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Compliance and data quality in GPS-based studies
Authors:Michal Isaacson  Noam Shoval  Hans-Werner Wahl  Frank Oswald  Gail Auslander
Affiliation:1. New England University Transportation Center, AgeLab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, E40-279, Cambridge, MA, 02139, USA
2. Department of Geography, Hebrew University of Jerusalem, Mt. Scopus, 91905, Jerusalem, Israel
3. Department of Psychological Ageing Research, Institute of Psychology, University of Heidelberg, Bergheimer Str. 20, 69115, Heidelberg, Germany
4. Interdisciplinary Ageing Research Group, Faculty of Educational Sciences, Goethe University, Frankfurt am Main, Grüneburgplatz 1, 60323, Frankfurt am Main, Germany
5. Paul Baerwald School of Social Work and Social Welfare, The Hebrew University of Jerusalem, Mt. Scopus, 91905, Jerusalem, Israel
Abstract:Recent years have witnessed a growing volume of papers describing the use of GPS technology and other tracking technologies for obtaining data on time–space activities. These methods have several advantages over traditional methods of time–space data collection in terms of accuracy, resolution and length of the possible data collection period. However, to date, no work has been done on the compliance rates among participants and the resulting validity of the collected data. This paper presents a method that combines the use of a GPS receiver with Radio Frequency Identification (RFID) technology that was implemented in research on time–space activities of elderly persons with cognitive impairment. The method presented in this paper enables monitoring the level of compliance of the participants during their participation in the study and presents a unique opportunity to examine the extent to which participants in a GPS based study are able to comply with study requirements. Healthy older adults and those with cognitive decline were found to be generally compliant with a complex study protocol. These results serve as another step into the acceptance of GPS based studies as a valid methodology for mobility data collection.
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