should not oppose your thematic analysis; rather, everything should be organised accurately.
If not, we can help. Our panel of experts makes sure to keep the 3 pillars of Research Methodology strong.
Also, read about discourse analysis , content analysis and survey conducting . we have provided comprehensive guides.
You need to make the final report of all the findings you have done at this stage. You should include the dataset, findings, and every aspect of your analysis in it.
While making the final report , do not forget to consider your audience. For instance, you are writing for the Newsletter, Journal, Public awareness, etc., your report should be according to your audience. It should be concise and have some logic; it should not be repetitive. You can use the references of other relevant sources as evidence to support your discussion.
What is meant by thematic analysis.
Thematic Analysis is a qualitative research method that involves identifying, analyzing, and interpreting recurring themes or patterns in data. It aims to uncover underlying meanings, ideas, and concepts within the dataset, providing insights into participants’ perspectives and experiences.
This article presents the key advantages and disadvantages of secondary research so you can select the most appropriate research approach for your study.
Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers.
Baffled by the concept of reliability and validity? Reliability refers to the consistency of measurement. Validity refers to the accuracy of measurement.
USEFUL LINKS
LEARNING RESOURCES
COMPANY DETAILS
Academia.edu no longer supports Internet Explorer.
To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser .
Enter the email address you signed up with and we'll email you a reset link.
Data analysis is central to credible qualitative research. Indeed the qualitative researcher is often described as the research instrument insofar as his or her ability to understand, describe and interpret experiences and perceptions is key to uncovering meaning in particular circumstances and contexts. While much has been written about qualitative analysis from a theoretical perspective we noticed that often novice, and even more experienced researchers, grapple with the 'how' of qualitative analysis. Here we draw on Braun and Clarke's (2006) framework and apply it in a systematic manner to describe and explain the process of analysis within the context of learning and teaching research. We illustrate the process using a worked example based on (with permission) a short extract from a focus group interview, conducted with undergraduate students.
Kevin Y F Cheung
Pedagogic interventions that focus on improving a writer’s ‘sense of self as an author’ have shown the potential to reduce unintentional plagiarism and improve student writing at undergraduate level. Development of the authorial identity concept has come from focus on student understandings of authorship. Research suggests that undergraduate students lack understanding of authorship and experience difficulties identifying with the role of ‘author’ when writing. Further development of authorial identity as a useful psychological and pedagogical construct requires exploration of the ways that other individuals understand authorial identity. One perspective that has not been examined is that of academic staff. The understandings of authorial identity held by professional academics is particularly important as they are readers and assessors of undergraduate assignments, as well as writers of journal articles that serve as examples of academic writing that students use as typical examples of academic writing. The current thesis presents original qualitative research exploring the understandings of authorial identity from the perspective of academic psychologists teaching at a post 1992 university in the UK. An interview schedule informed by the literature and two pilot interviews were used to conduct six semi structured interviews that were recorded and transcribed. Transcripts were then analysed using thematic analysis to identify recurring themes in the transcripts. The analysis suggests that academics hold two salient understandings of authorial identity. Academics understood authorial identity to be an attribute of the writer and an attribute of a piece of writing. This is represented by the two main themes of ‘an authorial writer’ and ‘an authorial piece’. From this analysis it is possible to conceptualise psychology academics’ understandings of a typical authorial writer and a typical authorial piece. These were identified by the subthemes within each main theme. An authorial writer was understood to use authorial thinking, value writing skills, take ownership of their writing, identify with the role of author, have positive self beliefs related to their writing, and communicate effectively in writing. Academics understood an authorial piece of writing to be high quality, inform about the reader, written in an authorial style, original, and an authorial genre of writing. These form the basis of a framework of authorial identity that builds on previous investigation conducted with students. This model will inform further research and pedagogies based on an authorial identity approach to plagiarism.
