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Essays on Media Analysis

What makes a good media analysis essay topic.

When embarking on the quest to find the perfect topic for a media analysis essay, it is crucial to select one that not only captivates but also provides ample opportunities for analysis. Here are some innovative recommendations to fuel your brainstorming process and aid in the selection of an outstanding essay topic:

Brainstorm: Begin by jotting down all the media-related subjects that pique your interest. Explore various forms of media, including television, movies, social media, news articles, and advertising campaigns.

Research potential topics: Once you have a list of potential topics, conduct preliminary research to ensure that there is enough information available to support your analysis. Seek out recent and relevant sources that offer diverse perspectives.

Choose a specific angle: Narrow down your topic by selecting a specific aspect or angle to analyze. Instead of analyzing generic "television shows," for example, you could focus on the portrayal of gender roles in reality TV programs.

Consider significance: Evaluate the significance of your chosen topic. Does it address a current issue or prevalent challenge in society? Opt for subjects that have broader implications and can generate meaningful discussions.

Uniqueness: Strive for a topic that stands out from the ordinary. Avoid overdone subjects and aim for creativity and originality. Look for unique angles or lesser-known media artifacts to analyze.

Personal interest: Lastly, choose a topic that genuinely interests you. A personal interest in the subject matter will make the writing process more enjoyable and result in a more engaging essay.

Remember, a good media analysis essay topic should be specific, relevant, unique, and align with your personal interests. Now, let's embark on an exploration of the best media analysis essay topics that meet these criteria.

The Best Media Analysis Essay Topics

The Influential Role of Social Media in Shaping Body Image Perception Among Teenagers

Analyzing the Portrayal of Mental Health in Popular TV Shows

The Impact of Media on Political Opinion Formation during Election Campaigns

Examining the Representation of Race and Ethnicity in Hollywood Movies

The Power of Advertising: Its Influence on Consumer Behavior and Purchasing Decisions

Provocative Questions to Guide Your Media Analysis

To delve deeper into these media analysis essay topics, ponder these ten thought-provoking questions:

How does social media contribute to the objectification of women?

In what ways does mainstream media perpetuate racial stereotypes?

How does the portrayal of violence in video games affect children's behavior?

To what extent do advertising campaigns exploit insecurities to sell products?

How does political bias influence news reporting in mainstream media?

Inspiring Prompts for Your Media Analysis Essay

Here are five imaginative essay prompts to ignite your creativity in the realm of media analysis:

Analyze the use of symbolism in a specific music video of your choice and examine its impact on the audience's interpretation.

Discuss how a particular news outlet's coverage of a recent event demonstrates media bias and explore its potential consequences.

Examine the marketing strategies employed in a successful viral advertising campaign and assess their effects on brand recognition and consumer behavior.

Compare and contrast the representation of technology and its impact on society in two science fiction films.

Critically analyze the portrayal of marginalized communities in a specific TV series and its influence on societal perceptions.

Frequently Asked Questions about Writing a Media Analysis Essay

Q: How should I structure a media analysis essay?

A: A media analysis essay typically follows an introduction, body paragraphs analyzing different aspects, and a conclusion. Ensure that each paragraph focuses on a specific argument or analysis point.

Q: Can I incorporate personal opinions in a media analysis essay?

A: While media analysis essays should strive for objectivity, you can include your interpretation and analysis of the media artifacts. However, always support your claims with evidence and examples.

Q: How can I find relevant sources for my media analysis essay?

A: Utilize academic databases, reputable news outlets, scholarly articles, books, and credible online sources to gather relevant information and support your analysis.

Q: Should I include a thesis statement in my media analysis essay?

A: Yes, a clear and concise thesis statement is essential in a media analysis essay. It should convey your main argument or analysis focus.

Q: Can I analyze media artifacts from different time periods in one essay?

A: It is generally recommended to focus on a specific time period or media artifact in each essay. This approach allows for a more in-depth analysis and prevents the essay from becoming overly broad.

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Media analysis refers to the systematic examination and interpretation of media content, including various forms of media such as print, broadcast, and digital media. It involves critically analyzing and evaluating the messages, themes, and techniques employed in media to understand their impact on individuals, society, and culture.

Media analysis aims to uncover underlying meanings, implicit messages, and societal implications within media texts. It involves studying elements such as narrative structures, visual aesthetics, language use, cultural representations, and ideological biases present in media productions. Through media analysis, researchers and scholars aim to gain insights into the construction of meaning, power dynamics, and social influences propagated by media. It helps uncover patterns, trends, and dominant discourses within media representations, shedding light on how media shapes public opinion, influences perceptions, and reflects societal values. By examining media content critically, media analysis contributes to a deeper understanding of the role of media in shaping narratives, influencing public discourse, and impacting social, cultural, and political dynamics.

Media Texts: Analysis of news articles, television shows, films, advertisements, social media posts, and websites. Representation: Analysis of the representation of individuals, groups, events, and ideas in media. It examines how different social, cultural, and political identities are portrayed and the impact of these representations on shaping perceptions, stereotypes, and biases. Audience Reception: This involves examining audience responses, interpretations, and the influence of media on attitudes, beliefs, and behaviors. Media Institutions: It examines the ownership structures, industry practices, and policies that shape media content and its dissemination. Media Effects: This involves studying the influence of media on public opinion, social behavior, cultural values, and political processes.

Content Analysis, Semiotic Analysis, Discourse Analysis, Audience Research, Comparative Analysis, Historical Analysis, Critical Cultural Analysis.

The topic of media analysis holds significant importance when writing an essay due to several reasons. Firstly, media plays a pervasive role in modern society, shaping our perceptions, attitudes, and behaviors. Analyzing media allows us to examine its influence and understand how it constructs narratives and shapes public opinion. Secondly, media analysis helps in critically evaluating the accuracy, credibility, and biases present in media content. By examining the techniques, messages, and underlying ideologies, we can uncover hidden agendas or misrepresentations. This analysis contributes to a more informed and nuanced understanding of media's impact. Furthermore, media analysis enables us to explore the social, cultural, and political implications of media representations. It allows for an examination of power dynamics, social inequalities, and the perpetuation of stereotypes. By scrutinizing media, we can uncover hidden meanings and challenge dominant narratives. Lastly, media analysis promotes media literacy and critical thinking skills. It equips us with the tools to navigate the complex media landscape, distinguishing between reliable information and misinformation. By engaging in media analysis, we become active participants in the media discourse, fostering a more informed and empowered society.

1. Anstead, N., & O'Loughlin, B. (2015). Social media analysis and public opinion: The 2010 UK general election. Journal of computer-mediated communication, 20(2), 204-220. (https://academic.oup.com/jcmc/article/20/2/204/4067564) 2. Ravaja, N. (2004). Contributions of psychophysiology to media research: Review and recommendations. Media Psychology, 6(2), 193-235. (https://www.tandfonline.com/doi/abs/10.1207/s1532785xmep0602_4) 3. Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3, 1277-1291. (https://link.springer.com/article/10.1007/s13278-012-0079-3) 4. Filo, K., Lock, D., & Karg, A. (2015). Sport and social media research: A review. Sport management review, 18(2), 166-181. (https://www.sciencedirect.com/science/article/abs/pii/S1441352314000904) 5. McQuail, D. (1985). Sociology of mass communication. Annual Review of Sociology, 11(1), 93-111. (https://www.annualreviews.org/doi/abs/10.1146/annurev.so.11.080185.000521) 6. Lockyer, S., & Pickering, M. (2008). You must be joking: The sociological critique of humour and comic media. Sociology Compass, 2(3), 808-820. (https://compass.onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-9020.2008.00108.x) 7. Arsenault, A., & Castells, M. (2008). Switching power: Rupert Murdoch and the global business of media politics: A sociological analysis. International Sociology, 23(4), 488-513. (https://journals.sagepub.com/doi/pdf/10.1177/0268580908090725 )

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Media Analysis – An Explanation for Undergraduates

Media analysis is a research methodology used in mass communication studies, media studies, cultural studies, and the social sciences. It is defined as the analysis and critique of media.

The aim of media analysis is to understand media’s potential to impact individuals and society. Media analysis has two main purposes:

• Critique of Media: It can identify how groups in society such as women and people of color are represented in the media to help us understand systemic racism and sexism, and can help expose media bias .
• Media Campaign Research: It can also help media companies identify gaps in the advertising landscape to better promote their own products.

What is Media Analysis?

Media analysis studies texts: books, letters, videos, television shows, blogs, movies, newspapers, etc. It looks directly at media texts (rather than interviewing media producers) and reflects on what they collectively say about an issue. Here are some useful scholarly definitions that you could use in an essay:

• Media analysis is the study of “what is said on a given subject in a given place at a given time” within the media (Lasswell, Lerner and Pool, 1952, p. 34) – this is one of the first ever definitions.
• “Content analysis is a research method that uses a set of procedures to make valid inferences from text” (Weber, 1990, p. 9)
• “Critical media analysis means thinking critically about the impact of the media on the distribution of power in society.” (Stocchetti & Kukkonen, 2011, p. 13)
• It “is a research technique that is based on measuring the amount of something (violence, negative portrayals of women, or whatever) in a representative sampling of some mass-mediated popular form of art” (Berger, 2005, p. 25)
• It is “a technique for gathering and analysing the content of text.” (Neuman, 1997, p. 272)

How to do Media Analysis

Media content analysis can be conducted in multiple ways. But, media analysis has two core elements that must always be looked at systematically: the text and its content.

The text is the thing you look at while conducting your analysis. Neuman (1997, p. 273) describes a text as: “anything written, visual, or spoken that serves as a medium for communication”. Usually, we try to look at a wide range of texts within a defined period of time (say, maybe all superhero movies in 2020; or, all newspaper articles published in national newspapers about Trump in July 2020). This helps increase the validity of the analysis. Texts can be:

• Newspaper articles
• Email chains
• Television shows
• Advertisements
• YouTube videos
• Etc. etc. etc.

The content is the ‘stuff’ that you analyze within the text. Neuman (1997, p. 273) defines content as “words, meanings, pictures, symbols, ideas, themes, or any message that can be communicated.” To analyze this content, we might count the amount of positive versus negative statements about someone, how a camera frames someone as powerful or weak, the amount of time someone is given to speak, and so forth. Generally, content can be broken down into four categories:

• Written: words, sentences, paragraphs, etc.
• Sonic / Audible: spoken words, music, sound effects, etc.
• Visual: Images, pictures, color schemes, camera angles, facial expressions, etc.
• Motive: The pace at which things move, the direction they move, etc.

Quantitative Techniques

‘Quantitative’ approaches to media analysis use measurable scientific approaches to analyze media texts. These approaches will involve counting exact numbers, ratios, percentages, etc. to get objective facts about media representation. Below are the two major quantitative approaches to media analysis.

1. Quantitative Content Analysis

Quantitative methods count the numbers of mentions, keywords, latent semantic keywords, etc. in order to create measurable comparisons. Comparisons can be made between media texts (e.g. “Which media are more inclusive of women?”, or between elements within a text (e.g. “What is the ratio between white and non-white representation within this text?”). Usually, software tools are employed during quantitative content analysis to create a reliable and objective overview of media representation.

2. Laswell’s Method

Laswell’s method is the oldest method of media analysis. For Laswell, you can do a simple critique of media representation by asking the following 5 questions:

• Who? Look at the media channel doing the communication. Are they respectable? Are they historically biased? Do they follow journalistic ethics? Who funds them?
• Says What? Look at what is being said. How does it frame the issue?
• In which Channel? Look at the means of communication. Is it television, blogs, podcasts, etc.? How does the channel / medium impact the message being communicated? Is it a medium that attracts millenials, or baby boomers?
• To Whom? Look at who the target audience is. What might this say about why the message is framed the way it is?
• With what Effect? Has the media had an impact on politics, public discourse, the growth of certain movements, or the increased sale of certain products?

3. Quantitative Approach – Advantages & Disadvantages

Advantages of a quantitative approach:

• It can seem more reliable because it provides objective figures.
• It provides direct measurable comparisons.

Disadvantages of a quantitative approach:

• Lack of context. Often, only subjective human analysis can identify how media manipulates people.
• Media analysis is about looking at how media is manipulative; it’s hard to use machines to pick up on the nuances of media techniques.

Qualitative Techniques

Qualitative methods are much more common for media analysis these days. Many researchers have realized it’s very hard to provide a deep analysis of media texts using hard scientific methods .

There are a lot of little human nuances in meda that require deep explanations and a critical human eye critiquing texts. This is where qualitative approaches are very beneficial. Below are the two major qualitative approaches to media analysis.

