Open Collections

Dietrich, Matthew

University of British Columbia

In this thesis, we address the challenge of acquiring high-quality measurement data from real-world experiments. Experiments with human participants can be expensive, both in terms of scheduling participants as well as equipment requirements. Because of these constraints limiting data collection, we desire software tools for data quality assurances that are active during each measurement session. We propose a modular approach to conducting experiments based on the inputs, outputs, and dependencies between individual data-generating operations that we call measurement services. Formally defining the output of each operation provides clear quality assurance targets to aim for during the experiment session. Our framework of modular components also emphasizes extensibility and reusability in the development of new experiments. We implemented our approach by developing BodyData, a web application-centered system designed to measure, store, and securely access data from experiments with human participants. BodyData was tested in our lab; two case studies are presented to illustrate the utility of the system in practice. We discuss how we provide improved quality assurance through the use of configurable data entry constraints as well as visual feedback during the measurement session. We also discuss how we support queries from authorized clients for use in analysis and visualization of stored data.

Text

2021-08-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/79266

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/