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E-grāmata: Handbook of Educational Measurement and Psychometrics Using R

(University of Alberta, Edmonton, Canada), (University of Minnesota, Center for Applied Research and Education Improvement, St. Paul, USA)
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Currently there are many introductory textbooks on educational measurement and psychometrics as well as R. However, there is no single book that covers important topics in measurement and psychometrics as well as their applications in R.

The Handbook of Educational Measurement and Psychometrics Using R covers a variety of topics, including classical test theory; generalizability theory; the factor analytic approach in measurement; unidimensional, multidimensional, and explanatory item response modeling; test equating; visualizing measurement models; measurement invariance; and differential item functioning.

This handbook is intended for undergraduate and graduate students, researchers, and practitioners as a complementary book to a theory-based introductory or advanced textbook in measurement. Practitioners and researchers who are familiar with the measurement models but need to refresh their memory and learn how to apply the measurement models in R, would find this handbook quite fulfilling. Students taking a course on measurement and psychometrics will find this handbook helpful in applying the methods they are learning in class. In addition, instructors teaching educational measurement and psychometrics will find our handbook as a useful supplement for their course.

Recenzijas

"This book provides excellent coverage of conducting psychometric analyses using R for all levels, including psychometricians, researchers, and graduate students. The book would also serve as a top-notch companion to a theoretical measurement/psychometric textbook. The book uses a hands-on approach with explanation through practical examples that provide readers with commentary about the input and output for each analysis covered. The R code provided in the examples will be helpful for both seasoned and new users of R. Overall, this book combines readability, practical examples, and many pieces of R code into a superb resource to aid in conducting psychometric analyses." Brandon LeBeau, Assistant Professor, University of Iowa

"The Handbook of Educational Measurement and Psychometrics Using R is an outstanding addition to literature. The content is both broad and comprehensive thereby providing researchers and practitioners alike with an excellent resource for dealing with a diverse range of psychometric analyses. Also, instructors could use the Handbook as a complimentary text for teaching introductory and advanced measurement courses because programming with R eliminates the need to use different software programs. In short, the Handbook is a very useful and much needed resource for our field." Dr. Mark Gierl, Professor of Educational Psychology, Canada Research Chair in Educational Measurement, Director, Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, Alberta, Canada

"This book may be the first attempt to demonstrate how to use R to analyze data in the fields of educational measurement and psychometrics. The rapid development of R has also facilitated the implementation of some data analyses which were conducted using costly software in the past. One of the unique features of this textbook is that it illustrates step by step how to analyze data focusing on educational measurement and psychometric methods. Such a detailed approach makes it an extremely useful textbook for learning. It is a must for my graduate-level educational measurement and psychometrics courses. Moreover, this book is a great resource when I am analyzing data using R in my own research. In sum, this book is highly recommended for anyone wishing to teach a measurement course using R and to advance their own research based on psychometric methods." Pey-Yan Liou, Graduate Institute of Learning and Instruction, National Central University

"I wish to commend the authors on writing their first book. After reading the text, it seemed they had been writing books for many years as they truly understood their audience. ...The text is appropriate for advanced undergraduate and graduate students with some knowledge of educational measurements or psychometrics, as well as researchers, who wish to learn these methods within R. The text has a balance of theory and applied examples, whereby the authors provide explanations of method assumptions, R syntax and output, and a library in R entitled hemp with datasets to run through the examples provided in the text. ...In addition to providing an array of datasets to test the various chapter functions, the hemp library also provides various functions, a help section, and the librarys correct citation for publication. Overall, I would recommend this book as a text for the classroom or a handbook reference source for researchers, who wish to expand their knowledge of educational measurements, and psychometrics, as well as advancing their understanding of R." - Stephanie A. Besser in ISCB, June 2019

