qualitative research meaning in tagalog

Translation of "qualitative" into Tagalog

Sample translated sentence: It was none other than Jesus Christ who pinpointed this qualitatively different source of evil. ↔ Walang iba kundi si Jesu-Kristo ang tuwirang tumukoy sa naiibang kauriang ito na pinagmumulan ng masasama.

of descriptions or distinctions based on some quality rather than on some quantity [..]

Automatic translations of " qualitative " into Tagalog

"qualitative" in english - tagalog dictionary.

Currently we have no translations for qualitative in the dictionary, maybe you can add one? Make sure to check automatic translation, translation memory or indirect translations.

Computer generated translations

kalidad (@2 : ka: ხარისხი pl: jakościowy )

klase (@2 : fa: چونی tr: nitelik )

uri (@2 : fa: چونی tr: nitelik )

pang-uri (@1 : ar: وَصْفِيّ )

Translations of "qualitative" into Tagalog in sentences, translation memory

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Kwantitatibo na Pananaliksik

Ang mga kwantitatib research ay nagbibigay diin sa mga sukat ng layunin at ang statistical, matematika, o numerical analysis ng data na nakolekta sa pamamagitan ng mga botohan, mga questionnaire, at mga survey, o sa pamamagitan ng pagmamanipula ng mga umiiral nang statistical data gamit ang computational techniques. Nagtutuos ang dami ng pananaliksik sa pag-iipon ng numerong datos at pangkalahatan sa mga grupo ng mga tao o upang ipaliwanag ang isang partikular na kababalaghan. Mga Katangian ng kwantitatib research Ang iyong layunin sa pagsasagawa ng quantitative research study ay upang matukoy ang ugnayan sa pagitan ng isang bagay [isang independiyenteng variable] at isa pang [dependent o outcome variable] sa loob ng isang populasyon. Ang mga dami ng mga disenyo ng pananaliksik ay alinman sa naglalarawang [mga saklaw na kadalasang nasusukat ng isang beses] o pang-eksperimentong [mga saklaw na sinukat bago at pagkatapos ng paggamot]. Ang isang naglalarawang pag-aaral ay nagtatatag lamang ng mga asosasyon sa pagitan ng mga variable; ang isang eksperimentong pag-aaral ay nagtatatag ng pananahilan. Ang mga dami ng mga pananaliksik sa pananaliksik sa mga numero, logic, at isang layunin na paninindigan. Ang dami ng pananaliksik ay nakatuon sa numeric at walang pagbabago na data at detalyadong, nagtatagpo ng pangangatwiran sa halip na divergent na pangangatwiran [ie, ang henerasyon ng iba't ibang mga ideya tungkol sa isang suliranin sa pananaliksik sa isang kusang-loob, walang bayad na paraan]. Ang mga pangunahing katangian nito ay: Ang data ay karaniwang natipon gamit ang nakabalangkas na mga instrumento sa pananaliksik. Ang mga resulta ay batay sa mas malaking laki ng sample na kinatawan ng populasyon. Ang pag-aaral ng pananaliksik ay kadalasang maaaring replicated o paulit-ulit, na ibinigay nito mataas na pagiging maaasahan. Ang mananaliksik ay may isang malinaw na tinukoy na pananaliksik na tanong kung saan ang mga layunin ay hinahangad. Ang lahat ng mga aspeto ng pag-aaral ay maingat na dinisenyo bago maipon ang data. Ang data ay nasa anyo ng mga numero at istatistika, madalas na nakaayos sa mga talahanayan, tsart, mga numero, o iba pang mga di-tekstong mga form. Maaaring gamitin ang proyekto upang gawing mas malawak ang mga konsepto, hulaan ang mga resulta sa hinaharap, o magsiyasat sa mga relasyon sa pananahilan. Ang mananaliksik ay gumagamit ng mga tool, tulad ng mga questionnaire o software ng computer, upang mangolekta ng numerical data. Ang napakahalagang layunin ng isang pag-aaral sa dami ng pananaliksik ay ang pag-uri-uriin ang mga tampok, bilangin ang mga ito, at bumuo ng mga istatistikang modelo sa pagtatangkang ipaliwanag kung ano ang naobserbahan. Mga bagay na dapat tandaan kapag nag-uulat ng mga resulta ng isang pag-aaral gamit ang mga dami ng pamamaraan: Ipaliwanag ang data na nakolekta at ang kanilang statistical treatment pati na rin ang lahat ng kaugnay na mga resulta na may kaugnayan sa problema sa

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Tagalog-Dictionary.com

Meaning of "research", research •.

