Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative. All three approaches are used to interpret meaning from the content of text data and, hence, adhere to the naturalistic paradigm. The major differences among the approaches are coding schemes, origins of codes, and threats to trustworthiness. In conventional content analysis, coding categories are derived directly from the text data. With a directed approach, analysis starts with a theory or relevant research findings as guidance for initial codes. A summative content analysis involves counting and comparisons, usually of keywords or content, followed by the interpretation of the underlying context. The authors delineate analytic procedures specific to each approach and techniques addressing trustworthiness with hypothetical examples drawn from the area of end-of-life care. Content analysis is a research method that has come into wide use in health studies in recent years. A search of content analysis as a subject heading term in the Cumulative Index to Nursing and Allied Health Literature produced more than 4,000 articles published between 1991 and 2002. The number of studies reporting the use of content analysis grew from only 97 in 1991 to 332 in 1997 and 601 in 2002. Researchers regard content analysis as a flexible method for analyzing text data (Cavanagh, 1997). Content analysis describes a family of analytic approaches ranging from impressionistic, intuitive, interpretive analyses to systematic, strict textual analyses (Rosengren, 1981). The specific type of content analysis approach chosen by a researcher varies with the theoretical and substantive interests of the researcher and the problem being studied (Weber, 1990). Although this flexibility has made content analysis useful for a variety of researchers, the lack of a firm definition and procedures has potentially limited the application of content analysis (Tesch, 1990). The differentiation of content analysis is usually limited to classifying it as primarily a qualitative versus quantitative research method. Amore thorough analysis of the ways in which qualitative content analysis can be used would potentially illuminate key issues for researchers to consider in the design of studies purporting to use content analysis and the analytic procedures employed in such studies, thus avoiding a muddling of methods (Morse, 1991). Our purpose in this article is to present the breadth of approaches categorized as qualitative content analysis. We have identified three distinct approaches: conventional, directed, and summative. All three approaches are used to interpret text data from a predominately naturalistic paradigm. We begin with a brief review of the history and definitions of content analysis. We then illustrate the three different approaches to qualitative content analysis with hypothetical studies to explicate the issues of study design and analytical procedures for each approach.

BACKGROUND ON THE DEVELOPMENT OF CONTENT ANALYSIS

Content analysis has a long history in research, dating back to the 18th century in Scandinavia (Rosengren, 1981). In the United States, content analysis was first used as an analytic technique at the beginning of the 20th century (Barcus, 1959). Initially, researchers used content analysis as either a qualitative or quantitative method in their studies (Berelson, 1952). Later, content analysis was used primarily as a quantitative research method, with text data coded into explicit categories and then described using statistics. This approach is sometimes referred to as quantitative analysis of qualitative data (Morgan, 1993) and is not our primary focus in this article. More recently, the potential of content analysis as a method of qualitative analysis for health researchers has been recognized, leading to its increased application and popularity (Nandy & sarvela, 1997). qualitative content analysis is one of numerous research methods used to analyze text data. Other methods include ethnography, grounded theory, phenomenology, and historical research. Research using qualitative content analysis focuses on the characteristics of language as communication with attention to the content or contextual meaning of the text (Budd, Thorp, & Donohew, 1967; Lindkvist, 1981; McTavish & Pirro, 1990; Tesch, 1990). Text data might be in verbal, print, or electronic form and might have been obtained from narrative responses, open-ended survey questions, interviews, focus groups, observations, or print media such as articles, books, or manuals (Kondracki & Wellman, 2002). Qualitative content analysis goes beyond merely counting words to examining language intensely for the purpose of classifying large amounts of text into an efficient number of categories that represent similar meanings (Weber, 1990). These categories can represent either explicit communication or inferred communication. The goal of content analysis is "to provide knowledge and understanding of the phenomenon under study" (Downe-Wamboldt, 1992, p. 314). In this article, qualitative content analysis is defined 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. To illustrate the possible applications of content analysis, we constructed hypothetical studies drawn from the area of end-of-life (EOL) research. Content analysis has been a popular analytic method in studies related to EOL care, an area of increasing emphasis as demonstrated by its inclusion as one of the five research themes supported by the National Institutes of Health, National Institute of Nursing research.

