Content analysis is a research method and type of textual analysis that analyzes the meanings of content, which could take the form of textual, visual, aural, and otherwise multimodal texts.
Generally, a content analysis will seek meanings and relationships of certain words and concepts within the text or corpus of texts, and generate thematic data that reveals deeper insights into the text’s meanings.
Prasad (2008) defines it as:
:…the study of the content with reference to the meanings, contexts and intentions contained in messages.” (p. 174)
Content analyses can involve deductive coding, where themes and concepts are asserted before the content is created; or, they can involve inductive coding, where themes and concepts emerge during a close reading of the text.
An example of a content analysis would be a study that analyzes the presence of ideological words and phrases in newspapers to ascertain the editorial team’s political biases.
Content Analysis Examples
1. Conceptual Analysis
Also called semantic content analysis, a conceptual analysis selects a concept and tries to count its occurrence within a text (Kosterec, 2016).
An example of a concept that you might examine is sentiment, such as positive, negative, and neutral sentiment. Here, you would need to conduct a semantic study of the text to find instances of words like ‘bad’, ‘terrible’, etc. for negative sentiment, and ‘good’, ‘great’, etc. for positive sentiment. A compare and contrast will demonstrate a balance of sentiment within the text.
A basic conceptual analysis has the weakness of lacking the capacity to read words in context, which would require a deeper qualitative analysis of paragraphs, which is offset by other types of analysis in this list.
Example of Conceptual Analysis
A company launches a new product and wants to understand the public’s initial reactions to it. They use conceptual analysis to analyze comments on their social media posts about the product. They could choose specific concepts such as “like”, “dislike”, “awesome”, “terrible”, etc. The frequency of these words in the comments give them an idea about the public’s sentiment towards the product.
2. Relational Analysis
Relational analysis addresses the above-mentioned weakness of conceptual analysis (i.e. that a mere counting of instances of terms lacks context) by examining how concepts in a text relate to one another.
Here, a scholar might analyze the overlap or sequences between certain concepts and sentiments in language (Kosterec, 2016). To combine the two examples from the above conceptual analysis, a scholar might examine all of a particular masthead newspaper’s columns on global warming. In the study, they would examine the proximity between the word ‘global warming’ and positive, negative, and neutral sentiment words (‘good’, ‘bad’, ‘great’, etc.) to ascertain the newspaper’s sentiment toward a specific concept.
Example of Relational Analysis
A political scientist wants to understand the relationship between the use of emotional rhetoric and audience reaction in political speeches. They carry out a relational analysis on a corpus of speeches and corresponding audience feedback. By exploring the co-occurrence of emotive words (“hope”, “fear”, “pride”) and audience responses (“applause”, “boos”, “silence”), they discover patterns in how different types of emotional language affect audience reactions.
3. Thematic Analysis
A thematic analysis focuses on identifying themes or major ideas running throughout the text.
This can follow a range of strategies, spanning from highly quantitative – such as using statistical software to thematically group words and terms – through to highly qualitative, where trained researchers take notes on each paragraph to extract key ideas that can be thematicized.
Many literature reviews take the form of a thematic analysis, where the scholar reads all recent studies on a topic and tries to ascertain themes, as well as gaps, across the recent literature.
Example of Thematic Analysis
A scholar searches on research bases for all published academic papers containing the keyword “back pain” from the past 10 years. She then uses inductive coding to generate themes that span the studies. From this thematic analysis, she produces a literature review on key emergent themes from the literature on back pain, as well as gaps in the research.
4. Narrative Analysis
This involves a close reading of the framing and structure of narrative elements within content. It can examine personal life stories, biographies, journals, and so on.
In literary research, this method generally explores the elements of the story, such as characters, plot, literary themes, and settings. But in life history research, it will generally involve deconstructing a real person’s life story, analyzing their perspectives and worldview to develop insights into their unique situation, life circumstances, or personality.
The focus generally expands out from the story itself to what it can tell us about the individuals or culture from which it originates.
Example of Narrative Analysis
A social work researcher takes a group of their patients’ personal journals and, after obtaining ethics clearance and permission from the patients, deconstructs the underlying messages in their journals in order to extract an understanding of the core mental hurdles each patient faces, which are then analyzed through the lens of Jungian psychoanalysis.
5. Discourse Analysis
Discourse analysis, the research methodology from which I conducted my PhD studies, involves the study of how language can create and reproduce social realities.
Based on the work of postmodern scholars such as Michel Foucault and Jaques Derrida, it attempts to deconstruct how texts normalize ways of thinking within specific historical, cultural, and social contexts.
Foucault, the most influential scholar in discourse analytic research, demonstrated through the study of how society spoke about madness that different societies constructed madness in different ways: in the renaissance era, mad people we spoken of as wise people, during the classical era, language changed, and they were framed as pariahs. Finally, in the modern era, they were spoken about as if they were sick.
Following Foucault (1988), many content analysis scholars now look at the differing ways societies frame different identities (gender, race, social class, etc.) in different times – and this can be revealed by looking at the language used in the content (i.e. the texts) produced throughout different eras (Johnstone, 2017).
Example of Discourse Analysis
A scholar examines a corpus of immigration speeches from a specific political party from the past 10 years and examines how refugees are discussed in the speeches, with a focus on how language constructs and defines refugees. It finds that refugees appear to be constructed as threats, dirty, and nefarious.