AISHE-J: The All Ireland Journal of Teaching and Learning in Higher Education
Gerry Gallagher
As the number of learning and teaching continuing professional development (CPD) courses increases in Higher Education Institutions (HEIs), so too does the accompanying number of learning innovations being implemented and evaluated. The evaluation process requires valid and reliable data collection and analysis procedures to be established. In many cases, qualitative methods such as interviews, focus groups and free-text responses are employed for this purpose. These methods generate large volumes of data, which must be coded and analysed in a thorough and professional manner. While commercial software packages can assist in this analysis, in a difficult economic climate, the cost of campus-wide licenses for such can be quite prohibitive. In a recent publication aimed at enhancing the learning environment in practical sessions, Bree et al. (2014) implemented a simple, cost-effective technology-based analysis of captured focus group data with a widely used software suite. This ...
Studies in Higher Education
Edward Stupple
Prof Alejandro Armellini
This article focuses on university students’ perceptions of their learning and social experiences in the context of an institution-wide pedagogic shift to Active Blended Learning (ABL). It explores students’ perceived enablers and barriers to learning in the new environment. Thematic analysis was conducted on data collected through focus groups involving 60 students. Three key categories emerged: (1) learning experiences, (2) social experiences and (3) support provision. Findings suggest that quality learning experiences are necessary but not sufficient to provide a quality overall student experience. Tutors play a key role in both. Staff-student partnerships are central to promote learner engagement and a sense of belonging. Students value, above all, regular synchronous and asynchronous interaction with peers, tutors and content, enabled by sound pedagogic design and the appropriate deployment of digital technologies. Employability-focused activities that explicitly link theory an...
Sinegugu Duma
Background: Problem-based learning (PBL) was introduced in Malawi in 2002 in order to improve the nursing education system and respond to the acute nursing human resources shortage. However, its implementation has been very slow throughout the country.Objectives: The objectives of the study were to explore and describe the goals that were identified by the college to facilitate the implementation of PBL, the resources of the organisation that facilitated the implementation of PBL, the factors related to sources of students that facilitated the implementation of PBL, and the influence of the external system of the organisation on facilitating the implementation of PBL, and to identify critical success factors that could guide the implementation of PBL in nursing education in Malawi.Method: This is an ethnographic, exploratory and descriptive qualitative case study. Purposive sampling was employed to select the nursing college, participants and documents for review.Three data collecti...
Research and Practice in Technology Enhanced Learning
Diogo Casanova
Digital assessment and feedback have been a growing area of research and practice in the past decade in higher education. Within this theme, research has been published highlighting the importance of learner agency in the assessment and feedback process as a way to develop assessment literacy in contrast with the existing lecturer-led approach. In this research, we aimed to find out whether lecturers are willing to let go of some of the power they currently have in the digital assessment and feedback process and how they see opportunities for agency being developed in the digital assessment and feedback systems. We collected data from 10 sandpits with 58 lecturers in which, using a storytelling technique and one mockup of a digital assessment and feedback system, we discussed and critiqued an assessment scenario intending to collect perceptions about digital assessment and feedback and the constraints felt by lecturers in their assessment practice. Based on these perceptions, we identify recommendations that may improve digital assessment and feedback systems and practices. We discuss the data and the recommendations based on three clusters of themes: (i) preparation for the assessment, (ii) formative feedback and (iii) feedback post-submission.
Small Group Research
Karin Frykedal
Group work assessment is often described by teachers as complex and challenging, with individual assessment and fair assessment emerging as dilemmas. The aim of this literature review is to explore and systematize research about group work assessment in educational settings. This is an integrated research area consisting of research combining group work and classroom assessment. A database search was conducted, inspired by the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). The analysis and categorization evolved into a typology consisting of five themes: (a) purpose of group work assessment, (b) what is assessed in group work, (c) methods for group work assessment, (d) effects and consequences of group work assessment, and (e) quality in group work assessment. The findings reveal that research in the field of group work assessment notably focuses on social skills and group processes. Peer assessment plays a prominent role and teachers ...
