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1. Introduction

Content analysis (CA) is a research methodology, or a family of methods, used for exploring patterns of words or phrases (or more generally of signs, e.g. in pictures) in a sample of → documents. Roberts (2015, 769) described the field as being about methods that have been “developed to draw inferences about large corpuses, or populations, of texts”. Content analysis can, for example, reveal cultural bias or gender bias in texts, map themes in scholarly domains, or identify demanded qualifications in job advertisements and thus be used to explore the world outside the documents, i.e., trends in the job market.

The present article is not intended for people interested in applying CA as a research method, but is about distinguishing different ways of analyzing the contents of documents and about clarifying some conceptual confusion in the use of the term content analysis. Those readers who are interested in applying CA as a research method may consult standard textbooks such as Krippendorff (2018).

CA is one among many methods used for analyzing messages or documents. Roberts (2015, 769) pointed out that this term does not involve “analysis”, but “measurement”:

Content analysis is a class of techniques for mapping symbolic data into a data matrix suitable for statistical analysis. These techniques may be applied to any representative sample of cultural artifacts (e.g., books, paintings, technological innovations, etc.), whereby various nonnumeric attributes of these artifacts are mapped into a matrix of statistically manipulable symbols. Thus, content analysis involves measurement, not ‘analysis’ in the usual sense of the word.

Neuendorf (2017, 11-18) considered it a myth that the term content analysis applies to all ways of examining documents. He rather stated that the truth is: “The term does not apply to every analysis of messages—only those investigations that meet a particular definition“. Further (2017, 11):

There are many forms of analysis—from frivolous to seminal—that may be applied to the human production of messages. Content analysis is only one type, a technique presented by this book as systematic and quantitative. Even in the scholarly literature, some contestation exists as to what may be called a content analysis. On a number of occasions, the term has been applied erroneously.

Neuendorf found it important to distinguish between CA and other ways of analyzing documents, including qualitative methods such as rhetorical analysis, narrative analysis, discourse analysis, structuralist or semiotic analysis, interpretative analysis, conversation analysis, critical analysis, and normative analysis. It seems that CA started out as a purely quantitative approach, but has developed to include more qualitative and “humanistic” approaches; however, in this process, CA has become more difficult to define in relation to other qualitative approaches, such as those listed above [1]. Scheufele (2015a, 111) wrote:

In a wider sense, the term ‘qualitative content analysis’ subsumes quite different and various methods and techniques of analyzing text material qualitatively or hermeneutically. Examples are → grounded theory or → discourse analysis. In a more narrow sense, qualitative content analysis is a label for a specific type of qualitative text analysis that was developed by Philipp Mayring (2002).

It is important with Neuendorf to realize that CA “does not apply to every analysis of messages”. This article argues that, for example, CA, subject analysis, concept analysis, literary criticism and other terms should not be confused. Why is this important? Are these terms not all about analysis of some kind of meaningful material? The justifications for not confusing these terms are:

• You do not learn how to make content analysis of a sample of documents by considering the literature of subject analysis or concept analysis. You have to go to the literature of CA — consider here, as Neuendorf (2017, 11) wrote, that ”Calling an investigation a content analysis does not make it so”, the same being true about e.g. subject analysis and concept analysis.
• You do not learn how to analyze the meaning of a word by looking into the literature of subject analysis, CA, or literary criticism. You need to go to the literature on conceptual analysis (although, again, the labels in the literature are sometimes confused).
• You do not learn how to analyze the subject of a document by looking into the literature of CA, conceptual analysis, or literary criticism. You have to go to the literature on → subject analysis (although knowledge of, for example, concept analysis, may be helpful in the further development of the theory of subject analysis).

The point is that processes like CA, concept analysis and subject analysis are different processes, requiring different methods, although they are sometimes confused, and although they may sometimes learn from each other, and the literatures on approaches such as subject analysis, concept analysis, and subject analysis are under discussion and development. This does not imply that these kinds of analysis should be considered closed. The debate about them should be considered open, but this does not justify conceptual confusion. Even if one may found the literature on a topic unsatisfactory, it is important to improve its literature rather than to confuse the terminology (see also Hjørland 2024, Section 7, about developing disciplines rather than confusing them).

