Thematic data analysis

Wikipedia, the free to: navigation, ic analysis is one of the most common forms of analysis in qualitative research. 2] themes are patterns across data sets that are important to the description of a phenomenon and are associated to a specific research question. 4] thematic analysis is performed through the process of coding in six phases to create established, meaningful patterns. These phases are: familiarization with data, generating initial codes, searching for themes among codes, reviewing themes, defining and naming themes, and producing the final report. Advantages and ic analysis is used in qualitative research and focuses on examining themes within data. Thematic analysis goes beyond simply counting phrases or words in a text and moves on to identifying implicit and explicit ideas within the data. 6] coding is the primary process for developing themes within the raw data by recognizing important moments in the data and encoding it prior to interpretation. 6] most researchers consider thematic analysis to be a very useful method in capturing the intricacies of meaning within a data set. 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 ic analysis takes the concept of supporting assertions with data from grounded theory. 9] this is reflective in thematic analysis because the process consists of reading transcripts, identifying possible themes, comparing and contrasting themes, and building theoretical models. Analysis is also related to phenomenology in that it focuses on the human experience subjectively. This allows the respondents to discuss the topic in their own words, free of constraints from fixed-response questions found in quantitative most research methods, this process of data analysis can occur in two primary ways—inductively or deductively. 11] in an inductive approach, the themes identified are strongly linked to the data because assumptions are data-driven. 7] this means that the process of coding occurs without trying to fit the data into a pre-existing model or frame. 12] 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. Theme represents a level of patterned response or meaning from the data that is related to the research questions at hand. 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 potential data analysis pitfall occurs when researchers use the research question to code instead of creating codes and fail to provide adequate examples from the data. This requires much interpretation of the data, so researchers might focus on one specific question or area of interest across the majority of the data set. The code is the label that is given to particular pieces of the data that contribute to a theme.

That qualitative work is inherently interpretive research, the biases, values, and judgments of the researchers need to be explicitly acknowledged so they are taken into account in data presentation. 17] researchers shape the work that they do and work as the instrument for collecting and analyzing data. In order to acknowledge the researcher as the tool of analysis, it is necessary for one to create and maintain a reflexivity journal. Fieldwork and interviews are complete and researchers are beginning the data analysis stages, they should take notes from the transcription and interviews. Researchers can take notes by writing down any words that may be of use during data analysis in a journal or notebook. The logging of ideas for future analysis can aid 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. 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 in showing distinctions between data and journaling. This will assist the researcher in the final stages of analysis and through the process of data complication and reduction. 22] analytic memos reveal information about the researchers thinking process pertaining to the codes and categories that have emerged throughout the analysis process. 23] 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. Questions above should be asked throughout all cycles of the coding process and the data analysis. Keep in mind that codes can emerge from data that is unexpected, so keeping a thick detailed reflexivity journal will assist researchers in identifying potential codes that were not initially pertinent to the study. Is little reliable guidance on what sample size is needed for a thematic analysis,[26][27][28] with suggestions ranging from 6 to 400+ depending on the type of data collection and size of the project. And re-read data in order to become familiar with what the data entails, paying specific attention to patterns that inary "start" codes and detailed start codes in journal, along with a description of what each code means and the source of the te the initial codes by documenting where and how patterns occur. This happens through data reduction where the researcher collapses data into labels in order to create categories for more efficient analysis. This involves the researcher making inferences about what the codes hensive codes of how data answers research e detailed information as to how and why codes were combined, what questions the researcher is asking of the data, and how codes are e codes into overarching themes that accurately depict the data. The researcher should also describe what is missing from the of candidate themes for further ivity journals need to note how the codes were interpreted and combined to form this stage, the researcher looks at how the themes support the data and the overarching theoretical perspective. If the analysis seems incomplete, the researcher needs to go back and find what is nt recognition of how themes are patterned to tell an accurate story about the need to include the process of understanding themes and how they fit together with the given codes. Answers to the research questions and data-driven questions need to be abundantly complex and well-supported by the researcher needs to define what each theme is, which aspects of data are being captured, and what is interesting about the themes. Comprehensive analysis of what the themes contribute to understanding the researcher should describe each theme within a few the researchers write the report, they must decide which themes make meaningful contributions to understanding what is going on within the data. Thick description of the why particular themes are more useful at making contributions and understanding what is going on within the data set.

