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Content Analysis – Methods, Types and Examples

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

Content Analysis

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

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

Key Features of Content Analysis

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

Importance of Content Analysis

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

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

Types of Content Analysis

1. qualitative content analysis.

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

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

2. Quantitative Content Analysis

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

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

3. Summative Content Analysis

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

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

4. Relational Content Analysis

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

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

Methods of Conducting Content Analysis

1. define research questions and objectives.

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

2. Select Data Sources

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

3. Develop a Coding Framework

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

4. Analyze Data

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

5. Interpret Results

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

Steps in Content Analysis

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

Examples of Content Analysis

Example 1: social media analysis.

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

Example 2: Political Campaigns

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

Example 3: Academic Research

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

Example 4: Market Research

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

Advantages of Content Analysis

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

Disadvantages of Content Analysis

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

Applications of Content Analysis

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

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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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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Chapter 17. Content Analysis

Introduction.

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

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

An Overview of the Content Analysis Process

The first step: selecting content.

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

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

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

The Second Step: Collecting and Storing

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

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

Researcher Note

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

The Third Step: Analysis

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

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

The Fourth and Final Step: The Write-Up

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

Analyzing Content: Helpful Hints and Pointers

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

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

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

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

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

Table 17.1. Examples of What to Note During Content Analysis

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

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

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

Table 17.2. Examples of Unit of Analysis in Content Analysis

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

Further Readings

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

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

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

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

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

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

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

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

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

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

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

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

Content Analysis for Research – Complete Guide

Published 16 October, 2023

Content analysis is an important part of the research. It allows for the systematic examination and interpretation of a large body of information that can be used to develop new knowledge or test existing theories. Content analysis is a research method that provides insights into the relationship between content and context, or what people are saying about a particular topic. The results of content analysis can be used to inform decision-making in marketing, public relations, and other fields. This blog post will discuss how to do content analysis, including what it is and its types, use, advantages, and disadvantages.

Concept of Content Analysis for Research

Content analysis is a research technique for systematically analyzing written, visual, or spoken communication. It involves breaking down and examining the meaning of communication into its separate elements in order to identify patterns, relationships, themes, and trends.

Content analysis can be either a quantitative or qualitative approach which the researcher applies for analyzing the text. It is a type of analytical technique that you can apply in different types of research such as media, marketing, sociology, etc. It is a research technique that mainly emphasizes actual content. You can also consider content analysis to be the procedure of data analysis where the researcher performs an investigation on content within a message.

For example, The report containing information about the weather conditions of a particular location in the next few days. By analyzing the text or content you can easily make your judgment about traveling to a specific location.

Example of content analysis in quantitative and qualitative research

Quantitative research: An investigation which the author performs with the purpose of identifying the issues in employment. Investigators by analyzing the speech of different speakers in a campaign researcher can easily identify the frequency of words like jobs, work, and employment. They can utilize statistical techniques for analyzing the difference in a speech of different people during the campaign.

Qualitative research: Using the same above example, but here the researcher will require to locate the terms in speech and the investigator will require to address the other phrases that candidates have used for describing the term employment issues. Some of the candidates may have to use the terms laziness, unemployment for describing the situation of employment.

Therefore, considering the fact the researcher will need to develop an understanding of the relationship between two different terms.

Different types of text in content analysis

The various types of texts in the content analysis are:

Written text: Such types of text include books journals etc. Oral text: It includes oral speech. Iconic texts: These are basically drawing, paintings, etc. Audio-visual text: It includes TV programs, videos, movies etc. Hyper texts: These texts are generally found on the internet.

Uses of content analysis for research

Uses of content analysis in research are:

• Content analysis is an analytical technique that which you can use for performing both qualitative and quantitative research methods .
• You can apply content analysis in different fields such as media, marketing, cultural studies, etc.
• It is the analytical technique that the researcher uses for analyzing the global differences in communication content. You can also use this technique for analyzing the effectiveness of communication during the first meeting with the supervisor.
• The researcher uses Content analysis for judging intention, the purpose of an individual or group of people. Students can use content analysis to judge the intention of the tutor before making the final choice of the Dissertation supervisor . 
• Content analysis also provides you with ease in analyzing or addressing the emotional state of an individual.
• You can utilize the content analysis for determining the effect of external components present in the environment such as laws, code of conduct, culture, etc on message.

