Volume 20, No. 3, Art. 36 – September 2019

Qualitative Content Analysis: Why is it Still a Path Less Taken?

Bammidi Devi Prasad

Abstract: The history of content analysis is largely the history of quantitative content analysis. Although qualitative content analysis (QCA) was used in scholarly writings, it remained largely limited to an explorative, impressionistic, and less pragmatic role. Researchers who laid the foundations for the method of content analysis and coined it as a significant quantitative research method were influenced by the logical positivism popular in the 1940s and the dominance of quantitative forms of analysis, especially in Anglo-Saxon regions. These and other trends overshadowed the methodological developments in QCA, although critical voices raised objections to the over-reliance on quantification and analysis of manifest content at the expense of the deeper meanings in the text. Against this background, I make an attempt to look back briefly at the history and significance of QCA, and then critically examine the main reasons for the marginalization of QCA in the broader Anglo-Saxon vs. Continental context in comparison to its quantitative counterpart. While the stronger presence of qualitative research, including QCA, is explained by the dominance of hermeneutic intellectual traditions in Germany and other non-English speaking countries, their general marginalization is related to the methodological uncertainty, positivist quantitative orthodoxy in evaluating qualitative methods, and epistemological and ontological ambiguity connected to the approach. Based on the discussion, I provide some reflections on the future developments of QCA.

Key words: qualitative content analysis; quantitative content analysis; qualitative research; quality criteria for content analysis; methods of social science research

Table of Contents

1. Introduction

2. History of Qualitative Content Analysis

3. What is Qualitative Content Analysis?

4. Reasons for Quantitative Orientation of Content Analysis and its Continued Dominance

5. Resistance to Over Fine Quantification in Content Analysis

6. Why is it Still a Path Less Taken? Reasons for the Marginalization of QCA

7. Conclusion







1. Introduction

The history of content analysis is for the most part, the history of quantitative content analysis (QnCA). Even though qualitative content analysis (QCA) was covered in scholarly writings (BERELSON, 1952; HOLSTI, 1969; KRIPPENDORFF, 2004; LASSWELL & LEITES, 1965), it was confined to an exploratory, impressionistic, and less pragmatic role. One of the reasons for this was the initial quantitative foundations laid down by BERELSON (1952), GERBNER, HOLSTI, KRIPPENDORF, PAISLEY and STONE (1969), HOLSTI (1969), LASSWELL and LEITES (1965), POOL (1959), STONE, DUNPHY, SMITH and OGILVIE (1966), and others during and after wartime research, which shaped the journey of content analysis as a substantive quantitative research method. Prior to the 1930s, social and behavioral scientists were using both qualitative and quantitative approaches side by side. As far back as the 1930s, Florian ZNANIEKI (1934) developed analytic induction as a major logical method of analysis of data to develop general principles from detailed facts, an opposite of enumerative induction, i.e., statistical generalizations arrived at from a large number of cases about characteristics of individual units. However, the 1930s and 1940s was the high time during which social scientists were under considerable influence from logical positivism and the associated quantitative forms of analysis (HAMMERSLEY & ATKINSON, 2007), and this influence seemed to sway the scholars who laid the foundations for the method of content analysis. During the 1950s, content analysis shifted from a simple description of message content to more sophisticated data analysis systems with a focus on quantitative descriptions of communication content and semantic relationships (STAN, 2010). By the mid-50s, dissenting voices began to be heard objecting to over dependency on quantification and analysis of manifest content to the neglect of deeper meanings embedded in the text (GEORGE, 1959; KRACAUER, 1952; MAYRING, 2000). [1]

An ambiguity while dealing with the qualitative dimension of content analysis is often seen in scholarly writings. For instance, LASSWELL's essay "Why be Quantitative?" (1965) and BERELSON's (1952) chapter on "'Qualitative' Content Analysis" in his book "Content Analysis in Communication Research" gave space for a careful discussion of the qualitative dimensions of content analysis, albeit treating it at the end as an unsystematic, non-numeric impressionistic analysis of communication content. BERELSON even proposed to use the term "content assessment" (p.128) in place of the term qualitative content analysis, indicating his reluctance to associate this variant with the method of content analysis at all. Similarly, most authors of the methodological texts either did not mention QCA or treated it as part of a quantitative method only. Scholars who used QCA were also unsure, felt as if they were using a semi-scientific method, and hence were encouraged to use external evidence to support the validity of findings arrived at using the method (HOLSTI, 1969). Thus, QCA was considered a black sheep and was condemned to a marginalized role in the methodological ghetto of content analysis (MARKOFF, SPAPIRO & WEITMAN, 1975). [2]

In the developments and trends outlined so far, I have reflected predominantly about the Anglo-Saxon situation and not much about non-English speaking Continental regions. It may be noted that the specific research traditions of these regions are found to be different (FLICK, 2005; ROSENGREN, 1981). As SLATER (1998) has pointed out, the ideals of quantification and natural science methodology influenced the main line of development of social science (particularly of Anglo-Saxon genre). The development of the method of content analysis in this region reflected these characteristics. [3]

