The ubiquity of mobile devices allows researchers to assess people’s real-life behaviors objectively, unobtrusively, and with high temporal resolution. As a result, psychological mobile sensing research has grown rapidly. However, only very few cross-cultural mobile sensing studies have been conducted to date. In addition, existing multi-country studies often fail to acknowledge or examine possible cross-cultural differences. In this chapter, we illustrate biases that can occur when conducting cross-cultural mobile sensing studies. Such biases can relate to measurement, construct, sample, device type, user practices, and environmental factors. We also propose mitigation strategies to minimize these biases, such as the use of informants with expertise in local culture, the development of cross-culturally comparable instruments, the use of culture-specific recruiting strategies and incentives, and rigorous reporting standards regarding the generalizability of research findings. We hope to inspire rigorous comparative research to establish and refine mobile sensing methodologies for cross-cultural psychology.

Social interaction and the subsequent generation of interpersonal relationships appear to be inherently linked to emotional well-being. Smartphones have increased an individual’s social footprint while remaining the primary way in which people communicate with each other via social media, phone calls and text messages. However, many researchers have questioned if the same technology is simultaneously preventing us from developing meaningful relationships?

At the same time, other research has started to focus on a variety of popular smartphone applications that have changed the way modern relationships are formed and maintained (e.g., Tinder, Snaphat). This work typically considers a participants’ own experience or data derived directly from applications themselves (Davidson, Joinson and Jones, 2018). However, it is also possible to explore real-wold social interaction via the variety of on-board sensors, which can also reveal group dynamics within the real-world (Piwek, Ellis and Andrews, 2016). For example, Bluetooth and location data derived from appropriate sensors can be used to infer when someone is meeting with others who are also running similar software of their device. This has also been referred to as Social fMRI whereby researchers can quantify social mechanisms in the real world (Aharony et al., 2011).

Smartphones can also generate data within other domains that psychology could take advantage of in the future including “smart cities.” For example, tracking and understanding individuals’ mobility using GPS location can allow for the forecasting of future movements. While smartphones have dramatically changed how large sections of society form and develop new relationships, this chapter points towards how the same technology can be leveraged further to understand how relationships and groups rapidly shift between offline and online contexts in the digital age.

While psychologists agree that individuals differ from each other on a variety of traits (e.g., personality), the theoretical and methodological assumptions used to develop pen and paper psychometric tests have failed to keep pace with recent computational developments that utilise digital traces to infer information about individuals and the world around them. For example, while self-report assessments are designed to predict behaviour in the absence of any real-world measure, digital devices (e.g., smartphones) have facilitated the measurement of many real-world outcomes (Piwek et al., 2016). Even the purchase of a specific device can reveal something about the individual behind the screen (Shaw, Ellis and Ziegler, 2018). Smartphones could therefore lead to a step change in how we study and conceptualise a variety of individual differences. This is particularly pertinent when it comes to understanding personality traits (e.g., levels of extraversion – a measure of sociability) that are automatically deployed in new situations seamlessly and non-consciously (Roberts and Hill, 2017).

A number of studies using smartphones have correlated data from these devices with traditional psychometric tests. However, following a brief review of this work, this chapter will question how much progress has been made in this domain. There remains a general consensus among many social scientists that, while traditional psychometric measures are far from perfect, they are the only option available. This chapter will challenge that assumption however, data derived from smartphone sensing may ultimately support the notion that existing psychometric tools remain valuable and reinforce existing conceptualisations of standard personality models.