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E-raamat: Computerized Adaptive and Multistage Testing with R: Using Packages catR and mstR

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  • Formaat: EPUB+DRM
  • Sari: Use R!
  • Ilmumisaeg: 23-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319692180
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Computerized Adaptive and Multistage Testing with R: Using Packages catR and mstRSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Use R!
  • Ilmumisaeg: 23-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319692180
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The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples. Implementation relies on the R packages catR and mstR that have been already or are being developed by the first author (with the team) and that include some of the newest research algorithms on the topic.

The book covers many topics along with the R-code: the basics of R, theoretical overview of CAT and MST, CAT designs, CAT assembly methodologies, CAT simulations, catR package, CAT applications, MST designs, IRT-based MST methodologies, tree-based MST methodologies, mstR package, and MST applications. CAT has been used in many large-scale assessments over recent decades, and MST has become very popular in recent years. R open source language also has become one of the most useful tools for applications in almost all fields, including business and education.

Though very useful and popular, R is a difficult language to learn, with a steep learning curve. Given the obvious need for but with the complex implementation of CAT and MST, it is very difficult for users to simulate or implement CAT and MST. Until this manual, there has been no book for users to design and use CAT and MST easily and without expense; i.e., by using the free R software. All examples and illustrations are generated using predefined scripts in R language, available for free download from the book's website.
1 Overview of Adaptive Testing
1(6)
1.1 Linear Test, CAT and MST
1(2)
1.1.1 Linear Test
1(1)
1.1.2 CAT
2(1)
1.1.3 MST
3(1)
1.2 Organization of This Book
3(4)
2 An Overview of Item Response Theory
7(28)
2.1 Principles and Assumptions of Item Response Theory
7(2)
2.2 Commonly Used IRT Models
9(11)
2.2.1 Unidimensional Dichotomous IRT Models
9(3)
2.2.2 Unidimensional Polytomous IRT Models
12(5)
2.2.3 Multidimensional IRT Models
17(2)
2.2.4 Other IRT Models
19(1)
2.3 Parameter Estimation
20(7)
2.3.1 Model Calibration
21(1)
2.3.2 Ability Estimation
22(3)
2.3.3 Information and Precision
25(2)
2.4 Further Topics
27(8)
2.4.1 Dimensionality
27(1)
2.4.2 Local Item Independence
28(1)
2.4.3 Model and Person Fit
28(1)
2.4.4 Differential Item Functioning
29(1)
2.4.5 IRT Linking and Equating
29(6)
Part I Item-Level Computerized Adaptive Testing
3 An Overview of Computerized Adaptive Testing
35(18)
3.1 Introduction and Background
35(1)
3.2 CAT Basics
36(1)
3.3 Test Design and Implementation
37(1)
3.4 Test Assembly
38(1)
3.5 The Item Bank
38(2)
3.6 IRT-Based CAT
40(8)
3.6.1 Initial Step
40(1)
3.6.2 Test Step
41(1)
3.6.3 Item Selection Method
42(5)
3.6.4 Stopping Step
47(1)
3.6.5 Final Step
48(1)
3.7 Content Balance, Exposure and Security
48(2)
3.8 CAT with Regression Trees
50(1)
3.9 Final Comments
51(2)
4 Simulations of Computerized Adaptive Tests
53(34)
4.1 The R Package catR
53(1)
4.2 Item Bank and Structure
54(2)
4.3 General Architecture of catR
56(1)
4.4 Basic IRT Functions
57(2)
4.5 IRT-Level Functions
59(7)
4.5.1 Item Parameter Generation
59(3)
4.5.2 Data Generation
62(1)
4.5.3 IRT Scoring
63(3)
4.6 CAT-Level Functions
66(7)
4.6.1 Technical CAT Functions
67(1)
4.6.2 CAT-Specific Functions
67(6)
4.7 Top-Level Function: randomCAT ()
73(8)
4.7.1 Input Information
74(1)
4.7.2 The Start List
75(2)
4.7.3 The Test List
77(2)
4.7.4 The Stop List
79(1)
4.7.5 The Final List
80(1)
4.7.6 Output Information
80(1)
4.8 Top-Level Function: simulateRespondents ()
81(6)
4.8.1 Input Arguments
81(2)
4.8.2 Output Information
83(4)
5 Examples of Simulations Using catR
87(26)
5.1 Item Banks
87(3)
5.1.1 The Dichotomous Item Bank
87(2)
5.1.2 The Polytomous Item Bank
89(1)
5.2 Example 1a: CAT with Dichotomous Item Bank
90(4)
5.3 Example 1b: CAT with Polytomous Item Bank
94(3)
5.4 Example 2: CAT for Placement Tests
97(5)
5.4.1 Data Generation and Linear Design
97(1)
5.4.2 CAT Design and Implementation
98(1)
5.4.3 Output Analysis
99(3)
5.5 Example 3: CAT with Unsuitable Data
102(3)
5.5.1 Data Generation
102(1)
5.5.2 CAT Design and Implementation
103(1)
5.5.3 Results
104(1)
5.6 Example 4: simulateRespondents () Function
105(8)
Part II Computerized Multistage Testing
6 An Overview of Computerized Multistage Testing
113(10)
6.1 Introduction and Background
113(1)
6.2 MST Basics
114(2)
6.3 Test Design and Implementation
116(1)
6.4 Test Assembly
117(1)
6.5 The Item Bank
117(1)
6.6 IRT-Based MST
118(2)
6.6.1 Module Selection
118(1)
6.6.2 Routing
118(1)
6.6.3 Latent Trait Estimation
119(1)
6.7 IRT Linking
120(1)
6.8 MST with Regression Trees
121(1)
6.9 Final Comments
121(2)
7 Simulations of Computerized Multistage Tests
123(18)
7.1 The R Package mstR
123(1)
7.2 Multistage Structure for Item Banks
124(4)
7.3 MST Functions
128(8)
7.3.1 The startModule () Function
128(2)
7.3.2 The nextModule () Function
130(6)
7.4 The randomMST () Function
136(5)
7.4.1 Input Information
136(1)
7.4.2 The start List
137(1)
7.4.3 The test List
138(1)
7.4.4 The final List
139(1)
7.4.5 Output Information
139(2)
8 Examples of Simulations Using mstR
141(20)
8.1 Introduction
141(3)
8.2 Example 1: MST Using randomMST ()
144(4)
8.3 Example 2: MST with Cut-Scores
148(5)
8.3.1 Thresholds for Ability Estimation
148(3)
8.3.2 Score-Based Thresholds
151(2)
8.4 Example 3: Comparing MST Designs
153(3)
8.4.1 Designs
153(1)
8.4.2 Implementation
154(1)
8.4.3 Results
155(1)
8.5 Example 4: MST Versus CAT
156(5)
8.5.1 Design and Code
157(1)
8.5.2 Results
158(3)
References 161(10)
Index 171
David Magis, PhD, is Research Associate of the Fonds de la Recherche Scientifique FNRS at the Department of Psychology, University of Liège, Belgium. His specialization is statistical methods in psychometrics, with special interest in item response theory, differential item functioning and computerized adaptive testing. His research interests include both theoretical and methodological development as well as open source implementation and dissemination in R. He is the main developer and maintainer of the packages catR and mstR, among others.

