Phases of thematic analysis

Phase 1

The initial phase in thematic analysis is to familiarize yourself with the data. Immersing yourself in the data in an active way will assist you in searching for meanings and patterns in the data set. At this stage it is tempting to skip over the data, however to assist in identifying possible themes and patterns it is advantageous to read and re-read the material until you are familiar with it. After re-reading the material note taking is important in the development of potential codes to explain a phenomenological event (Braun & Clarke, 2006).

Transcription

After completing the interview the researcher begins the first step in data analysis, transcription of the interview into written form.(intro,thematicanalysis.docx) Transcription Prior to reading the interview transcripts researchers should create a "start list" of potential codes. The start codes should be included in the reflexivity journal with a description of representations of each code and where the code was established (Miles and Huberman (1994)). Transcription of the data is imperative to the dependability of the analysis step. The early stages of data analysis include rigorous methods of data transcription. Data transcription can include verbal data, t.v. programs, interviews and speeches. During this stage in thematic analysis meanings are beginning to be created from the data through 'interpretive acts'. In this stage it is especially important to draw upon non-verbal utterances and verbal discussions to lead to a richer understanding of the meaning of data (Braun & Clarke, 2006).

Criteria for transcription of data must be established before the transcription phase is initiated to ensure that dependability it high (ITA). Inconsistencies in transcription can produce biases in data analysis that will be difficult to identify later in the analysis process. The protocol for transcription should explicitly state criteria of transcription. Inserting comments such as *voice lowered* would signal a change in the speech. A general guideline to follow when transcribing includes a ratio of 15 minutes of transcription for every 5 minutes of dialog. After this stage the researcher should be familiar with the content of the data and should be able to identify overt patterns or repeating issues in one or more interviews. These patterns should be recorded in a (reflexivity Journal) where they will be of use when coding and checking data for accuracy. Following the completion of the transcription process the researchers most important task to to begin to gain control over the data. At this point it is important to mark data that addresses the research question, this is the beginning of the coding process.(ITA.docx)

==Phase 2== Generating Initial Codes The second step in thematic analysis is to generate the initial list of items in the data set that have a reoccurring pattern. Coding is the process where a researcher develops a systematic way of organizing, and gaining meaningful parts of data as it relates to the research question (Intro to thematic analysis). The coding process evolves from the bottom using inductive analysis, this process is not linear but a cyclical one in which codes emerge throughout the research process (ITA). Coding involves both the process of data reduction and complication, through these means analysis of overreaching themes can be accomplished (ITA). Reduction of codes is initiated by assigning tags or labels to the data set based on the proposed research question. In this stage condensing large data sets into smaller units permit analysis of data by creating categories (ITA)In-vivo codes are produced by applying references and terminology produced by the participants to their interviews (ITA). The process of attaching labels or codes to the transcription interview allow researchers to recognize and organize relevant and salient data. Coding can be used to develop, transform and re-conceptualize the data seeking to find more possibilities for analysis of data. Coffey & Atkinson,(1996) suggest that researchers ask questions relating to the data, generating theories from the data, extending past what has been previously reported in the research of the given topic (ITA). Identifying features of the data both "semantic" and "Latent"(Thematic analysis revised)

===Questions to consider as you code=== (CM)

What are people doing? What are they trying to accomplish?
- How exactly do they do this? What specific means or strategies are used?
  - How do members talk about and understand what is going on?
    - What assumptions are they making?
      - What do I see going on here? What did I learn from note taking?
        Why did I include them?