• a careful hunting for facts or truth, inquiry, investigation: pagsaliksik, pananaliksik
• to make a research: manaliksik, magsuri, magsiyasat

» synonyms and related words:

• detailed, minute search, research

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Words rhyme with Qualitative

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• Eur J Ageing
• v.7(4); 2010 Dec

Language differences in qualitative research: is meaning lost in translation?

Fenna van nes.

1 Longitudinal Aging Study Amsterdam, EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands

2 Amsterdam School of Health Professions, Amsterdam, The Netherlands

Tineke Abma

3 Medical Humanities, VU University Medical Center, Amsterdam, The Netherlands

Hans Jonsson

4 Division of Occupational Therapy, Karolinska Institutet, Stockholm, Sweden

This article discusses challenges of language differences in qualitative research, when participants and the main researcher have the same non-English native language and the non-English data lead to an English publication. Challenges of translation are discussed from the perspective that interpretation of meaning is the core of qualitative research. As translation is also an interpretive act, meaning may get lost in the translation process. Recommendations are suggested, aiming to contribute to the best possible representation and understanding of the interpreted experiences of the participants and thereby to the validity of qualitative research.

English is the dominant language in cross-European projects and publications (Kushner 2003 ). With European research collaboration and knowledge circulation being stimulated by the European Union as well as by national governments, language differences play an increasingly important role in research. Language differences may have consequences, because concepts in one language may be understood differently in another language. This is in particular relevant for qualitative research, because it works with words; language is central in all phases ranging from data collection to analysis and representation of the textual data in publications. Language differences may occur in the first phase of a qualitative study, when interview data need to be translated to the researcher’s language, for example in qualitative research with immigrants. Consequences for the validity of moving across languages have gained considerably attention in these cross-cultural studies (Squires 2009 ). However, language differences also play a role, when translation is required in later phases. This is the case in most studies with participants and main researcher having the same non-English native language, because publication is sought mainly in English outlets. In these studies, moving to English has gained little methodological attention, although here validity might be threatened as well. This article discusses challenges of language differences in qualitative research, when participants and the main researcher have the same non-English native language and the non-English data lead to an English publication.

Interpretation of meanings

Qualitative research seeks to study meanings in subjective experiences. The relation between subjective experience and language is a two-way process; language is used to express meaning, but the other way round, language influences how meaning is constructed. Giving words to experiences is a complicated process as the meaning of experiences is often not completely accessible for subjects and difficult to express in language. To capture the richness of experience in language, people commonly use narratives and metaphors (Polkinghorne 2005 ). Metaphors vary from culture to culture and are language-specific (Lakoff and Johnson 1980 ). For example in Dutch it is a common saying to give a proposal ‘hands and feet’ (handen en voeten geven in Dutch) to express the physical work that is needed to make the proposal concrete. This expression is not easily understandable for native English speakers (Otis 2008 ). Language also influences what can be expressed, and some linguists even state that social reality as experienced is unique to one’s own language; those who speak different languages would perceive the world differently (Chapman 2006 ).

Qualitative research is considered valid when the distance between the meanings as experienced by the participants and the meanings as interpreted in the findings is as close as possible (Polkinghorne 2007 ). We would like to go one step further, and hold that the findings should be communicated in such a way that the reader of the publication understands the meaning as it was expressed in the findings, originating from data in the source language.

Translation between languages involves interpretation as well. The message communicated in the source language has to be interpreted by the translator (often the researcher him or herself) and transferred into the target language in such a way that the receiver of the message understands what was meant. Challenges in the interpretation and representation of meaning may be experienced in any communicative action, but are more complicated when cultural contexts differ and interlingual translation is required. Because interpretation and understanding meanings are central in qualitative research and text is the ‘vehicle’ with which meaning is ultimately transferred to the reader, language differences generate additional challenges that might hinder the transfer of meaning and might result in loss of meaning and thus loss of the validity of the qualitative study.

