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Content analysis

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overview

Content analysis is a research tool used to determine the presence of certain words, topics, or concepts in certain qualitative data (e.g., text). Content analysis enables researchers to quantify and analyze the presence, meaning, and relationships of such words, topics, or concepts. As an example, researchers can rate the language used in a news article to look for bias or partisanship. Researchers can then draw inferences about the messages in the texts, the author (s), the audience, and even the culture and time of the text's environment.

description

Data sources can come from interviews, open questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents). A single study can analyze different forms of text in its analysis. In order to analyze the text using content analysis, the text must be coded or broken down into manageable code categories for analysis (i.e. codes). Once the text is encoded into code categories, the codes can then be further categorized into code categories to further summarize the data.

Three different definitions of content analysis are provided below.

  • Definition 1: Any technique for drawing conclusions by systematically and objectively identifying specific characteristics of messages. (from Holsti, 1968)

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

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

Possible uses of content analysis

  • Identify the intentions, priorities or communication trends of a person, group or institution

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  • Describe attitude and behavioral responses to communications

  • Determining the psychological or emotional state of people or groups

  • Uncover international differences in communication content

  • Uncovering patterns in communication content

  • Test and improve an intervention or survey before starting

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

Types of content analysis

There are two general types of content analysis: conceptual analysis and relational analysis. The term analysis determines the existence and frequency of terms in a text. Relational analysis takes conceptual analysis further by examining the relationships between concepts in a text. Each type of analysis can lead to different results, conclusions, interpretations, and meanings.

Conceptual analysis

Usually people think of conceptual analysis when they think of content analysis. Conceptual analysis selects a concept to study, and the analysis includes quantifying and counting its presence. The main goal is to study the occurrence of selected terms in the data. Terms can be explicit or implicit. Explicit terms are easy to spot. Coding implicit terms is more complicated: you have to determine the degree of implication and make judgments based on subjectivity (problem of reliability and validity). Therefore, the coding of implicit terms involves the use of a dictionary or contextual translation rules, or both.

To begin a conceptual content analysis, first identify the research question and select one or more samples to analyze. Next, the text needs to be coded into manageable content categories. This is basically a selective reduction process. By reducing the text to categories, the researcher can focus on and code specific words or patterns that determine the research question.

General steps for performing a conceptual content analysis:

1. Define the level of analysis: word, literal sense, phrase, sentence, subject

2. Decide how many concepts you want to code for: develop predefined or interactive categories or concepts. Decide either: A. to allow flexibility in adding categories through the coding process, or B. to stick with the predefined categories.

  • Option A allows the introduction and analysis of new and important material that could have a significant impact on your research question.

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

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

  • In coding the existence of a concept, the researcher would only count a concept once if it occurs at least once in the data and no matter how often it occurs.

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

4. Decide how to differentiate between concepts:

  • Should text be coded exactly as it appears, or should it be coded the same if it appears in different forms? For example dangerous vs. dangerous. The point here is to create coding rules so that these word segments are transparently categorized in a logical manner. The rules could result in all of these word segments falling into the same category, or perhaps the rules could be formulated so that the researcher can distinguish these word segments into separate codes.

  • What level of implication is allowed? Words that imply the concept or words that explicitly state the concept? For example dangerous vs. the person is scary vs. that person could harm me. These word segments may not deserve separate categories because of the implicit meaning of dangerous.

5. Develop rules for coding your texts. After the decisions in steps 1-4 are completed, a researcher can begin developing rules for translating text into code. This keeps the coding process organized and consistent. The researcher can code exactly what he wants to code. The validity of the coding process is guaranteed when the researchers are consistent and coherent in their codings, that is, they follow their translation rules. In content analysis, compliance with the translation rules is synonymous with validity.

6. Decide what to do with irrelevant information: should it be ignored (e.g. common English words like that and and) or used to check the coding scheme in case it would add to the coding result?

7. Coding text: This can be done by hand or with the help of software. Using software, researchers can enter categories and have the coding done automatically, quickly and efficiently by the software program. When the coding is done by hand, it is much easier for a researcher to spot errors (e.g., typos, misspellings). Using computer coding, the text could be cleaned of errors to include all available data. This hand vs. computer coding decision is most relevant for implicit information where category preparation is essential for accurate coding.

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

Relational analysis

Relational analysis begins like concept analysis, in which a concept is selected for investigation. However, the analysis involves examining the relationships between the concepts. Individual concepts are viewed as having no inherent meaning, but meaning is a product of the relationships between the concepts.

To begin a relational content analysis, first identify a research question and select one or more samples to analyze. The research question must be focused so that the types of terms cannot be interpreted and summarized. Next, select text to analyze. Carefully select the text to analyze, having enough information to analyze it thoroughly, so that too much information does not limit the results, making the coding process too tedious and difficult to produce meaningful and worthwhile results.

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There are three sub-categories of relational analysis that you can choose from before proceeding with the general steps.

  1. Affect extraction: an emotional evaluation of concepts that are explicit in a text. A challenge to this method is that emotions can vary over time, population, and space. However, it could be effective to capture the emotional and psychological state of the speaker or author of the text.

  2. Proximity analysis: an assessment of the common occurrence of explicit concepts in the text. Text is defined as a series of words called a window that are scanned for the common occurrence of concepts. The result is the creation of a matrix of terms or a group of related, common terms that would suggest an overall meaning.