CONVENTIONAL CONTENT ANALYSIS

Researcher X used a conventional approach to content analysis in her study (Table 1). Conventional content analysis is generally used with a study design whose aim is to describe a phenomenon, in this case the emotional reactions of hospice patients. This type of design is usually appropriate when existing theory or research literature on a phenomenon is limited. Researchers avoid using preconceived categories (Kondracki & Wellman, 2002), instead allowing the categories and names for categories to flow from the data. Researchers immerse themselves in the data to allow new insights to emerge (Kondracki & Wellman, 2002), also described as inductive category development (Mayring, 2000). Many qualitative methods share this initial approach to study design and analysis. If data are collected primarily through interviews, open-ended questions will be used. Probes also tend to be open-ended or specific to the participant's comments rather than to a preexisting theory, such as "Can you tell me more about that?" Data analysis starts with reading all data repeatedly to achieve immersion and obtain a sense of the whole (Tesch, 1990) as one would read a novel. Then, data are read word by word to derive codes (Miles & Huberman, 1994; Morgan, 1993; Morse & Field, 1995) by first highlighting the exact words from the text that appear to capture key thoughts or concepts. Next, the researcher approaches the text by making notes of his or her first impressions, thoughts, and initial analysis. As this process continues, labels for codes emerge that are reflective of more than one key thought. These often come directly from the text and are then become the initial coding scheme. Codes then are sorted into categories based on how different codes are related and linked. These emergent categories are used to organize and group codes into meaningful clusters (Coffey & Atkinson, 1996; Patton, 2002). Ideally, the numbers of clusters are between 10 and 15 to keep clusters broad enough to sort a large number of codes (Morse & Field, 1995). Depending on the relationships between subcategories, researchers can combine or organize this larger number of subcategories into a smaller number of categories. Atree diagram can be developed to help in organizing these categories into a hierarchical structure (Morse & Field, 1995). Next, definitions for each category, subcategory, and code are developed. To prepare for reporting the findings, exemplars for each code and category are identified from the data. Depending on the purpose of the study, researchers might decide to identify the relationship between categories and subcategories further based on their concurrence, antecedents, or consequences (Morse & Field, 1995). With a conventional approach to content analysis, relevant theories or other research findings are addressed in the discussion section of the study. In Researcher X's study, she might compare and contrast her findings to Kbler-Ross's (1969) theory. The discussion would include a summary of how the findings from her study contribute to knowledge in the area of interest and suggestions for practice, teaching, and future research. The advantage of the conventional approach to content analysis is gaining direct information from study participants without imposing preconceived catego ries or theoretical perspectives. Researcher X's study depicts a research question appropriate for this approach. Knowledge generated from her content analysis is based on participants' unique perspectives and grounded in the actual data. Her sampling technique was designed to maximize diversity of emotional reactions, and the analysis techniques were structured to capture that complexity. One challenge of this type of analysis is failing to develop a complete understanding of the context, thus failing to identify key categories. This can result in findings that do not accurately represent the data. Lincoln and Guba (1985) described this as credibility within the naturalistic paradigm of trustworthiness or internal validity within a paradigm of reliability and validity. Credibility can be established through activities such as peer debriefing, prolonged engagement, persistent observation, triangulation, negative case analysis, referential adequacy, and member checks (Lincoln & Guba, 1985; Manning, 1997). Another challenge of the conventional approach to content analysis is that it can easily be confused with other qualitative methods such as grounded theory method (GTM) or phenomenology. These methods share a similar initial analytical approach but go beyond content analysis to develop theory or a nuanced understanding of the lived experience. The conventional approach to content analysis is limited in both theory development and description of the lived experience, because both sampling and analysis procedures make the theoretical relationship between concepts difficult to infer from findings. At most, the result of a conventional content analysis is concept development or model building (Lindkvist, 1981). For example, Researcher X might find that patients who are new to hospice care express worry about how their social obligations will be met (such as finding care for a pet), whereas patients who have been in hospice for long periods might express more anticipatory grief. Researcher X might compare her findings to those of Kbler-Ross (1969) and conclude that an additional emotional reaction to entering hospice care is the process of "tying up loose ends," which she might define as making both financial and social arrangements.

