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016 7 a| 0189189672| Uk;

019 a| 1042320952a| 1052180904;

020 a| 9781315154268q| (electronic bk.);

020 a| 1315154269q| (electronic bk.);

020 a| 9781498770149q| (electronic book);

020 a| 9781351650304q| (electronic book);

020 a| 1351650300q| (electronic book);

020 a| 9781351640770q| (electronic book);

020 a| 1351640771q| (electronic book);

020 a| 1498770142;

020 a| 9781498770149;

020 z| 9781498770132q| (hardcover);

020 z| 1498770134q| (hardcover);

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029 1 a| CHBISb| 011430676;

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035 a| (Sirsi) o1041707245;

035 a| (OCoLC)1041707245z| (OCoLC)1042320952z| (OCoLC)1052180904;

037 a| 9781351650304b| Ingram Content Group;

049 a| EREE;

050 4 a| LB3051b| .D4465 2018eb;

072 7 a| EDUx| 0010002| bisacsh;

072 7 a| EDUx| 0360002| bisacsh;

082 04 a| 371.262| 23;

100 1 a| Desjardins, Christopher David,e| author.=| ^A1393676;

245 10 a| Handbook of educational measurement and psychometrics using R /c| Christopher D. Desjardins, Okan Bulut.;

264 1 a| Boca Raton, FL :b| CRC Press,c| [2018];

300 a| 1 online resource (xxiii, 302 pages).;

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

490 1 a| Chapman & Hall/CRC the R series (CRC Press);

504 a| Includes bibliographical references and index.;

520 a| "This book provides a broad overview of methods in educational and psychological measurement focusing on applications using R. It includes the key introductory topics and extends to recent research developments, such as multidimensional item response theory models. The focus is on the practical implementation of the methods, with lots of real data examples and R code integrated throughout. The book will be supplemented by an R package with all code and data available for replication. The book could be used as a supplementary text for the computing component of a course on measurement in either department"--c| Provided by publisher.;

588 0 a| Print version record.;

505 0 a| Machine generated contents note: 1. Introduction to the R Programming Language -- 1.1. Chapter Overview -- 1.2. What Is R? -- 1.2.1. Our Approach to R -- 1.3. Obtaining and Installing R -- 1.3.1. Windows -- 1.3.2. Mac 1 -- 1.3.3. Linux -- 1.4. Obtaining and Installing RStudio -- 1.5. Using R -- 1.5.1. Basic R Usage -- 1.5.2.R Packages -- 1.5.2.1. Masked Functions -- 1.5.3. Assessing and Reading in Data -- 1.5.4. Data Manipulation -- 1.5.5. Descriptive and Inferential Statistics -- 1.5.6. Plotting in R -- 1.5.6.1. Base R Graphics -- 1.5.6.2. Lattice Graphics -- 1.6. Installing Packages Used in This Handbook -- 1.7. Chapter Summary -- 2. Classical Test Theory -- 2.1. Chapter Overview -- 2.2. What Is Measurement? -- 2.3. Issues in Measurement -- 2.3.1. Type of Scales -- 2.4. The Classical Test Theory Framework -- 2.4.1. Reliability -- 2.4.2. Validity -- 2.4.3. Item Analysis -- 2.5. Summary -- 3. Generalizability Theory -- 3.1. Chapter Overview -- 3.2. Introduction -- 3.3. Examples -- 3.3.1. One-Facet Design;

