Measuring SES-related traits relating to technology usage: Two validated surveys

TL;DR

This paper introduces two validated surveys to measure how well software products serve individuals across different socioeconomic statuses, aiding developers in creating more inclusive technology.

Contribution

It provides two new surveys, SES-Subjective and SES-Facets, for assessing SES-related traits in software users, validated through empirical deployments at two universities.

Findings

Both surveys are reliable and statistically align with ground truth SES data.

The surveys are actionable for software development and legal compliance.

Validated with over 900 responses across two institutions.

Abstract

Software producers are now recognizing the importance of improving their products' suitability for diverse populations, but little attention has been given to measurements to shed light on products' suitability to individuals below the median socioeconomic status (SES) -- who, by definition, make up half the population. To enable software practitioners to attend to both lower- and higher-SES individuals, this paper provides two new surveys that together facilitate measuring how well a software product serves socioeconomically diverse populations. The first survey (SES-Subjective) is who-oriented: it measures who their potential or current users are in terms of their subjective SES (perceptions of their SES). The second survey (SES-Facets) is why-oriented: it collects individuals' values for an evidence-based set of facet values (individual traits) that (1) statistically differ by SES and (2) affect how an individual works and problem-solves with software products. Our empirical validations with deployments at University A and University B (464 and 522 responses, respectively) showed that both surveys are reliable. Further, our results statistically agree with both ground truth data on respondents' socioeconomic statuses and with predictions from foundational literature. Finally, we explain how the pair of surveys is uniquely actionable by software practitioners, such as in requirements gathering, debugging, quality assurance activities, maintenance activities, and fulfilling legal reporting requirements such as those being drafted by various governments for AI-powered software.