An Introduction to the Rasch Model with Examples in R offers a clear, comprehensive introduction to the Rasch model along with practical examples in the free, open-source software R.It is accessible for readers without a background in psychometrics or statistics, while also providing detailed explanations of the relevant mathematical and statistical concepts for readers who want to gain a deeper understanding. Its worked examples in R demonstrate how to apply the methods t...

Statistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data.In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to:Understand and ...

Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated us...

Bayesian inference has become a standard method of analysis in many fields of science. Students and researchers in experimental psychology and cognitive science, however, have failed to take full advantage of the new and exciting possibilities that the Bayesian approach affords. Ideal for teaching and self study, this book demonstrates how to do Bayesian modeling. Short, to-the-point chapters offer examples, exercises, and computer code (using WinBUGS or JAGS, and supported by Matlab and R...

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs. Featuring examples applicable to psychology, education, business, and ot...

This new text provides a state-of the-art introduction to educational and psychological testing and measurement theory that reflects many intellectual developments of the past two decades. The book introduces psychometric theory using a latent variable modeling (LVM) framework and emphasizes interval estimation throughout, so as to better prepare readers for studying more advanced topics later in their careers. Featuring numerous examples, it presents an applied approach to conducting test...

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification.This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path mode...

Computational modeling is now ubiquitous in psychology, and researchers who are not modelers may find it increasingly difficult to follow the theoretical developments in their field. This book presents an integrated framework for the development and application of models in psychology and related disciplines. Researchers and students are given the knowledge and tools to interpret models published in their area, as well as to develop, fit, and test their own models. Both the development of ...

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

This comprehensiveSecond Edition offers readers a complete guide to carrying out research projects involving structural equation modeling (SEM). Updated to include extensive analysis of AMOS′ graphical interface, a new chapter on latent curve models and detailed explanations of the structural equation modeling process, this second edition is the ideal guide for those new to the field.The book includes:Learning objectives, key concepts and questions for further discuss...

This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http:/...

Contemporary Psychometrics features cutting edge chapters organized in four sections: test theory, factor analysis, structural equation modeling, and multivariate analysis.The section on test theory includes topics such as multidimensional item response theory (IRT), the relationship between IRT and factor analysis, estimation and testing of these models, and basic measurement issues that are often neglected.The factor analysis section reviews the history and devel...