Content analysis

Content analysis is best understood as a broad family of techniques. Effective researchers choose techniques that best help them answer their substantive questions. That said, according to Klaus Krippendorff, six questions must be addressed in every content analysis:[5]

1. Which data are analyzed?
2. How are the data defined?
3. From what population are data drawn?
4. What is the relevant context?
5. What are the boundaries of the analysis?
6. What is to be measured?

The simplest and most objective form of content analysis considers unambiguous characteristics of the text such as word frequencies, the page area taken by a newspaper column, or the duration of a radio or television program. Analysis of simple word frequencies is limited because the meaning of a word depends on surrounding text. Key Word In Context (KWIC) routines address this by placing words in their textual context. This helps resolve ambiguities such as those introduced by synonyms and homonyms.

A further step in analysis is the distinction between dictionary-based (quantitative) approaches and qualitative approaches. Dictionary-based approaches set up a list of categories derived from the frequency list of words and control the distribution of words and their respective categories over the texts. While methods in quantitative content analysis in this way transform observations of found categories into quantitative statistical data, the qualitative content analysis focuses more on the intentionality and its implications. There are strong parallels between qualitative content analysis and thematic analysis.[6]

Quantitative content analysis highlights frequency counts and objective analysis of these coded frequencies.[7] Additionally, quantitative content analysis begins with a framed hypothesis with coding decided on before the analysis begins. These coding categories are strictly relevant to the researcher's hypothesis. Quantitative analysis also takes a deductive approach.[8]

Siegfried Kracauer provides a critique of quantitative analysis, asserting that it oversimplifies complex communications in order to be more reliable. On the other hand, qualitative analysis deals with the intricacies of latent interpretations, whereas quantitative has a focus on manifest meanings. He also acknowledges an "overlap" of qualitative and quantitative content analysis.[7] Patterns are looked at more closely in qualitative analysis, and based on the latent meanings that the researcher may find, the course of the research could be changed. It is inductive and begins with open research questions, as opposed to a hypothesis.[8]

More generally, content analysis is research using the categorization and classification of speech, written text, interviews, images, or other forms of communication. In its beginnings, using the first newspapers at the end of the 19th century, analysis was done manually by measuring the number of columns given a subject. The approach can also be traced back to a university student studying patterns in Shakespeare's literature in 1893.[9] With the rise of common computing facilities like PCs, computer-based methods of analysis are growing in popularity.[10][11][12] Answers to open ended questions, newspaper articles, political party manifestos, medical records or systematic observations in experiments can all be subject to systematic analysis of textual data.

By having contents of communication available in form of machine readable texts, the input is analyzed for frequencies and coded into categories for building up inferences.

Computer-assisted analysis can help with large, electronic data sets by cutting out time and eliminating the need for multiple human coders to establish inter-coder reliability. However, human coders can still be employed for content analysis, as they are often more able to pick out nuanced and latent meanings in text. A study found that human coders were able to evaluate a broader range and make inferences based on latent meanings.[13]

Robert Weber notes: "To make valid inferences from the text, it is important that the classification procedure be reliable in the sense of being consistent: Different people should code the same text in the same way".[14] The validity, inter-coder reliability and intra-coder reliability are subject to intense methodological research efforts over long years.[5] Neuendorf suggests that when human coders are used in content analysis at least two independent coders should be used. Reliability of human coding is often measured using a statistical measure of inter-coder reliability or "the amount of agreement or correspondence among two or more coders".[4] Lacy and Riffe identify the measurement of inter-coder reliability as a strength of quantitative content analysis, arguing that, if content analysts do not measure inter-coder reliability, their data are no more reliable than the subjective impressions of a single reader.[15]

Holsti groups fifteen uses of content analysis into three basic categories:[21]

• make inferences about the antecedents of a communication
• describe and make inferences about characteristics of a communication
• make inferences about the effects of a communication.

He also places these uses into the context of the basic communication paradigm.

The following table shows fifteen uses of content analysis in terms of their general purpose, element of the communication paradigm to which they apply, and the general question they are intended to answer.

The process of the initial coding scheme or approach to coding is contingent on the particular content analysis approach selected. Through a directed content analysis, the scholars draft a preliminary coding scheme from pre-existing theory or assumptions. While with the conventional content analysis approach, the initial coding scheme developed from the data.

• Donald Wayne Foster
• Hermeneutics
• Text mining
• The Polish Peasant in Europe and America
• Transition words
• Video content analysis

• Graneheim, Ulla Hällgren; Lundman, Berit (2004). "Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness". Nurse Education Today. 24 (2): 105–112. doi:10.1016/j.nedt.2003.10.001. PMID 14769454.
• Budge, Ian (ed.) (2001). Mapping Policy Preferences. Estimates for Parties, Electors and Governments 1945-1998. Oxford, UK: Oxford University Press. ISBN 978-0199244003.
• Krippendorff, Klaus, and Bock, Mary Angela (eds) (2008). The Content Analysis Reader. Thousand Oaks, CA: Sage. ISBN 978-1412949668.
• Neuendorf, Kimberly A. (2017). The Content Analysis Guidebook, 2nd ed. Thousand Oaks, CA: Sage. ISBN 978-1412979474.
• Roberts, Carl W. (ed.) (1997). Text Analysis for the Social Sciences: Methods for Drawing Inferences from Texts and Transcripts. Mahwah, NJ: Lawrence Erlbaum. ISBN 978-0805817348.
• Wimmer, Roger D. and Dominick, Joseph R. (2005). Mass Media Research: An Introduction, 8th ed. Belmont, CA: Wadsworth. ISBN 978-0534647186.