Understand your pre-understandings.
While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?
Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.
Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.
All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.
In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?
Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.
A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.
It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.
Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.
It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.
When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.
During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.
Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.
From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:
Peer review under responsibility of African Federation for Emergency Medicine.
Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .
Data & Finance for Work & Life
Content analysis is a type of qualitative research (as opposed to quantitative research) that focuses on analyzing content in various mediums, the most common of which is written words in documents.
It’s a very common technique used in academia, especially for students working on theses and dissertations, but here we’re going to talk about how companies can use qualitative content analysis to improve their processes and increase revenue.
Whether you’re new to content analysis or a seasoned professor, this article provides all you need to know about how data analysts use content analysis to improve their business. It will also help you understand the relationship between content analysis and natural language processing — what some even call natural language content analysis.
Don’t forget, you can get the free Intro to Data Analysis eBook , which will ensure you build the right practical skills for success in your analytical endeavors.
Any content analysis definition must consist of at least these three things: qualitative language , themes , and quantification .
In short, content analysis is the process of examining preselected words in video, audio, or written mediums and their context to identify themes, then quantifying them for statistical analysis in order to draw conclusions. More simply, it’s counting how often you see two words close to each other.
For example, let’s say I place in front of you an audio bit, a old video with a static image, and a document with lots of text but no titles or descriptions. At the start, you would have no idea what any of it was about.
Let’s say you transpose the video and audio recordings on paper. Then you use a counting software to count the top ten most used words, excluding prepositions (of, over, to, by) and articles (the, a), conjunctions (and, but, or) and other common words like “very.”
Your results are that the top 5 words are “candy,” “snow,” “cold,” and “sled.” These 5 words appear at least 25 times each, and the next highest word appears only 4 times. You also find that the words “snow” and “sled” appear adjacent to each other 95% of the time that “snow” appears.
Well, now you have performed a very elementary qualitative content analysis .
This means that you’re probably dealing with a text in which snow sleds are important. Snow sleds, thus, become a theme in these documents, which goes to the heart of qualitative content analysis.
The goal of qualitative content analysis is to organize text into a series of themes . This is opposed to quantitative content analysis, which aims to organize the text into categories .
If you’ve heard about content analysis, it was most likely in an academic setting. The term itself is common among PhD students and Masters students writing their dissertations and theses. In that context, the most common type of content analysis is document analysis.
There are many types of content analysis , including:
This list gives you an idea for the possibilities and industries in which qualitative content analysis can be applied.
For example, marketing departments or public relations groups in major corporations might collect survey, focus groups, and interviews, then hand off the information to a data analyst who performs the content analysis.
A political analysis institution or Think Tank might look at legislature over time to identify potential emerging themes based on their slow introduction into policy margins. Perhaps it’s possible to identify certain beliefs in the senate and house of representatives before they enter the public discourse.
Non-governmental organizations (NGOs) might perform an analysis on public records to see how to better serve their constituents. If they have access to public records, it would be possible to identify citizen characteristics that align with their goal.
There are two types of logic we can apply to qualitative content analysis: inductive and deductive. Inductive content analysis is more of an exploratory approach. We don’t know what patterns or ideas we’ll discover, so we go in with an open mind.
On the other hand, deductive content analysis involves starting with an idea and identifying how it appears in the text. For example, we may approach legislation on wildlife by looking for rules on hunting. Perhaps we think hunting with a knife is too dangerous, and we want to identify trends in the text.
Neither one is better per se, and they each have carry value in different contexts. For example, inductive content analysis is advantageous in situations where we want to identify author intent. Going in with a hypothesis can bias the way we look at the data, so the inductive method is better
Deductive content analysis is better when we want to target a term. For example, if we want to see how important knife hunting is in the legislation, we’re doing deductive content analysis.
Two main methodologies exist for analyzing the text itself: coding and word frequency. Idea coding is the manual process of reading through a text and “coding” ideas in a column on the right. The reason we call this coding is because we take ideas and themes expressed in many words, and turn them into one common phrase. This allows researchers to better understand how those ideas evolve. We will look at how to do this in word below.
In short, coding in the context qualitative content analysis follows 2 steps:
Word frequency is simply counting the number of times a word appears in a text, as well as its proximity to other words. In our “snow sled” example above, we counted the number of times a word appeared, as well as how often it appeared next to other words. There’s are online tool for this we’ll look at below.
In short, word frequency in the context of content analysis follows 2 steps:
Many data scientists consider coding as the only qualitative content analysis, since word frequency turns to counting the number of times a word appears, making is quantitative.
While there is merit to this claim, I personally do not consider word frequency a part of quantitative content analysis. The fact that we count the frequency of a word does not mean we can draw direct conclusions from it. In fact, without a researcher to provide context on the number of time a word appears, word frequency is useless. True quantitative research carries conclusive value on its own.
There are four ways to approach qualitative content analysis given our two measurement types and inductive/deductive logical approaches. You could do inductive coding, inductive word frequency, deductive coding, and deductive word frequency.
The two best are inductive coding and deductive word frequency. If you would like to discover a document, trying to search for specific words will not inform you about its contents, so inductive word frequency is un-insightful.
Likewise, if you’re looking for the presence of a specific idea, you do not want to go through the whole document to code just to find it, so deductive coding is not insightful. Here’s simple matrix to illustrate:
Inductive (discovery) | Deductive (locating) | |
---|---|---|
(summarizing ideas) | GOOD. (Example: discovering author intent in a passage.) | BAD. (Example: coding an entire document to locate one idea.) |
(counting word occurrences) | OK. (Example: trying to understand author intent by pulling to 10% of words.) | GOOD. (Example: locating and comparing a specific term in a text.) |
We looked at a small example above, but let’s play out all of the above information in a real world example. I will post the link to the text source at the bottom of the article, but don’t look at it yet . Let’s jump in with a discovery mentality , meaning let’s use an inductive approach and code our way through each paragraph.
Qualitative Content Analysis Example Download
*Click the “1” superscript to the right for a link to the source text. 1
We could use word frequency analysis to find out which are the most common x% of words in the text (deductive word frequency), but this takes some time because we need to build a formula that excludes words that are common but that don’t have any value (a, the, but, and, etc).
As a shortcut, you can use online tools such as Text Analyzer and WordCounter , which will give you breakdowns by phrase length (6 words, 5 words, 4 words, etc), without excluding common terms. Here are a few insightful example using our text with 7 words:
Perhaps more insightfully, here is a list of 5 word combinations, which are much more common:
The downside to these tools is that you cannot find 2- and 1-word strings without excluding common words. This is a limitation, but it’s unlikely that the work required to get there is worth the value it brings.
OK. Now that we’ve seen how to go about coding our text into quantifiable data, let’s look at the deductive approach and try to figure out if the text contains a single word we’re looking for. (This is my favorite.)
We know the text now because we’ve already looked through it. It’s about the process of becoming literate, namely, the elements that impact our ability to learn to read. But we only looked at the first four sections of the article, so there’s more to explore.
Let’s say we want to know how a household situation might impact a student’s ability to read . Instead of coding the entire article, we can simply look for this term and it’s synonyms. The process for deductive word frequency is the following:
In my example, the process would be:
The results: 0! None of these words appeared in the text, so we can conclude that this text has nothing to do with a child’s home life and its impact on his/her ability to learn to read. Here’s a picture:
Content analysis can be intimidating because it uses data analysis to quantify words. This article provides a starting point for your analysis, but to ensure you get 90% reliability in word coding, sign up to receive our eBook Beginner Content Analysis . I went from philosophy student to a data-heavy finance career, and I created it to cater to research and dissertation use cases.
While similar, content analysis, even the deductive word frequency approach, and natural language processing (NLP) are not the same. The relationship is hierarchical. Natural language processing is a field of linguistics and data science that’s concerned with understanding the meaning behind language.
On the other hand, content analysis is a branch of natural language processing that focuses on the methodologies we discussed above: discovery-style coding (sometimes called “tokenization”) and word frequency (sometimes called the “bag of words” technique)
For example, we would use natural language processing to quantify huge amounts of linguistic information, turn it into row-and-column data, and run tests on it. NLP is incredibly complex in the details, which is why it’s nearly impossible to provide a synopsis or example technique here (we’ll provide them in coursework on AnalystAnswers.com ). However, content analysis only focuses on a few manual techniques.
Content analysis in marketing is the use of content analysis to improve marketing reach and conversions. has grown in importance over the past ten years. As digital platforms become more central to our understanding and interaction with others, we use them more.
We write out ideas, small texts. We post our thoughts on Facebook and Twitter, and we write blog posts like this one. But we also post videos on youtube and express ourselves in podcasts.
All of these mediums contain valuable information about who we are and what we might want to buy . A good marketer aims to leverage this information in three ways:
The challenge for marketers doing this is getting the rights to access this data. Indeed, data privacy laws have gone into play in the European Union (General Data Protection Regulation, or GDPR) as well as in Brazil (General Data Protection Law, or GDPL).
Content analysis is concerned with themes and ideas, whereas narrative analysis is concerned with the stories people express about themselves or others. Narrative analysis uses the same tools as content analysis, namely coding (or tokenization) and word frequency, but its focus is on narrative relationship rather than themes. This is easier to understand with an example. Let’s look at how we might code the following paragraph from the two perspectives:
I do not like green eggs and ham. I do not like them, Sam-I-Am. I do not like them here or there. I do not like them anywhere!
Content analysis : the ideas expressed include green eggs and ham. the narrator does not like them
Narrative analysis : the narrator speaks from first person. He has a relationship with Sam-I-Am. He orients himself with regards to time and space. he does not like green eggs and ham, and may be willing to act on that feeling.
Content analysis and document analysis are very similar, which explains why many people use them interchangeably. The core difference is that content analysis examines all mediums in which words appear , whereas document analysis only examines written documents .
For example, if I want to carry out content analysis on a master’s thesis in education, I would consult documents, videos, and audio files. I may transcribe the video and audio files into a document, but I wouldn’t exclude them form the beginning.
On the other hand, if I want to carry out document analysis on a master’s thesis, I would only use documents, excluding the other mediums from the start. The methodology is the same, but the scope is different. This dichotomy also explains why most academic researchers performing qualitative content analysis refer to the process as “document analysis.” They rarely look at other mediums.
Content gap analysis is a term common in the field of content marketing, but it applies to the analytical fields as well. In a sentence, content gap analysis is the process of examining a document or text and identifying the missing pieces, or “gap,” that it needs to be completed.
As you can imagine, a content marketer uses gap analysis to determine how to improve blog content. An analyst uses it for other reasons. For example, he/she may have a standard for documents that merit analysis. If a document does not meet the criteria, it must be rejected until it’s improved.
The key message here is that content gap analysis is not content analysis. It’s a way of measuring the distance an underperforming document is from an acceptable document. It is sometimes, but not always, used in a qualitative content analysis context.
Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.
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If you are a researcher in marketing, advertising, or academics, you know the importance and challenges of conducting effective and reliable content analysis. In this article, we’re breaking down content analysis into eight steps that will help yield credible and reliable research results.
We use content analysis as a research method to systematically analyze and categorize qualitative data, such as written or visual content, and identify patterns, themes, and meanings. Content analysis involves developing a coding framework or a set of categories to systematically analyze the content. Coding categories can be qualitative or quantitative and are based on predefined criteria.
We use content analysis in various research purposes and fields, such as communication studies, media studies, social sciences, marketing , and psychology. We can do the analysis manually or with the help of software tools, and the task requires attention to detail and rigorous examination for valid and reliable results.
