content analysis in research sociology

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Understanding Content Analysis in Sociology

Content analysis is a research method widely used in sociology to analyze and interpret the content of various forms of communication, such as written text, audio recordings, images, and videos. It involves systematically examining and categorizing the content to identify patterns, themes, and underlying meanings. This method allows sociologists to gain insights into social phenomena, cultural values , and the ways in which individuals and groups communicate and construct meaning.

Process of Content Analysis

The process of content analysis typically involves the following steps:

• Defining the Research Question: The first step is to clearly define the research question or objective. This helps in determining the scope and focus of the analysis.
• Selection of Data: Researchers select the relevant data sources, such as newspapers, books, social media posts, or interviews, based on the research question.
• Unit of Analysis: The unit of analysis refers to the specific elements within the data that will be examined. It could be words, sentences, paragraphs, or even entire documents.
• Coding: Coding involves creating a set of categories or codes to systematically classify the content. These codes can be predefined or developed during the analysis process.
• Data Analysis: Researchers analyze the coded data to identify patterns, themes, and relationships. This can involve quantitative techniques, such as frequency counts, or qualitative techniques, such as identifying recurring themes.
• Interpretation: The final step involves interpreting the findings and drawing conclusions based on the analysis. This may involve making connections to existing theories or generating new insights.

Applications of Content Analysis in Sociology

Content analysis has a wide range of applications in sociology. Some of the key areas where it is commonly used include:

Media Studies:

Content analysis is frequently used to examine media representations, such as news articles, television shows, or advertisements. It helps in understanding how different groups are portrayed, the dominant narratives, and the underlying ideologies within media content.

Political Communication:

Content analysis is utilized to analyze political speeches, campaign materials, and social media posts during elections. It helps in understanding the strategies employed by politicians, the framing of issues, and the impact of communication on public opinion.

Gender and Identity Studies:

Content analysis is valuable in studying gender and identity representations in various forms of media. It allows researchers to explore how gender roles, stereotypes, and identities are constructed and reinforced in society.

Health Communication:

Content analysis is used to examine health-related messages in public health campaigns, medical literature, and online forums. It helps in understanding the dissemination of health information, the portrayal of health issues, and the impact on public perception and behavior.

Social Movements and Activism:

Content analysis is employed to study the communication strategies and messages of social movements and activist groups. It helps in understanding the framing of issues, the mobilization of support, and the impact of communication on social change.

Strengths and Limitations of Content Analysis

Content analysis offers several strengths as a research method in sociology:

• It allows for the systematic and objective analysis of large amounts of data.
• It can be used to study both quantitative and qualitative aspects of communication.
• It provides insights into the cultural and social meanings embedded in communication.
• It can be replicated by other researchers to ensure validity and reliability.

However, content analysis also has some limitations:

• It relies on the availability and accessibility of relevant data sources.
• It may overlook the context and nuances of communication.
• It requires careful coding and interpretation, which can be subjective.
• It may not capture the dynamic nature of communication over time.

In conclusion, content analysis is a valuable research method in sociology that allows for the systematic examination and interpretation of various forms of communication. It provides insights into social phenomena, cultural values, and the ways in which individuals and groups construct meaning. By understanding the process, applications, and strengths and limitations of content analysis, sociologists can effectively utilize this method to gain deeper insights into the social world.

Mr Edwards has a PhD in sociology and 10 years of experience in sociological knowledge

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Content Analysis: Method to Analyze Social Life Through Words, Images

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Content analysis is a research method used by sociologists to analyze social life by interpreting words and images from documents, film, art, music, and other cultural products and media. The researchers look at how the words and images are used, and the context in which they are used to draw inferences about the underlying culture.

Content analysis can help researchers study fields of sociology that are otherwise difficult to analyze, such as gender issues, business strategy and policy, human resources, and organizational theory.

It has been used extensively to examine the place of women in society. In advertising, for example, women tend to be portrayed as subordinate, often through their lower physical positioning in relation to the males or the unassertive nature of their poses or gestures.

History of Content Analysis

Prior to the advent of computers , content analysis was a slow, painstaking process, and was impractical for large texts or bodies of data. At first, researchers mainly performed word counts in texts of particular words.

However, that changed once mainframe computers were developed, providing researchers with the ability to crunch larger amounts of data automatically. This allowed them to expand their work beyond individual words to include concepts and semantic relationships.

Today, content analysis is used in a huge number of fields, including marketing, political science, psychology, and sociology, in addition to gender issues within society.

Types of Content Analysis

Researchers now recognize several different types of content analysis, each of which embraces a slightly different approach. According to a report in the medical journal Qualitative Health Research , there are three different types: conventional, directed, and summative.

"In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, the analysis starts with a theory or relevant research findings as guidance for initial codes. Summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context," the authors wrote.

Other experts write about the difference between conceptual analysis and relational analysis. The conceptual analysis determines how often a text uses certain words or phrases, while relational analysis determines how those words and phrases relate to certain broader concepts. Conceptual analysis is the more traditionally used form of content analysis.

How Researchers Perform Content Analysis

Typically, researchers start by identifying questions they would like to answer through content analysis. For example, they might want to consider how women are portrayed in advertising. If so, the researchers would choose a data set of advertising—perhaps the scripts for a series of television commercials—to analyze.

They then would look at the use of certain words and images. To continue the example, the researchers might study the television ads for stereotypical gender roles, for language implying that women in the commercials were less knowledgeable than the men, and for the sexual objectification of either gender.

Content analysis can be used to provide insights into particularly complex subjects like gender relations. It does, however, have some disadvantages: it's labor-intensive and time-consuming, and researchers can bring inherent bias into the equation when formulating a research project .

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• Content Analysis | Guide, Methods & Examples

Content Analysis | Guide, Methods & Examples

Published on July 18, 2019 by Amy Luo . Revised on June 22, 2023.

Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual:

• Books, newspapers and magazines
• Speeches and interviews
• Web content and social media posts
• Photographs and films

Content analysis can be both quantitative (focused on counting and measuring) and qualitative (focused on interpreting and understanding).  In both types, you categorize or “code” words, themes, and concepts within the texts and then analyze the results.

Table of contents

What is content analysis used for, advantages of content analysis, disadvantages of content analysis, how to conduct content analysis, other interesting articles.

Researchers use content analysis to find out about the purposes, messages, and effects of communication content. They can also make inferences about the producers and audience of the texts they analyze.

Content analysis can be used to quantify the occurrence of certain words, phrases, subjects or concepts in a set of historical or contemporary texts.

Quantitative content analysis example

To research the importance of employment issues in political campaigns, you could analyze campaign speeches for the frequency of terms such as unemployment , jobs , and work  and use statistical analysis to find differences over time or between candidates.

In addition, content analysis can be used to make qualitative inferences by analyzing the meaning and semantic relationship of words and concepts.

Qualitative content analysis example

To gain a more qualitative understanding of employment issues in political campaigns, you could locate the word unemployment in speeches, identify what other words or phrases appear next to it (such as economy,   inequality or  laziness ), and analyze the meanings of these relationships to better understand the intentions and targets of different campaigns.

Because content analysis can be applied to a broad range of texts, it is used in a variety of fields, including marketing, media studies, anthropology, cognitive science, psychology, and many social science disciplines. It has various possible goals:

• Finding correlations and patterns in how concepts are communicated
• Understanding the intentions of an individual, group or institution
• Identifying propaganda and bias in communication
• Revealing differences in communication in different contexts
• Analyzing the consequences of communication content, such as the flow of information or audience responses

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• Unobtrusive data collection

You can analyze communication and social interaction without the direct involvement of participants, so your presence as a researcher doesn’t influence the results.

• Transparent and replicable

When done well, content analysis follows a systematic procedure that can easily be replicated by other researchers, yielding results with high reliability .

• Highly flexible

You can conduct content analysis at any time, in any location, and at low cost – all you need is access to the appropriate sources.

Focusing on words or phrases in isolation can sometimes be overly reductive, disregarding context, nuance, and ambiguous meanings.

Content analysis almost always involves some level of subjective interpretation, which can affect the reliability and validity of the results and conclusions, leading to various types of research bias and cognitive bias .

• Time intensive

Manually coding large volumes of text is extremely time-consuming, and it can be difficult to automate effectively.

If you want to use content analysis in your research, you need to start with a clear, direct  research question .

Example research question for content analysis

Is there a difference in how the US media represents younger politicians compared to older ones in terms of trustworthiness?

Next, you follow these five steps.

1. Select the content you will analyze

Based on your research question, choose the texts that you will analyze. You need to decide:

• The medium (e.g. newspapers, speeches or websites) and genre (e.g. opinion pieces, political campaign speeches, or marketing copy)
• The inclusion and exclusion criteria (e.g. newspaper articles that mention a particular event, speeches by a certain politician, or websites selling a specific type of product)
• The parameters in terms of date range, location, etc.

If there are only a small amount of texts that meet your criteria, you might analyze all of them. If there is a large volume of texts, you can select a sample .

2. Define the units and categories of analysis

Next, you need to determine the level at which you will analyze your chosen texts. This means defining:

• The unit(s) of meaning that will be coded. For example, are you going to record the frequency of individual words and phrases, the characteristics of people who produced or appear in the texts, the presence and positioning of images, or the treatment of themes and concepts?
• The set of categories that you will use for coding. Categories can be objective characteristics (e.g. aged 30-40 ,  lawyer , parent ) or more conceptual (e.g. trustworthy , corrupt , conservative , family oriented ).

Your units of analysis are the politicians who appear in each article and the words and phrases that are used to describe them. Based on your research question, you have to categorize based on age and the concept of trustworthiness. To get more detailed data, you also code for other categories such as their political party and the marital status of each politician mentioned.

3. Develop a set of rules for coding

Coding involves organizing the units of meaning into the previously defined categories. Especially with more conceptual categories, it’s important to clearly define the rules for what will and won’t be included to ensure that all texts are coded consistently.

Coding rules are especially important if multiple researchers are involved, but even if you’re coding all of the text by yourself, recording the rules makes your method more transparent and reliable.

In considering the category “younger politician,” you decide which titles will be coded with this category ( senator, governor, counselor, mayor ). With “trustworthy”, you decide which specific words or phrases related to trustworthiness (e.g. honest and reliable ) will be coded in this category.

4. Code the text according to the rules

You go through each text and record all relevant data in the appropriate categories. This can be done manually or aided with computer programs, such as QSR NVivo , Atlas.ti and Diction , which can help speed up the process of counting and categorizing words and phrases.

Following your coding rules, you examine each newspaper article in your sample. You record the characteristics of each politician mentioned, along with all words and phrases related to trustworthiness that are used to describe them.

5. Analyze the results and draw conclusions

Once coding is complete, the collected data is examined to find patterns and draw conclusions in response to your research question. You might use statistical analysis to find correlations or trends, discuss your interpretations of what the results mean, and make inferences about the creators, context and audience of the texts.

Let’s say the results reveal that words and phrases related to trustworthiness appeared in the same sentence as an older politician more frequently than they did in the same sentence as a younger politician. From these results, you conclude that national newspapers present older politicians as more trustworthy than younger politicians, and infer that this might have an effect on readers’ perceptions of younger people in politics.

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1 Introduction

• Published: November 2015
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This chapter offers an inclusive definition of content analysis. This helps in clarifying some key terms and concepts. Three approaches to content analysis are introduced and defined briefly: basic content analysis, interpretive content analysis, and qualitative content analysis. Long-standing differences between quantitative and qualitative approaches to content analysis that are still evident in contemporary published research are also touched on here. In addition, the chapter examines the origins, evolution, and conceptual foundations of content analysis, as well as the development of content analysis in the social work profession. Finally, the chapter offers illustrative examples of different approaches to content analysis to ground the discussion in examples of published research.

