video content analysis qualitative research

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A hands-on guide to doing content analysis

Christen erlingsson, petra brysiewicz.

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Corresponding author. [email protected]

Received 2017 Feb 21; Revised 2017 May 6; Accepted 2017 Aug 4; Issue date 2017 Sep.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. Novice qualitative researchers are often daunted by the prospect of qualitative data analysis and thus may experience much difficulty in the data analysis process. Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task.

Keywords: Qualitative research, Qualitative data analysis, Content analysis

African relevance

Qualitative research is useful to deepen the understanding of the human experience.

Novice qualitative researchers may benefit from this hands-on guide to content analysis.

Practical tips and data analysis templates are provided to assist in the analysis process.

Introduction

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including emergency care research. An increasing number of health researchers are currently opting to use various qualitative research approaches in exploring and describing complex phenomena, providing textual accounts of individuals’ “life worlds”, and giving voice to vulnerable populations our patients so often represent. Many articles and books are available that describe qualitative research methods and provide overviews of content analysis procedures [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Some articles include step-by-step directions intended to clarify content analysis methodology. What we have found in our teaching experience is that these directions are indeed very useful. However, qualitative researchers, especially novice researchers, often struggle to understand what is happening on and between steps, i.e., how the steps are taken.

As research supervisors of postgraduate health professionals, we often meet students who present brilliant ideas for qualitative studies that have potential to fill current gaps in the literature. Typically, the suggested studies aim to explore human experience. Research questions exploring human experience are expediently studied through analysing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. When reflecting on the proposed study aim together with the student, we often suggest content analysis methodology as the best fit for the study and the student, especially the novice researcher. The interview data are collected and the content analysis adventure begins. Students soon realise that data based on human experiences are complex, multifaceted and often carry meaning on multiple levels.

For many novice researchers, analysing qualitative data is found to be unexpectedly challenging and time-consuming. As they soon discover, there is no step-wise analysis process that can be applied to the data like a pattern cutter at a textile factory. They may become extremely annoyed and frustrated during the hands-on enterprise of qualitative content analysis.

The novice researcher may lament, “I’ve read all the methodology but don’t really know how to start and exactly what to do with my data!” They grapple with qualitative research terms and concepts, for example; differences between meaning units, codes, categories and themes, and regarding increasing levels of abstraction from raw data to categories or themes. The content analysis adventure may now seem to be a chaotic undertaking. But, life is messy, complex and utterly fascinating. Experiencing chaos during analysis is normal. Good advice for the qualitative researcher is to be open to the complexity in the data and utilise one’s flow of creativity.

Inspired primarily by descriptions of “conventional content analysis” in Hsieh and Shannon [3] , “inductive content analysis” in Elo and Kyngäs [5] and “qualitative content analysis of an interview text” in Graneheim and Lundman [1] , we have written this paper to help the novice qualitative researcher navigate the uncertainty in-between the steps of qualitative content analysis. We will provide advice and practical tips, as well as data analysis templates, to attempt to ease frustration and hopefully, inspire readers to discover how this exciting methodology contributes to developing a deeper understanding of human experience and our professional contexts.

Overview of qualitative content analysis

Synopsis of content analysis.

A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes is a process of further abstraction of data at each step of the analysis; from the manifest and literal content to latent meanings ( Fig. 1 and Table 1 ).

Example of analysis leading to higher levels of abstraction; from manifest to latent content.

Glossary of terms as used in this hands-on guide to doing content analysis. *

More information found in Refs. [1] , [2] , [3] , [5]

The initial step is to read and re-read the interviews to get a sense of the whole, i.e., to gain a general understanding of what your participants are talking about. At this point you may already start to get ideas of what the main points or ideas are that your participants are expressing. Then one needs to start dividing up the text into smaller parts, namely, into meaning units. One then condenses these meaning units further. While doing this, you need to ensure that the core meaning is still retained. The next step is to label condensed meaning units by formulating codes and then grouping these codes into categories. Depending on the study’s aim and quality of the collected data, one may choose categories as the highest level of abstraction for reporting results or you can go further and create themes [1] , [2] , [3] , [5] , [8] .