International Journal of Evaluation and Research in Education (IJERE)
Andi Anto Patak
People who plagiarize have a complex problem. Plagiarism could be by accident, by mistake, or on purpose. This research aims at exploring the reasons for plagiarizing and the significance of citing and referencing using Mendeley to avoid plagiarism. Four Indonesian Mendeley Advisors were interviewed online using convenient sampling technique. This study revealed that reasons for plagiarizing are time restriction, laziness, and busy. The significance of citing and referencing using Mendeley to avoid plagiarism are (1) confirm, justify, and claim the issue conveyed in scientific work; (2) highlight a particular idea; (3) criticize or approve the premise of others, and (4) build argument. Mendeley usage acquaintance for scientific writing is expected to be practical tools for avoiding plagiarism and promote academic honesty in the setting of higher education. However, the role of supervisor is crucial to provide useful feedback for their students’ writing to help students avoid plagiar...
SAGE Open Nursing
Philemon Amooba
Introduction The successful transition of nurses from clinical practice to academia is essential to the training of a proficient future nursing workforce. However, deprived of requisite support and guidance, novice nurse educators often find the transition from bedside nursing practice to the classroom challenging and hence, adopt some coping strategies to facilitate their transition. Yet, little is known about the strategies adopted by Ghanaian novice nurse educators to facilitate their transition. Objective This study explored the strategies adopted by novice nurse educators to facilitate their transition from practice to academia in three nursing training colleges in Ghana. Methods This study adopted a descriptive qualitative study design. The study used a purposive sampling technique to recruit 12 novice nurse educators. Data were generated through individual in-depth interviews using a semistructured interview guide. Interviews were audio-recorded, transcribed verbatim, and ana...
Joe Gregory
Model-Based Systems Engineering (MBSE) represents a move away from the traditional approach of Document-Based Systems Engineering (DBSE). It is claimed that MBSE promotes consistency, communication, clarity and maintainability within systems engineering projects and addresses issues associated with cost, complexity and safety. While these potential benefits of MBSE are generally agreed upon by would-be practitioners, its implementation is challenging and many organisations struggle to overcome the cultural and technical hurdles along the long and winding road to MBSE adoption. In this paper, we aim to ease the process of implementation by investigating where the current issues with the existing systems engineering processes lie, and where a model-based approach may be able to help, from the perspective of engineers working on spacecraft functional avionics in Airbus. A repeatable process has been developed to elicit this information. Semi-structured interviews have been conducted wi...
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
Bernadette Brereton
Sarah Ranby
Rahman Sahragard
Dr. Elaine Gregersen
Alani Ramos
Sheila sefhedi
Jose "Jay" Fulgencio
JURNAL ILMIAH DIDAKTIKA: Media Ilmiah Pendidikan dan Pengajaran
Safrul Muluk
Pharmacy Education
Bugewa Apampa
Irish Journal of Technology Enhanced Learning
Claire McAvinia
Teaching of Psychology
Olga Khokhlova
The Cognitive Behaviour Therapist
Alex Hassett
Asian-Pacific Journal of Second and Foreign Language Education
Davoud Amini
Margarida Coelho
Metathesis: Journal of English Language, Literature, and Teaching
Mora Siahaan
International Journal of Environmental Research and Public Health
Jens Skogen
ProQuest 2020
Carlos Gonzalez
NURFARADILLA NASRI
Dr. Ghadah Al Murshidi
Child Language Teaching and Therapy
Stanislava Antonijevic
BMC Medical Education
mohammad keshavarzi
Journal of Engineering, Design and Technology
Abimbola Windapo
Psychology Teaching Review
Julie Hulme
Barbara Tam
Journal of Practical Nurse Education and Practice
Viola Manokore
Richa Basra
Acta Paedagogica Vilnensia
Stefanija Alisauskiene
The Professional Counselor
Eric Beeson
Emerging from the third space chrysalis: Experiences in a non-hierarchical, collaborative research community of practice
Silvina Bishopp-Martin , Ian Johnson
Rasha AlOkaily
Irish Veterinary Journal
Vivienne Duggan
Daniel A Lee
Cogent Education
Geraldine McDermott
International Journal of Learning, Teaching and Educational Research
Dr Omkar Dastane
Academics Education International Journals
Isai amuthan krishnan
Andrea Lumbi Bustamante
English as a Foreign Language International Journal (formerly Asian EFL Journal)
Dr. John L Adamson , Nonie Chiang
Dissertations 5: findings, analysis and discussion: home.