1. Social Semiotics

‘Semiotics’ is the study of signs and symbols. It was invented by Ferdinand de Saussure who explored how ‘signs’ create ‘meaning’. ‘Social semiotics’ is a more contemporary approach, which not only looks at signs. It also looks at how signs get their meaning from culture. For example, a red octagon doesn’t naturally mean ‘Stop sign’. But, in our culture, we know that it nearly always means that because it’s the meaning our culture gave to the sign. To do a Social Semiotic Analysis, closely examine the texts you want to analyse. Watch / read / listen to them and take notes on the contents:

• Sounds: What sounds are present and how do they influence the message? For example, if there is classical music, it may mean a different audience is appealed to than rap music. We know this because we have a finger on the pulse of our culture – we know what social groups rap music would appeal to.
• Words: Are there words or phrases that jump out to you for the way they frame particular groups? Take note of these words and phrases and how frequently they’re used.
• Images: How do the images influence us? If the color scheme is mostly blue, perhaps the text is designed to soothe and calm us. If there are images of someone in a white doctor’s coat, is it an advertisement trying to tell us that the product is backed by science? If there are low camera angles looking up at someone, is it trying to make that person appear powerful? Etc.

A social semiotic analysis would then create a group of themes to discuss. A theme might be: “Women are represented as powerful in this text.” Another might be: “Most dental advertisements use scientific language to convince viewers.”

2. Discourse Analysis

Discourse analysis explores discourses (messages circulating in society). It was created by Michel Foucault in the 1970s. It has become a very popular way of examining media texts to figure out how power is reproduced through media bias . Discourse analysis is very similar to social semiotics. In fact, I would recommend combining the two. However, discourse analysis is unique in that its focus is on power. It wants to explore how media silences some people and empowers others. Here are some unique aspects of a discourse analysis to look out for when looking at media texts:

• Who is silenced by the text? When closely examining your texts, think about who is absent in the text. This means not just looking at what’s said and shown. You also need to look at what isn’t said. What’s not shown is just as important as what is shown.
• What do silences say about the message? Once you know what isn’t said and shown, what can you infer from this? Is the media conveniently excluding certain points because they don’t adhere to capitalist consumer society? Are marginalized groups and their views missing from mainstream media?
• What is presumed as ‘true’ and what is presumed as ‘untrue’ within media messages? According to discourse analysis, truth is produced by discourse (the messages that circulate in society). So, discourse analysis critiques what is presumed to be true and untrue within media and how this might change over time.

3. Qualitative Approach – Advantages & Disadvantages 

Advantages of a qualitative approach:

• Human communication is very hard to measure quantitatively. Quantitative methods can’t pick up the subtle cultural, social and political messages in media.
• Qualitative research gives deep, detailed explanations using ‘thick description’ of data. It can be very convincing, if done well.

Disadvantages of a qualitative approach:

• Validity and authority is hard to achieve because researcher interpretation is central to this style of research.
• It has been accused of bias and hyper-subjectivity. Many people see it as a psudo-science where any researcher can come up with any results they want so long as their arguments are convincing. See: the grievance studies hoax.

Example of Media Analysis

“How do Car Advertisements on Television Represent Women?”

You gather all car advertisements in the national archives of advertising from the past 3 years. It’s 250 advertisements. You decide to conduct a media discourse analysis. You watch all advertisements, and take notes on:

• How many advertisements depict women
• What roles women take in the advertisements
• How women are spoken about in the advertisements

You review your notes, and find three themes:

• Women are only shown in 25% of advertisements
• Women are driving trucks in only 5% of advertisements
• When women are depicted, they’re predominantly sexualized and shown as objects of men’s desire

Strengths & Weaknesses of Media Analysis

• It helps to show how media contributes to social and cultural biases which could marginalize some members of society.
• It helps us reflect on power relationships.
• It can create a case to media departments about how best to advertise a product in the marketplace.
• It is often accused of having very little real-life relevance . A descriptive overview of media’s biases may be a good academic exercise, but it’s not the most desirable skill to have for future employers.
• There is so much media these days that it’s hard to get a snapshot of the whole media landscape. You usually have to zoom-in on small market subsets which are case studies that cannot provide broad overgeneralizations .

Altheide, D. & Schneider, C. (2013). Qualitative Media Analysis. Los Angeles: SAGE.

Berger, A. (2005). Media research techniques. Newbury Park, CA: Sage.

Fairclough, N. (2010). Critical analysis of media discourse. In: Thornham, S., Bassett, C., & Marris, P. (Eds.). Media studies: A reader . New York: NYU Press.

Kress, G. R., & Van Leeuwen, T. (2006). Reading images: The grammar of visual design . Sydney: Psychology Press.

Macnamara, J. (2005). Media content analysis: Its uses, benefits and Best Practice Methodology. Asia Pacific Public Relations Journal, 6 (1), 1– 34.

Neuman, W. L. (1997). Social research methods: qualitative and quantitative approaches. Needham Heights, MA: Allyn & Bacon

Stocchetti, M. & Kukkonen, K. (2011). Critical Media Analysis: An Introduction for Media Professionals. Frankfurt: Peter Yang.

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Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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3 thoughts on “Media Analysis – An Explanation for Undergraduates”

Dear Chris, Thank you for mentioning our books. However, you have misspelled our names. ‘Stocchetti’ is with two ‘c’ and it is ‘Kukkonen’, and not ‘Kukkonon’

Matteo Stocchetti

Apologies – that’s been fixed. Thanks for stopping by and thanks for your useful book on Media Analysis!

Regards, Chris

I found this so interesting and useful as a media analyst in the making. Thanks to you Dr. Chris.

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How to Do a Media Analysis

Last Updated: February 9, 2024

This article was co-authored by Noah Taxis . Noah Taxis is an English Teacher based in San Francisco, California. He has taught as a credentialed teacher for over four years: first at Mountain View High School as a 9th- and 11th-grade English Teacher, then at UISA (Ukiah Independent Study Academy) as a Middle School Independent Study Teacher. He is now a high school English teacher at St. Ignatius College Preparatory School in San Francisco. He received an MA in Secondary Education and Teaching from Stanford University’s Graduate School of Education. He also received an MA in Comparative and World Literature from the University of Illinois Urbana-Champaign and a BA in International Literary & Visual Studies and English from Tufts University. This article has been viewed 37,711 times.

A media analysis reviews a broad swath of news stories on a given subject. Media professionals may use media analysis to decide how to frame a story that they want to publish, such as by helping them choose specific terms and rhetorical appeals to use. This is also a common assignment in communications and journalism courses, so you might also do this as a student. Start by collecting news stories and then analyze them by asking and answering questions about them.

Collecting Stories to Analyze

• For example, you may include the local newspaper, radio station, web news sources, and possibly any major news sources in the nearest big city if you’re in a rural area or suburb.
• Alternatively, you might want to focus on national or worldwide news sources to analyze a larger company or subject.

• For example, if you’re conducting a media analysis of the controversy over a major highway construction project in your city, then you might include terms like, “highway construction,” “highway controversy,” “construction budget concerns,” etc.

• Make sure to include a variety of different types of media sources unless you’re hoping to examine a specific medium, such as TV, radio, or print news.

Tip : If desired, you may expand your search to cover a longer period of time, such as 12 months. This may result in a more thorough study of the topic.

• Separating the data into categories can help you know what to expect when you start reading a story.

Analyzing the Stories

• Buzzwords, which are terms that come up again and again across different media channels.
• Bias, which is using emotional appeals to convince readers of something even if the evidence is lacking.
• Similar portrayals of a story, such as portraying it in a positive or negative light across different media channels.
• Positioning of the story, such as whether it’s a front-page or prime-time news story.

Tip : The length of the story may also help you to determine its importance. For example, if it's a short story that appears on one page, the news outlet may deem it less important than something that takes up multiple pages.

• How does the media frame this topic?
• Who are the spokespeople for the topic and how are they being represented?
• Are any voices noticeably absent from the articles on this subject?
• What topics are getting the most coverage within the category?
• What media outlets are covering this topic?
• Does coverage seem to peak or drop at certain times of the year?

• For example, if you have noted that most news outlets portray your subject using a set of buzzwords and a similar level of bias, then you may describe and discuss these.

• For example, if the sources you consulted all portray a public concern in a similar light, then you might want to adopt this method of framing your topic as well.

Structuring a Media Analysis Essay

• For example, you might begin by saying that your topic is an upcoming election in your community and that you wanted to do a media analysis to determine how to introduce your own story on the topic. Then, you might conclude by saying what media channels have in common in their presentation of this topic.

Tip : Make sure to clarify any special terms or details that your readers might not understand in this section as well.

• What aspects of the topic are being covered?
• What buzzwords do the media channels use?
• Do the media channels tend to show bias on the subject, and if so, how?

• This can help you to determine what types of spokespeople to include in your own article.

• For example, you might notice that the “hero takes a fall” archetype is used frequently for the articles in your topic area. This might mean that choosing this frame for your story could be beneficial.

• For example, if you recommend including a business professional, professor, and a member of the community in coverage of a story, cite the data you have collected that shows these spokespeople as the picks for stories on your topic.

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• ↑ https://penandthepad.com/how-2317746-write-media-analysis-papers.html
• ↑ http://www.pointk.org/resources/files/gould_media.pdf
• ↑ https://ecu.au.libguides.com/research-methodologies-creative-arts-humanities/media-analysis

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29 Media Content Analysis: Qualitative Methods

Michael Neal recevied an MA in Media Psychology in 2011 from Fielding Graduate University in Santa Barbara, California. He is founder, CEO, and Chairman of SignalDemand. He was previously co-founder of DemandTec (Nasdaq: DMAN), the largest provider of consumer demand management software. Previously he consulted at Deloitte and Accenture, applying mathematics to key problems in the Food and Consumer Goods industries, and he holds several patents in related fields. Neal earned his MBA from the J. B. Fuqua School of Business at Duke University and a BA in Economics and Statistics from the University of Florida. He serves as director on several corporate and industry association boards.

• Published: 28 January 2013
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Although qualitative methods traditionally suffer from concerns about reliability, validity, and researcher bias (see Chapter 8), new computer-assisted methods can assist in addressing these issues. Intelligent software, integrated with mixed methods and visualizations can be a valuable tool for media psychology researchers. Techniques for automated text coding assistance, concept, theme, and data relationship extraction with advanced text analytics capabilities provide the ability for researchers to examine large volumes of poor quality data, such as those found in social media studies. The current chapter examines quantitative, qualitative, and text analytics methods within the context of a qualitative media content analysis. For media researchers, an example within the chapter provides insight into many of the challenges of extracting meaning from text. As an exemplar of integrated methods, this chapter extends the exploration of a definition for media psychology from the Introduction of this handbook to derive a definition of media psychology from the actual content of the Handbook.

Content Analysis of the Oxford Handbook of Media Psychology

The proliferation of electronic data via social media, traditional media on the Internet, and digital translations of audio and video content provides a rich and vast source of data for analysis by social scientists. Although the availability of usable data has increased significantly, the ability to analyze that very data is challenged due to traditional manual analysis methods with significantly increased data volumes. For example, manual coding and analysis of hundreds of thousands of Twitter messages is impractical for many research projects. Therefore, many traditional methods require a sampling strategy to reduce data sizes to a manageable collection. Unbiased sampling strategies for data collection and reduction are often difficult to create for these data, and time to publish results is often shorter because of the timeliness and relevance of those data. New methods for content analysis are needed to meet these demands for emerging media psychology research. One approach is a content analysis that incorporates elements of grounded theory, intelligent and (at least partially) automated computer software, and generous use of visualization techniques to assist researchers with new and integrated analysis methods.

Early practitioners of media studies conducted content analyses on printed books, and these systematic analyses of texts were performed several times by religious scholars prior to 1900 (Krippendorff, 2004 ). Early media content analyses included propaganda studies from World War II, notably those by Harold Lasswell (Neuendorf, 2002 ). In 1954, James Flanagan provided a formal method for analysis called critical incident technique (CIT), which provided specific procedures for psychological research (Wertz et al., 2011 ). The General Inquirer Project at Harvard in the 1960s was created to analyze written messages (Neuendorf, 2002 ). More broadly, the field of qualitative research and the use of that term expanded greatly in the late 1980s and 1990s (Wertz et al., 2011 ). Today, qualitative research and content analysis techniques continue to evolve as a mainstream science, which strives to adapt to changing data and research needs (Wertz, et al., 2011 ).

In the 21st century, content and data for media psychologists are found in a range of sources from historical records and massive media collections, including newspapers, magazines, websites, blogs, text messages, tweets, Facebook pages, and emails. Audio content, such as radio programs, interview transcripts, and conversations, can be transcribed into text. Likewise, video, such as television, movies, news footage, and You Tube videos, can also be transcribed or computer translated into text. Finally, images can be described with added text and metadata (additional or ancillary information about the data) encoded with demographics and other descriptive data. All of these sources may be combined or examined individually as part of a study. This extensive proliferation of traditional, electronic, and social media data is leading to strong interest in content analyses, more powerful software tools, and integrated methods.

