The Theory and Practice of Item Response Theory pdf epub mobi txt 电子书 下载 2026
出版者:Guilford Press
作者:Ayala, R.J. de
出品人:
页数:428
译者:
出版时间:2008-10
价格:$ 75.71
装帧:Hardcover
isbn号码:9781593858698
丛书系列:
图书标签:
- 英文
- 教育
- Item Response Theory
- Psychometrics
- Educational Measurement
- Statistical Modeling
- Quantitative Research
- Assessment
- Testing
- Psychology
- Statistics
- Data Analysis
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具体描述
Item response theory (IRT) is a latent variable modeling approach used to minimize bias and optimize the measurement power of educational and psychological tests and other psychometric applications. Designed for advanced students, researchers, and psychometric professionals, this book clearly presents both the "how-to" and the "why" of IRT. It describes simple and more complex IRT models and shows how they are applied with the help of widely available software packages. Chapters follow a consistent format and build sequentially, taking the reader from model development through the fit analysis and interpretation phases that one would perform in practice. The use of common empirical data sets across the chapters facilitates understanding of the various models and how they relate to one another.
好的，下面是针对一本名为《项目反应理论的理论与实践》（The Theory and Practice of Item Response Theory）的书籍的详细简介，该简介内容不包含原书中的任何主题，而是基于一个完全不同且假设存在的学术主题，力求详实且自然。 --- 跨学科前沿：复杂系统中的自组织临界性与宏观涌现现象研究 导言：重塑我们对秩序与混沌的认知 在当代科学的多个领域——从地球物理学到生态学，再到金融市场动力学——研究者们正面临一个共同的挑战：如何理解并预测那些由大量简单元素相互作用而产生的复杂、非线性和宏观尺度的组织模式。本书《复杂系统中的自组织临界性与宏观涌现现象研究》正是在这一背景下应运而生的一部深度专著。它系统性地探讨了“自组织临界性”（Self-Organized Criticality, SOC）理论框架如何作为理解这些涌现现象（Emergent Phenomena）的强大工具，并超越了传统的平衡态物理学的局限。 本书的宗旨在于提供一个跨学科的视角，整合统计物理学、非线性动力学、信息论以及计算建模的前沿成果，为研究者和高级学生提供一个理解复杂系统内在机制的坚实基础。我们不将复杂系统视为一组随机事件的集合，而是将其视为一个处于“临界边缘”的动态实体，其宏观行为由内部反馈机制驱动，而非依赖于外部调谐参数。 第一部分：自组织临界性理论基础与数学建构 本部分聚焦于SOC理论的核心概念和严格的数学表述。我们从基础的元胞自动机模型（如著名的沙堆模型）出发，逐步深入到更抽象的、适用于现实世界的非平衡动力学模型。 第一章：从平衡态到临界边缘 本章对比了传统的热力学平衡态理论与复杂系统的非平衡特性。重点阐述了“临界点”的概念，并引入了相变理论的某些元素，但强调SOC系统是如何自发地达到并维持在这一临界状态的，无需外部微调。我们引入了“慢弛豫”（Slow Relaxation）和“长程关联”（Long-Range Correlations）的概念，这些是区分SOC现象的关键特征。 第二章：核心数学模型与尺度不变性 详细解析了用于描述SOC现象的数学框架，包括随机游走模型、基于信息的反馈机制模型，以及在网络科学中应用到的基于优先连接规则的模型。核心在于对“幂律分布”（Power-Law Distributions）的深入分析。本书强调，幂律分布并非随机巧合，而是系统达到临界状态的直接证据。我们对临界指数（Critical Exponents）进行了严格的推导，并探讨了如何通过信息熵和有效信息传递率来量化系统的组织程度。 第三章：非线性动力学与反馈回路 本章将SOC置于更广阔的非线性动力学背景之下。我们探讨了Hysteresis（迟滞现象）和 Bifurcation（分岔现象）在SOC系统中的体现，特别关注了内部反馈回路（如正反馈驱动的雪崩效应）如何将微小扰动放大为宏观事件。本章包含对Langevin方程在描述间歇性“爆发”（Bursts）和“静默期”（Quiescent Periods）方面的应用案例。 第二部分：跨学科应用与宏观涌现模式 在建立了坚实的理论基础后，本书的后半部分将理论应用于现实世界的复杂系统，展现SOC和涌现现象的普适性。 第四章：地球物理系统中的临界动力学 本章重点研究自然界中显著的临界现象。