CA is a research methodology used to make empirical studies of the contents of documents (whereas subject analysis is not a research method, but a method used to characterize documents in order to facilitate their findability). CA is used in many disciplines, including communication studies, public health, educational research, library and information science (LIS), psychology and business studies (whereas subject analysis is primarily connected to LIS). CA is mostly applied to written materials, but it is considered to be about text in the broad meaning of this term [2]. Krippendorff (2018, 19) thus exemplify types of documents, which may be analyzed: “works of art, images, maps, sounds, signs, symbols, and even numerical records may be included as data—that is, they may be considered as text—provided that they speak to someone about phenomena outside of what can be sensed or observed”.

A deeper analysis of the term content analysis must include an analysis of the term content, but this is only briefly discussed here and in Section 3, awaiting a special article devoted to this concept. See also discussion on → Table of Contents (Hjørland 2022). Content is usually understood as something contained in a container, and is often associated with the form-content dualism, but this is a philosophical issue with a rather comprehensive literature. Of relevance for this article is the question whether content can be explored apart from its container or, as in McLuhan’s (1964) slogan, “the medium is the message” (see, e.g., Robertson 1967a; 1967b; Harder 1967).

In the next section, we shall discuss some uses of the term CA which we find misleading. Although there are different definitions and methods of this term, there is a distinct interdisciplinary literature about it, dominated by communication studies. Among the examples of content analytic studies from LIS, the following can be mentioned: Chu 2015; Tuomaala, Järvelin and Vakkari 2014; White 1999; White and Marsh 2006; Yoon and Schultz 2017).

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2. Some confusions of content analysis with other terms

In her book Essential Library of Congress Subject Headings, Broughton (2012) titled Chapter 6 “Content analysis”. In it, she wrote (2012, 65):

Before you can do that [index or classify a document] it is necessary to decide what the item being catalogued is about. Whatever system of subject headings (or classification scheme or thesaurus) is being used to describe a document, you should try initially to make an independent assessment of what the subject of that document is. In practice, you will almost certainly be unable to represent this exactly using the artificial language of your system, but you should at least begin by deciding objectively what it is you want to express. This process may be called 'subject analysis', or 'document analysis', 'content analysis' or 'concept analysis'. The subject content of items is sometimes also referred to more grandly as 'intellectual content' or 'semantic content', but these are simply other ways of defining what a document is about.

This quote thus considered the following terms as synonyms: aboutness analysis, subject analysis, document analysis, content analysis, concept analysis, “analysis of intellectual content” or “of semantic content”. As stated in Section 1, we do not consider all these terms synonymous. Below we will consider these terms, as well as additional terms sometimes confused with content analysis. As Broughton’s quote use of “content analysis” is here understood as a misnomer for “subject analysis”, let us consider this last term first, followed by the other terms used in the quote, and then by a few additional concepts:

• Subject analysis is the intellectual or automated process by which the subjects of a document are analyzed for subsequent expression in the form of subject data by an indexing language (free or controlled language, verbal expression, classification notation or other expression). It is not generally considered a research method, but a practice meant to increase the retrievability of documents in libraries, bibliographical databases, and related contexts. Its definition presupposes the term → subject, which has a long and complicated history in LIS (see Hjørland 2017). Of particular importance is the conflict between two ways of understanding subjects: (a) as something inherent in a document, (b) as something ascribed to a document in order to facilitate the findability of that document for a particular purpose. For a brief but important introduction to subject analysis, see Albrechtsen (1993). In addition to Broughton, Lancaster also uses “content analysis” in relation to indexing (2003, 391-393: “Modular Content Analysis with Subject Modules”), just as “Content Analysis, Specification, and Control” before 1970 was a regular headline in Annual Review of Information Science and Technology, see e.g., Baxendale (1966). Portella and Lima (2024, 164-5) provide tables defining and comparing “subject analysis” and “content analysis”.
• Aboutness analysis presupposes the term aboutness, which Hjørland (2017, Section 3.2) argued should be considered synonymous with subject in LIS (although some researchers, e.g., Hutchins 1978, do not consider them to be synonymous, but have suggested a special meaning of aboutness analysis [3]). The concept of aboutness has a rather comprehensive philosophical literature, e,g., Yablo (2014).
• Document analysis is a term that has been used in LIS by Gardin (1973) and Salminen, Kauppinen and Lehtovaara (1997). Outside LIS, Bowen (2009) used this term about a qualitative research method. It is a vague term that overlaps with many other terms. (a) Like descriptive cataloging, it is about establishing authorship and origin of documents; (b) like content analysis, it may describe patterns of words, themes and messages in documents; (c) like historical bibliography, it may outline the historical, cultural, or social context in which documents were produced; (d) it may perform comparative analysis between different documents; (e) like source criticism it may study the reliability and credibility of documents (i.e., evaluate the value of documents as sources of information); (f) like reception analysis and kinds of bibliometric analyses, it may analyze a document’s reception by different audiences, including its impact and influence.
• Content analysis is, as we saw in Section 1, about exploring patterns of signs in a sample of documents.
• Concept analysis (or conceptual analysis) is a term with a long tradition in the philosophical literature, dating back at the least to Plato's early dialogues, asking questions like “what is knowledge?”, “what is justice?”, or “what is truth?”. In the rationalist tradition, it is believed that such questions can be answered solely based on one's grasp of the relevant concepts, but other epistemologies imply empirical, historical and/or pragmatic analysis. Concept analysis is today considered an interdisciplinary field, not the least involving the sciences (e.g., biological concepts are analyzed by biologists and by philosophers, among others). Concept analysis is very different from both CA and subject analysis, although analysis of relevant concepts plays an important role in these (if you state that a book is about subject X, you need to know what X means, which requires an analysis of the meaning of X). About the philosophical tradition of CA see Hanna (1998) [4]. An example of concept analysis in relation to LIS is the research about the concept → “information” (see Hjørland 2023b). Another example is this article about content analysis. Holley and Joudrey (2021) is a review article about aboutness and “conceptual analysis”, which confuses this concept with subject analysis. Cheti (1996) is another example of a title making the same confusion. The problems of concept analysis are also important for knowledge organization and information science, for example, in the approach known as formal concept analysis (see, e.g., Priss 2006).
• “Analysis of intellectual content” and “analysis of semantic content” are two phrases highlighting the meaning of texts (as in subject analysis) rather than formal aspects, such as size, number of pages, and what is generally understood as → “descriptive cataloging” (see Hjørland 2023a).
• Information analysis. Kent (1971) is an example of using this term, but this use seems just to be part of a trend to rename concepts without changing their meaning in order to promote the new field of information science. Information analysis was defined by Vickery and Vickery (2004, 119):

  Human analysis of primary information message consists of a scan to select from it terms, phrases, and other expressions that are believed best to express its information content. The structure of the primary message itself often guides the human indexer — for example, the title of a paper, or a summary provided by the author, or his conclusions.

  However, it seems strange that Vickery and Vickery limit this definition in two ways: (1) to be about primary messages, as also secondary messages are indexed by the same principle, (2) to be about derivative indexing, as also assigned indexing needs a definition. Dousa (2009) used the term information analysis to cover the analysis of both whole documents as well as smaller units of these. However, the retrieval of smaller units from documents is mostly termed “passage retrieval” (e.g. O’Connor 1980), or by Ranganathan (1963, 29) retrieval of “microdocuments”. In any case, the important issue is that what is retrieved are parts of documents, not something with an independent existence. In passage retrieval the criterion for what should be retrieved is the subject of the passage/microdocument. Nothing is gained by calling it “information analysis” rather than “subject analysis”.
• Literary criticism aims to interpret and explore deeper meanings and artistic qualities of literary works based on interpretation which often relies on theoretical frameworks such as Marxism, feminism, psychoanalysis, or structuralism. Literary criticism is seldom confused with content analysis, but Short’s (2016) “critical content analysis” [5] is an exception.
• Bibliometrics (with altmetrics, informetrics, scientometrics, sentiment analysis, stylometry, webometrics etc.) includes methods for analyzing contents of documents, such as identifying documents referring to particular sets of other documents. Hereby techniques such as bibliographical coupling and co-citation analysis are able to provide empirical clusters of sets of documents. Bibliometrics usually does not use predetermined categories (“deductive CA” [6]), but establishes sets of categories based on the documents studied (“inductive CA” [7]). Tuomaala, Järvelin and Vakkari (2014) used CA to classify articles in LIS according to topical content and methodology using a predetermined classification of LIS, but qualitative CA (or mixed qualitative and quantitative CA), like bibliometrics, is mostly based on an inductive methodology. Some bibliometrics methods represent kinds of CA, but bibliometrics tends to produce a separate literature and to form an independent field of study.
• Reviews (e.g. book reviews and film reviews) are about analyzing contents (and sometimes also form) of documents. They may be related to literary criticism, to argument analysis, and to domain specific issues in the domain of the book (e.g., research methodology), but reviews are clearly different from CA.