In contrast, the German, other European and non-English speaking countries reflected the continental philosophical traditions, which were closer to the hermeneutics approach (KNOBLAUCH, FLICK & MAEDER, 2005; KUCKARTZ, 2014). However, it should be remembered that during the 1950s and 1960s, the quantitative orientation was dominant, even in the German social sciences. It is only later, i.e., the early 1970s, qualitative research traditions in disciplines such as sociology and education in Germany started to grow, mostly because of the import of North American textbooks to the region. The beginning of original discourses in hermeneutics and their stronger presence in German qualitative research practices could be witnessed particularly in such fields as education, nursing research, anthropology, gender studies, psychology, health sciences and humanities (FLICK, 2005; MRUCK & MEY, 2000). One such interpretive practice thus developed and applied in the above fields was QCA. [4]

While the stronger presence of qualitative research including QCA is explained by the dominance of the hermeneutics intellectual traditions in the region, qualitative methods were often treated with neglect in research funding and teaching in Germany and in other European contexts. As with the actual state of qualitative research in these countries, one still witnesses the marginalization of these methods even in the 2000s (MRUCK & MEY, 2000). Though the international discourse is still mainly dominated by North American qualitative research methods, the overall mistrust regarding qualitative methodology persists (ERICKSON, 2018; GOBO, 2005). In fact, the dissenting voices came from within the advocates of qualitative research. For example, HAMMERSLY (2008) pointed out that the claims made in the second half of the 20th century regarding the potential to capture people's perspectives with qualitative inquiry, and to produce evidence that can be of practical value to policy makers, were not fulfilled. Others of the same camp (DENZIN, 2009) argued that these developments were part of a broader trend around the globe in which conservative scientific bodies (SHAVELSON & TOWNE, 2002), governments, and markets were attempting to enforce evidence-based research frameworks which followed mostly the logic of quantitative research. Obviously, the major debates in content analysis regarding its qualitative and quantitative variants reflected these developments and phases in social research. [5]

A glimpse into the scenario of QCA is provided in the above description. Though appearing to be gloomy, it will be useful in providing a backdrop for the discussion of the method in terms of strengths, limitations and future directions. In this article, after providing a brief account of the history and meaning of QCA (Sections 2 and 3), I shall discuss the reasons for and resistance to quantitative orientation in content analysis (Sections 4 and 5). This will be followed by a critique of the reasons for the marginalization of QCA in the larger context of content analysis (Section 6), and a few reflections on future directions (Section 7). [6]

2. History of Qualitative Content Analysis

Is there a history of QCA? Probably yes, though it was mostly overshadowed by the history of QnCA.1) The history of QCA can be traced more by the absence of its mention in important events or periods than by its visibility in these contexts. As MORGAN (1993) observed, until 1945-50, content analysis was used in a mixed mode. Both qualitative and quantitative approaches were evident in the early studies. It is after the 1950s that the field became conspicuously quantitative. There are many possible explanations. Two predominant influences are the uncompromising quantitative foundations of the method laid down by LASSWELL and LEITES (1965), LASSWELL, LERNER and POOL (1952), and others, and the quantitative orientation of most of the wartime studies taken up during and after World War II. More specifically, reasons such as the large-scale application of the technique in its initial stages mostly to data sources such as news, war propaganda materials, and radio broadcasts, greater emphasis placed on objectivity, and preference given to the use of statistical methods for presentation of findings obtained through the method have contributed to make it more quantitative (ROBERTS, 1997). In fact, by the 1960s, interesting and large-scale studies using QnCA were conducted. Studies such as "Language of Politics" (LASSWELL & LEITES, 1965) and "The Comparative Study of Symbols" (LASSWELL et al., 1952) are some examples. The two milestone conferences held at Allerton House of the University of Illinois, and the Annenberg School of Communications, University of Pennsylvania, also focused mostly on quantitative applications of content analysis. With the exception of GEORGE (1959), not much was covered about the qualitative approaches in content analysis by the contributions in the Allerton House conference. Content analysis was instead primarily viewed as a method for counting or measuring features of text for purposes of inference (POOL, 1959). Keeping to this perspective, while summarizing the proceedings of the Allerton House conference, POOL argued that qualitative analysis is more useful in the hypothesis-forming exploratory phase by providing a set of categories to be explored in a more rigorous kind of quantitative analysis. Roughly a decade later, in 1967, the Annenberg School conference brought together diverse theoretical and methodological contributions with a predominant focus on issues of quantification, drawing inferences from content data and use of computer techniques in content analysis (GERBNER et al., 1969). In the same period, BARCUS (1969) showed with a survey on education in content analysis that the teaching and training for the method, especially in subjects such as Journalism, Sociology, Education, and Library and Information Science, mostly emphasized quantitative approaches. Even the books recommended were those of authors who advocated quantitative content analysis.2) [7]

Later trends involving greater use of computers in content analysis led to methodologies leaning even more towards the quantitative mode. STONE et al. (1969) emphasized how computers can aid in the process of content analysis. In contrast, a qualitative approach is accepted/used for informal inspection in developing categories and application rules to be taken over by formal content analysis (which is quantitative) to describe the data as a whole. [8]