"Many books currently available in the field of educational measurement and psychometrics require the use of expensive software programs. This can make it challenging for readers to reproduce example analyses or apply the given methods to their own data. Desjardins and Buluts Handbook of Educational Measurement and Psychometrics Using R fills a need in the field with many practical examples of how to use R to analyze data for a wide variety of educational measurement and psychometric topics... For each of the methods covered, the authors present a detailed step-by-step analysis of the data that include reading in the data, conducting the analysis, and interpreting the output... Overall, the Handbook of Educational Measurement and Psychometrics Using R by Desjardins and Bulut is a unique and much-needed addition to the field of educational measurement and psychometrics. This book would be a great resource for professors, researchers, and graduate students who are not familiar with R and need a user-friendly introduction to educational measurement and psychometric analysis techniques." - Anelise G. Sabbag, The American Statistician, November 2019 "This book provides excellent coverage of conducting psychometric analyses using R for all levels, including psychometricians, researchers, and graduate students. The book would also serve as a top-notch companion to a theoretical measurement/psychometric textbook. The book uses a hands-on approach with explanation through practical examples that provide readers with commentary about the input and output for each analysis covered. The R code provided in the examples will be helpful for both seasoned and new users of R. Overall, this book combines readability, practical examples, and many pieces of R code into a superb resource to aid in conducting psychometric analyses." Brandon LeBeau, Assistant Professor, University of Iowa

"The Handbook of Educational Measurement and Psychometrics Using R is an outstanding addition to literature. The content is both broad and comprehensive thereby providing researchers and practitioners alike with an excellent resource for dealing with a diverse range of psychometric analyses. Also, instructors could use the Handbook as a complimentary text for teaching introductory and advanced measurement courses because programming with R eliminates the need to use different software programs. In short, the Handbook is a very useful and much needed resource for our field." Dr. Mark Gierl, Professor of Educational Psychology, Canada Research Chair in Educational Measurement, Director, Centre for Research in Applied Measurement and Evaluation, University of Alberta, Edmonton, Alberta, Canada

"This book may be the first attempt to demonstrate how to use R to analyze data in the fields of educational measurement and psychometrics. The rapid development of R has also facilitated the implementation of some data analyses which were conducted using costly software in the past. One of the unique features of this textbook is that it illustrates step by step how to analyze data focusing on educational measurement and psychometric methods. Such a detailed approach makes it an extremely useful textbook for learning. It is a must for my graduate-level educational measurement and psychometrics courses. Moreover, this book is a great resource when I am analyzing data using R in my own research. In sum, this book is highly recommended for anyone wishing to teach a measurement course using R and to advance their own research based on psychometric methods." Pey-Yan Liou, Graduate Institute of Learning and Instruction, National Central University

"I wish to commend the authors on writing their first book. After reading the text, it seemed they had been writing books for many years as they truly understood their audience. ...The text is appropriate for advanced undergraduate and graduate students with some knowledge of educational measurements or psychometrics, as well as researchers, who wish to learn these methods within R. The text has a balance of theory and applied examples, whereby the authors provide explanations of method assumptions, R syntax and output, and a library in R entitled hemp with datasets to run through the examples provided in the text. ...In addition to providing an array of datasets to test the various chapter functions, the hemp library also provides various functions, a help section, and the librarys correct citation for publication. Overall, I would recommend this book as a text for the classroom or a handbook reference source for researchers, who wish to expand their knowledge of educational measurements, and psychometrics, as well as advancing their understanding of R." - Stephanie A. Besser in ISCB, June 2019

"Many books currently available in the field of educational measurement and psychometrics require the use of expensive software programs. This can make it challenging for readers to reproduce example analyses or apply the given methods to their own data. Desjardins and Buluts Handbook of Educational Measurement and Psychometrics Using R fills a need in the field with many practical examples of how to use R to analyze data for a wide variety of educational measurement and psychometric topics... For each of the methods covered, the authors present a detailed step-by-step analysis of the data that include reading in the data, conducting the analysis, and interpreting the output... Overall, the Handbook of Educational Measurement and Psychometrics Using R by Desjardins and Bulut is a unique and much-needed addition to the field of educational measurement and psychometrics. This book would be a great resource for professors, researchers, and graduate students who are not familiar with R and need a user-friendly introduction to educational measurement and psychometric analysis techniques." - Anelise G. Sabbag, The American Statistician, November 2019

