The Theory and Practice of Item Response Theory
The Theory and Practice of Item Response Theory
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发表时间：
2008-12
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通讯作者：
De Ayala
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作者：
De Ayala
符号和缩略语。第 1 部分：测量简介。测量。一些测量问题。项目反应理论。经典测试理论。潜在类别分析。概括。第 2 部分：单参数模型。拉希模型的概念发展。单参数模型。单参数 Logistic 模型和 Rasch 模型。模型的假设。经验数据集：数学数据集。从概念上估计个人的位置。最大似然估计的一些实用特征。估计和信息的标准误差。仪器的估计能力。概括。第 3 部分：联合最大似然参数估计。联合最大似然估计。参数估计的不确定性。校准样本有多大？示例：Rasch 模型在数学数据中的应用，JMLE。概括。第 4 部分：边际最大似然参数估计。边际最大似然估计。估计个人的位置：预期的事后。示例：Rasch 模型在数学数据 (MMLE) 中的应用。度量变换和总特征函数。概括。第 5 部分：二参数模型。二参数模型的概念开发。二参数模型的信息。 2PL 模型的概念参数估计。校准样本有多大？公制转换，2PL 模型。示例：2PL 模型在数学数据 (MMLE) 中的应用。信息和相对效率。概括。第 6 部分。三参数模型。三参数模型的概念开发。关于伪猜测参数的附加注释。 3PL 模型的概念评估。校准样本有多大？评估条件独立性。示例：3PL 模型在数学数据 (MMLE) 中的应用。评估人员适合度：适当性测量。三参数模型的信息。公制转换，3PL 模型。处理缺失的响应。选择 1PL、2PL 和 3PL 模式时要考虑的问题。概括。第 7 部分：有序多分数据的 Rasch 模型。部分信用模型的概念发展。 PC 模型的概念参数估计。示例：PC 模型在推理能力仪器 MMLE 中的应用。评级量表模型。 RS 模型的概念估计。示例：RS 模型在安全套态度量表中的应用，JMLE。校准样本有多大？ PC 和 RS 型号的信息。公制转换、PC 和 RS 模型。概括。第 8 部分：有序多分数据的非 Rasch 模型。广义部分信用模型。示例：GPC 模型在推理能力工具 MMLE 中的应用。分级响应模型的概念开发。校准样本有多大？示例：GR 模型在安全套态度量表 (MMLE) 中的应用。分级数据信息。公制转换、GPC 和 GR 模型。概括。第 9 部分：标称多分数据模型。名义响应模型的概念开发。校准样本有多大？示例：NR 模型在科学测试中的应用，MMLE。示例：科学测试-NR 和 PC 模型的混合模型校准，MMLE。示例：科学测试的 NR 和 PC 混合模型校准、折叠选项、MMLE。 NR 模型的信息。度量转换，NR 模型。多项选择模型的概念发展。示例：MC 模型在科学测试（MMLE）中的应用。示例：BS 模型在科学测试（MMLE）中的应用。概括。第 10 部分：多维数据模型。多维 IRT 模型的概念开发。多维物品位置和辨别。项目向量和矢量图。多维三参数逻辑模型。 MIRT 模型的假设。 M2PL 模型的估计。 M2PL 模型的信息。 MIRT 中的不确定性。公制转换，M2PL 模型。示例：M2PL 模型的应用，正态-Ogive 谐波分析稳健方法。获取人员位置估计。概括。第 11 部分：链接和等同。定义相等。等式：数据收集阶段。等同：转变阶段。示例：总特征函数方程的应用。概括。第 12 部分：差异化项目功能。差异化项目功能和项目偏差。曼特尔-亨泽尔卡方。 TSW 似然比检验。逻辑回归。示例：DIF 分析。概括。附录 A：人员位置的最大似然估计。估计个人的位置：经验最大似然估计。估计个人位置：牛顿 MLE 方法。重新审视零方差二元响应模式。附录 B：项目位置的最大似然估计。附录 C：正常尖顶模型。正常 Ogive 模型的概念发展。 IRT统计与传统项目分析指数之间的关系。二参数正态 Ogive 和 Logistic 模型的关系。将二参数正态 Ogive 模型扩展到多维空间。附录 D：计算机化自适应测试。简史。固定分支技术。可变分支技术。可变分支相对于固定分支方法的优点。基于 IRT 的可变分支自适应测试算法。附录 E. 其他。线性 Logistic 测试模型 (LLTM)。使用主轴来估计项目歧视。无限项目辨别参数估计。示例：NOHARM 一维校准。 NOHARM 的近似卡方统计量。混合模型。相对效率、单调性和信息。 FORTRAN 格式。示例：科学测试-NR 和 2PL 模型的混合模型校准，MMLE。示例：科学测试 NR 和 GR 模型的混合模型校准，MMLE。赔率、赔率比和 Logits。人的反应功能。链接：温度类比示例。 DIF 分析应该基于潜在类别吗？分离和可靠性指数。传统项目统计和观察分数的依赖性。
Symbols and Acronyms. Part 1. Introduction to Measurement. Measurement. Some Measurement Issues. Item Response Theory. Classical Test Theory. Latent Class Analysis. Summary. Part 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. Part 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. Part 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. Part 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. Part 6. The Three-Parameter Model. Conceptual Development of the Three-Parameter Model. Additional Comments about the Pseudo-Guessing Parameter. 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. Part 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. Part 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. Part 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. Part 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. Part 11. Linking and Equating. Equating Defined. Equating: Data Collection Phase. Equating: Transformation Phase. Example: Application of the Total Characteristic Function Equating. Summary. Part 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.