The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 262-263).
As discussed in two earlier articles in Research Design Review (see “The Important Role of ‘Buckets’ in Qualitative Data Analysis” and “Finding Connections & Making Sense of Qualitative Data”), the selection of the unit of analysis is one of the first steps in the qualitative data analysis process. The “unit of analysis” refers to the portion of content that will be the basis for decisions made during the development of codes. For example, in textual content analyses, the unit of analysis may be at the level of a word, a sentence (Milne & Adler, 1999), a paragraph, an article or chapter, an entire edition or volume, a complete response to an interview question, entire diaries from research participants, or some other level of text. The unit of analysis may not be defined by the content per se but rather by a characteristic of the content originator (e.g., person’s age), or the unit of analysis might be at the individual level with, for example, each participant in an in-depth interview (IDI) study treated as a case. Whatever the unit of analysis, the researcher will make coding decisions based on various elements of the content, including length, complexity, manifest meanings, and latent meanings based on such nebulous variables as the person’s tone or manner.
Deciding on the unit of analysis is a very important decision because it guides the development of codes as well as the coding process. If a weak unit of analysis is chosen, one of two outcomes may result: 1) If the unit chosen is too precise (i.e., at too much of a micro-level than what is actually needed), the researcher will set in motion an analysis that may miss important contextual information and may require more time and cost than if a broader unit of analysis had been chosen. An example of a too-precise unit of analysis might be small elements of content such as individual words. 2) If the unit chosen is too imprecise (i.e., at a very high macro-level), important connections and contextual meanings in the content at smaller (individual) units may be missed, leading to erroneous categorization and interpretation of the data. An example of a too-imprecise unit of analysis might be the entire set of diaries written by 25 participants in an IDI research study, or all the comments made by teenagers on an online support forum. Keep in mind, however, that what is deemed too precise or imprecise will vary across qualitative studies, making it difficult to prescribe the “right” solution for all situations.
Although there is no perfect prescription for every study, it is generally understood that researchers should strive for a unit of analysis that retains the context necessary to derive meaning from the data. For this reason, and if all other things are equal, the qualitative researcher should probably err on the side of using a broader, more contextually based unit of analysis rather than a narrowly focused level of analysis (e.g., sentences). This does not mean that supra-macro-level units, such as the entire set of transcripts from an IDI study, are appropriate; and, to the contrary, these very imprecise units, which will obscure meanings and nuances at the individual level, should be avoided. It does mean, however, that units of analysis defined as the entirety of a research interview or focus group discussion are more likely to provide the researcher with contextual entities by which reasonable and valid meanings can be obtained and analyzed across all cases.
In the end, the researcher needs to consider the particular circumstances of the study and define the unit of analysis keeping in mind that broad, contextually rich units of analysis — maintained throughout coding, category and theme development, and interpretation — are crucial to deriving meaning in qualitative data and ensuring the integrity of research outcomes.
Milne, M. J., & Adler, R. W. (1999). Exploring the reliability of social and environmental disclosures content analysis. Accounting, Auditing & Accountability Journal, 12(2), 237–256.
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