Preface xiii
List of Figures
xix
List of Tables
xxiii
1 Introduction to the R Programming Language
1(30)
1.1
Chapter Overview
1(1)
1.2 What Is R?
1(2)
1.2.1 Our Approach to R
2(1)
1.3 Obtaining and Installing R
3(1)
1.3.1 Windows
4(1)
1.3.2 Mac 1
4(1)
1.3.3 Linux
4(1)
1.4 Obtaining and Installing RStudio
4(2)
1.5 Using R
6(22)
1.5.1 Basic R Usage
7(4)
1.5.2 R Packages
11(2)
1.5.2.1 Masked Functions
13(1)
1.5.3 Assessing and Reading in Data
14(2)
1.5.4 Data Manipulation
16(5)
1.5.5 Descriptive and Inferential Statistics
21(4)
1.5.6 Plotting in R
25(1)
1.5.6.1 Base R Graphics
25(2)
1.5.6.2 Lattice Graphics
27(1)
1.6 Installing Packages Used in This Handbook
28(1)
1.7
Chapter Summary
29(2)
2 Classical Test Theory
31(24)
2.1
Chapter Overview
31(1)
2.2 What Is Measurement?
31(1)
2.3 Issues in Measurement
32(8)
2.3.1 Type of Scales
33(7)
2.4 The Classical Test Theory Framework
40(13)
2.4.1 Reliability
41(6)
2.4.2 Validity
47(2)
2.4.3 Item Analysis
49(4)
2.5 Summary
53(2)
3 Generalizability Theory
55(20)
3.1
Chapter Overview
55(1)
3.2 Introduction
55(5)
3.3 Examples
60(13)
3.3.1 One-Facet Design
60(1)
3.3.1.1 G Study
60(4)
3.3.1.2 D Study
64(2)
3.3.2 Two-Facet Crossed Design
66(1)
3.3.2.1 G Study
66(2)
3.3.2.2 D Study
68(1)
3.3.3 Two-Facet Partially Nested Design
69(1)
3.3.3.1 G Study
70(1)
3.3.3.2 D Study
71(1)
3.3.4 Two-Facet Crossed Design with a Fixed Facet
72(1)
3.3.4.1 G Study
72(1)
3.3.4.2 D Study
73(1)
3.4 Summary
73(2)
4 Factor Analytic Approach in Measurement
75(32)
4.1
Chapter Overview
75(1)
4.2 Introduction
75(1)
4.3 Exploratory Factor Analysis (EFA)
76(17)
4.3.1 EFA of a Cognitive Inventory
77(12)
4.3.2 EFA Using the psych Package
89(2)
4.3.3 EFA with Categorical Data
91(2)
4.4 Confirmatory Factor Analysis (CFA)
93(12)
4.4.1 CFA of the WISC-R Data
93(10)
4.4.2 CFA with Categorical Data
103(1)
4.4.2.1 Ordinal CFA---Method 1
103(2)
4.4.2.2 Ordinal CFA---Method 2
105(1)
4.5 Summary
105(2)
5 Item Response Theory for Dichotomous Items
107(36)
5.1
Chapter Overview
107(1)
5.2 Introduction
107(6)
5.2.1 Comparison to Classical Test Theory
107(1)
5.2.2 Basic Concepts in IRT
108(4)
5.2.3 IRT Model Assumptions
112(1)
5.3 The Unidimensional IRT Models for Dichotomous Items
113(15)
5.3.1 One-Parameter Logistic Model and Rasch Model
113(1)
5.3.1.1 One-Parameter Logistic Model
113(6)
5.3.1.2 Rasch Model
119(3)
5.3.2 Two-Parameter Logistic Model
122(2)
5.3.3 Three-Parameter Logistic Model
124(2)
5.3.4 Four-Parameter Logistic Model
126(2)
5.4 Ability Estimation in IRT Models
128(5)
5.5 Model Diagnostics
133(8)
5.5.1 Item Fit
134(2)
5.5.2 Person Fit
136(3)
5.5.3 Model Selection
139(2)
5.6 Summary
141(2)
6 Item Response Theory for Polytomous Items
143(26)
6.1
Chapter Overview
143(1)
6.2 Polytomous Rasch Models for Ordinal Items
144(7)
6.2.1 Partial Credit Model
144(4)
6.2.2 Rating Scale Model
148(3)
6.3 Polytomous Non-Rasch Models for Ordinal Items
151(6)
6.3.1 Generalized Partial Credit Model
