Qualitative Thinking and Research in Data Science

 

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