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The purpose of this chapter is to provide researchers a summary of some of the latest developments in item response theory (IRT), and to help these groups realize that psychometric tools can now be used for theory testing in addition to the traditional role of improving construct measurement. The author first reviews some of the fundamental tenets of classical test theory to contrast with IRT. He then describes recent advances in goodness-of-fit tests that have helped turn IRT into a model-testing tool. Finally, the author reviews several new test models that provide new flexibilities, summarizing summarize several examples of research that has used these new models in organizational research. At the end of this review, the author provides suggestions to help researchers better use these new IRT tools. Although there have been significant advances in IRT in the past decade, there has not been a systematic review of these developments. This review places those developments in context to provide readers a real appreciation of these breakthroughs.

The chapter explores the developments in work on the history of quantification and sport, explaining how quantification in sport is generally understood, and then establishing what a sociological approach offers to scholars interested in exploring new expressions of these developments in biometrics and Big Data. It then outlines some potential directions scholars might pursue to further develop knowledge of these developments in the context of sport.

The chapter synthesizes existing literature from the sociology of quantification, sport sociology and quantification, and Big Data to provide historical, contemporary, and future oriented assessments of sport and datafication.

By situating a discussion of Big Data and biometrics in the context of sport, this chapter argues for the value of a sociological approach to these areas. The chapter engages prior work as a way to move scholars to challenge the assumed epistemological and political power of numbers for the way we engage sport.

The chapter argues for a number of future areas of study that may push the boundaries of existing research in the area.

The chapter provides a survey of the literature on sport, analytics, and Big Data as an impetus for future research into the importance of a sociological approach to these areas in the context of sport.

Purpose – The purpose is to present item response theory (IRT) as a promising approach to deal with theoretical and methodological challenges which may not be adequately addressed with linear factor analysis (LFA) techniques.

Approach – We address this limitations and present IRT's approach to counteracting these difficulties. We further present two IRT models in greater detail and make a theoretical comparison between these models and LFA techniques. Then, we illustrate how IRT is applied in practice by analyzing a scale of trust by an IRT and an LFA approach and comparing their results.

Practical implications – Scale properties may vary among countries because some items may function differently in different contexts. Moreover, a scale's reliability depends on the value of the latent variable being assessed, which implies that the scale may discriminate better among certain ranges of the latent trait than among others. The theory we hereby present offers a more adequate approach to dealing with these issues than extant LFA methods.

Originality/value – This chapter presents a methodology of recognized value in other fields of knowledge into the field of strategy and international business, thereby advancing the methodologies available for doing research in these domains.

This article begins with a brief reading of the state of the practice of empirical social science research on measurement before proceeding to the discussion of an exemplary instance of this researcher's ethnographic effort to improve indicators of social capital formation. Given the central role measurement plays in social science research, it is appropriate, that a volume on methodological innovations in ethnography would contain a chapter about the relationship of ethnography to measure development. However, it is worth acknowledging that the line of argumentation advanced in this chapter is unconventional. The central tenant of this chapter – that ethnography has much to offer to the field of measurement and that ethnographers ought to take the contribution that they have the potential to make to the field of measurement seriously – at present might be thought to have little agreement either among those researchers whose primary focus is measurement or among ethnographers. This chapter contends that the features and strengths of ethnography specifically, and qualitative research more generally, makes it uniquely suited to contribute to the development of new indicators and the improvement of existing indicators. This chapter modestly hopes to encourage discussion of this contention and illustrate how this author sees his own ethnographic research into indicators of social capital formation as an attempt to address a pressing methodological dilemma within the field, more general of social scientific measure development.

This chapter provides a contemporary view of state-of-the science research and thinking done in the areas of selection and classification. It takes as a starting point the observation that the world of work is undergoing important changes that are likely to result in different occupational and organizational structures. In this context, we review recent research on criteria, especially models of job performance, followed by sections on predictors, including ability, personality, vocational interests, biodata, and situational judgment tests. The paper also discusses person-organization fit models, as alternatives or complements to the traditional person-job fit paradigm.

A nurse home care scheduling system is described. The objective is to provide medical care at patients’ homes using the fewest number of nurses possible to deliver the required care. The heuristic scheduling system is easy to implement as a computerized adaptive system. As such, it is easy to use on a daily basis and easy to update as new data related to completed treatment and new requests are obtained. A case study illustrates the advantages of implementing such a system.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

Work organizations and the employees within these organizations face considerable environmental pressures requiring adaptive change. Several forces have contributed to this need for great adaptation. These are described in many excellent sources (e.g., Cascio, 2003; Ilgen & Pulakos, 1999); here we briefly review their implications for individual adaptability.

In this article, we survey experiments that are directly related to monetary policy and central banking. We argue that experiments can also be used as a tool for central bankers for bench testing policy measures or rules. We distinguish experiments that analyze the reasons for non-neutrality of monetary policy, experiments in which subjects play the role of central bankers, experiments that analyze the role of central bank communication and its implications, experiments on the optimal implementation of monetary policy, and experiments relevant for monetary policy responses to financial crises. Finally, we mention open issues and raise new avenues for future research.