Process-Driven Data Collection with Smart Mobile Devices

Schobel, Johannes; Schickler, Marc; Pryss, Rüdiger; Reichert, Manfred

doi:10.1007/978-3-319-27030-2_22

Cited by 6 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Participation was voluntary and participants had to sign an informed consent prior to the interview. With respect to the special vulnerability of the population, anonymity and confidentiality were ensured through the electronic coding and storage of the data, which fulfilled the highest and most secure data encryption standards (Schobel, Schickler, Pryss, Maier, & Reichert, 2014). Psychologists and advanced students of clinical psychology from the University Lumière of Bujumbura conducted the interviews in Kirundi, permanently supervised by clinical psychologists from the University of Konstanz.…”

Section: Methodsmentioning

confidence: 99%

Predicting domestic and community violence by soldiers living in a conflict region.

Nandi¹,

Elbert²,

Bambonyé³

et al. 2017

Psychological Trauma: Theory, Research, Practice, and Policy

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Methodsmentioning

confidence: 99%

Predicting domestic and community violence by soldiers living in a conflict region.

Nandi¹,

Elbert²,

Bambonyé³

et al. 2017

Psychological Trauma: Theory, Research, Practice, and Policy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Opinions were gathered after invitations were sent through social media and messaging applications, using the LimeSurvey platform, which is optimized for mobile devices. This method has proven to be effective, with strong motivation and acceptance from respondents due to its practicality in their daily lives (Salama et al, 2020;Schobel et al, 2015;Van Berkel et al, 2017).…”

Section: Sample and Data Collection Proceduresmentioning

confidence: 99%

How political skill and work engagement differ by hierarchical level: evidence from Brazil

Matos de Moraes,

Santos Crespo Istoe,

Afonso Miranda

2023

RBGN

View full text Add to dashboard Cite

“…Developing such a plethora of data collection instruments enabled us to elaborate crucial requirements in this context [16]. Although the involved investigators and clinicians were satisfied with the provided mobile data collection apps, they craved for more sophisticated features over time.…”

Section: Introductionmentioning

confidence: 99%

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Schobel¹,

Pryss²,

Probst³

et al. 2018

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

BackgroundMany research domains still heavily rely on paper-based data collection procedures, despite numerous associated drawbacks. The QuestionSys framework is intended to empower researchers as well as clinicians without programming skills to develop their own smart mobile apps in order to collect data for their specific scenarios.ObjectiveIn order to validate the feasibility of this model-driven, end-user programming approach, we conducted a study with 80 participants.MethodsAcross 2 sessions (7 days between Session 1 and Session 2), participants had to model 10 data collection instruments (5 at each session) with the developed configurator component of the framework. In this context, performance measures like the time and operations needed as well as the resulting errors were evaluated. Participants were separated into two groups (ie, novices vs experts) based on prior knowledge in process modeling, which is one fundamental pillar of the QuestionSys framework.ResultsStatistical analysis (t tests) revealed that novices showed significant learning effects for errors (P=.04), operations (P<.001), and time (P<.001) from the first to the last use of the configurator. Experts showed significant learning effects for operations (P=.001) and time (P<.001), but not for errors as the experts’ errors were already very low at the first modeling of the data collection instrument. Moreover, regarding the time and operations needed, novices got significantly better at the third modeling task than experts were at the first one (t tests; P<.001 for time and P=.002 for operations). Regarding errors, novices did not get significantly better at working with any of the 10 data collection instruments than experts were at the first modeling task, but novices’ error rates for all 5 data collection instruments at Session 2 were not significantly different anymore from those of experts at the first modeling task. After 7 days of not using the configurator (from Session 1 to Session 2), the experts’ learning effect at the end of Session 1 remained stable at the beginning of Session 2, but the novices’ learning effect at the end of Session 1 showed a significant decay at the beginning of Session 2 regarding time and operations (t tests; P<.001 for time and P=.03 for operations).ConclusionsIn conclusion, novices were able to use the configurator properly and showed fast (but unstable) learning effects, resulting in their performances becoming as good as those of experts (which were already good) after having little experience with the configurator. Following this, researchers and clinicians can use the QuestionSys configurator to develop data collection apps for smart mobile devices on their own.

show abstract

Process-Driven Data Collection with Smart Mobile Devices

Cited by 6 publications

References 12 publications

Predicting domestic and community violence by soldiers living in a conflict region.

Predicting domestic and community violence by soldiers living in a conflict region.

How political skill and work engagement differ by hierarchical level: evidence from Brazil

Learnability of a Configurator Empowering End Users to Create Mobile Data Collection Instruments: Usability Study

Contact Info

Product

Resources

About