Content analysis as a reflective process

You must mould the clay of the data , tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your pre-understanding. In qualitative methodology, it is imperative to vigilantly maintain an awareness of one’s pre-understanding so that this does not influence analysis and/or results. This is the difficult balancing task of keeping a firm grip on one’s assumptions, opinions, and personal beliefs, and not letting them unconsciously steer your analysis process while simultaneously, and knowingly, utilising one’s pre-understanding to facilitate a deeper understanding of the data.

Content analysis, as in all qualitative analysis, is a reflective process. There is no “step 1, 2, 3, done!” linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to reflect on your initial analysis. Are you still satisfied with the length of meaning units? Do the condensed meaning units and codes still “fit” with each other? Do the codes still fit into this particular category? Typically, a fair amount of adjusting is needed after the first analysis endeavour. For example: a meaning unit might need to be split into two meaning units in order to capture an additional core meaning; a code modified to more closely match the core meaning of the condensed meaning unit; or a category name tweaked to most accurately describe the included codes. In other words, analysis is a flexible reflective process of working and re-working your data that reveals connections and relationships. Once condensed meaning units are coded it is easier to get a bigger picture and see patterns in your codes and organise codes in categories.

Content analysis exercise

The synopsis above is representative of analysis descriptions in many content analysis articles. Although correct, such method descriptions still do not provide much support for the novice researcher during the actual analysis process. Aspiring to provide guidance and direction to support the novice, a practical example of doing the actual work of content analysis is provided in the following sections. This practical example is based on a transcribed interview excerpt that was part of a study that aimed to explore patients’ experiences of being admitted into the emergency centre ( Fig. 2 ).

Excerpt from interview text exploring “Patient’s experience of being admitted into the emergency centre”

This content analysis exercise provides instructions, tips, and advice to support the content analysis novice in a) familiarising oneself with the data and the hermeneutic spiral, b) dividing up the text into meaning units and subsequently condensing these meaning units, c) formulating codes, and d) developing categories and themes.

Familiarising oneself with the data and the hermeneutic spiral

An important initial phase in the data analysis process is to read and re-read the transcribed interview while keeping your aim in focus. Write down your initial impressions. Embrace your intuition. What is the text talking about? What stands out? How did you react while reading the text? What message did the text leave you with? In this analysis phase, you are gaining a sense of the text as a whole.

You may ask why this is important. During analysis, you will be breaking down the whole text into smaller parts. Returning to your notes with your initial impressions will help you see if your “parts” analysis is matching up with your first impressions of the “whole” text. Are your initial impressions visible in your analysis of the parts? Perhaps you need to go back and check for different perspectives. This is what is referred to as the hermeneutic spiral or hermeneutic circle. It is the process of comparing the parts to the whole to determine whether impressions of the whole verify the analysis of the parts in all phases of analysis. Each part should reflect the whole and the whole should be reflected in each part. This concept will become clearer as you start working with your data.

Dividing up the text into meaning units and condensing meaning units

You have now read the interview a number of times. Keeping your research aim and question clearly in focus, divide up the text into meaning units. Located meaning units are then condensed further while keeping the central meaning intact ( Table 2 ). The condensation should be a shortened version of the same text that still conveys the essential message of the meaning unit. Sometimes the meaning unit is already so compact that no further condensation is required. Some content analysis sources warn researchers against short meaning units, claiming that this can lead to fragmentation [1] . However, our personal experience as research supervisors has shown us that a greater problem for the novice is basing analysis on meaning units that are too large and include many meanings which are then lost in the condensation process.

Suggestion for how the exemplar interview text can be divided into meaning units and condensed meaning units ( condensations are in parentheses ).

Formulating codes

The next step is to develop codes that are descriptive labels for the condensed meaning units ( Table 3 ). Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. At this stage of analysis you are still keeping very close to your data with very limited interpretation of content. You may adjust, re-do, re-think, and re-code until you get to the point where you are satisfied that your choices are reasonable. Just as in the initial phase of getting to know your data as a whole, it is also good to write notes during coding on your impressions and reactions to the text.

Suggestions for coding of condensed meaning units.

Feeling helpless? Resigned, Powerless? “In god’s hands”? What do you think?

Worried? Feeling lost? Distraught? What do you think?

Developing categories and themes

The next step is to sort codes into categories that answer the questions who , what , when or where? One does this by comparing codes and appraising them to determine which codes seem to belong together, thereby forming a category. In other words, a category consists of codes that appear to deal with the same issue, i.e., manifest content visible in the data with limited interpretation on the part of the researcher. Category names are most often short and factual sounding.