Importance of content analysis for research

• Content analysis is very much important as it enables you to develop an understanding of the relationship between various concepts in a text.
• In addition to this, content analysis is important as it will help you in developing the understanding of content and would support in addressing its key features.
• The content analysis is significant as it will help you in eliminating the unstructured content in a text.
• It will also support you in identifying the important facts in the content. It will also help you in determining the behavioral response to communication.
• Content analysis has great importance as it helps you to increase the validity of outcomes by applying the techniques for eliminating biases.

Types of content analysis for research

The two most common types of content analysis for research are:

Conceptual analysis

It is basically a type of content analysis that mainly involves quantifying the presence of the specific term in a text. Conceptual analysis entails the identification of concepts in the text. Rather than identifying specific words as such, concept analysis allows you to search for groups of words as they relate to a specific meaning or concept.

Relational analysis

It is a procedure that includes the identification of concepts there in the text . The relational analysis mainly emphasizes on examination of the relationship between various concepts present in a text. The relational analysis goes one step beyond the identifying of concepts and attempts to find meaningful relationships between multiple occurrences.

For example, analysis of the speech given by the president of America on health care services. While analyzing the speech of the president on health care services, the investigator will be more interested in quantifying the number of times positive or negative words for health care services are used by the president which is considered as conceptual analysis. But in relational analysis, the researcher is more concerned about analyzing the relationship between the texts and positive and negative words in speech.

Advantages of content analysis for research

You can get various advantages by utilizing content analysis. Some of the advantages are:

• Through content analysis, you can easily analyze the pattern of communication.
• You can utilize content analysis for performing both Quantitative and Qualitative operations.
• Another biggest advantage of content analysis is that it enables to development of the understanding of complex models related to the use of language or human thoughts.
• You can perform content analysis anytime and anywhere.

Disadvantages of content analysis for research

There are some disadvantages of content analysis for research:

• It is quite a time-consuming procedure
• There are high chances of error in the case when you use relational analysis for the purpose of interpretation of a text.
• It could be quite difficult to automate.
• It comprises of word count.
• Content analysis disregards the context which creates text.
• There are high chances of bias ness while performing coding of units.

Process of conducting content analysis for research

Before planning to perform the content analysis for research, you will require to formulate research questions .

For example: Is there any difference in a speech given by different candidates on employment issues during a social campaign?.

The further process of content analysis in research includes the different phases these are:

Step 1 – Selection of content for analyzing

It is the research question that you have select that will help in the selection of text. You need to identify the problem as to the first and foremost step.

• After making the choice of text you need to determine the medium that is sources of content. The different sources of content could be a website, speeches, and newspapers.
• After the selection of medium, you will require to establish a criterion for inclusion such as a newspaper that contains a description of a particular program or website.
• Then at the next phase, you will need to set parameters such as location, date range. If in case you are planning to analyze the large volume of text then in such a situation you will need to perform sampling for electing few people as simple in research.

For example, Media representing employment issues, and you intend to examine the article in news. As it is a large volume of content then you will need to select a few newspapers by applying the sampling method in research .

Step 2 – Defining the Units and categories of analysis

It is basically a phase in the content analysis where you need to establish the standard in order to analyze text.

• Develop useful, valid categories based on researchable questions
• Test a few items as a team or unit (either independently or as a group) to identify items that may be ambiguous or still need further clarification
• Code a subset of items using two independent analysts
• Refine categories, defining specifications about what should be included as well what should not be included when there may be uncertainty to ensure the reliability of coding

For example, you need to record the frequency of the term. In addition to this, you will also require to determine the categories which you can use for the objective of coding.