152(2)
6.3.2 Graded Response Model
154(3)
6.4 Polytomous IRT Models for Nominal Items
157(9)
6.4.1 Nominal Response Model
158(3)
6.4.2 Nested Logit Model
161(5)
6.5 Model Selection
166(1)
6.6 Summary
167(2)
7 Multidimensional Item Response Theory
169(24)
7.1
Chapter Overview
169(1)
7.2 Multidimensional Item Response Modeling
170(5)
7.2.1 Compensatory and Noncompensatory MIRT
170(2)
7.2.2 Between-Item and Within-Item Multidimensionality
172(2)
7.2.3 Exploratory and Confirmatory MIRT Analysis
174(1)
7.3 Common MIRT Models
175(17)
7.3.1 Multidimensional 2PL Model
175(9)
7.3.2 Multidimensional Rasch Model
184(3)
7.3.3 Multidimensional Graded Response Model
187(2)
7.3.4 Bi-Factor IRT Model
189(3)
7.4 Summary
192(1)
8 Explanatory Item Response Theory
193(18)
8.1
Chapter Overview
193(1)
8.2 Explanatory Item Response Modeling
193(17)
8.2.1 Data Structure
194(2)
8.2.2 Rasch Model as a GLMM
196(3)
8.2.3 Linear Logistic Test Model
199(4)
8.2.4 Latent Regression Rasch Model
203(3)
8.2.5 Interaction Models
206(4)
8.3 Summary
210(1)
9 Visualizing Data and Measurement Models
211(22)
9.1
Chapter Overview
211(1)
9.2 Introduction
211(1)
9.3 Diagnostic Plots
212(10)
9.4 Path Diagrams
222(2)
9.5 Interactive Plots with shiny
224(7)
9.5.1 Example 1: Diagnostic Plot for Factor Analysis
225(3)
9.5.2 Example 2: The 3PL IRT Model
228(3)
9.6 Summary
231(2)
10 Equating
233(16)
10.1 Overview
233(1)
10.2 Introduction
233(3)
10.2.1 Equating Designs
234(1)
10.2.2 Equating Functions and Methods
235(1)
10.2.3 Evaluating the Results
236(1)
10.2.4 Further Reading
236(1)
10.3 Examples
236(11)
10.3.1 Equivalent Groups
237(1)
10.3.1.1 Identity, Mean, and Linear Functions
237(4)
10.3.1.2 Nonlinear Functions
241(2)
10.3.2 Nonequivalent Groups
243(1)
10.3.2.1 Linear Tucker Equating
243(4)
10.4 Summary
247(2)
11 Measurement Invariance and Differential Item Functioning
249(28)
11.1
Chapter Overview
249(1)
11.2 Measurement Invariance
249(9)
11.2.1 Assessing Measurement Invariance
250(1)
11.2.1.1 Configural Invariance
251(1)
11.2.1.2 Weak Invariance
251(2)
11.2.1.3 Strong Invariance
253(1)
11.2.1.4 Strict Invariance
254(2)
11.2.1.5 Assessing Partial Invariance
256(2)
11.3 Differential Item Functioning
258(16)
11.3.1 The Mantel-Haenszel (MH) Method
259(5)
11.3.2 Logistic Regression
264(5)
11.3.3 Item Response Theory Likelihood Ratio Test
269(5)
11.4 Summary
274(3)
12 More Advanced Topics in Measurement
277(10)
12.1
Chapter Overview
277(1)
12.2 CRAN Task Views
277(1)
12.3 Computerized Adaptive Testing
278(2)
12.4 Cognitive Diagnostic Modeling
280(1)
12.5 IRT Linking Procedures
280(1)
12.6 Bayesian Models of Measurement
281(2)
12.7 Hierarchical Linear Models
283(1)
12.8 Profile Analysis
284(1)
12.9 Summary
285(2)
References 287(12)
Index 299
Christopher Desjardins is a Research Associate at the University of Minnesota.

Okan Bulut is an Associate Professor of educational measurement at the University of Alberta.