In data that is rich with latent meaning, analysis can be carried on to create themes. In our practical example, we have continued the process of abstracting data to a higher level, from category to theme level, and developed three themes as well as an overarching theme ( Table 4 ). Themes express underlying meaning, i.e., latent content, and are formed by grouping two or more categories together. Themes are answering questions such as why , how , in what way or by what means? Therefore, theme names include verbs, adverbs and adjectives and are very descriptive or even poetic.

Suggestion for organisation of coded meaning units into categories and themes.

Some reflections and helpful tips

Understand your pre-understandings.

While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?

Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.

Use your intuition

Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.

Aspects of coding and categorising hard to place data

All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.

In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?

Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.

A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.

It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.

Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.

Additional tips

It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.

When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.

During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.

Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.

Lessons learnt/key points

From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:

A method description is a guideline supporting analysis and trustworthiness. Don’t get caught up too rigidly following steps. Reflexivity and flexibility are just as important. Remember that a method description is a tool helping you in the process of making sense of your data by reducing a large amount of text to distil key results.

It is important to maintain a vigilant awareness of one’s own pre-understandings in order to avoid bias during analysis and in results.

Use and trust your own intuition during the analysis process.

If possible, discuss and reflect together with other researchers who have analysed the same data. Be open and receptive to new perspectives.

Understand that it is going to take time. Even if you are quite experienced, each set of data is different and all require time to analyse. Don’t expect to have all the data analysis done over a weekend. It may take weeks. You need time to think, reflect and then review your analysis.

Keep reminding yourself how excited you have felt about this area of research and how interesting it is. Embrace it with enthusiasm!

Let it be chaotic – have faith that some sense will start to surface. Don’t be afraid and think you will never get to the end – you will… eventually!

Peer review under responsibility of African Federation for Emergency Medicine.

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .

Contributor Information

Christen Erlingsson, Email: [email protected].

Petra Brysiewicz, Email: [email protected].

Appendix A. Supplementary data

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Video in Qualitative Research: Analysing Social Interaction in Everyday Life

• By: Christian Heath , Jon Hindmarsh & Paul Luff
• Publisher: SAGE Publications, Inc.
• Publication year: 2010
• Online pub date: December 17, 2017
• Discipline: Anthropology
• Methods: Video research , Transcription , Professional communication
• DOI: https:// doi. org/10.4135/9781526435385
• Keywords: films , organizations , publications , social science , software , transcripts , videos Show all Show less
• Print ISBN: 9781412929431
• Online ISBN: 9781526435385
• Buy the book icon link

Subject index

Video in Qualitative Research provides unprecedented opportunities for social science research, enabling the fine-grained analysis of social organization, culture, and communication. Video in Qualitative Research is the first book to provide practical guidance for students and academics on how to use video in qualitative research, how to address the problems and issues that arise in undertaking video-based field studies, and how to subject video recordings to detailed scrutiny and analysis. The authors consider the ethical and practical issues that arise in recording and gathering data as well as how video enables new and distinctive ways of presenting insights, observations, and findings to both academic and practitioner audiences. The book discusses a range of video-based projects including studies of control centers, operating theatres, medical consultations, auction houses, and museums and galleries. Video in Qualitative Research is a valuable guide for students and researchers across the social sciences thinking of using video as part of their research.

Front Matter

• Chapter 1 | Video, Analysis and the Social Sciences
• Chapter 2 | Access, Ethics and Project Planning
• Chapter 3 | Collecting Audio-Visual Data
• Chapter 4 | Analysing Video: Developing Preliminary Observations
• Chapter 5 | Matters of Context: Objects, Participation and Institutional Practice
• Chapter 6 | Preparing Presentations and Publications
• Chapter 7 | Implications, Applications and New Developments

Back Matter

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Chapter 17. Content Analysis

Introduction.

Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or Facebook. Really, almost anything can be the “content” to be analyzed. This is a qualitative research method because the focus is on the meanings and interpretations of that content rather than strictly numerical counts or variables-based causal modeling. [1] Qualitative content analysis (sometimes referred to as QCA) is particularly useful when attempting to define and understand prevalent stories or communication about a topic of interest—in other words, when we are less interested in what particular people (our defined sample) are doing or believing and more interested in what general narratives exist about a particular topic or issue. This chapter will explore different approaches to content analysis and provide helpful tips on how to collect data, how to turn that data into codes for analysis, and how to go about presenting what is found through analysis. It is also a nice segue between our data collection methods (e.g., interviewing, observation) chapters and chapters 18 and 19, whose focus is on coding, the primary means of data analysis for most qualitative data. In many ways, the methods of content analysis are quite similar to the method of coding.

Although the body of material (“content”) to be collected and analyzed can be nearly anything, most qualitative content analysis is applied to forms of human communication (e.g., media posts, news stories, campaign speeches, advertising jingles). The point of the analysis is to understand this communication, to systematically and rigorously explore its meanings, assumptions, themes, and patterns. Historical and archival sources may be the subject of content analysis, but there are other ways to analyze (“code”) this data when not overly concerned with the communicative aspect (see chapters 18 and 19). This is why we tend to consider content analysis its own method of data collection as well as a method of data analysis. Still, many of the techniques you learn in this chapter will be helpful to any “coding” scheme you develop for other kinds of qualitative data. Just remember that content analysis is a particular form with distinct aims and goals and traditions.

An Overview of the Content Analysis Process

The first step: selecting content.

Figure 17.2 is a display of possible content for content analysis. The first step in content analysis is making smart decisions about what content you will want to analyze and to clearly connect this content to your research question or general focus of research. Why are you interested in the messages conveyed in this particular content? What will the identification of patterns here help you understand? Content analysis can be fun to do, but in order to make it research, you need to fit it into a research plan.

Figure 17.1. A Non-exhaustive List of "Content" for Content Analysis

To take one example, let us imagine you are interested in gender presentations in society and how presentations of gender have changed over time. There are various forms of content out there that might help you document changes. You could, for example, begin by creating a list of magazines that are coded as being for “women” (e.g., Women’s Daily Journal ) and magazines that are coded as being for “men” (e.g., Men’s Health ). You could then select a date range that is relevant to your research question (e.g., 1950s–1970s) and collect magazines from that era. You might create a “sample” by deciding to look at three issues for each year in the date range and a systematic plan for what to look at in those issues (e.g., advertisements? Cartoons? Titles of articles? Whole articles?). You are not just going to look at some magazines willy-nilly. That would not be systematic enough to allow anyone to replicate or check your findings later on. Once you have a clear plan of what content is of interest to you and what you will be looking at, you can begin, creating a record of everything you are including as your content. This might mean a list of each advertisement you look at or each title of stories in those magazines along with its publication date. You may decide to have multiple “content” in your research plan. For each content, you want a clear plan for collecting, sampling, and documenting.

The Second Step: Collecting and Storing

Once you have a plan, you are ready to collect your data. This may entail downloading from the internet, creating a Word document or PDF of each article or picture, and storing these in a folder designated by the source and date (e.g., “ Men’s Health advertisements, 1950s”). Sølvberg ( 2021 ), for example, collected posted job advertisements for three kinds of elite jobs (economic, cultural, professional) in Sweden. But collecting might also mean going out and taking photographs yourself, as in the case of graffiti, street signs, or even what people are wearing. Chaise LaDousa, an anthropologist and linguist, took photos of “house signs,” which are signs, often creative and sometimes offensive, hung by college students living in communal off-campus houses. These signs were a focal point of college culture, sending messages about the values of the students living in them. Some of the names will give you an idea: “Boot ’n Rally,” “The Plantation,” “Crib of the Rib.” The students might find these signs funny and benign, but LaDousa ( 2011 ) argued convincingly that they also reproduced racial and gender inequalities. The data here already existed—they were big signs on houses—but the researcher had to collect the data by taking photographs.

In some cases, your content will be in physical form but not amenable to photographing, as in the case of films or unwieldy physical artifacts you find in the archives (e.g., undigitized meeting minutes or scrapbooks). In this case, you need to create some kind of detailed log (fieldnotes even) of the content that you can reference. In the case of films, this might mean watching the film and writing down details for key scenes that become your data. [2] For scrapbooks, it might mean taking notes on what you are seeing, quoting key passages, describing colors or presentation style. As you might imagine, this can take a lot of time. Be sure you budget this time into your research plan.

