HOME > Detail View

Detail View

Introduction to item response theory models and applications

Introduction to item response theory models and applications (Loan 1 times)

Material type
단행본
Personal Author
Carlson, James E., author.
Title Statement
Introduction to item response theory models and applications / James E. Carlson, Ph.D.
Publication, Distribution, etc
New York :   Routledge/Taylor & Francis Group,   2021.  
Physical Medium
xiii, 165 p. ; cm.
Series Statement
Multivariate Applications series
ISBN
9780367471019 (paperback) 9780367476922 (hardback)
요약
"This is a highly accessible, comprehensive introduction to item response theory (IRT) models and their use in various aspects of assessment/testing. The book employs a mixture of graphics and simulated data sets to ease the reader into the material and covers the basics required to obtain a solid grounding in IRT. Written in an easily accessible way that assumes little mathematical knowledge, Carlson presents detailed descriptions of several commonly used IRT models, including those for items scored on a two-point (dichotomous) scale such as correct/incorrect, and those scored on multiple-point (polytomous) scales, such as degrees of correctness. One chapter describes a model in-depth and is followed by a chapter of instructions and illustrations showing how to apply the models to the reader's own work. This book is an essential text for instructors and higher level undergraduate and postgraduate students of statistics, psychometrics, and measurement theory across the behavioral and social sciences, as well as testing professionals"--
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Item response theory. Psychology --Mathematical models. Psychometrics.
000 00000cam u2200205 a 4500
001 000046067727
005 20210210160317
008 210209s2021 nyu b 001 0 eng d
010 ▼a 2020017872
020 ▼a 9780367471019 (paperback)
020 ▼a 9780367476922 (hardback)
020 ▼z 9781003035886 (ebook)
035 ▼a (KERIS)REF000019288066
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
042 ▼a pcc
050 0 0 ▼a BF39.2.I84 ▼b C37 2021
082 0 0 ▼a 150.28/7 ▼2 23
084 ▼a 150.287 ▼2 DDCK
090 ▼a 150.287 ▼b C284i
100 1 ▼a Carlson, James E., ▼e author.
245 1 0 ▼a Introduction to item response theory models and applications / ▼c James E. Carlson, Ph.D.
260 ▼a New York : ▼b Routledge/Taylor & Francis Group, ▼c 2021.
263 ▼a 2010
300 ▼a xiii, 165 p. ; ▼c cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a Multivariate Applications series
504 ▼a Includes bibliographical references and index.
520 ▼a "This is a highly accessible, comprehensive introduction to item response theory (IRT) models and their use in various aspects of assessment/testing. The book employs a mixture of graphics and simulated data sets to ease the reader into the material and covers the basics required to obtain a solid grounding in IRT. Written in an easily accessible way that assumes little mathematical knowledge, Carlson presents detailed descriptions of several commonly used IRT models, including those for items scored on a two-point (dichotomous) scale such as correct/incorrect, and those scored on multiple-point (polytomous) scales, such as degrees of correctness. One chapter describes a model in-depth and is followed by a chapter of instructions and illustrations showing how to apply the models to the reader's own work. This book is an essential text for instructors and higher level undergraduate and postgraduate students of statistics, psychometrics, and measurement theory across the behavioral and social sciences, as well as testing professionals"-- ▼c Provided by publisher.
650 0 ▼a Item response theory.
650 0 ▼a Psychology ▼x Mathematical models.
650 0 ▼a Psychometrics.
830 0 ▼a Multivariate Applications series.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 150.287 C284i Accession No. 111843362 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