Content analysis is an important research method that can provide valuable insights into qualitative data , help uncover patterns and themes, enhance the rigor of research findings, and inform decision-making and policy development in various fields. We use content analysis in different contexts:
Step 1: define your research questions and objectives.
Your research questions should identify the issues you will address in your study and will help you plan your investigation. Following your research questions, your research objectives should clearly state the steps you will take to fulfill the aim(s) of your research.
Making sure you have clearly defined questions and objectives is the foundational step of any research. Therefore, take the time to clearly identify what they are before you begin your content analysis.
Decide on the specific content you want to analyze. It could be documents, texts, images, videos, social media posts, or any other form of content that is relevant to your research question.
Create a coding framework or a set of categories that you’ll use to analyze the content systematically. Coding categories are the labels or codes that you’ll assign to different aspects of the content. They should be mutually exclusive and exhaustive, meaning that each piece of content should fit into one and only one category.
Coding categories can be qualitative or quantitative, depending on the nature of the data and the research goals. Qualitative coding categories typically involve assigning labels or codes to different themes, concepts, or patterns that emerge from the content. Quantitative coding categories, on the other hand, often involve counting the frequency or occurrence of specific features or characteristics in the content.
Some examples of coding categories include:
● Themes or concepts
● Sentiments or emotions
● Actors or sources
● Frames or perspectives
● Visual elements
● Time periods
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Develop a detailed coding guide that provides instructions on how to apply the coding categories to the content. The coding guide should include definitions of each category, examples, and guidelines for making coding decisions. These elements will ensure consistency and reliability in your analysis.
Conduct a pilot test by coding a small subset of your content to ascertain the effectiveness and clarity of your coding categories and coding guide. Based on the results of the pilot test, refine your coding categories and guide as needed.
Once you’ve finalized your coding categories and coding guide, apply them to the rest of your content. Doing this involves systematically reviewing and coding each piece of content according to the coding categories and guidelines in your coding guide. You can perform these tasks manually or by using software tools designed for content analysis. Some software tools you might consider include:
● NVivo
● MAXQDA
● Dedoose
● ATLAS.ti
● QDA Miner
● Coding Analysis Toolkit (CAT)
Once all the content has been coded, analyze the coded data to identify patterns, trends, and themes. This task may involve quantitative analysis, such as calculating frequencies or percentages of coded categories, as well as qualitative analysis, such as identifying recurring themes or interpreting the meaning behind the codes.
Based on your analysis, draw conclusions and report your findings . Clearly explain the results of your content analysis and their connection to your research questions or objectives. Use evidence from your coded data to support your conclusions.
Additionally, remember to document your coding process thoroughly, keep track of any decisions or changes made during the analysis, and be mindful of potential biases or limitations in your coding. Content analysis requires careful attention to detail and rigorous examination to ensure valid and reliable results.
Content analysis is a crucial research methodology for an array of fields. While the analysis can be time-consuming and often expensive to conduct, the results are invaluable to the validity and efficacy of your research.
Interested in learning about other research methodologies and techniques? Check our Research page to learn more.
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This guide provides an introduction to content analysis, a research methodology that examines words or phrases within a wide range of texts.
Content analysis is a research tool used to determine the presence of certain words or concepts within texts or sets of texts. Researchers quantify and analyze the presence, meanings and relationships of such words and concepts, then make inferences about the messages within the texts, the writer(s), the audience, and even the culture and time of which these are a part. Texts can be defined broadly as books, book chapters, essays, interviews, discussions, newspaper headlines and articles, historical documents, speeches, conversations, advertising, theater, informal conversation, or really any occurrence of communicative language. Texts in a single study may also represent a variety of different types of occurrences, such as Palmquist's 1990 study of two composition classes, in which he analyzed student and teacher interviews, writing journals, classroom discussions and lectures, and out-of-class interaction sheets. To conduct a content analysis on any such text, the text is coded, or broken down, into manageable categories on a variety of levels--word, word sense, phrase, sentence, or theme--and then examined using one of content analysis' basic methods: conceptual analysis or relational analysis.
Historically, content analysis was a time consuming process. Analysis was done manually, or slow mainframe computers were used to analyze punch cards containing data punched in by human coders. Single studies could employ thousands of these cards. Human error and time constraints made this method impractical for large texts. However, despite its impracticality, content analysis was already an often utilized research method by the 1940's. Although initially limited to studies that examined texts for the frequency of the occurrence of identified terms (word counts), by the mid-1950's researchers were already starting to consider the need for more sophisticated methods of analysis, focusing on concepts rather than simply words, and on semantic relationships rather than just presence (de Sola Pool 1959). While both traditions still continue today, content analysis now is also utilized to explore mental models, and their linguistic, affective, cognitive, social, cultural and historical significance.
Perhaps due to the fact that it can be applied to examine any piece of writing or occurrence of recorded communication, content analysis is currently used in a dizzying array of fields, ranging from marketing and media studies, to literature and rhetoric, ethnography and cultural studies, gender and age issues, sociology and political science, psychology and cognitive science, and many other fields of inquiry. Additionally, content analysis reflects a close relationship with socio- and psycholinguistics, and is playing an integral role in the development of artificial intelligence. The following list (adapted from Berelson, 1952) offers more possibilities for the uses of content analysis:
In this guide, we discuss two general categories of content analysis: conceptual analysis and relational analysis. Conceptual analysis can be thought of as establishing the existence and frequency of concepts most often represented by words of phrases in a text. For instance, say you have a hunch that your favorite poet often writes about hunger. With conceptual analysis you can determine how many times words such as hunger, hungry, famished, or starving appear in a volume of poems. In contrast, relational analysis goes one step further by examining the relationships among concepts in a text. Returning to the hunger example, with relational analysis, you could identify what other words or phrases hunger or famished appear next to and then determine what different meanings emerge as a result of these groupings.
Traditionally, content analysis has most often been thought of in terms of conceptual analysis. In conceptual analysis, a concept is chosen for examination, and the analysis involves quantifying and tallying its presence. Also known as thematic analysis [although this term is somewhat problematic, given its varied definitions in current literature--see Palmquist, Carley, & Dale (1997) vis-a-vis Smith (1992)], the focus here is on looking at the occurrence of selected terms within a text or texts, although the terms may be implicit as well as explicit. While explicit terms obviously are easy to identify, coding for implicit terms and deciding their level of implication is complicated by the need to base judgments on a somewhat subjective system. To attempt to limit the subjectivity, then (as well as to limit problems of reliability and validity ), coding such implicit terms usually involves the use of either a specialized dictionary or contextual translation rules. And sometimes, both tools are used--a trend reflected in recent versions of the Harvard and Lasswell dictionaries.
Conceptual analysis begins with identifying research questions and choosing a sample or samples. Once chosen, the text must be coded into manageable content categories. The process of coding is basically one of selective reduction . By reducing the text to categories consisting of a word, set of words or phrases, the researcher can focus on, and code for, specific words or patterns that are indicative of the research question.
An example of a conceptual analysis would be to examine several Clinton speeches on health care, made during the 1992 presidential campaign, and code them for the existence of certain words. In looking at these speeches, the research question might involve examining the number of positive words used to describe Clinton's proposed plan, and the number of negative words used to describe the current status of health care in America. The researcher would be interested only in quantifying these words, not in examining how they are related, which is a function of relational analysis. In conceptual analysis, the researcher simply wants to examine presence with respect to his/her research question, i.e. is there a stronger presence of positive or negative words used with respect to proposed or current health care plans, respectively.
Once the research question has been established, the researcher must make his/her coding choices with respect to the eight category coding steps indicated by Carley (1992).
The following discussion of steps that can be followed to code a text or set of texts during conceptual analysis use campaign speeches made by Bill Clinton during the 1992 presidential campaign as an example. To read about each step, click on the items in the list below:
First, the researcher must decide upon the level of analysis . With the health care speeches, to continue the example, the researcher must decide whether to code for a single word, such as "inexpensive," or for sets of words or phrases, such as "coverage for everyone."
The researcher must now decide how many different concepts to code for. This involves developing a pre-defined or interactive set of concepts and categories. The researcher must decide whether or not to code for every single positive or negative word that appears, or only certain ones that the researcher determines are most relevant to health care. Then, with this pre-defined number set, the researcher has to determine how much flexibility he/she allows him/herself when coding. The question of whether the researcher codes only from this pre-defined set, or allows him/herself to add relevant categories not included in the set as he/she finds them in the text, must be answered. Determining a certain number and set of concepts allows a researcher to examine a text for very specific things, keeping him/her on task. But introducing a level of coding flexibility allows new, important material to be incorporated into the coding process that could have significant bearings on one's results.
After a certain number and set of concepts are chosen for coding , the researcher must answer a key question: is he/she going to code for existence or frequency ? This is important, because it changes the coding process. When coding for existence, "inexpensive" would only be counted once, no matter how many times it appeared. This would be a very basic coding process and would give the researcher a very limited perspective of the text. However, the number of times "inexpensive" appears in a text might be more indicative of importance. Knowing that "inexpensive" appeared 50 times, for example, compared to 15 appearances of "coverage for everyone," might lead a researcher to interpret that Clinton is trying to sell his health care plan based more on economic benefits, not comprehensive coverage. Knowing that "inexpensive" appeared, but not that it appeared 50 times, would not allow the researcher to make this interpretation, regardless of whether it is valid or not.
The researcher must next decide on the , i.e. whether concepts are to be coded exactly as they appear, or if they can be recorded as the same even when they appear in different forms. For example, "expensive" might also appear as "expensiveness." The research needs to determine if the two words mean radically different things to him/her, or if they are similar enough that they can be coded as being the same thing, i.e. "expensive words." In line with this, is the need to determine the level of implication one is going to allow. This entails more than subtle differences in tense or spelling, as with "expensive" and "expensiveness." Determining the level of implication would allow the researcher to code not only for the word "expensive," but also for words that imply "expensive." This could perhaps include technical words, jargon, or political euphemism, such as "economically challenging," that the researcher decides does not merit a separate category, but is better represented under the category "expensive," due to its implicit meaning of "expensive."
After taking the generalization of concepts into consideration, a researcher will want to create translation rules that will allow him/her to streamline and organize the coding process so that he/she is coding for exactly what he/she wants to code for. Developing a set of rules helps the researcher insure that he/she is coding things consistently throughout the text, in the same way every time. If a researcher coded "economically challenging" as a separate category from "expensive" in one paragraph, then coded it under the umbrella of "expensive" when it occurred in the next paragraph, his/her data would be invalid. The interpretations drawn from that data will subsequently be invalid as well. Translation rules protect against this and give the coding process a crucial level of consistency and coherence.
The next choice a researcher must make involves irrelevant information . The researcher must decide whether irrelevant information should be ignored (as Weber, 1990, suggests), or used to reexamine and/or alter the coding scheme. In the case of this example, words like "and" and "the," as they appear by themselves, would be ignored. They add nothing to the quantification of words like "inexpensive" and "expensive" and can be disregarded without impacting the outcome of the coding.
Once these choices about irrelevant information are made, the next step is to code the text. This is done either by hand, i.e. reading through the text and manually writing down concept occurrences, or through the use of various computer programs. Coding with a computer is one of contemporary conceptual analysis' greatest assets. By inputting one's categories, content analysis programs can easily automate the coding process and examine huge amounts of data, and a wider range of texts, quickly and efficiently. But automation is very dependent on the researcher's preparation and category construction. When coding is done manually, a researcher can recognize errors far more easily. A computer is only a tool and can only code based on the information it is given. This problem is most apparent when coding for implicit information, where category preparation is essential for accurate coding.
