Electronic Data Collection from the Study Director's Viewpoint

2002 ◽  
pp. 40-43
Author(s):  
S. Scott Brady
Keyword(s):  
Data Collection ◽  
Electronic Data ◽  
Electronic Data Collection

scholarly journals Analysis of erroneous data entries in paper based and electronic data collection

2019 ◽  
Author(s):  
Benedikt Ley ◽  
Komal Raj Rijal ◽  
Jutta Marfurt ◽  
Nabaraj Adhikari ◽  
Megha Banjara ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Entry ◽  
Categorical Variables ◽  
Data Sets ◽  
Continuous Variables ◽  
Electronic Data ◽  
Suitable Alternative ◽  
Electronic Data Collection ◽  
Data Points ◽  
Time Variables

Abstract Objective: Electronic 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. Results: Data 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% (643/3,580) of continuous variables, 15.8% of time variables (113/716), 13.0% of date variables (140/1,074), 12.0% of text variables (86/716), and 10.9% of categorical variables (1,370/12,530). Overall 64% (1,499/2,352) of all discrepancies were due to data omissions, 76.6% (1,148/1,499) 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.

Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicentre Dementia Registry – digiDEM Bayern: a Mixed Method Study (Preprint)

2021 ◽  
Author(s):  
Michael Reichold ◽  
Miriam Hess ◽  
Peter L. Kolominsky-Rabas ◽  
Elmar Gräßel ◽  
Hans-Ulrich Prokosch
Keyword(s):  
Data Collection ◽  
Technology Acceptance ◽  
Mobile Devices ◽  
Mixed Method ◽  
User Group ◽  
Electronic Data ◽  
Thinking Aloud ◽  
Electronic Data Collection ◽  
Usability Problems ◽  
Dementia Registry

BACKGROUND Digital registries have shown to provide an efficient way better to understand the clinical complexity and long-term progression of diseases. The paperless way of electronic data collection during a patient interview saves both: time and resources. In the prospective multicenter 'Digital Dementia Registry Bavaria - digiDEM Bayern', interviews are also conducted on-site in rural areas with unreliable internet connectivity. It must be ensured that electronic data collection can still be performed there, and it is no need to fall back on paper-based questionnaires. Therefore, the EDC system REDCap offers, in addition to a web-based data collection solution, the option to collect data offline via an app and synchronize it afterward. OBJECTIVE This study evaluates the usability of the REDCap app as an offline electronic data collection option for a lay user group and examines the necessary technology acceptance using mobile devices for data collection. Thereby, the feasibility of the app-based offline data collection in the dementia registry project was evaluated before going live. METHODS The study was conducted with an exploratory mixed-method in the form of an on-site usability test with the 'Thinking Aloud' method combined with a tailored semi-standardized online questionnaire including System Usability Score (SUS). The acceptance of mobile devices for the data collection was surveyed based on the technology acceptance model (TAM) with five categories. RESULTS Using the Thinking Aloud method, usability problems were identified and solutions were derived therefore. The evaluation of the REDCap app resulted in a SUS score of 74, which represents 'good' usability. After evaluating the technology acceptance questionnaire, it can be stated that the lay user group is open to mobile devices as interview tools. CONCLUSIONS The usability evaluation results show that a lay user group like the data collecting partners in the digiDEM project can handle the REDCap app well overall. The usability test provided statements about positive aspects and was able to identify usability problems of the REDCap app. In addition, the current technology acceptance in the sample showed that heterogeneous groups of different ages with different experiences in handling mobile devices are also ready for the use of app-based EDC systems. Based on the results, it can be assumed that the offline use of an app-based EDC system on mobile devices is a viable solution to collect data in a registry-based research project.

Social Network Construction in the Information Age

2011 ◽  
pp. 131-155 ◽  
Author(s):  
Michael Farrugia ◽  
Neil Hurley ◽  
Diane Payne ◽  
Aaron Quigley
Keyword(s):  
Social Network ◽  
Data Collection ◽  
Information Age ◽  
Network Data ◽  
Electronic Data ◽  
Network Construction ◽  
Electronic Data Collection ◽  
Social Network Data

In this chapter, the authors will discuss the differences between manual data collection and electronic data collection to understand the advantages and the challenges brought by electronic social network data. They will discuss in detail the processes that are used to transform electronic data to social network data and the procedures that can be used to validate the resultant social network.

452 ASSESSING THE FEASIBILITY OF ELECTRONIC DATA COLLECTION FOR MEN WITH PROSTATE CANCER

2013 ◽  
Vol 189 (4S) ◽  
Author(s):  
Leslie D McIntosh ◽  
Linda Black ◽  
Joanne Morley ◽  
Sheri Long ◽  
Patricia Carter ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Data Collection ◽  
Electronic Data ◽  
Electronic Data Collection
Sign in / Sign up

Export Citation Format

Share Document