Challenges of language differences

We will now discuss the challenges that may arise when moving to English in qualitative research. We give examples to illustrate these challenges, although it is challenging in itself to formulate in English examples of the problems in translation between non-English to English. Where needed we have kept the original words in the source language.

Translation of findings

With participants and the main researcher speaking the same language, no language differences are present in data gathering, transcription and during the first analyses, because usually the first coding phase stays closely to the data. The first language differences may occur when interpretations are being discussed among members of a multi-national research team. This is a fragile phase with multiple interpretations being under discussion as even in the source language it is not yet clear how to express the meanings as interpreted. For discussion, these first interpretations need to be explained in English and a very good understanding of subtle meaning differences is needed to come to the best English wordings. A first example comes from a study with ageing couples. The multi-national team discussed how to express the particular way in which the couples experienced changes, namely as slow and almost unnoticed ‘movements down a slope’. The words ‘shifting’ and ‘gliding’ were considered, but both words seemed not fully suitable to express the intended meaning.

In this example, the translation challenges occurred in the first interpretation phase. In the following example, we were not aware of translation problems when translating the Dutch wandelen to walking, because according to several dictionaries, ‘walking’ was linguistically correct. However, native English speakers understood walking as the Dutch lopen , as to move from one place to another on feet, only as instrumental transportation. However, the activity wandelen consisted of a complex constellation of different meanings including the intrinsic enjoyment of the activity, enjoying nature and its associations with Sunday afternoons and holidays together. Ultimately ‘going for walk’ seemed more appropriate to represent the meaning expressed by the couple.

Challenges of translation may even occur when support of a professional translator is been used. This occurred in a narrative case study of an older couple after the wife had experienced a stroke (Van Nes et al. 2009 ). The findings had the form of narratives with the main meanings expressed in the titles of the narratives. The common narrative was that they acted as one organism, which was expressed as ‘One body, three hands and two minds’. The title of the narrative of the husband was constructed to express the sudden and complete shift in his valued activities. Before the stroke of his wife, he had his own engaging activities (bee-keeping and having a kitchen garden), which he experienced as a way to be independent and to support himself and the household with honey and vegetables. After the stroke of his wife, he was busy all day long with supporting her and there was no time left for these former valued activities. In the title ‘ From being self - supporting in an engaging occupation 1 to the absorbing occupation of supporting ’, the word engaging was meant to reflect a positive meaning. Absorbing was meant to contrast with engaging and to indicate that he was fully occupied all day long after the stroke. The reviewers of the submitted paper on this study, however, understood both English words as having the same positive connotation, so the intended meaning of the complete turn was lost.

Translation of quotations

Quotations of participants are commonly being used in qualitative research articles. Translation of quotes poses specific challenges, because it may be difficult to translate concepts for which specific culturally bound words were used by the participants. For example, the Dutch word gezellig was used commonly by late-life couples, expressing the feeling they had when doing things together. The meaning expressed with this typical Dutch word included experiencing togetherness in doing everyday activities together, often at specific times of the day and in the own home. Translating the word gezellig , only as ‘cosy’ would reduce the meaning. Using more words than in the original quote, however, changes the voice of the participant. This is especially problematic as giving voice to people is seen as an important aim of qualitative research (Denzin and Lincoln 2000 ).

Back translation

After publication, a new translation challenge may be faced, when back translation to the original source language is undertaken. This was the case when translating ‘One body, three hands and two minds’ back to Dutch. The literal translation of ‘One body’ would have been ‘ Eén lichaam ’, but this appeared to be more physical than one body, because in English the word body is also used in other ways, e.g. as in a body of literature. The chosen solution was translation as Samen Eén (‘Together One’), but here the meaning of functioning as one organism was lost.

We have shown that with interpretation of meaning being central in qualitative research, language differences may affect the understanding and interpretation of meanings in different phases on the way from participant to reader. If translation issues are not given adequate thought and attention, the meaning-transfer-chain may resemble the whispering game children play. In the game players line up in such a way that they can whisper to their immediate neighbours. The first player whispers a phrase to his neighbour, who then passes on the message until it reaches the end of the line. If the game has been ‘successful’, the final message differs considerably from the first. In qualitative research meaning is also transferred from one phase to the next, until it reaches the reader and in each transfer meaning might get lost. Such loss of meaning reduces the validity of the qualitative study.

