Abstract
Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Smartphones offer the possibility to gather longitudinal data at little cost to researchers and participants. They provide the option to verify self-report data with data from sensors built into most smartphones. How accurate this sensor data is when gathered via different smartphone devices, e.g., in a typical experience sampling framework, has not been investigated systematically. With the present study, we investigated the accuracy of orientation data about the spatial position of smartphones via a newly invented measurement device, the RollPitcher. Objective status of pitch (vertical orientation) and roll (horizontal orientation) of the smartphone was compared to data gathered from the sensors via web browsers and native apps. Bayesian ANOVAs confirmed that the deviations in pitch and roll differed between smartphone models, with mean inaccuracies per device of up to 2.1° and 6.6°, respectively. The inaccuracies for measurements of roll were higher than for pitch, d = .28, p < .001. Our results confirm the presence of heterogeneities when gathering orientation data from different smartphone devices. In most cases, measurement via a web browser was identical to measurement via a native app, but this was not true for all smartphone devices. As a solution to lack of sensor accuracy, we recommend the development and implementation of a coherent research framework and also discuss the implications of the heterogeneities in orientation data for different research designs.
Highlights
Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science
Roll values that were gathered at pitch angles of 85° were excluded from all combined analyses as these showed unusually large deviations from the objective values
The main hypothesis is that the angle of 85° is too close to 90°, at which there are no meaningful values for roll
Summary
Smartphone usage is increasing around the globe—in daily life and as a research device in behavioral science. Stieger and Reips (2019) investigated data from both smartphone sensors and from open-access Internet databases on temperature, longitude, latitude, altitude, wind speed, rainfall, and further environment-based variables to predict fluctuations of well-being by using a smartphone-based mobile experience sampling method. In their study, they found a high correlation between smartphone GPS measurement of altitudes and Google Maps measurement of altitudes, but a consistent difference in absolute measurement. Participants are using their own device and only download an app or open their web browser to participate This presents researchers with additional problems, as there may be a large heterogeneity of data across devices. Here we are investigating a special case of using heterogeneous consumer-grade equipment in Internet-based research
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