Using Sensors in Organizational Research—Clarifying Rationales and Validation Challenges for Mixed Methods

Abstract

Sensor-based data are becoming increasingly widespread in social, behavioral, and organizational sciences. Far from providing a neutral window on “reality,” sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral-, and computer science literature. Further debunking the myth of raw data, the present article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors.

Indexing dynamic collective constructs using computer-aided text analysis: Construct validity evidence and illustrations featuring team processes.

“…At issue, then, is determining on a minute by minute basis, to whom each paramedic was communicating. Müller et al (2019), both suggested that multimodal streams of information might be combined to index members' interactions. For instance, Bluetooth data from wearable sensors can permit one to determine which members are within speaking range of others (cf., Kim et al, 2012;Matusik et al, 2019).…”

Social Analytics of Team Interaction using Dynamic Complexity Heat Maps and Network Visualizations

“…Sociometers are wearable devices fitted with sensors that can include Bluetooth, RFID and infrared technologies for detecting proximity between individuals, microphones to detect nonverbal vocal activity, and/or accelerometers for detecting physical activity and energy levels [47,71]. If each member of a group is equipped with such a device, then a data set extending over time, multiple individuals, and multiple modalities can be collected.…”

“…Over time, several designs for sociometers have been developed, with an early example being presented by Choudhury and Pentland, who also coined the term sociometer [10]. Two prominent examples [47] of the latest generation of devices are the OpenBeacon proximity tag [9], from the SocioPatterns Collaboration 1 , and the Rhythm Badge [40] from Massachusetts Institute of Technology (MIT) 2 , which is used in this research.…”

Sensor-based proximity metrics for team research. A validation study across three organizational contexts

“…However, when assessing the evidence across the emerging body of research on wearable sensors, it is clear that a single physical measurement cannot be an equally valid indicator for such diverse concepts as "happiness", "friendship", "advice" or "personality". As a consequence, researchers need to address the gap that separates the physical measurement from social or psychological constructs and consider opportunities such as mixed methods in their research design in order to bridge it (Müller et al, 2019).…”