Survey researchers

Summarize survey data using tables, graphs, and fact sheets

AI can autonomously generate tables, graphs, and fact sheets from survey data with high quality. Data visualization and summary statistics are highly automatable tasks where AI tools already produce publication-ready outputs with minimal human editing.

BLS evidence: Survey researchers summarize survey data, using tables, graphs, and fact sheets.

Conduct background research on survey topics

AI excels at literature review, synthesizing research papers, and extracting relevant background information on survey topics. LLMs can rapidly process large bodies of academic and industry research, requiring only human validation of sources and relevance.

BLS evidence: Survey researchers typically conduct background research on survey topics as part of their duties.

Analyze survey data using statistical software and techniques

AI systems can perform statistical analysis, run regression models, and apply standard techniques autonomously. Tools like code-generating LLMs can write R/Python scripts for complex analyses, though human review of methodology choices and interpretation remains valuable.

BLS evidence: Survey researchers analyze data, using statistical software and techniques, and use a variety of statistical techniques and analytical software to analyze the results.

Account for and solve problems caused by nonresponse or sampling issues

AI can detect nonresponse patterns, apply weighting adjustments, and implement imputation techniques with minimal supervision. Statistical methods for handling missing data and sampling bias are well-codified and automatable, though methodology selection benefits from expert review.

BLS evidence: Survey researchers account for and solve problems caused by nonresponse or other sampling issues.

Evaluate surveys and their performance to improve future surveys

AI can analyze response rates, completion times, and data quality metrics to identify survey performance issues. It can suggest improvements based on patterns, though strategic decisions about future survey design still benefit from human expertise and organizational context.

BLS evidence: Survey researchers evaluate surveys, the methods underlying them, and their performance to improve future surveys.

Test surveys to ensure questions are understandable

AI can simulate respondent comprehension, flag ambiguous wording, and suggest improvements through cognitive testing simulations. However, actual pilot testing with diverse human populations still provides irreplaceable insights into real-world comprehension issues.

BLS evidence: Survey researchers test surveys to make sure that people will understand the questions being asked.

Design surveys and determine appropriate survey methods

AI can generate survey designs and suggest methodologies based on research objectives, but requires human judgment to balance stakeholder needs, cultural context, and practical constraints. Current LLMs excel at drafting questions but need oversight for validity and appropriateness.

BLS evidence: Survey researchers design surveys and analyze data, and they plan and design surveys, and determine appropriate survey methods.

Coordinate the work of survey interviewers and data collectors

AI can assist with scheduling, tracking progress, and flagging quality issues, but coordinating human workers requires interpersonal skills, real-time problem-solving, and motivational management that AI cannot fully replicate. Human judgment remains central to team coordination.

BLS evidence: Survey researchers coordinate the work of survey interviewers and data collectors, and sometimes supervise interviewers who collect survey data.