我们详细分析了地震学中的Gutenberg-Richter定律（地震震级-频率的幂律关系）如何被解释为地壳应力释放的SOC过程。此外，还探讨了火山爆发模式、森林火灾传播的临界阈值，以及河流侵蚀模式中的自组织特性。本章特别关注了“雪崩式”事件的预警指标与模型限制。 第五章：生态系统与生物网络中的组织与崩溃 在生物学和生态学领域，系统往往在稳定与崩溃的边缘徘徊。本章考察了生态群落的物种多样性如何通过竞争和捕食关系自发地达到一个临界点。我们应用网络理论工具，分析了食物网结构的鲁棒性与脆弱性，并展示了关键物种的移除如何引发系统级的“级联崩溃”（Trophic Cascades），这种崩溃的规模服从幂律分布。此外，对神经元网络中的同步化和信息处理效率的讨论，也将其置于SOC的框架下进行审视。 第六章：社会经济系统中的宏观涌现 本章探讨了人类社会活动中的复杂性。我们分析了金融市场中的资产价格波动，特别关注了“闪电崩盘”（Flash Crashes）和市场泡沫的形成，将其视为信息传播和投资者情绪反馈导致的临界行为。在社会动力学方面，我们研究了意见极化、技术采纳曲线以及城市交通流的拥堵现象，论证了在缺乏中央控制的情况下，群体行为如何通过本地交互涌现出高度结构化的宏观模式。 第三部分：计算方法、模拟与未来展望 本书的最后部分提供了实用的计算工具，并指出了当前研究的前沿挑战。 第七章：计算建模与模拟技术 本章提供了实现和分析SOC系统的具体计算方案。详细介绍了用于模拟复杂系统的Agent-Based Modeling (ABM) 方法，并提供了如何利用并行计算技术来处理大规模系统模拟的实践建议。重点讨论了蒙特卡洛方法在估计临界参数和验证理论预测中的应用，以及如何利用离线数据进行模式识别和“临界信号”的提取。 第八章：挑战与前沿方向 尽管SOC提供了强大的解释力，本书也坦诚地指出了其局限性，例如如何准确区分真正的SOC与仅仅是“接近临界”的系统，以及如何将时间演化信息更有效地整合到静态的幂律分析中。本章最后展望了量子系统中的自组织临界性、复杂网络中的信息流临界性以及在人工生命学中构建具备SOC特性的自适应系统的潜在路径。 总结与读者定位 《复杂系统中的自组织临界性与宏观涌现现象研究》是一部为物理学家、工程师、生态学家、经济学家以及高级计算机科学研究者量身打造的深度参考书。它不仅是理论的汇编，更是连接基础科学与实际复杂性问题的桥梁。通过本书，读者将能够掌握一套分析和建模非平衡动态系统的通用语言，从而更好地理解我们周围世界中秩序如何从看似混乱的交互中自然生成。本书要求读者具备扎实的微积分、线性代数和基础统计学知识。
作者简介
R. J. de Ayala is Professor of Educational Psychology at the University of Nebraska-Lincoln. His research interests include psychometrics, item response theory, computerized adaptive testing, applied statistics, and multilevel models. His work has appeared in Applied Psychological Measurement, Applied Measurement in Education, the British Journal of Mathematical and Statistical Psychology, Educational and Psychological Measurement, the Journal of Applied Measurement, and the Journal of Educational Measurement. He is a Fellow of the American Psychological Association’s Division 5: Evaluation, Measurement, and Statistics, as well as of the American Educational Research Association.
目录信息
Symbols and Acronyms
1. Introduction to Measurement
- Measurement
- Some Measurement Issues
- Item Response Theory
- Classical Test Theory
- Latent Class Analysis
- Summary
2. The One-Parameter Model
- Conceptual Development of the Rasch Model
- The One-Parameter Model
- The One-Parameter Logistic Model and the Rasch Model
- Assumptions underlying the Model
- An Empirical Data Set: The Mathematics Data Set
- Conceptually Estimating an Individual's Location
- Some Pragmatic Characteristics of Maximum Likelihood Estimates
- The Standard Error of Estimate and Information
- An Instrument's Estimation Capacity
- Summary
3. Joint Maximum Likelihood Parameter Estimation
- Joint Maximum Likelihood Estimation
- Indeterminacy of Parameter Estimates
- How Large a Calibration Sample?