It should be noted that the non-mentioning of or absence of references to the method of QCA or any of its forms speaks volumes about its position in the subject field. A longitudinal study (RIFFE & FREITAG, 1997) in which the researchers examined the trends in content analysis in The Journalism and Mass Communication Quarterly over the last quarter century (1971-95) indicated an increase in the publication of studies using content analysis in the journal mainly focusing on its quantitative dimensions. There was a conspicuous absence of any reference to the qualitative approach in the article. [9]

So far, I have focused much of my review on content analysis as a method and how QCA was marginalized in the Anglo-Saxon situation. Now, what is the current scenario in terms of their application in the larger context? To my mind it depends on what field one is looking at, and the intellectual tradition of the region where the methods are practiced. While QnCA is still used primarily in communication studies, it is also reported to be used in fields such as business management, and organization studies (DURIAU, REGER & PFARRER, 2007; GAUR & KUMAR, 2018). Researchers in fields such as information and library science, and political communication, primarily used QnCA until recently, though in many current studies QCA is used to compensate for some of the weaknesses of QnCA (NEUENDORF & KUMAR, 2015; ZHANG & WILDEMUTH, 2009). Currently, QCA is more widely applied in nursing research, education, medicine, and psychology (GRANEHEIM & LUNDMAN, 2004). [10]

Interest in qualitative research practices became prominent at the end of the twentieth and early twenty first century in the continental regions, where there was a stronger presence of the hermeneutics intellectual tradition. As such, studies using QCA might be more frequent in these regions compared to quantitative approaches to content analysis (MAYRING, 2014). However, this inference remains inconclusive, as I did not come across any comparative review of trends in the research approaches (qualitative and quantitative methods including content analysis) used by the German academics in social sciences or in other disciplines. Even a trend study comparing QCA and QnCA would be difficult, if not impossible, owing to the multiple names with which QCA came to be used in the literature. Sometimes the authors themselves may be unclear in differentiating the boundaries of the two variants, hence may not designate their article as either QCA or QnCA, or a mixture of both. Quite often, the word QCA or its synonyms such as for example, non-frequency content analysis, thematic coding or ethnographic content analysis may not figure in the title or abstract of the article. Unless all of these explanations are taken care of, it will be difficult to take up a trend study or even to come to a conclusion about the frequency of the application of QCA either within the respective fields or as a method on its own. [11]

Descriptions of QCA as a method in its own right began to appear in the literature only recently, primarily as an outcome of the interaction between researchers of the American and German intellectual traditions since the 1960s (FLICK, 2009; HSIEH & SHANNON, 2005; KUCKARTZ, 2014; MAYRING, 2014; SCHREIER, 2012). As MERTON (1968) pointed out in his interesting essay, the quantitative-manifest versus qualitative-latent orientations reflect the American vs. European intellectual traditions, respectively. According to him, the qualitative and latent approach to content is close to researchers of a European or more specifically German intellectual tradition, whose training stresses more on the meta perspective of the problem. Thus, individuals of these two traditions, broadly representing the continental and analytical philosophies, are distinct in terms of their understanding of the text as an aspect of reality and in comprehending its meanings. While researchers using an analytical approach assume that reality exists out there independent of the investigator who seeks to understand the reality as objectively as possible, those using the continental philosophical approaches see no such distinction. [12]

Further, in the continental philosophical approach, the method of inquiry is not a neutral process. This is in fact reflected in (the essence of) the Hermeneutics intellectual tradition. According to PALMER (1969), SCHLEIERMACHER states that the object under inquiry cannot be fully understood without examining the object in its context. KRACAUER (1952) was articulating this aspect when he was referring to ‘text' as organic and that the meanings were created not by the analyst (reader) alone but were co-created both by the reader and the text of the author. Even this co-creation is not independent of the cultural and historical contexts of the reader and the author, for if these contexts differ, meanings and the interpretations of meanings will differ. It is in this context that the interpretation of meanings of text within its context in QCA becomes important. [13]

Similarly, the American tradition's modernist phase (DENZIN & LINCOLN, 2005; GOBO, 2005) in qualitative research began with significant contributions from scholars like GLASER and STRAUSS (1967). This has indirectly influenced and softened the LASSWELLian quantitative orientation in content analysis, leading to the creation of a more neutral and mixed genre of intellectual traditions among scholars such as HOLSTI, KRIPPENDORFF, MORGAN and others. The dissent voices already existing over too much quantitative orientation in content analysis (GEORGE, 1959; KRACAUER, 1952) gave momentum to this shift. Despite these developments, the methodological vagueness of QCA remained (STEINKE, 2004). [14]

3. What is Qualitative Content Analysis?

Definitions of content analysis broadly come under three types. In the first type, which is the majority, the quantitative orientation of the method is emphasized. The second type, in which reference to the quantitative dimension is purposely avoided, contains no explicit reference to the qualitative dimension either. Only in the third type of definitions, either the subjective and qualitative dimensions of the method are boldly stated or both qualitative and quantitative orientations of the method are accommodated, while leaning more toward an interpretive approach. At this point, though I make a mention of a few important definitions of the first two types for purposes of comparison, I shall dwell more on the third type of definitions as they reflect the qualitative aspects of content analysis. [15]

