Algorithms for Measurement Invariance Testing


Book Description

Latent variable models are a powerful tool for measuring many of the phenomena in which developmental psychologists are often interested. If these phenomena are not measured equally well among all participants, this would result in biased inferences about how they unfold throughout development. In the absence of such biases, measurement invariance is achieved; if this bias is present, differential item functioning (DIF) would occur. This Element introduces the testing of measurement invariance/DIF through nonlinear factor analysis. After introducing models which are used to study these questions, the Element uses them to formulate different definitions of measurement invariance and DIF. It also focuses on different procedures for locating and quantifying these effects. The Element finally provides recommendations for researchers about how to navigate these options to make valid inferences about measurement in their own data.




Handbook of Structural Equation Modeling


Book Description

"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--




Cross-cultural Analysis


Book Description

Intended to bridge the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents the latest strategies for analyzing cross-cultural data. Techniques are demonstrated through the use of applications that employ cross national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationallyâe"prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods presented in the book. Syntax and graphical and verbal explanations of the techniques are included. A website features some of the data sets and syntax commands used in the book. Applications from the behavioral and social sciences that use real data-sets demonstrate: The use of samples from 17 countries to validate the resistance to change scale across these nations How to test the cross-national invariance properties of social trust The interplay between social structure, religiosity, values, and social attitudes A comparison of anti-immigrant attitudes and patterns of religious orientations across European countries. The book is divided into techniques for analyzing cross-cultural data within the generalized-latent-variable approach: multiple-group confirmatory factor analysis and multiple-group structural equation modeling; multi-level analysis; latent class analysis; and item-response theory. Since researchers from various disciplines often use different methodological approaches, a consistent framework for describing and applying each method is used so as to cross âe~methodological bordersâe(tm) between disciplines. Some chapters describe the basic strategy and how it relates to other techniques presented in the book, others apply the techniques and address specific research questions, and a few combine the two. A table in the preface highlights for each chapter: a description of the contents, the statistical methods used, the goal(s) of the analysis, and the data set employed. This book is intended for researchers, practitioners, and advanced students interested in cross-cultural research. Because the applications span a variety of disciplines, the book will appeal to researchers and students in: psychology, political science, sociology, education, marketing and economics, geography, criminology, psychometrics, epidemiology, and public health, as well as those interested in methodology. It is also appropriate for an advanced methods course in cross-cultural analysis.




Human Values and Beliefs


Book Description

Provides a wealth of information about values and beliefs of people all over the world




Bayesian Structural Equation Modeling


Book Description

This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.




Principles and Practice of Structural Equation Modeling


Book Description

This book has been replaced by Principles and Practice of Structural Equation Modeling, Fifth Edition, ISBN 978-1-4625-5191-0.





Book Description




Statistical Approaches to Measurement Invariance


Book Description

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.




Assessing Measurement Invariance for Applied Research


Book Description

This user-friendly guide illustrates how to assess measurement invariance using computer programs, statistical methods, and real data.




Introduction to Property Testing


Book Description

An extensive and authoritative introduction to property testing, the study of super-fast algorithms for the structural analysis of large quantities of data in order to determine global properties. This book can be used both as a reference book and a textbook, and includes numerous exercises.