Augmenting Survey Data with Digital Trace Data: Is There a Threat to Panel Retention?
Bibliographic record
Abstract
Abstract Linking digital trace data to existing panel survey data may increase the overall analysis potential of the data. However, producing linked products often requires additional engagement from survey participants through consent or participation in additional tasks. Panel operators may worry that such additional requests may backfire and lead to lower panel retention, reducing the analysis potential of the data. To examine these concerns, we conducted an experiment in the German PASS panel survey after wave 11. Three quarters of panelists (n = 4,293) were invited to install a research app and to provide sensor data over a period of 6 months, while one quarter (n = 1,428) did not receive an invitation. We find that the request to install a smartphone app and share data significantly decreases panel retention in the wave immediately following the invitation by 3.3 percentage points. However, this effect wears off and is no longer significant in the second and third waves after the invitation. We conclude that researchers who run panel surveys have to take moderate negative effects on retention into account but that the potential gain likely outweighs these moderate losses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.227 | 0.562 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".