With more tools and formal methods available to media psychology researchers, high quality analysis for a wide range of media is possible and can further advance this emerging field. There are many potential methods that are software assisted and augmented by visualization, and several of these techniques are discussed later in this chapter when meaning making is covered. One such analysis will be performed on this handbook as both an exemplar and a meaning extraction exercise. This will assist creating a definition of media psychology derived from this first edition of the Oxford Handbook of Media Psychology .

Media Content Analysis Methods

Traditionally, content analysis is divided into quantitative (counting) and qualitative (meaning) methods, which are used separately or together (i.e., mixed methods) to interpret data. Quantitative measurements are generally straightforward and noncontroversial because they primarily involve counting words or concepts. Concepts are symbols, often textual, that represent categories or actual objects in the world (Lakoff, 1987 ). Qualitative analyses, however, can be controversial and subject to criticism, replicability, and doubt because these analyses are not reduced to mathematical equations with accepted defined use-case scenarios. There are many qualitative methods for interpreting text, and researcher skill, experience, bias, and process affect the outcomes. In media content analyses, these issues also exist, as well as how to handle large amounts of data.

Media qualitative analysis, especially a content analysis, has the central problem of how to reduce the complete text corpus (i.e., text collection) to smaller sets of text and concepts (Weber, 1990 ). This reduction is often the first step in a meaning-making exercise where extraction of key elements is performed based on the researcher's knowledge and method. These key elements are expressed as concepts. These concepts then become their own unit of meaning (Popping, 2000 ).

Important aspects of content analysis include sampling, units of measure, coding, validity, and reliability, which can alleviate concerns over repeatability and bias. An overview of both quantitative and qualitative methods for content analyses is explored within the framework of media-specific analyses. Note this chapter is concerned with content analyses of text data or data converted to text. For an examination of multimedia analysis techniques see Ali, Lee, and Smeaton ( 2011 ).

Quantitative Analysis

Quantitative analysis is concerned primarily with counting and statistics. There are several aspects of measuring in content analysis. One is to focus on the concepts themselves. To begin a quantitative analysis, because of the volume of some data collections, the researcher should be cognizant of preferred parameters of concept extraction. Carley ( 1993 ) provided guidance for researchers for approximate quantitative ranges for a desired number of concepts by proposing that 100 to 500 concepts provides sufficient generalization for a proper analysis, whereas fewer than 25 concepts hides meaning. The number of concepts generated can typically be configured in a software program.

Once the general parameters of extraction are determined, the measurements should be designed. Numeric measurements often include word, or concept, frequencies. The existence of a word (i.e., frequency 〉0) may itself indicate meaning, or the number of times a word or concept appears and where it appears may indicate a different or expanded meaning. Words can be combined into more general concepts and concepts into themes to provide associations. One method of determining associations is the proximity between concepts as calculated by the distance between them, which indicates if the words “co-occur.” Co-occurrence frequencies of words and concepts can be used to determine relationships and assist with meaning extraction by providing the researcher an indication of the concepts that are discussed together most often.

To extract meaning, the researcher must make observations or establish a method for analysis. For example, the top ten concepts might be meaningful. The co-occurring words to specific concepts will also show relationships, especially if emotive words are used (i.e., potential sentiment indicators). The frequencies themselves and relative counts are indications of strength, or coverage, across the text collection. Substantial drop-offs in frequencies or other statistics may also serve to group similarities of concepts or relationships of concepts. Prior to completing the study design, it is incumbent on the researcher to explore all quantitative measures available though software tools and manual processes.

While quantitative measures can provide insight, it is important to also analyze the text in its own context to retain meaning (Miles & Huberman, 1994 ). Numbers often have a meaning specific in the context, and this should be part of the analysis. For example, one method of analysis is to create a category and assign word frequencies for words found in each category. For a study examining the number of violent incidents against women in a television drama series, example categories might be threatening language, demeaning statements, and actual violent acts. The strength of representations of the frequencies in these categories provides a measure of meaning (Weber, 1990 ). In addition to meaning extraction itself, differences in frequency counts of words and concepts can also be used for comparative analysis of texts or sub-collections of texts (Carley, 1993 ). Thus, meaning can be inferred from purely numerical analysis, but the context and richness found in textual data augment meaning with examples and details.

Qualitative Analysis

Qualitative research in media psychology focuses less on numeric data measurements and more on the meaning embedded in the data. The field of qualitative analysis has evolved over the previous decades with many distinct types of analyses. Qualitative data are unique and can offer insights into information because the data can preserve chronology, consequences of specific events, and often detailed explanations (Miles & Huberman, 1994 ). The analyses of these data in the social sciences are broad-based and can also provide insight into experiences, emotions, and cultural phenomena (Strauss & Corbin, 1998 ). Due to the varied nature of the data, wide-ranging researcher questions, and methods for examining these data, many methods have evolved with different approaches and techniques.

The research question itself guides the selection of the specific qualitative method by a set of characteristics (Richards & Morse, 2007 ). Whichever method is selected, however, the primary difficulty with analyzing any text collection is often the determination of meaning. Different analysts can and often do read the same text and extract different meanings. The researcher makes a determination of both what he or she sees in the data as well as how to describe it, and the process of interpretation is left to the researcher. In a purely traditional qualitative examination, it is solely researcher interpretation that makes meaning.

The phrase “interpretation of meaning” does not imply that no formal methods exist for this process. In fact, there is a strong historical basis for qualitative research as a science (see Packer, 2011 ). Many well-established qualitative analysis methods have been developed which inform media studies. In particular, Wertz et al. ( 2011 ) provided a case study for five qualitative analysis techniques: phenomenological psychology, grounded theory, discourse analysis, narrative analysis, and intuitive inquiry. Wertz and colleagues provided a running conversation among the five authors and experts in their respective fields analyzing the same data. This insightful book also provides an exemplar of how different approaches may be valid and answer similar or diverse research questions across a single data collection. Frost et al. ( 2010 ) described the project Pluralism in Qualitative Research (PQR) undertaken to explore integration of several methods. This project examined approaches and methods of several research assistants in analyzing data with four qualitative methods to better understand processes and nuances of differing methods. These efforts show that integration and use of multiple qualitative methods in a single study warrants attention. As Wertz et al. noted, qualitative traditions share roots and historically inform each other. Therefore, even if quantitative measurements and computer-assisted software are only part of the process, media psychology researchers should consider these traditional approaches for their analyses as well.

One qualitative methodology, which supports the discovery of concepts, themes, and relationships in the data, is grounded theory. Traditional grounded theory described by Glaser and Strauss ( 1967 ) leads to the discovery of theory and uncovers unknown qualities about the data. In media studies, a well-defined question at the inception of a study is not always the case. The more open the question, the more discovery is done and, typically, the more objective the analysis. Because data drive the results, grounded theory is often considered to be the best method to reduce researcher bias in qualitative analyses. The codes in grounded theory assist the researcher in segmenting, classifying, and dividing the data according to conceptual meaning (Wertz et al., 2011 ). This approach alone may or may not provide an answer to a specific research question or thoroughly illuminate a collection of media Therefore, researchers can and often should combine methodologies by adding additional forms of qualitative research to their overall process. For example, a combination of grounded theory and content analysis can be an excellent approach (Bernard & Ryan, 1998 ).

As previously observed, a central fundamental component of grounded theory or any of these formalized qualitative methods is the question of how to derive meaning from texts. If a researcher is examining a manageable collection of short texts and is an expert in his or her field, a particular method such as those listed by Wertz et al. ( 2011 ) is appropriate. However, if the number of texts is large or a simple content analysis is desired at the start of a project, either for meaning making, data reduction, or as a navigation and search aid, there are more technical approaches to be examined. These include several specific techniques informed by text analytics such as content extraction, semiotic analysis, semantic network, and part of speech analysis.

While grounded theory describes an approach for making meaning, there are many techniques to perform this task. First, computer assisted or automated text analysis performs the function of bringing organization and structure to naturally unstructured text collections (Popping, 2000 ). Next, data must be synthesized into output a researcher can analyze, make inferences from, and explore the underlying data in order to extract meaning.

Text coding is the process of examining text in a specific, measurable unit and extracting relevant data. Researchers look for words, phrases, and word sense from defined measurements of units of text (i.e., words, sentences, paragraphs, tweets). Coding is accomplished by progressively identifying and integrating categories to compose meaning (Willig, 2008 ). There are three general methods of coding: (1) manual: by person(s) coding from codebooks, instructional guides, or intuition; (2) computer-assisted: beginning with coding then often some automation for remaining documents; and (3) computer generated: by statistical algorithms or network analysis informed by text analytics techniques.

There are several computer-assisted techniques for data reduction and meaning making from large text collections. Hopkins and King ( 2010 ) provided a method analyzing multiple document sets and determining estimates of coverage for researcher input categories of interest. They provided document generalizations appropriate for many social science content analyses. Smith and Humphreys ( 2006 ) provided a system that performs statistical analysis over an entire collection, generates a coding dictionary from that specific corpus, and identifies concepts, themes, and co-occurrence frequencies. This system has a basis in grounded theory and implementation with quantitative foundations, and it is the theory underlying the product Leximancer used in the analysis later in this chapter.

Semiotic analysis is an approach for meaning assignment. Since a text collection is a series of text components used to convey a message, the meaning found in these texts can be discerned by using linguistic techniques of analysis (Berger, 2005 ). The search for signs in the text produces meaning points from that text. Another approach for content analysis is semantic network analysis (Van Atteveldt, 2008 ). Text content is represented as a network of objects. In another approach, Tausczik and Pennebaker ( 2010 ) examined part of speech analysis in text analytics. They contended that nouns, regular verbs, and adjective/adverbs represent content words, while parts of speech such as pronouns, prepositions, articles, and conjunctions (e.g., it , and , the ) comprise style words. They observed that these style words are how people communicate, and content words contain what is being communicated. In another method, Bernard and Ryan ( 1998 ) offered that text analysis has become a method to create and extract an entire model, or schema, from the data, especially for discourse analysis. Schema mapping is a well-known technique from psychology (see Lakoff, 1987 ).

In summary, there are many approaches in qualitative analysis of which content analysis is one. A content analysis is best when both quantitative and qualitative approaches are combined (Weber, 1990 ). This has been traditionally called mixed methods. With large and varied text collections found in media and social media, mixed methods may not be limited to quantitative versus qualitative, but may be an integration of multiple qualitative methods as a sequential process. Additional qualitative methods such as those demonstrated by Wertz et al. ( 2011 ) can be explored by following those concept links deep into the actual text to ensure context and associated meaning derivable by a specific technique, such as narrative analysis.

Issues in Content Analysis

No matter which content analysis approach is taken, from a manual analysis by an acknowledged expert to a fully automated, computer-driven statistical analysis of a text, there are several issues researchers must address. The primary concerns are sampling strategies and study reliability. These issues address researcher bias, which must be limited to ensure meaningful and worthy results. Reliability is critical in a qualitative study, so that results can be reproduced and understood by other researchers conducting similar and derivative studies.

Sampling is a method to take subsets of documents to study. Especially when manual coding is to be performed or research-intensive interpretation is required for the entire text collection, sampling is a practical and required method to enable a project to be considered and then completed. In theory, it simply requires two major activities: identify the complete text collection and identify units of analysis within these texts (Bernard & Ryan, 1998 ). In practice, sampling can be used to reduce voluminous data to a manageable amount of data. However, an artificial sampling plan to reduce data or to allow a particular analysis method may create an element of bias before the text is analyzed. It is also possible that data may be inadvertently omitted. With increasingly large media collections and research questions examining specific issues or subtleties, researchers should evaluate other options to avoid sampling simply to reduce analysis time.

Sample collections require a definition of data resolution. This decision is important for a study because it impacts both the data collected and the analysis segment size. For example, a media psychologist might study treatment of race in television comedies. A sample and resolution might be specified as follows: television comedies, 1/2 hour, and Wednesday nights between 8:00 pm and 11:00 pm. For a microblogging social media communication such as Twitter, samples might range from tweets from a single user or a collection of tweets on a specific topic during a specific time period. Content analysts must determine these units to measure. Importantly, these units of measure can significantly impact relationships of words and coding because frequencies of occurrence and concept discovery are restricted to occur within these units. Researchers must be cognizant of the sampling strategy chosen and the data generated from that strategy.

