Iterative

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Conceptual Overview and Discussion

The philosophy behind an iterative approach to research is that of flexibility and ongoing change that meets the needs of the research design, data requirements, and analysis methods in response to new information as it is collected. Loops of iterative cycles occur that may begin as small loops and then move into larger cycles. Iterative sampling is an example of an iterative process. Working back and forth between the research design and the initial data collection, adjustment can be made to the purposive sampling frame, followed by further data collection with another cycle of evaluation against the sampling frame, and further sampling adjustment and data collection as needed. Iterative sampling ensures that information-rich participants are included in the study. This process recognizes that rich information, purposefully focused on the collection of specific, rather than general, data related to the research topic, is necessary for reliable and valid qualitative research. Mistakenly criticized as repeatedly changing the objectives of the study, and as lacking rigor, an iterative approach is valuable for its sensitivity to the richness and variability of data and for ensuring data address the study's objectives.

In the coding of data, an iterative sequence of actions occur, in which a segment of data is selected, conceptualized, and tagged or labeled with a meaningful code. This sequence is executed multiple times in exactly the same way. A further iterative cycle occurs alongside the coding sequence as the researcher moves between the inductive discoveries of patterns to their deductive verification, yielding further inductive insight, beginning the cycle again.

Themes may emerge from iteratively induced patterns. In the visual display of themes in models and network diagrams connections between themes, as well as new themes, emerge. Working back and forth between raw data, codes, themes, and visual data display, another iterative cycle is begun.

Drawing together and lengthening out the inductive–deductive and thematic data display iterations are yet other cycles, those of reflection and writing. As the findings are written up, analysis continues with reflection on the results, returning to the analysis, writing up some of the findings, reflecting back to the results, and so on. By executing these sequences multiple times the analysis deepens and gains momentum, while at the same time a standard of reliability is applied to the subjective process of coding, interpretation, and analysis in qualitative research.

Application

Julia A. Walters, Emily C. Hansen, E. Haydn Walters, and Richard Wood-Baker describe an iterative approach in which each transcript was read after each interview with attention to repeated stories and links between events. Initial codes and memos were applied to the data. This cycle of reading, linking, coding, and memoing was repeated with each interview. Another iteration, in which interview transcripts were compared with each other, began following collection of the second interview. Codes were collated into categories, and categories were grouped into themes in further iterative cycles involving comparison among codes, categories, and themes.

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