- Example: Application of the Rasch Model to the Mathematics Data, JMLE
- Summary
4. Marginal Maximum Likelihood Parameter Estimation
- Marginal Maximum Likelihood Estimation
- Estimating an Individual's Location: Expected A Posteriori
- Example: Application of the Rasch Model to the Mathematics Data, MMLE
- Metric Transformation and the Total Characteristic Function
- Summary
5. The Two-Parameter Model
- Conceptual Development of the Two-Parameter Model
- Information for the Two-Parameter Model
- Conceptual Parameter Estimation for the 2PL Model
- How Large a Calibration Sample?
- Metric Transformation, 2PL Model
- Example: Application of the 2PL Model to the Mathematics Data, MMLE
- Information and Relative Efficiency
- Summary
6. The Three-Parameter Model
- Conceptual Development of the Three-Parameter Model
- Additional Comments about the Pseudo-Guessing Parameter, Xⱼ
- Conceptual Estimation for the 3PL Model
- How Large a Calibration Sample?
- Assessing Conditional Independence
- Example: Application of the 3PL Model to the Mathematics Data, MMLE
- Assessing Person Fit: Appropriateness Measurement
- Information for the Three-Parameter Model
- Metric Transformation, 3PL Model
- Handling Missing Responses
- Issues to Consider in Selecting among the 1PL, 2PL, and 3PL Models
- Summary
7. Rasch Models for Ordered Polytomous Data
- Conceptual Development of the Partial Credit Model
- Conceptual Parameter Estimation of the PC Model
- Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE
- The Rating Scale Model
- Conceptual Estimation of the RS Model
- Example: Application of the RS Model to an Attitudes toward Condom Scale, JMLE
- How Large a Calibration Sample?
- Information for the PC and RS Models
- Metric Transformation, PC and RS Models
- Summary
8. Non-Rasch Models for Ordered Polytomous Data
- The Generalized Partial Credit Model
- Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE
- Conceptual Development of the Graded Response Model
- How Large a Calibration Sample?
- Example: Application of the GR Model to an Attitudes toward Condom Scale, MMLE
- Information for Graded Data
- Metric Transformation, GPC and GR Models
- Summary
9. Models for Nominal Polytomous Data
- Conceptual Development of the Nominal Response Model
- How Large a Calibration Sample?
- Example: Application of the NR Model to a Science Test, MMLE
- Example: Mixed Model Calibration of the Science Test—NR and PC Models, MMLE
- Example: NR and PC Mixed Model Calibration of the Science Test, Collapsed Options, MMLE
- Information for the NR Model
- Metric Transformation, NR Model
- Conceptual Development of the Multiple-Choice Model
- Example: Application of the MC Model to a Science Test, MMLE
- Example: Application of the BS Model to a Science Test, MMLE
- Summary
10. Models for Multidimensional Data
- Conceptual Development of a Multidimensional IRT Model
- Multidimensional Item Location and Discrimination
- Item Vectors and Vector Graphs
- The Multidimensional Three-Parameter Logistic Model
- Assumptions of the MIRT Model
- Estimation of the M2PL Model
- Information for the M2PL Model
- Indeterminacy in MIRT
- Metric Transformation, M2PL Model
- Example: Application of the M2PL Model, Normal-Ogive Harmonic Analysis Robust Method
- Obtaining Person Location Estimates
- Summary
11. Linking and Equating
- Equating Defined
- Equating: Data Collection Phase
- Equating: Transformation Phase
- Example: Application of the Total Characteristic Function Equating
- Summary
12. Differential Item Functioning
- Differential Item Functioning and Item Bias
- Mantel–Haenszel Chi-Square
- The TSW Likelihood Ratio Test
- Logistic Regression
- Example: DIF Analysis
- Summary
Appendix A. Maximum Likelihood Estimation of Person Locations
- Estimating an Individual's Location: Empirical Maximum Likelihood Estimation
- Estimating an Individual's Location: Newton's Method for MLE
- Revisiting Zero Variance Binary Response Patterns
Appendix B. Maximum Likelihood Estimation of Item Locations
Appendix C. The Normal Ogive Models
- Conceptual Development of the Normal Ogive Model
