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Books > Social sciences > Psychology > Psychological methodology > Psychological testing & measurement
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Statistical Approaches to Measurement Invariance (Hardcover, New)
Loot Price: R4,587
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Statistical Approaches to Measurement Invariance (Hardcover, New)
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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.
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