qualitative analysis

Qualitative Research Analysis: Selected Articles from 2019

Research Design Review is a blog first published in November 2009. RDR currently consists of more thQualitative Research Analysisan 220 articles and has 650+ subscribers along with nearly 780,000 views. As in recent years, many of the articles published in 2019 centered on qualitative research. This paper — “Qualitative Research: Analysis” — represents a compilation of four of these articles pertaining to qualitative research analysis.

These articles cover a range of topics including: considerations when defining the unit of analysis; a discussion on handling “gaps” in the data; a cautionary perspective on coding, i.e., reminding researchers that an overemphasis on coding may miss the true intention of qualitative data analysis; and a look at a Total Quality Framework approach to the qualitative content analysis method.

A separate paper consisting of 14 2019 RDR articles on design and methods can be found here.

Qualitative Data Analysis: The Unit of Analysis

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 Read Full Text

The Qualitative Analysis Trap (or, Coding Until Blue in the Face)

There is a trap that is easy to fall into when conducting a thematic-style analysis of qualitative data. The trap revolves around coding and, specifically, the idea that after a general familiarization with the in-depth interview or focus group discussion content the researcher pores over the data scrupulously looking for anything deemed worthy of a code. If you think this process is daunting for the seasoned analyst who has categorized and themed many qualitative data sets, consider the newly initiated graduate student who is learning the process for the first time.

Recent dialog on social media suggests that graduate students, in particular, are susceptible to falling into the qualitative analysis trap, i.e., the belief that a well done analysis hinges on developing lots of codes and coding, coding, coding until…well, until the analyst is blue in the face. This is evident by overheard comments such as “I thought I finished coding but every day I am finding new content to code” and “My head is buzzing with all the possible directions for themes.”

Coding of course misses the point. The point of qualitative analysis is not to deconstruct the interview or discussion data into bits and pieces, i.e., codes, but rather to define the research question from participants’ perspectives Read Full Text