Statistical Test Theory for the Behavioral Sciences

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Overview

Since the development of the first intelligence test in the early 20th century, educational and psychological tests have become important measurement techniques to quantify human behavior. Focusing on this ubiquitous yet fruitful area of research, Statistical Test Theory for the Behavioral Sciences provides both a broad overview and a critical survey of assorted testing theories and models used in psychology, education, and other behavioral science fields.

Following a logical progression from basic concepts to more advanced topics, the book first explains classical test theory, covering true score, measurement error, and reliability. It then presents generalizability theory, which provides a framework to deal with various aspects of test scores. In addition, the authors discuss the concept of validity in testing, offering a strategy for evidence-based validity. In the two chapters devoted to item response theory (IRT), the book explores item response models, such as the Rasch model, and applications, including computerized adaptive testing (CAT). The last chapter looks at some methods used to equate tests.

Equipped with the essential material found in this book, advanced undergraduate and graduate students in the behavioral sciences as well as researchers involved in measurement and testing will gain valuable insight into the research methodologies and statistical data analyses of behavioral testing.

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Editorial Reviews

From the Publisher
“This book is a comprehensive and well-illustrated overview of the concepts and applications of statistical test theory in the social sciences. … Various concepts and methodologies are well explained and illustrated through examples that are accompanied with data-based examples. The language of the book is simple. The prerequisite knowledge of mathematics and statistics is kept to a minimum … The book will undoubtedly appeal to the students and application-oriented researchers in the social sciences who wish to obtain a detailed overview of statistical test theory. This book can be adopted as a textbook in advanced undergraduate and graduate courses in the social sciences and can also be comprehensively used as a part of any course in applied statistics.”
—Journal of the Royal Statistical Society
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Product Details

Table of Contents

PREFACE
Measurement and Scaling
Definition of a test
Measurement and scaling
Classical Test Theory
True score and measurement error
The population of persons
Classical Test Theory and Reliability
The definition of reliability and the standard error of measurement
The definition of parallel tests
Reliability and test length
Reliability and group homogeneity
Estimating the true score
Correction for attenuation
Estimating Reliability
Reliability estimation from a single administration of a test
Reliability estimation with parallel tests
Reliability estimation with the test–retest method
Reliability and factor analysis
Score profiles and estimation of true scores
Reliability and conditional errors of measurement
Generalizability Theory
Basic concepts of G theory
One-facet designs, the p × i design, and the i : p design
The two-facet crossed p × i × j design
An example of a two-facet crossed p × i × j design: The generalizability of job performance measurements
The two-facet nested p × (i : j) design
Other two-facet designs
Fixed facets
Kinds of measurement errors
Conditional error variance
Concluding remarks
Models for Dichotomous Items
The binomial model
The generalized binomial model
The generalized binomial model and item response models
Item analysis and item selection
Validity and Validation of Tests
Validity and its sources of evidence
Selection effects in validation studies
Validity and classification
Selection and classification with more than one predictor
Convergent and discriminant validation: A strategy for evidence-based validity
Validation and IRT
Research validity: Validity in empirical behavioral research
Principal Component Analysis, Factor Analysis, and Structural Equation Modeling: A Very Brief Introduction
Principal component analysis (PCA)
Exploratory factor analysis
Confirmatory factor analysis and structural equation modeling
Item Response Models
Basic concepts
The multivariate normal distribution and polytomous items
Item-test regression and item response models
Estimation of item parameters
Joint maximum likelihood estimation for item and person parameters
Joint maximum likelihood estimation and the Rasch model
Marginal maximum likelihood estimation
Markov chain Monte Carlo
Conditional maximum likelihood estimation in the Rasch model
More on the estimation of item parameters
Maximum likelihood estimation of person parameters
Bayesian estimation of person parameters
Test and item information
Model-data fit
Appendix: Maximum likelihood estimation of θ in the Rasch model
Applications of Item Response Theory
Item analysis and test construction
Test construction and test development
Item bias or DIF
Deviant answer patterns
Computerized adaptive testing (CAT)
IRT and the measurement of change
Concluding remarks
Test Equating
Some basic data collection designs for equating studies
The equipercentile method
Linear equating
Linear equating with an anchor test
A synthesis of observed score equating approaches: The Kernel method
IRT models for equating
Concluding remarks
Answers
References
Index
Each chapter contains an Introduction and Exercises.

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