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Statistical Approaches to  Measurement Invariance par [Millsap, Roger E.]
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Statistical Approaches to Measurement Invariance 1 , Format Kindle

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Description du produit

Présentation de l'éditeur

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.


Biographie de l'auteur

Roger E. Millsap is a Professor in the Department of Psychology and a faculty member in the Doctoral Program in Quantitative Psychology at Arizona State University. He received his Ph.D. in Psychology in 1983 from the University of California-Berkeley. Dr. Millsap’s research interests include psychometrics, latent variable models, and multivariate statistics. He has published more than 60 papers in professional journals and co-edited the Sage Handbook of Quantitative Methods in Psychology with Alberto Maydeu-Olivares in 2009. Dr. Millsap is a Past-President of the Psychometric Society, of Division 5 of the American Psychological Association, and of the Society of Multivariate Experimental Psychology. He is a Past –Editor of Multivariate Behavioral Research and is the current Executive Editor of Psychometrika.


Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 5199 KB
  • Nombre de pages de l'édition imprimée : 370 pages
  • Pagination - ISBN de l'édition imprimée de référence : 1848728190
  • Utilisation simultanée de l'appareil : Jusqu'à 4 appareils simultanés, selon les limites de l'éditeur
  • Editeur : Routledge; Édition : 1 (29 mars 2012)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
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Commentaires client les plus utiles sur (beta) (Peut contenir des commentaires issus du programme Early Reviewer Rewards) 5.0 étoiles sur 5 1 commentaire
5.0 étoiles sur 5 Measurement invariance is a pretty loaded concept and often misunderstood 24 mai 2017
Par Perman Gochyyev - Publié sur
Format: Broché Achat vérifié
Measurement invariance is a pretty loaded concept and often misunderstood. This book, written by the authority in the field, is a great read -- with its careful statistics and relevant analogies, it helped me in understanding the concept better. Millsap has an immense knowledge of the field, which can be seen from his meticulous citations.
5.0 étoiles sur 5 This is a fine book that summarizes the literature on measurement invariance up ... 9 octobre 2016
Par JVerkuilen - Publié sur
Format: Broché
This is a fine book that summarizes the literature on measurement invariance up until its publication date. While there have been important developments since then (such as Muthen & Asparuhov's alignment method), this book synthesizes everything up until then, focusing on latent variable methods from SEM or IRT but providing a useful comparison with observed score methods such as the Mantel-Haenszel test. It's mathematical level is not extraordinary but many of the results here are fairly deep and demanding of thought, especially in the early chapters. This book is not a how-to. If you want that, you'll need to look elsewhere, such as Osterlind & Everson's SAGE Monograph. About the only criticism I can level at it is that I wish it had been typeset in LaTeX, or if it was, that Routledge's typesetting styles were cleaner and more like a Springer or CRC Press book. But that's a small issue in an otherwise fine book.

As a personal addendum, I want to say that Roger Millsap is sorely missed. He was a first rate scholar, absolutely. But in addition, Roger was a first rate person. Those two things often don't go together.
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