DIRECTED CONTENT ANALYSIS

Sometimes, existing theory or prior research exists about a phenomenon that is incomplete or would benefit from further description. The qualitative researcher might choose to use a directed approach to content analysis, as Researcher Y did (Table 2). Potter and Levine-Donnerstein (1999) might categorize this as a deductive use of theory based on their distinctions on the role of theory. However the key tenets of the naturalistic paradigm form the foundation of Researcher Y's general approach to the study design and analysis. The goal of a directed approach to content analysis is to validate or extend conceptually a theoretical framework or theory. Existing theory or research can help focus the research question. It can provide predictions about the variables of interest or about the relationships among variables, thus helping to determine the initial coding scheme or relationships between codes. This has been referred to as deductive category application (Mayring, 2000). Content analysis using a directed approach is guided by a more structured process than in a conventional approach (Hickey & Kipping, 1996). Using existing theory or prior research, researchers begin by identifying key concepts or variables as initial coding categories (Potter & Levine-Donnerstein, 1999). Next, operational definitions for each category are determined using the theory. In Researcher Y's study, Kbler-Ross's (1969) five stages of grief served as an initial framework to identify emotional stages of terminally ill patients. If data are collected primarily through interviews, an open-ended question might be used, followed by targeted questions about the predetermined categories. After an open-ended question, Researcher Y used probes specifically to explore participants' experiences of denial, anger, bargaining, depression, and acceptance. Coding can begin with one of two strategies, depending on the research question. If the goal of the research is to identify and categorize all instances of a particular phenomenon, such as emotional reactions, then it might be helpful to read the transcript and highlight all text that on first impression appears to represent an emotional reaction. The next step in analysis would be to code all highlighted passages using the predetermined codes. Any text that could not be categorized with the initial coding scheme would be given a new code.

The second strategy that can be used in directed content analysis is to begin coding immediately with the predetermined codes. Data that cannot be coded are identified and analyzed later to determine if they represent a new category or a subcategory of an existing code. The choice of which of these approaches to use depends on the data and the researcher's goals. If the researcher wants to be sure to capture all possible occurrences of a phenomenon, such as an emotional reaction, highlighting identified text without coding might increase trustworthiness. If the researcher feels confident that initial coding will not bias the identification of relevant text, then coding can begin immediately. Depending on the type and breadth of a category, researchers might need to identify subcategories with subsequent analysis. For example, Researcher Y might decide to separate anger into subcategories depending on whom the anger was directed toward. The findings from a directed content analysis offer supporting and nonsupporting evidence for a theory. This evidence can be presented by showing codes with exemplars and by offering descriptive evidence. Because the study design and analysis are unlikely to result in coded data that can be compared meaningfully using statistical tests of difference, the use of rank order comparisons of frequency of codes can be used (Curtis et al., 2001). Researcher Y might choose to describe his study findings by reporting the incidence of codes that represented the five main categories derived from Kbler-Ross (1969) and the incidence of newly identified emotional reactions. He also could descriptively report the percent of supporting versus nonsupporting codes for each participant and for the total sample. The theory or prior research used will guide the discussion of findings. Newly identified categories either offer a contradictory view of the phenomenon or might further refine, extend, and enrich the theory. In Researcher Y's study, the discussion might focus on the extent to which participants' emotional journeys paralleled Kbler-Ross's (1969) model and the newly identified emotional reactions or stages that were experienced by participants in the study. The main strength of a directed approach to content analysis is that existing theory can be supported and extended. In addition, as research in an area grows, a directed approach makes explicit the reality that researchers are unlikely to be working from the naive perspective that is often viewed as the hallmark of naturalistic designs. The directed approach does present challenges to the naturalistic paradigm. Using theory has some inherent limitations in that researchers approach the data with an informed but, nonetheless, strong bias. Hence, researchers might be more likely to find evidence that is supportive rather than nonsupportive of a theory. Second, in answering the probe questions, some participants might get cues to answer in a certain way or agree with the questions to please researchers. In Researcher Y's study, some patients might agree with the suggested emotional stages even though they did not experience the emotion. Third, an overemphasis on the theory can blind researchers to contextual aspects of the phenomenon. In Researcher Y's study, the emphasis on Kbler-Ross's (1969) stages of emotional response to loss might have clouded his ability to recognize contextual features that influence emotions. For example, the cross-sectional design of the study might have overemphasized current emotional reactions. These limitations are related to neutrality or confirmability of trustworthiness as the parallel concept to objectivity (Lincoln & Guba, 1985). To achieve neutral or unbiased results, an audit trail and audit process can be used. In Researcher Y's study, the vague terminology used in Kbler-Ross's description of the model would be a challenge for the researcher in creating useful operational definitions. Having an auditor review and examine these definitions before the study could greatly increase the accuracy of predetermined categories.