Researcher Note

A note on data scraping : Data scraping, sometimes known as screen scraping or frame grabbing, is a way of extracting data generated by another program, as when a scraping tool grabs information from a website. This may help you collect data that is on the internet, but you need to be ethical in how to employ the scraper. A student once helped me scrape thousands of stories from the Time magazine archives at once (although it took several hours for the scraping process to complete). These stories were freely available, so the scraping process simply sped up the laborious process of copying each article of interest and saving it to my research folder. Scraping tools can sometimes be used to circumvent paywalls. Be careful here!

The Third Step: Analysis

There is often an assumption among novice researchers that once you have collected your data, you are ready to write about what you have found. Actually, you haven’t yet found anything, and if you try to write up your results, you will probably be staring sadly at a blank page. Between the collection and the writing comes the difficult task of systematically and repeatedly reviewing the data in search of patterns and themes that will help you interpret the data, particularly its communicative aspect (e.g., What is it that is being communicated here, with these “house signs” or in the pages of Men’s Health ?).

The first time you go through the data, keep an open mind on what you are seeing (or hearing), and take notes about your observations that link up to your research question. In the beginning, it can be difficult to know what is relevant and what is extraneous. Sometimes, your research question changes based on what emerges from the data. Use the first round of review to consider this possibility, but then commit yourself to following a particular focus or path. If you are looking at how gender gets made or re-created, don’t follow the white rabbit down a hole about environmental injustice unless you decide that this really should be the focus of your study or that issues of environmental injustice are linked to gender presentation. In the second round of review, be very clear about emerging themes and patterns. Create codes (more on these in chapters 18 and 19) that will help you simplify what you are noticing. For example, “men as outdoorsy” might be a common trope you see in advertisements. Whenever you see this, mark the passage or picture. In your third (or fourth or fifth) round of review, begin to link up the tropes you’ve identified, looking for particular patterns and assumptions. You’ve drilled down to the details, and now you are building back up to figure out what they all mean. Start thinking about theory—either theories you have read about and are using as a frame of your study (e.g., gender as performance theory) or theories you are building yourself, as in the Grounded Theory tradition. Once you have a good idea of what is being communicated and how, go back to the data at least one more time to look for disconfirming evidence. Maybe you thought “men as outdoorsy” was of importance, but when you look hard, you note that women are presented as outdoorsy just as often. You just hadn’t paid attention. It is very important, as any kind of researcher but particularly as a qualitative researcher, to test yourself and your emerging interpretations in this way.

The Fourth and Final Step: The Write-Up

Only after you have fully completed analysis, with its many rounds of review and analysis, will you be able to write about what you found. The interpretation exists not in the data but in your analysis of the data. Before writing your results, you will want to very clearly describe how you chose the data here and all the possible limitations of this data (e.g., historical-trace problem or power problem; see chapter 16). Acknowledge any limitations of your sample. Describe the audience for the content, and discuss the implications of this. Once you have done all of this, you can put forth your interpretation of the communication of the content, linking to theory where doing so would help your readers understand your findings and what they mean more generally for our understanding of how the social world works. [3]

Analyzing Content: Helpful Hints and Pointers

Although every data set is unique and each researcher will have a different and unique research question to address with that data set, there are some common practices and conventions. When reviewing your data, what do you look at exactly? How will you know if you have seen a pattern? How do you note or mark your data?

Let’s start with the last question first. If your data is stored digitally, there are various ways you can highlight or mark up passages. You can, of course, do this with literal highlighters, pens, and pencils if you have print copies. But there are also qualitative software programs to help you store the data, retrieve the data, and mark the data. This can simplify the process, although it cannot do the work of analysis for you.

Qualitative software can be very expensive, so the first thing to do is to find out if your institution (or program) has a universal license its students can use. If they do not, most programs have special student licenses that are less expensive. The two most used programs at this moment are probably ATLAS.ti and NVivo. Both can cost more than $500 [4] but provide everything you could possibly need for storing data, content analysis, and coding. They also have a lot of customer support, and you can find many official and unofficial tutorials on how to use the programs’ features on the web. Dedoose, created by academic researchers at UCLA, is a decent program that lacks many of the bells and whistles of the two big programs. Instead of paying all at once, you pay monthly, as you use the program. The monthly fee is relatively affordable (less than $15), so this might be a good option for a small project. HyperRESEARCH is another basic program created by academic researchers, and it is free for small projects (those that have limited cases and material to import). You can pay a monthly fee if your project expands past the free limits. I have personally used all four of these programs, and they each have their pluses and minuses.