Finally, reliability and repeatability must be examined, especially if automated tools and large text collections are used. One measure of accuracy is tied to statistical norms, and accuracy is the strongest form of reliability (Weber, 1990 ). Thus, a computer program, which uses proven methods and is repeatable, is a good tool to aid in a valid content analysis. By following a formal methodology, utilizing computer tools, and employing visual aids for both analysis and communication, media psychologists can limit these issues for their content analyses.

Making Meaning from Text

Whatever tools a researcher uses to assist in qualitative research, he or she must understand the basic theory for text reduction and concept extraction if it will be part of his or her research process. To understand how a software tool examines and processes text, an example text segment from this handbook will be examined and manually processed. This example of coding is one of the critical stages for a content analysis and meaning making. This paragraph is from Rutledge (Chapter 3 , this volume):

The ubiquity of technology is driving home the point that human experience is not separable from technology and vice versa. While emerging technologies and new models of communication add to the complexity of study, the integration of media into daily life highlights the importance of the human experience and systems of relationships as the focal point of study. It is here where media psychology brings a different perspective than other fields of study. It has the breadth of theory and tools to examine the behavior and interactions of individuals, groups, and organizations. It is not constrained by the type of technology, but understands the mediation that technology affords. It can, therefore, apply the lens of psychology to any form of human experience mediated by technology. (p. 56)

A human coder would easily ignore the many words that do not add to meaning, such as articles and prepositions. The pronoun it would be consciously replaced with actual nouns and those concepts carried forward. To examine how meaning might be made of this paragraph and data reduced to a few concepts, the paragraph will be examined in one potential scenario.

First, the resolution needs to be set for coding. For this example, the resolution is a sentence. Words are examined selected based on their importance. In a statistics-based program, several processing iterations are made to identify significant occurrences of words and create a coding dictionary. Since this example is only a single paragraph, it will be assumed that the full text collection was examined to generate the coding dictionary from all text. The paragraph is repeated with underline, bold, and italics denoting identified concepts and their counts shown afterwards.

The ubiquity of technology is driving home the point that human experience is not separable from technology and vice versa. While emerging technologies and new models of communication add to the complexity of study, the integration of media into daily life highlights the importance of the human experience and systems of relationships as the focal point of study. It is here where media psychology brings a different perspective than other fields of study. It has the breadth of theory and tools to examine the behavior and interactions of individuals, groups, and organizations. It is not constrained by the type of technology , but understands the mediation that technology affords. It can, therefore, apply the lens of psychology to any form of human experience mediated by technology . Concepts: technology (6), media (2), psychology (2)

In this paragraph, the concepts technology , media , and psychology all appear. Although study , human , experience , and point also have frequency counts greater than 1, an arbitrary assumption has been made that those concepts are less frequent elsewhere in the text. Thus, this text contains these three concepts of interest: technology , media , and psychology . There is an implied relationship because of their co-occurrence. A qualitative analysis other than content analysis would likely yield more results such as the context of the discussion connecting technology and how media psychology fits into that context. For the content analysis, this segment would be a prism into the entire text as part of the overall text reduction to meaningful concepts. It is imperative, however, that the researcher can query or visually navigate to this specific text to retrieve original context when desired.

This type of analysis performed over a large text collection sentence by sentence demonstrates how it is possible to derive concepts and relationships. In many studies using qualitative analysis, computer programs traditionally perform mundane tasks for researchers, such as the elimination of uninteresting words like prepositions and articles. These are generally called stop words (e.g., the , and , of , for ). Options are usually available to select stemming for frequency counts, which combines words with plural and singular forms (e.g., technology = technologies ). Researchers may also need to combine synonyms from these extraction lists to provide more accurate total counts. Finally, compound words (e.g., “media psychologist”) are important to identify and separate from individual counts where they are independent entities. Careful attention to these options, often subtle in text collections, will assist in accuracy and consistency of the results. Whatever approach is used, it is imperative that researchers understand the strengths and weaknesses of their approach and all tools used.

Automated and Intelligent Software Tools

A content analysis can be done without a computer. Although, at a minimum, a computer usually serves as a document file folder, backup mechanism, and a search tool for and within documents. As software continues to become more sophisticated, there will be a trend towards intelligent software integrated into multiple analysis methods that can enable studies not previously possible.

There are several areas where computer technology can expand to assist in automating and assisting in content analysis. For software to act with intelligence, it must be able to responsibly and consistently replace tasks that a human can provide at least as well. In one scenario, Van Atteveldt ( 2008 ) proposed three main areas for continued intelligent tool development: (1) expand concept abstraction beyond just counting word frequency counts, (2) extract relationships and their associated structures, and (3) develop algorithms where computers can do extractions. There are a number of software solutions available with varied capabilities that continually evolve. The availability and capability of these tools is not the scope of this chapter, and the reader is directed to evaluate the current capabilities of tools and products.

A primary capability of these software programs is to perform or assist with coding for later retrieval, comparison, and reporting. Coding is one of the critical aspects of qualitative research, and software can significantly enhance this capability with tools for organization, automated tagging, and document manipulation. Another key capability is search and retrieve, which is tied directly to coding and tagging source text segments and words and phrases of interest. For example, one traditional technique that researchers use is to manually code text on a line-by-line method, assigning an action to each line (Wertz et al., 2011 ). This time-consuming process allows for interpretation by the researcher as each line is manually coded, as in the earlier example of the Rutledge paragraph. However, manually coding tens of thousands of documents is not practical for most studies even if multiple human coders are employed (which then introduces coding inconsistencies known as interrater reliability).

Unfortunately, a simple computer analysis line by line can also be problematic. For example, the term North Carolina is a compound concept of the words North and Carolina . A software tool which looks strictly at word frequency counting will determine that North Carolina , South Carolina , and Dr. Carolina Smith yields three counts of the concept Carolina . A human coder recognizes these differences immediately and codes these as three distinct concepts. Therefore, computer-based tools must use algorithms to intelligently handle cases such as these.

Another issue for coding is the need for a coding dictionary. A predefined thesaurus applied prior to coding can miss concepts contained in the actual text, and a thesaurus constructed from a sample of the texts can miss data in the complete texts (Carley, 1993 ). Therefore, a tool that generates a corpus-specific (i.e., from the text collection itself) coding dictionary from analysis of the text in that particular collection will be most effective. If a coding dictionary is input, then it must be specific to the domain of the text collection at a minimum.

Automated and computer-assisted coding approaches, although occasionally creating issues, must be used in many media analyses wherein volumes are significant and manual coding can become time and cost prohibitive. For example, one recent study of public sentiment in Twitter used 1 billion tweets as its study data (O'Connor, Balasubramanyan, Routledge, & Smith, 2010 ). As more media psychologists endeavor to examine these large text collections, intelligent and at least partially automated tools will become a staple of the researcher process. Fortunately, a large amount of data lends itself to a statistics-based approach for data reduction and meaning extraction.

As software capability evolves to support more complexity and linguistic nuances, the promise is that approaches can be developed, refined, and customized to support multiple methods of qualitative analysis. Strides have been made for content analysis and extraction of meaning quantitatively and qualitatively with concept and theme identification and relationship inferences. However, truly intelligent software will be enabling in many ways by allowing integration of modes of multimedia, the fast and inexpensive processing of voluminous data, and the reproducibility and understanding of language required for many qualitative analyses. Visualizations and smart querying will also be a component of these new software programs. The ultimate solution may be a hybrid system in which automation provides insights and meaning indicators, but the researcher remains in the process to interpret and guide the software.

Leximancer Text Analytics Software

The software tool used in the content analysis of this handbook is a statistics-based software tool called Leximancer. Leximancer uses machine learning and is language, vernacular, and syntax agnostic. Leximancer performs automatic coding of concepts from the text. This is appropriate for a content analysis because the methods that employ both quantitative and qualitative methods achieve the best results (Weber, 1990 ). While Leximancer automatically creates its own coding dictionary, it also has the capability to input a researcher's own concept seeds for forcing a focus on specific concepts found in the text collection. It has a feature to input a predefined sentiment list to assist in extracting sentiment (positive and negative emotive words) associated with concepts. This reduces researcher bias in the initial steps of a content analysis, because Leximancer serves as a discovery agent for the text collection. The concepts generated from the text compose themes for further explorations.

One advantage to a statistically based tool such as Leximancer is its ability to simulate human concept extraction. For example, a literal examination of text would miss a concept in a text block if that exact text did not appear. By using an approach that identifies surrounding, co-occurring words, the existence of those words can be used to indicate occurrence of the concept. This has a significant benefit in that the actual word does not need to be present for the concept to be found in the text. Evidence words in the form of a thesaurus provide statistical evidence that clusters of words all relate to one or more concepts. This mimics the role of a researcher, who understands that context and related words indicate a concept occurrence. Finally, it is important to observe that, in this type of thematic analysis, the relationship is discovered by software, but the nature of the relationships is the domain of the researcher (Popping, 2000 ).

Visual Analysis and Display

Visual data display for analysis is an important tool for a researcher. Visual displays can be used to provide insight into a text collection, as an aid in navigation into the text collection, and to convey results and meaning. Historically, imagery has served a role to provide abstraction of complexity and amplification of relevance in the study of meaning (Barry, 1997 ). In text analytics, visualization has often been used for quantitative data wherein numerical relationships are represented by a variety of techniques. Traditional charts include bar graphs showing word frequencies. However, visualization techniques in many fields have expanded beyond a simple display of facts to an effort to convey ideas (Yau, 2011 ).

There are two equally important reasons for using visual data mapping, or an art-informed inquiry. First, the researcher may derive new meaning and discover new insights, and secondly, share these insights in an easily understandable manner for readers and other researchers (Butler-Kisbe & Poldma, 2010 ). The selection of visualization metaphors is also important, and researchers should consider both existing and meaningful representations.

Concept Maps

One method of visual representation of a text collection is a concept map. At its core, written communication represented by a text collection is composed of words. Novak and Cañas ( 2006 ) stated that the roughly 460,000 words in the English language are mostly concept labels and can be further combined into other representations. Text collections for studies naturally contain a subset of this number, and further reductions are used as previously discussed to reduce the concepts, or word representations, to a smaller number. A concept map then takes these remaining high value concepts and creates a visual representation in circles or boxes of the concept labels (see Figure 29.2 as an example).

Performing a content analysis is, in many ways, a data reduction exercise. The text is reduced and collated into themes, concepts, and thesauri describing evidence why these reductions were made. In data visualization, a technique called multidimensional scaling (MDS) serves the same function by clustering similar data into smaller collections, or groups for display (Yau, 2011 ). For text visualization, positioning of data on a coordinate system where similar text items cluster together form a meaningful visualization. These displays can be further heat-mapped to illustrate other dimensions of the concepts (i.e., hot or frequent topics are color coded to hot colors such as red and yellow, whereas low-occurring counts are cold colors such as blue). The purpose of these techniques is to view similar concepts from the data and visually allow for pattern searching and identification of outliers (Yau, 2011 ).

In Figure 29.1 , the concepts from the previous analysis of the paragraph by Rutledge (this volume) are displayed. This diagram was created in a simple drawing program, but it conveys considerable information.

For example, the size of the circles adjacent to the concept labels can be used to denote relative frequency counts. Relationships can be displayed by connecting lines, and proximity of concepts can represent general proximity in the text. Concept maps allow the researcher to create and study the visual representation and relationships while taking a break from detailed textual examination (Butler-Kisbe & Poldma, 2010 ). Finally, encircling concepts in proximity to each other in order to illustrate general topics found in the text can create a theme. For the Rutledge paragraph example, recall technology was the most frequent concept whereas media and psychology appeared in close proximity. All these concepts appeared together, which potentially represents a theme from the data. This example is intentionally small for illustration purposes, but the power of this type of visual display is readily observed when a large text collection such as the Handbook is represented with a dozen theme circles and their related concepts as shown in Figure 29.2 . When these representations are combined into a concept map representing an entire text collection, the researcher can quickly affirm the content or learn new concepts and themes shown in this map.

Theme Circle, Concept Labels, and Connections from Rutledge Paragraph.

Concept maps can be used in a systematic way for analysis. For example, ideas can be initially formed, emergent concepts explored, and representations created. Concept maps are particularly useful in initial stages of analysis when ideas are fuzzy and not yet organized (Butler-Kisbe & Poldma, 2010 ). Concept maps also provide a powerful psychological function in qualitative analysis. Butler-Kisbe and Poldma further elaborated:

Concept maps allow the researcher to step outside the constraints of linear thinking and to engage in, and encourage the messy and nonlinear work of, the brain, and in so doing, tease out ideas and connections in the data that might otherwise remain implicit. It is when these implicit thoughts become apparent that the analysis can be pushed to a deeper level. (p. 11)

The reinforcing use of imagery can confirm data or guide a researcher to more areas of the analysis. For example, in early analysis of draft chapters, the concept dill was an outlier not shown with other themes. This is an interesting result because Dr. Karen Dill is the editor of the Handbook , and her name appears often throughout this text in that capacity. Because the software counted each time her name appeared on editorial notes, her name was an artifact from metadata to the chapters. This example represents an instance when the appearance of a term can be misleading, and it also shows the power of a visual outlier when examining a large set of data.

