Abstract
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.
Highlights
In psychology and social sciences, self-report questionnaires are commonly used to collect data in various situations [1]
Our work significantly differs from these approaches as we focus on sophisticated data collection instruments based on advanced process management technology
Taking the above issues into account, the QuestionSys configurator that we developed applies sophisticated end-user programming techniques to properly abstract the modeling of data collection instruments
Summary
In psychology and social sciences, self-report questionnaires are commonly used to collect data in various situations [1] These data are predominantly collected using paper-based questionnaires, which are costly regarding the subsequent processing and analysis of the collected data. The latter has to be transferred to digital spreadsheet documents, which is a time-consuming and error-prone task, especially in the context of large-scale trials or studies. Electronic questionnaires do not differ from the paper-based versions in psychometric properties [3] They contribute to more complete datasets compared with the ones collected using pencil and paper [4], resulting in a better data quality [5]. 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
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