Using R for Item Response Theory Model Applications 1st Edition by Insu Paek, Ki Cole 1138542792 9781138542792

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Product details:

ISBN 10: 1138542792

ISBN 13: 9781138542792

Author: Insu Paek, Ki Cole

Item response theory (IRT) is widely used in education and psychology and is expanding its applications to other social science areas, medical research, and business as well. Using R for Item Response Theory Model Applications is a practical guide for students, instructors, practitioners, and applied researchers who want to learn how to properly use R IRT packages to perform IRT model calibrations with their own data. This book provides practical line-by-line descriptions of how to use R IRT packages for various IRT models. The scope and coverage of the modeling in the book covers almost all models used in practice and in popular research, including: dichotomous response modeling polytomous response modeling mixed format data modeling concurrent multiple group modeling fixed item parameter calibration modelling with latent regression to include person-level covariate(s) simple structure, or between-item, multidimensional modeling cross-loading, or within-item, multidimensional modeling high-dimensional modeling bifactor modeling testlet modeling two-tier modeling For beginners, this book provides a straightforward guide to learn how to use R for IRT applications. For more intermediate learners of IRT or users of R, this book will serve as a great time-saving tool for learning how to create the proper syntax, fit the various models, evaluate the models, and interpret the output using popular R IRT packages.

Table of contents:

1 Introduction

1.1 Basic concepts of item response theory (IRT)

1.2 R IRT programs

2 Unidimensional IRT with dichotomous item responses

2.1 Rasch model application

2.2 2-Parameter logistic model (2PLM) application

2.3 1-Parameter logistic model (1PLM) application

2.4 3-Parameter logistic model (3PLM) application

2.5 4-Parameter logistic model (4PLM) application

3 Unidimensional IRT with polytomous item responses

3.1 Partial credit model (PCM) application

3.2 Rating scale model (RSM) application

3.3 Generalized partial credit model (GPCM) application

3.4 Graded response model (GRM) application

3.5 Nominal response model (NRM) application

4 Unidimensional IRT with other applications

4.1 Mixed format response IRT modeling

4.2 Multiple group IRT modeling

4.3 Fixed item parameter calibration

4.4 Latent regression

5 Multidimensional IRT for simple structure

5.1 Multidimensional Rasch model application

5.2 Multidimensional 2PLM application

5.3 Multidimensional 3PLM application

5.4 Multidimensional partial credit model (PCM) application

5.5 Multidimensional generalized partial credit model (GPCM) application

5.6 Multidimensional graded response model (GRM) application

5.7 IRT modeling with high dimensionality

5.8 IRT modeling for within-item multidimensionality

6 Multidimensional IRT for bifactor structure

6.1 Bifactor modeling for dichotomous item responses

6.2 Bifactor modeling for polytomous item responses

6.3 Testlet model application

6.4 Two-tier IRT modeling

7 Limitations and caveat

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Tags: Insu Paek, Ki Cole, Response, Applications, Model