The coding process is rarely completed the first time, each time researchers should strive to refine themes by adding to some and subtracting from others, combining, and splitting potential codes (Coding manual). Start codes are produced through terminology used by participants during the interview and can be used as a reference point of their experiences during the interview. Dependability increases when the researcher begins to use concrete codes that are based on dialog and are descriptive in nature (ITA). These codes will facilitate the researchers ability to locate pieces of data later in the analysis process and identify why they included them. Initial coding sets the stage for detailed analysis later in the analysis process allowing the researcher to reorganize the data according to the ideas that have been obtained later in the data collection process. Reflexivity journal entries for new codes serve as a reference point for the participant and their data section, allowing the researcher to understand why and where they will include these start codes in final analysis (ITA). Throughout the coding process importance should be placed on giving full and equal attention to each data item, the identification of repeated patterns may develop at this point. Coding for as many themes as possible and coding individual aspects of the data may seem irrelevant but can potentially be crucial later in the analysis process (TAR)((thematic analysis revised).

==Phase 3== Searching For Themes Searching for themes and considering what works within the themes and what does not enables the researcher to begin the analysis of potential codes(TAR). At this phase it is important to begin the analysis of codes to consider how they combine to form over-reaching themes in the data (TAR). At this point researchers should have list of themes and will begin to focus on the broader patterns in the data combining the coded data with themes (TAR). Some codes may end up as main themes, sub themes, or miscellaneous themes, therefore it is important to begin thinking about relationships between codes and themes and between levels of existing themes (TAR). It may be helpful to use visual models to sort codes into potential themes (TAR). Themes differ from codes in that themes are phrases or sentences that identifies what data means, describing an outcome of coding for analytic reflection(CM). Themes consist of ideas and descriptions within a culture and can be used to explain causal events, statements and morals derived from participant stories. In subsequent phases it is important to narrow down potential themes to provide an overreaching theme. Thematic analysis allows for categories or themes to emerge from the data such as repeating ideas, indigenous terms, metaphors and analogies, shifts in topic, and similarities and differences of participant linguistic expression. Important at this point to address not only what is present in data but also what is missing from the data (CM). Conclusion of this phase should yield many candidate themes collected throughout the data process, it is crucial to avoid discarding themes even if they are initially insignificant as they may be important themes later in the analysis process (TAR).

==Phase 4== Reviewing Themes The reviewing themes phase requires the researchers to search the data set for data in support and against the proposed theory. This step allows for further expansion and revision of themes as the develop (TAR). At this point researchers should have a set of potential themes, as this phase is where reworking of initial themes takes place. Some existing themes may collapse into each other, other themes may need to be condensed into smaller units (TAR). This phase involves two levels of refining and reviewing themes. Connections between overlapping themes may serve as important sources of information and can alert researchers to the possibility of new patterns and issues in the data. Deviations from coded material can notify the researcher that a code may not exist. This should be noted in the reflexivity journal including absences of themes (ITA). Codes serve as a way to relate data to a persons idea of that data, information is derived prior to paraphrasing and identifying initial codes, at this point the researcher should focus on interesting aspects of the codes and why they fit together (ITA).

Level 1

Reviewing coded data extracts allows researchers to identify if themes form coherent patterns, if so researchers should move onto Level 2. If themes do not form clear patterns, consideration of potentially problematic themes should be considered in addition to determining if data does not fit into the theme (TAR). If themes are problematic it is important to rework the theme, during this process identification of new themes may emerge (TAR).

Level 2

Considering the validity of individual themes and how they connect to the data set is crucial at this stage. It is important to asses if the potential thematic map accurately reflects the meanings in the data set to provide an accurate representation of participants experiences. Once again at this stage it is important to read and re read the data to decide if current themes relate to data set. To assist you in this process it is imperative to code additional items within the themes may have been missed earlier in the initial coding stage. If the potential map works then the researcher should progress to phase 5. If the map does not work it is crucial to return to the data continuing to review and refine existing codes, researchers should repeat this process until they are satisfied with the thematic map. By the end of this phase researchers should have an idea of what themes are and how they fit together to convey a story about the data (TAR).