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Content Analysis

Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. As an example, researchers can evaluate language used within a news article to search for bias or partiality. Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of surrounding the text.

Description

Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study may analyze various forms of text in its analysis. To analyze the text using content analysis, the text must be coded, or broken down, into manageable code categories for analysis (i.e. “codes”). Once the text is coded into code categories, the codes can then be further categorized into “code categories” to summarize data even further.

Three different definitions of content analysis are provided below.

Definition 1: “Any technique for making inferences by systematically and objectively identifying special characteristics of messages.” (from Holsti, 1968)

Definition 2: “An interpretive and naturalistic approach. It is both observational and narrative in nature and relies less on the experimental elements normally associated with scientific research (reliability, validity, and generalizability) (from Ethnography, Observational Research, and Narrative Inquiry, 1994-2012).

Definition 3: “A research technique for the objective, systematic and quantitative description of the manifest content of communication.” (from Berelson, 1952)

Uses of Content Analysis

Identify the intentions, focus or communication trends of an individual, group or institution

Describe attitudinal and behavioral responses to communications

Determine the psychological or emotional state of persons or groups

Reveal international differences in communication content

Reveal patterns in communication content

Pre-test and improve an intervention or survey prior to launch

Analyze focus group interviews and open-ended questions to complement quantitative data

Types of Content Analysis

There are two general types of content analysis: conceptual analysis and relational analysis. Conceptual analysis determines the existence and frequency of concepts in a text. Relational analysis develops the conceptual analysis further by examining the relationships among concepts in a text. Each type of analysis may lead to different results, conclusions, interpretations and meanings.

Conceptual Analysis

Typically people think of conceptual analysis when they think of content analysis. In conceptual analysis, a concept is chosen for examination and the analysis involves quantifying and counting its presence. The main goal is to examine the occurrence of selected terms in the data. Terms may be explicit or implicit. Explicit terms are easy to identify. Coding of implicit terms is more complicated: you need to decide the level of implication and base judgments on subjectivity (an issue for reliability and validity). Therefore, coding of implicit terms involves using a dictionary or contextual translation rules or both.

To begin a conceptual content analysis, first identify the research question and choose a sample or samples for analysis. Next, the text must be coded into manageable content categories. This is basically a process of selective reduction. By reducing the text to categories, the researcher can focus on and code for specific words or patterns that inform the research question.

General steps for conducting a conceptual content analysis:

1. Decide the level of analysis: word, word sense, phrase, sentence, themes

2. Decide how many concepts to code for: develop a pre-defined or interactive set of categories or concepts. Decide either: A. to allow flexibility to add categories through the coding process, or B. to stick with the pre-defined set of categories.

Option A allows for the introduction and analysis of new and important material that could have significant implications to one’s research question.

Option B allows the researcher to stay focused and examine the data for specific concepts.

3. Decide whether to code for existence or frequency of a concept. The decision changes the coding process.

When coding for the existence of a concept, the researcher would count a concept only once if it appeared at least once in the data and no matter how many times it appeared.

When coding for the frequency of a concept, the researcher would count the number of times a concept appears in a text.

4. Decide on how you will distinguish among concepts:

Should text be coded exactly as they appear or coded as the same when they appear in different forms? For example, “dangerous” vs. “dangerousness”. The point here is to create coding rules so that these word segments are transparently categorized in a logical fashion. The rules could make all of these word segments fall into the same category, or perhaps the rules can be formulated so that the researcher can distinguish these word segments into separate codes.

What level of implication is to be allowed? Words that imply the concept or words that explicitly state the concept? For example, “dangerous” vs. “the person is scary” vs. “that person could cause harm to me”. These word segments may not merit separate categories, due the implicit meaning of “dangerous”.

5. Develop rules for coding your texts. After decisions of steps 1-4 are complete, a researcher can begin developing rules for translation of text into codes. This will keep the coding process organized and consistent. The researcher can code for exactly what he/she wants to code. Validity of the coding process is ensured when the researcher is consistent and coherent in their codes, meaning that they follow their translation rules. In content analysis, obeying by the translation rules is equivalent to validity.

6. Decide what to do with irrelevant information: should this be ignored (e.g. common English words like “the” and “and”), or used to reexamine the coding scheme in the case that it would add to the outcome of coding?

7. Code the text: This can be done by hand or by using software. By using software, researchers can input categories and have coding done automatically, quickly and efficiently, by the software program. When coding is done by hand, a researcher can recognize errors far more easily (e.g. typos, misspelling). If using computer coding, text could be cleaned of errors to include all available data. This decision of hand vs. computer coding is most relevant for implicit information where category preparation is essential for accurate coding.

8. Analyze your results: Draw conclusions and generalizations where possible. Determine what to do with irrelevant, unwanted, or unused text: reexamine, ignore, or reassess the coding scheme. Interpret results carefully as conceptual content analysis can only quantify the information. Typically, general trends and patterns can be identified.

Relational Analysis

Relational analysis begins like conceptual analysis, where a concept is chosen for examination. However, the analysis involves exploring the relationships between concepts. Individual concepts are viewed as having no inherent meaning and rather the meaning is a product of the relationships among concepts.

To begin a relational content analysis, first identify a research question and choose a sample or samples for analysis. The research question must be focused so the concept types are not open to interpretation and can be summarized. Next, select text for analysis. Select text for analysis carefully by balancing having enough information for a thorough analysis so results are not limited with having information that is too extensive so that the coding process becomes too arduous and heavy to supply meaningful and worthwhile results.

There are three subcategories of relational analysis to choose from prior to going on to the general steps.

Affect extraction: an emotional evaluation of concepts explicit in a text. A challenge to this method is that emotions can vary across time, populations, and space. However, it could be effective at capturing the emotional and psychological state of the speaker or writer of the text.

Proximity analysis: an evaluation of the co-occurrence of explicit concepts in the text. Text is defined as a string of words called a “window” that is scanned for the co-occurrence of concepts. The result is the creation of a “concept matrix”, or a group of interrelated co-occurring concepts that would suggest an overall meaning.

Cognitive mapping: a visualization technique for either affect extraction or proximity analysis. Cognitive mapping attempts to create a model of the overall meaning of the text such as a graphic map that represents the relationships between concepts.

General steps for conducting a relational content analysis:

1. Determine the type of analysis: Once the sample has been selected, the researcher needs to determine what types of relationships to examine and the level of analysis: word, word sense, phrase, sentence, themes. 2. Reduce the text to categories and code for words or patterns. A researcher can code for existence of meanings or words. 3. Explore the relationship between concepts: once the words are coded, the text can be analyzed for the following:

Strength of relationship: degree to which two or more concepts are related.

Sign of relationship: are concepts positively or negatively related to each other?

Direction of relationship: the types of relationship that categories exhibit. For example, “X implies Y” or “X occurs before Y” or “if X then Y” or if X is the primary motivator of Y.

4. Code the relationships: a difference between conceptual and relational analysis is that the statements or relationships between concepts are coded. 5. Perform statistical analyses: explore differences or look for relationships among the identified variables during coding. 6. Map out representations: such as decision mapping and mental models.

Reliability and Validity

Reliability : Because of the human nature of researchers, coding errors can never be eliminated but only minimized. Generally, 80% is an acceptable margin for reliability. Three criteria comprise the reliability of a content analysis:

Stability: the tendency for coders to consistently re-code the same data in the same way over a period of time.

Reproducibility: tendency for a group of coders to classify categories membership in the same way.

Accuracy: extent to which the classification of text corresponds to a standard or norm statistically.

Validity : Three criteria comprise the validity of a content analysis:

Closeness of categories: this can be achieved by utilizing multiple classifiers to arrive at an agreed upon definition of each specific category. Using multiple classifiers, a concept category that may be an explicit variable can be broadened to include synonyms or implicit variables.

Conclusions: What level of implication is allowable? Do conclusions correctly follow the data? Are results explainable by other phenomena? This becomes especially problematic when using computer software for analysis and distinguishing between synonyms. For example, the word “mine,” variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. Software can obtain an accurate count of that word’s occurrence and frequency, but not be able to produce an accurate accounting of the meaning inherent in each particular usage. This problem could throw off one’s results and make any conclusion invalid.

Generalizability of the results to a theory: dependent on the clear definitions of concept categories, how they are determined and how reliable they are at measuring the idea one is seeking to measure. Generalizability parallels reliability as much of it depends on the three criteria for reliability.

Advantages of Content Analysis

Directly examines communication using text

Allows for both qualitative and quantitative analysis

Provides valuable historical and cultural insights over time

Allows a closeness to data

Coded form of the text can be statistically analyzed

Unobtrusive means of analyzing interactions

Provides insight into complex models of human thought and language use

When done well, is considered a relatively “exact” research method

Content analysis is a readily-understood and an inexpensive research method

A more powerful tool when combined with other research methods such as interviews, observation, and use of archival records. It is very useful for analyzing historical material, especially for documenting trends over time.

Disadvantages of Content Analysis

Can be extremely time consuming

Is subject to increased error, particularly when relational analysis is used to attain a higher level of interpretation

Is often devoid of theoretical base, or attempts too liberally to draw meaningful inferences about the relationships and impacts implied in a study

Is inherently reductive, particularly when dealing with complex texts

Tends too often to simply consist of word counts

Often disregards the context that produced the text, as well as the state of things after the text is produced

Can be difficult to automate or computerize

Textbooks & Chapters  

Berelson, Bernard. Content Analysis in Communication Research.New York: Free Press, 1952.

Busha, Charles H. and Stephen P. Harter. Research Methods in Librarianship: Techniques and Interpretation.New York: Academic Press, 1980.

de Sola Pool, Ithiel. Trends in Content Analysis. Urbana: University of Illinois Press, 1959.

Krippendorff, Klaus. Content Analysis: An Introduction to its Methodology. Beverly Hills: Sage Publications, 1980.

Fielding, NG & Lee, RM. Using Computers in Qualitative Research. SAGE Publications, 1991. (Refer to Chapter by Seidel, J. ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’.)

Methodological Articles  

Hsieh HF & Shannon SE. (2005). Three Approaches to Qualitative Content Analysis.Qualitative Health Research. 15(9): 1277-1288.

Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K, & Kyngas H. (2014). Qualitative Content Analysis: A focus on trustworthiness. Sage Open. 4:1-10.

Application Articles  

Abroms LC, Padmanabhan N, Thaweethai L, & Phillips T. (2011). iPhone Apps for Smoking Cessation: A content analysis. American Journal of Preventive Medicine. 40(3):279-285.

Ullstrom S. Sachs MA, Hansson J, Ovretveit J, & Brommels M. (2014). Suffering in Silence: a qualitative study of second victims of adverse events. British Medical Journal, Quality & Safety Issue. 23:325-331.

Owen P. (2012).Portrayals of Schizophrenia by Entertainment Media: A Content Analysis of Contemporary Movies. Psychiatric Services. 63:655-659.

Choosing whether to conduct a content analysis by hand or by using computer software can be difficult. Refer to ‘Method and Madness in the Application of Computer Technology to Qualitative Data Analysis’ listed above in “Textbooks and Chapters” for a discussion of the issue.

QSR NVivo:  http://www.qsrinternational.com/products.aspx

Atlas.ti:  http://www.atlasti.com/webinars.html

R- RQDA package:  http://rqda.r-forge.r-project.org/

Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner, and Mike Palmquist. (1994-2012). Ethnography, Observational Research, and Narrative Inquiry. Writing@CSU. Colorado State University. Available at: https://writing.colostate.edu/guides/guide.cfm?guideid=63 .