Introduction






Background and Terminology







Contents of the Following Chapters












Models for Dichotomously-Scored Items









Introduction







Classical Test theory Models



The Model


Item Parameters and their Estimates


Test Parameters and their Estimates






Item Response Theory Models



Introduction


The Normal Ogive Three-Parameter Item Response Theory Model


The Three-Parameter Logistic (3PL) Model


Special Cases: The Two-Parameter and One-Parameter Logistic Models


Relationships Between Probabilities of Alternative Responses


Transformations of Scale


Effects of Changes in Parameters


The Test Characteristic Function


The Item Information Function


The Test Information Function and Standard Errors of Measurement






IRT Estimation Methodology



Estimation of Item Parameters


Estimation of Proficiency


Indeterminacy of the Scale in IRT Estimation






Summary












Analyses of Dichotomously-Scored Item and Test Data









Introduction







Example Classical Test Theory Analyses with a Small Dataset







Test and Item Analyses with a Larger Dataset



CTT Item and Test Analysis Results






IRT Item and Test Analysis



IRT Software


Missing Data


Iterative Estimation Methodology


Model Fit






IRT Analyses Using PARSCALE



PARSCALE Terminology


Some PARSCALE Options


PARSCALE Item Analysis


PARSCALE Test Analyses






IRT Analyses Using flexMIRT



flexMIRT Terminology


Some flexMIRT Options


flexMIRT Item Analyses and Comparisons Between Programs


flexMIRT Test Analyses and Comparisons Between Programs






Using IRT Results to Evaluate Items and Tests



Evaluating Estimates of Item Parameters


Evaluating Fit of Models to Items


Evaluating Tests as a Whole or Subsets of Test Items






Equating, Linking, and Scaling



Equating


Linking


Scaling


Vertical Scaling






Summary












Models for Polytomously-Scored Items









Introduction







The Nature of Polytomously-Scored Items







Conditional Probability Forms of Models for Polytomous Items







Probability-of-Response Form of the Polytomous Models



The 2PPC Model


The GPC Model


The Graded Response (GR) Model






Additional Characteristics of the GPC Model



Effects of Changes in Parameters


Alternative Parameterizations


The Expected Score Function


Functions of Scoring at or Above Categories


Comparison of Conditional Response and P+ Functions


Item Mapping and Standard Setting


The Test Characteristic Function


The Item Information Function


The Item Category Information Function


The Test Information Function


Conditional Standard Errors of Measurement






Summary












Analyses of Polytomously-Scored Item and Test Data









Generation of Example Data







Classical Test Theory Analyses



Item Analyses


Test Analyses






IRT Analyses



PARSCALE Item Analyses


flexMIRT Item Analyses and Comparisons with PARSCALE






Additional Methods of Using IRT Results to Evaluate Items



Evaluating Estimates of Item Parameters


Evaluating Fit of Models to Item Data


Additional Graphical Methods






Test Analyses



PARSCALE Test Analyses


flexMIRT Test Analyses






Placing the Results from Different Analyses on the Same Scale







Summary












Multidimensional Item Response Theory Models









Introduction







The Multidimensional 3PL Model for Dichotomous Items







The Multidimensional 2PL Model for Dichotomous Items







Is there a Multidimensional 1PL Model for Dichotomous Items







Further Comments on MIRT Models



Alternate Parameterizations


Additional Analyses of MIRT Data






Noncompensatory MIRT Models







MIRT Models for Polytomous Data







Summary












Analyses of Multidimensional Item Response Data









Response Data Generation







MIRT Computer Software







MIRT and Factor analyses







flexMIRT analyses of Example Generated Data



One-dimensional Solution with Two-Dimensional Data


Two-dimensional Solution






Summary












Overview of More Complex Item Response Theory Models









Some More Complex Unidimensional Models



Multigroup Models


Adaptive Testing


Mixture Models


Hierarchical Rater Models


Testlet Models






More General MIRT Models: Some Further Reading



Hierarchical Models






Cognitive Diagnostic Models







Summary










References





Appendix A. Some Technical Background


1. Slope of the 3PL Curve at the Inflection Point where


2. Simplifying Notation for GPC Expressions


3. Some Characteristics of GPC Model Items


Peaks of Response Curves


Crossing Point of Pk and Pk-1


Crossing Point of P0 and P2 for m = 3


Symmetry in the Case of m = 3


Limits of the Expected Score Function





Appendix B. Item Category Information Functions





Appendix C. Item Generating Parameters and Classical and IRT Parameter Estimates





Index

New Arrivals Books in Related Fields

강응섭 (2021)
Jung, C. G (2021)
Fine, Reuben (2021)
박주용 (2021)