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

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.

1 Introduction

In this position statement, I address the issue of evaluation methodology for visualization research by revisiting the long-standing and ongoing discussion of quantitative and qualitative research methods. With the rapid advances in machine learning (ML) and artificial intelligence (AI), there is already a massive impact on how qualitative research can be conducted, in particular, by using large-language models (LLMs) for analyzing a wide range of text documents acquired in typical qualitative research.