The Sort and Sift, Think and Shift qualitative data analysis method, created by Ray Maietta and his consulting team at ResearchTalk Inc. (Alison Hamilton, Paul Mihas, Kevin Swartout, Jeff Petruzzelli and Kristin Black) 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 direct next stages of analysis and to fit findings with current conversations in the field. The method 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.

The course prepares researchers at two different points of their analysis to use the Sort and Sift method.

• First, researchers starting analysis on a project will learn the Sort and Sift method and how to apply the tools from the beginning to the end of the project.
• Second, researchers who began analysis via different means will benefit from being exposed to tools they can use to gain traction and direction in their analysis.

The Sort and Sift method is built on a foundation of core principles that guide your analysis process and features a set of tools that you can introduce to any qualitative project. The focus of the course is on learning the what, when, why and how of each tool. Integrating these tools into your work will help you connect, or re-connect, with the depth of meaning in your data documents and ignite, or re-ignite, your motivation for the work.

Using the Sort and Sift method results in the following benefits:

1. Improving the quality of your work
2. Pursuing different analytic pathways as you look at data from different angles and vantage points
3. Bringing new elements to your discoveries
4. Assessing alignment with existing work in your field

Coding qualitative data serves as a heuristic (i.e., a method of discovery) to generate analytic outcomes such as major categories, themes, theories, and other representations such as visual displays and evocative narratives. The emergence of artificial intelligence/ChatGPT provides researchers with an intriguing new heuristic for qualitative data analysis.

This two-day course focuses on a select number of coding methods through manual applications and supplemental, exploratory data analysis with the ChatGPT platform. Participants will review:

• foundation principles for coding qualitative data
• how coding generates reflective analytic memos
• selected outcomes of qualitative data analysis (categories, themes, diagrams, etc.)
• applications with and appropriateness of AI/ChatGPT as a qualitative researcher’s resource

Participants are strongly encouraged (but are not required) to have access to a ChatGPT platform during the course for selected data analysis activities. Course content is derived from Saldaña’s The Coding Manual for Qualitative Researchers (5th edition, 2025; Sage Publishing).

Curious about observation but not sure how to get started? Concerned about potential pitfalls? Want to strengthen your triangulation of interview and focus group data? This workshop provides a strong foundation for conducting observation in qualitative research.

Observation was arguably the first qualitative data collection method: used by anthropologists dating back to the 1800s. Though now less common than in-depth interviews or focus group discussions, observation can be an invaluable asset to most qualitative studies. In formative, descriptive, and evaluative research, observation can generate insights and reveal details impossible to learn any other way.

Combining hands-on practice with examples from completed and ongoing research, this workshop will equip you with the tools to incorporate observation into your existing methods. You’ll learn about different approaches to observation, situations in which each approach is most useful, and how to design the data collection instruments you’ll need for each. You’ll learn how to minimize reactivity and how to account for it when it occurs.

In this workshop you’ll also learn about some common objections to observation and how to address them: Observation is too expensive. It takes too long. Observing is invasive, maybe even unethical. People don’t like being observed, they’ll never consent to it. Your data won’t be valid: people change what they’re doing if they know that you’re watching. We’ll discuss these concerns and how to build the strongest possible case for the value of observation in your own research.

This course is for researchers who want to adopt a qualitative orientation in their mixed methods research designs and examine its influence on integration planning, execution, and reporting. Designing qualitatively oriented integration procedures within mixed methods research requires specialized skills in addition to leveraging existing qualitative and quantitative researcher expertise. In this course, we draw examples from diverse settings, such as the study of literacy gaps in children, the effectiveness of public health messaging, and technology needs in higher education to explore how qualitatively oriented mixed methods research offers a unique lens to mitigate some of the inequities and ethical issues that arise by relying on sole sources of qualitative or quantitative evidence.

Core discussions will include:

• Distinguishing characteristics of qualitatively oriented mixed methods designs
• Describing mixed methods procedures that reflect a qualitative orientation to integration
• Creating visual diagrams and displays to convey evidence of qualitatively oriented mixed methods designs and integration evidence
• Incorporating recent advances and promising practices in mixed methods research into proposals, practices, and manuscripts.