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    978-1-58488-958-8
    August 31st 2007

Description

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.

Reviews

“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

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.

Name: Statistical Test Theory for the Behavioral Sciences (Hardback)Chapman and Hall/CRC 
Description: By Dato N. M. de Gruijter, Leo J. Th. van der KampSeries Editor: Andrew Gelman, Sophia Rabe-Hesketh, Anders Skrondal, J. Scott Long. 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,...
Categories: Psychological Methods & Statistics, Quantitative Methods, Statistical Theory & Methods, Statistical Computing