BERELSON's definition (1952, p.18) is a classic example of the first type. According to him "[c]ontent analysis is a research technique for the objective, systematic and quantitative description of the manifest content of communication." The second type of definitions are: "Content analysis is any technique for making inferences by objectively and systematically identifying specified characteristics of messages" (HOLSTI, 1969, p.14) and "[c]ontent analysis is a research technique for making replicable and valid inferences from data to their context" (KRIPPENDORFF, 1980, p.21). The third type of definitions are more explicit about the qualitative approach of the method. Thus, ROSENGREN (1981, p.11) described content analysis "as belonging to a family of analytic approaches ranging from impressionistic, intuitive, interpretive analyses to systematic strict textual analyses." According to SCHREIER (2014, p.170) "[q]ualitative content analysis is a method for systematically describing the meaning of qualitative data […] Three features characterize the method: qualitative content analysis reduces data, it is systematic, and it is flexible." While discussing flexibility, the third key feature of the method, SCHREIER explains that QCA combines both data driven and concept driven (inductive and deductive) categories in its quest to match coding frame to the content. Next, HSIEH and SHANNON (2005, p.1278) defined QCA as "a research method for the subjective interpretation of the content of text data through the systematic classification process of coding and identifying themes or patterns." According to PATTON (2002, p.453), QCA "is any qualitative data reduction and sense-making effort that takes a volume of qualitative material and attempts to identify core consistencies and meanings." [16]

Thus, it may be noted that when using QCA, the process goes beyond merely counting words or extracting objective content from texts—to examining meanings, themes and patterns that may be manifest or latent in a particular text. In essence, the focus is on meanings and patterns rather than on counting the physical characteristics of the text. Researchers are able to understand social reality in a subjective but systematic manner. In this sense, the products of both quantitative and qualitative content analyses are different. While numbers are produced in QnCA that can reach interval or ratio level of measurement to be manipulated by relevant statistical methods, nominal level data is produced in QCA—mostly narratives and themes which have emerged from the text inductively (ZHANG & WILDEMUTH, 2009). In other words, in QCA, capturing latent meanings is more context-dependent and interpretive, and therefore they are more likely to be subjective, and less precise. For this reason alone, mapping latent characteristics of content is often subjected to challenges relating to validity, i.e., whether the coder's interpretation of the meaning is converging with the contextual meaning of the text. Though context-dependency is pertinent to both qualitative and quantitative methods, it is more relevant in QCA when capturing latent meaning. From this point of view, KRIPPENDORFF's emphasis on the context to give meaning to the inferences drawn from data assumes importance. In his own words:

"Every content analysis requires a context within which the available texts are examined. The analyst must, in effect, construct a world in which the texts make sense and can answer the analyst's research questions [...] Once an analyst has chosen a context for a particular body of text and clearly understands that context, certain kinds of questions become answerable and others make no sense" (2004, p.24). [17]

The next attribute in the definitions including that of QCA is the systematic nature—in the sense that the selection of content to be analyzed, the deriving of units of analysis, and the application of coding procedures are carried out according to explicit rules which are consistently applied in the process of analysis. Depending on the purpose of research, content selection is done either by following probability or non-probability sampling procedures. The systematic approach brings in the dependability and credibility to the inferences drawn. At this juncture, it will be instructive to quote the summary given by MORGAN (1993, p.116) drawing an approximate picture of the two variants i.e., QnCA and QCA:

"At one extreme, quantitative content analysis begins with predetermined codes, locates these codes through mechanized search procedures, and treats the resulting counts as all that needs to be known about the data. At the other extreme, qualitative content analysis uses code categories that emerge from the data themselves, applies these codes through careful reading of the data, and treats counting as the detection of patterns to guide the further interpretation of the data." [18]

During the early 21st century, articles including the analysis steps and quality assessment procedures involved in conducting QCA started appearing (FRIESE, SORATTO & PIRES, 2018; HSIEH & SHANNON, 2005; SCHREIER, 2012; ZHANG & WILDEMUTH, 2009). Also authors of these articles gave examples of studies in an effort to help readers understand the boundaries of the method (HSIEH & SHANNON, 2005; ZHANG & WILDEMUTH, 2009). As it is important to have an idea of how a QCA study would look, it would be helpful to take a glance at the following two examples.

  • LÓPEZ, DETZ, RATANAWONGSA and SARKAR (2011) employed QCA utilizing both inductive and deductive coding to analyze 712 online reviews from two doctor rating websites to understand the predominant themes emerging out of the patients' reviews of their doctors. They began with a coding frame developed on the basis of their medical experiences and literature review and finalized incorporating new concepts that emerged during the reading of the reviews till no new codes were observed. A sample quote was given as an example for each code. They used counts of codes and high inter-rater reliabilities were reported based on the independent coding of 46 per cent of the reviews by two coders. The coding was done using the representative quotes following which the investigators reviewed and combined codes into larger themes namely global themes covering the medical encounter process (comprising of five thematic categories, i.e., overall excellence, recommendation, negative sentiment, intent not to return, and professionalism) and specific descriptions about interactions with the doctor (comprising 3 thematic categories i.e., interpersonal manner, competence, and system issues). The coding and analysis revolved around the latent meanings of the text.