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

General steps for performing a relational content analysis:

1. Determine the type of analysis: after the sample is selected, the researcher needs to determine what types of relationships to study and the level of analysis: word, literal sense, phrase, sentence, subjects.
3. Examine the relationship between concepts: once the words are coded, the text can be analyzed for:

  • Strength of Relationship: The degree to which two or more concepts are related.

  • Relationship signs: Are concepts positively or negatively related to one another?

  • Relationship Direction: the types of relationships that categories have. For example, X means that Y or X occurs before Y, or if X is then Y, or if X is Y's primary motivator.

4. Code the Relationships: One difference between conceptual and relational analysis is that the statements or relationships between concepts are coded.
6. Map representations: such as decision mapping and mental models.

Reliability and validity

reliability : Due to the human nature of the researcher, coding errors can never be eliminated, only minimized. In general, 80% is an acceptable margin for reliability. Three criteria include the reliability of a content analysis:

  1. Stability: The tendency for coders to recode the same data in the same way over a period of time.

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

  3. Accuracy: The degree to which the text classification statistically corresponds to a standard or norm.

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

  1. Category proximity: This can be achieved by using multiple classifiers to arrive at an agreed definition of each specific category. Using multiple classifiers, a concept category, which can be an explicit variable, can be expanded to include synonyms or implicit variables.

  2. Conclusions: what level of implication is allowed? Do the conclusions follow the data correctly? Can the results be explained by other phenomena? This becomes particularly problematic when using computer software to analyze and distinguish between synonyms. For example, the word mine variously denotes a personal pronoun, an explosive device and a deep hole in the ground from which ore is extracted. Software can accurately count the occurrences and frequency of this word, but it cannot accurately capture the meaning inherent in each particular use. This problem could skew the results and invalidate any conclusion.

  3. Generalizability of the results to a theory: Depends on the clear definition of the term categories, how they are determined and how reliably they measure the idea to be measured. Generalizability goes hand in hand with reliability, as much of it depends on the three criteria for reliability.

Benefits of content analysis

  • Directly examines communication with text

  • Allows both qualitative and quantitative analyzes

  • Provides valuable historical and cultural insight over time

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  • Enables proximity to data

  • Coded text form can be evaluated statistically

  • Inconspicuous ways to analyze interactions

  • Provides insight into complex models of human thought and language use

  • When done well, it is considered a relatively accurate research method

  • Content analysis is an easy to understand and inexpensive research method

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

Disadvantages of content analysis

  • Can be very time consuming

  • Subject to increased error, especially when using relational analysis to reach a higher level of interpretation

  • Often the theoretical basis is missing or tries too freely to draw meaningful conclusions about the connections and effects of a study

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  • Is inherently reductive, especially with complex texts

  • Too often only consists of word counts

  • Often ignores the context that produced the text and the state of affairs after the text was produced

  • Can be difficult to automate or computerize

Readings

Textbooks & Chapters

  • Berelson, Bernhard. Content analysis in communication research. New York: Free Press, 1952.

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

  • de Sola Pool, Ithiel. Trends in content analysis. Urbana: University of Illinois Press, 1959.

  • Krippendorff, Klaus. Content analysis: an introduction to their methodology. Beverly Hills: Sage Publications, 1980.

  • Fielding, NG & Lee, RM. Use of computers in qualitative research. SAGE Publications, 1991. (See chapter by Seidel, J. 'Method and Madness in the Application of Computer Technology to Qualitative Data Analysis'.)

Methodical articles

  • Hsieh HF & Shannon SE. (2005). Three approaches to qualitative content analysis. Qualitative health research. 15 (9): 1277-1288.

  • Elo S, Kaarianinen M, Kanste O, Polkki R, Utriainen K and Kyngas H. (2014). Qualitative content analysis: a focus on trustworthiness. Sage open. 4: 1-10.

Application item

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

  • Ullstrom S. Sachs MA, Hansson J, Ovretveit J and Brommels M. (2014). Suffering in Silence: A Qualitative Study of Second Victims of Adverse Events. British Medical Journal, Edition of Quality and Safety. 23: 325-331.

  • Owen P. (2012). Entertainment Media Representations of Schizophrenia: A Content Analysis of Contemporary Films. Psychiatric services. 63: 655-659.

Software

Deciding whether to perform content analysis manually or using computer software can be difficult. For more information on this topic, see 'Method and Madness in the Application of Computer Technology to Qualitative Data Analysis,' listed in textbooks and chapters above.

Web pages

  • Rolly Constable, Marla Cowell, Sarita Zornek Crawford, David Golden, Jake Hartvigsen, Kathryn Morgan, Anne Mudgett, Kris Parrish, Laura Thomas, Erika Yolanda Thompson, Rosie Turner and Mike Palmquist. (1994-2012). Ethnography, observational research and narrative investigation. Write @ CSU. Colorado State University. Available at: http://writing.colostate.edu/guides/guide.cfm?guideid=63 . As an introduction to content analysis by Michael Palmquist, this is the premier content analysis resource on the web. It's comprehensive yet concise. It contains examples and an annotated bibliography. The information contained in the narrative above relies heavily on and rounds up Michael Palmquist's excellent content analysis resource, but has been streamlined for postgraduate and early career researchers in epidemiology.

  • http://psychology.ucdavis.edu/faculty_sites/sommerb/sommerdemo/

  • http://depts.washington.edu/uwmcnair/chapter11.content.analysis.pdf

Courses

An der Mailman School of Public Health der Columbia University

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