Thematic content analysis refers to a qualitative method of research widely applied across different disciplines to report, identify, and analyze data patterns. It plays an important role in giving a complex and detailed data account. Similar to numerous other qualitative methods of research like discourse analysis, framework analysis, and narrative analysis, the coding process is crucial to thematic content analysis, acting as a bridge between insightful themes’ emergence and raw data.

This article will teach you the key thematic content analysis coding steps, from mastery of the coding purpose to arranging codes effectively.

Coding thematic analysis

Thematic content analysis also called thematic coding, is a qualitative data analytics type that finds text themes by evaluating sentence and word structure meaning.

When you apply thematic codes to analyze feedback from customers, for instance, you can understand the most frequent feedback themes. This assists you master what drives the satisfaction of customers in an actionable, accurate way.

How to code thematic analysis

1.      Conform to the pre-coding work (data)

Before you are capable of successfully working with information, you must understand it. Familiarize yourself with the information to see the general themes that appear. Transcribe files and audio and observe all patterns and meanings across your data set. Examine your transcript, and write down notes concerning possible codes to design.

2.      Create the data initial codes (code open work)

Create initial codes set to represent data meanings and patterns. Make codebooks to monitor the codes. Examine the information again to use the suitable codes and recognize interesting excerpts. You should utilize a similar code to display the same meaning excerpts.

3.      Compare codes with backup data (initial code clustering)

Now it is time to categorize all excerpts related to a specific code. If you are performing this manually, break down codes and assemble them. It’s the thematic content analysis software that automatically collates the codes.

4.      Group your codes into potential themes (selective codes clustering)

Once you have concluded the codes, group them into possible themes. Themes mirror data patterns and trends. You can merge several codes to design sub-themes.

5.      Final revision (finalize, review, and revise your themes)

Now you have decided upon your initial themes, adjust and review them as required. Each theme must be different, with adequate data to back it up. You can combine the same themes and eliminate those with insufficient supportive information. Begin formulating your themes into narratives.

6.      Write your report

The last step of narrating a data set story is writing your report. You must fully think about your themes to convey your analysis validity.

A normal thematic content analysis report has these components:

• The introduction
• The methodsection
• Findings and results
• The conclusion

The narrative should be logical, and it must include intense quotes that will support points. It must also include your claims interpretive argument and analysis. Additionally, consider reporting the findings in tree diagrams or flowcharts, which may be part of or independent of the report.

Deductive coding in thematic analysis

The deductive approach to coding starts with a predetermined code set, and then assigning those codes to your latest qualitative information. These codes may come from prior studies, or you may already be aware of the themes you are interested in evaluating. A deductive approach to coding can also be referred to as concept-driven coding of data.

For instance, let’s say you are conducting a customer experience survey. You need to comprehend the problems arising from long waiting times, so you opt to use “wait time” among your codes before starting to examine the information.

A deductive coding approach can help ensure that your interest areas are coded and save time. But you must also be keen on biases; when you begin with predetermined codes, you’ve biases to the nature of your answers. Ensure you do not miss other significant themes by concentrating too much on justifying your hypothesis.

How to thematically analyze data

After you have decided thematic content analysis tends to be the right approach for evaluating your information, and you have considered the approach you are going to adopt, you can follow these six steps to thematically analyze data.

1.      Data Familiarization

The initial step is getting to know your data. It is essential to get your collected data an in-depth overview before you start evaluating specified items.

This may involve writing initial notes, transcribing audio, assessing the text, and generally studying the information to know it.

2.      Data Coding

Next, you have to code your information. Coding refers to highlighting your text sections – usually sentences or phrases – and devising shorthand codes or “labels” to define the content of text.

At this step, you need to be keen: you study every interview transcript and highlight all that pops off as potentially interesting or relevant. Together with highlighting every phrase and sentence that matches the codes, you can continue to add extra codes as you study a text.

