LAPS: Automating Hypothesis-Driven Statistical Analysis of Public Survey Using Large Language Models

Abstract

Public surveys are indispensable resources for understanding social dynamics, yet their analysis often imposes a high cognitive load due to structural complexity. In this paper, we present LAPS, a Large Language Model (LLM)-assisted automated framework that supports end-to-end, hypothesis-driven statistical analysis of survey data. LAPS consists of four modules (i.e., Operationalization, Planning, Execution, and Reporting) with human-in-the-loop mechanisms to balance automation with user agency. To evaluate the applicability of LAPS, we conducted a within-subjects user study with 12 social science researchers across three analytical environments: traditional statistical tools, a general-purpose LLM, and LAPS. Our findings demonstrate that LAPS ensures researcher agency and analytical stability, reduces the cognitive burden in the analysis workflow, and produces trustworthy, coherent outputs. Based on these findings, we reflect on how LAPS improves researchers' workflows and discuss design implications for scalable and trustworthy human-AI collaboration in survey-based research.