Summary: Three Approaches to Qualitative Content Analysis (Hsiu-Fang Hsieh & Sarah E. Shannon)

-H i s t o r y-

Content analysis is a research method that has come into wide use in health studies in recent years. Asearch 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.

The differentiation of content analysis is usually limited to classifying it as primarily a qualitative versus quantitative research method.

The 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.

CONVENTIONAL CONTENT ANALYSIS

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.

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).

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. 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.

DIRECTED CONTENT ANALYSIS

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, Kübler-Ross’s (1969) five stages of grief served as an initial framework to identify emotional stages of terminally ill patients.

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

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. The findings from a directed content analysis offer supporting and nonsupporting evidence for a theory.

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.

SUMMATIVE CONTENT ANALYSIS

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 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.

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.

So, 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.