==Phase 5== Defining and naming themes Defining and refining existing themes that will be presented in the final analysis assist the researcher in analyzing the data within each theme. At this phase identification of the themes essence relates to how each theme effects the whole picture of the data. Analysis at this stage is characterized by identifying which aspects of data are being captured and what is interesting about the themes and why. To identify whether current themes contain sub themes and further depth of themes it is important to consider themes as the whole picture and as separate themes. Then researchers must conduct and write up a detailed analysis and identify the story of each of the themes and their significance (TAR). By the end of this phase researchers should be able to define what current themes are and are not and should posses the ability to explain a few sentences about each theme. It is important to note that researchers should begin thinking about names for themes that will give a reader a full sense of the theme and its importance (TAR).

==Phase 6== Producing the report After you have the reviewed final themes, researchers will begin the process of writing up the final report. When writing up the final report researchers should decide on themes that make meaningful contributions to answering research questions, and should be refined later as final themes. Researchers should present the dialog connected with each theme to support the theme and increase dependability through a thick description of results(Intro-thematicanalysis). The task in this phase is to write up thematic analysis to convey the complicated story of the data in a manner that convinces the reader of the validity and merit of your analysis. A clear, concise, and straightforward logical account of the story across and with themes is important for readers to understand the final report. The write up of the report should contain enough evidence that themes within the data are relevant to the data set. Extracts should be included in the narrative to capture the full meaning of the points in analysis. The argument should be in support of the research question (TAR). The final step in producing the report is to include member checking as a means to establish credibility, researchers should consider taking final themes and supporting dialog to participants to elicit feedback (ITA).

Reflexivity journals

The reflexivity process can be described as the documenting close reflections of potential findings and implications of the research study. Reflexivity journals are often referred to as analytic memos or memo writing, which can be useful for reflecting on emergent patterns, themes and concepts. ^[1] Throughout the coding process researchers should have detailed records of the development of each of their codes and potential themes. In addition, changes made to themes and connections between themes are incorporated into the final report to assist the reader in understanding decisions that were made throughout the coding process.^[2]

Once fieldwork and interviews are complete and you are beginning the data analysis stages, researchers should take notes from transcription, and interviews. Researchers can take notes by writing down any words that may be of use during further data analysis in a journal or notebook. The logging of ideas for future analysis can aid researchers in getting thoughts and reflections written down and may serve as a reference for potential coding ideas as one progresses from one stage to the next in the thematic analysis process. Items written in journal do not have to be accurate or final but instead should contain considerations for further analysis. Researchers must take into consideration that analytic memos will assist them in the future coding of potential overreaching themes ^[3]

While working on reflexivity journal entries it is important to make certain that notes written in journals are different from the data, the use of italics, bolding words, and adding brackets will assist researchers in showing distinctions between data and journaling. Researchers should write their reflexivity notes fully avoiding abbreviations, this will assist the researcher in the final stages of analysis and through the process of data complication and reduction. ^[4] Auerbach & Silverstein (2003) suggest keeping a log of concerns with the research, theoretical framework, central research questions, goals, and major issues to help researchers focus on the coding process.^[5] Analytic memos reveal information about the researchers thinking process pertaining to the codes and categories that have emerged throughout the analysis process.^[6] One of the most critical outcomes of qualitative data analysis is to interpret how each individual components of the study relate to each other, in particular researchers should focus on observations of the population to gain an image of the bigger picture that may lead to universal observations. ^[7] Emerson, Fretz & Shaw (1995) recommend the following questions should be considered when coding fieldwork notes:

The questions above should be asked throughout all cycles of the coding process and the data analysis. It is also important to to note what jumps out at you while coding, keep in mind that codes can emerge from the data that is unexpected, keeping a thick detailed reflexivity journal will assist researchers in identifying potential codes that were not initially pertinent to the study. ^[8]

