arXiv 23 Jun 2026 · Statistics — Methodology
arXiv:2606.24244 · PDF · DOI · OpenAlex · Extracted main text
AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Cochran, William G (1977) Sampling Techniques | 0.874 | 6 | 4 | 67% |
| 2 | Särndal, Carl-Erik and Swensson, Bengt and Wretman, Jan (1992) Model Assisted Survey Sampling | 0.874 | 6 | 4 | 67% |
| 3 | Schober, Michael F. and Conrad, Frederick G (1997) Does Conversational Interviewing Reduce Survey Measurement Error? | 0.843 | 4 | 4 | 75% |
| 4 | Tourangeau, Roger and Rips, Lance J. and Rasinski, Kenneth (2000) The Psychology of Survey Response | 0.843 | 4 | 4 | 75% |
| 5 | Conrad, Frederick G. and Schober, Michael F (2000) Clarifying Question Meaning in a Household Telephone Survey | 0.843 | 3 | 3 | 100% |
| 6 | Biemer, Paul P (2010) Total Survey Error: Design, Implementation, and Evaluation | 0.737 | 3 | 3 | 67% |
| 7 | Graham, John W and Taylor, Bonnie J and Olchowski, Allison E and Cum… (2006) Planned missing data designs in psychological research. | 0.737 | 3 | 3 | 67% |
| 8 | Groves, Robert M. and Lyberg, Lars (2010) Total Survey Error: Past, Present, and Future | 0.737 | 3 | 3 | 67% |
| 9 | Raghunathan, Trivellore E. and Grizzle, James E (1995) A Split Questionnaire Survey Design | 0.737 | 3 | 3 | 67% |
| 10 | Wang, Siruo and McCormick, Tyler H. and Leek, Jeffrey T (2020) Methods for Correcting Inference Based on Outcomes Predicted by Machine Learning self | 0.737 | 3 | 3 | 67% |
Showing the top 10 of 70 scored citations.