Regardless of which program you choose, you should know that none of them will actually do the hard work of analysis for you. They are incredibly useful for helping you store and organize your data, and they provide abundant tools for marking, comparing, and coding your data so you can make sense of it. But making sense of it will always be your job alone.

So let’s say you have some software, and you have uploaded all of your content into the program: video clips, photographs, transcripts of news stories, articles from magazines, even digital copies of college scrapbooks. Now what do you do? What are you looking for? How do you see a pattern? The answers to these questions will depend partially on the particular research question you have, or at least the motivation behind your research. Let’s go back to the idea of looking at gender presentations in magazines from the 1950s to the 1970s. Here are some things you can look at and code in the content: (1) actions and behaviors, (2) events or conditions, (3) activities, (4) strategies and tactics, (5) states or general conditions, (6) meanings or symbols, (7) relationships/interactions, (8) consequences, and (9) settings. Table 17.1 lists these with examples from our gender presentation study.

Table 17.1. Examples of What to Note During Content Analysis

One thing to note about the examples in table 17.1: sometimes we note (mark, record, code) a single example, while other times, as in “settings,” we are recording a recurrent pattern. To help you spot patterns, it is useful to mark every setting, including a notation on gender. Using software can help you do this efficiently. You can then call up “setting by gender” and note this emerging pattern. There’s an element of counting here, which we normally think of as quantitative data analysis, but we are using the count to identify a pattern that will be used to help us interpret the communication. Content analyses often include counting as part of the interpretive (qualitative) process.

In your own study, you may not need or want to look at all of the elements listed in table 17.1. Even in our imagined example, some are more useful than others. For example, “strategies and tactics” is a bit of a stretch here. In studies that are looking specifically at, say, policy implementation or social movements, this category will prove much more salient.

Another way to think about “what to look at” is to consider aspects of your content in terms of units of analysis. You can drill down to the specific words used (e.g., the adjectives commonly used to describe “men” and “women” in your magazine sample) or move up to the more abstract level of concepts used (e.g., the idea that men are more rational than women). Counting for the purpose of identifying patterns is particularly useful here. How many times is that idea of women’s irrationality communicated? How is it is communicated (in comic strips, fictional stories, editorials, etc.)? Does the incidence of the concept change over time? Perhaps the “irrational woman” was everywhere in the 1950s, but by the 1970s, it is no longer showing up in stories and comics. By tracing its usage and prevalence over time, you might come up with a theory or story about gender presentation during the period. Table 17.2 provides more examples of using different units of analysis for this work along with suggestions for effective use.

Table 17.2. Examples of Unit of Analysis in Content Analysis

Every qualitative content analysis is unique in its particular focus and particular data used, so there is no single correct way to approach analysis. You should have a better idea, however, of what kinds of things to look for and what to look for. The next two chapters will take you further into the coding process, the primary analytical tool for qualitative research in general.

Further Readings

Cidell, Julie. 2010. “Content Clouds as Exploratory Qualitative Data Analysis.” Area 42(4):514–523. A demonstration of using visual “content clouds” as a form of exploratory qualitative data analysis using transcripts of public meetings and content of newspaper articles.

Hsieh, Hsiu-Fang, and Sarah E. Shannon. 2005. “Three Approaches to Qualitative Content Analysis.” Qualitative Health Research 15(9):1277–1288. Distinguishes three distinct approaches to QCA: conventional, directed, and summative. Uses hypothetical examples from end-of-life care research.

Jackson, Romeo, Alex C. Lange, and Antonio Duran. 2021. “A Whitened Rainbow: The In/Visibility of Race and Racism in LGBTQ Higher Education Scholarship.” Journal Committed to Social Change on Race and Ethnicity (JCSCORE) 7(2):174–206.* Using a “critical summative content analysis” approach, examines research published on LGBTQ people between 2009 and 2019.