Step 3 – Develop a set of rules for coding

It is a stage in a content analysis where you need to arrange units according to the predetermined sequence. Here, it is very much essential for you to clearly define the regulation before performing coding as these tactics will help you in making the research methodology more consistent.

For example, considering the above example, you have to use the economy as a category. Then you have to determine that which term can relate to the employment issue,

Step 4 – Content coding according to the rules

You for performing the content-coding are needed to record all relevant information in their suitable categories. You code the text and record all the data in categories. This is done manually, but it can be computerized to make the process of counting and categorizing words and phrases a speedy task.

Step 5 – Analysis of outcomes and draw conclusions

Based on the coding scheme, review items by category – a number of items, percentage of items coded to this category, themes that emerge. It is a phase where you need to examine the information by doing so you will be able to identify pastern and make a research paper conclusion .

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The Essential Guide to Content Analysis in Research

Have you ever wondered how researchers make sense of the vast amount of content that media produces every day? From news articles to television shows and social media posts, there’s a treasure trove of data waiting to be analyzed. This is where content analysis comes into play—a meticulous method that researchers use to dissect and understand communication content. Let’s dive into the world of content analysis and uncover its significant role in media research.

Table of Contents

• What is Content Analysis?
• Quantitative Content Analysis
• Developing a Codebook
• Applications of Quantitative Analysis
• Qualitative Content Analysis
• Identifying Themes and Patterns
• Contextualizing Findings
• Research Questions Explored Through Content Analysis
• The Limitations of Content Analysis
• Overcoming Limitations
• Content Analysis in the Digital Age
• Advancements in Technology
• Challenges and Opportunities

What is Content Analysis? 🔗

Content analysis is a research technique used to systematically categorize and interpret text data from various media sources. The goal is to identify patterns, themes, or biases within the content and draw meaningful conclusions about the messages conveyed. This method can be applied in both quantitative and qualitative research, offering a versatile approach to understanding the nuances of communication.

Quantitative Content Analysis 🔗

Quantitative content analysis focuses on counting and comparing the frequency of certain words, phrases, or concepts within the media content. Researchers develop a codebook —a set of predefined categories that serve as a guide for systematically classifying the data. This process can offer insights into the prevalence of particular themes or issues across various media platforms.

Developing a Codebook 🔗

Establishing clear definitions: For a codebook to be effective, each category must have a clear and concise definition to ensure consistent coding.

Reliability: Multiple coders are often used to check for intercoder reliability , ensuring that the categorization is not subjective but consistent across different individuals.

Applications of Quantitative Analysis 🔗

Quantitative content analysis is particularly useful in tracking changes over time, such as how the representation of gender in advertising has evolved, or measuring the frequency of certain viewpoints in news reporting, providing a snapshot of media bias.

Qualitative Content Analysis 🔗

On the other hand, qualitative content analysis delves deeper into the context and meaning behind the content. Instead of counting occurrences, it aims to interpret the underlying themes and messages, offering a richer understanding of the communication content.

Identifying Themes and Patterns 🔗

Through meticulous reading and re-reading of the content, qualitative researchers identify recurring themes and patterns that may reveal societal norms or the subtext of media messaging.

Contextualizing Findings 🔗

Qualitative content analysis also involves placing the findings within the broader societal and cultural context, allowing researchers to draw connections between media content and its potential impact on audiences.

Research Questions Explored Through Content Analysis 🔗

Content analysis can address a variety of research questions, such as:

• Media Representation: How are different groups or issues portrayed in the media? Are there stereotypes or biases evident in the portrayal?
• Societal Norms: What does the content reveal about societal values or norms at a given time?
• Media Impact: How might media content influence public perception or behavior?

The Limitations of Content Analysis 🔗

Despite its utility, content analysis is not without limitations. The method depends heavily on the researcher’s interpretation, which can introduce subjectivity. Additionally, it does not account for the production process of the content or the audience’s reception of it—factors that are equally important in media studies.

Overcoming Limitations 🔗

Researchers can mitigate these limitations by employing triangulation —using multiple methods or data sources to validate the findings. Moreover, being transparent about the coding process and the development of the codebook helps bolster the credibility of the research.