Early Concept Map from Oxford Handbook of Media Psychology Analysis .

Future Media Psychology Studies

Media psychology researchers face many of the same challenges as qualitative researchers in the social sciences. In particular, validity of research must continue to be addressed. With the ever-increasing volumes of data and the availability of computer tools, researchers now must address validity in the form of reproducibility and stability as well as communication of complexities in their methods and tools. Procedures and documentation methods must be standardized to help address these issues and provide confidence in evolving research methods.

In an example of the current state of research reporting on qualitative studies, Bluhm, Harman, Lee, and Mitchell ( 2010 ) examined management sciences articles to summarize methods and approaches from the previous 10 years of journal articles in Europe and North America. Because of the lack of standard reporting parameters and inconsistency of data reported, Bluhm et al. did not examine techniques used in the analyses, but they discussed only what was reported. They found that 45% of the articles they reviewed had poor and incomplete descriptions of both analysis techniques and data collection methods.

The combination of computer assisted and automated text processing software tools with enhanced visualization provides further challenges for reporting study parameters. Table 29.1 is provided as an exemplar for the type of data that should be added in a summary table for media psychology qualitative research. Collection of this data will allow communication of methods as well as repeatability of results by other researchers using the same or similar tools and techniques. The study parameters for content analysis of this handbook will be summarized in this format in the Discussion section of this chapter.

The initial table rows are informational. They are included in the use case wherein multiple studies are compiled for reference. The analysis method includes the general method or techniques used in the study. For instance, Content Analysis and Semiotic Analysis are examples. The Data Collection Method is short descriptive text describing how the data were collected. The Data Summary provides quantitative measures of the data. The Software Tools row includes all tools used and their respective version numbers. The Software Configuration section is critically important for reliability by reproducibility. If another researcher uses the same data and applies the same software configurations with the same software tool, the results should be replicated. This is the qualitative researchers’ incarnation of quantitative researchers’ use of a tool such as SPSS for consistent and acceptable statistical analyses.

Note, there are no results per se of the actual study. The reporting of results is left to the definition of the particular discipline (e.g., specific journal policies). However, it is not necessarily the purview of a journal or style guide to explain what needs to be disclosed for transparency, trustworthiness, and peer evaluation.

Table 29.2 contains a fictitious study with values inserted to demonstrate how a study's parameters and data can be standardized. The use of methods distilled to short descriptions and tools, data collection, and parameters provide a solid foundation for understanding what was done in the media study.

The data contained in this summary table provide a quick summary of the methods, software, configuration, and data collection. They address many of the concerns of Bluhm et al. ( 2010 ) on reporting and also address the issue of reproducibility for computer tools as their usage becomes more prevalent in media psychology research.

Content Analysis of This Handbook

The opening chapter of this volume provided several definitions of media psychology and the results from a survey of the Oxford Handbook of Media Psychology authors. Several succinct definitions emerged. Now that the Handbook has been completed, there is a further approach for defining this emerging field that can provide a more detailed yet expansive view of media psychology, at least in the context of the content submitted to this handbook. A content analysis was performed on the Table of Contents and on the actual book chapter text to determine the nature of the content written by the Handbook authors.

One could claim that a review of the table of contents for a book is at least a cursory content analysis. However, the title of a chapter is necessarily limited in scope compared with the content of the chapter itself, and the chapter content may show emphasis and connections not revealed by a simple title. Furthermore, the editor drafted initial titles and the authors interpreted the editor's general content area themselves. Finally, a content analysis across all chapters helped determine dominant global themes, which may have existed embedded in chapters regardless of the theme of the chapter. Examples include elucidations of media psychology theory and methods, as well as themes (e.g., children and the media) and contextual factors (e.g., areas of controversy) and ideas (e.g., negative effects) that transcend specific content domains. Relationships are also important between concepts in different chapters and concepts within chapters. An examination of all themes and concepts and their relationships will best provide a survey of media psychology as found in this handbook.

Methodology

Two analyses will be performed on the Oxford Handbook of Media Psychology . First, a manual content analysis will be conducted on the Table of Contents. Then a computer-assisted content analysis will be done for each of the content chapters of the Handbook . The results of these two analyses will be compared, and a definition of media psychology will be produced.

The examination of the text of the Table of Contents will be a simple word frequency count of the titles with inconsequential parts of speech removed. This word count approach will use the repeated words for each chapter title to provide an indication of the aggregated summary of the Handbook contents in the succinct style of the short text descriptions.

The second analysis will be for chapters of the Handbook that are content chapters. Thus, the introduction and concluding chapters will be excluded. The content analysis of chapter text will be conducted using two distinct methods. First, a grounded theory approach will be used in which the concepts are identified and summarized according to themes. Evidence words in the form of a thesaurus of related words will be found automatically for each concept. This is done using a statistics-based approach in the software tool Leximancer to assist in an unbiased examination of raw text data. Concepts from the Handbook will be identified over several passes of the data, with configurations adjusted for data characteristics and data anomalies. Second, concepts related to media, psychology, and media psychology will be interpreted to understand the other topics that are explicitly tied to those concepts and to produce a definition of media psychology.

The Leximancer text analytics software program Version 4.0 ( www.Leximancer.com ) will perform coding and automated concept extraction for the Handbook . A coding dictionary will be generated automatically from the actual book text and successive iterations of coding, to generate themes, concepts, and a thesaurus of evidence words for those concepts. Quantitative data will be created, and the concept map is used to navigate to specific data excerpts for detailed examination of the source text. Frequency counts and concepts will be examined for the concepts media , psychology , and the compound media-psychology . Related concepts are first identified to see what concepts appear together, or co-occur, in the text. This provides insight into proximity of concepts and when particular themes are discussed.

Sampling for the study was straightforward. All text from chapters was included with the exception of the introduction and concluding chapters. The nature of these chapters, focusing on describing and summarizing the Handbook , might arguably skew the results.

The source chapter files were Microsoft Word format except the Video Games and Attention chapter (Chapter 22 , this volume), which was in Portable Data format (PDF). All changes and edits were accepted and saved. This removed metadata such as the anomaly of the editor's name (i.e., dill concept discussion from earlier as its own concept). The References were left in to participate in the analysis. Tables, appendices, and figures were included, although no text extraction or augmentation was performed on embedded images.

As with any content analysis, the method must address the issues of reliability and validity. Reliability refers to stability and reproducibility. Both stability and reproducibility are achieved by use of a computer-driven tool whereby there are no issues with interrater reliability. Additionally, a measure of accuracy is tied to statistical norms, and accuracy is the strongest form of reliability (Weber, 1990 ). Thus, the use of a statistic-based software tool such as Leximancer aids in reliability through accuracy. Another aspect of reproducibility is manifested in procedures and process decisions researchers make. For example, when using a tool to assist in text analytics, software configurations can alter results as well as editing of the source text. Editing source text can be problematic as it introduces version control issues for the data. For large data collections, this may impact reproducibility. Therefore, no modification should be made to the original source data. Also important is a careful documentation of software configurations to allow reproducibility. Explanation of the reason for configurations is included as an aid in understanding.

The first analysis is of the Table of Contents for the Oxford Handbook of Media Psychology . The Table of Contents contains chapter names for all 29 chapters, which are divided into six parts. Parts One and Six are administrative in nature, as they contain the introduction and conclusion. Therefore, content found in them for this study would be redundant to the chapters and Parts with content. Table 29.3 shows the Part titles with a description of the content for that Part.

The Part descriptions serve more as an organizing function than as a potential description of media psychology. Therefore, only the actual chapter titles were expanded into text paragraphs sans punctuation. Table 29.3 aggregates the text for each chapter title organized by Part. For the analysis, all of this text was combined into a single text extract for the word frequency counts.

Word frequencies were used as an indication of strength and order of importance. A free tool was used to count the frequencies ( http://writewords.org.uk/word_count.asp ). The words and, the, in, to, of, use, a, for, we, its , and from were excluded as inconsequential. These are typically listed in a stop list for computer programs to ignore and include parts of speech such as articles, prepositions, pronouns, and non-descriptive verbs. An analysis of the Merged Chapter Titles column then provided an indication of the concepts discussed by the chapter titles. Table 29.4 shows the results of the word frequency counts with a relative percentage to the most frequent word, media .

The Relevance Percentage column shows how each topic compares with media at 100% relevant as the most frequent word. For example, for psychology , 11/20 is 55%. This indicates that the word psychology appears slightly more than half as often as media (i.e., 55%). While not meant to be precise, this measure provides an initial overview of relative occurrences of terms. For example, the terms media and psychology appear substantially more often than the other terms. Media appears in 19 titles, psychology or a derivative in 11 titles, and both media and psychology in 10 titles. Six titles contained neither word.

Although this example is meant to be simple, several issues in analyzing text are illustrated. For example, a researcher has to decide which words are to be ignored. Although prepositions and articles make sense, words like approaches and effects are a researcher's decision. When a large amount of text is analyzed, the inclusion, exclusion, or combination of terms and concepts can have meaningful effects. In this example, including the term psychophysiology moved the psychology root term to above 50%. Stemming (i.e., including plurals and extended words as roots) also affects counts and relationship analysis.

Note : The terms psychology, psychological, and psychophysiology were combined for this count.

This type of manual examination can easily be done on a small text collection such as a table of contents. From this technique, a definition of media psychology is composed of the following concepts or topics: research, children, narrative, violence, adolescents, approach, effects, games, history, persuasion, sexual , and video . These are the results of the content analysis. Now the researcher's interpretation is required as to the meaning of these concepts. Several indicate content, such as media and games , whereas others indicate activity, such as research and approach . The Discussion section will provide the final analysis, as it is an interpretation of these results.

Oxford Handbook Entire Text Analysis

The next analysis was the chapter text of the Oxford Handbook of Media Psychology . Again, the Introduction and concluding chapters were omitted. A first run was performed with all default settings to provide an overview of the data and identify any issues or modifications required in settings.

The quantitative measurements for the Handbook were as follows: 25 book chapters with one chapter in each file. There were 1,271 pages in APA format, double-spaced with a mean 50.84 pages per chapter. The text was comprised of 23,833 sentences. A thesaurus of evidence words was generated with a total of 1,409 words.

The initial run produced a thematic summary, which serves as an indicator of the overall concepts grouped by themes. The themes are ranked for importance by summing the co-occurrence counts for all of the concepts within each theme. The logic is that more important concepts tend to be connected (i.e., co-occur) with other concepts on the map. The summary of ordered theme occurrences is shown in Figure 29.3 .

These theme connectivity scores are ranked relative to one another by dividing each by the highest score. The themes chart indicates that video , media , and db-id (an anomaly discussed below) are dominant concepts in the Handbook . Although the themes are grouped using a mathematical threshold, the researcher can adjust them for reclustering. The results presented use the default setting in Leximancer, which provides a generalized coverage analysis, and it is used here to provide an overall picture of the data.

This initial analysis by Leximancer identified concepts and reported their counts. Concepts are not simply word counts. A coding dictionary is developed automatically during the analysis, which evolves into a thesaurus, or list of evidence words, for each concept. This dictionary identifies word clusters and extracts concepts from the word occurrences. For example, if the thesaurus for media included psychology , game , and research , and those words occurred together without the explicit word media , Leximancer would still count an occurrence of the media concept. This is a powerful technique that mimics the interpretation capabilities of a manual coder. As noted earlier, the dynamically created thesaurus contained 1,409 word entries used for coded concepts specific to the Handbook content. The top concepts from the initial run are listed in Table 29.5 .

This list is where researchers can look to quickly obtain a view of the initial text analysis. For example, the concepts violence and violent will be merged to provide a single concept on the topic of violence. Other potential anomalies can be identified at this point. Quick queries of the underlying data provide an indication of the actual text and provide a sense of confidence in the researcher.

This first run discussion would typically not be represented in a study. These results are included here as an example of the process a researcher using automated tools typically performs. For example, the theme db-id is an anomaly of the use of Microsoft Word as a data storage mechanism. The top five thesaurus terms for this concept in the db-id theme are titlesecondary - title , secondary-title , app , db-idm , and contributorstitlesttle . These are all terms associated with the computer program Endnote and its internal codes. Data anomalies are easily recognized by human coders but can be problematic for computer-assisted techniques. This is why a general query capability should be a feature of any tool to facilitate investigation of actual text segments coded.

