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=Thematic Analysis=

Thematic analysis is the most common form of qualitative research analysis. It emphasizes pinpointing, examining, and recording patterns (or "themes") within data. Unfortunately, thematic analysis is widely used but there is no clear consensus on what exactly thematic analysis entails.

What is thematic analysis?
As mentioned above, thematic analysis is used in qualitative research analysis and focuses on examining themes within data. This method emphasizes organization and rich description of the data set. Thematic analysis goes beyond simply counting phrases or words in a text and moves on to identifying implicit and explicit ideas within the data. Coding is the primary process for developing themes within the raw data. The analysis of these codes can include comparing theme frequencies, identifying theme co-occurrence, and graphically displaying relationships between different themes. As mentioned earlier, researchers consider thematic analysis to be the most useful method in capturing the intricacies of meaning within a data set.

There is a wide range as to what a "data set" actually entails. Texts can range from a single-word response to an open-ended question or as complex as a body of a thousands of pages. As a consequence, data analysis strategies will likely vary according to size. Most qualitative researchers analyze transcribed in-depth interviews that can be 2-hours in length, resulting in nearly 40 pages of transcribed data per respondent. Also, it should be taken into consideration that complexity in a study can vary according to different data types.

Thematic analysis takes the concept of supporting assertions with data from grounded theory. This is considered by Charmaz as a set of methods that "consist of thematic, yet flexible guidelines for collecting and analyzing qualitative data to construct theories 'grounded' in the data themselves." This is reflective in thematic analysis because the process consists of reading transcripts, identifying possible themes, comparing and contrasting themes, and building theoretical models.

Thematic analysis is also related to phenomenology in that it focuses on the human experience subjectively. This approach emphasizes the participants' perceptions, feelings and experiences as the paramount object of study. Rooted in humanistic psychology, phenomenology notes giving voice to the "other" as a key component in qualitative research in general. This allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative studies.

Like most research methods, this process of data analysis can occur in two primary ways--inductively or deductively. In an inductive approach, the themes identified are strongly linked to the data because assumptions are data-driven. This means that the process of coding occurs without trying to fit the data into a pre-existing model or frame. It is important to note that throughout this inductive process, it is not possible for the researchers to free themselves from theoretical their epistemological responsibilities. Deductive approaches, on the other hand, are theory-drive. This form of analysis tends to be less descriptive overall because analysis is limited to the preconceived frames. The result tends to focus on one or two specific aspects of the data that were determined prior to data analysis. The choice between these two approaches generally depends on the researchers' epistemologies.

Advantages

 * Flexibility that it allows the researcher. Theories can be applied to this process across a variety of epistemologies.
 * Well suited to large data sets.
 * Great for multiple researchers.
 * Interpretation of themes supported by data.
 * Applicable to research questions that go beyond an individual's experience.

Disadvantages

 * Reliability is a concern because of the wide variety of interpretations that are possible (intro to app them anal) of the themes as well as applying the codes to large numbers of text. This is also important when multiple researchers are involved.  In order to conserve qualitative rigor, planning for monitoring agreement (and reliability) should be implemented throughout the process.
 * Another concern of thematic analysis is that it may miss nuanced data. In other words, slight differences between respondents may be missed.
 * Discovery and verification of themes and codes are inseparable because they perpetuate one another based on the data set.

Discussed below are several ways at avoiding these pitfalls. Pay specific attention to member checking and detailed reflexivity journals.

What is a theme?
A theme represents a level of patterned response or meaning from the data that is related to the research questions at hand. Determining what can be considered a theme can be used with deciding prevalence. This does not necessarily mean the frequency at which a theme occurs, but in terms of space within each data item and across the data set. It is ideal that the theme will occur numerous times across the data set, but a higher frequency does not necessarily mean that the theme is more important to understanding the data. A researcher's judgement is the key tool in determining which themes are more crucial.

There are also different levels at which themes can be identified--semantic and latent. A thematic analysis generally focuses wholly or mostly on one level. Semantic themes attempt to identify the explicit and surface meanings of the data. The researcher does not look beyond what the participant said or wrote. In this instance, the researcher wishes to give the reader a sense of the important themes. Thus, some depth and complexity is lost. However, a rich description of the entire data set is represented. Conversely, latent themes identify underlying ideas, patterns, and assumptions. This requires much interpretation of the of the data, so researchers might focus on one specific question or area of interest across the majority of the data set.

