PD24-05: Latent Code Identification: An Integrative Framework to Classify Qualitative Evidence and Build Educational Possibilities

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 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."
A version of this workshop was offered at AERA 2022 and 2023. This workshop will be presented at the 2023 American Sociological Association annual conference.

Sub Unit

• Professional Development and Training Committee

Director

• Manuel S. Gonzalez Canche, University of Pennsylvania