Once the coding is done, the researcher examines the data and attempts to draw whatever conclusions and generalizations are possible. Of course, before these can be drawn, the researcher must decide what to do with the information in the text that is not coded. One's options include either deleting or skipping over unwanted material, or viewing all information as relevant and important and using it to reexamine, reassess and perhaps even alter one's coding scheme. Furthermore, given that the conceptual analyst is dealing only with quantitative data, the levels of interpretation and generalizability are very limited. The researcher can only extrapolate as far as the data will allow. But it is possible to see trends, for example, that are indicative of much larger ideas. Using the example from step three, if the concept "inexpensive" appears 50 times, compared to 15 appearances of "coverage for everyone," then the researcher can pretty safely extrapolate that there does appear to be a greater emphasis on the economics of the health care plan, as opposed to its universal coverage for all Americans. It must be kept in mind that conceptual analysis, while extremely useful and effective for providing this type of information when done right, is limited by its focus and the quantitative nature of its examination. To more fully explore the relationships that exist between these concepts, one must turn to relational analysis.
Relational analysis, like conceptual analysis, begins with the act of identifying concepts present in a given text or set of texts. However, relational analysis seeks to go beyond presence by exploring the relationships between the concepts identified. Relational analysis has also been termed semantic analysis (Palmquist, Carley, & Dale, 1997). In other words, the focus of relational analysis is to look for semantic, or meaningful, relationships. Individual concepts, in and of themselves, are viewed as having no inherent meaning. Rather, meaning is a product of the relationships among concepts in a text. Carley (1992) asserts that concepts are "ideational kernels;" these kernels can be thought of as symbols which acquire meaning through their connections to other symbols.
The kind of analysis that researchers employ will vary significantly according to their theoretical approach. Key theoretical approaches that inform content analysis include linguistics and cognitive science.
Linguistic approaches to content analysis focus analysis of texts on the level of a linguistic unit, typically single clause units. One example of this type of research is Gottschalk (1975), who developed an automated procedure which analyzes each clause in a text and assigns it a numerical score based on several emotional/psychological scales. Another technique is to code a text grammatically into clauses and parts of speech to establish a matrix representation (Carley, 1990).
Approaches that derive from cognitive science include the creation of decision maps and mental models. Decision maps attempt to represent the relationship(s) between ideas, beliefs, attitudes, and information available to an author when making a decision within a text. These relationships can be represented as logical, inferential, causal, sequential, and mathematical relationships. Typically, two of these links are compared in a single study, and are analyzed as networks. For example, Heise (1987) used logical and sequential links to examine symbolic interaction. This methodology is thought of as a more generalized cognitive mapping technique, rather than the more specific mental models approach.
Mental models are groups or networks of interrelated concepts that are thought to reflect conscious or subconscious perceptions of reality. According to cognitive scientists, internal mental structures are created as people draw inferences and gather information about the world. Mental models are a more specific approach to mapping because beyond extraction and comparison because they can be numerically and graphically analyzed. Such models rely heavily on the use of computers to help analyze and construct mapping representations. Typically, studies based on this approach follow five general steps:
To create the model, a researcher converts a text into a map of concepts and relations; the map is then analyzed on the level of concepts and statements, where a statement consists of two concepts and their relationship. Carley (1990) asserts that this makes possible the comparison of a wide variety of maps, representing multiple sources, implicit and explicit information, as well as socially shared cognitions.
As with other sorts of inquiry, initial choices with regard to what is being studied and/or coded for often determine the possibilities of that particular study. For relational analysis, it is important to first decide which concept type(s) will be explored in the analysis. Studies have been conducted with as few as one and as many as 500 concept categories. Obviously, too many categories may obscure your results and too few can lead to unreliable and potentially invalid conclusions. Therefore, it is important to allow the context and necessities of your research to guide your coding procedures.
The steps to relational analysis that we consider in this guide suggest some of the possible avenues available to a researcher doing content analysis. We provide an example to make the process easier to grasp. However, the choices made within the context of the example are but only a few of many possibilities. The diversity of techniques available suggests that there is quite a bit of enthusiasm for this mode of research. Once a procedure is rigorously tested, it can be applied and compared across populations over time. The process of relational analysis has achieved a high degree of computer automation but still is, like most forms of research, time consuming. Perhaps the strongest claim that can be made is that it maintains a high degree of statistical rigor without losing the richness of detail apparent in even more qualitative methods.
Affect extraction: This approach provides an emotional evaluation of concepts explicit in a text. It is problematic because emotion may vary across time and populations. Nevertheless, when extended it can be a potent means of exploring the emotional/psychological state of the speaker and/or writer. Gottschalk (1995) provides an example of this type of analysis. By assigning concepts identified a numeric value on corresponding emotional/psychological scales that can then be statistically examined, Gottschalk claims that the emotional/psychological state of the speaker or writer can be ascertained via their verbal behavior.
Proximity analysis: This approach, on the other hand, is concerned with the co-occurrence of explicit concepts in the text. In this procedure, the text is defined as a string of words. A given length of words, called a window , is determined. The window is then scanned across a text to check for the co-occurrence of concepts. The result is the creation of a concept determined by the concept matrix . In other words, a matrix, or a group of interrelated, co-occurring concepts, might suggest a certain overall meaning. The technique is problematic because the window records only explicit concepts and treats meaning as proximal co-occurrence. Other techniques such as clustering, grouping, and scaling are also useful in proximity analysis.
Cognitive mapping: This approach is one that allows for further analysis of the results from the two previous approaches. It attempts to take the above processes one step further by representing these relationships visually for comparison. Whereas affective and proximal analysis function primarily within the preserved order of the text, cognitive mapping attempts to create a model of the overall meaning of the text. This can be represented as a graphic map that represents the relationships between concepts.
In this manner, cognitive mapping lends itself to the comparison of semantic connections across texts. This is known as map analysis which allows for comparisons to explore "how meanings and definitions shift across people and time" (Palmquist, Carley, & Dale, 1997). Maps can depict a variety of different mental models (such as that of the text, the writer/speaker, or the social group/period), according to the focus of the researcher. This variety is indicative of the theoretical assumptions that support mapping: mental models are representations of interrelated concepts that reflect conscious or subconscious perceptions of reality; language is the key to understanding these models; and these models can be represented as networks (Carley, 1990). Given these assumptions, it's not surprising to see how closely this technique reflects the cognitive concerns of socio-and psycholinguistics, and lends itself to the development of artificial intelligence models.
The following discussion of the steps (or, perhaps more accurately, strategies) that can be followed to code a text or set of texts during relational analysis. These explanations are accompanied by examples of relational analysis possibilities for statements made by Bill Clinton during the 1998 hearings.
The question is important because it indicates where you are headed and why. Without a focused question, the concept types and options open to interpretation are limitless and therefore the analysis difficult to complete. Possibilities for the Hairy Hearings of 1998 might be:
What did Bill Clinton say in the speech? OR What concrete information did he present to the public?
Once the question has been identified, the researcher must select sections of text/speech from the hearings in which Bill Clinton may have not told the entire truth or is obviously holding back information. For relational content analysis, the primary consideration is how much information to preserve for analysis. One must be careful not to limit the results by doing so, but the researcher must also take special care not to take on so much that the coding process becomes too heavy and extensive to supply worthwhile results.
Once the sample has been chosen for analysis, it is necessary to determine what type or types of relationships you would like to examine. There are different subcategories of relational analysis that can be used to examine the relationships in texts.
In this example, we will use proximity analysis because it is concerned with the co-occurrence of explicit concepts in the text. In this instance, we are not particularly interested in affect extraction because we are trying to get to the hard facts of what exactly was said rather than determining the emotional considerations of speaker and receivers surrounding the speech which may be unrecoverable.
Once the subcategory of analysis is chosen, the selected text must be reviewed to determine the level of analysis. The researcher must decide whether to code for a single word, such as "perhaps," or for sets of words or phrases like "I may have forgotten."
At the simplest level, a researcher can code merely for existence. This is not to say that simplicity of procedure leads to simplistic results. Many studies have successfully employed this strategy. For example, Palmquist (1990) did not attempt to establish the relationships among concept terms in the classrooms he studied; his study did, however, look at the change in the presence of concepts over the course of the semester, comparing a map analysis from the beginning of the semester to one constructed at the end. On the other hand, the requirement of one's specific research question may necessitate deeper levels of coding to preserve greater detail for analysis.
In relation to our extended example, the researcher might code for how often Bill Clinton used words that were ambiguous, held double meanings, or left an opening for change or "re-evaluation." The researcher might also choose to code for what words he used that have such an ambiguous nature in relation to the importance of the information directly related to those words.
Once words are coded, the text can be analyzed for the relationships among the concepts set forth. There are three concepts which play a central role in exploring the relations among concepts in content analysis.
One of the main differences between conceptual analysis and relational analysis is that the statements or relationships between concepts are coded. At this point, to continue our extended example, it is important to take special care with assigning value to the relationships in an effort to determine whether the ambiguous words in Bill Clinton's speech are just fillers, or hold information about the statements he is making.
This step involves conducting statistical analyses of the data you've coded during your relational analysis. This may involve exploring for differences or looking for relationships among the variables you've identified in your study.
In addition to statistical analysis, relational analysis often leads to viewing the representations of the concepts and their associations in a text (or across texts) in a graphical -- or map -- form. Relational analysis is also informed by a variety of different theoretical approaches: linguistic content analysis, decision mapping, and mental models.
The authors of this guide have created the following commentaries on content analysis.
The issues of reliability and validity are concurrent with those addressed in other research methods. The reliability of a content analysis study refers to its stability , or the tendency for coders to consistently re-code the same data in the same way over a period of time; reproducibility , or the tendency for a group of coders to classify categories membership in the same way; and accuracy , or the extent to which the classification of a text corresponds to a standard or norm statistically. Gottschalk (1995) points out that the issue of reliability may be further complicated by the inescapably human nature of researchers. For this reason, he suggests that coding errors can only be minimized, and not eliminated (he shoots for 80% as an acceptable margin for reliability).
On the other hand, the validity of a content analysis study refers to the correspondence of the categories to the conclusions , and the generalizability of results to a theory.
The validity of categories in implicit concept analysis, in particular, is achieved by utilizing multiple classifiers to arrive at an agreed upon definition of the category. For example, a content analysis study might measure the occurrence of the concept category "communist" in presidential inaugural speeches. Using multiple classifiers, the concept category can be broadened to include synonyms such as "red," "Soviet threat," "pinkos," "godless infidels" and "Marxist sympathizers." "Communist" is held to be the explicit variable, while "red," etc. are the implicit variables.
The overarching problem of concept analysis research is the challenge-able nature of conclusions reached by its inferential procedures. The question lies in what level of implication is allowable, i.e. do the conclusions follow from the data or are they explainable due to some other phenomenon? For occurrence-specific studies, for example, can the second occurrence of a word carry equal weight as the ninety-ninth? Reasonable conclusions can be drawn from substantive amounts of quantitative data, but the question of proof may still remain unanswered.
This problem is again best illustrated when one uses computer programs to conduct word counts. The problem of distinguishing between synonyms and homonyms can completely throw off one's results, invalidating any conclusions one infers from the results. The word "mine," for example, variously denotes a personal pronoun, an explosive device, and a deep hole in the ground from which ore is extracted. One may obtain an accurate count of that word's occurrence and frequency, but not have an accurate accounting of the meaning inherent in each particular usage. For example, one may find 50 occurrences of the word "mine." But, if one is only looking specifically for "mine" as an explosive device, and 17 of the occurrences are actually personal pronouns, the resulting 50 is an inaccurate result. Any conclusions drawn as a result of that number would render that conclusion invalid.