It is important to note that a theme is different from a code. Several texts recommend that researchers "code for themes". This can be misleading because the theme is considered the outcome or result of coding, not that which is coded. The code is the label that is given to particular pieces of the data that contribute to a theme. Saldana uses an example: "SECURITY can be a code, but A FALSE SENSE OF SECURITY can be a theme".

=Phases of Thematic Analysis=

Phase 1: Becoming Familiar with the Data
The initial phase in thematic analysis is for researchers to familiarize themselves with the data. Prior to reading the interview transcripts, researchers should create a "start list" of potential codes. These start codes should be included in a reflexivity journal with a description of representations of each code and where the code is established. analyzing data in an active way will assist reserachers in searching for meanings and patterns in the data set. At this stage it is tempting to skip over the data; however, it will aid researchers in identifying possible themes and patterns. Reading and re reading the material until the reseracher is comfortable is crucial to the initial phase of analysis. After becoming familiar with the material, note-taking is a crucial part of this step in order begin developing potential codes.

Transcription
After completing data collection, the researcher needs to begin transcribing the data into written form. For further information on this process, please refer to transcription. Transcription of the data is imperative to the dependability of the analysis. Transcribed data can come from television programs, interviews (see interviewing), and speeches, among others. During this stage in thematic analysis, meanings are created from the data through interpretation of verbal and non-verbal utterances.

Criteria for transcription of data must be established before the transcription phase is initiated to ensure that dependability is high. 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 like "*voice lowered*" will signal a change in the speech. 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. 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 feel 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 for accuracy. Following the completion of the transcription process the researcher's 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.

Phase 2: Generating Initial Codes
The second step in thematic analysis is to generate an initial list of items from the data set that have a reoccurring pattern. This process of developing a systematic way of organizing, and gaining meaningful parts of data as it relates to the research question is called coding. The coding process evolves from the bottom by using inductive analysis and is not considered to be linear process, but rather a cyclical process in which codes emerge throughout the research process.

Coding also involves the process of data reduction and complication. Reduction of codes is initiated by assigning tags or labels to the data set based on the research question(s). In this stage, condensing large data sets into smaller units permits further analysis of the data by creating useful categories. In-vivo codes are also produced by applying references and terminology from the participants in their interviews. Coding aids in development, transformation and re-conceptualization of the data and helps to find more possibilities for analysis. Researchers should ask questions related to the data and generate theories from the data, extending past what has been previously reported in previous research. Researchers also need to identify semantic and/or latent themes that are represented.

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 and by combining or splitting potential codes.. 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 dialogue and are descriptive in nature. These codes will facilitate the researcher's ability to locate pieces of data later in the process and identify why they included them. Initial coding sets the stage for detailed analysis later by allowing the researcher to reorganize the data according to the ideas that have been obtained throughout the process. Reflexivity journal entries for new codes serve as a reference point to the participant and their data section, reminding the researcher to understand why and where they will include these start codes in the final analysis. Throughout the coding process, full and equal attention needs to be paid to each data item because it will help in the identification of unnoticed repeated patterns. 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.

Questions to consider as you code

 * 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?

Phase 3: Searching For Themes
Searching for themes and considering what works and what does not work within themes enables the researcher to begin the analysis of potential codes. In this phase, it is important to begin by examining how codes combine to form over-reaching themes in the data. At this point, researchers have a list of themes and begin to focus on broader patterns in the data, combining coded data with proposed themes. Researchers also begin considering how relationships are formed between codes and themes and between different levels of existing themes. It may be helpful to use visual models to sort codes into the potential themes.

As previously mentioned, themes differ from codes in that themes are phrases or sentences that identifies what the data means. They describe an outcome of coding for analytic reflection. Themes consist of ideas and descriptions within a culture that can be used to explain causal events, statements, and morals derived from the participants' stories. In subsequent phases, it is important to narrow down the potential themes to provide an overreaching theme. Thematic analysis allows for categories or themes to emerge from the data like the following: repeating ideas; indigenous terms, metaphors and analogies; shifts in topic; and similarities and differences of participants' linguistic expression. It is important at this point to address not only what is present in data, but also what is missing from the data.. The 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.

Phase 4: Reviewing Themes
This phase requires the researchers to search the data set for data that supports or refutes the proposed theory. This allows for further expansion and revision of themes as the develop. 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. 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. 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.

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. If themes are problematic it is important to rework the theme, during this process identification of new themes may emerge.

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.

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. 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.

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. 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.