- The Relationship between IRT Statistics and Traditional Item Analysis Indices
- Relationship of the Two-Parameter Normal Ogive and Logistic Models
- Extending the Two-Parameter Normal Ogive Model to a Multidimensional Space
Appendix D. Computerized Adaptive Testing
- A Brief History
- Fixed-Branching Techniques
- Variable-Branching Techniques
- Advantages of Variable-Branching over Fixed-Branching Methods
- IRT-Based Variable-Branching Adaptive Testing Algorithm
Appendix E. Miscellanea
- Linear Logistic Test Model (LLTM)
- Using Principal Axis for Estimating Item Discrimination
- Infinite Item Discrimination Parameter Estimates
- Example: NOHARM Unidimensional Calibration
- An Approximate Chi-Square Statistic for NOHARM
- Mixture Models
- Relative Efficiency, Monotonicity, and Information
- FORTRAN Formats
- Example: Mixed Model Calibration of the Science Test—NR and 2PL Models, MMLE
- Example: Mixed Model Calibration of the Science Test—NR and GR Models, MMLE
- Odds, Odds Ratios, and Logits
- The Person Response Function
- Linking: A Temperature Analogy Example
- Should DIF Analyses Be Based on Latent Classes?
- The Separation and Reliability Indices
- Dependency in Traditional Item Statistics and Observed Scores
References
Author Index
Subject Index
· · · · · · (收起)
1. Introduction to Measurement
- Measurement
- Some Measurement Issues
- Item Response Theory
- Classical Test Theory
- Latent Class Analysis
- Summary
2. The One-Parameter Model
- Conceptual Development of the Rasch Model
- The One-Parameter Model
- The One-Parameter Logistic Model and the Rasch Model
- Assumptions underlying the Model
- An Empirical Data Set: The Mathematics Data Set
- Conceptually Estimating an Individual's Location
- Some Pragmatic Characteristics of Maximum Likelihood Estimates
- The Standard Error of Estimate and Information
- An Instrument's Estimation Capacity
- Summary
3. Joint Maximum Likelihood Parameter Estimation
- Joint Maximum Likelihood Estimation
- Indeterminacy of Parameter Estimates
- How Large a Calibration Sample?
- Example: Application of the Rasch Model to the Mathematics Data, JMLE
- Summary
4. Marginal Maximum Likelihood Parameter Estimation
- Marginal Maximum Likelihood Estimation
- Estimating an Individual's Location: Expected A Posteriori
- Example: Application of the Rasch Model to the Mathematics Data, MMLE
- Metric Transformation and the Total Characteristic Function
- Summary
5. The Two-Parameter Model
- Conceptual Development of the Two-Parameter Model
- Information for the Two-Parameter Model
- Conceptual Parameter Estimation for the 2PL Model
- How Large a Calibration Sample?
- Metric Transformation, 2PL Model
- Example: Application of the 2PL Model to the Mathematics Data, MMLE
- Information and Relative Efficiency
- Summary
6. The Three-Parameter Model
- Conceptual Development of the Three-Parameter Model
- Additional Comments about the Pseudo-Guessing Parameter, Xⱼ
- Conceptual Estimation for the 3PL Model
- How Large a Calibration Sample?
- Assessing Conditional Independence
- Example: Application of the 3PL Model to the Mathematics Data, MMLE
- Assessing Person Fit: Appropriateness Measurement
- Information for the Three-Parameter Model
- Metric Transformation, 3PL Model
- Handling Missing Responses
- Issues to Consider in Selecting among the 1PL, 2PL, and 3PL Models
- Summary
7. Rasch Models for Ordered Polytomous Data
- Conceptual Development of the Partial Credit Model
- Conceptual Parameter Estimation of the PC Model
- Example: Application of the PC Model to a Reasoning Ability Instrument, MMLE
- The Rating Scale Model
- Conceptual Estimation of the RS Model
- Example: Application of the RS Model to an Attitudes toward Condom Scale, JMLE
- How Large a Calibration Sample?
- Information for the PC and RS Models
- Metric Transformation, PC and RS Models
- Summary
8. Non-Rasch Models for Ordered Polytomous Data
- The Generalized Partial Credit Model
- Example: Application of the GPC Model to a Reasoning Ability Instrument, MMLE
- Conceptual Development of the Graded Response Model
- How Large a Calibration Sample?