Recommendations

In the following, we give some recommendations aimed to potentially reduce the loss of meaning and thereby to enhance the validity of cross-English qualitative research. Our first recommendation focuses on the thinking and reflection processes that are needed in the analyses. We experienced that talking and reading in English leads to thinking in the English language as well. The relationship between thinking and language has been studied from different scientific perspectives, e.g. in psychology and in the philosophy of language (see e.g. Jackendoff 2009 ). One view considers language to be an aid to thinking. It is beyond the scope of this article to examine this relationship further. However, we feel it can be stated that there is some influence when analysing in another language than your own. To avoid potential limitations in the analysis we therefore recommend staying in the original language as long and as much as possible.

In discussions with members of the research team or peers who do not speak the source language, we recommend to delay the use of fixed—one word—translations. Instead, the analyses might even benefit from using fluid descriptions of meanings using various English formulations. In doing so, it is important to check the interpretations by going back to the codes and preliminary findings in the source language. Keeping record of these discussions would be useful to make the development of the interpretations transparent when in later phases the translations need to be adapted.

For translation of the most meaningful language parts in the findings, like the titles in narrative research or the themes in phenomenological research, we recommend that the researcher operates as a translation moderator in cooperation with a professional translator. This would involve explaining to the translator the intended meaning and its context in the source language. We recommend this should be done in a side-by-side procedure, in which the researcher and the translator discuss possible wordings. Often, different linguistically correct translations are possible, but there will be subtle meaning differences, which need to be closely examined in order to decide on the best translation.

Rich descriptions with the use of quotes of participants are considered to contribute to trustworthiness in qualitative research. However, using quotes is not unproblematic, because participants might feel that they are not fairly represented, when they see their spoken words in written form. Translating the quotes to another language enlarges this problem, because in the translation the words are literally not their own anymore (Temple 2008 ). Therefore, we recommend that these translations are also undertaken with support of a professional translator. Special attention is needed when metaphors are translated, either in quotes or in the findings.

Currently, in method sections of English articles reporting research with non-English data, translation issues are seldom discussed. In line with cross-language research literature, we recommend to describe and discuss in the research article how translation has been undertaken. This will provide reviewers and readers alike with a better insight into the way potential meaning losses have been avoided in the procedures used (Squires 2009 ).

We are aware that using the services of a professional translator adds to the costs of a study. However, these costs contribute to improving the validity of the research and of the quality of the transference of the findings to the readers of the publication. Furthermore, we suggest that the use of a translator in earlier phases of the research reduces efforts to refine translations in later phases, and may prove to be enriching as the discussing about the best translation may reveal new layers of meanings.

The recommendations we presented here are formulated for qualitative researchers who present findings in English, while the data were gathered in their native non-English language. For cross-European research in general, we consider some of our recommendations also to be relevant. In particular, the recommendation to use fluid descriptions of meanings in discussions might enhance quality also in quantitative studies, because it contributes to a sound understanding among researchers of the concepts central to their research.

We consider these recommendations to be a first step, since more research is needed on this topic. First, we would recommend to undertake an inventory with a questionnaire among non-English researchers who published their qualitative research in English outlets in order to collect data how they handled language differences in their studies and to get insight in and rise their awareness of the potential threat to validity when meaning gets lost in translation. As a next step, a series of focus groups could result in guidelines for cross-English qualitative research. With these suggestions for further steps, we stress the importance of an ongoing dialogue regarding different aspects of translation as an important methodological issue for qualitative cross-English research.

Open Access

This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

1 Occupation is used in Occupational Science as a broad construct as “meaningful everyday activities”.

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What (Exactly) Is Thematic Analysis?

Plain-Language Explanation & Definition (With Examples)

By: Jenna Crosley (PhD). Expert Reviewed By: Dr Eunice Rautenbach | April 2021

Thematic analysis is one of the most popular qualitative analysis techniques we see students opting for at Grad Coach – and for good reason. Despite its relative simplicity, thematic analysis can be a very powerful analysis technique when used correctly. In this post, we’ll unpack thematic analysis using plain language (and loads of examples) so that you can conquer your analysis with confidence.

