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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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what's content analysis in research

  • 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.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Thematic analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias
  • Social desirability bias

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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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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

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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  • Methodology

Content Analysis | A Step-by-Step Guide with Examples

Published on 5 May 2022 by Amy Luo . Revised on 5 December 2022.

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 categorise or ‘code’ words, themes, and concepts within the texts and then analyse the results.

Table of contents

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

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 analyse.

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

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

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
  • Analysing the consequences of communication content, such as the flow of information or audience responses

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  • Unobtrusive data collection

You can analyse 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.

  • 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 .

Next, you follow these five steps.

Step 1: Select the content you will analyse

Based on your research question, choose the texts that you will analyse. 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 criteria for inclusion (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 number of texts that meet your criteria, you might analyse all of them. If there is a large volume of texts, you can select a sample .

Step 2: Define the units and categories of analysis

Next, you need to determine the level at which you will analyse 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).

Step 3: Develop a set of rules for coding

Coding involves organising 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.

Step 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 categorising words and phrases.

Step 5: Analyse 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.

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Chapter 17. Content Analysis

Introduction.

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

what's content analysis in research

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

An Overview of the Content Analysis Process

The first step: selecting content.

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

New stories Blogs Comment posts Lyrics
Letters to editor Films Cartoons Advertisements
Brand packaging Logos Instagram photos Tweets
Photographs Graffiti Street signs Personalized license plates
Avatars (names, shapes, presentations) Nicknames Band posters Building names

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

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

The Second Step: Collecting and Storing

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

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

Researcher Note

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

The Third Step: Analysis

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

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

The Fourth and Final Step: The Write-Up

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

Analyzing Content: Helpful Hints and Pointers

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

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

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

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

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

Table 17.1. Examples of What to Note During Content Analysis

What can be noted/coded Example from Gender Presentation Study
Actions and behaviors
Events or conditions
Activities
Strategies and tactics
States/conditions
Meanings/symbols
Relationships/interactions
Consequences
Settings

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

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

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

Table 17.2. Examples of Unit of Analysis in Content Analysis

Unit of Analysis How Used...
Words
Themes
Characters
Paragraphs
Items
Concepts
Semantics

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

Further Readings

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

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

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

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

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

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

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

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

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

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

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

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

  • 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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How to do a content analysis

Content analysis illustration

What is content analysis?

Why would you use a content analysis, types of content analysis, conceptual content analysis, relational content analysis, reliability and validity, reliability, the advantages and disadvantages of content analysis, a step-by-step guide to conducting a content analysis, step 1: develop your research questions, step 2: choose the content you’ll analyze, step 3: identify your biases, step 4: define the units and categories of coding, step 5: develop a coding scheme, step 6: code the content, step 7: analyze the results, frequently asked questions about content analysis, related articles.

In research, content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. Simply put, content analysis is a research method that aims to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data , depending on the specific use case.

As such, some of the objectives of content analysis include:

  • Simplifying complex, unstructured content.
  • Identifying trends, patterns, and relationships in the content.
  • Determining the characteristics of the content.
  • Identifying the intentions of individuals through the analysis of the content.
  • Identifying the implied aspects in the content.

Typically, when doing a content analysis, you’ll gather data not only from written text sources like newspapers, books, journals, and magazines but also from a variety of other oral and visual sources of content like:

  • Voice recordings, speeches, and interviews.
  • Web content, blogs, and social media content.
  • Films, videos, and photographs.

One of content analysis’s distinguishing features is that you'll be able to gather data for research without physically gathering data from participants. In other words, when doing a content analysis, you don't need to interact with people directly.

The process of doing a content analysis usually involves categorizing or coding concepts, words, and themes within the content and analyzing the results. We’ll look at the process in more detail below.

Typically, you’ll use content analysis when you want to:

  • Identify the intentions, communication trends, or communication patterns of an individual, a group of people, or even an institution.
  • Analyze and describe the behavioral and attitudinal responses of individuals to communications.
  • Determine the emotional or psychological state of an individual or a group of people.
  • Analyze the international differences in communication content.
  • Analyzing audience responses to content.

Keep in mind, though, that these are just some examples of use cases where a content analysis might be appropriate and there are many others.

The key thing to remember is that content analysis will help you quantify the occurrence of specific words, phrases, themes, and concepts in content. Moreover, it can also be used when you want to make qualitative inferences out of the data by analyzing the semantic meanings and interrelationships between words, themes, and concepts.

In general, there are two types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions. With that in mind, let’s now look at these two types of content analysis in more detail.

With conceptual analysis, you’ll determine the existence of certain concepts within the content and identify their frequency. In other words, conceptual analysis involves the number of times a specific concept appears in the content.

Conceptual analysis is typically focused on explicit data, which means you’ll focus your analysis on a specific concept to identify its presence in the content and determine its frequency.

However, when conducting a content analysis, you can also use implicit data. This approach is more involved, complicated, and requires the use of a dictionary, contextual translation rules, or a combination of both.

No matter what type you use, conceptual analysis brings an element of quantitive analysis into a qualitative approach to research.

Relational content analysis takes conceptual analysis a step further. So, while the process starts in the same way by identifying concepts in content, it doesn’t focus on finding the frequency of these concepts, but rather on the relationships between the concepts, the context in which they appear in the content, and their interrelationships.

Before starting with a relational analysis, you’ll first need to decide on which subcategory of relational analysis you’ll use:

  • Affect extraction: With this relational content analysis approach, you’ll evaluate concepts based on their emotional attributes. You’ll typically assess these emotions on a rating scale with higher values assigned to positive emotions and lower values to negative ones. In turn, this allows you to capture the emotions of the writer or speaker at the time the content is created. The main difficulty with this approach is that emotions can differ over time and across populations.
  • Proximity analysis: With this approach, you’ll identify concepts as in conceptual analysis, but you’ll evaluate the way in which they occur together in the content. In other words, proximity analysis allows you to analyze the relationship between concepts and derive a concept matrix from which you’ll be able to develop meaning. Proximity analysis is typically used when you want to extract facts from the content rather than contextual, emotional, or cultural factors.
  • Cognitive mapping: Finally, cognitive mapping can be used with affect extraction or proximity analysis. It’s a visualization technique that allows you to create a model that represents the overall meaning of content and presents it as a graphic map of the relationships between concepts. As such, it’s also commonly used when analyzing the changes in meanings, definitions, and terms over time.

Now that we’ve seen what content analysis is and looked at the different types of content analysis, it’s important to understand how reliable it is as a research method . We’ll also look at what criteria impact the validity of a content analysis.

There are three criteria that determine the reliability of a content analysis:

  • Stability . Stability refers to the tendency of coders to consistently categorize or code the same data in the same way over time.
  • Reproducibility . This criterion refers to the tendency of coders to classify categories membership in the same way.
  • Accuracy . Accuracy refers to the extent to which the classification of content corresponds to a specific standard.

Keep in mind, though, that because you’ll need to code or categorize the concepts you’ll aim to identify and analyze manually, you’ll never be able to eliminate human error. However, you’ll be able to minimize it.

In turn, three criteria determine the validity of a content analysis:

  • Closeness of categories . This is achieved by using multiple classifiers to get an agreed-upon definition for a specific category by using either implicit variables or synonyms. In this way, the category can be broadened to include more relevant data.
  • Conclusions . Here, it’s crucial to decide what level of implication will be allowable. In other words, it’s important to consider whether the conclusions are valid based on the data or whether they can be explained using some other phenomena.
  • Generalizability of the results of the analysis to a theory . Generalizability comes down to how you determine your categories as mentioned above and how reliable those categories are. In turn, this relies on how accurately the categories are at measuring the concepts or ideas that you’re looking to measure.

Considering everything mentioned above, there are definite advantages and disadvantages when it comes to content analysis:

AdvantagesDisadvantages

It doesn’t require physical interaction with any participant, or, in other words, it’s unobtrusive. This means that the presence of a researcher is unlikely to influence the results. As a result, there are also fewer ethical concerns compared to some other analysis methods.

It always involves an element of subjective interpretation. In many cases, it’s criticized for being too subjective and not scientifically rigorous enough. Fortunately, when applying the criteria of reliability and validity, researchers can produce accurate results with content analysis.

It uses a systematic and transparent approach to gathering data. When done correctly, content analysis is easily repeatable by other researchers, which, in turn, leads to more reliable results.

It’s inherently reductive. In other words, by focusing only on specific concepts, words, or themes, researchers will often disregard any context, nuances, or deeper meaning to the content.

Because researchers are able to conduct content analysis in any location, at any time, and at a lower cost compared to many other analysis methods, it’s typically more flexible.

Although it offers researchers an inexpensive and flexible approach to gathering and analyzing data, coding or categorizing a large number of concepts is time-consuming.

It allows researchers to effectively combine quantitative and qualitative analysis into one approach, which then results in a more rigorous scientific analysis of the data.

Coding can be challenging to automate, which means the process largely relies on manual processes.

Let’s now look at the steps you’ll need to follow when doing a content analysis.

The first step will always be to formulate your research questions. This is simply because, without clear and defined research questions, you won’t know what question to answer and, by implication, won’t be able to code your concepts.

Based on your research questions, you’ll then need to decide what content you’ll analyze. Here, you’ll use three factors to find the right content:

  • The type of content . Here you’ll need to consider the various types of content you’ll use and their medium like, for example, blog posts, social media, newspapers, or online articles.
  • What criteria you’ll use for inclusion . Here you’ll decide what criteria you’ll use to include content. This can, for instance, be the mentioning of a certain event or advertising a specific product.
  • Your parameters . Here, you’ll decide what content you’ll include based on specified parameters in terms of date and location.

The next step is to consider your own pre-conception of the questions and identify your biases. This process is referred to as bracketing and allows you to be aware of your biases before you start your research with the result that they’ll be less likely to influence the analysis.

Your next step would be to define the units of meaning that you’ll code. This will, for example, be the number of times a concept appears in the content or the treatment of concept, words, or themes in the content. You’ll then need to define the set of categories you’ll use for coding which can be either objective or more conceptual.

Based on the above, you’ll then organize the units of meaning into your defined categories. Apart from this, your coding scheme will also determine how you’ll analyze the data.

The next step is to code the content. During this process, you’ll work through the content and record the data according to your coding scheme. It’s also here where conceptual and relational analysis starts to deviate in relation to the process you’ll need to follow.

As mentioned earlier, conceptual analysis aims to identify the number of times a specific concept, idea, word, or phrase appears in the content. So, here, you’ll need to decide what level of analysis you’ll implement.

In contrast, with relational analysis, you’ll need to decide what type of relational analysis you’ll use. So, you’ll need to determine whether you’ll use affect extraction, proximity analysis, cognitive mapping, or a combination of these approaches.

Once you’ve coded the data, you’ll be able to analyze it and draw conclusions from the data based on your research questions.

Content analysis offers an inexpensive and flexible way to identify trends and patterns in communication content. In addition, it’s unobtrusive which eliminates many ethical concerns and inaccuracies in research data. However, to be most effective, a content analysis must be planned and used carefully in order to ensure reliability and validity.

The two general types of content analysis: conceptual and relational analysis . Although these two types follow largely similar processes, their outcomes differ. As such, each of these types can provide different results, interpretations, and conclusions.

In qualitative research coding means categorizing concepts, words, and themes within your content to create a basis for analyzing the results. While coding, you work through the content and record the data according to your coding scheme.

Content analysis is the process of analyzing content and its features with the aim of identifying patterns and the presence of words, themes, and concepts within the content. The goal of a content analysis is to present the trends, patterns, concepts, and ideas in content as objective, quantitative or qualitative data, depending on the specific use case.

Content analysis is a qualitative method of data analysis and can be used in many different fields. It is particularly popular in the social sciences.

It is possible to do qualitative analysis without coding, but content analysis as a method of qualitative analysis requires coding or categorizing data to then analyze it according to your coding scheme in the next step.

what's content analysis in research

what's content analysis in research

What Is Qualitative Content Analysis?

Qca explained simply (with examples).

By: Jenna Crosley (PhD). Reviewed by: Dr Eunice Rautenbach (DTech) | February 2021

If you’re in the process of preparing for your dissertation, thesis or research project, you’ve probably encountered the term “ qualitative content analysis ” – it’s quite a mouthful. If you’ve landed on this post, you’re probably a bit confused about it. Well, the good news is that you’ve come to the right place…

Overview: Qualitative Content Analysis

  • What (exactly) is qualitative content analysis
  • The two main types of content analysis
  • When to use content analysis
  • How to conduct content analysis (the process)
  • The advantages and disadvantages of content analysis

1. What is content analysis?

Content analysis is a  qualitative analysis method  that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants – this is called  unobtrusive  research.

In other words, with content analysis, you don’t necessarily need to interact with participants (although you can if necessary); you can simply analyse the data that they have already produced. With this type of analysis, you can analyse data such as text messages, books, Facebook posts, videos, and audio (just to mention a few).

The basics – explicit and implicit content

When working with content analysis, explicit and implicit content will play a role. Explicit data is transparent and easy to identify, while implicit data is that which requires some form of interpretation and is often of a subjective nature. Sounds a bit fluffy? Here’s an example:

Joe: Hi there, what can I help you with? 

Lauren: I recently adopted a puppy and I’m worried that I’m not feeding him the right food. Could you please advise me on what I should be feeding? 

Joe: Sure, just follow me and I’ll show you. Do you have any other pets?

Lauren: Only one, and it tweets a lot!

In this exchange, the explicit data indicates that Joe is helping Lauren to find the right puppy food. Lauren asks Joe whether she has any pets aside from her puppy. This data is explicit because it requires no interpretation.