  • Another example is a set of studies exploring sex differences in conversation topics. Though the authors did not explicitly state about employing QCA, the analysis process reflects the procedures adopted in QCA. In his 1922 study, MOORE collected, over a period of one month, conversation fragments that could be overheard while walking in a public place at about 7.30 in the evening. He coded 174 bits of conversation thus collected into 10 content categories (ex. person of same sex, person of opposite sex, academic, career plans, jobs, money, sports, leisure activities, personal appearance & clothes, and social & political issues) combined under five broad themes namely, person & relationships, work & money, leisure activities, appearances, and issues. BISCHOPING (1993) replicated the study using the typology of MOORE (1922) as closely as possible. Illustrative examples of conversation bits were given which were coded into the 10 categories. In both the studies, the coding was done with a focus on the meaning of the example quotes/words in the context of the conversational fragment and not on the syntactical characteristics of the conversational fragments. [19]

In summary, though I attempted in this section to focus on the latent meanings and systematic nature of the analysis procedures of QCA, it would be naïve to assume that all is well on its front. Scholars continued lamenting about its methodological vagueness, lack of concreteness in findings, and predominance of impressionism. [20]

4. Reasons for Quantitative Orientation of Content Analysis and its Continued Dominance

The quantitative orientation within content analysis is present for many reasons. By the end of the 1940s, content analysis had taken on a more quantitative and statistical character and this shift must be viewed within the context of a general shift in the social sciences towards behaviorism after the World War II and into the 1950s and the early 1960s (KUCKARTZ, 2014). More specifically, as I have already mentioned in the history of QCA, the emphasis on the goal of objectivity in pursuit of scientific status for the method, the need for policy relevant and generalizable findings, the dominance of Anglo-Saxon analytical intellectual traditions encouraging quantitative orientation, emergence of the use of computers in content analysis, and coverage of more ground on methodological and theoretical fronts in QnCA compared to QCA are some of the major reasons. [21]

The main preoccupation of content analysts during the 1930s and 1940s was with counting frequencies of the manifest content characteristics of text to draw conclusions (SPEED, 1893; YACOBSON & LASSWELL, 1965). During the 1950s when content analysis was employed in the study of symbols and political propaganda, QnCA was preferred to make symbol studies objective and quantifiable and findings more generalizable. LASSWELL et al. (1952) observed that, as certain questions raised by him were not answered by qualitative studies, he chose to move toward a quantitative approach. He argued that in a qualitative approach, reading words and understanding symbols by a skilled person might produce insights which are often brilliant but usually unverifiable. To him, content analysis is not for "reading between the lines" but is a "method for reading on the lines and for reporting the results which can be verified" (LASSWELL et al., 1952, p.32). As the qualitative approach was considered unscientific, qualitative elements gradually disappeared from content analysis limiting it to the quantitative analysis of the manifest content of communication. Subsequently, BERELSON's (1952, p.18) definition of content analysis as "a research technique for the objective, systematic, and quantitative description of the manifest content of communication" (italics added) froze the boundary of the method as predominantly quantitative until the 1960s with most of the studies devoting their focus to counting and comparing frequencies of manifest attributes of content (POOL, 1959). Thus, methodologies subscribing to the philosophical underpinnings encouraging positivist orientation were increasingly learnt and practiced. Over fine quantification, the search for higher reliability and methodological sophistication, all in the pursuit of making the method more scientific, gave precedence to the quantitative approach over the qualitative approach in content analysis. [22]

5. Resistance to Over Fine Quantification in Content Analysis

Critique of such a methodically narrow approach to content analysis came up early in 1952, as over-dependence on manifest characteristics of content, such as numbers and frequencies to draw inferences, led to difficulties. One difficulty is the assumption that there is a direct connection between behavioral states and content characteristics, i.e., that researchers can find out the motives or intentions of the communicator from the content characteristics. Another difficulty relates to the assumptions that frequencies can indicate the intensity and direction of the presence of a phenomenon. Both are difficult assumptions. Sometimes, the emphasis with which a word or a symbol is mentioned in the text decides the intensity of the same and not how many times a word figured in the text. Similarly, a simple frequency count may not always indicate the nature of a particular psychological state of the communicator unless the meaning of the content characteristic is deciphered in the overall context of the text. [23]

Therefore, in about the same year, as BERELSON (1952) froze the quantitative stance of the method of content analysis, KRACAUER (1952), a German political scientist, in his brilliant article "The Challenge of Qualitative Content Analysis," refuted it, stating that the quantitative approach is not all that objective and systematic as it is believed to be. In his article which is considered to be the manifesto of QCA, he not only negated the arguments marginalizing QCA but also in the process proved that a qualitative stance is the beginning of QnCA and the end to which it should return for confirmation and validation in the true epistemological sense. KRACAUER brought to focus with his brilliant examples the significance of latent meanings of content to understand text and to draw valid inferences about it. He looked at the text as more organic, vibrant and holistic rather than objective, physical and fragmentary. According to him, the latent meanings with which a content analyst is primarily concerned are context dependent and the content of the text must be considered as a meaningful whole to determine the meaning. To him text documents are not simply agglomerations of facts, but