Thematic Analysis 101

• Basic terminology relating to thematic analysis
• What is thematic analysis
• When to use thematic analysis
• The main approaches to thematic analysis
• The three types of thematic analysis
• How to “do” thematic analysis (the process)
• Tips and suggestions

First, the lingo…

Before we begin, let’s first lay down some terminology. When undertaking thematic analysis, you’ll make use of codes . A code is a label assigned to a piece of text, and the aim of using a code is to identify and summarise important concepts within a set of data, such as an interview transcript.

For example, if you had the sentence, “My rabbit ate my shoes”, you could use the codes “rabbit” or “shoes” to highlight these two concepts. The process of assigning codes is called qualitative coding . If this is a new concept to you, be sure to check out our detailed post about qualitative coding .

Codes are vital as they lay a foundation for themes . But what exactly is a theme? Simply put, a theme is a pattern that can be identified within a data set. In other words, it’s a topic or concept that pops up repeatedly throughout your data. Grouping your codes into themes serves as a way of summarising sections of your data in a useful way that helps you answer your research question(s) and achieve your research aim(s).

Alright – with that out of the way, let’s jump into the wonderful world of thematic analysis…

What is thematic analysis?

Thematic analysis is the study of patterns to uncover meaning . In other words, it’s about analysing the patterns and themes within your data set to identify the underlying meaning. Importantly, this process is driven by your research aims and questions , so it’s not necessary to identify every possible theme in the data, but rather to focus on the key aspects that relate to your research questions .

Although the research questions are a driving force in thematic analysis (and pretty much all analysis methods), it’s important to remember that these questions are not necessarily fixed . As thematic analysis tends to be a bit of an exploratory process, research questions can evolve as you progress with your coding and theme identification.

When should you use thematic analysis?

There are many potential qualitative analysis methods that you can use to analyse a dataset. For example, content analysis , discourse analysis , and narrative analysis are popular choices. So why use thematic analysis?

Thematic analysis is highly beneficial when working with large bodies of data ,  as it allows you to divide and categorise large amounts of data in a way that makes it easier to digest. Thematic analysis is particularly useful when looking for subjective information , such as a participant’s experiences, views, and opinions. For this reason, thematic analysis is often conducted on data derived from interviews , conversations, open-ended survey responses , and social media posts.

Your research questions can also give you an idea of whether you should use thematic analysis or not. For example, if your research questions were to be along the lines of:

• How do dog walkers perceive rules and regulations on dog-friendly beaches?
• What are students’ experiences with the shift to online learning?
• What opinions do health professionals hold about the Hippocratic code?
• How is gender constructed in a high school classroom setting?

These examples are all research questions centering on the subjective experiences of participants and aim to assess experiences, views, and opinions. Therefore, thematic analysis presents a possible approach.

In short, thematic analysis is a good choice when you are wanting to categorise large bodies of data (although the data doesn’t necessarily have to be large), particularly when you are interested in subjective experiences .

What are the main approaches?

Broadly speaking, there are two overarching approaches to thematic analysis: inductive and deductive . The approach you take will depend on what is most suitable in light of your research aims and questions. Let’s have a look at the options.

The inductive approach

The inductive approach involves deriving meaning and creating themes from data without any preconceptions . In other words, you’d dive into your analysis without any idea of what codes and themes will emerge, and thus allow these to emerge from the data.

For example, if you’re investigating typical lunchtime conversational topics in a university faculty, you’d enter the research without any preconceived codes, themes or expected outcomes. Of course, you may have thoughts about what might be discussed (e.g., academic matters because it’s an academic setting), but the objective is to not let these preconceptions inform your analysis.

The inductive approach is best suited to research aims and questions that are exploratory in nature , and cases where there is little existing research on the topic of interest.

The deductive approach

In contrast to the inductive approach, a deductive approach involves jumping into your analysis with a pre-determined set of codes . Usually, this approach is informed by prior knowledge and/or existing theory or empirical research (which you’d cover in your literature review ).

For example, a researcher examining the impact of a specific psychological intervention on mental health outcomes may draw on an existing theoretical framework that includes concepts such as coping strategies, social support, and self-efficacy, using these as a basis for a set of pre-determined codes.

The deductive approach is best suited to research aims and questions that are confirmatory in nature , and cases where there is a lot of existing research on the topic of interest.

