A Multimodal Approach Combining Biometrics and Self-Report Instruments for Monitoring Stress in Programming: Methodological Insights

TL;DR

This study compares self-report and biometric measures of stress during programming tasks, revealing discrepancies and methodological challenges in accurately assessing stress in software engineering contexts.

Contribution

It provides a methodological framework for integrating psychometric and biometric data to study stress, highlighting limitations and insights for future research.

Findings

No significant stress detected by psychometric instruments

Participants reported mixed stress levels in interviews

Biometric data showed only EDA phasic peaks differed significantly

Abstract

The study of well-being, stress and other human factors has traditionally relied on self-report instruments to assess key variables. However, concerns about potential biases in these instruments, even when thoroughly validated and standardised, have driven growing interest in alternatives in combining these measures with more objective methods, such as physiological measures. We aimed to (i) compare psychometric stress measures and biometric indicators and (ii) identify stress-related patterns in biometric data during software engineering tasks. We conducted an experiment where participants completed a pre-survey, then programmed two tasks wearing biometric sensors, answered brief post-surveys for each, and finally went through a short exit interview. Our results showed diverse outcomes; we found no stress in the psychometric instruments. Participants in the interviews reported a mix of feeling no stress and experiencing time pressure. Finally, the biometrics showed a significant difference only in EDA phasic peaks. We conclude that our chosen way of inducing stress by imposing a stricter time limit was insufficient. We offer methodological insights for future studies working with stress, biometrics, and psychometric instruments.