505 0 a| Note continued: 3.3.1.1.G Study -- 3.3.1.2.D Study -- 3.3.2. Two-Facet Crossed Design -- 3.3.2.1.G Study -- 3.3.2.2.D Study -- 3.3.3. Two-Facet Partially Nested Design -- 3.3.3.1.G Study -- 3.3.3.2.D Study -- 3.3.4. Two-Facet Crossed Design with a Fixed Facet -- 3.3.4.1.G Study -- 3.3.4.2.D Study -- 3.4. Summary -- 4. Factor Analytic Approach in Measurement -- 4.1. Chapter Overview -- 4.2. Introduction -- 4.3. Exploratory Factor Analysis (EFA) -- 4.3.1. EFA of a Cognitive Inventory -- 4.3.2. EFA Using the psych Package -- 4.3.3. EFA with Categorical Data -- 4.4. Confirmatory Factor Analysis (CFA) -- 4.4.1. CFA of the WISC-R Data -- 4.4.2. CFA with Categorical Data -- 4.4.2.1. Ordinal CFA -- Method 1 -- 4.4.2.2. Ordinal CFA -- Method 2 -- 4.5. Summary -- 5. Item Response Theory for Dichotomous Items -- 5.1. Chapter Overview -- 5.2. Introduction -- 5.2.1.Comparison to Classical Test Theory -- 5.2.2. Basic Concepts in IRT -- 5.2.3. IRT Model Assumptions;

505 0 a| Note continued: 5.3. The Unidimensional IRT Models for Dichotomous Items -- 5.3.1. One-Parameter Logistic Model and Rasch Model -- 5.3.1.1. One-Parameter Logistic Model -- 5.3.1.2. Rasch Model -- 5.3.2. Two-Parameter Logistic Model -- 5.3.3. Three-Parameter Logistic Model -- 5.3.4. Four-Parameter Logistic Model -- 5.4. Ability Estimation in IRT Models -- 5.5. Model Diagnostics -- 5.5.1. Item Fit -- 5.5.2. Person Fit -- 5.5.3. Model Selection -- 5.6. Summary -- 6. Item Response Theory for Polytomous Items -- 6.1. Chapter Overview -- 6.2. Polytomous Rasch Models for Ordinal Items -- 6.2.1. Partial Credit Model -- 6.2.2. Rating Scale Model -- 6.3. Polytomous Non-Rasch Models for Ordinal Items -- 6.3.1. Generalized Partial Credit Model -- 6.3.2. Graded Response Model -- 6.4. Polytomous IRT Models for Nominal Items -- 6.4.1. Nominal Response Model -- 6.4.2. Nested Logit Model -- 6.5. Model Selection -- 6.6. Summary -- 7. Multidimensional Item Response Theory -- 7.1. Chapter Overview;

505 0 a| Note continued: 7.2. Multidimensional Item Response Modeling -- 7.2.1.Compensatory and Noncompensatory MIRT -- 7.2.2. Between-Item and Within-Item Multidimensionality -- 7.2.3. Exploratory and Confirmatory MIRT Analysis -- 7.3.Common MIRT Models -- 7.3.1. Multidimensional 2PL Model -- 7.3.2. Multidimensional Rasch Model -- 7.3.3. Multidimensional Graded Response Model -- 7.3.4. Bi-Factor IRT Model -- 7.4. Summary -- 8. Explanatory Item Response Theory -- 8.1. Chapter Overview -- 8.2. Explanatory Item Response Modeling -- 8.2.1. Data Structure -- 8.2.2. Rasch Model as a GLMM -- 8.2.3. Linear Logistic Test Model -- 8.2.4. Latent Regression Rasch Model -- 8.2.5. Interaction Models -- 8.3. Summary -- 9. Visualizing Data and Measurement Models -- 9.1. Chapter Overview -- 9.2. Introduction -- 9.3. Diagnostic Plots -- 9.4. Path Diagrams -- 9.5. Interactive Plots with shiny -- 9.5.1. Example 1: Diagnostic Plot for Factor Analysis -- 9.5.2. Example 2: The 3PL IRT Model -- 9.6. Summary -- 10. Equating;