More concepts could be considered, including discourse analysis, genre analysis, media analysis, picture analysis, and text analysis, but these terms will not be discussed here.

We conclude this section by stating that although content analysis is a fairly well understood concept in the literature in which textbooks such as Krippendorff (2018) providing a fine overview and a good understanding, it is often confused with other terms.

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3. Epistemological issues

Epistemological issues already are apparent in definitions of the term content analysis. Krippendorff (2018, 24) suggested the following definition:

Content analysis is a research technique for making reliable and valid inferences from texts (or other meaningful matter) to the context of its use.

Krippendorff provided this definition after having considered other conceptions of this term. Berelson (1952, 18) defined content analysis as “a research technique for the objective, systematic and quantitative description of the manifest content of communication”. Krippendorff (2018, 25-26) discussed this definition and argued:

• that the words “objective” and “systematic” should be replaced by “replicability”viii and “validity; [9]
• that the word “quantitative” should be omitted from the definition because both quantitative and qualitative methods have been proven useful;
• most importantly, the words “manifest content” are problematic, because they do not consider that sources, receivers and content analysists often have different interpretations of the same message [10].

Berelson’s definition may be understood as part of a behaviorist tradition, which by Macnamara (2018) based on Shoemaker and Reese (1996, 31f) contrasts with a humanistic tradition in CA [11].

Krippendorff (2018, 25) found that three basic kinds of definitions of content analysis have been provided in the literature:

1. Definitions that take content to be contained in a text
2. Definitions that take content to be a property of the source of a text
3. Definitions that take content to emerge in the process of a researcher analyzing a text relative to a particular context

Each of these kinds of definitions leads to a particular way of conceptualizing content analysis and, consequently, its analytical procedures.

Krippendorff (2018, 27-31) presented the following six statements:

1. Texts have no objective—that is, no reader-independent—qualities.
2. Texts do not have single meanings that could be ‘found’, ‘identified’ or ‘described’ for what they are or correlated with states of their sources.
3. The meaning invoked by texts need not be shared.
4. Meanings (contents) speak to something other than the given text, even where convention suggests that messages ‘contain’ them or texts ‘have’ them.
5. Texts have meanings relative to particular contexts, discourses, or purposes.
6. The nature of text demands that content analysts draw specific inferences from a body of texts to their chosen context—from print to what that printed matter means to particular users, from how analysts regard a body of texts to how selected audiences are affected by those texts, and from available data to unobserved phenomena.

These points are not without implications for the methodologies used to perform CA. As Roberts (2015, 769) argues:

After having acknowledged that language is not a neutral medium through which ‘content’ is unambiguously transmitted, researchers must make four key decisions: Are words only to be counted or are word relations to be encoded? Are word relations to be depicted as network characteristics or as variants of a semantic grammar? Do researchers presume they know more or less than the texts’ sources regarding the latter’s words? Are the texts under analysis being viewed as windows into sources’ perspectives or, alternatively, into events experienced by sources? Only once these decisions are made, can one identify the types of research questions afforded by content analysis.

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4. Conclusion

CA is first and foremost a research methodology, whereas subject analysis, for example, is a practical activity in library and information contexts, as well as a broader, daily activity implying statements of what meaningful things are about and labeling things in ways that provide clues to their uses. The main message of this article has been to argue against terminological pollution and provide arguments for which terms should be considered synonymous, and which should not. This does not indicate, that relevant lessons cannot be learned from different concepts. It is striking, for example, that CA and subject analysis have both developed from a phase in which respectively “content” and “subject” were something that documents have to a phase in which content and subject is understood as something attributed to documents from certain perspectives and interests.

