In this section, we will highlight three other techniques that can aid qualitative data analysis: affinity diagramming, concept mapping and memoing. Affinity diagramming is a technique used to externalize, make sense of, and organize large amounts of unstructured, far-ranging, and seemingly dissimilar qualitative data (Lucero, 2015, p. 231). By creating tangible notes based on the data and organizing them visually, researchers can map relationships between codes, identify themes and recognize patterns. Affinity diagramming can proceed through four stages (Lucero, 2015): First, the researcher examines the data and records observations (and labels) on post-it notes. Only one idea is placed on a note. Second, the notes are placed on a surface or wall where they all can be scrutinized. Third, the notes are arranged in columns or piles to reflect themes or categories, which are then labeled accordingly. Finally, the researcher refines the categories and further arrange themes into hierarchies to determine dominant patterns.

Similar to affinity diagrams, concept mapping can be quite useful for data analysis. Concept mapping is a graphical representation of concepts and relationships between those concepts (e.g., using boxes and arrows). The major concepts are typically laid out on one or more sheets of paper, blackboards, or using graphical software programs, linked to each other using arrows, and readjusted to best fit the observed data” (Bhattacherjee, 2011, p.115).

A third strategy for analyzing qualitative data is memoing. Bhattacherjee (2011, p. 115) defines memos as “theorized write-ups of ideas about substantive concepts and their theoretically coded relationships as they evolve during ground theory analysis, and are important tools to keep track of and refine ideas that develop during the analysis”. Researchers use memoing to review memos in order to discover patterns and relationships between categories using two-by-two tables, diagrams, or figures, or other illustrative displays (see Bhattacherjee, 2011 for more details). Box 9.6.1 provides additional tips for organizing codes.

Additional tips on identifying patterns

Identifying patterns from codes requires practice and significant commitments to develop an intimate relationship with qualitative data. Rosaline (201, p.226-7) offers the following tips to help identify patterns:

• Be open to revising your coding frame as you become aware of new distinctions and categories
• Be clear about who the speaker is (what are their characteristics?)
• Formulate reasons why similarities and differences exist (e.g. are they due to setting, respondents’ characteristics or method of data collection?)
• Determine if there are differences between respondents or groups. If there are, identify the distinctions
• Consider your expectations about the results (if you had any). Determine if you have been surprised by any of the results (why or why not?)

Reference

Bhattacherjee, A. (2012). Social Science Research: Principles, Methods, and Practices https://scholarcommons.usf.edu/cgi/viewcontent.cgi?referer=&httpsredir=1&article=1002&context=oa_textbooks

Frankfort-Nachimas, C., & Nachimas, D. (2015). Research methods in the social sciences (8th ed.). Worth Publishers.

Lucero, A. (2015, September). Using affinity diagrams to evaluate interactive prototypes. In IFIP (ed.), Human-Computer Interaction –INTERAC 2015, (pp. 231-248). Springer, Cham.

Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Sage.

Rosaline