A Step By Step Guide To Applying The Rasch Model Using R
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A Step-by-Step Guide to Applying the Rasch Model Using R
Author: Iasonas Lamprianou
language: en
Publisher: Taylor & Francis
Release Date: 2024-12-17
This new edition provides a step-by-step guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable (“latent”) variables. Although the focus is on simple R code, the book provides updated guidance through the point-and-click menus of BlueSky Statistics software. The book covers all Rasch models frequently used in social sciences, from the Simple Rasch model to the Rating Scale, Partial Credit, and Many-Facets Rasch models. Using a pragmatic approach to model-data fit, this book offers helpful practical examples to investigate Rasch model assumptions. In addition to traditional Rasch model approaches, it introduces the Rasch model as a special case of a Generalized Mixed Effects Model. Readers will also benefit from the online support material which includes all the code used in the book in downloadable and useable files. It also provides a comprehensive guide to R programming and practical guidance on using BlueSky Statistics software's point-and-click menus. This dual approach enables readers to experiment with data analysis using the provided data sets, enhancing their understanding and application of statistical concepts. It will be a valuable resource for both students and researchers who want to use Rasch models in their research.
Applying the Rasch Model in Social Sciences Using R
This unique text provides a step-by-step beginner’s guide to applying the Rasch model in R, a probabilistic model used by researchers across the social sciences to measure unobservable ("latent") variables. Each chapter is devoted to one popular Rasch model, ranging from the least to the most complex. Through a freely available and user-friendly package, BlueSky Statistics, Lamprianou offers a range of options for presenting results, critically examines the strengths and weaknesses of applying the Rasch model in each instance, and suggests more effective methodologies where applicable. With a focus on simple software code which does not assume extensive mathematical knowledge, the reader is initially introduced to the so-called simple Rasch Model to construct a "political activism" variable out of a group of dichotomously scored questions. In subsequent chapters, the book covers everything from the Rating Scale to the Many-facets Rasch model. The final chapter even showcases a complete mock manuscript, demonstrating how a Rasch-based paper on the identification of online hate speech should look like. Combining theoretical rigor and real-world examples with empirical datasets from published papers, this book is essential reading for students and researchers alike who aspire to use Rasch models in their research.
Modern Psychometrics with R
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.