Krippendorff, Klaus. 2018. Content Analysis: An Introduction to Its Methodology . 4th ed. Thousand Oaks, CA: SAGE. A very comprehensive textbook on both quantitative and qualitative forms of content analysis.

Mayring, Philipp. 2022. Qualitative Content Analysis: A Step-by-Step Guide . Thousand Oaks, CA: SAGE. Formulates an eight-step approach to QCA.

Messinger, Adam M. 2012. “Teaching Content Analysis through ‘Harry Potter.’” Teaching Sociology 40(4):360–367. This is a fun example of a relatively brief foray into content analysis using the music found in Harry Potter films.

Neuendorft, Kimberly A. 2002. The Content Analysis Guidebook . Thousand Oaks, CA: SAGE. Although a helpful guide to content analysis in general, be warned that this textbook definitely favors quantitative over qualitative approaches to content analysis.

Schrier, Margrit. 2012. Qualitative Content Analysis in Practice . Thousand Okas, CA: SAGE. Arguably the most accessible guidebook for QCA, written by a professor based in Germany.

Weber, Matthew A., Shannon Caplan, Paul Ringold, and Karen Blocksom. 2017. “Rivers and Streams in the Media: A Content Analysis of Ecosystem Services.” Ecology and Society 22(3).* Examines the content of a blog hosted by National Geographic and articles published in The New York Times and the Wall Street Journal for stories on rivers and streams (e.g., water-quality flooding).

• There are ways of handling content analysis quantitatively, however. Some practitioners therefore specify qualitative content analysis (QCA). In this chapter, all content analysis is QCA unless otherwise noted. ↵
• Note that some qualitative software allows you to upload whole films or film clips for coding. You will still have to get access to the film, of course. ↵
• See chapter 20 for more on the final presentation of research. ↵
• . Actually, ATLAS.ti is an annual license, while NVivo is a perpetual license, but both are going to cost you at least $500 to use. Student rates may be lower. And don’t forget to ask your institution or program if they already have a software license you can use. ↵

A method of both data collection and data analysis in which a given content (textual, visual, graphic) is examined systematically and rigorously to identify meanings, themes, patterns and assumptions.  Qualitative content analysis (QCA) is concerned with gathering and interpreting an existing body of material.    

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content analysis is a widely used research technique that systematically examines and interprets textual, visual, or multimedia content to identify patterns, themes, and meanings. It is a cornerstone method in qualitative research but can also be employed quantitatively to measure the frequency of certain elements within data. This article explores the definition, methods, types, and examples of content analysis, highlighting its importance and applications across various fields.

Content Analysis

Content analysis is a research method used to analyze, categorize, and interpret the content of communication in a systematic and replicable manner. It involves breaking down material—such as text, images, or audio—into manageable data categories, often to identify trends, patterns, or underlying themes.

For example, a researcher analyzing political speeches might use content analysis to quantify how often certain keywords, like “freedom” or “equality,” are used and interpret their significance in shaping public opinion.

Key Features of Content Analysis

• Systematic Approach: Content analysis involves clearly defined rules and procedures to ensure consistency and replicability.
• Flexible Data Sources: It can analyze a variety of content types, including written documents, video recordings, and social media posts.
• Dual Purpose: It serves both qualitative purposes (understanding themes) and quantitative purposes (measuring frequency or volume).

Importance of Content Analysis

Content analysis plays a significant role in research for the following reasons:

• Understanding Communication: It helps researchers explore the meaning, structure, and function of communication.
• Tracking Trends: Content analysis is useful for monitoring changes in cultural norms, public opinion, or market behavior over time.
• Cross-Disciplinary Applications: This method is used in various fields, including sociology, marketing, media studies, and psychology.

Types of Content Analysis

1. qualitative content analysis.

Qualitative content analysis focuses on understanding the underlying themes, patterns, and meanings within a dataset. It is interpretative in nature, often exploring how content conveys emotions, opinions, or values.

For example, analyzing customer reviews to identify recurring sentiments about a product, such as satisfaction or dissatisfaction.

2. Quantitative Content Analysis

Quantitative content analysis involves counting the frequency of specific elements, such as words, phrases, or symbols, within a dataset. This type of analysis is used to quantify content trends.

For instance, studying how often particular political ideologies are mentioned in news articles during an election cycle.