The generalizability of one's conclusions, then, is very dependent on how one determines concept categories, as well as on how reliable those categories are. It is imperative that one defines categories that accurately measure the idea and/or items one is seeking to measure. Akin to this is the construction of rules. Developing rules that allow one, and others, to categorize and code the same data in the same way over a period of time, referred to as stability , is essential to the success of a conceptual analysis. Reproducibility , not only of specific categories, but of general methods applied to establishing all sets of categories, makes a study, and its subsequent conclusions and results, more sound. A study which does this, i.e. in which the classification of a text corresponds to a standard or norm, is said to have accuracy .
Content analysis offers several advantages to researchers who consider using it. In particular, content analysis:
Content analysis suffers from several disadvantages, both theoretical and procedural. In particular, content analysis:
The Palmquist, Carley and Dale study, a summary of "Applications of Computer-Aided Text Analysis: Analyzing Literary and Non-Literary Texts" (1997) is an example of two studies that have been conducted using both conceptual and relational analysis. The Problematic Text for Content Analysis shows the differences in results obtained by a conceptual and a relational approach to a study.
Related Information: Example of a Problematic Text for Content Analysis
In this example, both students observed a scientist and were asked to write about the experience.
Student A: I found that scientists engage in research in order to make discoveries and generate new ideas. Such research by scientists is hard work and often involves collaboration with other scientists which leads to discoveries which make the scientists famous. Such collaboration may be informal, such as when they share new ideas over lunch, or formal, such as when they are co-authors of a paper.
Student B: It was hard work to research famous scientists engaged in collaboration and I made many informal discoveries. My research showed that scientists engaged in collaboration with other scientists are co-authors of at least one paper containing their new ideas. Some scientists make formal discoveries and have new ideas.
Content analysis coding for explicit concepts may not reveal any significant differences. For example, the existence of "I, scientist, research, hard work, collaboration, discoveries, new ideas, etc..." are explicit in both texts, occur the same number of times, and have the same emphasis. Relational analysis or cognitive mapping, however, reveals that while all concepts in the text are shared, only five concepts are common to both. Analyzing these statements reveals that Student A reports on what "I" found out about "scientists," and elaborated the notion of "scientists" doing "research." Student B focuses on what "I's" research was and sees scientists as "making discoveries" without emphasis on research.
Related Information: The Palmquist, Carley and Dale Study
Consider these two questions: How has the depiction of robots changed over more than a century's worth of writing? And, do students and writing instructors share the same terms for describing the writing process? Although these questions seem totally unrelated, they do share a commonality: in the Palmquist, Carley & Dale study, their answers rely on computer-aided text analysis to demonstrate how different texts can be analyzed.
Literary texts
One half of the study explored the depiction of robots in 27 science fiction texts written between 1818 and 1988. After texts were divided into three historically defined groups, readers look for how the depiction of robots has changed over time. To do this, researchers had to create concept lists and relationship types, create maps using a computer software (see Fig. 1), modify those maps and then ultimately analyze them. The final product of the analysis revealed that over time authors were less likely to depict robots as metallic humanoids.
Non-literary texts
The second half of the study used student journals and interviews, teacher interviews, texts books, and classroom observations as the non-literary texts from which concepts and words were taken. The purpose behind the study was to determine if, in fact, over time teacher and students would begin to share a similar vocabulary about the writing process. Again, researchers used computer software to assist in the process. This time, computers helped researchers generated a concept list based on frequently occurring words and phrases from all texts. Maps were also created and analyzed in this study (see Fig. 2).
Resources On How To Conduct Content Analysis
Beard, J., & Yaprak, A. (1989). Language implications for advertising in international markets: A model for message content and message execution. A paper presented at the 8th International Conference on Language Communication for World Business and the Professions. Ann Arbor, MI.
This report discusses the development and testing of a content analysis model for assessing advertising themes and messages aimed primarily at U.S. markets which seeks to overcome barriers in the cultural environment of international markets. Texts were categorized under 3 headings: rational, emotional, and moral. The goal here was to teach students to appreciate differences in language and culture.
Berelson, B. (1971). Content analysis in communication research . New York: Hafner Publishing Company.
While this book provides an extensive outline of the uses of content analysis, it is far more concerned with conveying a critical approach to current literature on the subject. In this respect, it assumes a bit of prior knowledge, but is still accessible through the use of concrete examples.
Budd, R. W., Thorp, R.K., & Donohew, L. (1967). Content analysis of communications . New York: Macmillan Company.
Although published in 1967, the decision of the authors to focus on recent trends in content analysis keeps their insights relevant even to modern audiences. The book focuses on specific uses and methods of content analysis with an emphasis on its potential for researching human behavior. It is also geared toward the beginning researcher and breaks down the process of designing a content analysis study into 6 steps that are outlined in successive chapters. A useful annotated bibliography is included.
Carley, K. (1992). Coding choices for textual analysis: A comparison of content analysis and map analysis. Unpublished Working Paper.
Comparison of the coding choices necessary to conceptual analysis and relational analysis, especially focusing on cognitive maps. Discusses concept coding rules needed for sufficient reliability and validity in a Content Analysis study. In addition, several pitfalls common to texts are discussed.
Carley, K. (1990). Content analysis. In R.E. Asher (Ed.), The Encyclopedia of Language and Linguistics. Edinburgh: Pergamon Press.
Quick, yet detailed, overview of the different methodological kinds of Content Analysis. Carley breaks down her paper into five sections, including: Conceptual Analysis, Procedural Analysis, Relational Analysis, Emotional Analysis and Discussion. Also included is an excellent and comprehensive Content Analysis reference list.
Carley, K. (1989). Computer analysis of qualitative data . Pittsburgh, PA: Carnegie Mellon University.
Presents graphic, illustrated representations of computer based approaches to content analysis.
Carley, K. (1992). MECA . Pittsburgh, PA: Carnegie Mellon University.
A resource guide explaining the fifteen routines that compose the Map Extraction Comparison and Analysis (MECA) software program. Lists the source file, input and out files, and the purpose for each routine.
Carney, T. F. (1972). Content analysis: A technique for systematic inference from communications . Winnipeg, Canada: University of Manitoba Press.
This book introduces and explains in detail the concept and practice of content analysis. Carney defines it; traces its history; discusses how content analysis works and its strengths and weaknesses; and explains through examples and illustrations how one goes about doing a content analysis.
de Sola Pool, I. (1959). Trends in content analysis . Urbana, Ill: University of Illinois Press.
The 1959 collection of papers begins by differentiating quantitative and qualitative approaches to content analysis, and then details facets of its uses in a wide variety of disciplines: from linguistics and folklore to biography and history. Includes a discussion on the selection of relevant methods and representational models.
Duncan, D. F. (1989). Content analysis in health educaton research: An introduction to purposes and methods. Heatlth Education, 20 (7).
This article proposes using content analysis as a research technique in health education. A review of literature relating to applications of this technique and a procedure for content analysis are presented.
Gottschalk, L. A. (1995). Content analysis of verbal behavior: New findings and clinical applications. Hillside, NJ: Lawrence Erlbaum Associates, Inc.
This book primarily focuses on the Gottschalk-Gleser method of content analysis, and its application as a method of measuring psychological dimensions of children and adults via the content and form analysis of their verbal behavior, using the grammatical clause as the basic unit of communication for carrying semantic messages generated by speakers or writers.
Krippendorf, K. (1980). Content analysis: An introduction to its methodology Beverly Hills, CA: Sage Publications.
This is one of the most widely quoted resources in many of the current studies of Content Analysis. Recommended as another good, basic resource, as Krippendorf presents the major issues of Content Analysis in much the same way as Weber (1975).
Moeller, L. G. (1963). An introduction to content analysis--including annotated bibliography . Iowa City: University of Iowa Press.
A good reference for basic content analysis. Discusses the options of sampling, categories, direction, measurement, and the problems of reliability and validity in setting up a content analysis. Perhaps better as a historical text due to its age.
Smith, C. P. (Ed.). (1992). Motivation and personality: Handbook of thematic content analysis. New York: Cambridge University Press.
Billed by its authors as "the first book to be devoted primarily to content analysis systems for assessment of the characteristics of individuals, groups, or historical periods from their verbal materials." The text includes manuals for using various systems, theory, and research regarding the background of systems, as well as practice materials, making the book both a reference and a handbook.
Solomon, M. (1993). Content analysis: a potent tool in the searcher's arsenal. Database, 16 (2), 62-67.
Online databases can be used to analyze data, as well as to simply retrieve it. Online-media-source content analysis represents a potent but little-used tool for the business searcher. Content analysis benchmarks useful to advertisers include prominence, offspin, sponsor affiliation, verbatims, word play, positioning and notational visibility.
Weber, R. P. (1990). Basic content analysis, second edition . Newbury Park, CA: Sage Publications.
Good introduction to Content Analysis. The first chapter presents a quick overview of Content Analysis. The second chapter discusses content classification and interpretation, including sections on reliability, validity, and the creation of coding schemes and categories. Chapter three discusses techniques of Content Analysis, using a number of tables and graphs to illustrate the techniques. Chapter four examines issues in Content Analysis, such as measurement, indication, representation and interpretation.
Examples of Content Analysis
Adams, W., & Shriebman, F. (1978). Television network news: Issues in content research . Washington, DC: George Washington University Press.
A fairly comprehensive application of content analysis to the field of television news reporting. The books tripartite division discusses current trends and problems with news criticism from a content analysis perspective, four different content analysis studies of news media, and makes recommendations for future research in the area. Worth a look by anyone interested in mass communication research.
Auter, P. J., & Moore, R. L. (1993). Buying from a friend: a content analysis of two teleshopping programs. Journalism Quarterly, 70 (2), 425-437.
A preliminary study was conducted to content-analyze random samples of two teleshopping programs, using a measure of content interactivity and a locus of control message index.
Barker, S. P. (???) Fame: A content analysis study of the American film biography. Ohio State University. Thesis.
Barker examined thirty Oscar-nominated films dating from 1929 to 1979 using O.J. Harvey Belief System and the Kohlberg's Moral Stages to determine whether cinema heroes were positive role models for fame and success or morally ambiguous celebrities. Content analysis was successful in determining several trends relative to the frequency and portrayal of women in film, the generally high ethical character of the protagonists, and the dogmatic, close-minded nature of film antagonists.
Bernstein, J. M. & Lacy, S. (1992). Contextual coverage of government by local television news. Journalism Quarterly, 69 (2), 329-341.
This content analysis of 14 local television news operations in five markets looks at how local TV news shows contribute to the marketplace of ideas. Performance was measured as the allocation of stories to types of coverage that provide the context about events and issues confronting the public.
Blaikie, A. (1993). Images of age: a reflexive process. Applied Ergonomics, 24 (1), 51-58.
Content analysis of magazines provides a sharp instrument for reflecting the change in stereotypes of aging over past decades.
Craig, R. S. (1992). The effect of day part on gender portrayals in television commercials: a content analysis. Sex Roles: A Journal of Research, 26 (5-6), 197-213.
