On Patterns of Refusals Conversion and Propensity of Converted Refusals to Respond at Later Waves in a Longitudinal Survey
Bibliographic record
Abstract
When a selected sample member refuses to take part in a survey interview, the survey organization may not accept therefusal as a final outcome, but rather to make further attempts to convert the refusals into an interview. The aim of thisstudy was to investigate the pattern of refusals conversion and the propensity of the converted refusals to respond at laterwaves in a longitudinal survey. A two-stage stratified randon sampling scheme was used with households in Oyo as thesampling unit. A sample of 750 households were randomly selected from the community and sub-divided into five equalgroups with each group treated as a wave. The recording schedule was used to obtain information on demographic characteristicsincluding survey process, external environment, age, gender, educational qualification, religion, employmentstatus, family size, duration of interview and the type of questions. The data were collected through oral interview of thesubjects. Summary statistics were constructed to look at the patterns of conversion of refusals. Logistic model was fittedto investigate the propensity of converted refusals to respond at later waves following a conversion. At wave 1 of thesurvey, 109 house heads were interview in households with a response rates (in percentage) of 72.67.The interview periodwas an average of 8 minutes per house head. The response rate at wave 2, wave 3, wave 4 and wave 5 were 82, 81.33, 82and 80.67 respectively. Outcomes of a conversion attempt were a full interview and a proxy interview. Five house headswent through the conversion process at wave 1 and data were successfully collected on 2 of them (40%). All of them wereinterviewed again at wave 2 (100%). Those converted refusals at wave 1, 100% gave a full interview six months later. Forhouse heads who were converted between wave 1 and wave 5 continued to give full interviews at every wave up to wave5. For all other waves, the converted refusals participated throughout the survey. Logistic model showed that, those whowere converted to a full interview rather than proxy interview were the most likely to give a full interview at subsequentwave. When we included in the model, information on the wave in which the initial conversion was took place and thetime since conversion, we found that those whose initial conversions were in earlier and later waves were less likely togive a full interview compared with those were converted at wave 3. Adding demographic information suggested thatmale, people with their ages between ((30 − 50) years, respondents with primary education were likely to take part againfollowing a conversion.
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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 | 0.114 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".