Should We Offer Web, Paper, or Both? A Comparison of Single- and Mixed-Response Mode Treatments in a Mail Survey
Bibliographic record
Abstract
Abstract This article leverages a five-treatment response mode experiment (paper-only, web-only, sequential web-mail, choice, and choice-plus [choice with a promised incentive for responding online]) that was conducted within a nationally representative survey. Because this survey’s sample was drawn from respondents to another nationally representative survey, we have rich frame data that includes multiple indicators of comfort using the internet for our sample members and we can compare their response behavior across two surveys. We find that the paper-only treatment yielded a lower response rate than most of the other treatments, but there were not significant differences between the response rates for the other treatments. Among our mixed-mode treatments, the sequential web-mail treatment had the highest percentage of response by web and the lowest cost per response. When focusing on the subgroups that we expected to be the least—and the most—comfortable with the internet, we found that the paper-only treatment generally performed worse than the others, even among subgroups expected not to be comfortable with the internet. We generally did not find significant differences in the effect of response mode treatment on the response rate or percentage of response by web between the subgroups who were the most and least comfortable with the internet. In terms of the consistency of response mode choice over time, our results suggest that some people respond consistently—but also that response mode preferences are weak enough that they can be influenced by the way in which the modes are offered. We ultimately recommend using a sequential web-mail design to minimize costs while still providing people who cannot or will not respond by web with another response mode option. We also find evidence that there may be a growing lack of interest in responding by paper; more research is needed in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.057 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".