Regardless of whether you take the inductive or deductive approach, you’ll also need to decide what level of content your analysis will focus on – specifically, the semantic level or the latent level.

A semantic-level focus ignores the underlying meaning of data , and identifies themes based only on what is explicitly or overtly stated or written – in other words, things are taken at face value.

In contrast, a latent-level focus concentrates on the underlying meanings and looks at the reasons for semantic content. Furthermore, in contrast to the semantic approach, a latent approach involves an element of interpretation , where data is not just taken at face value, but meanings are also theorised.

“But how do I know when to use what approach?”, I hear you ask.

Well, this all depends on the type of data you’re analysing and what you’re trying to achieve with your analysis. For example, if you’re aiming to analyse explicit opinions expressed in interviews and you know what you’re looking for ahead of time (based on a collection of prior studies), you may choose to take a deductive approach with a semantic-level focus.

On the other hand, if you’re looking to explore the underlying meaning expressed by participants in a focus group, and you don’t have any preconceptions about what to expect, you’ll likely opt for an inductive approach with a latent-level focus.

Simply put, the nature and focus of your research, especially your research aims , objectives and questions will  inform the approach you take to thematic analysis.

What are the types of thematic analysis?

Now that you’ve got an understanding of the overarching approaches to thematic analysis, it’s time to have a look at the different types of thematic analysis you can conduct. Broadly speaking, there are three “types” of thematic analysis:

• Reflexive thematic analysis
• Codebook thematic analysis
• Coding reliability thematic analysis

Let’s have a look at each of these:

Reflexive thematic analysis takes an inductive approach, letting the codes and themes emerge from that data. This type of thematic analysis is very flexible, as it allows researchers to change, remove, and add codes as they work through the data. As the name suggests, reflexive thematic analysis emphasizes the active engagement of the researcher in critically reflecting on their assumptions, biases, and interpretations, and how these may shape the analysis.

Reflexive thematic analysis typically involves iterative and reflexive cycles of coding, interpreting, and reflecting on data, with the aim of producing nuanced and contextually sensitive insights into the research topic, while at the same time recognising and addressing the subjective nature of the research process.

Codebook thematic analysis , on the other hand, lays on the opposite end of the spectrum. Taking a deductive approach, this type of thematic analysis makes use of structured codebooks containing clearly defined, predetermined codes. These codes are typically drawn from a combination of existing theoretical theories, empirical studies and prior knowledge of the situation.

Codebook thematic analysis aims to produce reliable and consistent findings. Therefore, it’s often used in studies where a clear and predefined coding framework is desired to ensure rigour and consistency in data analysis.

Coding reliability thematic analysis necessitates the work of multiple coders, and the design is specifically intended for research teams. With this type of analysis, codebooks are typically fixed and are rarely altered.

The benefit of this form of analysis is that it brings an element of intercoder reliability where coders need to agree upon the codes used, which means that the outcome is more rigorous as the element of subjectivity is reduced. In other words, multiple coders discuss which codes should be used and which shouldn’t, and this consensus reduces the bias of having one individual coder decide upon themes.

Quick Recap: Thematic analysis approaches and types

To recap, the two main approaches to thematic analysis are inductive , and deductive . Then we have the three types of thematic analysis: reflexive, codebook and coding reliability . Which type of thematic analysis you opt for will need to be informed by factors such as:

• The approach you are taking. For example, if you opt for an inductive approach, you’ll likely utilise reflexive thematic analysis.
• Whether you’re working alone or in a group . It’s likely that, if you’re doing research as part of your postgraduate studies, you’ll be working alone. This means that you’ll need to choose between reflexive and codebook thematic analysis.

Now that we’ve covered the “what” in terms of thematic analysis approaches and types, it’s time to look at the “how” of thematic analysis.

Need a helping hand?

How to “do” thematic analysis

At this point, you’re ready to get going with your analysis, so let’s dive right into the thematic analysis process. Keep in mind that what we’ll cover here is a generic process, and the relevant steps will vary depending on the approach and type of thematic analysis you opt for.

Step 1: Get familiar with the data

The first step in your thematic analysis involves getting a feel for your data and seeing what general themes pop up. If you’re working with audio data, this is where you’ll do the transcription , converting audio to text.