Gender portrayals in 2,209 network television commercials were content analyzed. To compare differences between three day parts, the sample was chosen from three time periods: daytime, evening prime time, and weekend afternoon sportscasts. The results indicate large and consistent differences in the way men and women are portrayed in these three day parts, with almost all comparisons reaching significance at the .05 level. Although ads in all day parts tended to portray men in stereotypical roles of authority and dominance, those on weekends tended to emphasize escape form home and family. The findings of earlier studies which did not consider day part differences may now have to be reevaluated.
Dillon, D. R. et al. (1992). Article content and authorship trends in The Reading Teacher, 1948-1991. The Reading Teacher, 45 (5), 362-368.
The authors explore changes in the focus of the journal over time.
Eberhardt, EA. (1991). The rhetorical analysis of three journal articles: The study of form, content, and ideology. Ft. Collins, CO: Colorado State University.
Eberhardt uses content analysis in this thesis paper to analyze three journal articles that reported on President Ronald Reagan's address in which he responded to the Tower Commission report concerning the IranContra Affair. The reports concentrated on three rhetorical elements: idea generation or content; linguistic style or choice of language; and the potential societal effect of both, which Eberhardt analyzes, along with the particular ideological orientation espoused by each magazine.
Ellis, B. G. & Dick, S. J. (1996). 'Who was 'Shadow'? The computer knows: applying grammar-program statistics in content analyses to solve mysteries about authorship. Journalism & Mass Communication Quarterly, 73 (4), 947-963.
This study's objective was to employ the statistics-documentation portion of a word-processing program's grammar-check feature as a final, definitive, and objective tool for content analyses - used in tandem with qualitative analyses - to determine authorship. Investigators concluded there was significant evidence from both modalities to support their theory that Henry Watterson, long-time editor of the Louisville Courier-Journal, probably was the South's famed Civil War correspondent "Shadow" and to rule out another prime suspect, John H. Linebaugh of the Memphis Daily Appeal. Until now, this Civil War mystery has never been conclusively solved, puzzling historians specializing in Confederate journalism.
Gottschalk, L. A., Stein, M. K. & Shapiro, D.H. (1997). The application of computerized content analysis in a psychiatric outpatient clinic. Journal of Clinical Psychology, 53 (5) , 427-442.
Twenty-five new psychiatric outpatients were clinically evaluated and were administered a brief psychological screening battery which included measurements of symptoms, personality, and cognitive function. Included in this assessment procedure were the Gottschalk-Gleser Content Analysis Scales on which scores were derived from five minute speech samples by means of an artificial intelligence-based computer program. The use of this computerized content analysis procedure for initial, rapid diagnostic neuropsychiatric appraisal is supported by this research.
Graham, J. L., Kamins, M. A., & Oetomo, D. S. (1993). Content analysis of German and Japanese advertising in print media from Indonesia, Spain, and the United States. Journal of Advertising , 22 (2), 5-16.
The authors analyze informational and emotional content in print advertisements in order to consider how home-country culture influences firms' marketing strategies and tactics in foreign markets. Research results provided evidence contrary to the original hypothesis that home-country culture would influence ads in each of the target countries.
Herzog, A. (1973). The B.S. Factor: The theory and technique of faking it in America . New York: Simon and Schuster.
Herzog takes a look at the rhetoric of American culture using content analysis to point out discrepancies between intention and reality in American society. The study reveals, albeit in a comedic tone, how double talk and "not quite lies" are pervasive in our culture.
Horton, N. S. (1986). Young adult literature and censorship: A content analysis of seventy-eight young adult books . Denton, TX: North Texas State University.
The purpose of Horton's content analysis was to analyze a representative seventy-eight current young adult books to determine the extent to which they contain items which are objectionable to would-be censors. Seventy-eight books were identified which fit the criteria of popularity and literary quality. Each book was analyzed for, and tallied for occurrence of, six categories, including profanity, sex, violence, parent conflict, drugs and condoned bad behavior.
Isaacs, J. S. (1984). A verbal content analysis of the early memories of psychiatric patients . Berkeley: California School of Professional Psychology.
Isaacs did a content analysis investigation on the relationship between words and phrases used in early memories and clinical diagnosis. His hypothesis was that in conveying their early memories schizophrenic patients tend to use an identifiable set of words and phrases more frequently than do nonpatients and that schizophrenic patients use these words and phrases more frequently than do patients with major affective disorders.
Jean Lee, S. K. & Hwee Hoon, T. (1993). Rhetorical vision of men and women managers in Singapore. Human Relations, 46 (4), 527-542.
A comparison of media portrayal of male and female managers' rhetorical vision in Singapore is made. Content analysis of newspaper articles used to make this comparison also reveals the inherent conflicts that women managers have to face. Purposive and multi-stage sampling of articles are utilized.
Kaur-Kasior, S. (1987). The treatment of culture in greeting cards: A content analysis . Bowling Green, OH: Bowling Green State University.
Using six historical periods dating from 1870 to 1987, this content analysis study attempted to determine what structural/cultural aspects of American society were reflected in greeting cards. The study determined that the size of cards increased over time, included more pages, and had animals and flowers as their most dominant symbols. In addition, white was the most common color used. Due to habituation and specialization, says the author, greeting cards have become institutionalized in American culture.
Koza, J. E. (1992). The missing males and other gender-related issues in music education: A critical analysis of evidence from the Music Supervisor's Journal, 1914-1924. Paper presented at the annual meeting of the American Educational Research Association. San Francisco.
The goal of this study was to identify all educational issues that would today be explicitly gender related and to analyze the explanations past music educators gave for the existence of gender-related problems. A content analysis of every gender-related reference was undertaken, finding that the current preoccupation with males in music education has a long history and that little has changed since the early part of this century.
Laccinole, M. D. (1982). Aging and married couples: A language content analysis of a conversational and expository speech task . Eugene, OR: University of Oregon.
Using content analysis, this paper investigated the relationship of age to the use of the grammatical categories, and described the differences in the usage of these grammatical categories in a conversation and expository speech task by fifty married couples. The subjects Laccinole used in his analysis were Caucasian, English speaking, middle class, ranged in ages from 20 to 83 years of age, were in good health and had no history of communication disorders.
Laffal, J. (1995). A concept analysis of Jonathan Swift's 'A Tale of a Tub' and 'Gulliver's Travels.' Computers and Humanities, 29 (5), 339-362.
In this study, comparisons of concept profiles of "Tub," "Gulliver," and Swift's own contemporary texts, as well as a composite text of 18th century writers, reveal that "Gulliver" is conceptually different from "Tub." The study also discovers that the concepts and words of these texts suggest two strands in Swift's thinking.
Lewis, S. M. (1991). Regulation from a deregulatory FCC: Avoiding discursive dissonance. Masters Thesis, Fort Collins, CO: Colorado State University.
This thesis uses content analysis to examine inconsistent statements made by the Federal Communications Commission (FCC) in its policy documents during the 1980s. Lewis analyzes positions set forth by the FCC in its policy statements and catalogues different strategies that can be used by speakers to be or to appear consistent, as well as strategies to avoid inconsistent speech or discursive dissonance.
Norton, T. L. (1987). The changing image of childhood: A content analysis of Caldecott Award books. Los Angeles: University of South Carolina.
Content analysis was conducted on 48 Caldecott Medal Recipient books dating from 1938 to 1985 to determine whether the reflect the idea that the social perception of childhood has altered since the early 1960's. The results revealed an increasing "loss of childhood innocence," as well as a general sentimentality for childhood pervasive in the texts. Suggests further study of children's literature to confirm the validity of such study.
O'Dell, J. W. & Weideman, D. (1993). Computer content analysis of the Schreber case. Journal of Clinical Psychology, 49 (1), 120-125.
An example of the application of content analysis as a means of recreating a mental model of the psychology of an individual.
Pratt, C. A. & Pratt, C. B. (1995). Comparative content analysis of food and nutrition advertisements in Ebony, Essence, and Ladies' Home Journal. Journal of Nutrition Education, 27 (1), 11-18.
This study used content analysis to measure the frequencies and forms of food, beverage, and nutrition advertisements and their associated health-promotional message in three U.S. consumer magazines during two 3-year periods: 1980-1982 and 1990-1992. The study showed statistically significant differences among the three magazines in both frequencies and types of major promotional messages in the advertisements. Differences between the advertisements in Ebony and Essence, the readerships of which were primarily African-American, and those found in Ladies Home Journal were noted, as were changes in the two time periods. Interesting tie in to ethnographic research studies?
Riffe, D., Lacy, S., & Drager, M. W. (1996). Sample size in content analysis of weekly news magazines. Journalism & Mass Communication Quarterly,73 (3), 635-645.
This study explores a variety of approaches to deciding sample size in analyzing magazine content. Having tested random samples of size six, eight, ten, twelve, fourteen, and sixteen issues, the authors show that a monthly stratified sample of twelve issues is the most efficient method for inferring to a year's issues.
Roberts, S. K. (1987). A content analysis of how male and female protagonists in Newbery Medal and Honor books overcome conflict: Incorporating a locus of control framework. Fayetteville, AR: University of Arkansas.
The purpose of this content analysis was to analyze Newbery Medal and Honor books in order to determine how male and female protagonists were assigned behavioral traits in overcoming conflict as it relates to an internal or external locus of control schema. Roberts used all, instead of just a sample, of the fictional Newbery Medal and Honor books which met his study's criteria. A total of 120 male and female protagonists were categorized, from Newbery books dating from 1922 to 1986.
Schneider, J. (1993). Square One TV content analysis: Final report . New York: Children's Television Workshop.
This report summarizes the mathematical and pedagogical content of the 230 programs in the Square One TV library after five seasons of production, relating that content to the goals of the series which were to make mathematics more accessible, meaningful, and interesting to the children viewers.
Smith, T. E., Sells, S. P., and Clevenger, T. Ethnographic content analysis of couple and therapist perceptions in a reflecting team setting. The Journal of Marital and Family Therapy, 20 (3), 267-286.
An ethnographic content analysis was used to examine couple and therapist perspectives about the use and value of reflecting team practice. Postsession ethnographic interviews from both couples and therapists were examined for the frequency of themes in seven categories that emerged from a previous ethnographic study of reflecting teams. Ethnographic content analysis is briefly contrasted with conventional modes of quantitative content analysis to illustrate its usefulness and rationale for discovering emergent patterns, themes, emphases, and process using both inductive and deductive methods of inquiry.
Stahl, N. A. (1987). Developing college vocabulary: A content analysis of instructional materials. Reading, Research and Instruction , 26 (3).
This study investigates the extent to which the content of 55 college vocabulary texts is consistent with current research and theory on vocabulary instruction. It recommends less reliance on memorization and more emphasis on deep understanding and independent vocabulary development.
Swetz, F. (1992). Fifteenth and sixteenth century arithmetic texts: What can we learn from them? Science and Education, 1 (4).
Surveys the format and content of 15th and 16th century arithmetic textbooks, discussing the types of problems that were most popular in these early texts and briefly analyses problem contents. Notes the residual educational influence of this era's arithmetical and instructional practices.
Walsh, K., et al. (1996). Management in the public sector: a content analysis of journals. Public Administration 74 (2), 315-325.
The popularity and implementaion of managerial ideas from 1980 to 1992 are examined through the content of five journals revolving on local government, health, education and social service. Contents were analyzed according to commercialism, user involvement, performance evaluation, staffing, strategy and involvement with other organizations. Overall, local government showed utmost involvement with commercialism while health and social care articles were most concerned with user involvement.
For Further Reading
Abernethy, A. M., & Franke, G. R. (1996).The information content of advertising: a meta-analysis. Journal of Advertising, Summer 25 (2) , 1-18.