As an introduction to Content Analysis by Michael Palmquist, this is the main resource on Content Analysis on the Web. It is comprehensive, yet succinct. It includes examples and an annotated bibliography. The information contained in the narrative above draws heavily from and summarizes Michael Palmquist’s excellent resource on Content Analysis but was streamlined for the purpose of doctoral students and junior researchers in epidemiology.

At Columbia University Mailman School of Public Health, more detailed training is available through the Department of Sociomedical Sciences- P8785 Qualitative Research Methods.

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Related terms

Qualitative Research : A method of inquiry employed in several different academic disciplines such as sociology, whereby it seeks to understand how individuals construct meaning through their social world.

Coding : The process of categorizing qualitative data into smaller groups to identify patterns and relationships.

Thematic Analysis : A method for identifying, analyzing, and reporting patterns (themes) within data

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List of illustrations, introduction: interrogating textual material in today’s day and age, characteristics and contexts of use of content analysis, chapter 1 content analysis in social research: a methodological point of view, chapter 2 the analysis of digital campaigns, the case study of the italian general elections, chapter 3 local digital campaigns, the role of facebook in rome’s local elections, chapter 4 analyzing the blog of the five star movement, chapter 5 content analysis applied to in-depth interviews, drawing ideas from an empirical study on religiosity in italy, concluding remarks, past and present research practices, looking forward, biographical note, table of contents, share link with colleague or librarian, product details.

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Qualitative Content Analysis

In subject area: Social Sciences

Qualitative Content Analysis is a method of analysis that involves structuring content based on semantic relationships, aiming to deepen understanding beyond numerical data by focusing on the meanings and connections between concepts such as sustainability and circular economy.

AI generated definition based on: Resources, Conservation and Recycling , 2020

Chapters and Articles

You might find these chapters and articles relevant to this topic.

The narrative of sustainability and circular economy - A longitudinal review of two decades of research

Josef-Peter Schöggl , ... Rupert J. Baumgartner , in Resources, Conservation and Recycling , 2020

3.2.5 Qualitative content analysis (step 5)

Based on the results of the historiographic analysis and the corresponding historic citation network, a qualitative content analysis of the 30 most influential papers from the bibliographic collection on circular economy and sustainability was carried out. By adding a qualitative layer to the analysis, the quantitative findings are expanded and deepened. Furthermore, the qualitative analysis goes beyond the numeric relationships and reaches into semantics (see Elijido-Ten and Clarkson, 2019 , for a similar approach). The qualitative content analysis was based on Mayring's (2010) suggestions and entails a process of content structuring. Since the aim was to investigate the relationship between sustainability and circular economy semantically, the material was analyzed with respect to subcategories connecting both of these concepts.

Stuck on coal and persuasion? A critical review of carbon capture and storage communication

Danny Otto , Matthias Gross , in Energy Research & Social Science , 2021

2.2 Content analysis

We also conducted a qualitative content analysis of our article sample to establish an overview of the state of the art of CCS communication research. Such methods have often been linked to systematic reviews [see 27,30] and enable a readily comprehensible synthesis of the corpus material. Qualitative content analysis [see [33,34] ] means that, in addition to the prior selection of articles based on the framing ‘communication and trust,’ we analyzed the texts freely. Codes were formed in-vivo for the best practices and communication advice given in the articles. These codes were sorted into larger categories to summarize the guidance for outreach strategies.

Qualitative, Multimethod, and Mixed Methods Research

Philipp A.E. Mayring , in International Encyclopedia of Education(Fourth Edition) , 2023

Final remarks

Qualitative Content Analysis enables a systematic analysis of textual material especially important for educational research. Transcripts from open ended data collection methods present insights in inner processes of learning and development.

Especially because of its intermediate position between qualitative and quantitative analysis, qualitative content analysis appears to be an important methodical approach. It represents a mixed methods analysis, combining qualitative steps (assignment of categories to text segments) and quantitative steps (analyzing category frequencies). It provides a series of concrete techniques (summary, inductive category formation, explication, deductive category assignments, mixed techniques) for different research questions approaching texts.

The central element of qualitative content analysis is its rigor, comprehending clear units of analysis, step models of techniques, and final checks for coding agreements. By this systematic, qualitative content analysis can meet scientific quality criteria, representing an alternative to more free and less controlled interpretation in text analysis.

Qualitative data analysis

Kirsty Williamson , ... Paul Scifleet , in Research Methods (Second Edition) , 2018

Qualitative content analysis: An overview

Qualitative content analysis is a method for studying the meaning that is contained in the body of a message. It is done by classifying and organising the content of a communication systematically into categories that describe the topics, themes and context of that message. Although it is most often applied to text, qualitative content analysis can be applied to any type of media – textual, verbal or visual. It has been used in domains as varied as the analysis of political speeches, foreign policy documents, newspapers and television shows. The focus is on documents rather than on responses to stimuli, questions directly asked of people, or as a result of actively engaging with people. It is an unobtrusive method where the only requirement is that the communications have been made by people ( Krippendorf, 2004; Prasad, 2008 ). This is one of the key features that distinguish it from other qualitative approaches to research data analysis where similar coding techniques are employed. Yet, like other methods that apply qualitative coding techniques, such as discourse analysis and genre analysis, qualitative content analysis accepts that the meaning of communication is not immutable but that it is constructed in the context of the questions asked of it. Qualitative content analysis is thus mainly an interpretive approach that allows a researcher to describe the topics and themes that are most meaningful to the research objectives of the study.

Foundations of content analysis

The roots of qualitative content analysis reside in a quantitative approach to measuring the frequency of topics with word counts and statistics (described in Chapter 10 : Bibliometric research ). The adoption and growth of quantitative approaches to analysing content paralleled changes in mass communication and the rise of the mass media. In the mass media environment, the advantages – and value for understanding society – of being able to identify and measure the occurrence of the topics reported in the news, was quickly recognised. Stempel (1989) pointed out that drawing conclusions and formulating opinions on the basis of what we read and hear about is what we all do; analysing the content of media is simply a logical extension of that. One of the first studies to apply quantitative content analysis formally occurred in 1893, when an analysis of New York newspapers, published between 1881 and 1893, demonstrated a change in news coverage from religious, scientific and political matters, to gossip, sport and scandals ( Krippendorf, 2004 ). By identifying and tabulating the themes of news, the study set a trend for gauging social issues prevalent in media. Importantly, researchers realised that, if the findings from studies like the 1893 newspaper study are to be accepted, then the approach taken to content analysis needs to be systematic and reliable.

Quantitative approaches for establishing reliability in content analysis grew significantly during World War II when it was used as technique for understanding the content of wartime propaganda. Working from the Library of Congress, Laswell (1952) described content analysis as an actuarial method for documenting the language of politics. Among other cold war topics, he was interested in the symbolic expressions of hostility between the USA and the USSR. He found that, by measuring the relative frequency of the symbols of hostility in communications, and their relationship to changes in the intensity of hostility between the two geo-political powers, a meaningful indicator of the state of the relationship could be established. Content analysis was seen as a deliberate, statistical simplification designed to discover “uniformities in the mass” ( Laswell, 1952 , p. 52). Isolating explicit symbols of communication, however, is not without complications. Deciding the right categories of representation, tendencies to multiply categories, coding errors and difficulties in resolving agreement between coders have been perennial problems in content analysis that Laswell aimed to address. Methods for categorisation, sampling and measurement established during this time resulted in the now widely used definition of quantitative content analysis as “a research technique for the objective, systematic and quantitative description of the manifest content of communication” ( Berelson, 1952 , p. 18).

Berelson’s definition set the standard for understanding content analysis as an objective method for quantifying the manifest (i.e., explicit and apparent) topics that occur in texts. In its strictest sense, this stance is closely associated with post-positivism. It preferences a methodology with a priori research design, hypothesis testing and strict rules for investigation. Objectivity and reliability reside in predetermining the categories that will be used for measuring the occurrence of topics in texts. The most evident units of content are assigned to categories designed to test the research hypothesis. A systematic approach allows different people to apply the same categories and statistical models to corresponding data consistently, supporting the reliability, replication and validity of the study. Laswell (1952) , Neuendorf (2002) and Stempel (1989) present exemplary quantitative studies. Importantly, focussing on the manifest content “means that content must be coded as it appears rather than as the content analyst feels it is intended” ( Stempel, 1989 , p. 126). Any inferences, other than those evident in the surface of the communication, are excluded from quantitative studies.

Qualitative content analysis

The methodological distinctions between quantitative and qualitative content analysis come down to the scope of inferences that can be made through analysis. Both approaches accept that, at a minimum, any predetermination of the categories that will be used, requires a priori understanding of the universe in which the communication takes place; categories do not determine themselves ( Krippendorf 2004; Laswell 1952 ). Even so, quantitative analysis restricts itself to those categories of interpretation that verify and confirm relationships quantified and tested by hypothesis, while qualitative content analysis is less constrained. It accepts that human communications are not always apparent and extends an interpretivist methodology to allow for subjective interaction between the researcher and the communications that will allow new understandings of the content to emerge. That texts are socially constructed and open to interpretation is widely acknowledged in the methodological approach of most analysts, including Altheide (1996) , Groeben and Rustemeyer (1994) , Kracauer (1952) , Macnamara (2005) , Mayring (2000) , Prasad (2008) , Schreier (2012) , and Weber (1990) . Qualitative content analysts accept that counting the frequency of categories can be a very effective indicator of importance and often work with summary statistics that support the description of contents. However a reliance on quantifying only the most apparent topics in communication is avoided. For the qualitative analyst, simply because a topic occurs fewer times in a communication does not mean that it is a lesser topic and, more importantly, the meaning of a communication is not always evident on the surface. As human communications typically need to be understood holistically within their context, qualitative content analysis is premised on an understanding that interpretation is required to account for the intricacies of latency and the rhetorical intent a topic attains. An investigation of obesity in news media may indicate the rise to prominence of a major health issue but interpretation, beyond the frequency of a term, is required to understand the comedic, political and socio-cultural dimensions that load the topic. Rather than disregarding erroneous expression, innuendo, irony, metaphor and double entendre, as statistically negligible ( Laswell, 1952 ), qualitative content analysis is at liberty to explore their intent within communications.

Comparison with quantitative content analysis

The widespread and increasing availability of digital media, proliferated through desktop computerisation and the internet, has seen an increasing number of both quantitative and qualitative approaches to categorising the contents of communication under the rubric of content analysis. Both methods share common features: they emphasise a systematic and ordered approach to research; they prioritise the research problem or hypothesis from the outset; they have formal steps for selecting the unit of analysis, and for developing and applying categories to content; and they analyse and report the findings of the research.

Although qualitative content analysis is a systematic method, it acknowledges the central role of the investigator in a research process that is designed to support reflexivity and interaction. Most commonly applied to documents and textual data, it is closely aligned with hermeneutic traditions of induction and interpretation where the goal is not only to understand what any given unit of content may represent but to understand the characteristics, dialogue and process of the social context in which meaning is created, that is, to understand the sociality of the communication: “Documents which are not simply agglomerations of fact participate in the process of living, and every word in them vibrates with the intentions in which they originate and simultaneously foreshadows the indefinite effect they may produce” ( Kracauer, 1952 . p. 641).