There are two methods to deal with this anomaly. One is to change the storage mechanism to portable document format (PDF). The second option is to adjust configuration of the software tool. In this case, it is preferable to save the file as PDF to better emulate the state of the final chapter data for the Handbook . The use of draft chapters in Word proved a poor choice due to the inconsistent use and processing of metadata. The reason this issue was disclosed here is to highlight potential problems with the use of computer tools even though the tool was not incorrect, given the data. It was the data that were not correct. Although working through these issues is not difficult with software configuration, a better approach was to save the chapter drafts into PDF. This removed all the metadata and ensured consistent representations of the text (i.e., all chapters in the same format). The second run was performed and the anomalies were no longer present.

Ordered List of Themes, Their Connectivity, and Relevance from the Initial Run.

This detail is exposed to the reader to illustrate the need to both understand potential data anomalies and the capabilities of the chosen software tool to deal with those anomalies. All details concerning data preparation and software configuration must be provided in the study with justifications. It is important in media research, just as in traditional

qualitative research, to document decisions and provide reasoning and justification, so readers of the research can understand decision points during the analysis.

Once the data were prepared, configuration of Leximancer needed to be performed. First, word variants were enabled to merge stems. The compound concept video AND game was created because video game is a prevalent topic in the Handbook . The compound concept media AND psychology was also created to determine relationships this combination would have. The concepts violent and violence were merged to indicate one singular concept. The final configuration included the addition of et and al to the stop word list to exclude APA citation repetition as a concept. The words thus and use were also added to the stop list because they surfaced as a concept due to the large use of the term by several writers and the interpretation of the researcher as inconsequential words.

Leximancer was then rerun for the final time. The resulting Theme Summary is shown in Figure 29.4 .

The themes, which are based on dominant concepts found, changed in several ways. The anomalies from Endnote citation insertions are gone. Video remained the top theme and six of the original themes are present in the final run (i.e., video, research, media, television, information, and development). These themes are indicators of underlying clusters of concepts, so a more detailed analysis is required to extract sufficient meaning.

The final concept map is shown in Figure 29.5 . The Media theme is near the center bottom of the diagram. Spacing is indicative of relationships. This visualization is an excellent tool to explore the general content of the data as well as relationships and connections.

This visualization of the data in the concept map is not used for formal interpretation of the data, but it is used more for orientation and general verification by this researcher. In Leximancer, mouse clicking on the interactive concept map is also a way to navigate and query the data and relationships.

Ordered List of Themes, Their Connectivity, and Relevance from the Final Run.

Final Concept Map from Handbook Chapters Final Leximancer Run.

In order to make meaning, the concepts and their relationships are used as the foundation of the content analysis. The top concept frequency counts are shown in Table 29.6 .

These 15 concepts represent the top concepts by relative frequency. All represent a minimum of a 20% relevance threshold to the top concept media . That is, all of these concepts represent a relative proportion of representation when media is 100%. This measure provides an indication of relevant occurrence of each concept. This gives a statistical indication of the coverage of these topics by all authors in the Handbook . For example, video and game are each individual concepts and the compound video AND game all have similar frequency counts. One interesting observation is that m edia dominates substantially as a concept with a count nearly two times as much as the next concept research .

Another indicator of the topic coverage is the relationship of concepts to each other. The concepts most related to the concept media are shown for illustrative purposes in Table 29.7 . The Likelihood percentage is the likelihood that when a related concept (e.g., literacy ) appears in a text segment, the concept media appears there as well.

Thus, of all comments about entertainment , 65% of them mention media . This Likelihood score is derived from the count 132 divided into the total frequency count for the concept entertainment , which was 204 (132/204 = .65). The second

concept is literacy , which is part of a compound of media literacy. The researcher must consider if the concept literacy as well as mass , the next concept in the list, should be modified in reporting, as is done here to illustrate a single concept of a type of media. In many ways, this step is an exercise to determine what the concepts actually represent. These results provide an illustrative example for the need, even in a grounded theory technique, for researcher to interpret the results of automated software-driven content discovery. Query features in software are essential here to allow a researcher access to the source text segments.

The next concept to be further investigated is psychology . The top related concepts are shown in Table 29.8 .

The Likelihood score is important in this summary as well. For example, of all text segments containing journal , 41% of them mention psychology as well. An exploration of the actual text reveals that psychology journals are a topic of conversation. The next concepts of social, human , and development are more traditionally associated with psychology terms. Note the Likelihood score indicates the strength of the relationship relative to other co-occurring concepts. It does not indicate the meaning of the relationship, and that task is the responsibility of the researcher and should be in the Discussion section of a research paper.

Finally, a further exploration involved the examination of the compound concept media-psychology . This was manually added to the coding dictionary. It is often valuable to create a compound concept and investigate concepts related to it. For a study of the Oxford Handbook of Media Psychology , the compound concept media-psychology was created to examine the related, co-occurring concepts, and the results are shown in Table 29.9 .

As one would expect, the concepts psychology and media were the most related concepts. However, psychology , with a 52% Likelihood score (i.e., probability that media - psychology is mentioned given psychology ) differs significantly from media with a Likelihood score of 19% (i.e., probability that media-psychology given media ). The large difference in these two scores indicates that media appears with many more and diverse concepts than does psychology . The next most related concepts starting at human at 15% down to social at 10% represent the remaining most related concepts. After these concepts, the remaining related concepts had a likelihood of less than 10%; that is, less than a 10% chance they were found in close proximity to media-psychology and were not retained for the study. Table 29.10 shows the top concept counts for a comparison.

The overlap with psychology shows that media psychology is more closely tied to psychology than media . The bolded concepts show this overlap. Interestingly, in these top concept counts, media does not overlap at all with psychology . The overlap of psychology to media-psychology is 11 of 15 concepts, or 73.3 %. The representation of media in media-psychology is 3 per 15 concepts or 33%. This means that when media and psychology appear together in text, there is a greater than 2 to 1 probability that a psychology concept is being discussed rather than a media concept.

Note : Bolded text represents concepts shared between Media Psychology and Psychology . Italic text denotes shared concepts between Media Psychology and Media .

A final note from the results section is to include a table summarizing all pertinent information for the study. As discussed previously, reliability and reproducibility are issues with media qualitative analysis. Table 29.11 is an implementation of Table 29.2 , which is an exemplar for transparency and information summary and disclosure. Table 29.11 provides this summary to enable readers and researchers to understand the general parameters of the study and specific software tools used and their configurations. This should increase transparency and bring some standardized view to information disclosure to enable reproducibility.

The Table of Contents review calculated word frequencies of the titles to indicate what authors believed their content would cover. Words were eliminated that provided no addition to meaning, such as articles and prepositions. The result of this analysis was that media is the dominant term mentioned 20 times with psychology second at 11 mentions. The terms media and psychology appear substantially more often than the other terms in the title as well. Media appears in 19 titles, psychology or a derivative in 11 titles, and both media and psychology in 10 titles. Six titles contained neither word. From this data, a definition of media psychology is composed of the following concepts, or topics: research, children, narrative , violence, adolescents, approach, effects, games, history, persuasion, sexual , and video . Media dominates the discussion as evidenced by its nearly two times representations in both number of words (20) and number of titles (19) over psychology , mentioned 11 times across 11 titles. The remaining terms contain three action words in research , effects , and approaches , suggesting that research, study of effects, and approaches are considered. The remaining words indicate the subtopics to media psychology . Thus, a succinct definition from the Table of Contents is that media psychology is dominated by media topics and addresses research, effects, and approaches on the topics of children , narrative , violence , adolescents , effects , games , history , persuasion , sexual , and video .

Whereas the Table of Contents analysis indicates what the editor and chapter authors believe the contents to represent, a more detailed analysis explored the entire text of the Handbook . The themes discovered and concept map generated by Leximancer were used as navigation aids to explore text excerpts and the concepts discovered. It is these concepts and their relationships defined by co-occurrence that provide insight into the contents.

Two approaches were taken to examine the data. First, the major concepts were examined by frequency of occurrence. The fact that a concept appears more than others is an indicator of strength of coverage. From this analysis, the concept media at 3,428 occurrences was found to have more than twice the frequency count of the next most discussed concept of research at 1,637 occurrences. The frequencies are roughly divided into two bands after media , which include research , video , game , effects , video game , violent , studies , and psychology . This tier represents from 36% to 48% relative percentage. Video (1,582), game (1,532), and the compound concept video-game (1,447) represent nearly identical frequencies, which indicate that they rarely occur in separate contexts. The third tier contains social , behavior , aggressive , journal , television , and experience and represents from 20% to 27% relevance percentage. These banded counts provided a summary of the topics and amount of coverage the topics generated throughout the Handbook .

The second approach examined the relationship of concepts to the key concepts of media-psychology , media , and psychology . This added depth to the analysis by examining specific relationships. These relationships showed that media was most related and occurred in the chapters with literacy , mass (media), children's , and digital , all with more than 80% Likelihood that they occurred with media . Psychology , individually, was related to journals , social (psychology), developmental (psychology), theory , university , media , communication , research , technology , and public . Interestingly, journal , the most prevalent concept apart from media , was only a 41% Likelihood indicating that it occurred less than one half the time with psychology . One interpretation of these much lower counts is that psychology was a more independent concept and did not have strong relationships to many other concepts in this data. The only concepts with a greater than 20% Likelihood score for psychology were journal , social , human , development , and theory . Finally, when media and psychology were combined and related concepts examined, psychology exceeded 52% Likelihood score, indicating that slightly over one half of the time, psychology appeared with media psychology . Conversely, media , the next most related topic, occurred only 19% of the time with media psychology .

The two approaches of examining concept frequencies and concept relationships provide the first step in the content analysis, as the data gathered to this point is mostly quantitative in nature and an excellent indicator of the concepts and their relationships in the Handbook . For interpretation, much as in the Table of Contents review, the meaning of the dominant concepts’ frequency and ratios and relationships need to be examined in context of the research question, which is to define media psychology. The strength of concept coverage indicates a dominance of media discussion with mass media, digital media, and media literacy as subtopics. The final cluster of frequency bands represents more psychology-related concepts of social , behavior , aggressive , and experience . Television is media related and journal discusses publication in general. One of the more indicative findings is the top related concept comparison shown in Table 29.10 . The overlap of psychology shows that media psychology is 73% and more closely tied to psychology than media . Interestingly, in these top concept counts, media does not overlap at all with psychology . The representation of media in media-psychology is only 33%. This means that when media and psychology appear together in text, there is a greater than 2 to 1 probability that a psychology concept is being discussed rather than a media concept.

This detailed analysis of the Handbook chapters leads to the following observations of the content of the Handbook . The concept media , by a large margin, dominates media psychology. Media has many closely related topics including mass media and media literary and examines research and effects with adolescents and children. Psychology is less dominant and is much more closely related to the concept media psychology . Media is more connected to and interwoven with the rest of the text than psychology . Therefore, a definition that emerges from this content analysis is that media psychology is the study and research of effects and media literacy on children and adolescents with a focus on media, including topics of aggression, social psychology, and experiences.

Conclusions

The purpose of this chapter was twofold. First, a media content analysis approach with automated software tools was described. This included the growing interest in visual data mapping, the trend towards intelligent software integrated into methods that permits research not previously possible, and the automated analysis of large datasets in a way that addresses some of the concerns of qualitative research. Issues facing media psychology researchers were discussed, including the need to thoroughly document software choices, configurations, and iterations to aid in reproducibility and communication of study results. An example template was provided to illustrate the details computer-assisted analyses for media studies should include.

The second purpose of this chapter was to determine what the authors of this handbook discussed in their individual chapters and derive a map of the discipline through an analysis of the concepts most often used in the book itself. Two approaches were taken to extract a definition of media psychology from the Oxford Handbook of Media Psychology . First, the Table of Contents was reviewed to determine what the editor and chapter authors intended their content to cover. Second, the actual chapter contents were analyzed to determine what the authors of the Handbook actually discussed. The assistance of statistical algorithms in Leximancer, visualization for initial understanding, explorations, and further inquiry assisted in a deeper understanding and ease of use in further examination of actual text excerpts as evidence of the concept compositions.

Through these two analyses, a detailed examination of media psychology was conducted. The concepts found from the Table of Contents showed that media psychology is dominated by media topics and addresses research, effects, and approaches on the topics of children, narrative, violence, adolescents, effects, games, history, persuasion, sexual, and video. Finally, the definition that emerged from the chapters’ content analysis was that media psychology is the study and research of effects and media literacy on children and adolescents with a focus on media, including the topics of aggression, social psychology, and experience.

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Media Analysis Techniques

• Arthur Asa Berger - San Francisco State University, USA
• Description

“ This book provides a concise, thought-provoking, and cleverly-written introduction to major theories in media analysis, and it gives students new perspectives on the media they use.”