505 0 a| Note continued: 10.1. Overview -- 10.2. Introduction -- 10.2.1. Equating Designs -- 10.2.2. Equating Functions and Methods -- 10.2.3. Evaluating the Results -- 10.2.4. Further Reading -- 10.3. Examples -- 10.3.1. Equivalent Groups -- 10.3.1.1. Identity, Mean, and Linear Functions -- 10.3.1.2. Nonlinear Functions -- 10.3.2. Nonequivalent Groups -- 10.3.2.1. Linear Tucker Equating -- 10.4. Summary -- 11. Measurement Invariance and Differential Item Functioning -- 11.1. Chapter Overview -- 11.2. Measurement Invariance -- 11.2.1. Assessing Measurement Invariance -- 11.2.1.1. Configural Invariance -- 11.2.1.2. Weak Invariance -- 11.2.1.3. Strong Invariance -- 11.2.1.4. Strict Invariance -- 11.2.1.5. Assessing Partial Invariance -- 11.3. Differential Item Functioning -- 11.3.1. The Mantel-Haenszel (MH) Method -- 11.3.2. Logistic Regression -- 11.3.3. Item Response Theory Likelihood Ratio Test -- 11.4. Summary -- 12. More Advanced Topics in Measurement -- 12.1. Chapter Overview -- 12.2. CRAN Task Views;

505 0 a| Note continued: 12.3.Computerized Adaptive Testing -- 12.4. Cognitive Diagnostic Modeling -- 12.5. IRT Linking Procedures -- 12.6. Bayesian Models of Measurement -- 12.7. Hierarchical Linear Models -- 12.8. Profile Analysis -- 12.9. Summary.;

650 0 a| Educational tests and measurementsv| Handbooks, manuals, etc.=| ^A505119;

650 0 a| Psychometricsx| Methodologyv| Handbooks, manuals, etc.=| ^A4135;

650 0 a| R (Computer program language)v| Handbooks, manuals, etc.=| ^A523882;

650 7 a| EDUCATIONx| Administrationx| General.2| bisacsh;

650 7 a| EDUCATIONx| Organizations & Institutions.2| bisacsh;

650 7 a| Educational tests and measurements.2| fast0| (OCoLC)fst00903660;

650 7 a| R (Computer program language)2| fast0| (OCoLC)fst01086207;

655 4 a| Electronic books.;

655 7 a| Handbooks and manuals.2| fast0| (OCoLC)fst01423877;

700 1 a| Bulut, Okan,e| author.=| ^A1393677;

830 0 a| Chapman & Hall/CRC the R series (CRC Press)=| ^A1144284;

856 40 3| Taylor & Francisu| https://go.openathens.net/redirector/ecu.edu?url=https://www.taylorfrancis.com/books/9781315154268;

938 a| Askews and Holts Library Servicesb| ASKHn| AH33740367;

938 a| ProQuest Ebook Centralb| EBLBn| EBL5432958;

938 a| EBSCOhostb| EBSCn| 1835579;

938 a| Taylor & Francisb| TAFRn| 9781315154268;

938 a| YBP Library Servicesb| YANKn| 15560405;

938 a| YBP Library Servicesb| YANKn| 15686584;

938 a| YBP Library Servicesb| YANKn| 15566775;

949 a| Click on web addressw| asish| joyner96o| jwjh;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5221188-2001l| HSLELECm| HSLr| Ys| Yt| HGENEBKu| 2/17/2020x| EBOOKz| HERESOURCEo| .STAFF. jwjh;

Handbook of educational measurement and psychometrics using R / Christopher D. Desjardins, Okan Bulut.

Author/creator	Desjardins, Christopher David author.
Other author	Bulut, Okan, author.
Format	Electronic
Publication	Boca Raton, FL : CRC Press, [2018]
Description	1 online resource (xxiii, 302 pages).
Supplemental Content	Taylor & Francis
Subjects	Educational tests and measurements--Handbooks, manuals, etc. Psychometrics--Methodology. --Handbooks, manuals, etc. R (Computer program language)--Handbooks, manuals, etc.