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Endnotes

1. Scheufele (2015b, 112) wrote: “While qualitative content analysis works rather inductively by summarizing and classifying elements and by assigning labels or categories to them, quantitative content analysis works deductively and measures quantitatively by assigning numeric codes to parts of the material to be coded”.

2. Jensen (2015 , 619): “Texts are vehicles of communication. While traditionally reserved for written and other verbal messages, the term refers to any meaningful entity, including images, everyday interaction, and cultural artifacts. Deriving from classical Latin 'texo' (to weave, to construct), texts emphasize the complex process in which ideas are articulated and communicated”.

3. Hutchins (1978, 180), contrasting presupposed knowledge with new knowledge in a text, wrote: “My general conclusion is that in most contexts indexers might do better to work with a concept of 'aboutness' which associates the subject of a document not with some 'summary' of its total content but with the 'presupposed knowledge' of its text”. Hutchins does not provide an example of how traditional subject analysis differs from his proposed “aboutness analysis”. His only example (1978, 181) concludes: “Thus both approaches to indexing would result in the same index entry Industrial archaeology”. What Hutchins associates with the traditional subject analysis is indexing as a kind of document summary, which has been termed document-oriented indexing. Hutchins is right in problematizing this approach. Request oriented indexing, on the other hand, does not intend to summarize the overall contents of documents, but to make documents findable in relation to the questions they may answer for the users. The important point here is that the concepts “subject” and “aboutness” are both associated with both document oriented indexing and request oriented indexing, and both views contain the same problems about the objectivity and subjectivity of indexing. Thus, there are no needs for the term aboutness analysis as distinguished from subject analysis.

4. Hanna (1998) wrote “by the end of the 1970s the movement [i.e., conceptual analysis] was widely regarded as defunct”.

5. Short (2016, 3) wrote: “Adding the word ‘critical’ in front of content analysis signals a political stance by the researcher, particularly in searching for and using research tools to examine inequities from multiple perspectives. Researchers who adopt a critical stance focus on locating power in social practices by understanding, uncovering, and transforming conditions of inequity embedded in society”.

6. Concerning deductive CA, see Kyngäs and Kaakinen (2020).

7. Concerning inductive CA, see Kyngäs (2020).

8. It is outside the scope of the present paper to discuss whether the concepts “replicability” and “validity” should form part of the definition of CA. These concepts are discussed in the philosophy of science (see, e.g., Guttinger 2020; Matarese and McCoy 2024; Pownall 2024).

9. Krippendorff (2018, 25) wrote: “His [Berelson’s] requirement that content analysis be 'objective' and 'systematic' is subsumed under the dual requirement of replicability and validity in our definition. For a process to be replicable, it must be governed by rules that are explicitly stated and applied equally to all units of analysis. Berelson argued for 'systematicity' in order to combat the human tendency to read textual material selectively, in support of expectations rather than against them. Our requirement of validity goes further, demanding that the researcher’s process of sampling, reading, and analyzing messages ultimately satisfy external criteria. Replicability is measurable, and validity is testable, but objectivity is neither”.

10. Krippendorff (2018, 26): “Berelson felt no need to elaborate on the crucial concept of 'content' in his definition, because for him and his contemporaries, at the time of his writing, there seemed to be no doubt about the nature of content—it was believe to reside inside a text”.

11. Macnamara (2018, 3): “Shoemaker and Reese argue that there are two traditions of content analysis—the behaviourist tradition and the humanist tradition. The behaviourist approach to content analysis, pursued by social scientists, is primarily concerned with the effects that content produces. Whereas the behaviourist approach looks forwards from media content to try to identify or predict future effects, the humanist approach looks backwards from media content to try to identify what it says about society and the culture producing it. Humanist media scholars draw on psychoanalysis and cultural anthropology to analyze how media content such as film and television dramas reveal 'truths' about a society—what Shoemaker and Reese term 'the media’s symbolic environment' (1996: 31–32)”.

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