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Multidimensional Item Response Theory [Mīkstie vāki]

  • Formāts: Paperback / softback, 152 pages, height x width: 215x139 mm, weight: 190 g
  • Sērija : Quantitative Applications in the Social Sciences
  • Izdošanas datums: 07-May-2020
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1506384250
  • ISBN-13: 9781506384252
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  • Mīkstie vāki
  • Cena: 50,80 €
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  • Formāts: Paperback / softback, 152 pages, height x width: 215x139 mm, weight: 190 g
  • Sērija : Quantitative Applications in the Social Sciences
  • Izdošanas datums: 07-May-2020
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1506384250
  • ISBN-13: 9781506384252
Citas grāmatas par šo tēmu:

Several decades of psychometric research have led to the development of sophisticated models for multidimensional test data, and in recent years, multidimensional item response theory (MIRT) has become a burgeoning topic in psychological and educational measurement. Considered a cutting-edge statistical technique, the methodology underlying MIRT can be complex, and therefore doesn’t receive much attention in introductory IRT courses. However author Wes Bonifay shows how MIRT can be understood and applied by anyone with a firm grounding in unidimensional IRT modeling. His volume includes practical examples and illustrations, along with numerous figures and diagrams. Multidimensional Item Response Theory includes snippets of R code interspersed throughout the text (with the complete R code included on an accompanying website) to guide readers in exploring MIRT models, estimating the model parameters, generating plots, and implementing the various procedures and applications discussed throughout the book.

Recenzijas

Multidimensional IRT in a clear, accessible, and compelling writing style. -- Gustavo Gonzįlez-Cuevas * Review *

Series Editor's Introduction ix
Acknowledgments xi
About the Author xiii
Chapter 1 Introduction
1(4)
Chapter 2 Unidimensional Item Response Theory
5(22)
What Is a Latent Trait?
5(1)
Assumptions of UIRT
5(1)
UIRT Models for Dichotomous Data
6(7)
RCode
13(1)
UIRT Models for Polytomous Data
13(4)
Additional UIRT Models
17(1)
RCode
17(1)
UIRT Estimation
17(9)
Other Estimation Methods
26(1)
R Code
26(1)
Chapter 3 MIRT Models for Dichotomous Data
27(20)
Compensation in MIRT Modeling
27(4)
Compensatory MIRT Models
31(6)
R Code
37(1)
Partially Compensatory MIRT Models
38(3)
R Code
41(1)
Additional MIRT Models for Dichotomous Data
41(4)
R Code
45(1)
Recent Advances in Dichotomous MIRT Modeling
45(2)
Chapter 4 MIRT Models for Polytomous Data
47(8)
R Code
54(1)
Additional Polytomous MIRT Models
54(1)
Chapter 5 Descriptive MIRT Statistics
55(22)
The 9-Space
55(2)
The Item Response Surface
57(1)
Conditional Response Functions
58(2)
The Direction of Measurement
60(7)
Person Parameters in MIRT
67(3)
MIRT Information
70(1)
Polytomous MIRT Descriptives
71(4)
Test-Level MIRT Descriptives
75(2)
Chapter 6 Item Factor Structures
77(6)
Two-Tier Model
77(3)
Correlated-Traits Model
80(1)
Bifactor Model
81(1)
Testlet Response Model
81(1)
R Code
82(1)
Chapter 7 Estimation in MIRT Models
83(10)
Conceptual Illustration
83(1)
Missing Data Formulation
84(3)
Two Challenges
87(1)
Adaptive Quadrature
88(1)
Bayesian Estimation
89(1)
MH-RM Estimation
90(1)
RCode
91(2)
Chapter 8 MIRT Model Diagnostics and Evaluation
93(12)
Dimensionality Assessment
93(4)
Test-Level Fit Assessment
97(3)
Item-Level Fit Assessment
100(3)
Model Comparison Methods
103(2)
Chapter 9 MIRT Applications
105(10)
Linking and Equating
105(2)
R Code
107(1)
Differential Item Functioning
107(2)
RCode
109(1)
Computerized Adaptive Testing
109(2)
R Code
111(1)
Applications of the Two-Tier Item Factor Structure
111(2)
Further MIRT Applications
113(2)
References 115(18)
Index 133