9.1: Introduction- Learning to Swim in the Data

Learning Objectives

By the end of this chapter, you should be able to:

• Understand how to present the characteristics of your data
• Learn various styles for presenting quotes
• Discover how to create and use simple data visualizations
• Determine how to weave data into a narrative

Suggested Timeline: January – Mid February

Box 9.1 – Student Testimony – The Key Principles and Motivations of Qualitative Research

Qualitative work is never ‘objective,’ and it does not seek to be. A qualitative researcher inhabits multiple subjectivities and produces work that draws from their lived experiences, their command of the methodological and scholarly texts they corral to interrogate their topic, and through the position they advocate for in their work. As a qualitative researcher, my work in Mumbai, Delhi, and Pune, India (2011), Chicago, U.S. (2015), and Vancouver, Canada (2017) has happened in community with others. I have written work on research topics ranging from the migration and displacement of Afghan students and refugees who lived in India, intervened—through sexual prevention campaigns and government-funded studies—in the HIV epidemic brutalizing black gay men, transwomen, and women of colour in the US, and documented a small part of the emerging constellation of temporary placemaking events—or pop-ups—queers in Vancouver create to make space to salaciously play, dance, forge friendships, and find their tribe.

My advice to emerging scholars is to leverage what you know as a set of strengths, to be open to what you don’t know, to learn from others already doing the work, and to find a supportive set of mentors who value and support your intellectual growth. Good work happens through the community you keep with your research participants and the mentors who want the best for you. That requires regular communication and setting realistic and achievable expectations given the capacity of everyone, while establishing healthy boundaries for yourself to do the work asked of you.

DO: regularly and reflexively challenge your views about your topic, and how ‘uncomfortable information’ you receive dislodges former assumptions held and generates new lines of inquiry

DON’T: engage in research for the final product such as publications or presentations—research is a long journey that depends on the people you work with and who support your work. The final product will reflect this.

Ryan Stillwagon, Graduate Student, UBC Sociology

Data analysis is the process by which we make meaning from the data that we collect. In qualitative research, we do this by: (1) searching for and identifying patterns and themes; (2) providing evidence (textual data and narratives) to support the patterns and themes identified; and (3) telling a cohesive story from the data and enable us to provide answers to our research question.

As with the preceding two chapters (methods and data collection), analysis will depend on the paradigm chosen. This section will therefore relate itself to the last couple sections, aiming to distinguish the main social science paradigms for data analysis. This chapter is divided into four sections: first, we outline the basic steps of qualitative analysis; second, we discuss common frameworks for conducting qualitative data analysis with emphasis on grounded theory and content analysis; third, we discuss data analysis and presentation strategies; and finally, we discuss storylining, the process of mapping concepts into a narrative.