Social media is an ever-increasing aspect of the internet presence and daily life. Despite certain challenges in defining the construct, researchers have realized the possibility that social media can allow for the measurement and assessment of a wide variety of variables. Throughout the ever-growing number of social media sites and apps across countries and languages, there is an abundance of formats that researchers can utilize, such as photo, text, location, video, and more. In this book chapter, we conducted a literature search and identified four constructs that are most frequently studied using social media (i.e., personality, emotion/affect/mood, life satisfaction, and political views). We then summarized a list of studies that use social media to investigate these four constructs. Additionally, social media offers unique opportunities for researchers to assess various cross-cultural data, which can present its own challenges. We also provide examples of the potential opportunities and challenges, as well as ethical and technical considerations for researchers to keep in mind.

Technology, that is, the output of human innovation, has always been central to human progress worldwide. Early on, the ancients developed the wheel, concrete, calculus, and paper, which led to advances in transportation, construction, and communication. Today, the incarnation of technology falls in the realm of the digital and computational, and its progress has been rapid, even arguably exponential. In his chapter, “The Law of Accelerating Returns,” Ray Kurzweil writes, “An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense ‘intuitive linear’ view. So we won’t experience 100 years of progress in the 21st century – it will be more like 20,000 years of progress (at today’s rate)” (Kurzweil, 2004, p. 381).

Behavioral measurement is the hallmark of research in the field of computational social science. We are witnessing innovative as well as clever use of existing and novel, commercial, or research-grade “sensors” to measure various aspects of human behavior and well-being. Passive sensing, a version of measurement where data is gathered and tracked unobtrusively using pervasive and ubiquitous sensors, is increasingly recognized and utilized in organizational science research. This chapter presents an overview of where passive sensing has been successful in workplace measurement, ranging from assessing worker personality and productivity, to their well-being, and understanding the overall organizational pulse. A range of passive sensing infrastructures are described (e.g., smartphones, wearable devices, social media) and several machine-learning-based predictive approaches are noted in this body of research. The chapter then highlights outstanding challenges as this field matures, which include issues of limited generalizability in computational measurement of workplace behaviors, gaps and limitations of gold standard assessment, model simplicity and sophistication tradeoffs, and, importantly, privacy risks. The chapter concludes with recommendations on important areas that need further or altogether new investments, so as to fully realize the potential of passive sensing technologies in more accurate, actionable, and ethical workplace measurement.

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.

The quality of psychological assessment processes in talent management is influenced by our choices about which measurement technologies to use. Technology with relevance to assessing talent is also advancing at great speed in many domains. These advances include processing power and speed, human computer interaction research, and machine learning and artificial intelligence. Given these rapid developments, it is an appropriate time to pause and take stock of how emerging assessment approaches (e.g., game-based assessment) that leverage these new developments are used, relative to more traditional approaches such as questionnaires and interviews. To achieve this objective, we report here on a survey of European assessment practitioners. We ask about the technology they use for psychological assessment, the constructs they measure with those approaches, and the levels of organisations they are used at. We also asked about how traditional approaches are being enhanced with technology and about practitioner perceptions of the reliability, validity and adverse impact and privacy of their technological choices.

In this essay, we review the Technology and Measurement around the Globe chapters with an eye toward integration and synthesis. We primarily focus on implications for testing, and then make connections to the broader world of nontest assessment. We identify themes of privacy, fairness, workplace applications, and emerging technologies, and offer a research agenda for future investigations that seek to understand culture, technology, and measurement.

An overview of testing and measurement in North America is provided, covering topics related to privacy laws and regulations, online proctoring, artificial intelligence, accommodations, accessibility, and the “opt out of testing” movement that are currently defining measurement in North America. This is not to say that these challenges are unique to North America; in fact, the challenges related to these topics are being faced all over the world in varying degrees and the same opportunities exist, but these topics are of particular importance when it comes to measurement and assessment in North America. Building on these observations, a discussion of how advances in technology and computing power provide an opportunity to challenge the status quo related to assessment; these advancements will allow assessment of skills in more authentic ways that will provide better insight into someone’s knowledge, skills, and abilities. The question we should be asking and attempting to answer is “How can assessment developers leverage the power of the cloud and technology to measure skills more accurately and create higher fidelity in the assessment process?”

This chapter provides an overview of the common machine learning algorithms used in psychological measurement (to measure human attributes). They include algorithms used to measure personality from interview videos; job satisfaction from open-ended text responses; and group-level emotions from social media posts and internet search trends. These algorithms enable effective and scalable measures of human psychology and behavior, driving technological advancements in measurement. The chapter consists of three parts. We first discuss machine learning and its unique contribution to measurement. We then provide an overview of the common machine learning algorithms used in measurement and their example applications. Finally, we provide recommendations and resources for using machine learning algorithms in measurement.

When used adequately, technology-enabled measurement can help researchers and practitioners better assess various constructs and phenomena of interest and hence better understand, predict, and influence them in order to address social and behavioral issues. This article examines key issues and experiences in Singapore associated with digital transformation and data society, including challenges and opportunities in technology-enabled measurement that may be applicable as well in other cities and countries. Using Singapore’s digitization transformation journey to apply technology systematically and extensively to improve the lives of its people as an example, critical issues of contexts, changes, and collaborations in research, policy, and practice involving technology-enabled measurement of psychological constructs and processes are discussed.

This chapter reviews the potential of technological innovations to advance assessment of psychological variables in education and the labor market in South America. We discuss in more detail SENNA kids, an electronic assessment tool developed in Brazil, to facilitate the formative assessment of social-emotional skills in young children. For the labour market, we describe an employee-experience tool developed by BONDI-X, a South American start-up, to track employees’ experience and foster communication between employees and organizations. We discuss how features of these two systems and their technologies can be integrated and contribute to a technology-supported self-directed experience system putting individuals in the driver seat of their personal development across their educational and employment careers.

The current chapter provides an overview of technology and measurement in Asia. In the first half of the chapter, we summarize the current use of technology in research, as well as related regulations and legal environments. In the second half of the chapter, we compare the existing technological applications in Asia with the rest of the world, discuss factors influencing the applications in Asia, and highlight potential developmental areas.