Using a meaning-based approach that emphasizes the “why” over the “how to,” Psychometrics: An Introduction provides thorough coverage of fundamental issues in psychological measurement. Author R. Michael Furr discusses traditional psychometric perspectives and issues including reliability, validity, dimensionality, test bias, and response bias as well as advanced procedures and perspectives including item response theory and generalizability theory. The substantially updated Third Edition includes broader and more in-depth coverage with new references, a glossary summarizing over 200 key terms, and expanded suggested readings consisting of highly relevant papers to enhance the book’s overall accessibility, scope, and usability.
Psychometrics is the field of study concerned with the theory and technique of educational measurement and psychological measurement, which includes the measurement of knowledge, abilities, attitudes, and personality traits. The field is primarily concerned with the construction and validation of measurement instruments, such as questionnaires, tests, and personality assessments. Since the founding of modern psychometrics, both the theory and practice have progressed vigorously. Tests are continuously developed, classical theories are extended and subsumed under modern theories, and new applications are designed. Unfortunately, test construction and test theory have developed separately. Test constructors are often badly informed about modern theories, and test theorists are badly informed about test construction. This book bridges the gap between theory and practice by discussing both test construction and test theory in psychometrics.
Focusing on the conceptual understanding of psychometric issues such as validity and reliability this textbook introduces psychometric principles at a level that goes into more detail than introductory undergraduate texts, yet also more intuitive than more technical publications intended for postgraduate level. By emphasizing conceptual development and practical significance over mathematical proofs, this book assists students in appreciating how measurement problems can be addressed and why it is important to address them.
A systematic, innovative introduction to the field of network analysis, Network Psychometrics with R: A Guide for Behavioral and Social Scientists provides a comprehensive overview of and guide to both the theoretical foundations of network psychometrics as well as modelling techniques developed from this perspective. Written by pioneers in the field, this textbook showcases cutting-edge methods in an easily accessible format, accompanied by problem sets and code. After working through this book, readers will be able to understand the theoretical foundations behind network modelling, infer network topology, and estimate network parameters from different sources of data. This book features an introduction on the statistical programming language R that guides readers on how to analyse network structures and their stability using R. While Network Psychometrics with R is written in the context of social and behavioral science, the methods introduced in this book are widely applicable to data sets from related fields of study. Additionally, while the text is written in a non-technical manner, technical content is highlighted in textboxes for the interested reader. Network Psychometrics with R is ideal for instructors and students of undergraduate and graduate level courses and workshops in the field of network psychometrics as well as established researchers looking to master new methods. This book is accompanied by a companion website with resources for both students and lecturers.
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 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.
In An Introduction to Psychological Assessment and Psychometrics, Keith Coaley outlines the key ingredients of psychological assessment, providing case studies to illustrate their application, making it an ideal textbook for courses on psychometrics or psychological assessment. New to the Second Edition: Includes occupational and educational settings Covers ethical and professional issues with a strong practical focus Case study material related to work selection settings End of chapter self-assessments to facilitate students’ progress Compliant with the latest BPS Certificate of Testing curriculum
This book helps readers apply testing and measurement theories. Featuring 22 self-standing modules, instructors can pick and choose the ones that are most appropriate for their course. Each module features an overview of a measurement issue and a step-by-step application of that theory. Best practices provide recommendations for ensuring the appropriate application of the theory. Practical questions help students assess their understanding of the topic while the examples allow them to apply the material using real data. Two cases in each module depict typical dilemmas faced when applying measurement theory followed by Questions to Ponder to encourage critical examination of the issues noted in the cases. Each module contains exercises some of which require no computer access while others involve the use of SPSS to solve the problem. The book’s website houses the accompanying data sets and more. The book also features suggested readings, a glossary of the key terms, and a continuing exercise that incorporates many of the steps in the development of a measure of typical performance. Updated throughout to reflect recent changes in the field, the new edition also features: --A new co-author, Michael Zickar, who updated the advanced topics and added the new module on generalizability theory (Module 22). -Expanded coverage of reliability (Modules 5 & 6) and exploratory and confirmatory factor analysis (Modules 18 & 19) to help readers interpret results presented in journal articles. -Expanded Web Resources, Instructors will now find: suggested answers to the book’s questions and exercises; detailed worked solutions to the exercises; and PowerPoint slides. Students and instructors can access the SPSS data sets; additional exercises; the glossary; and website references that are helpful in understanding psychometric concepts. Part 1 provides an introduction to measurement theory and specs for scaling and testing and a review of statistics. Part 2 then progresses through practical issues related to text reliability, validation, meta-analysis and bias. Part 3 reviews practical issues related to text construction such as the development of measures of maximal performance, CTT item analysis, test scoring, developing measures of typical performance, and issues related to response styles and guessing. The book concludes with advanced topics such as multiple regression, exploratory and confirmatory factor analysis, item response theory (IRT), IRT applications including computer adaptive testing and differential item functioning, and generalizability theory. Ideal as a text for any psychometrics, testing and measurement, or multivariate statistics course taught in psychology, education, marketing and management, professional researchers in need of a quick refresher on applying measurement theory will also find this an invaluable reference.
An Introduction to Psychometrics and Psychological Assessment is the successor to Cooper’s prize-winning book Psychological Testing: Theory and Practice. This expanded and updated volume shows how psychological questionnaires and tests can be chosen, administered, scored, interpreted and developed. In providing students, researchers, test users, test developers and practitioners in the social sciences, education and health with an evaluative guide to choosing, using, interpreting and developing tests, it provides readers a thorough grasp of the principles (and limitations) of testing, together with the necessary methodological detail. This book has three distinctive features. First, it stresses the basic logic of psychological assessment without getting bogged down with mathematics; the spreadsheet simulations and utilities which are integrated into the text allow users to explore how numbers behave, rather than reading equations. Readers will "learn by doing". Second, it covers both the theory behind psychological assessment and the practicalities of locating, designing and using tests and interpreting their scores. Finally, it is evaluative. Rather than just describing concepts such as test reliability or adaptive testing, it stresses the underlying principles, merits and drawbacks of each approach to assessment, and methods of developing and evaluating questionnaires and tests. Unusually for an introductory text, it includes coverage of several cutting-edge techniques, and this new edition expands the discussion on measurement invariance, methods of detecting/quantifying bias and hierarchical factor models, and features added sections on: Best practices for translation of tests into other languages and problems of cultural bias Automatic item generation The advantages, drawbacks and practicalities of internet-based testing Generalizability theory Network analysis Dangerous assumptions made when scoring tests The accuracy of tests used for assessing individuals The two-way relationship between psychometrics and psychological theory Aimed at non-mathematicians, this friendly and engaging text will help you to understand the fundamental principles of psychometrics that underpin the measurement of any human characteristic using any psychological test. Written by a leading figure in the field and accompanied by additional resources, including a set of spreadsheets which use simulated data and other techniques to illustrate important issues, this is an essential introduction for all students of psychology and related disciplines. It assumes very little statistical background and is written for students studying psychological assessment or psychometrics, and for researchers and practitioners who use questionnaires and tests to measure personality, cognitive abilities, educational attainment, mood or motivation.
A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