The time has come to show and discuss the findings of your research. How to structure this part of your dissertation?
Dissertations can have different structures, as you can see in the dissertation structure guide.
Dissertations organised by sections
Many dissertations are organised by sections. In this case, we suggest three options. Note that, if within your course you have been instructed to use a specific structure, you should do that. Also note that sometimes there is considerable freedom on the structure, so you can come up with other structures too.
A) More common for scientific dissertations and quantitative methods:
- Results chapter
- Discussion chapter
Example:
if you write a scientific dissertation, or anyway using quantitative methods, you will have some objective results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter.
B) More common for qualitative methods
- Analysis chapter. This can have more descriptive/thematic subheadings.
- Discussion chapter. This can have more descriptive/thematic subheadings.
C) More common for qualitative methods
- Analysis and discussion chapter. This can have more descriptive/thematic titles.
If your dissertation uses qualitative methods, it is harder to identify and report objective data. Instead, it may be more productive and meaningful to present the findings in the same sections where you also analyse, and possibly discuss, them. You will probably have different sections dealing with different themes. The different themes can be subheadings of the Analysis and Discussion (together or separate) chapter(s).
Thematic dissertations
If the structure of your dissertation is thematic , you will have several chapters analysing and discussing the issues raised by your research. The chapters will have descriptive/thematic titles.
Intended for healthcare professionals
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.
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
Step 1: reading.
All manuscript authors read the data
All manuscript authors write summary memos
Coders perform both data management and early data analysis
Codes are complete thoughts or sentences, not categories
Researchers host a thematic analysis session and share different perspectives
Themes are complete thoughts or sentences, not categories
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
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.
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.
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
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.
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.
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.
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
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.
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 ).
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
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.
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.
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
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.
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.
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
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
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.
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 ).
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
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
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.
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.
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
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.
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.
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.
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.
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.
We are a place for students of psychology to discuss study methods, receive assistance with homework, enquire for job-searching advice, and all else that come to mind. This community is aimed at those at the beginner to intermediate level, generally in or around undergraduate studies. Graduate students and professionals are recommended to our sister subreddit, r/AcademicPsychology.
I’m a student and have just transcribed all my data. This is my first time doing a qualitative research using thematic analysis. Do you have any tips?
Priestley, Jemma (2015) Experiences of Living with a Partner with Depression: A Thematic Analysis. PhD thesis, University of Essex.
Copy to clipboard Copy Priestley, Jemma (2015) Experiences of Living with a Partner with Depression: A Thematic Analysis. PhD thesis, University of Essex.
According to the Office of National Statistics (2011), approximately six million people provide unpaid care to a family member. The growth of interest in the carer role has helped establish the idea that the provision of informal care warrants attention because of the relationship between caring and burden. It has been suggested that living with someone with depression is comparable to that of other serious mental health problems, such as schizophrenia or dementia. Furthermore, there is evidence that partners are most at risk of burden within the informal caregiving context. The meta-ethnography of existing research indicates that qualitative studies which specifically explore the experiences of living with a family member with depression are somewhat heterogeneous regarding types of relationship with the depressed individual. Combining different relationships (e.g. partners, siblings and parents) within the same study makes it difficult to disentangle data and therefore gaining an in-depth understanding of specific experiences is almost impossible. This study therefore aimed to explore the experiences of living with a partner with depression. In-depth interviews were conducted with nine female and four male participants who live with a partner with depression. A critical realist perspective was held and data was analysed using Braun and Clarke’s six phases of thematic analysis (2006), with the assistance of MAXQDA. Results identified five key themes: ‘making sense of the depression’; ‘the depression cannot be compartmentalised’; ‘a light at the end of the tunnel’; ‘learning to navigate the ‘depression’ maze’; and ‘gaining a new perspective’. The findings illustrate that living with a partner with depression is not a static process and that the needs of the depressed partner are constantly changing. Furthermore, although the findings outline a sequential process that appears cyclical in nature, recognition is given that the phases are dynamic and may overlap. Clinical implications and recommendations are discussed within the context of the Care Act (2014).