On the other hand, implicit data , in this case, includes the fact that the speakers are in a pet store. This information is not clearly stated but can be inferred from the conversation, where Joe is helping Lauren to choose pet food. An additional piece of implicit data is that Lauren likely has some type of bird as a pet. This can be inferred from the way that Lauren states that her pet “tweets”.

As you can see, explicit and implicit data both play a role in human interaction  and are an important part of your analysis. However, it’s important to differentiate between these two types of data when you’re undertaking content analysis. Interpreting implicit data can be rather subjective as conclusions are based on the researcher’s interpretation. This can introduce an element of bias , which risks skewing your results.

Explicit and implicit data both play an important role in your content analysis, but it’s important to differentiate between them.

2. The two types of content analysis

Now that you understand the difference between implicit and explicit data, let’s move on to the two general types of content analysis : conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.

Conceptual analysis focuses on the number of times a concept occurs in a set of data and is generally focused on explicit data. For example, if you were to have the following conversation:

Marie: She told me that she has three cats.

Jean: What are her cats’ names?

Marie: I think the first one is Bella, the second one is Mia, and… I can’t remember the third cat’s name.

In this data, you can see that the word “cat” has been used three times. Through conceptual content analysis, you can deduce that cats are the central topic of the conversation. You can also perform a frequency analysis , where you assess the term’s frequency in the data. For example, in the exchange above, the word “cat” makes up 9% of the data. In other words, conceptual analysis brings a little bit of quantitative analysis into your qualitative analysis.

As you can see, the above data is without interpretation and focuses on explicit data . Relational content analysis, on the other hand, takes a more holistic view by focusing more on implicit data in terms of context, surrounding words and relationships.

There are three types of relational analysis:

  • Affect extraction
  • Proximity analysis
  • Cognitive mapping

Affect extraction is when you assess concepts according to emotional attributes. These emotions are typically mapped on scales, such as a Likert scale or a rating scale ranging from 1 to 5, where 1 is “very sad” and 5 is “very happy”.

If participants are talking about their achievements, they are likely to be given a score of 4 or 5, depending on how good they feel about it. If a participant is describing a traumatic event, they are likely to have a much lower score, either 1 or 2.

Proximity analysis identifies explicit terms (such as those found in a conceptual analysis) and the patterns in terms of how they co-occur in a text. In other words, proximity analysis investigates the relationship between terms and aims to group these to extract themes and develop meaning.

Proximity analysis is typically utilised when you’re looking for hard facts rather than emotional, cultural, or contextual factors. For example, if you were to analyse a political speech, you may want to focus only on what has been said, rather than implications or hidden meanings. To do this, you would make use of explicit data, discounting any underlying meanings and implications of the speech.

Lastly, there’s cognitive mapping, which can be used in addition to, or along with, proximity analysis. Cognitive mapping involves taking different texts and comparing them in a visual format – i.e. a cognitive map. Typically, you’d use cognitive mapping in studies that assess changes in terms, definitions, and meanings over time. It can also serve as a way to visualise affect extraction or proximity analysis and is often presented in a form such as a graphic map.

Example of a cognitive map

To recap on the essentials, content analysis is a qualitative analysis method that focuses on recorded human artefacts . It involves both conceptual analysis (which is more numbers-based) and relational analysis (which focuses on the relationships between concepts and how they’re connected).

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what's content analysis in research

3. When should you use content analysis?

Content analysis is a useful tool that provides insight into trends of communication . For example, you could use a discussion forum as the basis of your analysis and look at the types of things the members talk about as well as how they use language to express themselves. Content analysis is flexible in that it can be applied to the individual, group, and institutional level.

Content analysis is typically used in studies where the aim is to better understand factors such as behaviours, attitudes, values, emotions, and opinions . For example, you could use content analysis to investigate an issue in society, such as miscommunication between cultures. In this example, you could compare patterns of communication in participants from different cultures, which will allow you to create strategies for avoiding misunderstandings in intercultural interactions.

Another example could include conducting content analysis on a publication such as a book. Here you could gather data on the themes, topics, language use and opinions reflected in the text to draw conclusions regarding the political (such as conservative or liberal) leanings of the publication.

Content analysis is typically used in projects where the research aims involve getting a better understanding of factors such as behaviours, attitudes, values, emotions, and opinions.

4. How to conduct a qualitative content analysis

Conceptual and relational content analysis differ in terms of their exact process ; however, there are some similarities. Let’s have a look at these first – i.e., the generic process:

  • Recap on your research questions
  • Undertake bracketing to identify biases
  • Operationalise your variables and develop a coding scheme
  • Code the data and undertake your analysis

Step 1 – Recap on your research questions

It’s always useful to begin a project with research questions , or at least with an idea of what you are looking for. In fact, if you’ve spent time reading this blog, you’ll know that it’s useful to recap on your research questions, aims and objectives when undertaking pretty much any research activity. In the context of content analysis, it’s difficult to know what needs to be coded and what doesn’t, without a clear view of the research questions.

For example, if you were to code a conversation focused on basic issues of social justice, you may be met with a wide range of topics that may be irrelevant to your research. However, if you approach this data set with the specific intent of investigating opinions on gender issues, you will be able to focus on this topic alone, which would allow you to code only what you need to investigate.

With content analysis, it’s difficult to know what needs to be coded  without a clear view of the research questions.

Step 2 – Reflect on your personal perspectives and biases

It’s vital that you reflect on your own pre-conception of the topic at hand and identify the biases that you might drag into your content analysis – this is called “ bracketing “. By identifying this upfront, you’ll be more aware of them and less likely to have them subconsciously influence your analysis.

For example, if you were to investigate how a community converses about unequal access to healthcare, it is important to assess your views to ensure that you don’t project these onto your understanding of the opinions put forth by the community. If you have access to medical aid, for instance, you should not allow this to interfere with your examination of unequal access.

You must reflect on the preconceptions and biases that you might drag into your content analysis - this is called "bracketing".

Step 3 – Operationalise your variables and develop a coding scheme

Next, you need to operationalise your variables . But what does that mean? Simply put, it means that you have to define each variable or construct . Give every item a clear definition – what does it mean (include) and what does it not mean (exclude). For example, if you were to investigate children’s views on healthy foods, you would first need to define what age group/range you’re looking at, and then also define what you mean by “healthy foods”.

In combination with the above, it is important to create a coding scheme , which will consist of information about your variables (how you defined each variable), as well as a process for analysing the data. For this, you would refer back to how you operationalised/defined your variables so that you know how to code your data.

For example, when coding, when should you code a food as “healthy”? What makes a food choice healthy? Is it the absence of sugar or saturated fat? Is it the presence of fibre and protein? It’s very important to have clearly defined variables to achieve consistent coding – without this, your analysis will get very muddy, very quickly.

When operationalising your variables, you must give every item a clear definition. In other words, what does it mean (include) and what does it not mean (exclude).

Step 4 – Code and analyse the data

The next step is to code the data. At this stage, there are some differences between conceptual and relational analysis.

As described earlier in this post, conceptual analysis looks at the existence and frequency of concepts, whereas a relational analysis looks at the relationships between concepts. For both types of analyses, it is important to pre-select a concept that you wish to assess in your data. Using the example of studying children’s views on healthy food, you could pre-select the concept of “healthy food” and assess the number of times the concept pops up in your data.

Here is where conceptual and relational analysis start to differ.

At this stage of conceptual analysis , it is necessary to decide on the level of analysis you’ll perform on your data, and whether this will exist on the word, phrase, sentence, or thematic level. For example, will you code the phrase “healthy food” on its own? Will you code each term relating to healthy food (e.g., broccoli, peaches, bananas, etc.) with the code “healthy food” or will these be coded individually? It is very important to establish this from the get-go to avoid inconsistencies that could result in you having to code your data all over again.

On the other hand, relational analysis looks at the type of analysis. So, will you use affect extraction? Proximity analysis? Cognitive mapping? A mix? It’s vital to determine the type of analysis before you begin to code your data so that you can maintain the reliability and validity of your research .

what's content analysis in research

How to conduct conceptual analysis

First, let’s have a look at the process for conceptual analysis.

Once you’ve decided on your level of analysis, you need to establish how you will code your concepts, and how many of these you want to code. Here you can choose whether you want to code in a deductive or inductive manner. Just to recap, deductive coding is when you begin the coding process with a set of pre-determined codes, whereas inductive coding entails the codes emerging as you progress with the coding process. Here it is also important to decide what should be included and excluded from your analysis, and also what levels of implication you wish to include in your codes.

For example, if you have the concept of “tall”, can you include “up in the clouds”, derived from the sentence, “the giraffe’s head is up in the clouds” in the code, or should it be a separate code? In addition to this, you need to know what levels of words may be included in your codes or not. For example, if you say, “the panda is cute” and “look at the panda’s cuteness”, can “cute” and “cuteness” be included under the same code?

Once you’ve considered the above, it’s time to code the text . We’ve already published a detailed post about coding , so we won’t go into that process here. Once you’re done coding, you can move on to analysing your results. This is where you will aim to find generalisations in your data, and thus draw your conclusions .

How to conduct relational analysis

Now let’s return to relational analysis.

As mentioned, you want to look at the relationships between concepts . To do this, you’ll need to create categories by reducing your data (in other words, grouping similar concepts together) and then also code for words and/or patterns. These are both done with the aim of discovering whether these words exist, and if they do, what they mean.

Your next step is to assess your data and to code the relationships between your terms and meanings, so that you can move on to your final step, which is to sum up and analyse the data.

To recap, it’s important to start your analysis process by reviewing your research questions and identifying your biases . From there, you need to operationalise your variables, code your data and then analyse it.

Time to analyse

5. What are the pros & cons of content analysis?

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

For example, with conceptual analysis, you can count the number of times that a term or a code appears in a dataset, which can be assessed from a quantitative standpoint. In addition to this, you can then use a qualitative approach to investigate the underlying meanings of these and relationships between them.

Content analysis is also unobtrusive and therefore poses fewer ethical issues than some other analysis methods. As the content you’ll analyse oftentimes already exists, you’ll analyse what has been produced previously, and so you won’t have to collect data directly from participants. When coded correctly, data is analysed in a very systematic and transparent manner, which means that issues of replicability (how possible it is to recreate research under the same conditions) are reduced greatly.

On the downside , qualitative research (in general, not just content analysis) is often critiqued for being too subjective and for not being scientifically rigorous enough. This is where reliability (how replicable a study is by other researchers) and validity (how suitable the research design is for the topic being investigated) come into play – if you take these into account, you’ll be on your way to achieving sound research results.

One of the main advantages of content analysis is that it allows you to use a mix of quantitative and qualitative research methods, which results in a more scientifically rigorous analysis.

Recap: Qualitative content analysis

In this post, we’ve covered a lot of ground – click on any of the sections to recap:

If you have any questions about qualitative content analysis, feel free to leave a comment below. If you’d like 1-on-1 help with your qualitative content analysis, be sure to book an initial consultation with one of our friendly Research Coaches.

what's content analysis in research

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18 Comments

Abhishek

If I am having three pre-decided attributes for my research based on which a set of semi-structured questions where asked then should I conduct a conceptual content analysis or relational content analysis. please note that all three attributes are different like Agility, Resilience and AI.

Ofori Henry Affum

Thank you very much. I really enjoyed every word.

Janak Raj Bhatta

please send me one/ two sample of content analysis

pravin

send me to any sample of qualitative content analysis as soon as possible

abdellatif djedei

Many thanks for the brilliant explanation. Do you have a sample practical study of a foreign policy using content analysis?

DR. TAPAS GHOSHAL

1) It will be very much useful if a small but complete content analysis can be sent, from research question to coding and analysis. 2) Is there any software by which qualitative content analysis can be done?

Carkanirta

Common software for qualitative analysis is nVivo, and quantitative analysis is IBM SPSS

carmely

Thank you. Can I have at least 2 copies of a sample analysis study as my reference?

Yang

Could you please send me some sample of textbook content analysis?

Abdoulie Nyassi

Can I send you my research topic, aims, objectives and questions to give me feedback on them?

Bobby Benjamin Simeon

please could you send me samples of content analysis?

Obi Clara Chisom

Yes please send

Gaid Ahmed

really we enjoyed your knowledge thanks allot. from Ethiopia

Ary

can you please share some samples of content analysis(relational)? I am a bit confused about processing the analysis part

eeeema

Is it possible for you to list the journal articles and books or other sources you used to write this article? Thank you.

Upeksha Hettithanthri

can you please send some samples of content analysis ?

can you kindly send some good examples done by using content analysis ?

samuel batimedi

This was very useful. can you please send me sample for qualitative content analysis. thank you

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  • Am J Pharm Educ
  • v.84(1); 2020 Jan

Demystifying Content Analysis

A. j. kleinheksel.

a The Medical College of Georgia at Augusta University, Augusta, Georgia

Nicole Rockich-Winston

Huda tawfik.

b Central Michigan University, College of Medicine, Mt. Pleasant, Michigan

Tasha R. Wyatt

Objective. In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio files.

Findings. Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis.

Summary. This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.

