Comparability of Internet and Telephone Data in a Survey on the Respiratory Health of Children
Bibliographic record
Abstract
BACKGROUND: Mixing survey administration modes has generated concern about the comparability of responses between modes. OBJECTIVE: To explore the differences in respondent profiles, and responses between Internet and telephone questionnaires in a survey on respiratory diseases. METHODS: The data were generated from a mixed Internet and telephone survey of respiratory diseases among children in Montreal (Quebec), in 2006. Comparison of 12 selected questions was performed after standardization for respondent education and income. Stratification of analysis on education and income categories was also performed for the questions with significantly divergent responses. RESULTS: Six questions showed significant differences in responses between modes after standardization. The largest differences among the closed-ended questions were observed for highly prevalent symptoms, dry cough during the night (difference of 9% for positive answer [P<0.01]) and symptoms of allergic rhinitis (difference of 7% for positive answer [P<0.01]). A large discrepancy was also found in the multiple choice question and with an open-ended response (ie, free answer). For the three potentially sensitive questions, a desirability bias was probably present in one question on smoking habits (difference of 2.6 % for positive answer [P<0.05]). CONCLUSION: The differences observed between Internet and telephone responses to selected questions were not completely explained by socioeconomic disparities among the respondents. In a mixed-mode survey (Internet and telephone), caution should be used when formulating sensitive, complex, open-ended and long-ended questions, and those related to highly prevalent and nonspecific symptoms.
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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 | 0.058 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".