Comparison of response patterns in different survey designs: a longitudinal panel with mixed-mode and online-only design
Bibliographic record
Abstract
BACKGROUND: Increasing availability of the Internet allows using only online data collection for more epidemiological studies. We compare response patterns in a population-based health survey using two survey designs: mixed-mode (choice between paper-and-pencil and online questionnaires) and online-only design (without choice). METHODS: We used data from a longitudinal panel, the Hygiene and Behaviour Infectious Diseases Study (HaBIDS), conducted in 2014/2015 in four regions in Lower Saxony, Germany. Individuals were recruited using address-based probability sampling. In two regions, individuals could choose between paper-and-pencil and online questionnaires. In the other two regions, individuals were offered online-only participation. We compared sociodemographic characteristics of respondents who filled in all panel questionnaires between the mixed-mode group (n = 1110) and the online-only group (n = 482). Using 134 items, we performed multinomial logistic regression to compare responses between survey designs in terms of type (missing, "do not know" or valid response) and ordinal regression to compare responses in terms of content. We applied the false discovery rates (FDR) to control for multiple testing and investigated effects of adjusting for sociodemographic characteristic. For validation of the differential response patterns between mixed-mode and online-only, we compared the response patterns between paper and online mode among the respondents in the mixed-mode group in one region (n = 786). RESULTS: Respondents in the online-only group were older than those in the mixed-mode group, but both groups did not differ regarding sex or education. Type of response did not differ between the online-only and the mixed-mode group. Survey design was associated with different content of response in 18 of the 134 investigated items; which decreased to 11 after adjusting for sociodemographic variables. In the validation within the mixed-mode, only two of those were among the 11 significantly different items. The probability of observing by chance the same two or more significant differences in this setting was 22%. CONCLUSIONS: We found similar response patterns in both survey designs with only few items being answered differently, likely attributable to chance. Our study supports the equivalence of the compared survey designs and suggests that, in the studied setting, using online-only design does not cause strong distortion of the results.
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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 | 0.071 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".