Response Quality and Demographic Characteristics of Respondents Using a Mobile Device on a Web-based Survey
Bibliographic record
Abstract
As quickly as the Internet grew throughout the 1990s and 2000s, the growth of devices such as smartphones and tablets that allow one to access the Internet while mobile has been even more explosive. Given their increasing popularity and the conveniences they offer, it is no surprise that people are using these mobile devices to participate in Web-based surveys, even when those surveys have not been optimized for those devices. It is important that we understand the impact of mobile Internet access on Web surveys, particularly as we uncover who is using mobile devices to participate in Web surveys and the relative quality of their responses. This study uses data from the 2011 administration of the National Survey of Student Engagement (NSSE), a survey of undergraduate college and university students in the U.S. and Canada. Server-side paradata were used to classify the 414,056 respondents in this study’s sample to (a) determine the demographic characteristics of respondents who exclusively used a smartphone or tablet and (b) compare the quality of their responses to those provided by other respondents using indicators such as survey abandonment, item non-response, and response differentiation. Results of the study were mixed, indicating that mobile device users do not necessarily provide responses of lower quality even when responding to a Web-based survey that
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How this classification was reachedexpand
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 | 0.086 | 0.039 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".