At this stage, you’ll want to come up with preliminary thoughts about what you’ll code , what codes you’ll use for them, and what codes will accurately describe your content. It’s a good idea to revisit your research topic , and your aims and objectives at this stage. For example, if you’re looking at what people feel about different types of dogs, you can code according to when different breeds are mentioned (e.g., border collie, Labrador, corgi) and when certain feelings/emotions are brought up.

As a general tip, it’s a good idea to keep a reflexivity journal . This is where you’ll write down how you coded your data, why you coded your data in that particular way, and what the outcomes of this data coding are. Using a reflexive journal from the start will benefit you greatly in the final stages of your analysis because you can reflect on the coding process and assess whether you have coded in a manner that is reliable and whether your codes and themes support your findings.

As you can imagine, a reflexivity journal helps to increase reliability as it allows you to analyse your data systematically and consistently. If you choose to make use of a reflexivity journal, this is the stage where you’ll want to take notes about your initial codes and list them in your journal so that you’ll have an idea of what exactly is being reflected in your data. At a later stage in the analysis, this data can be more thoroughly coded, or the identified codes can be divided into more specific ones.

Step 2: Search for patterns or themes in the codes

Step 2! You’re going strong. In this step, you’ll want to look out for patterns or themes in your codes. Moving from codes to themes is not necessarily a smooth or linear process. As you become more and more familiar with the data, you may find that you need to assign different codes or themes according to new elements you find. For example, if you were analysing a text talking about wildlife, you may come across the codes, “pigeon”, “canary” and “budgerigar” which can fall under the theme of birds.

As you work through the data, you may start to identify subthemes , which are subdivisions of themes that focus specifically on an aspect within the theme that is significant or relevant to your research question. For example, if your theme is a university, your subthemes could be faculties or departments at that university.

In this stage of the analysis, your reflexivity journal entries need to reflect how codes were interpreted and combined to form themes.

Step 3: Review themes

By now you’ll have a good idea of your codes, themes, and potentially subthemes. Now it’s time to review all the themes you’ve identified . In this step, you’ll want to check that everything you’ve categorised as a theme actually fits the data, whether the themes do indeed exist in the data, whether there are any themes missing , and whether you can move on to the next step knowing that you’ve coded all your themes accurately and comprehensively . If you find that your themes have become too broad and there is far too much information under one theme, it may be useful to split this into more themes so that you’re able to be more specific with your analysis.

In your reflexivity journal, you’ll want to write about how you understood the themes and how they are supported by evidence, as well as how the themes fit in with your codes. At this point, you’ll also want to revisit your research questions and make sure that the data and themes you’ve identified are directly relevant to these questions .

Step 4: Finalise Themes

By this point, your analysis will really start to take shape. In the previous step, you reviewed and refined your themes, and now it’s time to label and finalise them . It’s important to note here that, just because you’ve moved onto the next step, it doesn’t mean that you can’t go back and revise or rework your themes. In contrast to the previous step, finalising your themes means spelling out what exactly the themes consist of, and describe them in detail . If you struggle with this, you may want to return to your data to make sure that your data and coding do represent the themes, and if you need to divide your themes into more themes (i.e., return to step 3).

When you name your themes, make sure that you select labels that accurately encapsulate the properties of the theme . For example, a theme name such as “enthusiasm in professionals” leaves the question of “who are the professionals?”, so you’d want to be more specific and label the theme as something along the lines of “enthusiasm in healthcare professionals”.

It is very important at this stage that you make sure that your themes align with your research aims and questions . When you’re finalising your themes, you’re also nearing the end of your analysis and need to keep in mind that your final report (discussed in the next step) will need to fit in with the aims and objectives of your research.

In your reflexivity journal, you’ll want to write down a few sentences describing your themes and how you decided on these. Here, you’ll also want to mention how the theme will contribute to the outcomes of your research, and also what it means in relation to your research questions and focus of your research.

By the end of this stage, you’ll be done with your themes – meaning it’s time to write up your findings and produce a report.