Data reduction

Coding can be thought as a means of reduction of data or data simplification. Using simple but broad analytic codes it is possible to reduce the data to a more manageable feat. In this stage of data analysis the analyst must focus on the identification of a more simple way of organizing data. using data reductionism researchers should include a process of indexing the data texts which could include: field notes, interview transcripts, or other documents. Data at this stage are reduced to classes or categories in which the researcher is able to identify segments of the data that share a common category or code. ^[9] Siedel and Kelle (1995) suggest three ways to aid with the process of data reduction and coding: (a) noticing relevant phenomena, (b) collecting examples of the phenomena, and (c) analyzing phenomena to find similarities, differences, patterns and overlying structures. This aspect of data collection is important because during this stage researchers should be attaching codes to the data to allow the researcher to think about the data in different ways.^[10] Coding can not be viewed as strictly data reduction, data complication can be used as a way to open up the data to examine further.^[11]The below section addresses the process of data complication and its significance to data analysis in qualitative analysis. ^[12]

Data complication

The process of creating codes can be described as both data reduction and data complication. Data complication can be described as going beyond the data and asking questions about the data to generate frameworks and theories. The complication of data is used to expand on data to create new questions and interpretation of the data. Researchers should make certain that the coding process does not lose more information than is gained.^[13] Tesch (1990) defines data complication as the process of reconceptualizing the data giving new contexts for the data segments. Data complication serves as a means of providing new contexts for the way data is viewed and analyzed.^[14]

Coding is a process of breaking data up through analytical ways and in order to produce questions about the data, providing temporary answers about relationships within and among the data. ^[15]Decontextualizing and recontextualizing help to reduce and expand the data in new ways with new theories.^[16]

level 2

==Phase 5== Defining and Naming Themes Begin this phase by thinking about potential theme names, names should give the reader an understanding of the theme and be clear and concise (TAR). Defining and refining existing themes will assist in analyzing data within each theme (TAR). Identifying the essence of each theme in addition to which aspects of the data are being captured will aid in the identification of interesting aspects of themes.(TAR). At this point researchers should conduct and write a detailed analysis and identify the story that each theme tells, in addition to identifying interesting aspects of each theme and why you chose them. Researchers should consider themes as separate and as the whole picture of data analysis. During this stage of thematic analysis it is important to identify whether current themes contain sub-themes and even further themes within existing themes. At the conclusion of this phase researchers should be able to explain a few sentences about each theme (TAR).

==Phase 6== Producing the Report Once final themes have been established researchers must begin the final analysis phase of writing up the report. At this phase the task is to write up the final thematic analysis. The analysis should provide a clear straightforward, non-repetitive and logical explanation of the story across and within final themes. The final write up should contain sufficient evidence liking data to the theme. Researchers should decide which themes make a meaningful contribution to answer the researcher questions and should be presented with dialog for each theme (ITA). Extracts from data set need to be included in the narrative to capture the essence of important parts. Researchers should include a convincing explanation for its inclusion in the analytic narrative. Ultimately the argument made should be in support of the research question (TAR). Member checking at this stage is crucial to establishing credibility, the researcher should consider taking the final analysis and supporting dialog to participants to elicit feedback (ITA).

^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 36.
^ Lincoln (1995). "Criteria For Rigor in Qualitative research". {{cite journal}}: Cite journal requires |journal= (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 17.
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 157.
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. pp. 36–37.
^ Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 28.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 29.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)
^ Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)

[1] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 36.

[2] Lincoln (1995). "Criteria For Rigor in Qualitative research". {{cite journal}}: Cite journal requires |journal= (help); Unknown parameter |coauthors= ignored (|author= suggested) (help)

[3] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 17.

[4] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.

[5] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.

[6] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 157.

[7] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. pp. 36–37.

[8] Saldana, Johnny (2009). The Coding Manual for Qualitative Researchers. Thousand Oaks, California: Sage Publications. p. 18.

[9] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 28.{{cite book}}: CS1 maint: multiple names: authors list (link)

[10] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 29.{{cite book}}: CS1 maint: multiple names: authors list (link)

[11] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)

[12] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)

[13] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 30.{{cite book}}: CS1 maint: multiple names: authors list (link)

[14] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)

[15] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)

[16] Coffey, Atkinson, Amanda, Paul (1996). Making Sense of qualitative data. Thousand Oaks, California: Sage Publications. p. 31.{{cite book}}: CS1 maint: multiple names: authors list (link)

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