Statistical Approaches to Measurement Invariance

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.

Product details

Hardcover : 199 pages
Title : Statistical Approaches to Measurement Invariance
Language : English
ISBN-10 : 1848728190
Ranting : 3.6

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Statistical Approaches to Measurement Invariance | Taylor
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.
Statistical Approaches to Measurement Invariance
Measurement invariance is a statistical property of an instrument (e.g., a questionnaire) indicating that it measures the same construct(s) in the same way across subgroups of respondents ...
Statistical Approaches to Measurement Invariance 1st
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 ...
Statistical Approaches to Measurement Invariance | Roger E
To illustrate, let X = (X1â†œ,â†œX2,â†œ…,â†œXp) be a 13 f14 Statistical Approaches to Measurement Invariance vector of scores on p observed measures, such as scores on p test items. Let W = (W1, W2, …â†œ, Wr) be a vector of scores on r latent variables believed to underlie X.
Statistical Approaches to Measurement Invariance
(2013). Statistical Approaches to Measurement Invariance. Structural Equation Modeling: A Multidisciplinary Journal: Vol. 20, No. 1, pp. 168 174.
Statistical approaches to measurement invariance
Millsap, R. E. (2011). Statistical approaches to measurement invariance. Routledge Taylor & Francis Group. Abstract. I have written this book with several goals in mind. The first goal is to acquaint the reader with the broad set of statistical procedures currently used to approach the problem of detecting measurement bias.
Statistical Approaches To Measurement Invariance
Statistical Approaches to Measurement Invariance Statistical Approaches to Measurement Invariance by Roger E. Millsap (2011, Trade Paperback) The lowest priced brand new, unused, unopened, undamaged item in its original packaging (where packaging is applicable). Statistical Approaches to Measurement Invariance by Roger ...
Statistical Approaches to Measurement Invariance | Request PDF
Request PDF | On Jan 1, 2013, Adam W. Meade and others published Statistical Approaches to Measurement Invariance | Find, read and cite all the research you need on ResearchGate
Approaches to Studying Measurement Invariance
Measurement invariance exists when a scale functions equivalently across people and is therefore essential for making meaningful group comparisons. Often, measurement invariance is examined with independent and identically distributed data; however, there are times when the participants are clustered within units, creating dependency in the data.
Statistical Approaches To Measurement Invariance PDF
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