Item Type: | Thesis (PhD) |
---|---|
Subjects: | |
Divisions: | |
Depositing User: | Jemma Priestley |
Date Deposited: | 07 Jun 2016 15:40 |
Last Modified: | 07 Jun 2016 15:40 |
URI: |
Available files, --> --> unspecified -->.
Filename: Thesis Final with Amendments.pdf
View detailed statistics
The Ohio State University
...but we are including them in this guide because these reviews are often mistakenly called 'systematic reviews', so we feel they should be discussed. These are also called 'literature reviews' and while they may include a large review of the literature in a given research area, the methodology used is not considered 'systematic'. These projects may or may not include comprehensive search strategies and quality assessments and analysis can be exploratory, chronological, conceptual, thematic or other.
One similar type of review that may be relevant, especially for graduate and postgraduate students is something called a 'systematized review' which are reviews that attempt to include elements of a systematic review, but stop short of following a fully systematic methodology.
According to The SAGE Encyclopedia of Communication Research Methods , "A literature review is a thorough and critical evaluation of previous research on a topic of interest to the author. The review summarizes a particular area of research that helps to explain why an author is interested in a particular topic. A literature review is primarily associated with formal academic writing, such as a master’s thesis, dissertation, or a peer-reviewed journal article. It is commonly part of a proposal written by someone pursuing a thesis or dissertation, known as a research prospectus. A literature review is also a common writing assignment in undergraduate- and graduate-level courses. An effective review of literature will define key terminology, identify a theoretical framework for the topic being addressed, and describe relevant past research in support of a research question or hypothesis.
A narrative literature review provides a synthesis or examination of the literature by considering issues and the development of the research over time. Narrative literature reviews can be contrasted with meta-analysis or the quantitative review or synthesis of literature."
Kysh, Lynn (2013): Difference between a systematic review and a literature review. figshare. Poster. https://doi.org/10.6084/m9.figshare.766364.v1
© The Ohio State University - University Libraries 1858 Neil Avenue Mall, Columbus, OH 43210 Phone: (614) 292-OSUL (6785) | Fax: (614) 292-9101
Request an alternate format of this page | Accessibility | Privacy Policy
Print Page Login to LibApps
IMAGES
VIDEO
COMMENTS
A THEMATIC ANALYSIS OF THE EXCEL PRE-COLLEGIATE PROGRAM AS AN AVENUE OF SUCCESSFUL POSTSECONDARY ENROLLMENT FOR LATINA/O STUDENTS College access and college enrollment rates are significantly lower for students of color, students from lower socioeconomic backgrounds, and first-generation students (Reese, 2008).
How to Do Thematic Analysis | Step-by-Step Guide & Examples. Published on September 6, 2019 by Jack Caulfield.Revised on June 22, 2023. Thematic analysis is a method of analyzing qualitative data.It is usually applied to a set of texts, such as an interview or transcripts.The researcher closely examines the data to identify common themes - topics, ideas and patterns of meaning that come up ...
When undertaking thematic analysis, you'll make use of codes. A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript. For example, if you had the sentence, "My rabbit ate my shoes", you could use the codes "rabbit ...
Thematic analysis (TA) was used to analysis the transcripts. The analysis revealed three main themes; 'Temptation', 'Stay Away' and 'What Would Others Think?'. ... Prof Doc Thesis. Rajmangal, T. 2017. A Thematic Analysis of Young Adults' Perspectives of Gambling and its Representation on Media. Prof Doc Thesis University of East ...
A thematic analysis dissertation is a special kind of research project where you look closely at a specific theme or a group of related themes. You study the patterns in the information you gather and figure out if they have anything to do with the main research question. If you want to get a better idea of how this works, you can check out a ...
Thematic Analysis - A Guide with Examples. Thematic analysis is one of the most important types of analysis used for qualitative data. When researchers have to analyse audio or video transcripts, they give preference to thematic analysis. A researcher needs to look keenly at the content to identify the context and the message conveyed by the ...