Series	Chapman & Hall/CRC the R series (CRC Press) Chapman & Hall/CRC the R series (CRC Press) ^A1144284
Contents	Machine generated contents note: 1. Introduction to the R Programming Language -- 1.1. Chapter Overview -- 1.2. What Is R? -- 1.2.1. Our Approach to R -- 1.3. Obtaining and Installing R -- 1.3.1. Windows -- 1.3.2. Mac 1 -- 1.3.3. Linux -- 1.4. Obtaining and Installing RStudio -- 1.5. Using R -- 1.5.1. Basic R Usage -- 1.5.2.R Packages -- 1.5.2.1. Masked Functions -- 1.5.3. Assessing and Reading in Data -- 1.5.4. Data Manipulation -- 1.5.5. Descriptive and Inferential Statistics -- 1.5.6. Plotting in R -- 1.5.6.1. Base R Graphics -- 1.5.6.2. Lattice Graphics -- 1.6. Installing Packages Used in This Handbook -- 1.7. Chapter Summary -- 2. Classical Test Theory -- 2.1. Chapter Overview -- 2.2. What Is Measurement? -- 2.3. Issues in Measurement -- 2.3.1. Type of Scales -- 2.4. The Classical Test Theory Framework -- 2.4.1. Reliability -- 2.4.2. Validity -- 2.4.3. Item Analysis -- 2.5. Summary -- 3. Generalizability Theory -- 3.1. Chapter Overview -- 3.2. Introduction -- 3.3. Examples -- 3.3.1. One-Facet Design
Contents	Note continued: 3.3.1.1.G Study -- 3.3.1.2.D Study -- 3.3.2. Two-Facet Crossed Design -- 3.3.2.1.G Study -- 3.3.2.2.D Study -- 3.3.3. Two-Facet Partially Nested Design -- 3.3.3.1.G Study -- 3.3.3.2.D Study -- 3.3.4. Two-Facet Crossed Design with a Fixed Facet -- 3.3.4.1.G Study -- 3.3.4.2.D Study -- 3.4. Summary -- 4. Factor Analytic Approach in Measurement -- 4.1. Chapter Overview -- 4.2. Introduction -- 4.3. Exploratory Factor Analysis (EFA) -- 4.3.1. EFA of a Cognitive Inventory -- 4.3.2. EFA Using the psych Package -- 4.3.3. EFA with Categorical Data -- 4.4. Confirmatory Factor Analysis (CFA) -- 4.4.1. CFA of the WISC-R Data -- 4.4.2. CFA with Categorical Data -- 4.4.2.1. Ordinal CFA -- Method 1 -- 4.4.2.2. Ordinal CFA -- Method 2 -- 4.5. Summary -- 5. Item Response Theory for Dichotomous Items -- 5.1. Chapter Overview -- 5.2. Introduction -- 5.2.1.Comparison to Classical Test Theory -- 5.2.2. Basic Concepts in IRT -- 5.2.3. IRT Model Assumptions
Contents	Note continued: 5.3. The Unidimensional IRT Models for Dichotomous Items -- 5.3.1. One-Parameter Logistic Model and Rasch Model -- 5.3.1.1. One-Parameter Logistic Model -- 5.3.1.2. Rasch Model -- 5.3.2. Two-Parameter Logistic Model -- 5.3.3. Three-Parameter Logistic Model -- 5.3.4. Four-Parameter Logistic Model -- 5.4. Ability Estimation in IRT Models -- 5.5. Model Diagnostics -- 5.5.1. Item Fit -- 5.5.2. Person Fit -- 5.5.3. Model Selection -- 5.6. Summary -- 6. Item Response Theory for Polytomous Items -- 6.1. Chapter Overview -- 6.2. Polytomous Rasch Models for Ordinal Items -- 6.2.1. Partial Credit Model -- 6.2.2. Rating Scale Model -- 6.3. Polytomous Non-Rasch Models for Ordinal Items -- 6.3.1. Generalized Partial Credit Model -- 6.3.2. Graded Response Model -- 6.4. Polytomous IRT Models for Nominal Items -- 6.4.1. Nominal Response Model -- 6.4.2. Nested Logit Model -- 6.5. Model Selection -- 6.6. Summary -- 7. Multidimensional Item Response Theory -- 7.1. Chapter Overview