—Donna Halper, Lesley University

In the Sixth Edition of Media Analysis Techniques, author Arthur Asa Berger once again provides students with a clearly written, user-friendly, hands-on guide to media criticism. The book empowers readers to make their own analyses of the media rather than just accept how others interpret the media. Media Analysis Techniques begins by examining four basic techniques of media interpretation—semiotic theory, Marxist theory, psychoanalytic theory, and sociological theory—that Berger considers critical for creative people to acknowledge if they are to understand how their creations translate to the real world. Application chapters then link popular culture to these four theories. Written in an accessible style that demystifies complex concepts, Media Analysis Techniques includes learning exercises, a glossary, study guides, and the author’s own illustrations.

New to the Sixth Edition:

• A new chapter on discourse analysis offers students techniques for analyzing the language in texts.
• New content on psychological impact of social media shows that there are often negative consequences to using social media.
• Increased coverage of technology and social media helps readers apply time-tested analysis techniques to the latest media.
• Updated examples from popular culture bring theory to life.
• New drawings and photo images illustrate concepts and enhance the visual attractiveness of this book.
• New material around generational differences describe to students how each generation interacts with media differently, particularly the millennials.    
• Daniel Chandler on semiotic codes
• Michel Foucault on change in cultures
• Mark Gottdiener on sign vehicles in semiotic theory
• Guy Debord on the Society of Spectacle
• Mark Thompson et al on Marx’s neglect of egalitarian political culture
• Marcel Danesi on myth and popular culture
• Ernest Kris on the Oedipus Complex
• Sigmund Freud on the purposes of jokes
• Clotaire Rapaille on the new “Global code.”
• Teun van Dyk on discourse analysis and ideology
• Wolfgang Iser on reception theory

Available with Perusall—an eBook that makes it easier to prepare for class! Perusall is an award-winning eBook platform featuring social annotation tools that allow students and instructors to collaboratively mark up and discuss their SAGE textbook. Backed by research and supported by technological innovations developed at Harvard University, this process of learning through collaborative annotation keeps your students engaged and makes teaching easier and more effective. Learn more .

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email [email protected] . Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html .

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NEW TO THIS EDITION:

• New material addresses generational differences and presents to students how each generation interacts with media differently, particularly the millennials.    
• New discussions by thinkers who have made major impacts on popular culture, such as
• Guy Debord on the Society of the Spectacle
• Clotaire Rapaille on the new “Global code”

KEY FEATURES:

• End-of-chapter study resources help students practice media analysis and focus on and retain important topics.
• Vivid applications from popular culture link theory to practice through teaching games and activities that show readers how to apply theories and concepts to various kinds of texts.
• A comprehensive glossary serves as a ready reference for students. 

Sample Materials & Chapters

Chapter 1- Semiotic Analysis

Chapter 2- Marxist Analysis

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David Wallace-Wells

Are smartphones driving our teens to depression.

By David Wallace-Wells

Opinion Writer

Here is a story. In 2007, Apple released the iPhone, initiating the smartphone revolution that would quickly transform the world. In 2010, it added a front-facing camera, helping shift the social-media landscape toward images, especially selfies. Partly as a result, in the five years that followed, the nature of childhood and especially adolescence was fundamentally changed — a “great rewiring,” in the words of the social psychologist Jonathan Haidt — such that between 2010 and 2015 mental health and well-being plummeted and suffering and despair exploded, particularly among teenage girls.

For young women, rates of hospitalization for nonfatal self-harm in the United States, which had bottomed out in 2009, started to rise again, according to data reported to the C.D.C., taking a leap beginning in 2012 and another beginning in 2016, and producing , over about a decade, an alarming 48 percent increase in such emergency room visits among American girls ages 15 to 19 and a shocking 188 percent increase among girls ages 10 to14.

Here is another story. In 2011, as part of the rollout of the Affordable Care Act, the Department of Health and Human Services issued a new set of guidelines that recommended that teenage girls should be screened annually for depression by their primary care physicians and that same year required that insurance providers cover such screenings in full. In 2015, H.H.S. finally mandated a coding change, proposed by the World Health Organization almost two decades before, that required hospitals to record whether an injury was self-inflicted or accidental — and which seemingly overnight nearly doubled rates for self-harm across all demographic groups. Soon thereafter, the coding of suicidal ideation was also updated. The effect of these bureaucratic changes on hospitalization data presumably varied from place to place. But in one place where it has been studied systematically, New Jersey, where 90 percent of children had health coverage even before the A.C.A., researchers have found that the changes explain nearly all of the state’s apparent upward trend in suicide-related hospital visits, turning what were “essentially flat” trendlines into something that looked like a youth mental health “crisis.”

Could both of these stories be partially true? Of course: Emotional distress among teenagers may be genuinely growing while simultaneous bureaucratic and cultural changes — more focus on mental health, destigmatization, growing comfort with therapy and medication — exaggerate the underlying trends. (This is what Adriana Corredor-Waldron, a co-author of the New Jersey study, believes — that suicidal behavior is distressingly high among teenagers in the United States and that many of our conventional measures are not very reliable to assess changes in suicidal behavior over time.) But over the past several years, Americans worrying over the well-being of teenagers have heard much less about that second story, which emphasizes changes in the broader culture of mental illness, screening guidelines and treatment, than the first one, which suggests smartphones and social-media use explain a whole raft of concerns about the well-being of the country’s youth.

When the smartphone thesis first came to prominence more than six years ago, advanced by Haidt’s sometime collaborator Jean Twenge, there was a fair amount of skepticism from scientists and social scientists and other commentators: Were teenagers really suffering that much? they asked. How much in this messy world could you pin on one piece of technology anyway? But some things have changed since then, including the conventional liberal perspective on the virtues of Big Tech, and, in the past few years, as more data has rolled in and more red flags have been raised about American teenagers — about the culture of college campuses, about the political hopelessness or neuroticism or radicalism or fatalism of teenagers, about a growing political gender divide, about how often they socialize or drink or have sex — a two-part conventional wisdom has taken hold across the pundit class. First, that American teenagers are experiencing a mental health crisis; second, that it is the fault of phones.

“Smartphones and social media are destroying children’s mental health,” the Financial Times declared last spring. This spring, Haidt’s new book on the subject, The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness, debuted at the top of the New York Times best-seller list. In its review of the book, The Guardian described the smartphone as “a pocket full of poison,” and in an essay , The New Yorker accepted as a given that Gen Z was in the midst of a “mental health emergency” and that “social media is bad for young people.” “Parents could see their phone-obsessed children changing and succumbing to distress,” The Wall Street Journal reflected . “Now we know the true horror of what happened.”

But, well, do we? Over the past five years, “Is it the phones?” has become “It’s probably the phones,” particularly among an anxious older generation processing bleak-looking charts of teenage mental health on social media as they are scrolling on their own phones. But however much we may think we know about how corrosive screen time is to mental health, the data looks murkier and more ambiguous than the headlines suggest — or than our own private anxieties, as parents and smartphone addicts, seem to tell us.

What do we really know about the state of mental health among teenagers today? Suicide offers the most concrete measure of emotional distress, and rates among American teenagers ages 15 to 19 have indeed risen over the past decade or so, to about 11.8 deaths per 100,000 in 2021 from about 7.5 deaths per 100,000 in 2009. But the American suicide epidemic is not confined to teenagers. In 2022, the rate had increased roughly as much since 2000 for the country as a whole, suggesting a national story both broader and more complicated than one focused on the emotional vulnerabilities of teenagers to Instagram. And among the teenagers of other rich countries, there is essentially no sign of a similar pattern. As Max Roser of Our World in Data recently documented , suicide rates among older teenagers and young adults have held roughly steady or declined over the same time period in France, Spain, Italy, Austria, Germany, Greece, Poland, Norway and Belgium. In Sweden there were only very small increases.

Is there a stronger distress signal in the data for young women? Yes, somewhat. According to an international analysis by The Economist, suicide rates among young women in 17 wealthy countries have grown since 2003, by about 17 percent, to a 2020 rate of 3.5 suicides per 100,000 people. The rate among young women has always been low, compared with other groups, and among the countries in the Economist data set, the rate among male teenagers, which has hardly grown at all, remains almost twice as high. Among men in their 50s, the rate is more than seven times as high.

In some countries, we see concerning signs of convergence by gender and age, with suicide rates among young women growing closer to other demographic groups. But the pattern, across countries, is quite varied. In Denmark, where smartphone penetration was the highest in the world in 2017, rates of hospitalization for self-harm among 10- to 19-year-olds fell by more than 40 percent between 2008 and 2016. In Germany, there are today barely one-quarter as many suicides among women between 15 and 20 as there were in the early 1980s, and the number has been remarkably flat for more than two decades. In the United States, suicide rates for young men are still three and a half times as high as for young women, the recent increases have been larger in absolute terms among young men than among young women, and suicide rates for all teenagers have been gradually declining since 2018. In 2022, the latest year for which C.D.C. data is available, suicide declined by 18 percent for Americans ages 10 to 14 and 9 percent for those ages 15 to 24.

None of this is to say that everything is fine — that the kids are perfectly all right, that there is no sign at all of worsening mental health among teenagers, or that there isn’t something significant and even potentially damaging about smartphone use and social media. Phones have changed us, and are still changing us, as anyone using one or observing the world through them knows well. But are they generating an obvious mental health crisis?

The picture that emerges from the suicide data is mixed and complicated to parse. Suicide is the hardest-to-dispute measure of despair, but not the most capacious. But while rates of depression and anxiety have grown strikingly for teenagers in certain parts of the world, including the U.S., it’s tricky to disentangle those increases from growing mental-health awareness and destigmatization, and attempts to measure the phenomenon in different ways can yield very different results.

According to data Haidt uses, from the U.S. National Survey on Drug Use and Health, conducted by the Substance Abuse and Mental Health Services Administration, the percent of teenage girls reporting major depressive episodes in the last year grew by about 50 percent between 2005 and 2017, for instance, during which time the share of teenage boys reporting the same grew by roughly 75 percent from a lower level. But in a biannual C.D.C. survey of teenage mental health, the share of teenagers reporting that they had been persistently sad for a period of at least two weeks in the past year grew from only 28.5 percent in 2005 to 31.5 percent in 2017. Two different surveys tracked exactly the same period, and one showed an enormous increase in depression while the other showed almost no change at all.

And if the rise of mood disorders were a straightforward effect of the smartphone, you’d expect to see it everywhere smartphones were, and, as with suicide, you don’t. In Britain, the share of young people who reported “feeling down” or experiencing depression grew from 31 percent in 2012 to 38 percent on the eve of the pandemic and to 41 percent in 2021. That is significant, though by other measures British teenagers appear, if more depressed than they were in the 2000s, not much more depressed than they were in the 1990s.

Overall, when you dig into the country-by-country data, many places seem to be registering increases in depression among teenagers, particularly among the countries of Western Europe and North America. But the trends are hard to disentangle from changes in diagnostic patterns and the medicalization of sadness, as Lucy Foulkes has argued , and the picture varies considerably from country to country. In Canada , for instance, surveys of teenagers’ well-being show a significant decline between 2015 and 2021, particularly among young women; in South Korea rates of depressive episodes among teenagers fell by 35 percent between 2006 and 2018.

Because much of our sense of teenage well-being comes from self-reported surveys, when you ask questions in different ways, the answers vary enormously. Haidt likes to cite data collected as part of an international standardized test program called PISA, which adds a few questions about loneliness at school to its sections covering progress in math, science and reading, and has found a pattern of increasing loneliness over the past decade. But according to the World Happiness Report , life satisfaction among those ages 15 to 24 around the world has been improving pretty steadily since 2013, with more significant gains among women, as the smartphone completed its global takeover, with a slight dip during the first two years of the pandemic. An international review published in 2020, examining more than 900,000 adolescents in 36 countries, showed no change in life satisfaction between 2002 and 2018.

“It doesn’t look like there’s one big uniform thing happening to people’s mental health,” said Andrew Przybylski, a professor at Oxford. “In some particular places, there are some measures moving in the wrong direction. But if I had to describe the global trend over the last decade, I would say there is no uniform trend showing a global crisis, and, where things are getting worse for teenagers, no evidence that it is the result of the spread of technology.”

If Haidt is the public face of worry about teenagers and phones, Przybylski is probably the most prominent skeptic of the thesis. Others include Amy Orben, at the University of Cambridge, who in January told The Guardian, “I think the concern about phones as a singular entity are overblown”; Chris Ferguson, at Stetson University, who is about to publish a new meta-analysis showing no relationship between smartphone use and well-being; and Candice Odgers, of the University of California, Irvine, who published a much-debated review of Haidt in Nature, in which she declared “the book’s repeated suggestion that digital technologies are rewiring our children’s brains and causing an epidemic of mental illness is not supported by science.”