Steps in qualitative content analysis

Qualitative content analysis is an approach to analysis that focusses on interpreting and describing, meaningfully, the topics and themes that are evident in the contents of communications when framed against the research objectives of the study. While different analysts vary in their approaches (for a discussion of major differences see Hsieh & Shannon, 2005 ) the following steps, based on Altheide (1996) , Mayring (2000) , and Schreier (2012) , outline the conventions of qualitative content analysis.

Step 1. Focussing research objectives on communications

Qualitative content analysis needs to pursue a research objective that can be answered from the content of communications ( Altheide, 1996; Mayring, 2000 ). It is, in this sense, communications-centric. The research problem should be specific enough to inform the type of communication appropriate to the investigation (e.g., a particular set of speeches by a political leader, printed news media in general or, comparisons across different media) as well as the appropriate unit for analysis within the chosen media (e.g., particular sections of a speech, entire newspaper edition, advertisements, segments of TV news). In other words, the research problem needs to be defined sufficiently to allow the researcher to locate suitable sources of communication. The researchers need to familiarise themselves with the determinate universe, including both context and process for the creation of the communications ( Altheide, 1996 ).

Step 2. Establishing the frame for the research

The research frame can be considered as very similar to a ‘picture frame’, shaping the view in which the analysis takes place ( Altheide, 1996 ). In qualitative content analysis , the emphasis a researcher places on one frame over another will influence category development and analysis. For example, the objective of research may be to understand ‘how obesity is being represented in news media’, but whether the research is framed as ‘obesity as a public health issue’ or ‘obesity as a social stigma’ will affect issues ranging from selection of the unit of analysis through to the themes and categories of analysis ( Altheide, 1996 ). In this example, ‘treatment’ and ‘health policy’ are likely to be important themes when analysing ‘obesity as a public health issue’ but they may not be as significant within the frame of ‘obesity as a social stigma’.

Step 3. Selecting the unit of analysis, sampling and coding

The unit of analysis refers to the level of communication that a researcher decides to analyse, that is, whether it is to be individual documents, a collection of documents or, in the case of social media research, a collection of messages ( Altheide, 1996; Schreier, 2012 ). The appropriate unit of analysis should emerge through the process of developing the research objectives and framing the study. When selecting the materials for analysis, both statistical and purposeful sampling techniques can be used. For a study of short message communications in social media, sampling messages statistically, with techniques ranging from random sampling through to stratified sampling, might be the best approach for dealing with the sheer mass of communications. More often, where a researcher has a detailed understanding of the study’s objectives, purposefully selecting communications that will inform the conceptual and theoretical issues is often the best approach ( Altheide, 1996; Schreier, 2012 ). The researcher can also make distinctions between individual communications and parts of communications through using different approaches to coding within, and across, communications. Determining the depth and level of granularity for coding content is dependent on research objectives. When coding, the convention is to include the immediate context of the surrounding text that is assigned to a category so that the meaning of the segment is maintained (as illustrated in Box 19.1 ).

Step 4. Developing content categories

A researcher’s conceptual understanding of a research problem and frame develops from the outset and many of the themes and categories emerge inductively very early on. The process of category development should start from the moment the research commences by listing key concepts and categories emerging from the research problem ( Altheide, 1996; Mayring, 2000 ). The researcher should establish criteria for category use by recording definitions and descriptions of categories. By documenting the key concepts as they emerge and applying these during analysis, the researcher develops a sequence of categories that can grow. Mayring (2000) described the process of category development as a ‘feedback loop’ with categories derived inductively from the theoretical background of the research, formalised and applied from the top-down, while the researcher remains open to adjusting and modifying categories. Although there is no definitive number of categories best suited to qualitative content analysis , analysts often report using about 20 categories ( Altheide, 1996 ). Altheide (1996) and Schreier (2012) present useful accounts of category development in their work.

Step 5. Protocols for analysis

Researchers using qualitative content analysis cannot assert, with certainty, that either the original sender or the recipient of the message share their understanding. Even so, providing the categories used for coding and analysis are an adequate representation of the research objective and, if they have been applied consistently, researchers can support the reliability of the research ( Schreier, 2012 ). Reliability is established by placing emphasis on the process of category definition and the procedures that will be used for assigning codes to content. This means that the protocols developed (e.g., for coding content) need to be treated as part of the research process ( Altheide, 1996 ). Category definition should involve procedures for agreement about the meaning of categories, and their use, to be evaluated by an independent party. This can be achieved by involving a second researcher as a conversant in category and content definition to confirm suitability of the categories and their use. Reliability can be evaluated by checking consistency in the application of the categories to the content either, over time for a single coder, or by checking the agreement of multiple coders in the use of categories to the same content, that is, to determine inter-coder reliability.

Step 6. Performing data analysis and preparing the findings

There is no one single technique for assigning content to categories ( Hsieh & Shannon, 2005 ). While some researchers continue to work with pen and paper to code content and develop reports, there are also sophisticated software packages available (e.g., ATLAS.ti, NVivo) that support the same processes and add many advanced features to assist analysis, including the automatic generation of word counts and keyword lists, matching and sorting facilities, audio and video tagging. Even when aided by technology, the process of qualitative content analysis remains one of constant iteration and verification. It is important to compare and contrast key differences and extreme cases as the analysis is undertaken ( Altheide, 1996 ). Developing analytical notes is essential. The analyst usually develops a narrative including descriptive summaries, illustrative examples and overviews of each category that may include both quotations from the content and frequency counts of category occurrence.

Examples of the use of qualitative content analysis

‘What is the Australian news media saying about obesity? How is obesity portrayed in social media? Is it a social stigma? Or public health issue?’ Qualitative content analysis rests on an assumption that questions like these can be answered by comparing, contrasting and describing the contents of communication ( Schreier, 2012 ). It does so without any strong theoretical commitment of its own and this is one criticism that has been levelled at it ( Weber, 1990 ). Nevertheless, while some researchers have seen qualitative content analysis’s apparent ‘neutrality’ as an issue, others have seen it as an advantage ( Macnamara, 2005 ). Qualitative content analysis offers a set of procedures to support whichever theoretical lens a researcher brings to it and because of this it is often used as the supporting method for other qualitative approaches including ethnomethodology, semiotics, genre analysis and discourse analysis ( Macnamara, 2005 ). Riemer, Overfeld, Scifleet and Richter’s (2012) categorisation of short message communications is an example of using qualitative content analysis to support a genre analysis of the types of communication taking place on the enterprise social network of the international consultancy firm, Capgemini. The study used both summary statistics and the description of messages communicated within the network to demonstrate how the appropriation of technology is being socially constructed in the workplace. The research demonstrated an important shift in emphasis in the organisational use of social media, from top-down models of adopting technology to bottom-up models of appropriation.

Conducting qualitative content analysis in urban planning research and urban studies

Ailin Sheydayi , Hashem Dadashpoor , in Habitat International , 2023

Qualitative content analysis as a kind of systematic analysis of qualitative data in which the latent values and meanings of the text are tried to be identified, described, and interpreted, is one of the most widely used qualitative methods in urban planning research and urban studies. However, the excessive flexibility and development of the method in other theoretical fields have obscured how to use it in the field of planning and urban studies. In this paper, we have tried to propose a procedural model that is suitable for the specific conditions and issues of the field by analyzing the content of the documents obtained from the systematic review of the theoretical literature of planning and urban studies that have used this method. This procedural model explains the process of qualitative content analysis within five stages: choosing the type and theoretical context of data, explaining the objectives, deciding on how to analyze the data, deciding on how to interpret the data, and application of the interpretation results. Proposing the procedural model of qualitative content analysis that comes from the field of planning and urban studies can help, in addition to disambiguating the function of the method, make the method more visible and more correctly used in the field.

Social Psychology: Research Methods

Lia Figgou , Vassilis Pavlopoulos , in International Encyclopedia of the Social & Behavioral Sciences (Second Edition) , 2015

Methods of Qualitative Data Analysis

Content analysis is basically a quantitative method that involves establishing categories and counting the number of instances that these categories appear in a corpus of data. However, the method is also employed in qualitative research in which systematic classification procedures are used to categorize qualitative textual data into clusters of meaning (categories or themes). Qualitative content analysis is more interpretative in comparison with its quantitative counterpart and it is interested not only in the ‘manifest’ meaning of words or phrases but also in its ‘latent’ underlying meaning ( Mayring, 2000 ). Coding systems consist of rules for assigning specified units into categories. Normally, categories are intended to be unidimensional, exhaustive, and mutually exclusive, but this is not always the case in qualitative content analysis (a piece of text may be relevant to more than one category). The implementation of content analysis has been drastically affected by the development of software packages, such as NVivo, Atlas ti, Dedoose.com , and the recently developed QCAmap. These tools can assist the researcher in handling and organizing large quantities of data but they cannot diminish the need for intellectual effort on the part of the analyst. Content analysis can be applied to a whole variety of data (including nonverbal data, such as pictures, drawings, gestures, etc.) and in relation to a variety of research questions. The fragmentation of texts and the decontextualization of data (as instances, pieces of text or answers are separated from their contexts) constitute potential disadvantages of the method.

• Grounded Theory

A methodological approach was first developed by the sociologists Glaser and Strauss (1967) . Glaser and Strauss criticized research derived from highly abstract theories that it fails to come up with explanations that are of relevance to those being studied. Hence, they developed a method aiming to generate a local, contextual theory, grounded on data. Therefore, the term grounded theory refers both to a method of inquiry, as well as to the theory produced by the implementation of method. The method includes an initial coding phase in which researchers work systematically through the data corpus generating codes (analytic labels) to describe both low-level concepts, as well as, more abstract categories. Throughout the process, they write memos elaborating their codes, identifying analytic gaps, and comparing analytic categories. Constant comparison and theoretical sampling constitute principal analytic tasks of the method. Constant comparison involves continually comparing elements (analytic categories and theoretical presuppositions), while theoretical sampling involves the active sampling of new data that inform theoretical categories as the analysis proceeds. The emphasis of the original grounded theory method on discovering theory from data with its empiricist connotations was criticized by scholars who emphasized the constitutive nature of the analysis and developed more social constructionist revisions of the method ( Charmaz, 2006 ).

Thematic Analysis

There is no agreement if it really constitutes a specific method in its own right. It is often rather unclear which its differences with qualitative content analysis are, and its use has been fairly inconsistent (see, however, Braun and Clarke, 2006 ; for a systematic introduction to the method). Thematic analysis also involves (usually inductive) coding of qualitative data into clusters of similar entities, or conceptual categories and the identification of consistent patterns and relationships between themes, so as to come up with a theoretical explanation of the phenomenon under study.

Interpretative Phenomenological Analysis

Its central focus is on the understanding of subjective experience and on the meaning attached by participants themselves to their lived experience. Unstructured or semistructured interview data are considered to be the ideal path to the participants' subjectivity. In terms of initial coding and the process of identification of emergent themes within the corpus of data Interpretative Phenomenological Analysis is also very close to some versions of thematic analysis and grounded theory ( Smith et al., 2009 ).

Narrative Analysis

Narrative analysis refers to a cluster of analytic methods for interpreting texts or visual data that have a storied form. A common assumption of narrative methods is that people tell stories to help organize and make sense of their lives and their storied accounts are functional, and purposeful. Different approaches to narrative analysis are categorized on the basis of whether they focus on the narrative content or structure, with the thematic version interrogating what a story is about, while the structural version asks how a story is composed to achieve particular communicative aims. To this basic typology, according to Kohler Riessman (2008) , one could also add the dialogic/performance narrative analysis, which focusses on the context and view of narratives as being multivoiced and coconstructed and the visual which links words and images in a coherent narrative.