3.      Generate themes

Next, you analyze your created codes, identify their patterns, and start devising themes.

A theme is normally broader than a code. Mostly, you will combine various codes to form one theme.

4.      Review themes

Now you’ve to ensure that your themes are accurate and useful data representations. Here, you go back to your data set to compare your themes as opposed to it.

If you encounter these problems, you may break the themes, design new ones, combine them, or discard them: whatever renders them more accurate and useful.

5.      Naming and defining themes

Since you’ve got a final themes list, it is time to define and name them.

Defining your themes includes formulating absolutely what you mean by all themes and considering how this helps you understand the information.

Naming your themes includes devising an easily understandable and succinct name for all the themes.

6.      Writing up

Eventually, you’ll write up your data analysis. Like every academic text, writing up your thematic analysis needs an introduction to set up your research approach, question, and aims.

You must also incorporate the methodology section, defining how you gathered the information (for example through open-ended survey queries or semi-structured interviews) and describing how you carried out the thematic content analysis.

Content analysis thematic analysis

Thematic content analysis refers to a qualitative approach used to reveal textual data themes, while analysis of content refers to either a qualitative or a quantitative method that also includes some data quantification.

Content Analysis normally counts the keywords or concept’s occurrence to conclude meaning, while thematic analysis gives meaning by extorting high-level insights.

The thematic analysis concentrates on data’s overarching themes and how the themes associate with one another, whereas in content analysis, researchers count coded keywords and concepts instances within large textual data amounts with less concentration on contrasting or comparing those codes.

Content and thematic analysis in qualitative research

Analysis of content is a method of research used to analyze, identify, and quantify patterns. It is a systematic method that often includes analyzing and identifying data, splitting data into little units, grouping them, and afterward counting these category’s frequency.

Why do you need content analysis?

By frequency counting, researchers can make conclusions concerning the meaning of content, its intentions, and even its possible impact.

Contrary to the analysis of content, the thematic content analysis goes above counting phrases or words, which is concisely what analysis of content does. Thematic content analysis concentrates on interpreting and identifying themes (i.e., meaning patterns) that arise from the information.

The themes describe the underlying experiences, ideas, or concepts that are interpreted by the participant or communicated in your text.

Thematic analysis and analysis of content are two approaches to qualitative analysis of information that share few basic similarities. Both of them involve evaluating written data by splitting a text into smaller units of content. Despite employing different methods, their shared aim is to produce fresh knowledge and insights from qualitative sources of data. Both approaches need an in-depth data understanding through a duplication process. Applying these methods, you can make research theoretical claims.

To get a comprehensive thematic analysis and content analysis applications understanding, it is beneficial to examine their differences.

Difference Point	Analysis of Content	Thematic Analysis
The approach aim	Category occurrences frequency emphasis	Identifying cohesive analysis and recurring themes
The focus of the researcher	Content analysis and variables relationships	Specified research question interpretation and exploration
The data analysis process	Uses coding schemes and predefined categories	Analyzed and iteratively coded, flexible method
The findings presentation	Results are normally presented in the form of conceptual models or maps	Supporting excerpts themes presented in your final report
Quality of interpretation	The more objective method with quantifiable evidence-based analysis	Subjective analysis and higher interpretation level

Given these main similarities and differences, a researcher must master both aspects to choose the most suitable approach depending on their study goals, questions, characteristics, and data. Informed decision-making allows you to use the approach that’ll best generate new data, interpret insights, and correspond with the general research aims. Identifying their differences is crucial to discovering yourself for precise interpretation of data.

Conclusion

Because it’s easy to use, thematic content analysis suits new researchers not familiar with more complex qualitative studies. It allows researchers to choose theoretical frameworks freely.

Thematic analysis versatility allows you to define your information in a sophisticated, rich, and intricate way. This approach might be used with whichever theory a researcher selects, unlike other analysis methods that are strongly bound to particular approaches.

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