Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However, there is no single book that covers important topics in measurement and psychometrics as well as their applications in R. The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics, including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional, multidimensional, and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning. This handbook is intended for undergraduate and graduate students, researchers, and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R, would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition, instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However, there is no single book that covers important topics in measurement and psychometrics as well as their applications in R. The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics, including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional, multidimensional, and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning. This handbook is intended for undergraduate and graduate students, researchers, and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R, would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition, instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

"This book provides a broad overview of methods in educational and psychological measurement focusing on applications using R. It includes the key introductory topics and extends to recent research developments, such as multidimensional item response theory models. The focus is on the practical implementation of the methods, with lots of real data examples and R code integrated throughout. The book will be supplemented by an R package with all code and data available for replication. The book could be used as a supplementary text for the computing component of a course on measurement in either department."--Provided by publisher.

Grounded in current knowledge and professional practice, this book provides up-to-date coverage of psychometric theory, methods, and interpretation of results. Essential topics include measurement and statistical concepts, scaling models, test design and development, reliability, validity, factor analysis, item response theory, and generalizability theory. Also addressed are norming and test equating, topics not typically covered in traditional psychometrics texts. Examples drawn from a dataset on intelligence testing are used throughout the book, elucidating the assumptions underlying particular methods and providing SPSS (or alternative) syntax for conducting analyses. The companion website presents datasets for all examples as well as PowerPoint slides of figures and key concepts. Pedagogical features include equation boxes with explanations of statistical notation, and end-of-chapter glossaries. The Appendix offers extensions of the topical chapters with example source code from SAS, SPSS, IRTPRO, BILOG-MG, PARSCALE, TESTFACT, and DIMTEST.

This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification. The handbook also offers applications and special topics and practical guidelines how to plan and conduct research studies with the help of DCMs. Commonly used models in educational measurement and psychometrics typically assume a single latent trait or at best a small number of latent variables that are aimed at describing individual differences in observed behavior. While this allows simple rankings of test takers along one or a few dimensions, it does not provide a detailed picture of strengths and weaknesses when assessing complex cognitive skills. DCMs, on the other hand, allow the evaluation of test taker performance relative to a potentially large number of skill domains. Most diagnostic models provide a binary mastery/non-mastery classification for each of the assumed test taker attributes representing these skill domains. Attribute profiles can be used for formative decisions as well as for summative purposes, for example in a multiple cut-off procedure that requires mastery on at least a certain subset of skills. The number of DCMs discussed in the literature and applied to a variety of assessment data has been increasing over the past decades, and their appeal to researchers and practitioners alike continues to grow. These models have been used in English language assessment, international large scale assessments, and for feedback for practice exams in preparation of college admission testing, just to name a few. Nowadays, technology-based assessments provide increasingly rich data on a multitude of skills and allow collection of data with respect to multiple types of behaviors. Diagnostic models can be understood as an ideal match for these types of data collections to provide more in-depth information about test taker skills and behavioral tendencies.

This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.

As part of their research activities, researchers in all areas of education develop measuring instruments, design and conduct experiments and surveys, and analyze data resulting from these activities. Educational research has a strong tradition of employing state-of-the-art statistical and psychometric (psychological measurement) techniques. Commonly referred to as quantitative methods, these techniques cover a range of statistical tests and tools. Quantitative research is essentially about collecting numerical data to explain a particular phenomenon of interest. Over the years, many methods and models have been developed to address the increasingly complex issues that educational researchers seek to address. This handbook serves to act as a reference for educational researchers and practitioners who desire to acquire knowledge and skills in quantitative methods for data analysis or to obtain deeper insights from published works. Written by experienced researchers and educators, each chapter in this handbook covers a methodological topic with attention paid to the theory, procedures, and the challenges on the use of that particular methodology. It is hoped that readers will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area.