Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in Light of Advanced AI

TL;DR

This paper proposes a process model that integrates quantitative and qualitative visualization research methods through semantic enrichment, leveraging AI advancements to enhance data analysis and interpretation.

Contribution

It introduces a novel process model for bridging data and semantics, facilitating integrated analysis in visualization research using AI-driven semantic transformation.

Findings

The model supports iterative semantic enrichment of user study data.

It demonstrates improved integration of qualitative and quantitative methods.

Open research issues in AI-human-analyst interactions are discussed.

Abstract

This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how we can bridge between the different methods in an integrated process of analyzing user study data. To this end, a process model of - potentially iterated - semantic enrichment and transformation of data is proposed. This joint perspective of data and semantics facilitates the integration of quantitative and qualitative methods. The model is motivated by examples of own prior work, especially in the area of eye tracking user studies and coding data-rich observations. Finally, there is a discussion of open issues and research opportunities in the interplay between AI, human analyst, and qualitative and quantitative methods for visualization research.