Why the Data Revolution Needs Qualitative Thinking
by Anissa Tanweer, Emily Kalah Gade, P.M. Krafft, and Sarah Dreier
Published on
Jul 30, 2021
Harvard Data Science Review
https://hdsr.mitpress.mit.edu/pub/u9s6f22y/release/4
In this article, we focus on a set of concepts that are intrinsically informed by particular epistemological and ontological positions common in qualitative social sciences—positions that seek to understand the contingently and subjectively constructed nature of the social world. We refer to these concepts as ‘sensibilities’ because we intend them to intervene on methodology in a sensitizing rather than prescriptive way. The three sensibilities we discuss, have certain kinds of methodological practices, and they can be coupled with multiple modes of data collection and analysis.
Sensibility
Interpretivism
Working definition
An epistemological approach probing the multiple and contingent ways that meaning is ascribed to objects, actions, and situations.
Example of related methods
Trace ethnography (Geiger & Ribes, 2011; Geiger & Halfaker, 2017)
Sensibility
Abductive reasoning
Working definition
A mode of inference that updates and builds upon preexisting assumptions based on new observations in order to generate a novel explanation for a phenomenon.
Example of related methods
Iterations of open coding, theoretical coding, and selective coding (Thornberg & Charmaz, 2013)
Sensibility
Reflexivity
Working definition
A process by which researchers systematically reflect upon their own positions relative to their object, context, and method of inquiry.
Example of related methods
Brain dumps, situational mapping, and toolkit critiques (Markham, 2017)
Abduction
Abduction is often described as “inference to the best explanation” (Douven, 2011).
Abductive reasoning updates and builds upon preexisting assumptions (in other words, theories) based on new observations in order to generate a novel explanation for a phenomenon. As such, it demarks “a creative outcome which engenders a new idea,"
When using abductive reasoning, qualitative researchers have developed ways of addressing the relationships between prior assumptions, new observations, and newly derived explanations. This can be incorporated in data science.
The labeling of data in qualitative methods (what qualitative researchers would instead call ‘coding’) is not a matter of mere assumption, but rather a systematic part of the theory-building process.
LinkedIn AI article
What are the most common reasoning frameworks for data science?
https://www.linkedin.com/advice/0/what-most-common-reasoning-frameworks-data-science-41fif
I made a contribution to the above article.
The above article in is decision making series
https://www.linkedin.com/pulse/topics/soft-skills-s2976/decision-making-s2506/
Jan 11, 2023
AI concepts for beginners - Exploring abductive reasoning in AI
https://indiaai.gov.in/article/exploring-abductive-reasoning-in-ai
Qualitative Data Analysis Methodologies and Methods
Last Updated : 23 Jul, 2025
https://www.geeksforgeeks.org/data-analysis/qualitative-data-analysis-methodologies-and-methods/
Leveraging LLM and Data Science Methods to Quantify Qualitative Data: A Workflow and Method Example
ud. 8.11.2026
pub. 22.11.2023