Discursive Methods (Discourse Analysis/Discursive Psychology)

There are a variety of related – but also fundamentally distinct – methods grouped under the rubric ‘discursive’ or, most commonly, ‘discourse analysis.’ What is common between different methods is the recognition of the vital role of discourse in social life and an approach to language as social practice, instead of a pathway to inner cognitive entities ( see Social Constructionism ). Nevertheless there is little consensus between approaches on the ancestors and the epistemological basis of the analysis, on the analytic objectives, and on the conceptualization of the very notion of discourse . Potter and Wetherell (1987) – influenced by speech act theory, ethnomethodology, semiology, and post-structuralism – used the term ‘discourse’ to refer to virtually any language use and considered interpretative repertoires (recurrently used units of content, situated in certain – usually interview – contexts and oriented toward different interactional but also macro-social functions) as the units of analysis. On the other hand, scholars influenced by Foucault's work defined discourse as “a system of statements which constructs an object” ( Parker, 1990 : 191) and tended to fracture texts into discrete discourses which subjectify speakers and reproduce power relations.

The early 1990s witnessed also the emergence of discursive psychology ( Edwards and Potter, 1992 ) which is informed by conversation analysis (CA) and prioritizes the use of naturalistic data. Its focus is on the role that descriptions of the world (including descriptions of psychological states) play in the management of speakers' accountability and in the formation of action. In the years to come, discursive psychologists became more deeply engaged with CA, working with conversational corpora from everyday and institutional settings, transcribed by the use of conventions developed by Gail Jefferson. Between the boundary lines among this strand of work, which affiliates with ethnomethodological traditions, and analytic perspectives which follow Foucaultian lines, other scholars proclaimed ( Wetherell, 1998 ) an eclectic engagement with both CA and post-structuralism (and therefore engagement with both the interactional as well as the broader ideological functions of language use) as the most productive starting point for discursive methods in Social Psychology. This approach is known as critical discursive psychology.

Conversation Analysis

CA refers to a specific approach to the analysis of interaction that emerged in the 1960s in the work of Harvey Sacks ( Sacks, 1995 ). CA is interested to understand social order by focusing analytically on the sequence of talk in interaction and on the ways participants organize mundane conversation. The recent engagement of discursive psychologists with CA blurs the boundaries between the two as analytic methods in Social Psychology.

Rhetorical Analysis

Interest in rhetoric in Social Psychology also arose as part of the discursive turn. Key text through which rhetoric was introduced in social psychological analyses constitutes Billig's (1987) ‘ Arguing and Thinking .’ Given his preference for scholarship, instead of methodology, Billig did not intend to introduce an analytic method in the narrow sense of the term. The text did not adhere to any specific methodology and neither included any methodological guidelines. Nevertheless, it served to deepen and enrich the understanding of how to approach analytically context and content in qualitative research, by advocating the need to consider the rhetorical relation between topics (as units of analysis). According to Billig (1987) , in order to capture the meaning of any commonplace assumption, we should put it in its argumentative context and examine it in relation to the commonplace that it aims to downgrade. Rhetorical analysis is also interested in relating meaning, or rather disputes over meaning, to the broader historical ideological context.

Validity and Reliability in Qualitative Research

There is considerable debate regarding the extent to which validity and reliability criteria – which have been defined in quantitative research and are imbued with its assumptions – are appropriate to evaluate the quality of qualitative research. According to some authors, qualitative researchers can and should incorporate criteria and techniques that deal with issues of reliability and validity and adjust them in their own epistemological framework. Silverman (1993) , for example, maintained that triangulation techniques (of researchers or/and of data sources) can vitally contribute to the quality of research inquiry and can be compatible with the constructionist epistemology that underlies many qualitative research methods, if they are considered as highlighting the situated use of different accounts, instead of being treated as a means to prioritize one account vis-à-vis others.

Other researchers, however, prefer to replace validity and reliability with terms and criteria more compatible to the interpretivist background of qualitative research. Potter and Wetherell (1987) suggested coherence of the analytic framework (the potential of the analytic framework to give coherence in a body of data by accounting both for regularity and variability within it), fruitfulness (the extent to which the analytic scheme generates novel explanations), new problems (the identification of contradictions and exceptions from the explanatory scheme that necessitates new analytic questions and answers), and participants' own orientation (the way in which participants themselves see what analysts may consider as consistent, contradictory, etc.) as validation criteria of discourse analysis. Interest in understanding the phenomena from the participant's view as a validation criterion of the quality of the analysis has been developed also in other methods. Grounded theorists, for example, also maintained that the extent to which results may be recognizable and may be of relevance to those studied constitutes a vital criterion of research quality. Finally, a usually mentioned criterion of qualitative analysis's validation concerns the extent to which researchers' reflexivity is built into the analytic process.

What is qualitative content analysis?

The chapter gives an overview on theoretical background and specific techniques of qualitative content analysis . Its role in qualitative research is described, central elements of definition introduced, and basic characteristics discussed. The importance for educational research is outlined, and the historical and methodological background described. We present two basic procedures, inductive category formation and deductive category assignment, by step models and by an example from educational research. Differences to other approaches to qualitative content analysis are worked out. An open access software program especially for our techniques of qualitative content analysis is presented.

First, I want to give a definition and characterization of the approach. Qualitative content analysis (cf. Mayring, 2015b , 2020 , 2021 ; Kuckartz, 2014 ) is a methodological approach to text analysis mainly in social sciences, which can be used in empirical research projects working with textual material like interview or group discussion transcripts, open observational protocols (field notes) or document analyses (files or webpages for example). In a systematic bibliometric analysis of the literature and research databases FORIS, SOLIS, Sociofile, Psyndex and MLA, Titscher et al. (2000) found open content analysis as the most frequent text analytical procedure with 39% from 1621 citations. Qualitative content analysis was followed by conversation analysis (21%), standardized content analysis (19%), grounded theory (12%), objective hermeneutics (5%), and ethnography (2%). Carrera-Fernandez et al. (2014) found that particularly in psychology (based on the Web of Science database) the qualitative content analysis was by far the first choice of analysis procedure, followed by grounded theory, discourse analysis and action research in their qualitative orientated journal article analysis.

What are the central characteristics of qualitative content analysis? I want to outline four points:

The qualitative content analysis approach (the same in quantitative content analysis) is category-based , that is its distinguishing feature. Categories refer to aspects within the text, which put the meaning of those aspects in a short textual form. Text analysis is, therefore, restricted to the category system. Text contents that are not addressed by the categories or holistic impressions are not considered or would have to be addressed with a further content analytical conduct or other text analysis methods. In so far it is a selective procedure in contrast to a holistic text understanding.

The qualitative content analysis procedure is research question oriented . Text analytical questions (possibly several) are derived from the main aims of the research project. These questions should be answered at the end of the analysis. This clearly distinguishes the qualitative content analysis from other more open, explorative approaches such as grounded theory.

Qualitative content analysis is characterized by strict rule orientation and systematic ongoing . Process models enable the procedure to be described step-by-step, and this has proven itself in previous research processes. The specific rules for each technique are reviewed in a pilot study and should not be changed after that.

Qualitative content analysis is an approach of mixed methods , standing between pure qualitative and pure quantitative ongoing. The first step to assign categories to text passages or to develop categories from text passages is a qualitative-interpretive operation, the comparing, accumulating text passages with similar categories, analyzing frequencies of categories means a quantitative ongoing (the second quantitative step is not obligatory in qualitative content analysis, but mostly obvious).

A central quality criterium for qualitative content analysis consists in high coder agreements , which means that several procedures of categorization of the same material should lead to similar results. Intra-coder agreement, similar to reliability (retest reliability), means that the same content analyst restarts with coding after finishing all material. Inter-coder agreement means passing the material (or at least a selection) to a second person, a measure of objectivity of the analysis. We would not expect an identical result (e.g., in the wordings of inductive categories), but a similar solution.

We can summarize: Qualitative content analysis is a systematic category-based set of techniques for analyzing texts with strict theory-based rules, containing qualitative and quantitative steps of analysis.

The adoption of conservation agriculture by smallholder farmers in southern Africa: A scoping review of barriers and enablers

Morgan Lee , James Gambiza , in Journal of Rural Studies , 2022

2.3 Coding and analysis of extracted data

Qualitative synthesis of the collected data, extracted via the data charting form, was undertaken through a qualitative content analysis . Qualitative content analysis (QCA) consists of multiple techniques for systematic text analysis which endeavour to preserve the methodological strengths of quantitative analysis ( Mayring, 2014 ). QCA allowed for a mixed methods approach where inductive and iterative categorisation of text from included records was followed by an analysis of category frequencies as a quantitative step, as outline by Mayring (2014) . QCA is methodologically dynamic and allows for a rigorous review of literature that is thorough and theoretically relevant while establishing well-rooted links between identified themes. The solid methodological value of QCA is seen in its transparency, and its constant comparative and iterative analysis ( Mayring, 2014 ). While not without criticism, the use of QCA in the review allowed for systematic categorisation of the barriers and enablers of CA across a diverse range of literature and the creation of the Conservation Agriculture Suitability Framework for Smallholder Farmers.

Related terms:

• Urban Studies
• Content Analysis
• Systematic Review
• Health Policy
• Urban Planning
• Drug Policy
• Civic Education
• Planning Research

Instant insights, infinite possibilities

• What is content analysis?

Last updated

20 March 2023

Reviewed by

Miroslav Damyanov

When you're conducting qualitative research, you'll find yourself analyzing various texts. Perhaps you'll be evaluating transcripts from audio interviews you've conducted. Or you may find yourself assessing the results of a survey filled with open-ended questions.

Streamline content analysis

Bring all your qualitative research into one place to code and analyze with Dovetail

Content analysis is a research method used to identify the presence of various concepts, words, and themes in different texts. Two types of content analysis exist: conceptual analysis and relational analysis . In the former, researchers determine whether and how frequently certain concepts appear in a text. In relational analysis, researchers explore how different concepts are related to one another in a text. 

Both types of content analysis require the researcher to code the text. Coding the text means breaking it down into different categories that allow it to be analyzed more easily.

• What are some common uses of content analysis?

You can use content analysis to analyze many forms of text, including:

Interview and discussion transcripts

Newspaper articles and headline

Literary works

Historical documents

Government reports

Academic papers

Music lyrics

Researchers commonly use content analysis to draw insights and conclusions from literary works. Historians and biographers may apply this approach to letters, papers, and other historical documents to gain insight into the historical figures and periods they are writing about. Market researchers can also use it to evaluate brand performance and perception.

Some researchers have used content analysis to explore differences in decision-making and other cognitive processes. While researchers traditionally used this approach to explore human cognition, content analysis is also at the heart of machine learning approaches currently being used and developed by software and AI companies.

• Conducting a conceptual analysis

Conceptual analysis is more commonly associated with content analysis than relational analysis. 

In conceptual analysis, you're looking for the appearance and frequency of different concepts. Why? This information can help further your qualitative or quantitative analysis of a text. It's an inexpensive and easily understood research method that can help you draw inferences and conclusions about your research subject. And while it is a relatively straightforward analytical tool, it does consist of a multi-step process that you must closely follow to ensure the reliability and validity of your study.

When you're ready to conduct a conceptual analysis, refer to your research question and the text. Ask yourself what information likely found in the text is relevant to your question. You'll need to know this to determine how you'll code the text. Then follow these steps:

1. Determine whether you're looking for explicit terms or implicit terms.

Explicit terms are those that directly appear in the text, while implicit ones are those that the text implies or alludes to or that you can infer. 