Carley, K., & Palmquist, M. (1992). Extracting, representing and analyzing mental models. Social Forces , 70 (3), 601-636.
Fan, D. (1988). Predictions of public opinion from the mass media: Computer content analysis and mathematical modeling . New York, NY: Greenwood Press.
Franzosi, R. (1990). Computer-assisted coding of textual data: An application to semantic grammars. Sociological Methods and Research, 19 (2), 225-257.
McTavish, D.G., & Pirro, E. (1990) Contextual content analysis. Quality and Quantity , 24 , 245-265.
Palmquist, M. E. (1990). The lexicon of the classroom: language and learning in writing class rooms . Doctoral dissertation, Carnegie Mellon University, Pittsburgh, PA.
Palmquist, M. E., Carley, K.M., and Dale, T.A. (1997). Two applications of automated text analysis: Analyzing literary and non-literary texts. In C. Roberts (Ed.), Text Analysis for the Social Sciences: Methods for Drawing Statistical Inferences from Texts and Tanscripts. Hillsdale, NJ: Lawrence Erlbaum Associates.
Roberts, C.W. (1989). Other than counting words: A linguistic approach to content analysis. Social Forces, 68 , 147-177.
Issues in Content Analysis
Jolliffe, L. (1993). Yes! More content analysis! Newspaper Research Journal , 14 (3-4), 93-97.
The author responds to an editorial essay by Barbara Luebke which criticizes excessive use of content analysis in newspaper content studies. The author points out the positive applications of content analysis when it is theory-based and utilized as a means of suggesting how or why the content exists, or what its effects on public attitudes or behaviors may be.
Kang, N., Kara, A., Laskey, H. A., & Seaton, F. B. (1993). A SAS MACRO for calculating intercoder agreement in content analysis. Journal of Advertising, 22 (2), 17-28.
A key issue in content analysis is the level of agreement across the judgments which classify the objects or stimuli of interest. A review of articles published in the Journal of Advertising indicates that many authors are not fully utilizing recommended measures of intercoder agreement and thus may not be adequately establishing the reliability of their research. This paper presents a SAS MACRO which facilitates the computation of frequently recommended indices of intercoder agreement in content analysis.
Lacy, S. & Riffe, D. (1996). Sampling error and selecting intercoder reliability samples for nominal content categories. Journalism & Mass Communication Quarterly, 73 (4) , 693-704.
This study views intercoder reliability as a sampling problem. It develops a formula for generating sample sizes needed to have valid reliability estimates. It also suggests steps for reporting reliability. The resulting sample sizes will permit a known degree of confidence that the agreement in a sample of items is representative of the pattern that would occur if all content items were coded by all coders.
Riffe, D., Aust, C. F., & Lacy, S. R. (1993). The effectiveness of random, consecutive day and constructed week sampling in newspaper content analysis. Journalism Quarterly, 70 (1), 133-139.
This study compares 20 sets each of samples for four different sizes using simple random, constructed week and consecutive day samples of newspaper content. Comparisons of sample efficiency, based on the percentage of sample means in each set of 20 falling within one or two standard errors of the population mean, show the superiority of constructed week sampling.
Thomas, S. (1994). Artifactual study in the analysis of culture: A defense of content analysis in a postmodern age. Communication Research, 21 (6), 683-697.
Although both modern and postmodern scholars have criticized the method of content analysis with allegations of reductionism and other epistemological limitations, it is argued here that these criticisms are ill founded. In building and argument for the validity of content analysis, the general value of artifact or text study is first considered.
Zollars, C. (1994). The perils of periodical indexes: Some problems in constructing samples for content analysis and culture indicators research. Communication Research, 21 (6), 698-714.
The author examines problems in using periodical indexes to construct research samples via the use of content analysis and culture indicator research. Issues of historical and idiosyncratic changes in index subject category heading and subheadings make article headings potentially misleading indicators. Index subject categories are not necessarily invalid as a result; nevertheless, the author discusses the need to test for category longevity, coherence, and consistency over time, and suggests the use of oversampling, cross-references, and other techniques as a means of correcting and/or compensating for hidden inaccuracies in classification, and as a means of constructing purposive samples for analytic comparisons.
Busch, Carol, Paul S. De Maret, Teresa Flynn, Rachel Kellum, Sheri Le, Brad Meyers, Matt Saunders, Robert White, and Mike Palmquist. (2005). Content Analysis. Writing@CSU . Colorado State University. https://writing.colostate.edu/guides/guide.cfm?guideid=61
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Methodology
Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.
Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.
There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.
This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.
When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.
Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .
Some types of research questions you might use thematic analysis to answer:
To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.
However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.
Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.
Once you’ve decided to use thematic analysis, there are different approaches to consider.
There’s the distinction between inductive and deductive approaches:
Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?
There’s also the distinction between a semantic and a latent approach:
Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?
After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .
The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.
This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.
Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.
Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:
Interview extract | Codes |
---|---|
Personally, I’m not sure. I think the climate is changing, sure, but I don’t know why or how. People say you should trust the experts, but who’s to say they don’t have their own reasons for pushing this narrative? I’m not saying they’re wrong, I’m just saying there’s reasons not to 100% trust them. The facts keep changing – it used to be called global warming. |
In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.
At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.
After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.
Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.
Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:
Codes | Theme |
---|---|
Uncertainty | |
Distrust of experts | |
Misinformation |
At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.
Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.
Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.
Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?
If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.
For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.
Now that you have a final list of themes, it’s time to name and define each of them.
Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.
Naming themes involves coming up with a succinct and easily understandable name for each theme.
For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.
Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.
We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.
The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.
In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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Home » Documentary Analysis – Methods, Applications and Examples
Table of Contents
Definition:
Documentary analysis, also referred to as document analysis , is a systematic procedure for reviewing or evaluating documents. This method involves a detailed review of the documents to extract themes or patterns relevant to the research topic .
Documents used in this type of analysis can include a wide variety of materials such as text (words) and images that have been recorded without a researcher’s intervention. The domain of document analysis, therefore, includes all kinds of texts – books, newspapers, letters, study reports, diaries, and more, as well as images like maps, photographs, and films.
Documentary analysis provides valuable insight and a unique perspective on the past, contextualizing the present and providing a baseline for future studies. It is also an essential tool in case studies and when direct observation or participant observation is not possible.
The process usually involves several steps:
Documentary analysis as a qualitative research method involves a systematic process. Here are the main steps you would generally follow:
Before you start any research , you need a clear and focused research question . This will guide your decision on what documents you need to analyze and what you’re looking for within them.
Once you know what you’re looking for, you can start to select the relevant documents. These can be a wide range of materials – books, newspapers, letters, official reports, diaries, transcripts of speeches, archival materials, websites, social media posts, and more. They can be primary sources (directly from the time/place/person you are studying) or secondary sources (analyses created by others).
You need to closely read the selected documents to identify the themes and patterns that relate to your research question. This might involve content analysis (looking at what is explicitly stated) and discourse analysis (looking at what is implicitly stated or implied). You need to understand the context in which the document was created, the author’s purpose, and the audience’s perspective.
After the initial reading, the data (text) can be broken down into smaller parts or “codes.” These codes can then be categorized based on their similarities and differences. This process of coding helps in organizing the data and identifying patterns or themes.
Once the data is organized, it can be analyzed to make sense of it. This can involve comparing the data with existing theories, examining relationships between categories, or explaining the data in relation to the research question.
The researcher needs to ensure that the findings are accurate and credible. This might involve triangulating the data (comparing it with other sources or types of data), considering alternative explanations, or seeking feedback from others.
The final step is to report the findings in a clear, structured way. This should include a description of the methods used, the findings, and the researcher’s interpretations and conclusions.
Documentary analysis is widely used across a variety of fields and disciplines due to its flexible and comprehensive nature. Here are some specific applications:
Historical Research
Documentary analysis is a fundamental method in historical research. Historians use documents to reconstruct past events, understand historical contexts, and interpret the motivations and actions of historical figures. Documents analyzed may include personal letters, diaries, official records, newspaper articles, photographs, and more.
Social Science Research
Sociologists, anthropologists, and political scientists use documentary analysis to understand social phenomena, cultural practices, political events, and more. This might involve analyzing government policies, organizational records, media reports, social media posts, and other documents.
Legal Research
In law, documentary analysis is used in case analysis and statutory interpretation. Legal practitioners and scholars analyze court decisions, statutes, regulations, and other legal documents.
Business and Market Research
Companies often analyze documents to gather business intelligence, understand market trends, and make strategic decisions. This might involve analyzing competitor reports, industry news, market research studies, and more.
Media and Communication Studies
Scholars in these fields might analyze media content (e.g., news reports, advertisements, social media posts) to understand media narratives, public opinion, and communication practices.
Literary and Film Studies
In these fields, the “documents” might be novels, poems, films, or scripts. Scholars analyze these texts to interpret their meaning, understand their cultural context, and critique their form and content.
Educational Research
Educational researchers may analyze curricula, textbooks, lesson plans, and other educational documents to understand educational practices and policies.
Health Research
Health researchers may analyze medical records, health policies, clinical guidelines, and other documents to study health behaviors, healthcare delivery, and health outcomes.
Some Examples of Documentary Analysis might be:
Documentary analysis can be used in a variety of research contexts, including but not limited to:
The purpose of documentary analysis in research can be multifold. Here are some key reasons why a researcher might choose to use this method:
Documentary analysis offers several advantages as a research method:
While documentary analysis offers several benefits as a research method, it also has its limitations. It’s important to keep these in mind when deciding to use documentary analysis and when interpreting your findings:
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An A.I.-powered version of Mr. Musk has appeared in thousands of inauthentic ads, contributing to billions in fraud.
By Stuart A. Thompson
All Steve Beauchamp wanted was money for his family. And he thought Elon Musk could help.
Mr. Beauchamp, an 82-year-old retiree, saw a video late last year of Mr. Musk endorsing a radical investment opportunity that promised rapid returns. He contacted the company behind the pitch and opened an account for $248. Through a series of transactions over several weeks, Mr. Beauchamp drained his retirement account, ultimately investing more than $690,000.
Then the money vanished — lost to digital scammers on the forefront of a new criminal enterprise powered by artificial intelligence.
The scammers had edited a genuine interview with Mr. Musk, replacing his voice with a replica using A.I. tools. The A.I. was sophisticated enough that it could alter minute mouth movements to match the new script they had written for the digital fake. To a casual viewer, the manipulation might have been imperceptible.
“I mean, the picture of him — it was him,” Mr. Beauchamp said about the video he saw of Mr. Musk. “Now, whether it was A.I. making him say the things that he was saying, I really don’t know. But as far as the picture, if somebody had said, ‘Pick him out of a lineup,’ that’s him.”
Thousands of these A.I.-driven videos, known as deepfakes, have flooded the internet in recent months featuring phony versions of Mr. Musk deceiving scores of would-be investors. A.I.-powered deepfakes are expected to contribute to billions of dollars in fraud losses each year, according to estimates from Deloitte .
The videos cost just a few dollars to produce and can be made in minutes. They are promoted on social media, including in paid ads on Facebook, magnifying their reach.
“It’s probably the biggest deepfake-driven scam ever,” said Francesco Cavalli, the co-founder and chief of threat intelligence at Sensity, a company that monitors and detects deepfakes.
The videos are often eerily lifelike, capturing Mr. Musk’s iconic stilted cadence and South African accent.
Scammers will start with a genuine video , like this interview from The Wall Street Journal conducted by Thorold Barker, an editor whose voice is also heard in the clip.