Content Analysis in the Digital Age 🔗

With the advent of digital media, content analysis has expanded to include online content such as blogs, social media posts, and web pages. Digital tools and software have also emerged to assist researchers in handling large datasets, making the process more efficient and accurate.

Advancements in Technology 🔗

Text mining and natural language processing are examples of technologies that can automate the coding process, allowing for the analysis of vast amounts of data that would be unmanageable manually.

Challenges and Opportunities 🔗

While technology provides new opportunities for content analysis, it also presents challenges, such as the need for researchers to possess technical skills to handle complex software and the ever-changing landscape of digital media content.

Conclusion 🔗

Content analysis is a cornerstone of communication research, offering a window into the world of media content and its societal implications. Whether through quantitative or qualitative lenses, this method provides a structured way to dissect and understand the complex messages that bombard us daily. As media continues to evolve, content analysis will undoubtedly adapt, using new tools and techniques to keep pace with the changing landscape of communication.

What do you think? How do you see content analysis shaping our understanding of media in the future? Can the insights gained from content analysis influence the way media is produced and consumed?

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Communication Research Methods

1 Research: Concept, Nature and Scope

• Research: Concept and Role
• Growth and Development
• Importance of Research
• Research: Nature and Characteristics
• Purpose of Research
• Scope of Communication Research

2 Classification of Research

• Based on Design
• Based on Stage
• Based on Nature
• Based on Location
• Based on Approach
• Communicators
• Media Content
• Distribution

3 Defining and Formulating Research Problems

• Difference between a Social Problem and a Research Problem
• Importance of Review of Literature
• Questions of Relevance, Feasibility, and Achievability
• Research Questions, Objectives, and Hypotheses
• Defining the Terms of Enquiry

4 Sampling Methods

• Types of Sampling
• Sampling Error
• Non-Probability Sampling
• Probability Sampling
• Sample Size

5 Review of Literature

• Literature Review: Need and Importance
• Objectives of Review of Literature
• Evaluation of Material for Review
• Writing Review of Literature

6 Data Collection Sources

• Primary and Secondary Data
• Sources of Secondary data
• Sources of Primary Data
• How to Store and Save Your Data

7 Survey Method

• Salient Features
• Types of Surveys
• Data collection tools
• Types of Questions
• Designing a Questionnaire
• The Process

8 Content Analysis

• Conceptual Foundations
• Characteristics of Content Analysis
• Types of Content Analysis
• Process of Content Analysis
• Let Us Sum Up

9 Experimental Method

• Nature of Experimental Method
• Classic Experimental Research Design
• Process of Experimental Research
• Experimental Design
• Field Experiments
• Merits and Demerits of Experimental Method

10 Interview Techniques

• Interview: Concept and Types
• Informal Interviews
• Structured Interviews
• Semi-structured Interviews
• Unstructured (Indepth) Interviews
• Interviewing Skills
• Ethical Issues

11 Case Study Method

• Case Study: A Qualitative Method
• Research Paradigms
• Main Features of Case Study Method
• Functions of Case Study
• Types of Case Studies
• Case Study Method: Strengths and Limitations
• The Process of Case Study

12 Observation Method

• Characteristics of Observation Method
• Strengths and Limitations
• Types of Observation
• Process of Observation
• Ethical Issues in Observation

13 Semiotics

• Texts and the Study of Signs
• Classification of Signs
• Paradigms and Syntagms
• Encoding and Decoding
• Social Semiotics

14 Basic Statistical Analysis

• Introduction to Statistics
• Populations and Samples
• Scales of Measurement
• Frequency Distribution
• Measures of Central Tendency
• Variability

15 Data Analysis

• Different Research Perspectives
• Handling Quantitative Data
• Qualitative Data Analysis
• Drawing Conclusion Through Data Analysis

16 Report Writing

• Stages in Report Writing
• The Beginning
• Main Body of the Report
• The Final Section
• Effective Writing

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• Knowledge Base
• 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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