Coding for explicit terms is straightforward. For example, if you're looking to code a text for an author's explicit use of color,  you'd simply code for every instance a color appears in the text. However, if you're coding for implicit terms, you'll need to determine and define how you're identifying the presence of the term first. Doing so involves a certain amount of subjectivity and may impinge upon the reliability and validity of your study .

2. Next, identify the level at which you'll conduct your analysis.

You can search for words, phrases, or sentences encapsulating your terms. You can also search for concepts and themes, but you'll need to define how you expect to identify them in the text. You must also define rules for how you'll code different terms to reduce ambiguity. For example, if, in an interview transcript, a person repeats a word one or more times in a row as a verbal tic, should you code it more than once? And what will you do with irrelevant data that appears in a term if you're coding for sentences? 

Defining these rules upfront can help make your content analysis more efficient and your final analysis more reliable and valid.

3. You'll need to determine whether you're coding for a concept or theme's existence or frequency.

If you're coding for its existence, you’ll only count it once, at its first appearance, no matter how many times it subsequently appears. If you're searching for frequency, you'll count the number of its appearances in the text.

4. You'll also want to determine the number of terms you want to code for and how you may wish to categorize them.

For example, say you're conducting a content analysis of customer service call transcripts and looking for evidence of customer dissatisfaction with a product or service. You might create categories that refer to different elements with which customers might be dissatisfied, such as price, features, packaging, technical support, and so on. Then you might look for sentences that refer to those product elements according to each category in a negative light.

5. Next, you'll need to develop translation rules for your codes.

Those rules should be clear and consistent, allowing you to keep track of your data in an organized fashion.

6. After you've determined the terms for which you're searching, your categories, and translation rules, you're ready to code.

You can do so by hand or via software. Software is quite helpful when you have multiple texts. But it also becomes more vital for you to have developed clear codes, categories, and translation rules, especially if you're looking for implicit terms and concepts. Otherwise, your software-driven analysis may miss key instances of the terms you seek.

7. When you have your text coded, it's time to analyze it.

Look for trends and patterns in your results and use them to draw relevant conclusions about your research subject.

• Conducting a relational analysis

In a relational analysis, you're examining the relationship between different terms that appear in your text(s). To do so requires you to code your texts in a similar fashion as in a relational analysis. However, depending on the type of relational analysis you're trying to conduct, you may need to follow slightly different rules.

Three types of relational analyses are commonly used: affect extraction , proximity analysis , and cognitive mapping .

Affect extraction

This type of relational analysis involves evaluating the different emotional concepts found in a specific text. While the insights from affect extraction can be invaluable, conducting it may prove difficult depending on the text. For example, if the text captures people's emotional states at different times and from different populations, you may find it difficult to compare them and draw appropriate inferences.

Proximity analysis

A relatively simpler analytical approach than affect extraction, proximity analysis assesses the co-occurrence of explicit concepts in a text. You can create what's known as a concept matrix, which is a group of interrelated co-occurring concepts. Concept matrices help evaluate and determine the overall meaning of a text or the identification of a secondary message or theme.

Cognitive mapping

You can use cognitive mapping as a way to visualize the results of either affect extraction or proximity analysis. This technique uses affect extraction or proximity analysis results to create a graphic map illustrating the relationship between co-occurring emotions or concepts.

To conduct a relational analysis, you must start by determining the type of analysis that best fits the study: affect extraction or proximity analysis. 

Complete steps one through six as outlined above. When it comes to the seventh step, analyze the text according to the relational analysis type they've chosen. During this step, feel free to use cognitive mapping to help draw inferences and conclusions about the relationships between co-occurring emotions or concepts. And use other tools, such as mental modeling and decision mapping as necessary, to analyze the results.

• The advantages of content analysis

Content analysis provides researchers with a robust and inexpensive method to qualitatively and quantitatively analyze a text. By coding the data, you can perform statistical analyses of the data to affirm and reinforce conclusions you may draw. And content analysis can provide helpful insights into language use, behavioral patterns, and historical or cultural conventions that can be valuable beyond the scope of the initial study.

When content analyses are applied to interview data, the approach provides a way to closely analyze data without needing interview-subject interaction, which can be helpful in certain contexts. For example, suppose you want to analyze the perceptions of a group of geographically diverse individuals. In this case, you can conduct a content analysis of existing interview transcripts rather than assuming the time and expense of conducting new interviews.

What is meant by content analysis?

Content analysis is a research method that helps a researcher explore the occurrence of and relationships between various words, phrases, themes, or concepts in a text or set of texts. The method allows researchers in different disciplines to conduct qualitative and quantitative analyses on a variety of texts.

Where is content analysis used?

Content analysis is used in multiple disciplines, as you can use it to evaluate a variety of texts. You can find applications in anthropology, communications, history, linguistics, literary studies, marketing, political science, psychology, and sociology, among other disciplines.

What are the two types of content analysis?

Content analysis may be either conceptual or relational. In a conceptual analysis, researchers examine a text for the presence and frequency of specific words, phrases, themes, and concepts. In a relational analysis, researchers draw inferences and conclusions about the nature of the relationships of co-occurring words, phrases, themes, and concepts in a text.

What's the difference between content analysis and thematic analysis?

Content analysis typically uses a descriptive approach to the data and may use either qualitative or quantitative analytical methods. By contrast, a thematic analysis only uses qualitative methods to explore frequently occurring themes in a text.

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Content Analysis

Content analysis is a research method where the researcher attempts to codify or thematically analyse a written piece of work. This aims to provide a reliable interpretation of the qualitative data that is being analysed, by creating quantitative data from it (e.g. the use of certain words or phrases in media reports).

An advantage of content analysis is that it can reveal patterns or trends which may not have been obvious before the research, or might have been considered an impression gained by a biased or subjective reading.

A disadvantage of content analysis is that the method cannot reveal the reasons for certain patterns of content, or the meanings placed on it, it can merely describe it.

An example of a piece of research that used content analysis as a method is when the Glasgow Media Group researched media bias against trade unionists in the reporting of strikes and industrial relations. It noted that overwhelmingly employers "offered" while unions "demanded" but it would take further research to explore the reasons for this and the meanings that readers and listeners would place on the different language used.

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.

Content analysis can be used to study a wide range of topics, including media coverage of social issues, political speeches, advertising messages, and online discussions, among others. It is often used in qualitative research and can be combined with other methods to provide a more comprehensive understanding of a particular phenomenon.

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

This type of content analysis involves the systematic and objective counting and categorization of the content of a particular form of communication, such as text or video. The data obtained is then subjected to statistical analysis to identify patterns, trends, and relationships between different variables. Quantitative content analysis is often used to study media content, advertising, and political speeches.

Qualitative Content Analysis

This type of content analysis is concerned with the interpretation and understanding of the meaning and context of the content. It involves the systematic analysis of the content to identify themes, patterns, and other relevant features, and to interpret the underlying meanings and implications of these features. Qualitative content analysis is often used to study interviews, focus groups, and other forms of qualitative data, where the researcher is interested in understanding the subjective experiences and perceptions of the participants.

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

This method involves analyzing the meanings of key concepts used in the content being analyzed. The researcher identifies key concepts and analyzes how they are used, defining them and categorizing them into broader themes.

Content Analysis by Frequency

This method involves counting and categorizing the frequency of specific words, phrases, or themes that appear in the content being analyzed. The researcher identifies relevant keywords or phrases and systematically counts their frequency.

Comparative Analysis

This method involves comparing the content of two or more sources to identify similarities, differences, and patterns. The researcher selects relevant sources, identifies key themes or concepts, and compares how they are represented in each source.

Discourse Analysis

This method involves analyzing the structure and language of the content being analyzed to identify how the content constructs and represents social reality. The researcher analyzes the language used and the underlying assumptions, beliefs, and values reflected in the content.

Narrative Analysis

This method involves analyzing the content as a narrative, identifying the plot, characters, and themes, and analyzing how they relate to the broader social context. The researcher identifies the underlying messages conveyed by the narrative and their implications for the broader social context.

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

• Define your research question or objective: Before starting your content analysis, you need to define your research question or objective clearly. This will help you to identify the content you need to analyze and the type of analysis you need to conduct.
• Select your sample: Select a representative sample of the content you want to analyze. This may involve selecting a random sample, a purposive sample, or a convenience sample, depending on the research question and the availability of the content.
• Develop a coding scheme: Develop a coding scheme or a set of categories to use for coding the content. The coding scheme should be based on your research question or objective and should be reliable, valid, and comprehensive.
• Train coders: Train coders to use the coding scheme and ensure that they have a clear understanding of the coding categories and procedures. You may also need to establish inter-coder reliability to ensure that different coders are coding the content consistently.
• Code the content: Code the content using the coding scheme. This may involve manually coding the content, using software, or a combination of both.
• Analyze the data: Once the content is coded, analyze the data using appropriate statistical or qualitative methods, depending on the research question and the type of data.
• Interpret the results: Interpret the results of the analysis in the context of your research question or objective. Draw conclusions based on the findings and relate them to the broader literature on the topic.
• Report your findings: Report your findings in a clear and concise manner, including the research question, methodology, results, and conclusions. Provide details about the coding scheme, inter-coder reliability, and any limitations of the study.

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

• Media Research: Content analysis is commonly used in media research to examine the representation of different groups, such as race, gender, and sexual orientation, in media content. It can also be used to study media framing, media bias, and media effects.
• Political Communication : Content analysis can be used to study political communication, including political speeches, debates, and news coverage of political events. It can also be used to study political advertising and the impact of political communication on public opinion and voting behavior.
• Marketing Research: Content analysis can be used to study advertising messages, consumer reviews, and social media posts related to products or services. It can provide insights into consumer preferences, attitudes, and behaviors.
• Health Communication: Content analysis can be used to study health communication, including the representation of health issues in the media, the effectiveness of health campaigns, and the impact of health messages on behavior.
• Education Research : Content analysis can be used to study educational materials, including textbooks, curricula, and instructional materials. It can provide insights into the representation of different topics, perspectives, and values.
• Social Science Research: Content analysis can be used in a wide range of social science research, including studies of social media, online communities, and other forms of digital communication. It can also be used to study interviews, focus groups, and other qualitative data sources.

Examples of Content Analysis

Here are some examples of content analysis:

• Media Representation of Race and Gender: A content analysis could be conducted to examine the representation of different races and genders in popular media, such as movies, TV shows, and news coverage.
• Political Campaign Ads : A content analysis could be conducted to study political campaign ads and the themes and messages used by candidates.
• Social Media Posts: A content analysis could be conducted to study social media posts related to a particular topic, such as the COVID-19 pandemic, to examine the attitudes and beliefs of social media users.
• Instructional Materials: A content analysis could be conducted to study the representation of different topics and perspectives in educational materials, such as textbooks and curricula.
• Product Reviews: A content analysis could be conducted to study product reviews on e-commerce websites, such as Amazon, to identify common themes and issues mentioned by consumers.
• News Coverage of Health Issues: A content analysis could be conducted to study news coverage of health issues, such as vaccine hesitancy, to identify common themes and perspectives.
• Online Communities: A content analysis could be conducted to study online communities, such as discussion forums or social media groups, to understand the language, attitudes, and beliefs of the community members.