Mr. Musk’s mouth movements are edited with lip-synching technology, which tweaks how someone speaks. Scammers will add an A.I. voice using voice-cloning tools, which copies any voice from sample clips.
The final ad , which can include fake graphics mimicking news organizations, can be quite convincing for casual internet users._
Source: The Wall Street Journal (original clip)
Mr. Musk was by far the most common spokesperson in the videos, according to Sensity, which analyzed more than 2,000 deepfakes.
He was featured in nearly a quarter of all deepfake scams since late last year, Sensity found. Among those focused on cryptocurrencies, he was featured in nearly 90 percent of the videos.
The deepfake ads also featured Warren Buffett, the prominent investor, and Jeff Bezos, the founder of Amazon, among others.
Mr. Musk did not respond to requests for comment.
Prime Video India (original clip)
It is difficult to quantify exactly how many deepfakes are floating online, but a search of Facebook’s ad library for commonly used language that advertised the scams uncovered hundreds of thousands of ads, many of which included the deepfake videos. Though Facebook has already taken down many of them for violating its policies and disabled some of the accounts that were responsible, other videos remained online and more seemed to appear each day.
YouTube was also flooded with the fakes, often using a label that suggests the video is “live.” In fact, the videos are prerecorded deepfakes.
Search results on YouTube for “Elon Bitcoin conference” showed dozens of supposedly live videos featuring a deepfake Mr. Musk hawking crypto scams. Some videos were watched by hundreds of thousands of people.
After former President Donald J. Trump spoke at a Bitcoin conference Saturday, YouTube hosted dozens of videos using the “live” label that showed a prerecorded deepfake version of Elon Musk saying he would personally double any cryptocurrency sent to his account. Some of the videos had hundreds of thousands of viewers, though YouTube said scammers can use bots to artificially inflate the number.
One Texan said he lost $36,000 worth of Bitcoin after seeing an “impersonation” of Mr. Musk speaking on a so-called live YouTube video in February 2023, according to a report with the Better Business Bureau , the nonprofit consumer advocacy group.
“I send my bitcoin, and never got anything back,” the person wrote.
Source: CNET (original clip)
YouTube said in a statement that it had removed more than 15.7 million channels and over 8.2 million videos for violating its guidelines from January to March of this year, with most of those violating its policies against spam .
The prevalence of the phony ads prompted Andrew Forrest, an Australian billionaire whose videos were also used to create deepfake ads on Facebook, to file a civil lawsuit against Meta, its parent company, for negligence in how its ad business is run. He claimed that Facebook’s advertising business lured “innocent users into bad investments.”
Meta, which owns Facebook, said the company was training automated detection systems to catch fraud on its platform, but also described a cat-and-mouse game where well-funded scammers constantly shifted their tactics to evade detection.
YouTube pointed to its policies prohibiting scams and manipulated videos. The company in March made it a requirement that creators disclose when they use A.I. to create realistic content.
The internet is now rife with similar reports from people scammed out of thousands of dollars, some of them losing their life savings. Hong Kong’s Securities and Futures Commission issued a warning in May about scams featuring Mr. Musk. Earlier this year, the Federal Trade Commission and the Federal Bureau of Investigation warned Americans that A.I.-powered cybercrime and deepfake scams were on the rise.
“Criminals are leveraging A.I. as a force multiplier” in ways that make “cyberattacks and other criminal activity more effective and harder to detect,” the F.B.I. said in an emailed statement.
Digital scams are as old as the internet itself. But the new-wave deepfakes featuring Mr. Musk emerged last year after sophisticated A.I. tools were released to the public, allowing anyone to clone celebrity voices or manipulate videos with eerie accuracy. Pornographers , meme-makers and, increasingly, scammers took notice.
Sources: TED Talks (first and second videos); Fox News (third video)
“It’s shifting now because organized crime has figured out, ‘we can make money at this,’” according to Lou Steinberg, the founder of CTM Insights , a cybersecurity research lab. “So we’re going to see more and more of these fake attempts to separate you from your money.”
The A.I.-generated videos are hardly perfect. Mr. Musk can sound robotic in some videos and his mouth does not always line up with his words. But they appear convincing enough for some targets of the scam — and are improving all the time, experts said.
Such videos cost as little as $10 to create, according to Mr. Cavalli from Sensity. The scammers — based mostly in India, Russia, China and Eastern Europe — cobble together the fake videos using a mix of free and cheap tools in less than 10 minutes.
“It works,” Mr. Cavalli said. “So they’ll keep amplifying the campaign, across countries, translating into multiple languages, and continuously spreading the scam to even more targets.”
Some of the scams often advertise phony A.I.-powered software, with claims that they can produce incredible returns on an investment. Targets are encouraged to send a small sum at first — about $250 — and are slowly lured into investing more as scammers claim that the initial investment is increasing in value.
In one video, taken from a shareholder meeting at Tesla, the deepfake Mr. Musk explains a product for automated trading powered by A.I. that can double a given investment each day.
Source: Tesla (original clip)
Experts who have studied crypto communities said Mr. Musk’s unique global fanbase of conservatives, anti-establishment types and crypto enthusiasts are often drawn to alternative paths for earning their fortunes — making them perfect targets for the scams.
“There’s definitely a group of people who believe that the secret to wealth is being hidden from them,” said Molly White, a researcher who has studied crypto communities. They think that “if they can find the secret to it, then that’s all they need.”
Scammers often target older internet users who may be familiar with cryptocurrency, A.I. or Mr. Musk, but unfamiliar with the safest ways to invest.
“The elderly have always been a very scammable, profitable population,” said Finn Brunton, a professor of science and technology studies at the University of California, Davis, who is an expert in the crypto market. He added that the elderly had been targets of fraud long before platforms like Facebook made them easier to scam.
Mr. Beauchamp, who is a widower and worked until he was 75 as a sales representative at a company in Ontario, Canada, came across an ad shortly after joining Facebook in 2023. Though he remembers seeing the video live on CNN, a spokeswoman for CNN said Mr. Musk had not appeared for an interview in years. (The New York Times could not identify a video matching Mr. Beauchamp’s description, but he said his story was nearly identical to that of another woman scammed online by a deepfaked Mr. Musk.)
He sent $27,216 last December to a company calling itself Magna-FX, according to emails between Mr. Beauchamp and the company that were shared with The New York Times. Magna-FX made it seem like his investment was increasing in value. At one point, a sales agent used software to take control of Mr. Beauchamp’s computer, moving funds around to apparently invest them.
To withdraw the money, Mr. Beauchamp was told to pay a $3,500 administration fee and another $3,500 commission fee. He sent the money only to be told that he needed to pay $20,000 to release a portion of the funds — about $200,000. He paid that, too.
Though Mr. Beauchamp told the scammers that he had exhausted his retirement savings, maxed out his credit cards, tapped a line of credit and borrowed money from his sister to invest and pay the fees, the scammers wanted more. They asked him to pay yet another fee. Mr. Beauchamp contacted the police.
Most traces of Magna-FX were taken offline, including the company website, phone number and email addresses used by the agents Mr. Beauchamp spoke with. Another company bearing a nearly identical name and advertising similar services did not respond to requests for comment.
“I guess now is the time to call me dumb, stupid, idiot and what other superlatives you can think of,” Mr. Beauchamp wrote in a report filed to the Better Business Bureau .
Mr. Beauchamp said he was managing to pay his bills using a smaller retirement account that he had not shared with the scammers, along with his pensions. He had planned to travel the world during his retirement.
Mr. Beauchamp filed a report with the local police but little movement has been made on the case, he said.
“Because of the amount of fraud that is going on everywhere, my case got put in a queue,” he said. “I’m not getting my hopes up.”
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Combination of near-infrared spectroscopy and transient flow method for efficient kinetic analysis of claisen rearrangement.
Kinetic analysis of the Claisen rearrangement of allyl phenyl ether (APE) to 2-allylphenol was conducted in pressurized N -methylpyrrolidone (NMP) at various temperatures from 240 to 280°C using an automated flow reactor. Rapid inline analysis by a compact near-infrared (NIR) spectrometer coupled with a flow rate ramp as a reciprocal function of the experimental time allowed a high-density data acquisition (900 points in 15 min) of the conversion of APE over the residence time ranging from 0.8 to 10.3 min. Inline NIR monitoring was also employed to measure the residence time of the NMP solution in the reactor. The residence time was shown to decrease by 26% with increasing temperature from 20 to 300°C due to the thermal expansion of the solution. The APE conversion exhibited first-order kinetics with an activation energy of 137 ± 1 kJ mol -1 and a pre-exponential factor of 7.3×10 10 s -1 . The result of the flow rate ramp experiment was consistent with that of the temperature ramp experiment, while the latter gave a continuous Arrhenius plot in a wider temperature range from 230 to 290°C. The rate constant in NMP was found to be 10 and 1.5 times smaller than those reported in subcritical water and alcohol solvents, respectively.
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Y. Takebayashi, K. Sue and S. Kataoka, React. Chem. Eng. , 2024, Accepted Manuscript , DOI: 10.1039/D4RE00301B
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The following segment was excerpted from this fund letter.
"The Internet's Final Frontier: Remote Amazon Tribes." The New York Times. June 2, 2024.
Jack Nicas and Victor Moriyama, two The New York Times reporters, "hiked more than 50 miles through the Amazon to reach remote Marubo villages." That was to see what happened to villagers who had just received a Starlink satellite internet antenna...which "connected" the tiny, closed civilization, among the most remote indigenous villages on the planet, to the rest of us...courtesy of Elon Musk's SpaceX ( SPACE ). The benefits of "video chats with faraway loved ones and calls for help in emergencies" are obvious. Connecting to the outside world also has drawbacks for villagers who need to plant and harvest and hunt to survive...and whose "young people have gotten lazy…learning the ways of the white people." "But please don't take our Internet away," the female leader of one village plaintively asked the reporters. Gwynne Shotwell, SpaceX's President and Chief Operating Officer, described her recent visit to a school in one of these villages. She was repeatedly "hugged" by young students. She said, "It was one of the best days in my life." I will interview Gwynne at the Annual Baron Investment Conference on November 15. That will be her second appearance at our annual meeting.
"Want to Buy SpaceX stock? You have to know someone." Wall Street Journal. May 19, 2024
Sounds almost like "Would you like to buy the Brooklyn Bridge?" doesn't it?
On May 19, 2024, WSJ reporter/analyst Micah Maidenberg wrote about extraordinary investor demand to invest in Elon Musk's "privately owned" SpaceX. Since 2017, Baron has consistently purchased SpaceX shares annually on behalf of Baron mutual funds, partnerships, private clients, and proprietary accounts. Baron's approximately $1 billion investments at cost are now valued at $2.68 billion. We think those investments could increase materially in value in the next 10 to 15 years. We expect to continue adding to this investment whenever we have an opportunity to do so.
SpaceX's current two most important businesses are "Starlink" and "Launch." Starlink is a satellite broadband Internet service. Starlink's satellite broadband serves the entire Planet Earth from its 6,500 low earth orbital ('LEO'), low latency satellites. Starlink's LEO constellation will ultimately consist of more than 15,000 satellites. Starlink's business is enabled by competitively advantaged SpaceX rocket ships that are "reflyable." "Reflyable" means that SpaceX rockets can be used over and over and over again…just like an airplane…and just like Star Trek spaceships. SpaceX reflyable rockets provide a dramatically "lower cost to space" than governments and commercial interests have previously achieved. We estimate SpaceX cost to orbit will soon be less than 10% the cost of traditional launch businesses!
