This inaugural issue is devoted to exploring measurement, research design, and statistics issues in six subdisciplines of exercise and sport science. Originally presented at the Eighth Measurement and Evaluation Symposium, all papers in this issue reflect the work of many renowned measurement specialists and content experts in their respective fields. The articles discuss the following topics: * standards of assessment quality for physical educators and the problem of providing adequate assessment without adequate resources; * the importance of properly conceptualizing and defining appropriate research questions as the "source and solution" for measurement and design issues in reference to motor learning/control and sport and exercise psychology; * the study of individuals -- single-subject and other small-sample designs -- in contrast to the more traditional study of groups; and * the importance of computing and reporting statistical power in research.

This volume highlights research and conceptual insights into one of the most basic, and yet, perplexing research issues in management-handling and assessing the comparability of our measurement devices across groups and measures. One of the most consistently difficult concerns in management research over the past three decades has been trying to reconcile measurement equivalence issues utilizing diverse samples. Given the emphasis on diversity in the human resources area and the internationalization of business and management, measurement equivalence is more of a general concern now than ever before. If we are not able to successfully address concerns about measurement equivalence, research examining differences between groups could be highly misleading and/or erroneous. Consequently, we hope that the thoughtful contributions of the scholars in this volume will help future scholars to better address measurement equivalence concerns.

In this volume prominent scholars from both psychology and education describe how these new rules of measurement work and how they differ from the old rules. Several contributors have been involved in the recent construction or revision of a major test, while others are well-known for their theoretical contributions to measurement. The goal is to provide an integrated yet comprehensive reference source concerned with contemporary issues and approaches in testing and measurement.

Eureka Math is a comprehensive, content-rich PreK–12 curriculum that follows the focus and coherence of the Common Core State Standards in Mathematics (CCSSM) and carefully sequences the mathematical progressions into expertly crafted instructional modules. The companion Study Guides to Eureka Math gather the key components of the curriculum for each grade into a single location, unpacking the standards in detail so that both users and non-users of Eureka Math can benefit equally from the content presented. Each of the Eureka Math Curriculum Study Guides includes narratives that provide educators with an overview of what students should be learning throughout the year, information on alignment to the instructional shifts and the standards, design of curricular components, approaches to differentiated instruction, and descriptions of mathematical models. The Study Guides can serve as either a self-study professional development resource or as the basis for a deep group study of the standards for a particular grade. For teachers who are new to the classroom or the standards, the Study Guides introduce them not only to Eureka Math but also to the content of the grade level in a way they will find manageable and useful. Teachers familiar with the Eureka Math curriculum will also find this resource valuable as it allows for a meaningful study of the grade level content in a way that highlights the coherence between modules and topics. The Study Guides allow teachers to obtain a firm grasp on what it is that students should master during the year. The Eureka Math Curriculum Study Guide, Grade 4 provides an overview of all of the Grade 4 modules, including Place Value, Rounding, and Algorithms for Addition and Subtraction; Unit Conversions and Problem Solving with Metric Measurement; Multi-Digit Multiplication and Division; Angle Measure and Plane Figures; Fraction Equivalence, Ordering, and Operations; Decimal Fractions; and Exploring Measurement with Multiplication.

This book serves as a roadmap for the development and application of patient-reported outcome (PRO) measures, supporting beginners through to experts, as a practical guide. To elucidate on key concepts in the book, examples from clinical research in hyperhidrosis and health-related quality of life and medicines clinical development context, are used. Health-related quality of life represents one of the most commonly measured PROs in both routine clinical practice and research. The book demonstrates the importance of PROs to patients with chronic disease and how such outcomes can assist clinicians in managing patients and monitoring their response to treatment in terms of both symptoms and impacts. This book will benefit readers as a single-source practical guide on the development of modern PRO measures and may also serve as a blueprint for the conceptualization and planning of evidence generation related to PROs in various settings. Ideas and suggestions on how to navigate recent developments shaping the field of PRO measurement are also offered.

This book reviews the statistical procedures used to detect measurement bias. Measurement bias is examined from a general latent variable perspective so as to accommodate different forms of testing in a variety of contexts including cognitive or clinical variables, attitudes, personality dimensions, or emotional states. Measurement models that underlie psychometric practice are described, including their strengths and limitations. Practical strategies and examples for dealing with bias detection are provided throughout. The book begins with an introduction to the general topic, followed by a review of the measurement models used in psychometric theory. Emphasis is placed on latent variable models, with introductions to classical test theory, factor analysis, and item response theory, and the controversies associated with each, being provided. Measurement invariance and bias in the context of multiple populations is defined in chapter 3 followed by chapter 4 that describes the common factor model for continuous measures in multiple populations and its use in the investigation of factorial invariance. Identification problems in confirmatory factor analysis are examined along with estimation and fit evaluation and an example using WAIS-R data. The factor analysis model for discrete measures in multiple populations with an emphasis on the specification, identification, estimation, and fit evaluation issues is addressed in the next chapter. An MMPI item data example is provided. Chapter 6 reviews both dichotomous and polytomous item response scales emphasizing estimation methods and model fit evaluation. The use of models in item response theory in evaluating invariance across multiple populations is then described, including an example that uses data from a large-scale achievement test. Chapter 8 examines item bias evaluation methods that use observed scores to match individuals and provides an example that applies item response theory to data introduced earlier in the book. The book concludes with the implications of measurement bias for the use of tests in prediction in educational or employment settings. A valuable supplement for advanced courses on psychometrics, testing, measurement, assessment, latent variable modeling, and/or quantitative methods taught in departments of psychology and education, researchers faced with considering bias in measurement will also value this book.

Adapting Psychological Tests and Measurement Instruments for Cross-Cultural Research provides an easy-to-read overview of the methodological issues and best practices for cross-cultural adaptation of psychological instruments. Although the development of cross-cultural test adaption methodology has advanced in recent years, the discussion is often pitched at an expert level and requires an advanced knowledge of statistics, psychometrics and scientific methodology. This book, however, introduces the history and concepts of cross-cultural psychometrics in a pedagogic and simple manner. It evaluates key ethical, cultural, methodological and legal issues in cross-cultural psychometrics and provides a guide to test adaptation, data analysis and interpretation. Written in an accessible manner, this book builds an understanding of the methodological, ethical and legal complexities of cross-cultural test adaptation and presents methods for test adaptation, including the basic statistical procedures for evaluating the equivalence of test versions. It would be the ideal companion for undergraduate students and those new to psychometrics.

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.