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

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 s ocio e conomic s tatus (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 can 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. The surveys’ design goal is worldwide applicability, but as a first step, here we empirically validated both these surveys with deployments at University A and University B (464 and 522 responses, respectively), which showed reliability of both the surveys in a US context. Our results also 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 can be 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.

Authors

Institutions

Publication Details

Journal
Empirical Software Engineering
Published
2025-09-03
DOI
https://doi.org/10.1007/s10664-025-10683-5
Primary Topic
Technology Adoption and User Behaviour
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Patricia Morreale, Sadia Afroz, Margaret Burnett, Anita Sarma et al.
Empirical Software Engineering
Technology Adoption and User Behaviour
article

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

Patricia Morreale, Sadia Afroz, Margaret Burnett, Anita Sarma, Christopher Bogart, Pannapat Chanpaisaeng, Rudrajit Choudhuri, A. W. Anderson, Puja Agarwal, Prisha Velhal, Chimdi Chikezie, Bhavika Madhwani
article en

Abstract

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 s ocio e conomic s tatus (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 can 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. The surveys’ design goal is worldwide applicability, but as a first step, here we empirically validated both these surveys with deployments at University A and University B (464 and 522 responses, respectively), which showed reliability of both the surveys in a US context. Our results also 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 can be 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.

Empirical Software EngineeringVol. 30(6)
Oregon State University (US), Kasetsart University (TH), Kean University (US), Carnegie Mellon University (US)
Carnegie Mellon University
Openalex Percentile: Top 77%
Technology Adoption and User Behaviour
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.