"[...] every word in them [i.e., the text documents3)] vibrates with the intentions in which they originate and simultaneously fore-shadows the indefinite effects they may produce. Their content is no longer their content if it is detached from the texture of intimations and implications to which it belongs [...] They challenge the reader or the analyst to absorb them and react to them. Only in approaching these wholes with his whole being will the analyst be able both to discover and determine their meaning [...]" (p.641). [24]

There is no better argument than the above quote to underline the importance of latent content to discover meanings contained in the text4) and to emphasize the contours of QCA. KRACAUER's intervention did not conclude but, in fact, began the debate. Several others followed his course of arguments. Overtime, methodologically different forms of QCA have developed drawing from different subject sources such as anthropology, communications, linguistics, and so on. A glance at the nomenclature shows the variations in its form. Examples are names such as: non-frequency content analysis (GEORGE, 1959); non-quantitative content analysis (BERELSON, 1952); non-statistical latent content analysis (BERELSON, 1952; GEORGE, 1959; HOLSTI, 1969); ethnographic content analysis and qualitative media analysis (ALTHEIDE, 1987, 1996); linguistic content analysis (ROBERTS, 1989); thematic coding (BOYATZIS, 1998); flexible content analysis (RUSTEMEYER, 1992, cited in SCHREIER, 2014, p.172); conventional content analysis (HSIEH & SHANNON, 2005); thematic content analysis (SMITH, 1992), and so on. As one can see, they broadly reflect the distinction between numbers and manifest characteristics vs. themes and latent meanings in contextualizing the method of QCA, though the description is still undertaken within the parameters of positivist paradigm. [25]

Moreover, the methods and procedures used in QCA for capturing the latent meanings in texts, though attempted, were not yet well developed. For mapping latent content, the measurement was seen as more subjective, interpretive and not infrequently imprecise. Hence, the vagueness about the methodological rigor of the procedures for ascertaining latent meanings of text continued (GEORGE, 1959; HSIEH & SHANNON, 2005; SCHREIER, 2014). For example, according to ROBERTS (1989), in QCA, the assignment of codes to content depends on the coder's subjective impressions of the latent contextual meanings of words. This may sometimes lead to insufficient inter-coder agreement and to lower levels of replicability of the findings from such analyses. Therefore, in an effort to bridge this gap, he advocated the use of linguistic content analysis as a via media between the qualitative (or impressionistic) approach and a computer-aided approach in which word frequencies within the categories are analyzed. Developments such as these show the attempts made by scholars to improve the methodological base of QCA. However, looking back, the line between counting and contextualizing meaning seems to have never been resolved from a methodological point of view. While BERELSON took it to one extreme, KRACAUER took it to the other. Some adopted a middle ground. Despite these positions, one invariably witnesses in all these debates, a leaning toward positivist criteria either in evaluating the functions of QCA or in developing its assessment standards (DELAMONT & ATKINSON, 2009). [26]

6. Why is it Still a Path Less Taken? Reasons for the Marginalization of QCA

So far, I have discussed the reasons for the predominance of quantitative orientation of the method of content analysis and also the dissenting voices against such orientation to the neglect of richness of text and its embedded meanings. Based on what has been discussed thus far, the following reasons can be mentioned as to why QCA still continues to be a path less taken by the researchers. [27]

Most important is the inconclusiveness and ambiguity regarding the epistemological foundations of the procedures of QCA to understand reality, or the reliability and validity of the reality measured by the method. Research findings should be consistent and dependable in order to be usable in policy and practice, and to contribute to knowledge building. QCA is often criticized for being impressionistic and subjective in its conclusions, lacking transparency in the analytical procedures adopted, and the trustworthiness of findings arrived at based on such procedures. Therefore, a more focused discussion about the quality of QCA findings is needed, particularly in view of the larger amount of work carried out on the validity and reliability of QnCA (NEUENDORF, 2011; ROURKE & ANDERSON, 2004) in comparison to QCA. [28]

Textbooks in which the qualitative approach can be learned usually begin with a general statement that there are no standard procedures and methods to analyze qualitative data (HUBERMAN & MILES, 1994; KUCKARTZ, 2014). While qualitative data is by no means a weak form of data but a different form of data that requires different, complex and systematic analysis (GOBO, 2005), such introductory statements are likely to give a novice researcher an impression that he/she is heading nowhere. [29]

Added to this, the issue of checking objectivity of the findings arrived at using QCA remains unresolved. Scholars have dealt with this in two major ways: 1. extending the quality criteria of positivist-quantitative paradigm to QCA studies,5) and 2. evolving alternative criteria for QCA approaches.6) Scholars who followed the former approach (e.g.,, CARTWRIGHT, 1953; LONG & JOHNSON, 2000; MAYRING, 2000; MORGAN, 1993; SCHREIER, 2012) are of the opinion that the two approaches are not different and that developing new quality criteria, which in essence are assessing similar dimensions, will create more complexity rather than add any further value. However, a few other scholars clearly departed from this position and took the second approach. They argue that as both qualitative and quantitative content analysis approaches are premised on entirely different philosophical foundations, i.e., positivist vs. interpretivist paradigms, the criteria for one approach cannot be applied to the other (FLICK, 2005; NOBLE & SMITH, 2015). Foremost among the proponents are MILES and HUBERMAN (1994), LINCOLN and GUBA (1985), STEINKE (2004) followed by ELO et al. (2014), GRANEHEIM et al. (2017), and others. Thus, MILES and HUBERMAN (1994, p.277) proposed alternative quality criteria for QCA (Table 1).

