ArticleBMC research notes2019
Analysis of erroneous data entries in paper based and electronic data collection.
Article in BMC research notes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- The effect of personalised versus non-personalised study invitations on recruitment within the ENGAGE feasibility trial: an embedded randomised controlled recruitment trial.BMC medical research methodology · 2022Trial
- Digital Quality Monitoring for Type 2 Diabetes in Swiss Primary Care: Qualitative Interview Study.Journal of medical Internet research · 2026Article
- A care quality dashboard for general practitioners managing patients with diabetes mellitus type 2: user-centered design and prototype evaluation.BMC medical informatics and decision making · 2026Article
- Implementation of good clinical practice in clinical research in the context of limited resources settings: Lessons learnt from the freeBILy trial using an embedded mixed methods approach.PLoS neglected tropical diseases · 2026Article
- Usability of a digital tool to support long-lasting insecticide net distribution in Northern Bahr el Ghazal State, South Sudan.Malaria journal · 2024Article
- Assessment of expanded programme on immunization routine data quality in the upper east region of Ghana.BMC health services research · 2024Article
- Field assessment of the operating procedures of a semi-quantitative G6PD Biosensor to improve repeatability of routine testing.PloS one · 2024Article
- Field evaluation of a novel semi-quantitative point-of-care diagnostic for G6PD deficiency in Indonesia.PloS one · 2024Article
- Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study.JMIR formative research · 2022Article
- Glucose-6-phosphate dehydrogenase activity in individuals with and without malaria: Analysis of clinical trial, cross-sectional and case-control data from Bangladesh.PLoS medicine · 2021Article
- Electronic data collection for multi-country, hospital-based, clinical observation of maternal and newborn care: EN-BIRTH study experiences.BMC pregnancy and childbirth · 2021Observational
- Electronic data collection in a multi-site population-based survey: EN-INDEPTH study.Population health metrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveElectronic data collection (EDC) has become a suitable alternative to paper based data collection (PBDC) in biomedical research even in resource poor settings. During a survey in Nepal, data were collected using both systems and data entry errors compared between both methods. Collected data were checked for completeness, values outside of realistic ranges, internal logic and date variables for reasonable time frames. Variables were grouped into 5 categories and the number of discordant entries were compared between both systems, overall and per variable category.
resultsData from 52 variables collected from 358 participants were available. Discrepancies between both data sets were found in 12.6% of all entries (2352/18,616). Differences between data points were identified in 18.0% (643/3580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1370/12,530). Overall 64% (1499/2352) of all discrepancies were due to data omissions, 76.6% (1148/1499) of missing entries were among categorical data. Omissions in PBDC (n = 1002) were twice as frequent as in EDC (n = 497, p < 0.001). Data omissions, specifically among categorical variables were identified as the greatest source of error. If designed accordingly, EDC can address this short fall effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.