PD25-03 Machine-Driven Text Classification and Analysis for Storytelling Using Free and No-Code Tools for a More Just Education Research

Session Type: Professional Development Course

Abstract

Labeling or classifying textual data is an expensive and consequential challenge for Mixed Methods and Qualitative researchers. The rigor and consistency behind the construction of these labels may ultimately shape research findings and conclusions. A methodological conundrum to address this challenge is the need for human reasoning for classification that leads to deeper and more nuanced understandings, but at the same time manual human classification comes with the well-documented increase in classification inconsistencies and errors, particularly when dealing with vast amounts of texts and teams of coders.
With a development grant of 2022 SAGE Concept Grant, this workshop offers an analytic framework designed to leverage the power of machine learning to classify textual data while also leveraging the importance of human reasoning in this classification process. This framework was designed to mirror as close as possible the line-by-line coding employed in manual code identification, but relying instead on latent Dirichlet allocation, text mining, MCMC, Gibbs sampling and advanced data retrieval and visualization. A set of output provides complete transparency of the classification process and aids to recreate the contextualized meanings embedded in the original texts.
In the pursuit of truly expanding access to data science, advance visualization tools, and machine learning to non-programmers, this analytic framework has been packaged in an open-access software application and is the second product of the analytic movement "Democratizing Data Science."
I offered versions of this workshop in AERA 2022, 2023, and 2024. Based on excellent reviews, Dr. Wimberly recommended me to resubmit this proposal. There is an additional course fee: Member: $120/Non-member: $150

Sub Unit

• Professional Development and Training Committee

Director

• Manuel S. Gonzalez Canche, University of Pennsylvania