Content Analysis
Content analysis is the intellectual process of categorizing qualitative textual data into clusters of similar entities, or conceptual categories, to identify consistent patterns and relationships between variables or themes. Qualitative content analysis is sometimes referred to as latent content analysis.
Content analysis is a method that is independent of theoretical perspective or framework (e.g., grounded theory, phenomenology) but has its beginnings as a quantitative method.
Where quantitative content analysis is helpful in answering “what” questions, qualitative content analysis can be helpful in answering “why” questions and analyzing perceptions.
In qualitative research, content analysis is interpretive, involving close reading of text. Qualitative researchers using a content analytic approach recognize that text is open to subjective interpretation, reflects multiple meanings, and is context dependent (e.g., part of a larger discourse).
When analyzing qualitative data such as interview transcripts, analyses across the whole set of data typically produce clusters or codes that translate into “themes.” For example, an interview study that explores the experience of new parenthood may produce interview transcripts that are analyzed for content related to themes ranging from stress to social isolation to joy.
Textual data include non-written text, such as photographic data, equally open to content analysis. In this case, the researcher may identify content as straightforwardly as identifying objects evident in photographs or may conduct more subtle analyses of symbolic communications that can be unconsciously discerned from a physical space.
Content analysis could be applied to the official reports and policies of an organization; such an analysis may identify the stated priorities of that organization as well as reveal implicit political perspectives.
The results of a content analysis may reveal recurrent instances of “items” or themes, or they may reveal broader discourses. The “categories” or clusters of data identified may represent discrete instances (i.e., something is apparent or not), or they may be represented as degrees of attributes, such as direction and intensity, or qualities (i.e., a quality such as joy is evident to some degree rather than simply present or absent).
In quantitative work, content analysis is applied in a deductive manner, producing frequencies of preselected categories or values associated with particular variables. A qualitative approach to content analysis, however, is typically inductive, beginning with deep close reading of text and attempting to uncover the less obvious contextual or latent content therein.
Validity and reliability are key to robust content analysis. In qualitative terms, the researcher doing a qualitative content analysis seeks trustworthiness and credibility by conducting iterative analyses, seeking negative or contradictory examples, seeking confirmatory data through methodological triangulation, and providing supporting examples for conclusions drawn.