# CAREER: Statistical Solutions for Survey Measurement Errors

> **NSF 01002627DB NSF RESEARCH & RELATED ACTIVIT** · William Marsh Rice University (TX) · $400,000

## Abstract

This research develops new statistical theory, methodology, algorithms, and software to produce more robust survey estimates. The new methods address a central challenge in the field of survey methods: the widespread problem of survey‐taker satisficing. Satisficing respondents provide low-effort responses without sufficiently considering the survey questions. Improving survey estimates benefits a range of disciplines that rely heavily on surveys or experiments with survey-based measures, including the social sciences, public health, and education. The research assists the countless decision makers and organizations that rely on survey data to inform their decision making. The project’s education component invests in the next generation of survey researchers through short courses, software development, and summer research experiences for undergraduates. By forging methodological innovations and training the next generation of survey researchers, the project expands survey methods expertise and produces widely disseminated tools.

This research draws on techniques from causal inference, econometrics, and convex optimization to develop new statistical estimators that are robust to measurement error caused by satisficing. While survey researchers have developed ad hoc techniques, such as attention checks, to try and exclude satisficing respondents from survey samples, these techniques are often detached from statistical theory. This research demonstrates the inadequacy of exist

## Key facts

- **NSF award ID:** 2442402
- **Awardee organization:** William Marsh Rice University (TX)
- **SAM.gov UEI:** K51LECU1G8N3
- **PI:** Matthew D Tyler
- **Primary program:** 01002627DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** CAREER-Faculty Erly Career Dev, METHOD, MEASURE & STATS, UNDERGRADUATE EDUCATION
- **Estimated total:** $400,000
- **Funds obligated:** $148,606
- **Transaction type:** Continuing Grant
- **Period:** 07/01/2025 → 06/30/2030

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2442402

## Citation

> US National Science Foundation, Award 2442402, CAREER: Statistical Solutions for Survey Measurement Errors. Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nsf/2442402. Licensed CC0.

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