SUMMATIVE CONTENT ANALYSIS

Typically, a study using a summative approach to qualitative content analysis starts with identifying and quantifying certain words or content in text with the purpose of understanding the contextual use of the words or content (Table 3). This quantification is an attempt not to infer meaning but, rather, to explore usage. Analyzing for the appearance of a particular word or content in textual material is referred to as manifest content analysis (Potter & Levine-Donnerstein, 1999). If the analysis stopped at this point, the analysis would be quantitative, focusing on counting the frequency of specific words or content (Kondracki & Wellman, 2002). A summative approach to qualitative content analysis goes beyond mere word counts to include latent content analysis. Latent content analysis refers to the process of interpretation of content (Holsti, 1969). In this analysis, the focus is on discovering underlying meanings of the words or the content (Babbie, 1992; Catanzaro, 1988; Morse & Field, 1995). In Researcher Z's study, the initial part of the analysis technique, to count the frequency of death, die, and dying is more accurately viewed as a quantitative approach. However, Researcher Z went on to identify alternative terms for death and to examine the contexts within which direct versus euphemistic terms were used. Hence, Researcher Z used a summative approach to qualitative content analysis. Researchers report using content analysis from this approach in studies that analyze manuscript types in a particular journal or specific content in textbooks. Examples include studies examining content related to EOL care in medical textbooks (Rabow, Hardie, Fair, & McPhee, 2000), EOL care in critical care nursing textbooks (Kirchhoff, Beckstrand, & Anumandla, 2003), palliative care in nursing textbooks (Ferrell, Virani, Grant, & Juarez, 2000), death and bereavement in nursing textbooks (Ferrell, Virani, Grant, & Borneman, 1999), and spirituality in nursing textbooks (McEwen, 2004). These researchers started with counting the pages that covered specific topics followed by descriptions and interpretations of the content, including evaluating the quality of the content. Others have compared the results of a content analysis with other data collected within the same research project, such as comparing preferences for various types of television programming with socioeconomic indicators of participants (Krippendorff, 1980). In a summative approach to qualitative content analysis, data analysis begins with searches for occurrences of the identified words by hand or by computer. Word frequency counts for each identified term are calculated, with source or speaker also identified. Researcher Z wanted to know the frequency of words that were used to refer to death but also to understand the underlying contexts for the use of explicit versus euphemistic terms. He or she illuminated the context of euphemistic versus explicit terms by reporting how their usage differed by variables such as the speaker (patient versus clinician), the clinician's specialization, and the age of the patient. Counting is used to identify patterns in the data and to contextualize the codes (Morgan, 1993). It allows for interpretation of the context associated with the use of the word or phrase. Researchers try to explore word usage or discover the range of meanings that a word can have in normal use. A summative approach to qualitative content analysis has certain advantages. It is an unobtrusive and nonreactive way to study the phenomenon of interest (Babbie, 1992). It can provide basic insights into how words are actually used. However, the findings from this approach are limited by their inattention to the broader meanings present in the data. As evidence of trustworthiness, this type of study relies on credibility. Amechanism to demonstrate credibility or internal consistency is to show that the textual evidence is consistent with the interpretation (Weber, 1990). For Researcher Z's study, validation by content experts on what terms are used to replace the death terms would be essential. Alternatively, researchers can check with their participants as to their intended meaning through the process of member check (Lincoln & Guba, 1985).

Assignment

Using the above article as a guide, how should a researcher go about deciding which form of data analysis is most appropriate for their qualitative study?

Provide example and in-text citation to back up your answer.