 

Duanli Yan, PhD, is Manager of Data Analysis and Computational Research for Automated Scoring group in the Research and Development division at the Educational Testing Service (ETS).  She is also an Adjunct Professor at Rutgers University.  At ETS, Dr. Yans responsibilities include the EXADEP test, the TOEIC® Institutional programs, and automated scoring engines upgrade and scoring. She has been a statistical coordinator and a Psychometrician for several operational programs and a Development Scientist for innovative research applications.  Dr. Yan received many awards including the 2011 ETS Presidential Award, the 2013 NCME Brenda Lyod award, the 2015 IACAT Early Career Award, and 2016 AERA Significant Contribution to Educational Measurement and Research Methodology Award.  She is a co-author for Bayesian Networks in Educational Assessment and a co-editor for Computerized Multistage Testing: Theory and Applications. 

 

Alina A. von Davier, PhD, is Vice-President at ACTNext and an Adjunct Professor at Fordham University. She was also Senior Research Director of the Computational Psychometrics Research Center at Educational Testing Service (ETS), where she was responsible for developing a team of experts and a psychometric research agenda in support of next generation assessments.  Computational psychometrics, which include machine learning and data mining techniques, Bayesian inference methods, stochastic processes and psychometric models are the main set of tools employed in her current work.  She also works with psychometric models applied to educational testing: test score equating methods, item response theory models, and adaptive testing.
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