Describe the process of choosing the way in which the results would be 1: becoming familiar with the data[edit]. 36] analyzing data in an active way will assist researchers in searching for meanings and patterns in the data set. At this stage, it is tempting to skip over the data; however, this will aid researchers in identifying possible themes and patterns. Reading and re-reading the material until the researcher is comfortable is crucial to the initial phase of analysis. For transcription of data must be established before the transcription phase is initiated to ensure that dependability is high. 6] inconsistencies in transcription can produce biases in data analysis that will be difficult to identify later in the analysis process. 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. 38] a general guideline to follow when transcribing includes a ratio of 15 minutes of transcription for every 5 minutes of 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. Following the completion of the transcription process the researcher's most important task is to begin to gain control over the data. Second step in thematic analysis is generating an initial list of items from the data set that have a reoccurring pattern. This systematic way of organizing, and gaining meaningful parts of data as it relates to the research question is called coding. The coding process evolves through an inductive analysis and is not considered to be linear process, but a cyclical process in which codes emerge throughout the research process. This cyclical process involves going back and forth between phases of data analysis as needed until you are satisfied with the final themes. 40] researchers conducting thematic analysis should attempt to go beyond surface meanings of the data to make sense of the data and tell an accurate story of what the data means. 6] 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. 6] 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. 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. 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.

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. 44] 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. 45] 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. 46] the below section addresses the process of data complication and its significance to data analysis in qualitative analysis. 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. 46] 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. 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. 47] decontextualizing and recontextualizing help to reduce and expand the data in new ways with new theories. 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. Differ from codes in that themes are phrases or sentences that identifies what the data means. 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. 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 requires the researchers to search for data that supports or refutes the proposed theory. 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.

6] codes serve as a way to relate data to a person's conception of that concept. At this point, the researcher should focus on interesting aspects of the codes and why they fit ing coded data extracts allows researchers to identify if themes form coherent patterns. If themes do not form clear patterns, consideration of the potentially problematic themes should be considered in addition to determining if data does not fit into the theme. If this occurs, data may need to be recognized in order to create cohesive, mutually exclusive themes. The validity of individual themes and how they connect to the data set is crucial to completing this stage. It is imperative to assess whether the potential thematic map accurately reflects the meanings in the data set in order to provide an accurate representation of participants' experiences. Once again, at this stage it is important to read and re-read the data to determine if current themes relate back to the data set. If the potential map works then the researcher should progress to the next phase of analysis. If the map does not work it is crucial to return to the data in order to continue to review and refine existing codes. Mismatches between data and analytic claims reduce the amount of support that can be provided by the data. This can be avoided if the researcher is certain that their interpretations of the data and analytic analysis correspond. By the end of this phase, researchers have an idea of what themes are and how they fit together so that they convey a story about the data set. And refining existing themes that will be presented in the final analysis assists the researcher in analyzing the data within each theme. At this phase, identification of the themes' essences relate to how each specific theme affects the entire picture of the data. Analysis at this stage is characterized by identifying which aspects of data are being captured, what is interesting about the themes, and why themes are order to identify whether current themes contain sub-themes and to discover further depth of themes, it is important to consider themes within the whole picture and also as autonomous themes. Researchers must then conduct and write a detailed analysis to identify the story of each theme and its significance. 42] failure to fully analyze the data occurs when researchers do not use the data to support their analysis beyond the content. Researchers conducting thematic analysis should attempt to go beyond surface meanings of the data to make sense of the data and tell an accurate story of what the data means. 6] the goal of this phase is to write the 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. 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 method of analysis should be driven by both theoretical assumptions and the research questions. Thematic analysis provides a flexible method of data analysis and allows for researchers with various methodological backgrounds to engage in this type of analysis.

