Consistency and Quality Check of Survey Data in India
Bibliographic record
Abstract
OBJECTIVE: Reliability of survey responses on topics such as utilization of health facilities by mother and child has long been a subject of concern. This paper explores consistency of responses from the same individuals over time on utilization of health services involving child delivery and child care. METHODS: A sub-sample survey was carried out by an independent monitoring agency in 13 states as a part of a larger Coverage Evaluation Survey of all states of India in 2009 by UNICEF, to recheck the responses to improve data quality. Our randomly chosen sub-sample consisted of 510 questionnaires regarding mothers and 497 regarding children. Differences of responses were noted and conveyed to field agencies to rectify recurring errors. Statistical analysis was conducted to find consistency of responses. RESULTS: Matching between the original and rechecked responses varied. Generally, however, the overall match was greater than 90%. CONCLUSION: Findings suggest that response inconsistencies and the manner in which they are resolved are shown to have important implications for the overall estimate of indicators of utilization of health facilities. The monitoring exercise has, therefore, addressed the quality and consistency issue, which needs further consideration in large-scale surveys. Otherwise, it poses a validity threat to data quality and results on which national policy is framed.
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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 | 0.134 | 0.247 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".