- Example: Application of the GR Model to an Attitudes toward Condom Scale, MMLE
- Information for Graded Data
- Metric Transformation, GPC and GR Models
- Summary
9. Models for Nominal Polytomous Data
- Conceptual Development of the Nominal Response Model
- How Large a Calibration Sample?
- Example: Application of the NR Model to a Science Test, MMLE
- Example: Mixed Model Calibration of the Science Test—NR and PC Models, MMLE
- Example: NR and PC Mixed Model Calibration of the Science Test, Collapsed Options, MMLE
- Information for the NR Model
- Metric Transformation, NR Model
- Conceptual Development of the Multiple-Choice Model
- Example: Application of the MC Model to a Science Test, MMLE
- Example: Application of the BS Model to a Science Test, MMLE
- Summary
10. Models for Multidimensional Data
- Conceptual Development of a Multidimensional IRT Model
- Multidimensional Item Location and Discrimination
- Item Vectors and Vector Graphs
- The Multidimensional Three-Parameter Logistic Model
- Assumptions of the MIRT Model
- Estimation of the M2PL Model
- Information for the M2PL Model
- Indeterminacy in MIRT
- Metric Transformation, M2PL Model
- Example: Application of the M2PL Model, Normal-Ogive Harmonic Analysis Robust Method
- Obtaining Person Location Estimates
- Summary
11. Linking and Equating
- Equating Defined
- Equating: Data Collection Phase
- Equating: Transformation Phase
- Example: Application of the Total Characteristic Function Equating
- Summary
12. Differential Item Functioning
- Differential Item Functioning and Item Bias
- Mantel–Haenszel Chi-Square
- The TSW Likelihood Ratio Test
- Logistic Regression
- Example: DIF Analysis
- Summary
Appendix A. Maximum Likelihood Estimation of Person Locations
- Estimating an Individual's Location: Empirical Maximum Likelihood Estimation
- Estimating an Individual's Location: Newton's Method for MLE
- Revisiting Zero Variance Binary Response Patterns
Appendix B. Maximum Likelihood Estimation of Item Locations
Appendix C. The Normal Ogive Models
- Conceptual Development of the Normal Ogive Model
- The Relationship between IRT Statistics and Traditional Item Analysis Indices
- Relationship of the Two-Parameter Normal Ogive and Logistic Models
- Extending the Two-Parameter Normal Ogive Model to a Multidimensional Space
Appendix D. Computerized Adaptive Testing
- A Brief History
- Fixed-Branching Techniques
- Variable-Branching Techniques
- Advantages of Variable-Branching over Fixed-Branching Methods
- IRT-Based Variable-Branching Adaptive Testing Algorithm
Appendix E. Miscellanea
- Linear Logistic Test Model (LLTM)
- Using Principal Axis for Estimating Item Discrimination
- Infinite Item Discrimination Parameter Estimates
- Example: NOHARM Unidimensional Calibration
- An Approximate Chi-Square Statistic for NOHARM
- Mixture Models
- Relative Efficiency, Monotonicity, and Information
- FORTRAN Formats
- Example: Mixed Model Calibration of the Science Test—NR and 2PL Models, MMLE
- Example: Mixed Model Calibration of the Science Test—NR and GR Models, MMLE
- Odds, Odds Ratios, and Logits
- The Person Response Function
- Linking: A Temperature Analogy Example
- Should DIF Analyses Be Based on Latent Classes?
- The Separation and Reliability Indices
- Dependency in Traditional Item Statistics and Observed Scores
References
Author Index
Subject Index
· · · · · · (收起)
读后感
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用户评价
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这本书还算好理解了，起码在略过很多复杂公式后还能看懂内容。
评分Almost the best book for introducing IRT, easy to read and perfect balance between technical and practice part.被说可以当成小说那样读，嗯差不多。
评分Almost the best book for introducing IRT, easy to read and perfect balance between technical and practice part.被说可以当成小说那样读，嗯差不多。
评分这本书还算好理解了，起码在略过很多复杂公式后还能看懂内容。
评分Almost the best book for introducing IRT, easy to read and perfect balance between technical and practice part.被说可以当成小说那样读，嗯差不多。
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