More thematic analysis

 

Thematic analysis papers

Javadi and Zarea (2016) Understanding Thematic Analysis and its Pitfall.

(Javadi and Zarea, 2016) For example, the themes obtained from the study may not be necessarily the most common themes. Overall, it should be said that the prevalence is not important much

and it is the researcher’s assessment on what to consider as theme is important. (p.35)

(Javadi and Zarea, 2016) Thematic analysis (TA) is one of the most common forms of analysis in qualitative research (1).

·        1, Guest GM. KM and Namey, EE Applied Thematic Analysis. Thousand Oaks California: Sage; 2012.

(Javadi and Zarea, 2016) Here, a part of flexibility in TA is to allow the themes to reveal themselves to you (in terms of significance or number and assessment of them) (Braun and Clarke, 2006, Saldaña2015).

·        14. Saldaña J. The coding manual for qualitative researchers: Sage; 2015.

(Javadi and Zarea, 2016) Rubin and Rubin suggest that this analysis is very exciting as you discover themes and concepts from the interviews you have had (9, 10). (p.34)

·        9. Rubin HJ, Rubin IS. Qualitative interviewing: The art of hearing data: Sage; 2011.

·        10. Rubin H. Rubin, l. S.(1995). Qualitative interviewing: The art of hearing data. Thousand Oaks, CA: Sage

(Javadi and Zarea, 2016) Data corpus refers to all of the collected data for a special research subject and data set refers to all the data employed for a special analysis (5,12). (p.34)

·        5. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative research in psychology. 2006;3(2):77-101.

·        12. Vaismoradi M, Turunen H, Bondas T. Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nursing & health sciences. 2013;15(3):398-405.

(Javadi and Zarea, 2016) Theme is the outcome of coding. The code is the label referred to special parts of the data that contribute to a theme (14). (p.34) 

·        14. Saldaña J. The coding manual for qualitative researchers: Sage; 2015.

(Javadi and Zarea, 2016) There is no definite answer to the question “what ratio of data is necessary for emergence of theme?” (p.34). 

(Javadi and Zarea, 2016) Brink, Wood (1997) The term “theme” is used for describing the fact that the data are grouped around a main issue (17) (p.35). 

·        17. Brink PJ, Wood MJ. Advanced design in nursing research: Sage Publications; 1997.

(Javadi and Zarea, 2016) In sematic approach the themes are detected at “the surface or semantic appearance” and the researcher is not after something beyond what the participant has said or what is explicitly written in the text. This is simplest and the most evident type of theme. In this method the data are explained, and it is simply for showing patterns that exist in the data and are organized in the forms of content, summarized or interpreted meanings. Here efforts are made to theorize the importance of patterns and their wider meanings (4, 16, 18).(p.36)

(Javadi and Zarea, 2016) Whereas Latent themes (interpretative)

In this level of analysis we go beyond what is obtained in the semantic method. This level is the beginning of efforts for detecting and testing beliefs, presumptions and conceptualization for forming semantic content of the data and with a level of the researcher’s interpretation (4, 16, 18). In fact, it can be said that the semantic approach is after the literal meaning while the latent or analytical approach requires going from description in which the data are just organized to reveal some patterns in semantic content and made concise, to interpretation in which efforts are made to create a theory based on the importance of the patterns and a wider framework of meanings and connotations (5). (p.36).

·        5. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative research in psychology. 2006;3(2):77-101

·        4. Boyatzis RE. Transforming qualitative information: Thematic analysis and code development: Sage; 1998.

·        16. Morse JM. Qualitative research methods for health professionals1995.

·        18. Speziale HS, Streubert HJ, Carpenter DR. Qualitative research in nursing: Advancing the humanistic imperative: Lippincott Williams & Wilkins; 2011.

(Javadi and Zarea, 2016) Researchers recommend active repeated reading so that you become familiar with all aspects of your data. It is necessary to read the whole set of data, before coding, in order to obtaining an overall understanding. In fact, it is through examining the data that specific patterns and meanings in the writings gradually emerge. (p.36). 

