Data Cleaning Needs and Issues: A Case Study of the National Reproductive Health Assessment (RHA) Data from Solomon Islands
Bibliographic record
Abstract
Data cleaning is an essential part of any research work without which the validity and reliability of the data could come under the spotlight. Aim: to document common errors found during the cleaning of datasets and suggests ways of minimizing errors during data entry process, reducing human errors throughout data cleaning. Design and Setting: a case study based on the national Reproductive Health Assessment (RHA) data conducted in Solomon Islands in 2013. Objective: The main objective of the Solomon Islands RHA was to establish the health status of reproductive aged women between the ages of 15 – 49 for the Solomon Islands. Method: data was collected using questionnaires and entered on to the SPSS database in the country by the local Solomon Islands research assistants who were trained by the Pacific Sexual and Reproductive Health Research Center (PSRHRC). The data was brought back to Fiji where the cleaning process took place. Results: findings of this case study showed that there were issues with the standardization of databases, database familiarization and data merging. Conclusion: more training is needed for researchers who are involved in data collection, data entry and data cleaning to minimize such errors which could give results which may not be a true representation of the indented study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.005 |
| 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; a candidate call from one teacher head, 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".