000			05184nam a2200145Ia 4500
999			_c172810 _d172810
020			_a9780805828283
040			_cCUS
082			_a153.93 _bTAT/C
100			_aTatsuoka, Kikumi
245		0	_aCognitive Assessment/ _c Kikumi K.Tatsuoka
260			_aNew York: _bRoutledge, _c2009.
300			_a330p.p. _bHB
505			_a1 Dimensionality of Test Data and Aberrant Response Patterns 1.1 General Overview of Cognitively Diagnostic Methodologies 1.2 Dimensionality of Tests 1.3 Detection of Aberrant Response Patterns and Their Effect on Dimensionality 1.4 Nonparametric Rule Space: Spotting Erroneous Rules of Operations 2 Parametric Person-Fit Statistics, Zeta (Q, and Generalized Zetas (^1/ • • •/ 2.1 History of Person-Fit Statistics 2.2 Using Person-Fit Statistics and IRT 6 for Representing Response Patterns 2.3 The Zeta (Q Index 2.4 Semantic Meaning of ^ 2.5 Generalized ^ 3 Cognitive Modeling by Developing an Incidence Q Matrix 3.1 Preparation of the Q-Matrix Theory 3.2 Analysis of Knowledge States and Their Partially Ordered Structure 3.3 A Q Matrix as a Cognitive Model 3.4 Stable Attributes as Knowledge and Cognitive Processing Skills 3.5 Relationship Among Attributes From the Graph Theory Approach 3.6 Partial-Order Relations Embedded in a Q Matrix: Boolean Algebra 3.7 Attribute Space and Item Response Space 3.8 Universal Set of Knowledge States and Ideal Item Score Patterns 4 Knowledge Space Generated From a Q Matrix 4.1 More About Attribute Space and Item Response Space 4.2 Boolean Description Function (BDF): Determination of Ideal Response Patterns as Error-Free States of Knowledge and Capabilities 4.3 Prerequisite Relationships in a Q Matrix 4.4 Equivalent Classes of Attribute Patterns 4.5 Item Construction 5 A Classification Space: Rule Space as a Cartesian Product of the Person Parameter 0 in Item Response Theory, C, and Generalized 5.1 Cognitive Sensitivity for Item Response Curves 5.2 Clustering Item Response Functions Into Homogeneous Categories and the Relationship Among Categories 5.3 Representing Items in a Tree Based on the Underlying Tasks 5.4 Rule Space as the Cartesian Product of Two Variables, IRT 0 and ^ 5.5 Variability of Response Errors and a Cluster of Response Patterns Around a Knowledge State (or, Equivalently, Ideal Item Score Patterns) 5.6 Bug Distributions; Distribution of Responses Around an Ideal Item Score Pattern 5.7 Expanding the Rule Space to a Multidimensional Space 5.8 Conceptual Framework of the Rule Space Methodology 6 Classification Rules 6.1 Intuitive Classification Using Generalized Distance 6.2 Conditional Density Function of Group 6.3 Conditional Probabilities and the Bayes Theorem 6.4 Classification Into One of Two Groups (Knowledge States) 6.5 Classification Into One of Several Groups, 6.6 Error Probabilities or Misclassification Probabilities 6.7 Distribution of D^ 7 Rule Space Decision Rules and Attribute Mastery Probabilities 7.1 Classification Rule for Two Groups in Two Dimensional Rule Space 7.2 Probabilities of Misclassifications. 7.3 Classification of Observed Response X Into One of Many Predetermined Knowledge States 7.4 Attribute Mastery Probabilities 7.5 Reduction of Irrelevant Knowledge States 8 Posterior Probabilities With Different Prior Probabilities and Their Effect on the Attribute Mastery Probabilities 8.1 Two or More Higher Dimensional Rule Space and Density Functions of Knowledge States 8.2 Classification Criterion: Cutoff and Errors 8.3 Prior Probabilities: Gamma Function, Uniform Function, and Observed Frequencies 8.4 Attribute Mastery Probabilities and the Effects From Different Priors as the Classification Criterion a Becomes Larger 8.5 Slippage Probabilities 8.6 The Length of Tests, the Size of Sets of Attributes, and a Sample Size 8.7 Latent Class Models, the Hybrid Model, the Mixture Distribution Diagnostic Model, and the POSET Model 9 Reliabilities of Item Score, Person's Score, Attribute Mastery Probability, and Their Relationship to the Classical Test Theory 9.1 Summary List of the Information Obtainable from a Rule Space Analysis 9.2 True Score and Pseudotrue Score of Item j for Person i and the Reliability of Observed Item Score X; and Attribute Mastery Probability for an Individual 9.3 True Scores of Item j for Person i 9.4 Relationship Between the RS True Scores Estimated From a Set of Ideal Item Score Patterns and Those From Attribute Mastery Probabilities. 9.5 Nonparametric Attribute Characteristic Curves (ACCs) 9.6 Nonparametric Item Response Curves 9.7 Test-Retest Reliability 10 Validation of Attributes, a Q Matrix Coded by the Involvement of Attributes to Items, and a Test 10.1 Selection of Attributes 10.2 Validation of a Q Matrix 10.3 Selection of a Smaller Number of Attributes From a Larger Number of Attributes 10.4 Validation of the Rule Space Analysis Results 10.5 Controlled Remediation: Cognitively Diagnostic Computerized Adaptive Testing and Remediation 10.6 An RSM Analysis as a Hypothesis Testing Method 10.7 Using RSM in Examining the Construct Validity of a Test
942			_cWB16