Purpose of Content Analysis

The purpose of content analysis is to systematically analyze and interpret the content of various forms of communication, such as written, oral, or visual, to identify patterns, themes, and meanings. Content analysis is used to study communication in a wide range of fields, including media studies, political science, psychology, education, sociology, and marketing research. The primary goals of content analysis include:

• Describing and summarizing communication: Content analysis can be used to describe and summarize the content of communication, such as the themes, topics, and messages conveyed in media content, political speeches, or social media posts.
• Identifying patterns and trends: Content analysis can be used to identify patterns and trends in communication, such as changes over time, differences between groups, or common themes or motifs.
• Exploring meanings and interpretations: Content analysis can be used to explore the meanings and interpretations of communication, such as the underlying values, beliefs, and assumptions that shape the content.
• Testing hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the effects of media on attitudes and behaviors or the framing of political issues in the media.

When to use Content Analysis

Content analysis is a useful method when you want to analyze and interpret the content of various forms of communication, such as written, oral, or visual. Here are some specific situations where content analysis might be appropriate:

• When you want to study media content: Content analysis is commonly used in media studies to analyze the content of TV shows, movies, news coverage, and other forms of media.
• When you want to study political communication : Content analysis can be used to study political speeches, debates, news coverage, and advertising.
• When you want to study consumer attitudes and behaviors: Content analysis can be used to analyze product reviews, social media posts, and other forms of consumer feedback.
• When you want to study educational materials : Content analysis can be used to analyze textbooks, instructional materials, and curricula.
• When you want to study online communities: Content analysis can be used to analyze discussion forums, social media groups, and other forms of online communication.
• When you want to test hypotheses and theories : Content analysis can be used to test hypotheses and theories about communication, such as the framing of political issues in the media or the effects of media on attitudes and behaviors.

Characteristics of Content Analysis

Content analysis has several key characteristics that make it a useful research method. These include:

• Objectivity : Content analysis aims to be an objective method of research, meaning that the researcher does not introduce their own biases or interpretations into the analysis. This is achieved by using standardized and systematic coding procedures.
• Systematic: Content analysis involves the use of a systematic approach to analyze and interpret the content of communication. This involves defining the research question, selecting the sample of content to analyze, developing a coding scheme, and analyzing the data.
• Quantitative : Content analysis often involves counting and measuring the occurrence of specific themes or topics in the content, making it a quantitative research method. This allows for statistical analysis and generalization of findings.
• Contextual : Content analysis considers the context in which the communication takes place, such as the time period, the audience, and the purpose of the communication.
• Iterative : Content analysis is an iterative process, meaning that the researcher may refine the coding scheme and analysis as they analyze the data, to ensure that the findings are valid and reliable.
• Reliability and validity : Content analysis aims to be a reliable and valid method of research, meaning that the findings are consistent and accurate. This is achieved through inter-coder reliability tests and other measures to ensure the quality of the data and analysis.

Advantages of Content Analysis

There are several advantages to using content analysis as a research method, including:

• Objective and systematic : Content analysis aims to be an objective and systematic method of research, which reduces the likelihood of bias and subjectivity in the analysis.
• Large sample size: Content analysis allows for the analysis of a large sample of data, which increases the statistical power of the analysis and the generalizability of the findings.
• Non-intrusive: Content analysis does not require the researcher to interact with the participants or disrupt their natural behavior, making it a non-intrusive research method.
• Accessible data: Content analysis can be used to analyze a wide range of data types, including written, oral, and visual communication, making it accessible to researchers across different fields.
• Versatile : Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research.
• Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives.

Limitations of Content Analysis

While content analysis has many advantages, there are also some limitations to consider, including:

• Limited contextual information: Content analysis is focused on the content of communication, which means that contextual information may be limited. This can make it difficult to fully understand the meaning behind the communication.
• Limited ability to capture nonverbal communication : Content analysis is limited to analyzing the content of communication that can be captured in written or recorded form. It may miss out on nonverbal communication, such as body language or tone of voice.
• Subjectivity in coding: While content analysis aims to be objective, there may be subjectivity in the coding process. Different coders may interpret the content differently, which can lead to inconsistent results.
• Limited ability to establish causality: Content analysis is a correlational research method, meaning that it cannot establish causality between variables. It can only identify associations between variables.
• Limited generalizability: Content analysis is limited to the data that is analyzed, which means that the findings may not be generalizable to other contexts or populations.
• Time-consuming: Content analysis can be a time-consuming research method, especially when analyzing a large sample of data. This can be a disadvantage for researchers who need to complete their research in a short amount of time.

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Unfortunately, that's not possible. We have to use our brains, resources, and efforts to not only go out there and find the answers to our questions but also to make sense of the information we find. One popular way of doing this is by conducting a content analysis .

• In this explanation, we will introduce the method of 'content analysis'.
• We'll start by defining 'content analysis', and discussing each of its relevant concepts one-by-one.
• After this, we will take a look at the basic stages involved in conducting a content analysis.
• Next, we will explore the various types of content analysis that can be conducted in the social sciences.
• Finally, we will close the explanation by evaluating the content analysis method's advantages and disadvantages.

What is the content analysis method?

Content analysis is a popular systematic approach to data interpretation that is widely used in the social sciences.

Defining 'content analysis'

Content analysis is not a method of data collection, but a way of interpreting data.

According to Merriam-Webster , content analysis is the "analysis of the manifest and latent content of a body of communicated material (such as a book or film) through a classification, tabulation, and evaluation of its key symbols and themes in order to ascertain its meaning and probable effect".

There are several aspects of this definition that we need to unpack to truly make sense of content analysis, how it's conducted, and why it is useful. Let's take a closer look...

First and foremost, it's important to note that this process is described as an analysis of content - meaning that it is not a method of collecting data. As such, researchers may need to use other primary or secondary research methods through which they will obtain data, that can then be interpreted using content analysis.

Primary research methods involve the collecting of data by the researcher who is carrying out the research themselves. Examples of primary research methods include interviews, observations, and surveys. In secondary research , the researcher analyses data that has already been collected.

You can find out more about these two forms of research in our dedicated Vaia explanations on "Primary Research" and "Secondary Research"!

The distinction between manifest and latent content is a crucial one. Sigmund Freud first identified these terms in his analysis of dreams. The term 'manifest content' refers to the exact, literal object that we see. However, the term 'latent content' refers to the hidden meaning behind the manifest content.

In sociological research, the communicated material chosen by the researcher will depend largely on their research aims and questions, as well as the types of resources that are available to them. Note that this content tends to be qualitative - it can include text, audio recordings, images, and videos.

The meaning and probable effects derived from content analysis also depend on the researcher's specific aims and questions. For example, a researcher might want to examine somebody's social development by studying their personal diaries. Alternatively, they may aim to study political trends by analysing government archives, newspapers, and Official Statistics. Another popular use of content analysis is to study media representation by examining the content, such as TV shows or news stories, and its effects on audiences.

What are the basic stages of content analysis?

As we have seen, there are many purposes and applications of the content analysis method in sociology. While there may be differences in the steps depending on the exact research aims, let's now look at the general steps that should be followed in every content analysis.

Conducting a content analysis

The basic steps to follow in content analysis are as follows:

1. First, the data needs to be collected and prepared . Of course, the process by which this is done will depend on the researcher's specific aims and resources.

For instance, if a researcher wants to analyse how students speak about their IGCSE experiences, they might choose to interview them and then transcribe those interviews so that they are ready for analysis. Another researcher might want to study how Muslims are represented in news articles in The Sun newspaper. Their data could then be issues of The Sun from the past year.

2. Next, depending on the researcher's aims, they must decide how they want to measure their data. Examples of measures include the use of particular adjectives, the types of images and colours used, or even what is not mentioned (in addition to what is mentioned).

3. Next is the coding step, arguably one of the most important in content analysis. Coding involves looking at the selected units of measurement and placing them into distinct categories that the researcher must identify.

The reason why the coding step is so important is that it sets guidelines that the researcher can follow to analyse all of their data consistently . The resulting document, which is called a coding frame , is also useful for other researchers to use so that they can replicate this research in the future.

4. Now that the researcher has established a coding frame , they can begin to code their data. Some researchers do this manually, while others use computer software that can help them out.

5. Next, the researcher must study their coded data and think about how it relates to their research aims and questions. They can then draw conclusions and report them to others.

6. Finally, a crucial last step of all research is to evaluate it once it is done. Researchers must reflect on their research methods and data, considering what was done well and what could be improved or elaborated in further research.

What are the various types of content analysis used in sociology?

There are various ways of conducting a sociological content analysis.

Researchers often distinguish between conceptual and relational content analyses.

The methods which researchers opt for can depend on many factors, such as the research aims, the available resources and the researcher's own theoretical stance.

Conceptual versus relational analysis

The most common distinction that is made within the method of content analysis distinguishes conceptual content analysis from relational content analysis .

Conceptual content analysis

Conceptual content analysis is usually the method which people think of when they talk about content analysis, generally. This involves choosing a particular concept and analysing selected content to test for it, specifically. The researcher will construct a coding frame and categories based on the chosen topic.

In a conceptual analysis, the instances of the chosen concept are usually identified in two ways:

explicit terms are more obvious, whereas

implicit terms depend on the interpretation and judgements of the researcher

Note that we made a similar distinction in our definition of content analysis, between manifest and latent content.

Imagine you are watching a brand-new Netflix series with your best friend. You both seem to enjoy the show, snacking on popcorn and laughing at all the jokes together. Once the series is complete, you ask your best friend what they thought. They tell you that they really enjoyed it, but that it would have been nice to see more people of colour in the main cast. You realise that you didn't notice this, and decide to go back to see for yourself.

You set aside a day to rewatch the series on your own, and make a note of all the White and non-White people who play main and side roles in the show, based on how much time screen time they have (explicit term) and the types of contributions that they make to the plot (implicit term). You aim to calculate a difference between the number of White main characters and non-White main characters throughout the series. The task that you now have on your hands is a conceptual content analysis .

Relational content analysis

A relational content analysis is quite similar to a conceptual analysis, except that there is an added step: examining the relationships between particular concepts.

A core assumption of relational analysis is that individual concepts don't have any meaning on their own - but that their meaning is created when they are combined and related with other concepts.

Imagine that you would like to explore the effect of the COVID-19 pandemic on people's feelings of loneliness. In this instance, the concept of 'loneliness' might not have much independent meaning. This could well be enhanced by combining it with the concept of 'isolation', which serves as an important link between the pandemic and loneliness.

Content analysis in sociology: advantages and disadvantages

As we know, each method of data collection and analysis comes with its own set of advantages and disadvantages. While these can differ on a case-by-case basis, there are some general evaluative points that we can consider when thinking about content analysis.

Strengths of content analysis

Identifying and counting up the instances of a particular topic means that the data generated from content analyses tend to be quantitative. This means that it becomes easier to identify trends and patterns.

Furthermore, creating a standardised coding frame makes this method highly replicable . Other researchers can apply the same guidelines to test the reliability and validity of results.

Considering explicit and implicit terms in a conceptual analysis makes for a more holistic analysis.

Limitations of content analysis

• On the other hand, researchers may experience a sense of bias where they actively search for content related to their concept, thereby overlooking content which does not line up with their research aims.
• Content analyses are vulnerable to the omission of context. By failing to consider the situational aspects of the concepts, the researcher misses a key component of sociological research. This can lead to inaccurate inferences, especially where the reliability of results is being cross-checked by other researchers who do not see the content in the same way.

Content Analysis in Sociology - Key takeaways

• Content analysis involves studying a piece of content for its themes, symbols and meanings.
• Content analysis involves collecting and selecting data, formulating a coding frame using the chosen analytical concepts, studying the selected content using this code, and drawing conclusions based on results.
• Researchers distinguish between conceptual and relational hypotheses, where the latter involves relating concepts to one another to establish meaning.
• Key advantages of content analysis include the ease with which it can be conducted, as well as the high level of standardisation and replicability that it enables.
• On the other hand, researchers who conduct content analyses are vulnerable to bias, which can lead to the exclusion of certain instances and concepts.