Increasing reliability may occur if multiple researchers are coding simultaneously, which is possible with this form of analysis. 52] this method of analysis contains several advantages and disadvantages, it is up to the researchers to decide if this method of analysis best explains their ility it allows researchers, in that multiple theories can be applied to this process across a variety of epistemologies. Demonstrating rigor using thematic analysis: a hybrid approach of inductive and deductive coding and theme development". Ries: qualitative researchhidden categories: cs1 maint: multiple names: authors logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable page was last edited on 23 october 2017, at 02: is available under the creative commons attribution-sharealike license;. Ori and pacific ch groups and aduate research ipate in our and pacific research ethics within the school of advisors in the school of s and ch groups and thematic of psychology - leading psychological science, scholarship and thematic ic analysis (ta) is a widely-used qualitative data analysis method. It is one of a cluster of methods that focus on identifying patterned meaning across a dataset. We initially outlined our approach in a 2006 paper, using thematic analysis in gh the title suggests ta is for, or about, psychology, that’s not the case! Purpose of ta is to identify patterns of meaning across a dataset that provide an answer to the research question being addressed. Patterns are identified through a rigorous process of data familiarisation, data coding, and theme development and of the advantages of (our version of) ta is that it’s theoretically-flexible. Note these different question types would require different versions of ta, informed by different theoretical to approach tathere are different ways ta can be approached: an inductive way – coding and theme development are directed by the content of the data; a deductive way – coding and theme development are directed by existing concepts or ideas; a semantic way – coding and theme development reflect the explicit content of the data; a latent way – coding and theme development report concepts and assumptions underpinning the data; a realist or essentialist way – focuses on reporting an assumed reality evident in the data; a constructionist way – focuses on looking at how a certain reality is created by the data. What is vitally important is that the analysis is theoretically coherent and approach to tathe approach to ta that we developed involves a six-phase process: familiarisation with the data: this phase involves reading and re-reading the data, to become immersed and intimately familiar with its content. That identify important features of the data that might be relevant to answering the research question. It involves coding the entire dataset, and after that, collating all the codes and all relevant data extracts, together for later stages of analysis. Searching for themes: this phase involves examining the codes and collated data to identify significant broader patterns of meaning (potential themes). It then involves collating data relevant to each candidate theme, so that you can work with the data and review the viability of each candidate theme. Reviewing themes: this phase involves checking the candidate themes against the dataset, to determine that they tell a convincing story of the data, and one that answers the research question. Defining and naming themes: this phase involves developing a detailed analysis of each theme, working out the scope and focus of each theme, determining the ‘story’ of each. Writing up: this final phase involves weaving together the analytic narrative and data extracts, and contextualising the analysis in relation to existing literature. Although these phases are sequential, and each builds on the previous, analysis is typically a recursive process, with movement back and forth between different phases. So it’s not rigid, and with more experience (and smaller datasets), the analytic process can blur some of these phases together. Ori and pacific ch groups and aduate research ipate in our and pacific research ethics within the school of advisors in the school of s and s and for brain d behaviour mentary application aduate study ipate in our aduate study ipate in our s and for brain d behaviour mentary application r: michi by christine ing qualitative data from research is a challenging but necessary task in the innovation process. Data acquired during the learn phase will reveal insights and challenges that lead to research question development and can often provide clues to potential ic analysis can be used to make sense of seemingly unrelated material. It can be used to analyze qualitative information and to systematically gain knowledge and empathy about a person, an interaction, a group, a situation, an organization or a thematic analysis has been practised for some time, it has not been subject to a great deal of rigorous scholarly documentation.

Richard boyatzis, a professor of organizational behavior at case western reserve university  who wrote the book,  qualitative information: thematic analysis and code development (1998) which outlines the principles of thematic analysis. During problem framing, thematic analysis helps researchers move from a broad reading of the data toward discovering patterns and framing a specific research chers use thematic analysis as a means to gain insight and knowledge from data gathered. By using thematic analysis to distill data, researchers determine broad patterns that will allow them to conduct more granular research and analysis. It is highly inductive: themes emerge from the data that is gathered and are not imposed or predetermined by the researcher. In practice, depending on the context of the research study, thematic analysis could include a bit of grounded theory, positivism, interpretivism and phenomenology. Coding is an explicit and iterative process in which the researcher will alter and modify the analysis as reflected by the data and as ideas emerge. The researcher(s) read and re-read the data, double-checking the codes for consistency and validation. The integration of the codes from the data becomes the codebook from which themes emerge. Themes/frameworks identification – from the codebook, the researcher identifies themes and sub-themes: patterns that have emerged from the coded data. Information consolidation, finalize theme names – the researcher finalizes the name of each theme, writes its description and illustrates it with a few quotations from the original text to help communicate its meaning to the ic analysis: information from semi-structured interviews has been transcribed. Paper id: us paper      next tanding thematic analysis and its best meaning for the term “research” is in the term itself. Thematic analysis is one of the types of qualitative research methods which has become applicable in different fields. Then the issues and advantages of thematic analysis are ic analysis, qualitative research, theme, inductive analysis. Introductiont hematic analysis (ta) is one of the most common forms of analysis in qualitative research (1). Ta is an approach for extraction of meanings and concepts from data and includes pinpointing, examining, and recording patterns or themes (3). Data can be in any form including: transcription of an interview, notes in the field, political documents, pictures, and videos (6, 7). It is the minimum organization and description of a set of data that is widely used in qualitative data analysis (4, 8). Rubin and rubin suggest that this analysis is very exciting as you discover themes and concepts from the interviews you have had (9, 10). This method is widely used in interpretative phenomenological analysis (ipa) and even in other methods for qualitative research such as grounded theory (1). Of course, it is better to use discourse analysis and narrative analysis in grounded theory (12). 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). Fact theme mentions some important points regarding the research data and shows a pattern or meaning related to data sets (4). The code is the label referred to special parts of the data that contribute to a theme (14).

