Effects of Mentioning the Incentive Prize in the Email Subject Line on Survey Response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective – This study examined the effects that mentioning the survey incentive prize in the subject line of a reminder email had on the response rate and data quality. To date, manipulation of the subject line, specifically in terms of mentioning the incentive prize, has received limited attention in the survey design literature. Methods – The delivery of the survey invitation is discussed in terms of the timing of the launch and reminder emails. Particular emphasis is given to the design of the email subject line and justification of the format. Weekly response rates from four LibQUAL+TM surveys were compared. In addition, weekly responses for one year were analyzed using SPSS to investigate if there were any between means differences in terms of three elements of data quality. The three elements were: length of time it took to complete the survey, the number of core questions with an N/A response, and the number of illogical responses where minimum scores were higher than desired. Results – The response rates for the second week were grouped together based on the presence or absence of the subject line manipulation. There was a significant difference between these means (4.75%, p 0.033). There was no statistical difference in regards to the measures of data quality as determined by a one-way ANOVA test. Conclusions – Reminding survey participants with an email that mentions the incentive prize in the subject line appears to increase response rates with no deleterious effects on data quality. The results of this investigation are encouraging, and those running the LibQUAL+TM survey in their universities should consider implementing this method to increase response rates. Further research to replicate these findings in other contexts and using an experimental design would be beneficial.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.125 | 0.290 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.031 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it