6. Multimodal Analysis
As audiovisual texts became more important in society, many scholars began to critique the fact that content analysis tends to only look at written texts. In response, a methodology called multimodal analysis emerged.
In multimodal analysis, scholars don’t just decode the meanings in written texts, but also in multimodal texts. This involves the study of the signs, symbols, movements, and sounds that are within the text.
This opens up space for the analysis of television advertisements, billboards, and so forth.
For an example, a multimodal analysis of a television advertisement might not just study what is said, but it’ll explore how the camera angles frame some people as powerful (low to high angle) and some people as weak (high to low angle). Similarly, they may examine the colors to see if a character is positioned as sad (dark colors, walking through rain) or joyful (bright colors, sunshine).
Example of Multimodal Analysis
A cultural studies scholar examines the representation of Gender in Disney films, looking not only at the spoken words, but also the dresses worn, the camera angles, and the princesses’ tone of voice when speaking to other characters to assess how Disney’s construction of gender has changed over time.
7. Semiotic Analysis
Semiotic analysis takes multimodal analysis to the next step by providing the specific methods for the analysis of multimodal texts.
Seminal scholars Kress and van Leeuwen (2006) have created a significant repertoire of texts demonstrating how semiotics shape meaning. In their works, they present deconstructions of various modes of address:
- Visual: How images, signs, and symbols create meaning in social contexts. For example, in our modern world, a red octagon has a specific social meaning: stop!
- Textual: How words shape meaning, such as through a sentiment analysis as discussed earlier.
- Motive: How movement can create a sense of pace, distance, the movement of time, and so forth, which shapes meaning.
- Aural: How sounds shape meaning. For example, the words spoken are not the only way we interpret a speech, but also how they’re spoken (shakily, confidently, assertively, etc.)
Example of Semiotic Analysis
A communications studies scholar examines the body language of leaders during meetings at an international political event, using it to explore how the leaders subtly send messages about who they are allied with, where they view themselves in geopolitical terms, and their attitudes toward the event overall.
8. Latent Content Analysis
This involves the interpretation of the underlying, inferred meanings of the words or visuals. The focus here is on what is being implied by the content rather than just what is explicitly said.
For example, in the context of the same newspaper articles, a latent content analysis might examine the way the event is framed, the language or rhetoric used, the themes or narratives that are implied, or the attitudes and ideologies that are expressed or endorsed, either overtly or covertly.
Returning to the work of Foucault, he demonstrated how silence also constructs meaning. The question emerges: what is left unsaid in the content, and how does this shape our understanding of the biases and assumptions of the author?
Example of Latent Content Analysis
A sociologist studying gender roles in films watches the top 10 movies from last year and doesn’t just count instances of words – rather, they analyze the underlying, implicit messages about gender roles. This could include exploring how female characters are portrayed (do they tend to be passive and in need of rescue, or are they active, independent and resourceful?) and how male characters are portrayed (emotional or unemotional?) What kind of occupations do characters of each gender typically have?
9. Manifest Content Analysis
A manifest content analysis is the counterpoint to latent content analysis. It involves a direct and surface-level reading of the visible aspects of the content.
It concerns itself primarily with what is visible, obvious and countable. This approach asserts that we should not read too deeply into anything beyond what is manifest (i.e. present), because the deeper we try to read into the missing or latent elements, the more we stray into the real of guessing and assuming.
Scholars will often do both latent and manifest content analyses side-by-side, exploring how each type of analysis might reveal different interpretations or insights.
Example of Manifest Content Analysis
A researcher is interested in studying bias in media coverage of a particular political event. They might conduct a conceptual analysis where the concept is the tone of language used – positive, neutral, or negative. They would examine a number of articles from different newspapers, tallying up instances of positive, negative, or neutral language to see if there is a bias towards positivity or negativity in coverage of the event.
10. Longitudinal Content Analysis
A longitudinal content analysis analyzes trends in content over a long period of time.
Earlier, I explored the idea in discourse analysis that different eras have different ideas about terms and concepts (consider, for example, evolving ideas of gender and race). A longitudinal analysis would be very useful here. It would involve collecting cross-sectional moments in time, at varying points in time, which would then be compared and contrasted for the representation of varying concepts and terms.
Example of Longitudinal Content Analsis
A scholar might look at newspaper reports on texts from each decade for 100 years, examining environmental terms (‘global warming’, ‘climate change’, ‘recycling’) to identify when and how environmental concepts entered public discourse.
Content analysis is a form of empirical research that uses texts rather than interviews or naturalistic observation to gather data that can then be analyzed. There are a range of methods and approaches to the analysis of content, but their unifying feature is that they involve close readings of texts to identify concepts and themes that might be revealing of core or underlying messages within the content.
The above examples are not mutually exclusive types, but rather different approaches that researchers can use based on their specific goals and the nature of the data they are working with.
Foucault, M. (1988). Madness and civilization: A history of insanity in the age of reason. London: Vintage.
Johnstone, B. (2017). Discourse analysis. London: John Wiley & Sons.
Kosterec, M. (2016). Methods of conceptual analysis. Filozofia, 71(3).
Kress, G., & Van Leeuwen, T. (2006). The grammar of visual design. London and New York: Routledge.
Prasad, B. D. (2008). Content analysis: A method of Social Science Research. In D.K. Lal Das (ed) Research Methods for Social Work, (pp.174-193). New Delhi: Rawat Publications.
Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]