Flashcards in Content Analysis in Sociology 3

Conceptual analysis and relational analysis.

Explicit terms are more obvious, whereas

implicit terms depend on interpretation and judgements of the researcher.

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Frequently Asked Questions about Content Analysis in Sociology

What is a content analysis in sociology? 

A content analysis involves studying a particular material or piece of content for the symbols, themes and meanings that it portrays. 

What is an example of content analysis in sociology?

As a sociologist, a researcher might conduct a content analysis on news articles to make inferences about whether certain groups are written about in loaded ways. For example, they might seek to examine whether ethnic minorities are framed as perpetrators more often than they are represented as victims. 

What are the main features of content analysis? 

The main features of content analysis include the aims of the analysis, the content itself, and the coding frame with which the content will be analysed.

What are the 8 steps of content analysis?

The eight steps of content analysis are as follows:

• Prepare the data.
• Decide on the units of measure.
• Establish a coding frame.
• Test the coding frame on a small fraction of the content, to see whether it works well.
• Code the text.
• Test the validity and reliability of results by using the coding frame on another piece of similar content. Do you get the same results?
• Use results to draw conclusions and decide whether it proves or disproves the hypothesis.
• Evaluate the methods used for their strengths and limitations, then report the results.

Who defined content analysis?

Bernard Berelson (1912-1979) defined content analysis as we understand it today.

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Category: content analysis

The internet as an object of content analysis.

There are, however, many difficulties in using web sites as sources of content analysis. Following Scott’s (1990) four criteria of assessing the quality of documents, we need consider why a web site is constructed in the first place, whether it is there for commercial purposes, and whether it has a political motive.

Three examples of content analysis of documents online

Boepple and Thompson (2014) conducted quantitative analysis of 21 ‘healthy living blogs’. Their sampling frame was only blogs which had received an award, and from those, they selected the blogs with the largest number of page views.

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Gap year blogs – a thematic analysis, evidence of right wing media bias.

NB – I am aware that how you answer this question depends on how you define left and right, and that not only are there different dimensions to left and right (YES I have come across the political compass!), but that the meanings of left and right shift over time, so they are relative concepts.

Having said that, we have to start somewhere – so I broadly define ‘right wing’ as neoliberal – pro-privatisation of public services, deregulation and lowering taxation, an emphasis on economic growth rather than social progress, and a current commitment to austerity. I also include within my broad definition of ‘right wing’ anti-immigration sentiments (sorry, I know it’s vague!). Left wing I define as against further privatisation of public services (more to the left is in favour of re-nationalisation), an enhanced role of the state in regulating especially big business, and a belief in higher levels of taxation of especially the wealthy (those earning over £50K a year for example). Also included within a broadly leftist perspective is a commitment to end austerity and a commitment to internationalism – the free movement of people across boarders and so a much more relaxed attitude to migration than the right.

Something I find very interesting is that the first piece of evidence below gets around the whole tricky issue of operationalizing right and left wing… just by asking people ‘do you think the media is right or left wing’? This raises all sorts of sociological questions about objectivity and subjectivity and categories. From a teaching perspective I’m currently thinking this ‘measuring political attitudes’ topic could be the perfect one for explaining the difference between positivist and phenomenological approaches to social research.

Four pieces of evidence of right wing bias in the media

A 2016 London School of Economic Report: Journalistic Representations of Jeremy Corbyn in the British Press   found that Jeremy Corbyn was represented unfairly in the media before he was elected party leader.

SOCG 3310: Sociological Research Methods : Primary Vs. Secondary Sources

• Search Strategy
• Research help

Primary Vs. Secondary Sources

• Sociology Research
• Copyright and Plagiarism
• Evaluating Sources

Primary Sources

Primary Sources  are immediate, first-hand accounts of a topic, from people who had a direct connection with it. Primary sources can include:

Texts of laws and other original documents.

Newspaper reports, by reporters who witnessed an event or who quote people who did.

Speeches, diaries, letters and interviews - what the people involved said or wrote.

Original research.

Datasets, survey data, such as census or economic statistics.

Photographs, video, or audio that capture an event.

Secondary Sources

Secondary Sources  are one step removed from primary sources, though they often quote or otherwise use primary sources. They can cover the same topic, but add a layer of interpretation and analysis. Secondary sources can include:

Most books about a topic.

Analysis or interpretation of data.

Scholarly or other articles about a topic, especially by people not directly involved.

Documentaries (though they often include photos or video portions that can be considered primary sources).

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357 IMAGES 10 Content Analysis Examples (2024) AQA Sociology: Research Methods: Content Analysis Content Analysis AQA Sociology: Research Methods: Content Analysis Content Analysis For Research Content Analysis in Social Research VIDEO Content Analysis || Research Methodology || Dr.vivek pragpura || sociology with vivek || Sociology नोट्स। सामाजिक शोध का अर्थ क्या होता है।। Content Analysis Method || Content Analysis Method in hindi || Content Analysis Research Method What is Thematic Content Analysis 68 Content Analysis Research Method for Consumer Behavior and Marketing Unitising in content analysis COMMENTS 15.1. Content Analysis Understand how to present the results from content analysis. As we noted earlier, content analysis is a materials-based research method that focuses on texts and their meanings. Sociologists use a more expansive definition of "text" than the word typically has. In a research context, the content being analyzed is essentially any recorded ... Understanding Content Analysis in Sociology Content analysis offers several strengths as a research method in sociology: It allows for the systematic and objective analysis of large amounts of data. It can be used to study both quantitative and qualitative aspects of communication. It provides insights into the cultural and social meanings embedded in communication. Content Analysis: An Introduction to Its Methodology The Fourth Edition has been completely revised to offer readers the most current techniques and research on content analysis, including new information on reliability and social media. Readers will also gain practical advice and experience for teaching academic and commercial researchers how to conduct content analysis. Available with Perusall ... Content Analysis: Research Method to Study Social Life Updated on February 04, 2020. Content analysis is a research method used by sociologists to analyze social life by interpreting words and images from documents, film, art, music, and other cultural products and media. The researchers look at how the words and images are used, and the context in which they are used to draw inferences about the ... 11.3: Content Analysis Table 11:.1 Content Analysis Examples; Data Research question Author(s) (year) Spam e-mails: ... Gender & Society, 4(4), 500-533. conducted a content analysis of introductory sociology textbooks, but their aim was not to learn about the content of sociology as a discipline. PDF Content Analysis Brief Outline of Method As the name suggests, Content Analysis is used by sociologists (and other social scientists) to investigate the content of the Mass Media (, although it has applications across a wider range of spheres - the analysis of historical documents, for example). In other words, it's used to explore the content of various ... Content Analysis Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual: Books, newspapers and magazines. Speeches and interviews. Web content and social media posts. Photographs and films. Sage Research Methods Content analysis is one of the most important but complex research methodologies in the social sciences. In this thoroughly updated Second Edition of The Content Analysis Guidebook, author Kimberly Neuendorf draws on examples from across numerous disciplines to clarify the complicated aspects of content analysis through step-by-step instruction and practical advice. Content Analysis In his 1952 text on the subject of content analysis, Bernard Berelson traced the origins of the method to communication research and then listed what he called six distinguishing features of the approach. As one might expect, the six defining features reflect the concerns of social science as taught in the 1950s, an age in which the calls for an "objective," "systematic," and ... Introduction Abstract. This chapter offers an inclusive definition of content analysis. This helps in clarifying some key terms and concepts. Three approaches to content analysis are introduced and defined briefly: basic content analysis, interpretive content analysis, and qualitative content analysis. Long-standing differences between quantitative and ... Content Analysis Method and Examples Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts. Content analysis Content analysis is a research method used in sociology to systematically analyze and interpret the content of communication. It involves identifying patterns, themes, or biases within qualitative data like texts, interviews, or media. Content Analysis in Social Research It is a useful research tool that scholars use to examine human thoughts and actions. During content analysis, researchers compile qualitative data based on human language in written form or even ... Content Analysis in Social Research Maria Paola Faggiano, Ph.D. in Methodology of Social Sciences, Tenure-track assistant professor of Sociology, Sapienza-University of Rome.She has published monographs and many articles on methodological issues. Among her recent publications, Content Analysis and Digital Data: Methodological Solutions between Tradition and Innovation.The Case of Digital Campaigns as Meeting Ground of Expertises ... Qualitative Content Analysis Qualitative content analysis (cf. Mayring, 2015b, 2020, 2021; Kuckartz, 2014) is a methodological approach to text analysis mainly in social sciences, which can be used in empirical research projects working with textual material like interview or group discussion transcripts, open observational protocols (field notes) or document analyses ... Using Content Analysis to Study the Sociology Curriculum in the United By focusing particularly on four published studies that used content analysis to look at the sociology curriculum in the United States, this case study examines issues related to clarifying your research question, operationalizing your variables, choosing a sample, and collecting and analyzing your data when content analysis is the method used ... What is Content Analysis? Uses, Types & Advantages Content analysis is a research method that helps a researcher explore the occurrence of and relationships between various words, phrases, themes, or concepts in a text or set of texts. The method allows researchers in different disciplines to conduct qualitative and quantitative analyses on a variety of texts. Content Analysis Content analysis is a research method where the researcher attempts to codify or thematically analyse a written piece of work. This aims to provide a reliable interpretation of the qualitative data that is being analysed, by creating quantitative data from it (e.g. the use of certain words or phrases in media reports). An advantage of content analysis is that it can reveal patterns or trends ... Content Analysis Versatile: Content analysis can be used to study communication in a wide range of contexts and fields, including media studies, political science, psychology, education, sociology, and marketing research. Cost-effective: Content analysis is a cost-effective research method, as it does not require expensive equipment or participant incentives. Content Analysis in Sociology: Types & Stages Content Analysis in Sociology - Key takeaways. Content analysis involves studying a piece of content for its themes, symbols and meanings. Content analysis involves collecting and selecting data, formulating a coding frame using the chosen analytical concepts, studying the selected content using this code, and drawing conclusions based on results. Content Analysis of The Mass Media in Social Research Formal Content Analysis is a quantitative approach to analysing mass media content and involves developing a system of classification to analyse the key features of media sources and then simply counting how many times these features occur in a given text. The simplest form of content analysis is a word or phrase count, which these days can be ... Sociology Research Best Sociology Databases . JSTOR. JSTOR is a highly selective digital library of academic content in many formats and disciplines. The collections include top peer-reviewed scholarly journals as well as respected literary journals, academic monographs, research reports from trusted institutes, and primary sources. PDF UNIT 2 CONTENT ANALYSIS QUANTITATIVE AND QUALITATIVE ASPECTS 1) Decide on how you will distinguish among concepts. m) Develop rules for coding your texts. no) Code the textsp) Analyze your r. sultsContent Analysis2) Relational Analysis has two parts. The first step involves quantitative technique for analysis and the second step is identification of the r. content analysis The Internet as an Object of Content Analysis. Websites, social media posts and similar virtual documents are all forms of secondary data, and thus amenable to both quantitative and qualitative content analysis. The sheer number of internet users creating online documents makes researching them a challenge. There are, however, many difficulties ... Primary Vs. Secondary Sources Original research. Datasets, survey data, such as census or economic statistics. Photographs, video, or audio that capture an event. Secondary Sources . Secondary Sources are one step removed from primary sources, though they often quote or otherwise use primary sources. They can cover the same topic, but add a layer of interpretation and analysis. essay homework paper phd project review speech study thesis