Step 5: Produce your report

You’re nearly done! Now that you’ve analysed your data, it’s time to report on your findings. A typical thematic analysis report consists of:

• An introduction
• A methodology section
• Your results and findings
• A conclusion

When writing your report, make sure that you provide enough information for a reader to be able to evaluate the rigour of your analysis. In other words, the reader needs to know the exact process you followed when analysing your data and why. The questions of “what”, “how”, “why”, “who”, and “when” may be useful in this section.

So, what did you investigate? How did you investigate it? Why did you choose this particular method? Who does your research focus on, and who are your participants? When did you conduct your research, when did you collect your data, and when was the data produced? Your reflexivity journal will come in handy here as within it you’ve already labelled, described, and supported your themes.

If you’re undertaking a thematic analysis as part of a dissertation or thesis, this discussion will be split across your methodology, results and discussion chapters . For more information about those chapters, check out our detailed post about dissertation structure .

It’s absolutely vital that, when writing up your results, you back up every single one of your findings with quotations . The reader needs to be able to see that what you’re reporting actually exists within the results. Also make sure that, when reporting your findings, you tie them back to your research questions . You don’t want your reader to be looking through your findings and asking, “So what?”, so make sure that every finding you represent is relevant to your research topic and questions.

Quick Recap: How to “do” thematic analysis

Getting familiar with your data: Here you’ll read through your data and get a general overview of what you’re working with. At this stage, you may identify a few general codes and themes that you’ll make use of in the next step.

Search for patterns or themes in your codes : Here you’ll dive into your data and pick out the themes and codes relevant to your research question(s).

Review themes : In this step, you’ll revisit your codes and themes to make sure that they are all truly representative of the data, and that you can use them in your final report.

Finalise themes : Here’s where you “solidify” your analysis and make it report-ready by describing and defining your themes.

Produce your report : This is the final step of your thematic analysis process, where you put everything you’ve found together and report on your findings.

Tips & Suggestions

In the video below, we share 6 time-saving tips and tricks to help you approach your thematic analysis as effectively and efficiently as possible.

Wrapping Up

In this article, we’ve covered the basics of thematic analysis – what it is, when to use it, the different approaches and types of thematic analysis, and how to perform a thematic analysis.

If you have any questions about thematic analysis, drop a comment below and we’ll do our best to assist. If you’d like 1-on-1 support with your thematic analysis, be sure to check out our research coaching services here .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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21 Comments

I really appreciate the help

Hello Sir, how many levels of coding can be done in thematic analysis? We generate codes from the transcripts, then subthemes from the codes and themes from subthemes, isn’t it? Should these themes be again grouped together? how many themes can be derived?can you please share an example of coding through thematic analysis in a tabular format?

I’ve found the article very educative and useful

Excellent. Very helpful and easy to understand.

This article so far has been most helpful in understanding how to write an analysis chapter. Thank you.

My research topic is the challenges face by the school principal on the process of procurement . Thematic analysis is it sutable fir data analysis ?

It is a great help. Thanks.

Best advice. Worth reading. Thank you.

Where can I find an example of a template analysis table ?

Finally I got the best article . I wish they also have every psychology topics.

Hello, Sir/Maam

I am actually finding difficulty in doing qualitative analysis of my data and how to triangulate this with quantitative data. I encountered your web by accident in the process of searching for a much simplified way of explaining about thematic analysis such as coding, thematic analysis, write up. When your query if I need help popped up, I was hesitant to answer. Because I think this is for fee and I cannot afford. So May I just ask permission to copy for me to read and guide me to study so I can apply it myself for my gathered qualitative data for my graduate study.

Thank you very much! this is very helpful to me in my Graduate research qualitative data analysis.

Thank you very much. I find your guidance here helpful. Kindly let help me understand how to write findings and discussions.

i am having troubles with the concept of framework analysis which i did not find here and i have been an assignment on framework analysis

I was discouraged and felt insecure because after more than a year of writing my thesis, my work seemed lost its direction after being checked. But, I am truly grateful because through the comments, corrections, and guidance of the wisdom of my director, I can already see the bright light because of thematic analysis. I am working with Biblical Texts. And thematic analysis will be my method. Thank you.

lovely and helpful. thanks

very informative information.

thank you very much!, this is very helpful in my report, God bless……..

Thank you for the insight. I am really relieved as you have provided a super guide for my thesis.

Thanks a lot, really enlightening

excellent! very helpful thank a lot for your great efforts

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