INTRODUCTION

The Academy’s growing interest in qualitative research indicates an important shift in the field’s scientific paradigm. Whereas health science researchers have historically looked to quantitative methods to answer their questions, this shift signals that a purely positivist, objective approach is no longer sufficient to answer pharmacy education’s research questions. Educators who want to study their teaching and students’ learning will find content analysis an easily accessible, robust method of qualitative data analysis that can yield rigorous results for both publication and the improvement of their educational practice. Content analysis is a method designed to identify and interpret meaning in recorded forms of communication by isolating small pieces of the data that represent salient concepts and then applying or creating a framework to organize the pieces in a way that can be used to describe or explain a phenomenon. 1 Content analysis is particularly useful in situations where there is a large amount of unanalyzed textual data, such as those many pharmacy educators have already collected as part of their teaching practice. Because of its accessibility, content analysis is also an appropriate qualitative method for pharmacy educators with limited experience in educational research. This article will introduce and illustrate the process of content analysis as a way to analyze existing data, but also as an approach that may lead pharmacy educators to ask new types of research questions.

Content analysis is a well-established data analysis method that has evolved in its treatment of textual data. Content analysis was originally introduced as a strictly quantitative method, recording counts to measure the observed frequency of pre-identified targets in consumer research. 1 However, as the naturalistic qualitative paradigm became more prevalent in social sciences research and researchers became increasingly interested in the way people behave in natural settings, the process of content analysis was adapted into a more interesting and meaningful approach. Content analysis has the potential to be a useful method in pharmacy education because it can help educational researchers develop a deeper understanding of a particular phenomenon by providing structure in a large amount of textual data through a systematic process of interpretation. It also offers potential value because it can help identify problematic areas in student understanding and guide the process of targeted teaching. Several research studies in pharmacy education have used the method of content analysis. 2-7 Two studies in particular offer noteworthy examples: Wallman and colleagues employed manifest content analysis to analyze semi-structured interviews in order to explore what students learn during experiential rotations, 7 while Moser and colleagues adopted latent content analysis to evaluate open-ended survey responses on student perceptions of learning communities. 6 To elaborate on these approaches further, we will describe the two types of qualitative content analysis, manifest and latent, and demonstrate the corresponding analytical processes using examples that illustrate their benefit.

Qualitative Content Analysis

Content analysis rests on the assumption that texts are a rich data source with great potential to reveal valuable information about particular phenomena. 8 It is the process of considering both the participant and context when sorting text into groups of related categories to identify similarities and differences, patterns, and associations, both on the surface and implied within. 9-11 The method is considered high-yield in educational research because it is versatile and can be applied in both qualitative and quantitative studies. 12 While it is important to note that content analysis has application in visual and auditory artifacts (eg, an image or song), for our purposes we will largely focus on the most common application, which is the analysis of textual or transcribed content (eg, open-ended survey responses, print media, interviews, recorded observations, etc). The terminology of content analysis can vary throughout quantitative and qualitative literature, which may lead to some confusion among both novice and experienced researchers. However, there are also several agreed-upon terms and phrases that span the literature, as found in Table 1 .

Terms and Definitions Used in Qualitative Content Analysis

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There is more often disagreement on terminology in the methodological approaches to content analysis, though the most common differentiation is between the two types of content: manifest and latent. In much of the literature, manifest content analysis is defined as describing what is occurring on the surface, what is and literally present, and as “staying close to the text.” 8,13 Manifest content analysis is concerned with data that are easily observable both to researchers and the coders who assist in their analyses, without the need to discern intent or identify deeper meaning. It is content that can be recognized and counted with little training. Early applications of manifest analysis focused on identifying easily observable targets within text (eg, the number of instances a certain word appears in newspaper articles), film (eg, the occupation of a character), or interpersonal interactions (eg, tracking the number of times a participant blinks during an interview). 14 This application, in which frequency counts are used to understand a phenomenon, reflects a surface-level analysis and assumes there is objective truth in the data that can be revealed with very little interpretation. The number of times a target (ie, code) appears within the text is used as a way to understand its prevalence. Quantitative content analysis is always describing a positivist manifest content analysis, in that the nature of truth is believed to be objective, observable, and measurable. Qualitative research, which favors the researcher’s interpretation of an individual’s experience, may also be used to analyze manifest content. However, the intent of the application is to describe a dynamic reality that cannot be separated from the lived experiences of the researcher. Although qualitative content analysis can be conducted whether knowledge is thought to be innate, acquired, or socially constructed, the purpose of qualitative manifest content analysis is to transcend simple word counts and delve into a deeper examination of the language in order to organize large amounts of text into categories that reflect a shared meaning. 15,16 The practical distinction between quantitative and qualitative manifest content analysis is the intention behind the analysis. The quantitative method seeks to generate a numerical value to either cite prevalence or use in statistical analyses, while the qualitative method seeks to identify a construct or concept within the text using specific words or phrases for substantiation, or to provide a more organized structure to the text being described.

Latent content analysis is most often defined as interpreting what is hidden deep within the text. In this method, the role of the researcher is to discover the implied meaning in participants’ experiences. 8,13 For example, in a transcribed exchange in an office setting, a participant might say to a coworker, “Yeah, here we are…another Monday. So exciting!” The researcher would apply context in order to discover the emotion being conveyed (ie, the implied meaning). In this example, the comment could be interpreted as genuine, it could be interpreted as a sarcastic comment made in an attempt at humor in order to develop or sustain social bonds with the coworker, or the context might imply that the sarcasm was meant to convey displeasure and end the interaction.

Latent content analysis acknowledges that the researcher is intimately involved in the analytical process and that the their role is to actively use mental schema, theories, and lenses to interpret and understand the data. 10 Whereas manifest analyses are typically conducted in a way that the researcher is thought to maintain distance and separation from the objects of study, latent analyses underscore the importance of the researcher co-creating meaning with the text. 17 Adding nuance to this type of content, Potter and Levine‐Donnerstein argue that within latent content analysis, there are two distinct types: latent pattern and latent projective . 14 Latent pattern content analysis seeks to establish a pattern of characteristics in the text itself, while latent projective content analysis leverages the researcher’s own interpretations of the meaning of the text. While both approaches rely on codes that emerge from the content using the coder’s own perspectives and mental schema, the distinction between these two types of analyses are in their foci. 14 Though we do not agree, some researchers believe that all qualitative content analysis is latent content analysis. 11 These disagreements typically occur where there are differences in intent and where there are areas of overlap in the results. For example, both qualitative manifest and latent pattern content analyses may identify patterns as a result of their application. Though in their research design, the researcher would have approached the content with different methodological approaches, with a manifest approach seeking only to describe what is observed, and the latent pattern approach seeking to discover an unseen pattern. At this point, these distinctions may seem too philosophical to serve a practical purpose, so we will attempt to clarify these concepts by presenting three types of analyses for illustrative purposes, beginning with a description of how codes are created and used.

Creating and Using Codes

Codes are the currency of content analysis. Researchers use codes to organize and understand their data. Through the coding process, pharmacy educators can systematically and rigorously categorize and interpret vast amounts of text for use in their educational practice or in publication. Codes themselves are short, descriptive labels that symbolically assign a summative or salient attribute to more than one unit of meaning identified in the text. 18 To create codes, a researcher must first become immersed in the data, which typically occurs when a researcher transcribes recorded data or conducts several readings of the text. This process allows the researcher to become familiar with the scope of the data, which spurs nascent ideas about potential concepts or constructs that may exist within it. If studying a phenomenon that has already been described through an existing framework, codes can be created a priori using theoretical frameworks or concepts identified in the literature. If there is no existing framework to apply, codes can emerge during the analytical process. However, emergent codes can also be created as addenda to a priori codes that were identified before the analysis begins if the a priori codes do not sufficiently capture the researcher’s area of interest.

The process of detecting emergent codes begins with identification of units of meaning. While there is no one way to decide what qualifies as a meaning unit, researchers typically define units of meaning differently depending on what kind of analysis is being conducted. As a general rule, when dialogue is being analyzed, such as interviews or focus groups, meaning units are identified as conversational turns, though a code can be as short as one or two words. In written text, such as student reflections or course evaluation data, the researcher must decide if the text should be divided into phrases or sentences, or remain as paragraphs. This decision is usually made based on how many different units of meaning are expressed in a block of text. For example, in a paragraph, if there are several thoughts or concepts being expressed, it is best to break up the paragraph into sentences. If one sentence contains multiple ideas of interest, making it difficult to separate one important thought or behavior from another, then the sentence can be divided into smaller units, such as phrases or sentence fragments. These phrases or sentence fragments are then coded as separate meaning units. Conversely, longer or more complex units of meaning should be condensed into shorter representations that still retain the original meaning in order to reduce the cognitive burden of the analytical process. This could entail removing verbal ticks (eg, “well, uhm…”) from transcribed data or simplifying a compound sentence. Condensation does not ascribe interpretation or implied meaning to a unit, but only shortens a meaning unit as much as possible while preserving the original meaning identified. 18 After condensation, a researcher can proceed to the creation of codes.

Many researchers begin their analyses with several general codes in mind that help guide their focus as defined by their research question, even in instances where the researcher has no a priori model or theory. For example, if a group of instructors are interested in examining recorded videos of their lectures to identify moments of student engagement, they may begin with using generally agreed upon concepts of engagement as codes, such as students “raising their hands,” “taking notes,” and “speaking in class.” However, as the instructors continue to watch their videos, they may notice other behaviors which were not initially anticipated. Perhaps students were seen creating flow charts based on information presented in class. Alternatively, perhaps instructors wanted to include moments when students posed questions to their peers without being prompted. In this case, the instructors would allow the codes of “creating graphic organizers” and “questioning peers” to emerge as additional ways to identify the behavior of student engagement.

Once a researcher has identified condensed units of meaning and labeled them with codes, the codes are then sorted into categories which can help provide more structure to the data. In the above example of recorded lectures, perhaps the category of “verbal behaviors” could be used to group the codes of “speaking in class” and “questioning peers.” For complex analyses, subcategories can also be used to better organize a large amount of codes, but solely at the discretion of the researcher. Two or more categories of codes are then used to identify or support a broader underlying meaning which develops into themes. Themes are most often employed in latent analyses; however, they are appropriate in manifest analyses as well. Themes describe behaviors, experiences, or emotions that occur throughout several categories. 18 Figure 1 illustrates this process. Using the same videotaped lecture example, the instructors might identify two themes of student engagement, “active engagement” and “passive engagement,” where active engagement is supported by the category of “verbal behavior” and also a category that includes the code of “raising their hands” (perhaps something along the lines of “pursuing engagement”), and the theme of “passive engagement” is supported by a category used to organize the behaviors of “taking notes” and “creating graphic organizers.”

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The Process of Qualitative Content Analysis

To more fully demonstrate the process of content analysis and the generation and use of codes, categories, and themes, we present and describe examples of both manifest and latent content analysis. Given that there are multiple ways to create and use codes, our examples illustrate both processes of creating and using a predetermined set of codes. Regardless of the kind of content analysis instructors want to conduct, the initial steps are the same. The instructor must analyze the data using codes as a sense-making process.

Manifest Content Analysis

The first form of analysis, manifest content analysis, examines text for elements that exist on the surface of the text, the meaning of which is taken at face value. Schools and colleges of pharmacy may benefit from conducting manifest content analyses at a programmatic level, including analysis of student evaluations to determine the value of certain courses, or analysis of recruitment materials for addressing issues of cultural humility in a uniform manner. Such uses for manifest content analysis may help administrators make more data-based decisions about students and courses. However, for our example of manifest content analysis, we illustrate the use of content analysis in informing instruction for a single pharmacy educator ( Figure 2 ).

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A Student’s Completed Beta-blocker Case with Codes in Underlined Bold Text

In the example, a pharmacology instructor is trying to assess students’ understanding of three concepts related to the beta-blocker class of drugs: indication of the drug, relevance of family history, and contraindications and precautions. To do so, the instructor asks the students to write a patient case in which beta-blockers are indicated. The instructor gives the students the following prompt: “Reverse-engineer a case in which beta-blockers would be prescribed to the patient. Include a history of the present illness, the patients’ medical, family, and social history, medications, allergies, and relevant lab tests.” Figure 2 is a hypothetical student’s completed assignment, in which they demonstrate their understanding of when and why a beta-blocker would be prescribed.

The student-generated cases are then treated as data and analyzed for the presence of the three previously identified indicators of understanding in order to help the instructor make decisions about where and how to focus future teaching efforts related to this drug class. Codes are created a priori out of the instructor’s interest in analyzing students’ understanding of the concepts related to beta-blocker prescriptions. A codebook ( Table 2 ) is created with the following columns: name of code, code description, and examples of the code. This codebook helps an individual researcher to approach their analysis systematically, but it can also facilitate coding by multiple coders who would apply the same rules outlined in the codebook to the coding process.

Example Code Book Created for Manifest Content Analysis

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Using multiple coders introduces complexity to the analysis process, but it is oftentimes the only practical way to analyze large amounts of data. To ensure that all coders are working in tandem, they must establish inter-rater reliability as part of their training process. This process requires that a single form of text be selected, such as one student evaluation. After reviewing the codebook and receiving instruction, everyone on the team individually codes the same piece of data. While calculating percentage agreement has sometimes been used to establish inter-rater reliability, most publication editors require more rigorous statistical analysis (eg, Krippendorf’s alpha, or Cohen’s kappa). 19 Detailed descriptions of these statistics fall outside the scope of this introduction, but it is important to note that the choice depends on the number of coders, the sample size, and the type of data to be analyzed.