The goal of a thematic analysis is to identify themes, i.e. patterns in the data that are important or interesting, and use these themes to address the research or say something about an issue. This is much more than simply summarising the data; a good thematic analysis interprets and makes sense of it.
In a thematic structure, the core chapters present analysis and discussion of different themes relevant to answer the research question and support the overall argument of the dissertation. The chapters will include analysis of texts/ research material. They can explore and connect academic theories/research to develop an argument.
Thematic analysis was conducted on data collected through focus groups involving 60 students. ... process using a worked example based on (with permission) a short extract from a focus group interview, conducted with undergraduate students. Key words: Thematic analysis, qualitative methods. ... often a journal article or dissertation. Table 4 ...
The undergraduate dissertation is often seen as the ^jewel in the crown _ of a degree programme, offering students a chance ... choice (why a student should choose Thematic Analysis [TA] versus Interpretative Phenomenological Analysis [IPA] for example) then this is a good starting point. It would be worth
Thematic analysis is a qualitative method for uncovering a collection of themes, 'some level of patterned response or meaning' (Braun & Clarke, 2006, p. 82) within a data-set. It goes beyond word or phrase counting to analyses involving 'identifying and describing both implicit and explicit ideas' (Guest, MacQueen, & Namey, 2012, p. 10).
A thesis submitted in partial fulfilment of the requirements for the degree of PsychD Department of Psychology University of Roehampton 2019. 1 ... using semi structured interviews. Thematic analysis was used to analyse the data. Four main themes were generated: 1. anonymity, 2. access and availability, 3. communication, and 4. control. The way ...
if you write a scientific dissertation, or anyway using quantitative methods, you will have some objective results that you will present in the Results chapter. You will then interpret the results in the Discussion chapter. B) More common for qualitative methods. - Analysis chapter. This can have more descriptive/thematic subheadings.
The method of analysis chosen for my study was a qualitative approach of thematic analysis. Generally, thematic analysis is the most widely used qualitative approach to analysing interviews. The conceptual framework of the thematic analysis for my interviews was mainly built upon the theoretical positions of Braun and Clarke (2006).
The purpose and design of the website is simple: Pinterest is a virtual pin board that allows the user to sort, collect, and organize online items. Users are equipped with their own personal online 'boards,' to which they can 'pin' items and group according to themes or topics (see Appendix 1 for an example).
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 ...
Take your time with the 6 steps, especially the first steps (data familiarisation,reading, re-reading) and really make sure u understand the dataset you are working with. Reply. mscocomuffin. •. Yeah…I'm in that step rn. I'm afraid I am going to rush in that stage 😅. Reply More replies. DenseRhubarb. •.
Welcome to White Rose eTheses Online - White Rose eTheses Online
practices. Section 3 is the bulk of the report and outlines the results of a thematic analysis of the thirteen focus groups, dividing the text into thirteen separate over-arching themes. Section 4 offers a summary and Section 5 concludes and offers directions for future research.
The data obtained from the best undergraduate student's thesis were analyzed from the perspective of Halliday's theme system and its progression. The analysis shows that the three types of ...
thematic analysis of the data, several overarching themes have been found to form the positive influences of video games. The four main positive aspects have been conceptualised as themes: the social aspect of games, confidence building, desire for transcendence and immersion, and creativity which has
A critical realist perspective was held and data was analysed using Braun and Clarke's six phases of thematic analysis (2006), with the assistance of MAXQDA. Results identified five key themes: 'making sense of the depression'; 'the depression cannot be compartmentalised'; 'a light at the end of the tunnel'; 'learning to ...
Also, for a synthesis, you would need to decide whether you are analyzing the participants' narratives or the researchers'. Overall, for an undergraduate thesis, I would recommend choosing one ...
These projects may or may not include comprehensive search strategies and quality assessments and analysis can be exploratory, chronological, conceptual, thematic or other. ... in a particular topic. A literature review is primarily associated with formal academic writing, such as a master's thesis, dissertation, or a peer-reviewed journal ...