Does that overstate the case? In a technical sense, I think, no: There may be some concerning changes in the underlying incidence of certain mood disorders among American teenagers over the past couple of decades, but they are hard to separate from changing methods of measuring and addressing mental health and mental illness. There isn’t great data on international trends in teenage suicide — but in those places with good reporting, the rates are generally not worsening — and the trends around anxiety, depression and well-being are ambiguous elsewhere in the world. And the association of those local increases with the rise of the smartphone, while now almost conventional wisdom among people like me, is, among specialists, very much a contested claim. Indeed, even Haidt, who has also emphasized broader changes to the culture of childhood , estimated that social media use is responsible for only about 10 percent to 15 percent of the variation in teenage well-being — which would be a significant correlation, given the complexities of adolescent life and of social science, but is also a much more measured estimate than you tend to see in headlines trumpeting the connection. And many others have arrived at much smaller estimates still.

But this all also raises the complicated question of what exactly we mean by “science,” in the context of social phenomena like these, and what standard of evidence we should be applying when asking whether something qualifies as a “crisis” or “emergency” and what we know about what may have caused it. There is a reason we rarely reduce broad social changes to monocausal explanations, whether we’re talking about the rapid decline of teenage pregnancy in the 2000s, or the spike in youth suicide in the late ’80s and early 1990s, or the rise in crime that began in the 1960s: Lives are far too complex to easily reduce to the influence of single factors, whether the factor is a recession or political conditions or, for that matter, climate breakdown.

To me, the number of places where rates of depression among teenagers are markedly on the rise is a legitimate cause for concern. But it is also worth remembering that, for instance, between the mid-1990s and the mid-2000s, diagnoses of American youth for bipolar disorder grew about 40-fold , and it is hard to find anyone who believes that change was a true reflection of underlying incidence. And when we find ourselves panicking over charts showing rapid increases in, say, the number of British girls who say they’re often unhappy or feel they are a failure, it’s worth keeping in mind that the charts were probably zoomed in to emphasize the spike, and the increase is only from about 5 percent of teenagers to about 10 percent in the first case, or from about 15 percent to about 20 percent in the second. It may also be the case, as Orben has emphasized , that smartphones and social media may be problematic for some teenagers without doing emotional damage to a majority of them. That’s not to say that in taking in the full scope of the problem, there is nothing there. But overall it is probably less than meets the eye.

If you are having thoughts of suicide, call or text 988 to reach the 988 Suicide and Crisis Lifeline or go to SpeakingOfSuicide.com/resources for a list of additional resources.

Further reading (and listening):

On Jonathan Haidt’s After Babel Substack , a series of admirable responses to critics of “The Anxious Generation” and the smartphone thesis by Haidt, his lead researcher Zach Rausch, and his sometime collaborator Jean Twenge.

In Vox, Eric Levitz weighs the body of evidence for and against the thesis.

Tom Chivers and Stuart Ritchie deliver a useful overview of the evidence and its limitations on the Studies Show podcast.

Five experts review the evidence for the smartphone hypothesis in The Guardian.

A Substack survey of “diagnostic inflation” and teenage mental health.

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Gross Domestic Product, First Quarter 2024 (Advance Estimate)

• News Release
• Related Materials
• Additional Information

Real gross domestic product (GDP) increased at an annual rate of 1.6 percent in the first quarter of 2024 (table 1), according to the "advance" estimate released by the Bureau of Economic Analysis. In the fourth quarter of 2023, real GDP increased 3.4 percent.

The GDP estimate released today is based on source data that are incomplete or subject to further revision by the source agency (refer to “Source Data for the Advance Estimate” on page 3). The “second” estimate for the first quarter, based on more complete source data, will be released on May 30, 2024.

The increase in real GDP primarily reflected increases in consumer spending, residential fixed investment, nonresidential fixed investment, and state and local government spending that were partly offset by a decrease in private inventory investment. Imports, which are a subtraction in the calculation of GDP, increased (table 2).

The increase in consumer spending reflected an increase in services that was partly offset by a decrease in goods. Within services, the increase primarily reflected increases in health care as well as financial services and insurance. Within goods, the decrease primarily reflected decreases in motor vehicles and parts as well as gasoline and other energy goods. Within residential fixed investment, the increase was led by brokers’ commissions and other ownership transfer costs as well as new single-family housing construction. The increase in nonresidential fixed investment mainly reflected an increase in intellectual property products. The increase in state and local government spending reflected an increase in compensation of state and local government employees. The decrease in inventory investment primarily reflected decreases in wholesale trade and manufacturing. Within imports, the increase reflected increases in both goods and services.

Compared to the fourth quarter, the deceleration in real GDP in the first quarter primarily reflected decelerations in consumer spending, exports, and state and local government spending and a downturn in federal government spending. These movements were partly offset by an acceleration in residential fixed investment. Imports accelerated.

Current‑dollar GDP increased 4.8 percent at an annual rate, or $327.5 billion, in the first quarter to a level of $28.28 trillion. In the fourth quarter, GDP increased 5.1 percent, or $346.9 billion (tables 1 and 3).

The price index for gross domestic purchases increased 3.1 percent in the first quarter, compared with an increase of 1.9 percent in the fourth quarter (table 4). The personal consumption expenditures (PCE) price index increased 3.4 percent, compared with an increase of 1.8 percent. Excluding food and energy prices, the PCE price index increased 3.7 percent, compared with an increase of 2.0 percent.

Personal Income

Current-dollar personal income increased $407.1 billion in the first quarter, compared with an increase of $230.2 billion in the fourth quarter. The increase primarily reflected increases in compensation and personal current transfer receipts (table 8).

Disposable personal income increased $226.2 billion, or 4.5 percent, in the first quarter, compared with an increase of $190.4 billion, or 3.8 percent, in the fourth quarter. Increases in compensation and personal current transfer receipts were partly offset by an increase in personal current taxes, which are a subtraction in the calculation of DPI. Real disposable personal income increased 1.1 percent, compared with an increase of 2.0 percent.

Personal saving was $755.7 billion in the first quarter, compared with $815.5 billion in the fourth quarter. The personal saving rate —personal saving as a percentage of disposable personal income—was 3.6 percent in the first quarter, compared with 4.0 percent in the fourth quarter.

Source Data for the Advance Estimate

The GDP estimate released today is based on source data that are incomplete or subject to further revision by the source agency. Information on the source data and key assumptions used in the advance estimate is provided in a Technical Note and a detailed " Key Source Data and Assumptions " file posted with the release. The second estimate for the first quarter, based on more complete data, will be released on May 30, 2024. For information on updates to GDP, refer to the "Additional Information" section that follows.

*          *          *

Next release, May 30, 2024, at 8:30 a.m. EDT Gross Domestic Product (Second Estimate) Corporate Profits (Preliminary Estimate) First Quarter 2024

Full Release & Tables (PDF)

Technical note (pdf), tables only (excel), release highlights (pdf), historical comparisons (pdf), key source data and assumptions (excel), revision information.

• GDP Lisa Mataloni  301-278-9083 [email protected]
• Corporate Profits Kate Pinard 301-278-9417 [email protected]
• Media (BEA) Connie O’Connell 301-278-9003 [email protected]

Additional resources available at www.bea.gov :

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• Historical time series for these estimates can be accessed in BEA's interactive data application .
• Access BEA data by registering for BEA's data Application Programming Interface (API).
• For more on BEA's statistics, refer to our online journal, the Survey of Current Business .
• BEA's news release schedule
• NIPA Handbook : Concepts and Methods of the U.S. National Income and Product Accounts

Definitions

Gross domestic product (GDP), or value added , is the value of the goods and services produced by the nation's economy less the value of the goods and services used up in production. GDP is also equal to the sum of personal consumption expenditures, gross private domestic investment, net exports of goods and services, and government consumption expenditures and gross investment.

Gross domestic income (GDI) is the sum of incomes earned and costs incurred in the production of GDP. In national economic accounting, GDP and GDI are conceptually equal. In practice, GDP and GDI differ because they are constructed using largely independent source data.

Gross output is the value of the goods and services produced by the nation's economy. It is principally measured using industry sales or receipts, including sales to final users (GDP) and sales to other industries (intermediate inputs).

Current-dollar estimates are valued in the prices of the period when the transactions occurred—that is, at "market value." Also referred to as "nominal estimates" or as "current-price estimates."

Real values are inflation-adjusted estimates—that is, estimates that exclude the effects of price changes.

The gross domestic purchases price index measures the prices of final goods and services purchased by U.S. residents.

The personal consumption expenditure price index measures the prices paid for the goods and services purchased by, or on the behalf of, "persons."

Personal income is the income received by, or on behalf of, all persons from all sources: from participation as laborers in production, from owning a home or business, from the ownership of financial assets, and from government and business in the form of transfers. It includes income from domestic sources as well as the rest of world. It does not include realized or unrealized capital gains or losses.

Disposable personal income is the income available to persons for spending or saving. It is equal to personal income less personal current taxes.

Personal outlays is the sum of personal consumption expenditures, personal interest payments, and personal current transfer payments.

Personal saving is personal income less personal outlays and personal current taxes.

The personal saving rate is personal saving as a percentage of disposable personal income.

Profits from current production , referred to as corporate profits with inventory valuation adjustment (IVA) and capital consumption (CCAdj) adjustment in the National Income and Product Accounts (NIPAs), is a measure of the net income of corporations before deducting income taxes that is consistent with the value of goods and services measured in GDP. The IVA and CCAdj are adjustments that convert inventory withdrawals and depreciation of fixed assets reported on a tax-return, historical-cost basis to the current-cost economic measures used in the national income and product accounts. Profits for domestic industries reflect profits for all corporations located within the geographic borders of the United States. The rest-of-the-world (ROW) component of profits is measured as the difference between profits received from ROW and profits paid to ROW.

For more definitions, refer to the Glossary: National Income and Product Accounts .

Statistical conventions

Annual-vs-quarterly rates . Quarterly seasonally adjusted values are expressed at annual rates, unless otherwise specified. This convention is used for BEA's featured, seasonally adjusted measures to facilitate comparisons with related and historical data. For details, refer to the FAQ " Why does BEA publish estimates at annual rates? "

Quarterly not seasonally adjusted values are expressed only at quarterly rates.

Percent changes . Percent changes in quarterly seasonally adjusted series are displayed at annual rates, unless otherwise specified. For details, refer to the FAQ " How is average annual growth calculated? " and " Why does BEA publish percent changes in quarterly series at annual rates? " Percent changes in quarterly not seasonally adjusted values are calculated from the same quarter one year ago. All published percent changes are calculated from unrounded data.

Calendar years and quarters . Unless noted otherwise, annual and quarterly data are presented on a calendar basis.

Quantities and prices . Quantities, or "real" volume measures, and prices are expressed as index numbers with a specified reference year equal to 100 (currently 2017). Quantity and price indexes are calculated using a Fisher-chained weighted formula that incorporates weights from two adjacent periods (quarters for quarterly data and annuals for annual data). For details on the calculation of quantity and price indexes, refer to Chapter 4: Estimating Methods in the NIPA Handbook .

Chained-dollar values are calculated by multiplying the quantity index by the current dollar value in the reference year (2017) and then dividing by 100. Percent changes calculated from real quantity indexes and chained-dollar levels are conceptually the same; any differences are due to rounding. Chained-dollar values are not additive because the relative weights for a given period differ from those of the reference year. In tables that display chained-dollar values, a "residual" line shows the difference between the sum of detailed chained-dollar series and its corresponding aggregate.

Updates to GDP

BEA releases three vintages of the current quarterly estimate for GDP. "Advance" estimates are released near the end of the first month following the end of the quarter and are based on source data that are incomplete or subject to further revision by the source agency. "Second" and "third" estimates are released near the end of the second and third months, respectively, and are based on more detailed and more comprehensive data as they become available.

The table below shows the average revisions to the quarterly percent changes in real GDP between different estimate vintages, without regard to sign.

Annual and comprehensive updates are released in late September. Annual updates generally cover at least the five most recent calendar years (and their associated quarters) and incorporate newly available major annual source data as well as some changes in methods and definitions to improve the accounts. Comprehensive (or benchmark) updates are carried out at about 5-year intervals and incorporate major periodic source data, as well as major conceptual improvements.

Unlike GDP, advance current quarterly estimates of GDI and corporate profits are not released because data on domestic profits and net interest of domestic industries are not available. For fourth quarter estimates, these data are not available until the third estimate.

GDP by industry and gross output estimates are released with the third estimate of GDP.