Qualitative Analysis

Because of the iterative and emergent nature of qualitative research (see Figure 2) it is sometimes difficult to draw a hard line distinguishing data collection from analysis. According to Patton (2002),

In the course of fieldwork, ideas about directions for analysis will occur. Patterns take shape. Possible themes spring to mind. Hypotheses emerge that inform subsequent fieldwork. While earlier states of fieldwork tend to be generative and emergent, following wherever the data lead, later stages bring closure by moving toward confirmatory data collection—deepening insights into and confirming (or disconfirming) patterns that seem to have appeared. (p. 436)

In contrast to quantitative research, there are no shared ground rules for qualitative analysis, except to represent the data fairly and completely, and to communicate what patterns, themes, and conclusions they reveal. Qualitative analysis involves sifting through large amounts of information, identifying important patterns, and reporting "thick" or rich descriptions of what was found. Patton (2002) identifies several ways to organize and report qualitative data:

• Storytelling approaches: chronological, flashback (working backward)
• Case study approaches: focus of analysis is on individuals, groups, major events, or settings
• Analytical framework approaches: analysis is focused on processes, key issues, topics, concepts, or interview questions

Strategies for Ensuring Validity or Trustworthiness in Qualitative Analysis

Because qualitative research is subjective in nature it is difficult to establish the reliability and validity of the approach and the information produced. Guba (1981) proposed four criteria for judging the "trustworthiness" of qualitative research:

• Credibility: Accomplished by confirming that the results of the research are credible to the participants in the study. The researcher must also establish his or her own credibility by describing any personal or professional information that may have influenced the study (Krefting, 1991; Patton, 2002). This is analogous to internal validity in quantitative research.
• Transferability: Accomplished in two steps: (a) the investigator must thoroughly describe the context of the research and the assumptions of the study, and (b) the reader or user of the research must decide how well the described study fits another context. This is analogous to external validity or generalizability in quantitative research.
• Dependability: To establish dependability the naturalistic researcher must explain both the stable, consistent elements of research findings, and also the contextual changes that occurred during the study. The researcher must also provide a dense description of the research methodology so that someone else could replicate it, if desired. This is analogous to reliability in quantitative research.
• Confirmability: The researcher is responsible for describing the research results in such a way that they can be confirmed by others. According to Patton (2002), this can be accomplished in several ways, (a) generating and assessing rival conclusions; (b) finding and analyzing negative cases that contradict prior understandings; (c) triangulating by using multiple methods, sources, analysts, or theories to test for consistency in results; (d) keeping methods and data in context by considering how design constraints may have affected the data available for analysis; and (e) articulating lessons learned and best practices emanating from the research. Confirmability is analogous to objectivity in the quantitative approach.