(Javadi and Zarea, 2016) Think about the relationship between different codes, themes and theme levels. Using the designs in the form of concept map on paper, software and schematic diagrams is highly helpful (5, 28). (p.37).

·        28. Clarke V, Braun V. Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The psychologist. 2013;26(2):120-3.

·        5. Braun V, Clarke V. Using thematic analysis in psychology. Qualitative research in psychology. 2006;3(2):77-101

 (Javadi and Zarea, 2016) Subthemes are in fact themes inside themes and a set of subthemes make a complex and big theme and show the meaning hierarchy in the data.(p.38)

 (Javadi and Zarea, 2016) Sometimes a part of the questions for data collection or interview guidance is introduced as theme. It is obvious that in such cases the researcher has not done any analytical work for identifying themes in the data sets and the themes are made of the researcher’s assumptions and not data analysis. Also, the interview questions may be impacted by the researcher’s presumptions and thus, the researcher presents his/her presumptions instead of the data tell what they mean. (p.38).

 (Javadi and Zarea, 2016) Gibson (2006) points out three issues for thematic analysis, the main part of which is theoretical issue: it is the interpretivism which is in fact the interpretation of others’ actions through our understanding. (p.39). 

·Gibson W. Thematic analysis. Retrieved August. 2006;2:2008.

(Javadi and Zarea, 2016) The results of this method are understandable for the public who have a low education level.(p.39).

(Javadi and Zarea, 2016) TA is an approach for extraction of meanings and concepts from data and includes pinpointing, examining, and recording patterns or themes.(p.39).

Ibrahim, M (2012) Thematic analysis: a critical review of its process and evaluation. In WEI international European academic conference proceedings, Zagreb, Croatia.

 (Ibrahim, 2012) Qualitative data collection is usually dependent on interpretation and there is an overlap of analysis and interpretation to reach a conclusion. (p.8).

(Ibrahim, 2012) Software such as NVivo is usefully able to analyse qualitative data in terms of gathering all the evidence and subsequently organising and grouping it into similar themes or ideas. Using software for analysing qualitative data is valuable in terms of improving the rigours of the analytical steps for validating that which does not reflect the researcher’s impressions of the data (p.8)

  Braun and Clarke (2006) argue that Grounded Theory is very similar to Thematic Analysis in terms their procedures for coding „themes‟ or coding from data (pp. 8-10). their data collection and analysis processes run parallel to start with.

Secondly, the flexibility of Thematic Analysis allows it to be used in both inductive and deductive methodologies (Frith and Gleeson 2004; Hayes 1997). For example, using an inductive approach the majority of the data that is collected will start with a precise content and then move to broader generalisations and finally to theories. This tends to ensure the themes are effectively linked to the data (Patton, 1990).

·        Hayes, N. 1997. Doing qualitative analysis in psychology. Psychology Press.

·        Patton,, M.Q. 1990. Qualitative evaluation and research methods. 2nd ed. California, USA: Thousand Oaks.

(Ibrahim, 2012) Thematic Analysis could be appropriate when the study aims to understand the current practices of any individual. In particular the influence of any variable, which- is utilised by participants in a practical way in order to investigate and identify how current situations are influenced by their points of view. This approach fits in with analysing the different phases of data collection, e.g. pre-/post-data. Further, it works when the research seeks to examine impact. In two phases of data collection, it will allow researchers to observe impact and differences and similarities that take place before and after adoption. (p.11).

The researcher can highlight the differences and similarities apparent within the data set (Creswell 2009; Boyatzis 1998).

·        Creswell 2009. Research design: qualitative, quantitative, and mixed methods approaches. Sage Publications.

Processed data can be displayed and classified according to its similarities and differences (Miles and Huberman 1994).

·        Miles, M.B. and Huberman, A.M. 1994. Qualitative data analysis: an expanded sourcebook. Sage Publications.

In order to achieve the above, the process should include coding, categorisation and noting patterns, i.e. different level of themes could be provide (Braun and Clarke 2006), also to provide a relationship between the variables and factors in order to create a reasonable and logical chain of evidence (Creswell 2009; Braun and Clarke 2006; Miles and Huberman 1994).