3. Summative Content Analysis

Summative analysis combines both qualitative and quantitative approaches. It starts with quantitative counting and progresses into qualitative interpretation, providing a richer understanding of the context.

For example, counting mentions of “sustainability” in corporate reports and then examining how the term is used to frame environmental initiatives.

4. Relational Content Analysis

Relational analysis explores relationships between concepts, phrases, or themes in a text. It identifies connections and assesses how ideas are interrelated within the content.

For instance, analyzing a novel to determine how often two characters are mentioned together and what this implies about their relationship.

Methods of Conducting Content Analysis

1. define research questions and objectives.

Clearly articulate what you aim to discover through content analysis. For example, a marketing researcher might ask: “How do customers describe our brand on social media?”

2. Select Data Sources

Choose appropriate content sources, such as books, social media posts, videos, or interviews, depending on the research objectives.

3. Develop a Coding Framework

Establish categories and codes to classify data systematically. Codes can be predefined (deductive approach) or generated from the data itself (inductive approach).

4. Analyze Data

• Quantitative Approach: Count the frequency of codes or themes.
• Qualitative Approach: Interpret the significance of patterns and relationships.

5. Interpret Results

Evaluate findings in the context of the research questions, identifying key insights, trends, or patterns.

Steps in Content Analysis

• Data Preparation: Gather and organize the content to be analyzed.
• Coding: Segment data into meaningful categories or codes.
• Categorization: Group similar codes into broader themes.
• Analysis: Examine the data for trends, patterns, or relationships.
• Validation: Ensure reliability by double-checking the coding process or using multiple coders.
• Reporting: Present findings in a structured format, such as tables, graphs, or narratives.

Examples of Content Analysis

Example 1: social media analysis.

A business analyzing customer feedback on Twitter might use content analysis to identify common themes, such as product satisfaction, customer service complaints, or brand loyalty.

Example 2: Political Campaigns

Researchers studying election campaigns might examine speeches, advertisements, or social media posts to determine the frequency of keywords like “progress” or “change” and interpret their appeal to voters.

Example 3: Academic Research

A scholar analyzing gender representation in children’s books might classify characters based on gender roles and count their frequency to highlight disparities.

Example 4: Market Research

Content analysis of customer reviews on e-commerce platforms can reveal recurring themes, such as product durability, value for money, or delivery experiences.

Advantages of Content Analysis

• Versatility: Applicable to diverse data types, including text, visuals, and multimedia.
• Non-Intrusive: Uses pre-existing data, eliminating the need for direct interaction with subjects.
• Quantitative and Qualitative Integration: Combines numerical and interpretative insights.
• Rich Insights: Provides an in-depth understanding of communication patterns and underlying themes.

Disadvantages of Content Analysis

• Time-Intensive: Coding and analyzing large datasets can be laborious.
• Subjectivity in Interpretation: Qualitative content analysis is prone to bias, especially if coding frameworks are inconsistent.
• Limited Context: Analyzing isolated content may overlook broader contextual factors.
• Over-Reliance on Frequency: Quantitative content analysis may prioritize volume over significance.

Applications of Content Analysis

• Media Studies: Analyzing news articles or advertisements to identify biases, trends, or representations.
• Marketing: Exploring customer feedback to understand brand perception and preferences.
• Health Communication: Evaluating public health campaigns to determine their effectiveness in raising awareness.
• Education: Studying educational materials to assess inclusivity or curriculum focus.
• Sociology: Investigating societal attitudes by examining cultural artifacts, such as films, books, or songs.

Content analysis is a versatile and powerful research method for examining communication and extracting meaningful insights. By categorizing and interpreting data systematically, researchers can uncover patterns and trends across diverse fields, from media and marketing to sociology and education. While it requires careful planning and execution, the ability to analyze and interpret both qualitative and quantitative aspects of content makes it an invaluable tool for academic and practical applications.

• Krippendorff, K. (2018). Content Analysis: An Introduction to Its Methodology (4th ed.). SAGE Publications.
• Neuendorf, K. A. (2017). The Content Analysis Guidebook (2nd ed.). SAGE Publications.
• Weber, R. P. (1990). Basic Content Analysis (2nd ed.). SAGE Publications.
• Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of Advanced Nursing , 62(1), 107-115.
• Mayring, P. (2000). Qualitative content analysis. Forum: Qualitative Social Research , 1(2).

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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