The Sort and Sift, Think and Shift qualitative data analysis approach, created by Ray Maietta and his consulting team at ResearchTalk Inc, is an iterative process in which analysts dive into data to understand its content, dimensions, and properties, and then step back to assess what they have learned to bridge findings with current conversations in the field. The approach is a data-driven process that is both flexible and fluid. Data content is directive as it helps researchers determine what to do when. The goal of the process is to arrive at an evidence-based meeting point that is a hybrid story of data content and researcher knowledge.
Five principles direct the Sort and Sift process.
- Principle 1 is to adopt and maintain a flexible posture to facilitate the evolution of your thinking and your use of analytic tools.
- Principle 2 is to let the data be your guide; qualitative data content directs project decisions from fieldwork to analysis to final presentation.
- According to Principle 3, the holistic picture of each data collection episode is of paramount importance.
- In Principle 4, topics that direct analysis emerge and evolve throughout the life of a project and should be monitored actively by diagramming and memoing.
- Principle 5 reflects that, through iterative phases, the Sort and Sift toolkit focuses on where and how key concepts integrate and work together (i.e., bridge and thread) to define participants’ lived experience.
The Process
Data engagement is defined by an iterative and synergistic feedback loop between the “diving in” and “stepping back” phases of analysis. We encourage you to dive into data early and often to understand its content, properties, and dimensions. The diving in phase is designed to privilege the holistic narrative of each data file (i.e., data collection episode). During the stepping back phase researchers reflect, re-strategize and re-orient after your diving in phases of analysis.
The Sort and Sift Toolkit - Three types of tools comprise the Sort and Sift toolkit: reflecting, engaging and integrating.
Reflecting tools:
Two reflecting tools, memoing and diagramming, can help you monitor your thoughts about data content. We introduce each tool separately here, but in practice, they are commonly used interchangeably and/or in combination.
- Initially, you can use memoing as a vehicle to think aloud about how your project expands existing conversations in the literature in your field and affects current practices, policies, and research your colleagues are doing. As analysis progresses, you will introduce and reflect on data examples and analytic discoveries.
- Diagramming facilitates exploration of how multiple parts of an analysis work together to reveal and shape new ideas. Diagramming facilitates synthesis and presentation of quotations, topics, and variables.
Engaging tools:
The Sort and Sift approach includes four data engagement tools that can be used independently or in combination with one another.
- Reading each datafile allows you to get the whole picture of each datafile, including a sense of the movement and flow within each file.
- Working with quotations by identifying “power quotations” and using them to direct decisions you make during analysis is the most powerful way to “let the data be your guide.”
- Episode profiling is used to depict the holistic story of each data collection episode and enables you to capture the lived experience of participants.
- Topic monitoring represents a dynamic and active form of working with categories. Topic names may change, topics may be combined, and some topics may be eliminated as your understanding of topics alone and in combination shifts and changes.
Integrating tools:
Three tools facilitate the integrating process. Mining is a pathway to using bridging and threading tools. Bridging and threading help you depict and discuss thematic connections you detect in your work.
- Mining is a thorough review of your work where you search through memos, diagrams, topics, and episode profiles to discover meaningful content and connections within and across data documents.
- Bridging is a process where researchers shape themes by finding connections between two or more topics.
- Threading helps you discover themes that hold component parts of stories together. “Threads” often serve as overarching concepts in project papers and presentations.
