This popular text introduces the reader to all aspects of psychometric assessment, including its history, the construction and administration of traditional tests, and the latest techniques for psychometric assessment online. Rust, Kosinski, and Stillwell begin with a comprehensive introduction to the increased sophistication in psychometric methods and regulation that took place during the 20th century, including the many benefits to governments, businesses, and customers. In this new edition, the authors explore the increasing influence of the internet, wherein everything we do on the internet is available for psychometric analysis, often by AI systems operating at scale and in real time. The intended and unintended consequences of this paradigm shift are examined in detail, and key controversies, such as privacy and the psychographic microtargeting of online messages, are addressed. Furthermore, this new edition includes brand-new chapters on item response theory, computer adaptive testing, and the psychometric analysis of the digital traces we all leave online. Modern Psychometrics combines an up-to-date scientific approach with full consideration of the political and ethical issues involved in the implementation of psychometric testing in today's society. It will be invaluable to both undergraduate and postgraduate students, as well as practitioners who are seeking an introduction to modern psychometric methods.

This popular text introduces the reader to all aspects of psychometric assessment, including its history, the construction and administration of traditional tests, and the latest techniques for psychometric assessment online. Rust, Kosinski, and Stillwell begin with a comprehensive introduction to the increased sophistication in psychometric methods and regulation that took place during the 20th century, including the many benefits to governments, businesses, and customers. In this new edition, the authors explore the increasing influence of the internet, wherein everything we do on the internet is available for psychometric analysis, often by AI systems operating at scale and in real time. The intended and unintended consequences of this paradigm shift are examined in detail, and key controversies, such as privacy and the psychographic microtargeting of online messages, are addressed. Furthermore, this new edition includes brand-new chapters on item response theory, computer adaptive testing, and the psychometric analysis of the digital traces we all leave online. Modern Psychometrics combines an up-to-date scientific approach with full consideration of the political and ethical issues involved in the implementation of psychometric testing in today's society. It will be invaluable to both undergraduate and postgraduate students, as well as practitioners who are seeking an introduction to modern psychometric methods.

Causal Inferences in Capital Markets Research is an attempt to promote a broad interdisciplinary debate about the notion of causality and the role of causal inference in the social sciences. At the risk of oversimplifying, the issue of causality divides the accounting research community in two polar views: the view that causality is an unattainable ideal for the social sciences and must be given up as a standard, and the view that, on one hand, causality should be the ultimate goal of all scientific endeavors and, on the other hand, theory and causal inference are inextricable. Reflecting and discussing these views was the main motivation for this volume. This volume contains eight articles on three topics: I) Econometrics; III) Accounting, and III) Finance. First, Nancy Cartwright addresses the problem of external validity and the reliability of scientific claims that generalize individual cases. Then, John Rust discusses the role of assumptions in empirical research and the possibility of assumption-free inference. Peter Reiss considers the question how sensitive are instrumental variables to functional form transformations. Finally, Charles Manski studies the logical issues that affect the interpretation of point predictions. Second, Jeremy Bertomeu, Anne Beyer and Daniel Taylor provide a critical overview of empirical accounting research focusing on the benefits of theory-based estimation, while Qi Chen and Katherine Schipper consider the question whether all research should be causal and assess the existing gap between theory and empirical research in accounting. Third, R. Jay Kahn and Toni Whited clarifies and contrasts the notions of identification and causality, whereas Ivo Welch adopts a sociology of science approach to understand the consequences of the researchers' race for discovering novel and surprising results. This volume allows researchers and Ph.D students in accounting, and the social sciences in general, to acquire a deeper understanding of the notion of causality and the nature, limits, and scope of empirical research in the social sciences.