Latent Content Analysis

Latent content analysis is another option for pharmacy educators, especially when there are theoretical frameworks or lenses the educator proposes to apply. Such frameworks describe and provide structure to complex concepts and may often be derived from relevant theories. Latent content analysis requires that the researcher is intimately involved in interpreting and finding meaning in the text because meaning is not readily apparent on the surface. 10 To illustrate a latent content analysis using a combination of a priori and emergent codes, we will use the example of a transcribed video excerpt from a student pharmacist interaction with a standardized patient. In this example, the goal is for first-year students to practice talking to a customer about an over-the-counter medication. The case is designed to simulate a customer at a pharmacy counter, who is seeking advice on a medication. The learning objectives for the pharmacist in-training are to assess the customer’s symptoms, determine if the customer can self-treat or if they need to seek out their primary care physician, and then prescribe a medication to alleviate the patient’s symptoms.

To begin, pharmacy educators conducting educational research should first identify what they are looking for in the video transcript. In this case, because the primary outcome for this exercise is aimed at assessing the “soft skills” of student pharmacists, codes are created using the counseling rubric created by Horton and colleagues. 20 Four a priori codes are developed using the literature: empathy, patient-friendly terms, politeness, and positive attitude. However, because the original four codes are inadequate to capture all areas representing the skills the instructor is looking for during the process of analysis, four additional codes are also created: active listening, confidence, follow-up, and patient at ease. Figure 3 presents the video transcript with each of the codes assigned to the meaning units in bolded parentheses.

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A Transcript of a Student’s (JR) Experience with a Standardized Patient (SP) in Which the Codes are Bolded in Parentheses

Following the initial coding using these eight codes, the codes are consolidated to create categories, which are depicted in the taxonomy in Figure 4 . Categories are relationships between codes that represent a higher level of abstraction in the data. 18 To reach conclusions and interpret the fundamental underlying meaning in the data, categories are then organized into themes ( Figure 1 ). Once the data are analyzed, the instructor can assign value to the student’s performance. In this case, the coding process determines that the exercise demonstrated both positive and negative elements of communication and professionalism. Under the category of professionalism, the student generally demonstrated politeness and a positive attitude toward the standardized patient, indicating to the reviewer that the theme of perceived professionalism was apparent during the encounter. However, there were several instances in which confidence and appropriate follow-up were absent. Thus, from a reviewer perspective, the student's performance could be perceived as indicating an opportunity to grow and improve as a future professional. Typically, there are multiple codes in a category and multiple categories in a theme. However, as seen in the example taxonomy, this is not always the case.

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Example of a Latent Content Analysis Taxonomy

If the educator is interested in conducting a latent projective analysis, after identifying the construct of “soft skills,” the researcher allows for each coder to apply their own mental schema as they look for positive and negative indicators of the non-technical skills they believe a student should develop. Mental schema are the cognitive structures that provide organization to knowledge, which in this case allows coders to categorize the data in ways that fit their existing understanding of the construct. The coders will use their own judgement to identify the codes they feel are relevant. The researcher could also choose to apply a theoretical lens to more effectively conceptualize the construct of “soft skills,” such as Rogers' humanism theory, and more specifically, concepts underlying his client-centered therapy. 21 The role of theory in both latent pattern and latent projective analyses is at the discretion of the researcher, and often is determined by what already exists in the literature related to the research question. Though, typically, in latent pattern analyses theory is used for deductive coding, and in latent projective analyses underdeveloped theory is used to first deduce codes and then for induction of the results to strengthen the theory applied. For our example, Rogers describes three salient qualities to develop and maintain a positive client-professional relationship: unconditional positive regard, genuineness, and empathetic understanding. 21 For the third element, specifically, the educator could look for units of meaning that imply empathy and active listening. For our video transcript analysis, this is evident when the student pharmacist demonstrated empathy by responding, "Yeah, I understand," when discussing aggravating factors for the patient's condition. The outcome for both latent pattern and latent projective content analysis is to discover the underlying meaning in a text, such as social rules or mental models. In this example, both pattern and projective approaches can discover interpreted aspects of a student’s abilities and mental models for constructs such as professionalism and empathy. The difference in the approaches is where the precedence lies: in the belief that a pattern is recognizable in the content, or in the mental schema and lived experiences of the coder(s). To better illustrate the differences in the processes of latent pattern and projective content analyses, Figure 5 presents a general outline of each method beginning with the creation of codes and concluding with the generation of themes.

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Flow Chart of the Stages of Latent Pattern and Latent Projective Content Analysis

How to Choose a Methodological Approach to Content Analysis

To determine which approach a researcher should take in their content analysis, two decisions need to be made. First, researchers must determine their goal for the analysis. Second, the researcher must decide where they believe meaning is located. 14 If meaning is located in the discrete elements of the content that are easily identified on the surface of the text, then manifest content analysis is appropriate. If meaning is located deep within the content and the researcher plans to discover context cues and make judgements about implied meaning, then latent content analysis should be applied. When designing the latent content analysis, a researcher then must also identify their focus. If the analysis is intended to identify a recognizable truth within the content by uncovering connections and characteristics that all coders should be able to discover, then latent pattern content analysis is appropriate. If, on the other hand, the researcher will rely heavily on the judgment of the coders and believes that interpretation of the content must leverage the mental schema of the coders to locate deeper meaning, then latent projective content analysis is the best choice.

To demonstrate how a researcher might choose a methodological approach, we have presented a third example of data in Figure 6 . In our two previous examples of content analysis, we used student data. However, faculty data can also be analyzed as part of educational research or for faculty members to improve their own teaching practices. Recall in the video data analyzed using latent content analysis, the student was tasked to identify a suitable over-the-counter medication for a patient complaining of heartburn symptoms. We have extended this example by including an interview with the pharmacy educator supervising the student who was videotaped. The goal of the interview is to evaluate the educator’s ability to assess the student’s performance with the standardized patient. Figure 6 is an excerpt of the interview between the course instructor and an instructional coach. In this conversation, the instructional coach is eliciting evidence to support the faculty member’s views, judgements, and rationale for the educator’s evaluation of the student’s performance.

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A Transcript of an Interview in Which the Interviewer (IN) Questions a Faculty Member (FM) Regarding Their Student’s Standardized Patient Experience

Manifest content analysis would be a valid choice for this data if the researcher was looking to identify evidence of the construct of “instructor priorities” and defined discrete codes that described aspects of performance such as “communication,” “referrals,” or “accurate information.” These codes could be easily identified on the surface of the transcribed interview by identifying keywords related to each code, such as “communicate,” “talk,” and “laugh,” for the code of “communication.” This would allow coders to identify evidence of the concept of “instructor priorities” by sorting through a potentially large amount of text with predetermined targets in mind.

To conduct a latent pattern analysis of this interview, researchers would first immerse themselves in the data to identify a theoretical framework or concepts that represent the area of interest so that coders could discover an emerging truth underneath the surface of the data. After immersion in the data, a researcher might believe it would be interesting to more closely examine the strategies the coach uses to establish rapport with the instructor as a way to better understand models of professional development. These strategies could not be easily identified in the transcripts if read literally, but by looking for connections within the text, codes related to instructional coaching tactics emerge. A latent pattern analysis would require that the researcher code the data in a way that looks for patterns, such as a code of “facilitating reflection,” that could be identified in open-ended questions and other units of meaning where the coder saw evidence of probing techniques, or a code of “establishing rapport” for which a coder could identify nonverbal cues such as “[IN leans forward in chair].”

Conducting latent projective content analysis might be useful if the researcher was interested in using a broader theoretical lens, such as Mezirow’s theory of transformative learning. 22 In this example, the faculty member is understood to have attempted to change a learner’s frame of reference by facilitating cognitive dissonance or a disorienting experience through a standardized patient simulation. To conduct a latent projective analysis, the researcher could analyze the faculty member’s interview using concepts found in this theory. This kind of analysis will help the researcher assess the level of change that the faculty member was able to perceive, or expected to witness, in their attempt to help their pharmacy students improve their interactions with patients. The units of meaning and subsequent codes would rely on the coders to apply their own knowledge of transformative learning because of the absence in the theory of concrete, context-specific behaviors to identify. For this analysis, the researcher would rely on their interpretations of what challenging educational situations look like, what constitutes cognitive dissonance, or what the faculty member is really expecting from his students’ performance. The subsequent analysis could provide evidence to support the use of such standardized patient encounters within the curriculum as a transformative learning experience and would also allow the educator to self-reflect on his ability to assess simulated activities.

OTHER ASPECTS TO CONSIDER

Navigating terminology.

Among the methodological approaches, there are other terms for content analysis that researchers may come across. Hsieh and Shannon 10 proposed three qualitative approaches to content analysis: conventional, directed, and summative. These categories were intended to explain the role of theory in the analysis process. In conventional content analysis, the researcher does not use preconceived categories because existing theory or literature are limited. In directed content analysis, the researcher attempts to further describe a phenomenon already addressed by theory, applying a deductive approach and using identified concepts or codes from exiting research to validate the theory. In summative content analysis, a descriptive approach is taken, identifying and quantifying words or content in order to describe their context. These three categories roughly map to the terms of latent projective, latent pattern, and manifest content analyses respectively, though not precisely enough to suggest that they are synonyms.

Graneheim and colleagues 9 reference the inductive, deductive, and abductive methods of interpretation of content analysis, which are data-driven, concept-driven, and fluid between both data and concepts, respectively. Where manifest content produces phenomenological descriptions most often (but not always) through deductive interpretation, and latent content analysis produces interpretations most often (but not always) through inductive or abductive interpretations. Erlingsson and Brysiewicz 23 refer to content analysis as a continuum, progressing as the researcher develops codes, then categories, and then themes. We present these alternative conceptualizations of content analysis to illustrate that the literature on content analysis, while incredibly useful, presents a multitude of interpretations of the method itself. However, these complexities should not dissuade readers from using content analysis. Identifying what you want to know (ie, your research question) will effectively direct you toward your methodological approach. That said, we have found the most helpful aid in learning content analysis is the application of the methods we have presented.

Ensuring Quality

The standards used to evaluate quantitative research are seldom used in qualitative research. The terms “reliability” and “validity” are typically not used because they reflect the positivist quantitative paradigm. In qualitative research, the preferred term is “trustworthiness,” which is comprised of the concepts of credibility, transferability, dependability, and confirmability, and researchers can take steps in their work to demonstrate that they are trustworthy. 24 Though establishing trustworthiness is outside the scope of this article, novice researchers should be familiar with the necessary steps before publishing their work. This suggestion includes exploration of the concept of saturation, the idea that researchers must demonstrate they have collected and analyzed enough data to warrant their conclusions, which has been a focus of recent debate in qualitative research. 25

There are several threats to the trustworthiness of content analysis in particular. 14 We will use the terms “reliability and validity” to describe these threats, as they are conceptualized this way in the formative literature, and it may be easier for researchers with a quantitative research background to recognize them. Though some of these threats may be particular to the type of data being analyzed, in general, there are risks specific to the different methods of content analysis. In manifest content analysis, reliability is necessary but not sufficient to establish validity. 14 Because there is little judgment required of the coders, lack of high inter-rater agreement among coders will render the data invalid. 14 Additionally, coder fatigue is a common threat to manifest content analysis because the coding is clerical and repetitive in nature.

For latent pattern content analysis, validity and reliability are inversely related. 14 Greater reliability is achieved through more detailed coding rules to improve consistency, but these rules may diminish the accessibility of the coding to consumers of the research. This is defined as low ecological validity. Higher ecological validity is achieved through greater reliance on coder judgment to increase the resonance of the results with the audience, yet this often decreases the inter-rater reliability. In latent projective content analysis, reliability and validity are equivalent. 14 Consistent interpretations among coders both establishes and validates the constructed norm; construction of an accurate norm is evidence of consistency. However, because of this equivalence, issues with low validity or low reliability cannot be isolated. A lack of consistency may result from coding rules, lack of a shared schema, or issues with a defined variable. Reasons for low validity cannot be isolated, but will always result in low consistency.

Any good analysis starts with a codebook and coder training. It is important for all coders to share the mental model of the skill, construct, or phenomenon being coded in the data. However, when conducting latent pattern or projective content analysis in particular, micro-level rules and definitions of codes increase the threat of ecological validity, so it is important to leave enough room in the codebook and during the training to allow for a shared mental schema to emerge in the larger group rather than being strictly directed by the lead researcher. Stability is another threat, which occurs when coders make different judgments as time passes. To reduce this risk, allowing for recoding at a later date can increase the consistency and stability of the codes. Reproducibility is not typically a goal of qualitative research, 15 but for content analysis, codes that are defined both prior to and during analysis should retain their meaning. Researchers can increase the reproducibility of their codebook by creating a detailed audit trail, including descriptions of the methods used to create and define the codes, materials used for the training of the coders, and steps taken to ensure inter-rater reliability.

In all forms of qualitative analysis, coder fatigue is a common threat to trustworthiness, even when the instructor is coding individually. Over time, the cases may start to look the same, making it difficult to refocus and look at each case with fresh eyes. To guard against this, coders should maintain a reflective journal and write analytical memos to help stay focused. Memos might include insights that the researcher has, such as patterns of misunderstanding, areas to focus on when considering re-teaching specific concepts, or specific conversations to have with students. Fatigue can also be mitigated by occasionally talking to participants (eg, meeting with students and listening for their rationale on why they included specific pieces of information in an assignment). These are just examples of potential exercises that can help coders mitigate cognitive fatigue. Most researchers develop their own ways to prevent the fatigue that can seep in after long hours of looking at data. But above all, a sufficient amount of time should be allowed for analysis, so that coders do not feel rushed, and regular breaks should be scheduled and enforced.

Qualitative content analysis is both accessible and high-yield for pharmacy educators and researchers. Though some of the methods may seem abstract or fluid, the nature of qualitative content analysis encompasses these concerns by providing a systematic approach to discover meaning in textual data, both on the surface and implied beneath it. As with most research methods, the surest path towards proficiency is through application and intentional, repeated practice. We encourage pharmacy educators to ask questions suited for qualitative research and to consider the use of content analysis as a qualitative research method for discovering meaning in their data.