Miles & Huberman (1994) model for the thematic analysis process. It consists of three link stages or „streams‟, i.e. data reduction, data display and data conclusion-drawing/verifying. Miles & Huberman (1994, p.12).

(Ibrahim, 2012) Data reduction refers to the process of choosing, focusing, simplifying, building and transforming data (Miles & Huberman, 1994). During this stage, new thoughts and ideas are developed in terms of what should be included in the data display. Importantly these stages focus on visualising the data.

 

Clarke, V. and Braun, V., 2013. Teaching thematic analysis: Overcoming challenges and developing strategies for effective learning. The psychologist, 26(2), pp.120-123.

(Braun and Clarke, 2013) TA is essentially a method for identifying and analysing patterns in qualitative data.

(Braun and Clarke, 2013) TA works with a wide range of research questions, from those about people’s experiences or understandings, to those about the representation and construction of particular phenomena in particular contexts. It can be used to analyse different types of data, from secondary sources such as media to transcripts of focus groups or interviews; c) it works with large or small datasets; and d) it can be applied to produce data-driven or theory-driven analyses.

(Braun & Clarke, 2006) The six phases of thematic analysis should not be viewed as a linear model, where one cannot proceed to the next phase without completing the prior phase (correctly); rather analysis is a recursive process. 

 (Braun and Clarke, 2013) A common feature of a weak TA is using the data collection questions as themes. They explain that findings can be informed by thematic questions, but further themes can emerge beyond them.

 When teaching thematic analysis (Braun and Clarke, 2013) ask their students to spend a few minutes reflecting and making notes on two things prior to beginning analysis: 1) the assumptions, if any, they hold about the research topic; 2) their values and life experiences, and how all this might shape how they read and interpret the data. They explain this is useful for getting student researchers to reflect on the assumptions underpinning different analytic observations and developing personal reflexivity.

Braun, V. and Clarke, V. (2019) Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health, 11(4), pp.589-597.

 Since their paper in 2006, Braun and Clarke (2019) go on to consider the centrality of researcher subjectivity and reflexivity to the articulation of thematic analysis, as well as highlight the importance of methodological scholars locating their stance and acknowledging their position(s)  (p.4).

 For us, qualitative research is about meaning and meaning-making, and viewing these as always context-bound, positioned and situated, and qualitative data analysis is about telling “stories”, about interpreting, and creating, not discovering and finding the ‘truth’ that is either ‘out there’ and findable from, or buried deep within, the data. (p.7).

 Our approach is sometimes presented as involving a rigid, linear series of stages. Or as offering the researcher ‘either or’ choices: coding can be semantic or latent, inductive or deductive, rather than a mix of semantic and latent, inductive and deductive. These are the common ‘misapplications’ that we see. (p.8).

 describe some key shifts in their thinking around TA:

 The researcher’s role in knowledge production is at the heart of our approach! Reflexive TA needs to be implemented with theoretical knowingness and transparency; the researcher strives to be fully cognisant of the philosophical sensibility and theoretical assumptions informing their use of TA; and these are consistently, coherently and transparently enacted throughout the analytic process and reporting of the research. (p.13).

 The coding process requires a continual bending back on oneself – questioning and querying the assumptions we are making in interpreting and coding the data. Themes are analytic outputs developed through and from the creative labour of our coding. They reflect considerable analytic ‘work,’ and are actively created by the researcher at the intersection of data, analytic process and subjectivity. Themes do not passively emerge from either data or coding; they are not ‘in’ the data, waiting to be identified and retrieved by the researcher. Themes are creative and interpretive stories about the data, produced at the intersection of the researcher’s theoretical assumptions, their analytic resources and skill, and the data themselves. (p.14).

 Quality reflexive TA is not about following procedures ‘correctly’ (or about ‘accurate’ and ‘reliable’ coding, or achieving consensus between coders), but about the researcher’s reflective and thoughtful engagement with their data and their reflexive and thoughtful engagement with the analytic process. (p.14). 

Assumptions and positionings are always part of qualitative research. Reflexive practice is vital to understand and unpack these. It is good practice to reflect on and identify what you’re assuming, and then interrogate whether those assumptions hold for any particular project. (p.15).