Contents	Note continued: 7.2. Multidimensional Item Response Modeling -- 7.2.1.Compensatory and Noncompensatory MIRT -- 7.2.2. Between-Item and Within-Item Multidimensionality -- 7.2.3. Exploratory and Confirmatory MIRT Analysis -- 7.3.Common MIRT Models -- 7.3.1. Multidimensional 2PL Model -- 7.3.2. Multidimensional Rasch Model -- 7.3.3. Multidimensional Graded Response Model -- 7.3.4. Bi-Factor IRT Model -- 7.4. Summary -- 8. Explanatory Item Response Theory -- 8.1. Chapter Overview -- 8.2. Explanatory Item Response Modeling -- 8.2.1. Data Structure -- 8.2.2. Rasch Model as a GLMM -- 8.2.3. Linear Logistic Test Model -- 8.2.4. Latent Regression Rasch Model -- 8.2.5. Interaction Models -- 8.3. Summary -- 9. Visualizing Data and Measurement Models -- 9.1. Chapter Overview -- 9.2. Introduction -- 9.3. Diagnostic Plots -- 9.4. Path Diagrams -- 9.5. Interactive Plots with shiny -- 9.5.1. Example 1: Diagnostic Plot for Factor Analysis -- 9.5.2. Example 2: The 3PL IRT Model -- 9.6. Summary -- 10. Equating
Contents	Note continued: 10.1. Overview -- 10.2. Introduction -- 10.2.1. Equating Designs -- 10.2.2. Equating Functions and Methods -- 10.2.3. Evaluating the Results -- 10.2.4. Further Reading -- 10.3. Examples -- 10.3.1. Equivalent Groups -- 10.3.1.1. Identity, Mean, and Linear Functions -- 10.3.1.2. Nonlinear Functions -- 10.3.2. Nonequivalent Groups -- 10.3.2.1. Linear Tucker Equating -- 10.4. Summary -- 11. Measurement Invariance and Differential Item Functioning -- 11.1. Chapter Overview -- 11.2. Measurement Invariance -- 11.2.1. Assessing Measurement Invariance -- 11.2.1.1. Configural Invariance -- 11.2.1.2. Weak Invariance -- 11.2.1.3. Strong Invariance -- 11.2.1.4. Strict Invariance -- 11.2.1.5. Assessing Partial Invariance -- 11.3. Differential Item Functioning -- 11.3.1. The Mantel-Haenszel (MH) Method -- 11.3.2. Logistic Regression -- 11.3.3. Item Response Theory Likelihood Ratio Test -- 11.4. Summary -- 12. More Advanced Topics in Measurement -- 12.1. Chapter Overview -- 12.2. CRAN Task Views
Contents	Note continued: 12.3.Computerized Adaptive Testing -- 12.4. Cognitive Diagnostic Modeling -- 12.5. IRT Linking Procedures -- 12.6. Bayesian Models of Measurement -- 12.7. Hierarchical Linear Models -- 12.8. Profile Analysis -- 12.9. Summary.
Abstract	"This book provides a broad overview of methods in educational and psychological measurement focusing on applications using R. It includes the key introductory topics and extends to recent research developments, such as multidimensional item response theory models. The focus is on the practical implementation of the methods, with lots of real data examples and R code integrated throughout. The book will be supplemented by an R package with all code and data available for replication. The book could be used as a supplementary text for the computing component of a course on measurement in either department"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Source of description	Print version record.
Issued in other form	Print version: Desjardins, Christopher David. Handbook of educational measurement and psychometrics using R. Boca Raton, FL : CRC Press, [2018] 9781498770132
Genre/form	Electronic books.
Genre/form	Handbooks and manuals.
ISBN	9781315154268 (electronic bk.)
ISBN	1315154269 (electronic bk.)
ISBN	9781498770149 (electronic book)
ISBN	9781351650304 (electronic book)
ISBN	1351650300 (electronic book)
ISBN	9781351640770 (electronic book)
ISBN	1351640771 (electronic book)
ISBN	1498770142
ISBN	9781498770149
ISBN	(hardcover)
ISBN	(hardcover)
Stock number	9781351650304 Ingram Content Group

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Handbook of educational measurement and psychometrics using R / Christopher D. Desjardins, Okan Bulut.

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