Evidence on the Comparison of Telephone and Internet Surveys for Respondent Recruitment
Bibliographic record
Abstract
Internet surveys have a potential use for survey research when compared against costs and declining response rates of traditional modes as they form a powerful tool for reducing respondents' burden in complex questionnaires. On the other hand, there exists scepticism about the reliability and robustness of the collected data. Arenze et al . (2005) argue that case studies involving Internet surveys cannot be generalised to other countries and have recommended systematic collection and reporting of experiences worldwide. Such studies have had limited exposure in the transport literature. This paper provides empirical evidence on the comparison between telephone and Internet surveys in the context of a car ownership study. The comparison between telephone and Internet modes focuses on performance measures such as response speed, response rates, survey costs, demographic profiles and geographical representation of the sample. The results indicate the cost effectiveness of Internet surveys. Moreover, they show that the time and cost for data collection significantly vary by sampling and recruitment method. Finally, Internet survey response rates are lower than those in the telephone interview, which implies that Internet surveys can only be used to complement traditional data collection methods.
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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 | 0.339 | 0.653 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".