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Content analysis

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Content analysis is a research method in the social sciences used to reduce large amounts of unstructured textual content into manageable data relevant to the (evaluation) research questions.

Texts refer to any occurrence of communications - including websites, social media, books, essays, interviews, focus groups, diaries, discussions, articles, speeches, conversations, advertising, theatre, informal conversation, and so on. To conduct a content analysis on any such text, the text is broken down into manageable categories on a variety of levels (ie, keywords, word sense, phrase, sentence, or theme) and coded. The coded content can then be quantitatively analyzed for trends, patterns, relationships, similarities, differences etc., from which researchers can get insights and make inferences about the messages within the texts, the writer(s) and the context.

Content analysis uses thematic coding in order to perform a quantitative analysis of particular occurrences of themes in an unstructured text. The coding schedule consists of a table in which each row is a unit for which data is being collected. Each column is a dimension or theme to be analyzed, according to the evaluation questions. Elements of the content are described and organized using these categories. This process is called 'coding' and can enable more efficient sorting and retrieval of data, particularly with the aid of appropriate software. Interpretation of the data may be based on:

  • frequency of occurrences (e.g. in different samples, or at different times)
  • patterns of co-occurrence (e.g. ‘Boolean operators’, cluster analysis)
  • sequence of occurrences.
  • General advice for using content analysis (archive link)
  • Two examples in which the content analysis option was used

Busch C, De Maret P S, Flynn T, Kellum R, Le, Brad Meyers S, Saunders M, White R, and Palmquist M. (2005). Content Analysis . Writing@CSU. Colorado State University Department of English. Retrieved from https://writing.colostate.edu/guides/guide.cfm?guideid=61

Power Point Presentation (2007) Introduction to qualitative analysis , Lecture from Psychology course. Retrieved from www.psychology.soton.ac.uk/researchmethods/lectures/media/2007-10-29/qual_lecture3.ppt

List D (2012) Know Your Audience Chapter 16 ? Audience Dialogue Website. Retrieved from http://www.audiencedialogue.net/kya16a.html  (archive link)

Expand to view all resources related to 'Content analysis'

  • Using Word & Excel to analyze qualitative data with Seth Tucker

'Content analysis' is referenced in:

Framework/guide.

  • Rainbow Framework :  Analyse data
  • Thematic coding

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Research Methodologies Guide

  • Action Research
  • Bibliometrics
  • Case Studies

Content Analysis

  • Digital Scholarship This link opens in a new window
  • Documentary
  • Ethnography
  • Focus Groups
  • Grounded Theory
  • Life Histories/Autobiographies
  • Longitudinal
  • Participant Observation
  • Qualitative Research (General)
  • Quasi-Experimental Design
  • Usability Studies

Content analysis is defined as 

"the systematic reading of a body of texts, images, and symbolic matter, not necessarily from an author's or user's perspective" ( Krippendorff , 2004).

Content analysis is distinguished from other kinds of social science research in that it does not require the collection of data from people. Like documentary research, content analysis is the study of recorded information, or information which has been recorded in texts, media, or physical items. 

For more information about content analysis, review the resources below:

Books and articles

Below, a few tools and online guides that can help you start your Content Analysis research are listed. These include free online resources and resources available only through ISU Library.

  • Quantitative Content Analysis by Kate Huxley Publication Date: 2020 This entry examines quantitative content analysis, which is a method based on the systematic coding and quantification of content—be that written, visual, or oral content.
  • Qualitative Content Analysis The article describes an approach of systematic, rule guided qualitative text analysis, which tries to preserve some methodological strengths of quantitative content analysis and widen them to a concept of qualitative procedure.
  • Basic Content Analysis by Robert Philip Weber Call Number: H61 W422 1990 Publication Date: 1990

Additional Resources

  • An Introduction to Content Analysis A tutorial-type guide to content analysis from Colorado State University.
  • Overview of Content Analysis An article from the peer-reviewed online journal, Practical Assessment, Research & Evaluation by Steve Stemler of Yale University.
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  • Last Updated: Dec 19, 2023 2:12 PM
  • URL: https://instr.iastate.libguides.com/researchmethods

Qualitative Research Guide : Content Analysis

  • Gray Literature
  • Action Research
  • Bibliometrics
  • Case Studies
  • Content Analysis

What is Content Analysis?

Books about content analysis, resources on content analysis, content analysis in the literature.

  • Ethnographic Research
  • Focus Groups
  • Grounded Theory
  • Life Histories
  • Mixed Methods
  • Participant Observation
  • Statistics and Data
  • Data Analysis and Software
  • Writing About Qualitative Research
  • Sharing Qualitative Data

"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." Source: Columbia Public Health

The following books are available as ebooks through the UCSF Library unless otherwise noted

Cover Art

  • Content Analysis A great overview of content analysis from Columbia University School of Public Health. Describes uses, approaches, advantages and disadvantages.
  • Flowchart for the Typical Process of Content Analysis Research From the Content Analysis Guidebook by Kimberly A. Neuendorf (linked above)
  • Overview: Content Analysis A tutorial-type guide to content analysis from Colorado State University.
  • Qualitative Content Analysis A very readable explanation of qualitative content analysis with links for further information.
  • Qualitative Content Analysis: A Focus on Trustworthiness This article from Elo et al. includes a checklist for researchers attempting to improve the trustworthiness of a content analysis study.

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This chapter examines qualitative content analysis, a recent methodological innovation. Qualitative content analysis is defined and distinguished here from basic and interpretive approaches to content analysis. Qualitative content analysis is also distinguished from other qualitative research methods, though features and techniques overlap with other qualitative methods. Key differences in the predominant use of newly collected data and use of non-quantitative analysis techniques are detailed. Differences in epistemology and the role of researcher self-awareness and reflexivity are also discussed. Methods of graphic data presentation are illustrated. Three short exemplar studies using qualitative content analysis are described and examined. Qualitative content analysis is explored in detail in terms of its characteristic components: (1) the research purposes of content analysis, (2) target audiences, (3) epistemological issues, (4) ethical issues, (5) research designs, (6) sampling issues and methods, (7) collecting data, (8) coding and categorization methods, (9) data analysis methods, and (10) the role of researcher reflection.

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What is Content Analysis – Steps & Examples

Published by Alvin Nicolas at August 16th, 2021 , Revised On August 29, 2023

“The content analysis identifies specific words, patterns, concepts, themes, phrases, characters, or sentences within the recorded communication content.”

To conduct content analysis, you need to gather data from multiple sources; it can be anything or any form of data, including text, audio, or videos.

Depending on the requirements of your analysis, you may have to use a  primary or secondary form of data , including:

Videos Transcripts Images Newspaper Books Literature Biographies Documents Oral statements/conversations Text books Encyclopedia Newspapers Periodicals Social media posts Articles

The Purpose of Content Analysis

There are so many objectives of content analysis. Some fundamental objectives are given below.

  • To simplify the content.
  • To get a clear, in-depth meaning of the language.
  • To identify the uses of language.
  • To know the impact of language on society.
  • To find out the association of the language with cultures, interpersonal relationships, and communication.
  • To gain an in-depth understanding of the concept.
  • To find out the context, behaviour, and response of the speaker.
  • To analyse the trends and association between the text and multimedia.

When to Use Content Analysis? 

There are many uses of the content analysis; some of them are listed below:

The content analysis is used.

  • To represent the content precisely, breaking it into short form.
  • To describe the characteristics of the content.
  • To support an argument.
  • It is used in many walks of life, including marketing, media, literature, etc.
  • It is used for extracting essential information from a large amount of data.

Types of Content Analysis

Content analysis is a broad concept, and it has various types depending on various fields. However, people from all walks of life use it at their convenience. Some of the popular methods are given below:

Sr. no Types Definition Example
1 Relational Analysis It helps to understand the association between concepts in humans. What other words are used next to the word  it’s synonyms such as  is used in the communication?

What kind of meaning is produced by this group of words?

2 Unobtrusive Research It’s a method of studying social behaviour without collecting data directly from the subject group Durkheim’s analysis of suicide
3 Conceptual analysis It analyses the existence and frequency of concepts in human communication. Smoking can have adverse   on your health.

Here you can find out how many times the word  its synonyms such as  communication.

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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Advantages and Disadvantages of Content Analysis

Content analysis has so many benefits, which are given below.

Content analysis:

  • Offers both qualitative and quantitative analysis of the communication.
  • Provides an in-depth understanding of the content by making it precise.
  • Enables us to understand the context and perception of the speaker.
  • Provides insight into complex models of human thoughts and language use.
  • Provides historical/cultural insight.
  • It can be applied at any given time, place, and people.
  • It helps to learn any language, its origin, and association with society and culture

Disadvantages

There are also some disadvantages of using the method of content analysis which are given below:

  • is very time-consuming.
  • Cannot interpret a large amount of data accurately and is subjected to increased error.
  • Cannot be computerised easily.

How to Conduct a Content Analysis?

If you want to conduct the content analysis, so here are some steps that you have to follow for that purpose. Those steps are given below.

Develop a Research Question and Select the Content

It’s essential to have a  research question to proceed with your study.  After selecting your research question, you need to find out the relevant resources to analyse.

Example:  If you want to find out the impact of plagiarism on the credibility of the authors. You can examine the relevant materials available on the topic from the internet, newspapers, and books published during the past 5-10 years.

Could you read it Thoroughly?

At this point, you have to read the content thoroughly until you understand it. 

Condensation

It would help if you broke the text into smaller portions for clear interpretation. In short, you have to create categories or smaller text from a large amount of given data.

The unit of analysis  is the basic unit of text to be classified. It can be a word, phrase, a theme, a plot, a newspaper article.

Code the Content

It takes a long to go through the textual data. Coding is a way of tagging the data and organising it into a sequence of symbols, numbers, and letters to highlight the relevant points. At this point, you have to draw meanings from those condensed parts. You have to understand the meaning and context of the text and the speaker clearly. 

Analyse and Interpret the Data

You can use statistical analysis to analyse the data. It is a method of collecting, analysing, and interpreting ample data to discover underlying patterns and details. Statistics are used in every field to make better decisions. It would help if you aimed to retain the meaning of the content while making it precise.

Frequently Asked Questions

How to perform content analysis.

To perform content analysis:

  • Define research objectives.
  • Select a representative sample.
  • Develop coding categories.
  • Analyze content systematically.
  • Apply coding to data.
  • Interpret results to draw insights about themes, patterns, and meanings.

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Inductive and deductive reasoning takes into account assumptions and incidents. Here is all you need to know about inductive vs deductive reasoning.

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Content Analysis vs Thematic Analysis: What's the Difference?

what's content analysis in research

This is part of our Practical Guide to Qualitative Content Analysis | Start a Free Trial of Delve | Take Our Free Online Qualitative Data Analysis Course

Thematic analysis and qualitative content analysis are two popular approaches used to analyze qualitative data. Confusingly, the two research approaches are often defined in similar ways or even used interchangeably in defining literature. 

Joffe (2012) points out that thematic analysis originally emerged from content analysis, but it developed into a separate approach with its own unique research goals. This evolution over time contributes to the mix-up between the two methods. Sub-categories like conventional content analysis and relational content analysis add another wrinkle of complexity by introducing variations and nuances to content analysis as a whole.

In this article, we clarify the difference between thematic analysis and the common forms of qualitative content analysis—and offer researchers a rational way to match the purpose of their intended research with the appropriate method of data analysis.

Thematic vs Content Analysis: Tl;dr Version

Thematic analysis is an intuitive approach to qualitative data analysis that allows researchers to explore patterns across their data. It involves identifying and understanding key themes in the data and how they relate to one another. “Themes” are overarching categories of common information related to a research phenomenon, which tells a story about its dimensions. 

On the other hand, content analysis is a more practical approach that can be used as a quantitative or qualitative method of data analysis. It can be applied to both textual and visual data but is more often applied to the latter. At its core, content analysis is a data collection technique used to determine the presence of certain words, themes, or concepts within data.

[Streamline your coding—regardless of the method—with Delve . Try it free for 14 days .]

Understanding Content vs Thematic Analysis

What is thematic analysis.

Thematic analysis is a qualitative research method for analyzing data that entails searching across a data set to identify, analyze, and report repeated patterns (Braun and Clarke 2006). You can conduct thematic analysis alone or with others through collaborative thematic analysis .

Eponymously, the themes derived from the data actively construct the patterns of meaning to answer a research question. In short, themes are ‘a patterned response or meaning’ derived from coded data that represent overarching ideas embedded within the larger data set. [1][2] 

As a result, thematic analysis is an effective qualitative research method for describing data that also involves your own interpretation to select codes and construct themes.

What are the main goals of thematic analysis?

The three main goals of thematic analysis are:

To identify important themes from the data.

To understand how themes relate to one another and how they are manifested in the data.

To use themes to generate new insights about a particular phenomenon.

When to use thematic analysis

Thematic analysis is a useful way to understand experiences, thoughts, or behaviors across a data set. Additionally, due to the clear, easy-to-follow processes outlined by Braun and Clarke (2006, 2012, 2017), researchers have suggested that thematic analysis is an ideal analytic method for novice qualitative researchers (Nowell et al. 2017).

What is Content Analysis?

Content analysis is a data collection technique used to determine the presence of certain words, themes, or concepts within qualitative data—either inductively or deductively —to explain a phenomenon. In short, the purpose of content analysis is to describe the characteristics of the document's content by examining who says what, to whom, and with what effect [3].