There is no definite answer to the question “what ratio of data is necessary for emergence of theme? Desantis and ugarriza (2000) have pointed out four criteria for theme: emergence from the data, having a essence nature, recurrence or iteration, levels for recognizing the theme (15). Morse (1995) have considered 5 dimension for themes based on a content analysis they have done on theme:overall nature: experiencestructure: nature or basis of the experienceperformance: capturing and uniforming the nature or basis of the experience into a meaningful wholeshape: stable or multitude of multiple experience instancesstate: recurrent of the experience (16)based on a study by desantis and ugarriza (2000) conducted on qualitative papers between 1979 and 1998, 40% of the papers had used the word “theme” in their studies. However, several definitions of the word “theme” which exist in different sources are as follows:brink, wood (1997):the term “theme” is used for describing the fact that the data are grouped around a main issue (17)speziale , streubert (2011): theme is a structural meaningful unit of data which is necessary for providing qualitative findings (18). Analysis methods can roughly be divided into two groups:in the first group are those methods which exist or epistemologically placed like conventional analysis (ca) and interpretative phenomenological analysis (ipa) and some variables limited the way the method is applied in a framework. In these methods, the analysis is done based on a specific guidance and may be limited in the framework of a theory. Through this theoretical freedom, ta is a flexible research tool which is also useful and can do data calculations in a potentially rich way with details and even with complexities. Therefore, flexibility is one of the advantages of ta and of course result in some criticisms of this type of analysis (4, 12). Decision-making on whether to make the reported themes in the form of rich description of the data set or detailed account of one particular aspect is upon the researcher. Depth and complexity are impacted if the researcher wants to create a paper with a limited number of words or around specific topics on which few number of papers have been published, or if the research question is broad or if the codes and themes have to be an accurate reflection of the whole content of data set. Inductive thematic analysis an inductive analysis means that the recognized themes are strongly made related to the data (4, 22). In this method in which the data are collected for a specific research subject, for example with focus group method, the recognized themes may have little relationship with the questions asked from the participants (4, 5). Theoretical ta theoretical ta is mostly done based on the theory or the analysis liked by the researcher (22). This method of analysis presents a description of the data with less richness and the details are presented based on the initial theory (4, 5). There is no definite answer to this question yet as other qualitative studies are also disputed, if the results of similar studies are considered before the analysis there is a risk of the researcher’s scope being limited and the researcher may ignore some the critical aspects of the data or pay too much attention to some specific parts of the data (18, 22). 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. 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). 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). It's depending on the type of data collection, size of the project, and how are themes analyzed and reported (25). Ta phases have many similarities with other phases of qualitative studies and they are not specific to thematic analysis (5, 22). The researcher frequently refers to the extracted codes and the entire data set and validates them (2, 5). Writing is an indispensable part of the analysis and thus the researcher should not do it the last phases.