Quality criteria for quantitative research

Proposed quality criteria for qualitative research




Dependability and auditability

Internal validity

Credibility and authenticity

External validity

Transferability and theoretical generalization

Table 1: Alternative quality criteria proposed in the place of criteria for quantitative research [30]

As can be seen, in QnCA, objectivity is ensured by reliability checks, that is, inter-coder reliabilities. But in QCA, inter-subjectivity (inter-subjective comprehensibility) is argued as relevant, that is, observers of similar backgrounds will arrive at similar themes while coding a sample text (STEINKE, 2004). On the contrary, SCHREIER (2012) supported the use of positivist constructs such as reliability and validity as quality criteria for QCA. According to her, as there is no clear dividing line between qualitative and quantitative content analysis, similar terms and criteria for reliability and validity can be used for both the methods. Although alternative quality criteria such as credibility, trustworthiness, authenticity etc. are suggested as standards to assess the quality of research based on QCA (HUBERMAN & MILES, 1994; LINCOLN & GUBA, 1985), there is little agreement about the rigor of these criteria among the qualitative researchers themselves. It is also felt that using the new criteria would cause more confusion than clarity (SCHREIER, 2012). Thus, these debates remain inconclusive. [31]

Further, the issue of external validity or generalizability is addressed differently by the two approaches. In QCA, theoretical generalization, as against the statistical generalization, is considered relevant. It is further argued that the understanding of the text on the basis of the patterns observed can be transferred to similar text situations by meeting the requirements of credibility and dependability and by providing thick descriptions of the thematic patterns of the text (ELO et al., 2014). This is termed transferability which comes under the category of theoretical generalization. [32]

Another major issue pointed out was about the theoretical base of the method of content analysis. In the theoretical discussions about the epistemological and ontological premises of the method, development of the methods and procedures relating to quality criteria, its contribution to other disciplines and so on are covered. The position of content analysis in the larger social science research methodology is established by critiquing the knowledge built in the above-mentioned areas. Content analysis as a quantitative method has almost a hundred years of work in this respect (McCORMACK, 1982) though in the 70s its position as a stand-alone method was seriously contested (BARCUS, 1969; MARKOFF et al., 1975). Comparatively, QCA is lacking such established knowledge as a method. It is still standing on a shaky foundation. [33]

There exists lack of clarity in terms of steps followed in QCA and qualitative data analysis (QDA). As a result, many articles which claim to have used QCA to study the topic often end up doing QDA. Students often struggle to get an understanding of the qualitative research approaches. It becomes even more difficult when it comes to differentiating between QDA and QCA. Efforts were made to comprehend the boundaries of different qualitative analyses including the position of content analysis in the larger picture (RITCHIE & LEWIS, 2004; TESCH, 1990). While content analysis is similar to these approaches, it differs from them in terms of its primary focus and analytic procedures. Several works (SCHREIER, 2012, 2014) discussed this aspect and interested readers may go through these readings. Despite these debates, there is ambiguity regarding the distinction between the boundaries of the two approaches. Hence, there was not much change in the uncertainty of QCA as a method and its position in the overall social science research. [34]

It was pointed out that content analysis, including QCA, is a descriptive methodological technique and inferences drawn from content data cannot go beyond it to explain the attributes of those who produced the content or its effects on those who received it (HOLSTI, 1969). Thus, if causal inferences are to be made, they require corroboration by independent evidence (BRYMAN, 2012; CARLSON, 2008; MARKOFF et al., 1975; SCHREIER, 2012). For example, though the method is good at capturing the changing trends in the subject content of professional articles published in a journal (LOY, 1979), it cannot answer why there were changes in the subject content. Similarly, in the hypothetical example given by CARLSON (2008) about advertisements promoting high sugared candies for children, he argues that one cannot assume cause and effect relationship between the advertisements and their effect on the behavior of children simply because the method does not constitute an experimental design in which dependent and independent variables are manipulated to draw causal inferences. Though quantitative analyses also cannot always make claims of causality, given the other hurdles of conducting QCA, this is yet another reason that contributes to making it a supplementary rather than a standalone technique. [35]

Not only in the past (GERBNER et al., 1969; STONE et al., 1966) but also in the days to come, growth in the information and communication technology and use of computers in content analysis (STEMLER, 2015; STEMPEL & STEWART, 2000; STONE, 2000) made the method lean more towards quantitative orientation. If computer aided content analysis is going to be used with the method, then a focus on quantitative approaches will continue, though computer software such as Atlas.ti and NVivo have come to be used for QCA operations. [36]

7. Conclusion

In retrospect, two aspects need to be emphasized at this point: 1. the still continuing ambivalent methodological stance toward QCA, in comparison with its quantitative counterpart, as less pragmatic and unscientific hence not dependable, and 2. the development of its quality criteria still as methodological extensions of that of QnCA. To my mind, we have reached a stage where we need to raise pertinent questions in the context of QCA. Some of them are:

  • Why are subjectivity and impressionism seen as hurdles to understanding reality when they form the basis for initial category formation and formulation of codes even for quantitative analysis?