For example, researchers could use content analysis to evaluate language used within poems to search for a collective understanding of a phenomenon within a specific community—such as malaria in rural Africa . Researchers can then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time surrounding the poems.[4]

What are the main goals of qualitative content analysis?

The three main goals of qualitative content analysis are:

To identify and understand themes, patterns, and relationships within the data.

To explore how the data can inform theoretical claims made in research studies.

To quantify qualitative data.

When to use qualitative content analysis?

You can use qualitative content analysis to quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts within textual data. You can also consider using qualitative content analysis when you want to apply a more interpretive level of analysis to your data than would be possible through quantitative content analysis. 

Qualitative analysis doesn't have to be overwhelming

Take delve's free online course to learn how to find themes and patterns in your qualitative data. get started here..

what's content analysis in research

The difference between thematic analysis and content analysis in qualitative research

Thematic analysis focuses on extracting high-level themes from within data, while content analysis—especially subcategorical methods like summative content analysis —focus on the reoccurrence of concepts or keywords at a more surface-level of analysis i.e. their frequency. 

In essence, the main difference between the two methods lies in the possibility of quantification of data in content analysis by measuring the frequency of different categories and themes. [4] While frequency is generally a core tenet of qualitative content analysis where statistical findings are tabulated or visualized in the final write-up, it is not a focus of thematic analysis. 

Instead, in contrast to tallying concepts or keywords to infer meaning as you would in content analysis, a theme is not necessarily reflective of the frequency of its appearance within the data in a thematic analysis (Braun and Clarke 2006; Nowell et al. 2017).

In summary, statistical data is core to most content analysis but is not typically cited in thematic analysis. And while the former tends to focus on more manifest data that is apparent through surface-level analysis, neither method is inherently more beneficial or astute than the other. 

Main differences between thematic analysis and content analysis are:

Thematic analysis (TA) is a qualitative method used to uncover themes in textual data, while content analysis (CA) is either a quantitative or a qualitative approach that also involves some quantification of data.

CA generally counts the occurrence of concepts or keywords to infer meaning, while TA assigns meaning by extracting high-level ideas.

TA focuses on the overarching themes in the data and how those themes relate to one another, while in CA researchers count instances of coded concepts and keywords within large amounts of textual data with less focus on comparing or contrasting those codes. 

Some differences in how thematic analysis and content analysis are used:

To elaborate further, these next differences exemplify how thematic analysis and content analysis are commonly used in practice. Though it is important to note that there are exceptions to each.

Thematic analysis always involves an inductive portion of analysis. While there are forms of inductive content analysis, it is more common in content analysis to apply existing theories and frameworks through a deductive analytical technique.

As the name implies, content analysis was historically applied to “content”. This includes qualitative data such as newspapers, books, research journals, and letters. The data for thematic analysis is often directly collected by the researcher, such as through semi-structured interviews . That being said, you may still apply thematic analysis to newspaper articles, and content analysis to semi-structured interviews.

Content analysis is able to use “automated” forms of analysis, and the researcher may not need to read their entire dataset. For example, in summative content analysis, you only seek specific keywords and could use Delve’s search functionality to quickly find those keywords and code them. In thematic analysis, automated forms of analysis are still a valuable aid, but the researcher will almost always still need to read the entire data set.

It's important to note that both methods have their advantages and disadvantages depending on the research question being asked and the type of data being analyzed.

Thematic analysis vs content analysis: the similarities

Now that we have covered the differences between qualitative content analysis and thematic analysis, it is important to note that similarities also exist between each method. 

For instance, both content analysis and thematic analysis share the same aim of analytically examining narrative materials from life stories by breaking the text into relatively small units of content and submitting them to descriptive treatment (Sparkes, 2005). Both are descriptive qualitative approaches to data analysis that achieve a similar goal, just in different ways.

Beyond that, these are some other overlapping characteristics:

They both involve examining qualitative data.

Both are used to generate new knowledge from the data.

Both are iterative processes that require intimate knowledge of the data you study. 

Both approaches can be used to inform theoretical claims in research studies.

No matter which method you choose, it's important to understand how each qualitative research method works so you can confidently decide which one best suits your research needs. Now that you’ve read this article, you are equipped with the knowledge to do just that!

Ready to streamline your qualitative data analysis?

Whether for thematic analysis or content analysis, Delve can simplify your qualitative data analysis. Delve users also appreciate its robust features for collaborative qualitative analysis , simplifying teamwork across locations and with various team members.

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Michelle E. Kiger & Lara Varpio (2020): Thematic analysis of qualitative data: AMEE Guide No. 131, Medical Teacher.

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3 (2), 77–101.

Vaismoradi M, Turunen H, Bondas T. Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study. Nurs Health Sci . 2013 Sep;15(3):398-405. 

Content Analysis. (n.d.). https://www.publichealth.columbia.edu/research/population-health-methods/content-analysis

Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic Analysis: Striving to Meet the Trustworthiness Criteria. International Journal of Qualitative Methods , 16(1). 

Bloor, M. and Wood, F. (2006) Keywords in Qualitative Methods. Sage Publications, Inc., London.

Joffe, H. (2011). Thematic analysis. In D. Harper & A. R. Thompson (Eds.), Qualitative methods in mental health and psychotherapy: A guide for students and practitioners (pp. 209–223). Chichester, UK: Wiley.

Sparkes A. Narrative analysis: exploring the whats and hows of personal stories. In: Holloway I (ed.). Qualitative Research in Health Care (1st edn). Berkshire: Open University Press, 2005; 191–208.

Cite this blog post:

Delve, Ho, L., & Limpaecher, A. (2023c, February 15). Content Analysis vs Thematic Analysis: What's the Difference? https://delvetool.com/blog/content-analysis-vs-thematic-analysis

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  • Estimated changes in free sugar consumption one year after the UK soft drinks industry levy came into force: controlled interrupted time series analysis of the National Diet and Nutrition Survey (2011–2019)
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  • http://orcid.org/0000-0003-1857-2122 Nina Trivedy Rogers 1 ,
  • http://orcid.org/0000-0002-3957-4357 Steven Cummins 2 ,
  • Catrin P Jones 1 ,
  • Oliver Mytton 3 ,
  • Mike Rayner 4 ,
  • Harry Rutter 5 ,
  • Martin White 1 ,
  • Jean Adams 1
  • 1 MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus , University of Cambridge , Cambridge , UK
  • 2 Department of Public Health, Environments & Society , London School of Hygiene & Tropical Medicine , London , UK
  • 3 Great Ormond Street Institute of Child Health , University College London , London , UK
  • 4 Nuffield Department of Population Health , University of Oxford , Oxford , UK
  • 5 Department of Social and Policy Sciences , , University of Bath , Bath , UK
  • Correspondence to Dr Nina Trivedy Rogers, MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 1TN, UK; nina.rogers{at}mrc-epid.cam.ac.uk

Background The UK soft drinks industry levy (SDIL) was announced in March 2016 and implemented in April 2018, encouraging manufacturers to reduce the sugar content of soft drinks. This is the first study to investigate changes in individual-level consumption of free sugars in relation to the SDIL.

Methods We used controlled interrupted time series (2011–2019) to explore changes in the consumption of free sugars in the whole diet and from soft drinks alone 11 months after SDIL implementation in a nationally representative sample of adults (>18 years; n=7999) and children (1.5–19 years; n=7656) drawn from the UK National Diet and Nutrition Survey. Estimates were based on differences between observed data and a counterfactual scenario of no SDIL announcement/implementation. Models included protein consumption (control) and accounted for autocorrelation.

Results Accounting for trends prior to the SDIL announcement, there were absolute reductions in the daily consumption of free sugars from the whole diet in children and adults of 4.8 g (95% CI 0.6 to 9.1) and 10.9 g (95% CI 7.8 to 13.9), respectively. Comparable reductions in free sugar consumption from drinks alone were 3.0 g (95% CI 0.1 to 5.8) and 5.2 g (95% CI 4.2 to 6.1). The percentage of total dietary energy from free sugars declined over the study period but was not significantly different from the counterfactual.

Conclusion The SDIL led to significant reductions in dietary free sugar consumption in children and adults. Energy from free sugar as a percentage of total energy did not change relative to the counterfactual, which could be due to simultaneous reductions in total energy intake associated with reductions in dietary free sugar.

  • PUBLIC HEALTH

Data availability statement

Data are available in a public, open access repository. Data from the National Diet and Nutrition Survey years 1–11 (2008–09 to 2018–19) can be accessed on the UK Data Service ( https://ukdataservice.ac.uk/ ).

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/jech-2023-221051

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WHAT IS ALREADY KNOWN ON THIS TOPIC

High intakes of free sugars are associated with a range of non-communicable diseases. Sugar sweetened beverages constitute a major source of dietary free sugars in children and adults.

The UK soft drink industry levy (SDIL) led to a reduction in the sugar content in many sugar sweetened beverages and a reduction in household purchasing of sugar from drinks.

No previous study has examined the impact of the SDIL on total dietary consumption of free sugars at the individual level.

WHAT THIS STUDY ADDS

There were declining trends in the intake of dietary free sugar in adults and children prior to the UK SDIL.

Accounting for prior trends, 1 year after the UK SDIL came into force children and adults further reduced their free sugar intake from food and drink by approximately 5 g/day and 11 g/day, respectively. Children and adults reduced their daily free sugar intake from soft drinks alone by approximately 3 g/day and approximately 5 g/day, respectively.

Energy intake from free sugars as a proportion of total energy consumed did not change significantly following the UK SDIL, indicating energy intake from free sugar was reducing simultaneously with overall total energy intake.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

The UK SDIL was associated with significant reductions in consumption of free sugars from soft drinks and across the whole diet and reinforces previous research indicating a reduction in purchasing. This evidence should be used to inform policy when extending or considering other sugar reduction strategies.

Energy intake from free sugars has been falling but levels remain higher than the 5% recommendation set by the WHO. Reductions in dietary sugar in relation to the SDIL may have driven significant reductions in overall energy.

Introduction

High consumption of free sugars is associated with non-communicable diseases. 1 Guidelines from the World Health Organization (WHO) and the UK Scientific Advisory Committee on Nutrition (SACN) suggest limiting free sugar consumption to below 5% of total energy intake to achieve maximum health benefits, 1 2 equivalent to daily maximum amounts of 30 g for adults, 24 g for children (7–10 years) and 19 g for young children (4–6 years). In the UK, consumption of free sugar is well above the recommended daily maximum, although levels have fallen over the last decade. 3 For example, adolescents consume approximately 70 g/day 4 and obtain 12.3% of their energy from free sugars. 3 Sugar sweetened beverages (SSBs) constitute a major source of free sugar in the UK diet, 2 5 and are the largest single source for children aged 11–18 years where they make up approximately one-third of their daily sugar intake. 6 A growing body of evidence has shown a link between consumption of SSBs and higher risk of weight gain, type 2 diabetes, coronary heart disease and premature mortality, 7 such that the WHO recommends taxation of SSBs in order to reduce over-consumption of free sugars and to improve health. 8 To date, >50 countries have introduced taxation on SSBs, which has been associated with a reduction in sales and dietary intake of free sugar from SSBs. 9 Reductions in the prevalence of childhood obesity 10 11 and improvements in dental health outcomes 12 13 have also been reported.

In March 2016 the UK government announced the UK soft drink industry levy (SDIL), a two-tier levy on manufacturers, importers and bottlers of soft drinks which would come into force in March 2018. 14 The levy was designed to incentivise manufacturers to reformulate and reduce the free sugar content of SSBs (see details in online supplemental text 1 ).

Supplemental material

One year after the UK SDIL was implemented there was evidence of a reduction in the sugar content of soft drinks 15 and households on average reduced the amount of sugar purchased from soft drinks by 8 g/week with no evidence of substitution with confectionary or alcohol. 16 However, lack of available data meant it was not possible to examine substitution of purchasing other sugary foods and drinks, which has previously been suggested in some but not all studies. 17 18 Household purchasing only approximates individual consumption because it captures only those products brought into the home, products may be shared unequally between household members, and it does not account for waste.

To examine the effects of the SDIL on total sugar intake at the individual level, in this study we used surveillance data collected using 3- or 4-day food diaries as part of the UK National Diet and Nutrition Survey (NDNS). We aimed to examine changes in absolute and relative consumption of free sugars from soft drinks alone and from both food and drinks (allowing us to consider possible substitutions with other sugary food items), following the announcement and implementation of the UK SDIL.

Data source

We used 11 years of data (2008–2019) from the NDNS. Data collection, sampling design and information on response is described in full elsewhere. 19 In brief, NDNS is a continuous national cross-sectional survey capturing information on food consumption, nutritional status and nutrient intake inside and outside of the home in a representative annual sample of approximately 500 adults and 500 children (1.5–18 years) living in private households in the UK. Participants are sampled throughout the year, such that in a typical month about 40 adults and 40 children participate (further details are shown in online supplemental text 2 ).

Outcomes of interest

Outcomes of interest were absolute and relative changes in the total intake of dietary free sugar from (1) all food and soft drinks combined and (2) from soft drinks alone. A definition of free sugar is given in online supplemental text 3 . Drink categories examined were those that fell within the following NDNS categories: soft drinks – not low calorie; soft drinks – low calorie; semi-skimmed milk; whole milk; skimmed milk; fruit juice, 1% fat milk and other milk and cream. Additionally, we examined absolute and relative changes in percentage energy from free sugar in (1) food and soft drinks and (2) soft drinks alone. While examination of changes in sugar consumption and percentage energy from sugar across the whole diet (food and drink) captures overall substitutions with other sugar-containing products following the UK SDIL, examination of sugar consumption from soft drinks alone provides a higher level of specificity to the SDIL.