The researcher should continuously take notes from his/her analysis and write the ideas that come to his/her mind regarding the codes in the first step (18, 22). Flexibility as a principle should be considered in the analysis and what are recommended as analysis do not that are rules (5). Familiarizing yourself with your data first, each word in the content of the interviews and speeches should be transcribed with correct spelling and this phase is one of the most significant phases in interpretative qualitative studies (9). This is very difficult and time consuming but highly valuable and familiarization with the data occurs during it (4, 27). In order to find out the content depth, the immersion of the researcher in the data is necessary. 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. You should remember that all parts of the data are important and if you study some parts selectively, you may ignore other parts. In fact, it is through examining the data that specific patterns and meanings in the writings gradually emerge. Then the verbal data should be turned into a manuscript that has minimum ambiguity grammatically and you know what you mean whenever you refer to it (5). The codes can be explicit or implicit meanings (semantic or latent) that are related to the most basic part of the data or raw information and can be evaluated in a meaningful way with regard to a phenomenon. Pay complete and equal attention to all data and identify the important aspects in the data that may or may not be repeated in the data. Give a related code to the data itself and pay less attention to its surroundings. It is better to have access to raw data whenever you refer to the text for coding later. Thematic map of the study by ghiyasvandian (2014)and the developed themes from the same study:figure 2. This means that the data inside the themes should be meaningfully related to each other and the themes should be explicitly and expelled differentiable. When you refer to your initial themes you will see that some of them are not really theme or do not have enough supportive data. If they did, you go to the second phase in which a process similar to the one in the previous phase is done but we consider the validity of themes regarding the whole data in the entire data set. In this state our thematic map should be an accurate document for our data set as a whole. At the end of this phase you should have a good idea on what differentiates the themes, how they are matched and the whole story they tell about the data. As pointed out before, analysis is done in a cyclic process and there may be a need to refer to previous phases at each phase (4, 9); of course, to the extent that you are not lost in the never-ending cycle of analysis! You reach what the theme says and what it is about and what aspects of the data are covered by the theme. Here, in addition to interpretation of the data content, you should determine the things that are interesting regarding the data and the way they become important.

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. Producing the report the sixth phase begins when you have a good set of themes and you do the final analysis by writing and reporting them. Morse (1995) provide 5 steps for thematic analysis:recognizing and listing cognitive data (parts of patterns) or nursing observations and experiencescombination of related data and patterns into meaningful units based on having relationship with bigger units that are known as izing subthemes and subpatterns and determining the way they become related to patterns and themessynthesis of several small themes for obtaining a general, comprehensive and broad viewformulation of phrases of themes or patterns for more retesting or reconfirmation of nursing phenomena (16). Is a clear, uncomplicated and straightforward qualitative study which does not need some theoretical details and technical knowledge such as discourse analysis or conversational analysis. Therefore, it can be said that doing a good ta in data is a simple, enjoying and flexible work. However, like other qualitative methods, some potential pitfalls may result in weak analysis and they should be dealt with. Is not just collection of a series of similar or organized data together with a little a low level of analytical narrative that simply paraphrases the data content and turns them into their initial phrases. The essences in ta indicates the analytical points that are processed by the researcher about the data and should be used for making sense and supporting the analysis which is beyond a specific content and to tell the reader what the data content means or may mean and not for providing a summary of data in the form of a series of extracted words (5, 12). As in any scientific study the conclusions and judgments should be based on the data from the study, the ta is not an exception and should refrain from personal inferences and specific prejudgments by the researcher on the research content and should pay attention to explicit or latent content of the text or message as it is. For this reason, sometimes a lack of proportionality between the data and analytical claims created for it is seen. In such cases there is no coherence and consistency between the claims and the data; and in the worst scenario, the data extract requires another analysis or is even in contrast with the claims mentioned (5). The researcher should find out whether his/her interpretations and analytical points are compatible with the data extract or mes 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. The involvement of direct view of the researchers that may be originated in the mental background should be avoided in every phase of the analysis (5). This issue happens when all the aspects of the data are not analyzed or there is a defect in providing a rich description or interpretation of an aspect or several aspects of the data. Unconvincing or weak analysis can be due to failure to provide enough examples of the data, for example one or two extract for each theme. Material analysis (analysis of a subject) is an assessed, self-conscious and artistic creation that is made coherent by the researcher for convincing the reader in presenting a discussion as justifiable. It is necessary that the data interpretations match theoretical framework in a performing a good thematic analysis. Even if an analysis is good and interesting but does not explain what its theoretical presumption or purpose is, it will lack crucial information and thus it is defective in one (2006) points out three issues for thematic analysis, the main part of which is theoretical issue: it is the interpretativism which is in fact the interpretation of others’ actions through our understanding. Some questions can be asked in this section which indicate the issues in this type of analysis: do the researcher’s activities make sense of others’ actions? There are some criticisms of thematic analysis, this method is simple and simpler than other qualitative research methods. High level of flexibility and simplicity and tangibility of analysis phase have made less-experienced researchers in qualitative studies not hopeless and have attracted them towards this method.

Conclusionthematic analysis is the most common and the simplest form of analysis in qualitative research. Ta is an approach for extraction of meanings and concepts from data and includes pinpointing, examining, and recording patterns or themes. Ta not only provides a flexible method of data analysis in qualitative research, it establishes the more systematic and explicit form of it without threatening depth of analysis.