  • Why are findings generated by QCA seen as less valid when precisely because of its impressionism and subjectivity it can attain accuracy by carrying its exploration beyond the words leading to "classifications and descriptions which conform far more closely to the texts than those commonly produced by quantitative analysis" (KRACAUER, 1952, p.640)?

  • Why are its products seen as not replicable when there is evidence to show that in qualitative analysis, while assessing a particular piece of content, coders may broadly come up with certain themes on which for most of the time they are likely to converge?7)

  • And lastly, why emphasize objectivity when the focus is on the nature of reality which is more contextual and interactive and differently conceived from that of the positivists? [37]

I am aware of the methodological debates and advances made in qualitative research around these questions. I argue that answers to some of the questions keeping QCA in the center will not only explain reasons for its marginalization in a more fundamental way but also will indicate directions to improve its position as a method in the larger context of social sciences. [38]

From this perspective, future methodological developments in QCA may be used to draw from subject sources such as hermeneutics, communications, anthropology, linguistics and psychology as these branches of knowledge have procedures more similar, and close to the ones used in QCA. [39]

More importantly, besides challenging the positivistic understanding of reality, the epistemological and ontological reasoning of the significance of the reality captured by qualitative or interpretive approaches should be emphasized, validated and legitimized (DENZIN, 2009; HAMMERSLEY & ATKINSON, 2007). Though efforts have been made in this direction, they still appear to be weak and less concrete in the light of habits of quantitative frameworks deeply embedded in the thinking of intellectuals of certain regions. The intellectual traditions from the German, Swedish and Canadian regions value qualitative frameworks, but their presence seems to be weak in the international context compared to the quantitative traditions of the West such as that of the US and UK (FLICK, 2005; MERTON, 1968; MRUCK & MEY, 2000; SCHREIER, 2014). [40]

Finally, in a more fundamental sense, the differences between the qualitative and quantitative approaches in content analysis are about positions as to how the reality is perceived and understood. In other words, these two premises are related to the epistemological and ontological ambiguities about the method - the hesitancy of its proponents and practitioners to take a position about the method's stance in terms of capturing reality, and about the nature of reality it says it captures. [41]


I would like to thank the two anonymous reviewers for their valuable feedback on this article. I am also thankful to my student, Ms. Khawla ZAINAB for her helpful comments.


1) KUCKARTZ (2014, pp.17-23) provides an excellent and balanced exposition of the history and development of the method of QCA. <back>

2) BERELSON (1952). BUDD, THORP and DONOHEW (1967), LASSWELL et al. (1952). NORTH, HOLSTI, ZANINOVICH and ZINNES (1963), POOL (1959), and STONE et al. (1966). <back>

3) Words in the parentheses are added. <back>

4) Martin Luther KING Jr.'s prophetic speech "I have a dream," can be mentioned as an excellent example for the above quote (McCORMACK, 1982, pp.179-182). <back>

5) Based on the nature of reality one perceives or one's world view. That is, in the positivist sense, the reality exists out there independent of the observer and we use methods to capture it. Alternatively, for an interpretivist, data and interpretation are co-creations of the researchers and reality is what the observer perceives. Therefore, reality is subjective and multiple in its interpretation. In this sense, the reliability and validity checks predominantly fall into the positivistic realm of understanding reality (CRESWELL & PLANO CLARK, 2011; GRANEHEIM, LINDGREN & LUNDMAN, 2017). <back>

6) Post modernists hold the third position and argue against the possibility of formulating criteria for qualitative research. <back>

7) What needs to be recognized is that the same text analyzed by different analysts may result in almost similar conclusions or themes. Therefore, it is reasonable to argue that QCA will not result in some different conclusions of same text if analyzed by a group of coders. However, it does not mean that the conclusions are a reflection of the reality, if there is such a one. It must be remembered that the similarity of arrived-at conclusions is to a great extent a reflection of the broadly similar cultural, aesthetic, moral or philosophical assumptions possessed by the coders in question. <back>


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Bammidi DEVI PRASAD is former professor and chair at the Centre for Equity and Justice for Children and Families, School of Social Work, Tata Institute of Social Sciences, Mumbai, India. Prior to that he was professor at Andhra University, Visakhapatnam, India. His main research interests are families, children, gender, social policy, and the development sector. On the methodological front, his main interests include the methodologies of qualitative and quantitative research and more specifically, content analysis.


Bammidi Devi Prasad

Dr.No. 10-182/7, Visalakshinagar
Andhra Pradesh, India

Tel.: +91 9702871115

E-mail: deviprasadb2@gmail.com


Devi Prasad, Bammidi (2019). Qualitative Content Analysis: Why is it Still a Path Less Taken? [41 paragraphs]. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 20(3), Art. 36, http://dx.doi.org/10.17169/fqs-20.3.3392.

Copyright (c) 2019 Devi Prasad Bammidi

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