Protein intake was selected as a non-equivalent dependent control. It was not a nutritional component specifically targeted by the intervention or other government interventions and therefore is unlikely to be affected by the SDIL but could still be affected by confounding factors such as increases in food prices 20 (see online supplemental text 4 ).

Statistical analysis

Controlled interrupted time series (ITS) analyses were performed to examine changes in the outcomes in relation to the UK SDIL separately in adults and children. We analysed data at the quarterly level over 11 years with the first data point representing dates from April to June 2008 and the last representing dates from January to March 2019. Model specifications are shown in online supplemental text 5 . Where diary date entries extended over two quarters, the earlier quarter was designated as the time point for analysis. Generalised least squares models were used. Autocorrelation in the time series was determined using Durbin–Watson tests and from visualisations of autocorrelation and partial correlation plots. Autocorrelation-moving average correlation structure with order (p) and moving average (q) parameters were used and selected to minimise the Akaike information criterion in each model. Trends in free sugar consumption prior to the announcement of SDIL in April 2016 were used to estimate counterfactual scenarios of what would have happened if the SDIL had not been announced or come into force. Thus, the interruption point was the 3-month period beginning April 2016. Absolute and relative differences in consumption of free sugars/person/day were estimated by calculating the difference between the observed and counterfactual values at quarterly time point 45. To account for non-response and to ensure the sample distribution represented the UK distribution of females and males and age profile, weights provided by NDNS were used and adapted for analysis of adults and children separately. 21 A study protocol has been published 22 and the study is registered ( ISRCTN18042742 ). For changes to the original protocol see online supplemental text 6 . All statistical analyses were performed in R version 4.1.0.

Data from 7999 adults and 7656 children were included across 11 years representing approximately 40 children and 40 adults each month. Table 1 gives descriptive values for the outcomes of interest. Compared with the pre-announcement period, free sugars consumed from all soft drinks reduced by around one-half in children and one-third in adults in the post-announcement period. Total dietary free sugar consumption and percentage of total dietary energy derived from free sugars also declined. Mean protein consumption was relatively stable over both periods in children and adults. The age and sex of the children and adults were very similar in the pre- and post-announcement periods.

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Mean amount of free sugar (g) consumed in children and adults per day during the study period before and after the announcement of the soft drinks industry levy (SDIL)

All estimates of change in free sugar consumption referred to below are based on g/individual/day in the 3-month period beginning January 2019 and compared with the counterfactual scenario of no UK SDIL announcement and implementation.

Change in free sugar consumption (soft drinks only)

In children, consumption of free sugars from soft drinks was approximately 27 g/day at the start of the study period but fell steeply throughout. By the end of the study period mean sugar consumption from soft drinks was approximately 10 g/day ( figure 1 ). Overall, relative to the counterfactual scenario, there was an absolute reduction in daily free sugar consumption from soft drinks of 3.0 g (95% CI 0.1 to 5.8) or a relative reduction of 23.5% (95% CI 46.0% to 0.9%) in children ( table 2 ). In adults, free sugar consumption at the beginning of the study was lower than that of children (approximately 17 g/day) and was declining prior to the SDIL announcement, although less steeply ( figure 1 ). Following the SDIL announcement, free sugar consumption from soft drinks appeared to decline even more steeply. There was an absolute reduction in free sugar consumption from soft drinks of 5.2 g (95% CI 4.2 to 6.1) or a relative reduction of 40.4% (95% CI 32.9% to 48.0%) in adults relative to the counterfactual ( figure 1 , table 2 ).

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Observed and modelled daily consumption (g) of free sugar from drink products per adult/child from April 2008 to March 2019. Red points show observed data and solid red lines (with light red shadows) show modelled data (and 95% CIs) of free sugar consumed from drinks. The dashed red line indicates the counterfactual line based on pre-announcement trends and if the announcement and implementation had not happened. Modelled protein consumption from drinks (control group) was removed from the graph to include resolution but is available in the supplementary section. The first and second dashed lines indicate the announcement and implementation of the soft drinks industry levy (SDIL), respectively.

Change in free sugar consumption in food and drink and energy from free sugar as a proportion of total energy compared with the counterfactual scenario of no announcement and implementation of the UK soft drinks industry levy (SDIL)

Change in total dietary free sugar consumption (food and soft drinks combined)

Consumption of total dietary free sugars in children was approximately 70 g/day at the beginning of the study but this fell to approximately 45 g/day by the end of the study ( figure 2 ). Relative to the counterfactual scenario, there was an absolute reduction in total dietary free sugar consumption of 4.8 g (95% CI 0.6 to 9.1) or relative reduction of 9.7% (95% CI 18.2% to 1.2%) in children ( figure 2 ; table 2 ). In adults, consumption of total dietary free sugar consumption at the beginning of the study was approximately 60 g/day falling to approximately 45 g/day by the end of the study ( figure 2 ). Relative to the counterfactual scenario there was an absolute reduction in total dietary free sugar consumption in adults of 10.9 g (95% CI 7.8 to 13.9) or a relative reduction of 19.8% (95% CI 25.4% to 14.2%). Online supplemental figures show that, relative to the counterfactual, dietary protein consumption and energy from protein was more or less stable across the study period (see online supplemental figures S3–S6 ).

Observed and modelled daily consumption (g) of free sugar from food and drink products per adult/child from April 2008 to March 2019. Red points show observed data and solid red lines (with light red shadows) show modelled data (and 95% CIs) of free sugar consumed from food and drinks. The dashed red line indicates the counterfactual line based on pre-announcement trends and if the announcement and implementation had not happened. Modelled protein consumption from food and drinks (control group) was removed from the graph to include resolution but is available in the supplementary section. The first and second dashed lines indicate the announcement and implementation of the soft drinks industry levy (SDIL), respectively.

Change in energy from free sugar as a proportion of total energy

The percentage of energy from total dietary free sugar decreased across the study period but did not change significantly relative to the counterfactual scenario in children or adults, with relative changes in free sugar consumption of −7.6 g (95% CI −41.7 to 26.5) and −24.3 g (95% CI −54.0 to 5.4), respectively (see online supplemental figure S1 and table 2 ). Energy from free sugar in soft drinks as a proportion of total energy from soft drinks also decreased across the study period but did not change significantly relative to the counterfactual (see online supplemental figure S2 ).

Summary of main findings

This study is the first to examine individual level consumption of free sugars in the total diet (and in soft drinks only) in relation to the UK SDIL. Using nationally representative population samples, we found that approximately 1 year following the UK SDIL came into force there was a reduction in total dietary free sugar consumed by children and adults compared with what would have been expected if the SDIL had not been announced and implemented. In children this was equivalent to a reduction of 4.8 g of free sugars/day from food and soft drinks, of which 3 g/day came from soft drinks alone, suggesting that the reduction of sugar in the diet was primarily due to a reduction of sugar from soft drinks. In adults, reductions in dietary sugar appeared to come equally from food and drink with an 11 g reduction in food and drink combined, of which 5.2 g was from soft drinks only. There was no significant reduction compared with the counterfactual in the percentage of energy intake from free sugars in the total diet or from soft drinks alone in both children and adults, suggesting that energy intake from free sugar was reducing simultaneously with overall total energy intake.

Comparison with other studies and interpretation of results

Our finding of a reduction in consumption of free sugars from soft drinks after accounting for pre-SDIL announcement trends is supported by previous research showing a large reduction in the proportion of available soft drinks with over 5 g of sugar/100 mL, the threshold at which soft drinks become levy liable. 15 Furthermore, efforts of the soft drink industry to reformulate soft drinks were found to have led to significant reductions in the volume and per capita sales of sugar from these soft drinks. 23

Our findings are consistent with recent research showing reductions in purchasing of sugar from soft drinks of approximately 8 g/household/week (equivalent to approximately 3 g/person/week or approximately 0.5 g/person/day) 1 year after the SDIL came into force. 16 The estimates from the current study suggest larger reductions in consumption (eg, 3 g free sugar/day from soft drinks in children) than previously reported for purchasing. Methodological differences may explain these differences in estimated effect sizes. Most importantly, the previous study used data on soft drink purchases that were for consumption in the home only. In contrast, we captured information on consumption (rather than purchasing) in and out of the home. Consumption of food and particularly soft drinks outside of the home in young people (1–21 years) increases with age and makes a substantial contribution to total free sugar intakes, highlighting the importance of recording both in home and out of home sugar consumption. 4 Purchasing and consumption data also treat waste differently; purchase data record what comes into the home and therefore include waste, whereas consumption data specifically aim to capture leftovers and waste and exclude it from consumption estimates. While both studies use weights to make the population samples representative of the UK, there may be differences in the study participant characteristics in the two studies, which may contribute to the different estimates.

Consistent with other studies, 24 we found that across the 11-year study period we observed a downward trend in free sugar and energy intake in adults and children. 3 A decline in consumption of free sugars was observed in the whole diet rather than just soft drinks, suggesting that consumption of free sugar from food was also declining from as early as 2008. One reason might be the steady transition from sugar in the diet to low-calorie artificial sweeteners, which globally have had an annual growth of approximately 5.1% between 2008 and 2015. 25

Public health signalling around the time of the announcement of the levy may also have contributed to the changes we observed. Public acceptability and perceived effectiveness of the SDIL was reported to be high 4 months before and approximately 20 months after the levy came into force. 26 Furthermore, awareness of the SDIL was found to be high among parents of children living in the UK, with most supporting the levy and intending to reduce purchases of SSBs as a result. 27 Health signalling was also found following the implementation of the SSB tax in Mexico, with one study reporting that most adults (65%) were aware of the tax and that those aware of the tax were more likely to think the tax would reduce purchases of SSBs, 28 although a separate study found that adolescents in Mexico were mostly unaware of the tax, 29 suggesting that public health signalling may differ according to age.

In 2016 the UK government announced a voluntary sugar reduction programme as part of its childhood obesity plan (which also included SDIL) with the aim of reducing sugar sold by industry by 5% no later than 2018 and by 20% in time for 2020 through both reformulation and portion size reduction. 30 While the programme only managed to achieve overall sugar reductions of approximately 3.5%, this did include higher reductions in specific products such as yoghurts (−17%) and cereals (−13%) by 2018 which may have contributed to some of the observed reductions in total sugar consumption (particularly from foods) around the time of the SDIL. While there is strong evidence that the UK SDIL led to significant reformulation 15 and reductions in purchases of sugar from soft drinks, 16 the products targeted by the sugar reduction programme were voluntary with no taxes or penalties if targets were not met, possibly leading to less incentive for manufacturers to reformulate products that were high in sugar. The 5-year duration of the voluntary sugar reduction programme also makes it challenging to attribute overall reductions using interruption points that we assigned to the ITS to align with the date of the SDIL announcement. The soft drinks categories in our study included levy liable and non-levy liable drinks because we wanted to examine whether individuals were likely to substitute levy liable drinks for high sugar non-liable options. The decline in sugar consumed overall and in soft drinks in relation to the levy suggests that individuals did not change their diets substantially by substituting more sugary foods and drinks. This is consistent with findings from a previous study that found no changes in relation to the levy in sugar purchased from fruit juice, powder used to make drinks or confectionery. 16

Consistent with previous analyses, 3 our findings showed that there was a downward trend in energy intake from sugar as a proportion of total energy across the duration of the study. While there was no reduction compared with the counterfactual scenario (which was also decreasing), our estimates suggest that, by 2019, on average energy from sugar as a proportion of all energy appears to be in line with the WHO recommendation of 10% but not the more recent guidelines of 5% which may bring additional health benefits. 1 31 This finding may suggest that reductions in energy intake from sugar were reducing in concert with overall energy intake and indeed may have been driving it. However, the magnitude of calories associated with the reduction in free sugars, compared with the counterfactual scenario in both adults and children, was modest and thus potentially too small to reflect significant changes in the percentage of energy from sugar. In children, a daily reduction of 4.8 g sugar equates to approximately 19.2 kilocalories out of an approximate daily intake of approximately 2000 kilocalories which is equivalent to approximately 1% reduction in energy intake. Furthermore, overall measures of dietary energy are also likely to involve a degree of error reducing the level of precision in any estimates.

Our estimates of changes in sugar consumption in relation to SDIL suggest that adults may have experienced a greater absolute reduction in sugar than children, which is not consistent with estimates of the distributional impact of the policy. 32 However, our understanding may be aided by the visualisations afforded by graphical depictions of our ITS graphs. Children’s consumption of sugar at the beginning of the study period, particularly in soft drinks, was higher than in adults but reducing at a steeper trajectory, which will have influenced our estimated counterfactual scenario of what would have happened without the SDIL. This steep downward trajectory could not have continued indefinitely as there is a lower limit for sugar consumption. No account for this potential ‘floor effect’ was made in the counterfactual. Adults had a lower baseline of sugar consumption, but their trajectory of sugar consumption decreased at a gentler trajectory, potentially allowing more scope for improvement over the longer run.