We think of SpaceX as the "railroad to space" … and analogize SpaceX rockets to America's railroads in the late 1800s. Railroads enabled our nation to settle America's West. Railroads then were a dramatic improvement over wagon trains. Just like reflyable rockets are a dramatic improvement over expensive rockets that can be used only once. No other commercial enterprise or government has been able to refly rockets…which, when you watch SpaceX landings, you too will quickly understand why it is such an awesome feat.
Since the 1960s, the United States has reached orbit with rockets mainly powered by Russian technology. Those rockets can be used only once before burning up in our atmosphere! Each launch could easily cost a lot more than $100 million. One of the unusual coincidences in my life is that from 1966-1969, when I attended George Washington Law School in the evenings, I worked during the days as a patent examiner in the U.S. Patent Office. There, I was assigned an unusual "art"... chemical coatings. In that role, I granted patents on golf ball covers…and heat resistant coatings shielding nose cones that carry astronauts returning to Earth!!!!
Approximately 90% of the mass to orbit from Planet Earth is currently launched by SpaceX. Elon estimates that when SpaceX Starship, the 400-foot tall, largest rocket ever launched from our planet, has been "derisked," 99% of all mass to orbit will be flown by SpaceX! Whether for commercial interests or governments.
Micah noted the growth prospects for SpaceX are so favorable that many investors seeking to purchase SpaceX shares willingly pay unusually hefty annual management fees plus "carried interest" in profits they earn. "Carried interest," most often 20% of profits, are paid to managers who do not risk their own capital but are paid a percentage of your profits. A much different model than for "active" mutual fund managers like Baron, which typically charges annual management fees of 1% or less of net assets…with no carried interest.
I interviewed SpaceX's President and Chief Operating Officer Gwynne Shotwell at the 2019 Annual Baron Investment Conference at New York's Metropolitan Opera House. The theme then, "What's Next?," couldn't have been more appropriate. That is since it was only a few months before the COVID-19 pandemic. After my interview with Gwynne, she was asked by several Baron Funds' shareholders in the audience how they could invest in SpaceX? "Talk to Ron," she answered. Several Baron mutual funds have significant investments in SpaceX.
The largest holdings are Baron Partners Fund (13.2% of total assets) and Baron Focused Growth Fund (10.3% of net assets). Tom Pritzker, Hyatt Hotel's Chairman, and my friend since 1979, wrote me recently. "Just saw Gwynne Shotwell interviewed at Aspen Ideas Festival. OMG!!!! What an awesome endeavor. I'm so glad to be associated. You could see on her face the joy of what she is doing. Thank you for convincing me to invest."
We expect a lot more businesses to be in the path of demand created by SpaceX. Like "Starshield," a satellite network to protect our Homeland…cargo and ordinance transport anywhere on our planet in 32 minutes point-to-point… manufacturing in zero gravity… data centers in space powered by the sun which Elon calls that "giant nuclear reactor in the sky"… cooling for SpaceX's massive orbiting GPU data centers from absolute zero space temperatures…and data for AI "training" transported to and from Earth by Starlink to those data centers... to list just a few of the possibilities...in addition to Starlink's base business of connecting virtually every square inch of our planet...whether on land, including deserts and mountains...on sea...or in the air.
Goldman Sachs and Morgan Stanley estimate very high annual profit margin revenues will be available to Starlink during the 2030s, which they currently approximate at $1.25 trillion...which is growing double digits annually. During the 2030s, Musk expects SpaceX to obtain a substantial percentage of the highly profitable revenues while enabling terrestrial telcos to improve and increase the services they provide to their customers.
Baron Funds (Institutional Shares) and Benchmark Performance 6/30/2024 Baron Capital's Top 30 Holdings - As of 3/31/2024 This is a hypothetical ranking created by Baron Capital using Morningstar data and is as of 6/30/2024. There were 2,059 share classes in the nine Morningstar Categories mentioned below for the period from 4/30/2003 to 6/30/2024. Note, the peer group used for this analysis includes all U.S. equity share classes in Morningstar Direct domiciled in the U.S., including obsolete funds, index funds, and ETFs. The individual Morningstar Categories used for this analysis are the Morningstar Large Blend, Large Growth, Large Value, Mid-Cap Blend, Mid-Cap Growth, Mid-Cap Value, Small Blend, Small Growth, and Small Value Categories. As of 6/30/2024, the Morningstar Large Growth Category consisted of 1,162, 1,019, and 794 share classes for the 1-, 5-, and 10-year periods. Morningstar ranked Baron Partners Fund (Institutional Shares) in the 100th, 1st, 6th, and 1st percentiles for the 1-, 5-, 10-year, and since conversion periods, respectively. The Fund converted into a mutual Fund on 4/30/2003, and the category consisted of 728 share classes. On an absolute basis, Morningstar ranked Baron Partners Fund Institutional Share Class as the 1,160th, 2nd, 31st, and 1st best performing share class in its Category, for the 1-, 5-, 10-year, and since conversion periods, respectively. |
Editor's Note: The summary bullets for this article were chosen by Seeking Alpha editors.
This article was written by
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Content analysis is a research method used to identify patterns in recorded communication. To conduct content analysis, you systematically collect data from a set of texts, which can be written, oral, or visual: Books, newspapers and magazines. Speeches and interviews. Web content and social media posts. Photographs and films.
2. The two types of content analysis. Now that you understand the difference between implicit and explicit data, let's move on to the two general types of content analysis: conceptual and relational content analysis. Importantly, while conceptual and relational content analysis both follow similar steps initially, the aims and outcomes of each are different.
Chapter 17. Content Analysis Introduction. Content analysis is a term that is used to mean both a method of data collection and a method of data analysis. Archival and historical works can be the source of content analysis, but so too can the contemporary media coverage of a story, blogs, comment posts, films, cartoons, advertisements, brand packaging, and photographs posted on Instagram or ...
Step 1: Select the content you will analyse. Based on your research question, choose the texts that you will analyse. You need to decide: The medium (e.g., newspapers, speeches, or websites) and genre (e.g., opinion pieces, political campaign speeches, or marketing copy)
Content analysis is a research tool used to determine the presence of certain words, themes, or concepts within some given qualitative data (i.e. text). Using content analysis, researchers can quantify and analyze the presence, meanings, and relationships of such certain words, themes, or concepts.
A step-by-step guide to conducting a content analysis. Step 1: Develop your research questions. Step 2: Choose the content you'll analyze. Step 3: Identify your biases. Step 4: Define the units and categories of coding. Step 5: Develop a coding scheme. Step 6: Code the content. Step 7: Analyze the Results. In Closing.
Content analysis, as in all qualitative analysis, is a reflective process. There is no "step 1, 2, 3, done!" linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to ...
Content analysis is concerned with themes and ideas, whereas narrative analysis is concerned with the stories people express about themselves or others. ... On the other hand, if I want to carry out document analysis on a master's thesis, I would only use documents, excluding the other mediums from the start. The methodology is the same, but ...
Step 8: Draw Conclusions and Report Findings. Based on your analysis, draw conclusions and report your findings. Clearly explain the results of your content analysis and their connection to your research questions or objectives. Use evidence from your coded data to support your conclusions.
This thesis uses content analysis to examine inconsistent statements made by the Federal Communications Commission (FCC) in its policy documents during the 1980s. Lewis analyzes positions set forth by the FCC in its policy statements and catalogues different strategies that can be used by speakers to be or to appear consistent, as well as ...
Step 1: Prepare the Data. Qualitative content analysis can be used to analyze various types of data, but generally the data need to be transformed into written text before analysis can start. If the data come from existing texts, the choice of the content must be justified by what you want to know (Patton, 2002).
Abstract. Content analysis is a highly fl exible research method that has been. widely used in library and infor mation science (LIS) studies with. varying research goals and objectives. The ...
Content analysis and thematic analysis as qualitative descriptive approaches. According to Sandelowski and Barroso research findings can be placed on a continuum indicating the degree of transformation of data during the data analysis process from description to interpretation.The use of qualitative descriptive approaches such as descriptive phenomenology, content analysis, and thematic ...
They include making a great amount of money, being charitable, being a law-abiding citizen, making a good marriage and raising a large family. To get there, the cartoon suggests making a large amount of money (i.e. money features both as an end and a means), using force, and working hard.
content analysis as a system atic, rudimentary, quantitative approach, and other approaches. that are more qualitative or interpretative (Neuendorf, 2001). Content analysis should hence be ...
Content analysis is a research method used to analyze and interpret the characteristics of various forms of communication, such as text, images, or audio. It involves systematically analyzing the content of these materials, identifying patterns, themes, and other relevant features, and drawing inferences or conclusions based on the findings.
Content analysis is also referred to as qualitative content analysis (Elo & Kyngäs, 2008) and ethnographic c ontent analysis (Altheide, 1987). It is a sys tematic process of coding
Through the use of postcolonial feminist theory and qualitative content analysis methodology of ten articles from the Winter/Spring 2003 Special Issue : Native Experiences in the Ivory Tower of the American Indian Quarterly , this study examined the
Table of contents. When to use thematic analysis. Different approaches to thematic analysis. Step 1: Familiarization. Step 2: Coding. Step 3: Generating themes. Step 4: Reviewing themes. Step 5: Defining and naming themes. Step 6: Writing up.
Thematic analysis (TA) is a qualitative method used to uncover themes in textual data, while content analysis (CA) is either a quantitative or a qualitative approach that also involves some quantification of data. CA generally counts the occurrence of concepts or keywords to infer meaning, while TA assigns meaning by extracting high-level ideas.
Common methods include thematic analysis, content analysis, and discourse analysis. Thematic Analysis. Thematic analysis is a method for identifying, analyzing, and reporting patterns (themes) within data. It involves coding the data, searching for themes, reviewing and defining these themes, and reporting the findings.
This chapter provides a critical analysis of the relevant literature on the research topic. It should demonstrate the gap in the existing knowledge and justify the need for the research. ... Note: That's just a basic template, but it should give you an idea of the structure and content that a typical thesis might include. Be sure to consult ...
Documentary Analysis. Definition: Documentary analysis, also referred to as document analysis, is a systematic procedure for reviewing or evaluating documents.This method involves a detailed review of the documents to extract themes or patterns relevant to the research topic.. Documents used in this type of analysis can include a wide variety of materials such as text (words) and images that ...
Verify your identity, personalize the content you receive, or create and administer your account. Provide specific products and services to you, such as portfolio management or data aggregation ...
A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes ...
In a dramatic fashion, on August 5, 2024, Tesla (NASDAQ:TSLA) market shares dropped 12.4%. This was in direct response to the Bank of Japan hiking interest rates. The interest rate hikes wiped out ...
Officials are beginning to think about targeting the ayatollahs, not merely their nuclear program.
An A.I.-powered version of Mr. Musk has appeared in thousands of inauthentic ads, contributing to billions in fraud. By Stuart A. Thompson All Steve Beauchamp wanted was money for his family. And ...
Kinetic analysis of the Claisen rearrangement of allyl phenyl ether (APE) to 2-allylphenol was conducted in pressurized N-methylpyrrolidone (NMP) at various temperatures from 240 to 280°C using an automated flow reactor.Rapid inline analysis by a compact near-infrared (NIR) spectrometer coupled with a flow rate ramp as a reciprocal function of the experimental time allowed a high-density data ...
We think of SpaceX as the "railroad to space" and analogize SpaceX rockets to America's railroads in the late 1800s. Click here for our thesis on SPACE stock.