Reductions in the levels of sugar in food and drink may have also impacted different age groups and children and adults differently. For example, the largest single contributor to free sugars in younger children aged 4–10 years is cereal and cereal products, followed by soft drinks and fruit juice. By the age of 11–18 years, soft drinks provide the largest single source (29%) of dietary free sugar. For adults the largest source of free sugars is sugar, preserves and confectionery, followed by non-alcoholic beverages. 5

Strengths and limitations

The main strengths of the study include the use of nationally representative data on individual consumption of food and drink in and out of the home using consistent food diary assessment over a 4-day period, setting it apart from other surveys which have used food frequency questionnaires, 24 hour recall, shortened dietary instruments or a mixture of these approaches across different survey years. 33 The continual collection of data using consistent methods enabled us to analyse dietary sugar consumption and energy quarterly over 11 years (or 45 time points) including the announcement and implementation period of the SDIL. Information on participant age allowed us to examine changes in sugar consumption in adults and children separately. Limited sample sizes restricted our use of weekly or monthly data and prevented us from examining differences between sociodemographic groups. At each time point we used protein consumption in food and drink as a non-equivalent control category, strengthening our ability to adjust for time-varying confounders such as contemporaneous events. The trends in counterfactual scenarios of sugar consumption and energy from free sugar as part of total energy were based on trends from April 2008 to the announcement of the UK SDIL (March 2016); however, it is possible that the direction of sugar consumption may have changed course. Ascribing changes in free sugar consumption to the SDIL should include exploration of other possible interventions that might have led to a reduction in sugar across the population. We are only aware of the wider UK government’s voluntary sugar reduction programme implemented across overlapping timelines (2015–2020) and leading to reductions in sugar consumption that were well below the targets set. 30 In turn, under-reporting of portion sizes and high energy foods, which may be increasingly seen as less socially acceptable, has been suggested as a common error in self-reported dietary intake with some groups including older teenagers and females, especially those who are living with obesity, more likely to underestimate energy intake. 34 35 However, there is no evidence to suggest this would have changed as a direct result of the SDIL. 36

Conclusions

Our findings indicate that the UK SDIL led to reductions in consumption of dietary free sugars in adults and children 1 year after it came into force. Energy from free sugar as a proportion of overall energy intake was falling prior to the UK SDIL but did not change in relation to the SDIL, suggesting that a reduction in sugar may have driven a simultaneous reduction in overall energy intake.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

For NDNS 2008–2013, ethical approval was obtained from the Oxfordshire A Research Ethics Committee (Reference number: 07/H0604/113). For NDNS 2014–2017, ethical approval was given from the Cambridge South NRES Committee (Reference number: 13/EE/0016). Participants gave informed consent to participate in the study before taking part.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

  • Data supplement 1

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Contributors OM, SC, MR, HR, MW and JA conceptualised and acquired funding for the study. NTR carried out statistical analyses. NTR and JA drafted the manuscript. All authors contributed to the article and approved the submitted version.

As the guarantor, NTR had access to the data, controlled the decision to publish and accepts full responsibility for the work and the conduct of the study.

Funding NTR, OM, MW and JA were supported by the Medical Research Council (grant Nos MC_UU_00006/7). This project was funded by the NIHR Public Health Research programme (grant nos 16/49/01 and 16/130/01) to MW. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the Department of Health and Social Care, UK. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Retraction Note: The spillover of tourism development on CO 2 emissions: a spatial econometric analysis

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Bridgewise Launches AI-Driven Analysis for Global ETFs and Mutual Funds

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10 Jul, 2024, 12:23 CST

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  • Bridgewise's groundbreaking Fund Analysis solution enables holdings-based analysis to provide in-depth clarity on the risk and performance of funds
  • The solution offers institutional investors a unique, interactive experience for exploring investment options and making informed buy/sell decisions
  • Bridgewise adds Deborah Fuhr , award-winning ETF industry veteran, to its advisory board, deepening the company's network and credentials in the ETF sector

SINGAPORE , July 10, 2024 /PRNewswire/ -- Bridgewise, the financial research intelligence platform for global securities, announced today the launch of a new fund analysis solution that will provide deeper levels of detail into funds and their underlying assets. Powered by AI, including machine learning and advanced language models, Bridgewise will enable for the first time, deep analysis of fund holdings, past performance, and fee structure, while providing extensive coverage for almost 90% of funds on the market. This enables institutional investors more accuracy for investments, and overcomes a long-standing challenge of a lack of data to analyze the risk and performance of a fund of which the underlying holdings are unknown.

As part of deepening the company's network and credentials, Bridgewise has also appointed Deborah Fuhr , managing partner and founder of ETFGI, to its advisory board. She brings close to 30 years' experience in exchange-traded funds (ETFs) and investment strategies, where her insights and strategic vision will support Bridgewise's continuous innovation and expansion.

ETFs, mutual funds, and other similar vehicles have become some of the most popular choices for investors, with ETFs seeing an impressive 24% annual growth in 2023 globally [1] . In APAC, the ETF industry also reached new milestones in February 2024 with $846.38 billion of investments, surpassing records set in 2023 [2] . However, due to the sheer number of assets held in funds, less than 20% of global securities are covered by professional analysts. The lack of comprehensive analyses makes it challenging for investors to make proper, informed decisions.

Bridgewise's latest Fund Analysis solution is powered by dual AI technologies - machine learning analysis of the global equities it covers and a custom Micro Language Model (MLM). It is able to break down funds into their constituent assets and provide a detailed analysis of each one, as well as buy/sell recommendations. Furthermore, Bridgewise analysis can be provided in any language, helping to close barriers for global funds. This is particularly important in Asia , where diverse company structures and language barriers have deterred foreign investors.

Gaby Diamant , Co-Founder and CEO at Bridgewise , says, "Until now, fund analysts have faced a daunting challenge. There is no way for a human to provide a detailed fundamental analysis on each and every asset in popular funds, especially when some funds are composed of thousands of individual stocks. The time it would take to complete such an analysis could stretch to a full year or more. Our AI technologies not only allow for an unprecedented level of depth of fund analysis, but also nearly universal coverage of funds, each one with the same level of detailed analysis along with buy/sell recommendations for the individual stocks in the fund. This emphasizes the potential for AI to bridge the gap between available data and insightful analysis, enabling a more thorough understanding of market dynamics and investment opportunities."

Deborah Fuhr , managing partner and founder of ETFGI , says: "ETFs are increasingly gaining popularity with retail investors and financial advisors around the world providing simple, liquid, transparent, cost efficient, diversified exposure to global markets, regions, countries, themes. At the end of May our research reported the global ETFs industry has had 60 months or 5 years of consecutive monthly net inflows and the assets invested in the global ETFs industry reached a record of $12.89 trillion .  Bridgewise is developing a solution to fill a growing need for detailed information about mutual funds and ETFs to allow retail investors to make fully informed decisions."

For investors, the new fund analysis solution will provide a unique experience featuring interactive elements, contextual ratings, and other features designed to help them find investment opportunities that are completely aligned with their goals. Features include:

  • Holdings Analysis - detailed insights into every underlying asset and how fund holdings are distributed across sectors, countries, exposure and other factors.
  • Alternatives Analysis - Interactive comparisons of alternative funds make it easy for investors to choose the one that is right for them.
  • Buy/Sell Recommendations - Bridgewise provides recommendations for every security it analyzes, and does so with the necessary regulatory compliance.
  • Category Benchmarks - Standard industry metrics are given context through graded comparisons to category benchmarks, helping investors of any experience level.
  • Asset Discovery - Investors are guided to additional underlying assets from within the fund, allowing them to pursue additional opportunities.

About Bridgewise:

Bridgewise is a global financial research technology company that powers the investment decisions of institutional and retail investors in more than 22 languages and across 15 countries , including over 50 institutional clients . Its proprietary generative AI-based technology platform has delivered over 10 million analyses, which provides comprehensive insights into global stocks and securities.

Founded in 2019, Bridgewise aims to bridge the knowledge gap in the investment world and democratize access to financial market information, providing easy to understand, comprehensive equity research that was previously exclusively available only to major financial institutions.

Its unbiased analysis, scoring and research can be seamlessly integrated into brokerage platforms and other financial institutions' infrastructures, offering instant analysis of global stocks alongside bespoke investment strategies, enabling informed investment decisions to investors of all types, worldwide.

More about Bridgewise can be  found here .

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Bridgewise secures $21 million in new funding to support the global expansion of its AI-powered financial intelligence platform for global securities

Bridgewise, the innovative AI-based analysis platform for global securities, has announced the completion of a $21 million funding round, bringing...

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    Abstract. In this chapter, the focus is on ways in which content analysis can be used to investigate and describe interview and textual data. The chapter opens with a contextualization of the method and then proceeds to an examination of the role of content analysis in relation to both quantitative and qualitative modes of social research.

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    Content analysis is a qualitative analysis method that focuses on recorded human artefacts such as manuscripts, voice recordings and journals. Content analysis investigates these written, spoken and visual artefacts without explicitly extracting data from participants - this is called unobtrusive research. In other words, with content ...

  11. Three Approaches to Qualitative Content Analysis

    Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm.

  12. Demystifying Content Analysis

    Qualitative Content Analysis. Content analysis rests on the assumption that texts are a rich data source with great potential to reveal valuable information about particular phenomena. 8 It is the process of considering both the participant and context when sorting text into groups of related categories to identify similarities and differences, patterns, and associations, both on the surface ...

  13. The Practical Guide to Qualitative Content Analysis

    Qualitative content analysis is a research method used to analyze and interpret the content of textual data, such as written documents, interview transcripts, or other forms of communication. ... What is content analysis? At a top level, content analysis in research allows you to examine and understand the content of textual data. There are two ...

  14. Content analysis

    Content analysis. Content analysis is a research method in the social sciences used to reduce large amounts of unstructured textual content into manageable data relevant to the (evaluation) research questions. Texts refer to any occurrence of communications - including websites, social media, books, essays, interviews, focus groups, diaries ...

  15. Content Analysis

    Content Analysis. Content analysis is defined as. "the systematic reading of a body of texts, images, and symbolic matter, not necessarily from an author's or user's perspective" (Krippendorff, 2004). Content analysis is distinguished from other kinds of social science research in that it does not require the collection of data from people.

  16. Content analysis

    Content analysis is the study of documents and communication artifacts, which might be texts of various formats, pictures, audio or video. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. One of the key advantages of using content analysis to analyse social phenomena is their non-invasive nature, in contrast to simulating social ...

  17. (PDF) Content Analysis: A Flexible Methodology

    Content analysis is a highly flexible research method that has been widely used in library and information science (LIS) studies with varying research goals and objectives. The research method is ...

  18. UCSF Guides: Qualitative Research Guide: Content Analysis

    What is 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." ...

  19. Qualitative Content Analysis

    It is a flexible research method ( Anastas, 1999 ). Qualitative content analysis may use either newly collected data, existing texts and materials, or a combination of both. It may be used in exploratory, descriptive, comparative, or explanatory research designs, though its primary use is descriptive.

  20. What is Content Analysis

    Content analysis: Offers both qualitative and quantitative analysis of the communication. Provides an in-depth understanding of the content by making it precise. Enables us to understand the context and perception of the speaker. Provides insight into complex models of human thoughts and language use.

  21. Content Analysis vs Thematic Analysis: What's the Difference?

    Thematic analysis and qualitative content analysis are two popular approaches used to analyze qualitative data. Confusingly, the two research approaches are often defined in similar ways or even used interchangeably in defining literature. We clarify the difference between thematic analysis and the common forms of qualitative content analysis.

  22. (PDF) Content Analysis

    In simple terms, content analysis is. the analysis of what is being said, written or recorded. Through systematic. classification process of coding and identifying themes or patterns, content ...

  23. Home

    Zillow Research aims to be the most open, authoritative source for timely and accurate housing data and unbiased insight. News. The Rental Market Slowdown is Leveling Off (June 2024 Rental Market Report) The Numbers May 2024 U.S. Typical Home Value (Zillow Home Value Index)

  24. Retraction Note: Empirical analysis of the feasible solution to

    The Publisher has retracted this article in agreement with the Editor-in-Chief. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised peer review process, inappropriate or irrelevant references, containing nonstandard phrases or not being in scope of the journal.

  25. Estimated changes in free sugar consumption one year after the UK soft

    Background The UK soft drinks industry levy (SDIL) was announced in March 2016 and implemented in April 2018, encouraging manufacturers to reduce the sugar content of soft drinks. This is the first study to investigate changes in individual-level consumption of free sugars in relation to the SDIL. Methods We used controlled interrupted time series (2011-2019) to explore changes in the ...

  26. Evaluation and Analysis of Trace Impurity Migration Pathways on the

    Separation and purification of fine chemicals with minor or trace amounts of impurity, for instance, metal ions, to produce high-end and high-pure chemicals has been of utmost importance in recent years. Understanding the key factors and impurity migration pathways is critical to designing a highly efficient crystallization-based purification process. This study combines theoretical models ...

  27. Retraction Note: The spillover of tourism development on CO2 ...

    The Publisher has retracted this article in agreement with the Editor-in-Chief. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised peer review process, inappropriate or irrelevant references, containing nonstandard phrases or not being in scope of the journal.

  28. United States

    Analysis and insights for driving a rapid transition to net-zero while building resilience to physical climate impacts Development co-operation Standards and guidelines for development co-operation with concrete examples of their implementation

  29. Bridgewise Launches AI-Driven Analysis for Global ETFs and Mutual Funds

    Its unbiased analysis, scoring and research can be seamlessly integrated into brokerage platforms and other financial institutions' infrastructures, offering instant analysis of global stocks ...

  30. How DataDome Detects Puppeteer Extra Stealth

    What is Puppeteer Extra Stealth? Puppeteer Extra Stealth is a plugin that enhances Puppeteer with advanced stealth features, making its detection more difficult. It achieves this by masking various browser characteristics commonly used to identify bots—such as modifying JavaScript global properties and mimicking human-like behavior.