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Handbook of diagnostic classification modelsmodels and model extensions, applications, software packages /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Handbook of diagnostic classification modelsedited by Matthias von Davier, Young-Sun Lee.
其他題名:	models and model extensions, applications, software packages /
其他作者:	von Davier, Matthias.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	xiv, 656 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Educational tests and measurementsHandbooks, manuals, etc.
電子資源:	https://doi.org/10.1007/978-3-030-05584-4
ISBN:	9783030055844$q(electronic bk.)

Handbook of diagnostic classification modelsmodels and model extensions, applications, software packages /
Handbook of diagnostic classification modelsmodels and model extensions, applications, software packages /[electronic resource] :edited by Matthias von Davier, Young-Sun Lee. - Cham :Springer International Publishing :2019. - xiv, 656 p. :ill. (some col.), digital ;24 cm. - Methodology of educational measurement and assessment,2367-170X. - Methodology of educational measurement and assessment..

1. Introduction: From Latent Class Analysis to DINA and Beyond -- PART 1: Approaches to Cognitive Diagnosis -- 2. Nonparametric Item Response Theory and Mokken Scale Analysis, with Relations to Latent Class Models and Cognitive Diagnostic Models -- 3. The Reparameterized Unified Model System: A Diagnostic Assessment Modeling Approach -- 4.Bayesian Networks -- 5. Nonparametric Classification Models -- 6. General Diagnostic Model (GDM) -- 7. Generalized Deterministic Inputs, Noisy "and" Gate Model (G-DINA) -- 8. Loglinear Cognitive Diagnostic Model (LCDM) -- 9. Diagnostic Modeling of Skill Hierarchies and Cognitive Process with MLTM-D -- 10. Explanatory Diagnostic Models -- 11. Insights from Reparametrized DINA and Beyond -- PART 2: Special Topics -- 12. Q Matrix Learning via Latent Variable Selection and Identifiability -- 13. Global Model and Item-level Fit Indices -- 14.Exploratory Data Analysis and Cognitive Diagnostic Model -- 15. CDM-CAT -- 16. Identifiability and Cognitive Diagnostic Model -- 17. Classification Consistency and Reliability -- 18.Differential Item Functioning in CDM -- 19. Parameter Invariance and Skill Attribute Continuity in DCMs: Bifactor MIRT as an Appealing and Related Alternative -- PART 3: Applications -- 20.Application of CDMs to Process Data Analysis -- 21. Application of CDMs to Learning Systems -- 22. CDMs in Vocational Education -- 23. Analyzing Large Scale Assessment Data with Diagnostic Models -- 24. Reduced Reparameterized Unified Model Applied to Learning Spatial Reasoning Skills -- 25. How to Conduct a Study with Diagnostic Models -- PART 4: Software, Data, and Tools -- 26. The R package CDM for Diagnostic Modeling -- 27. Diagnostic Classification Modeling with flexMIRT -- 28. Using Mplus to Estimate the Log-Linear Cognitive Diagnosis Model -- 29. The GDINA R-package -- 30. GDM software mdltm including Parallel EM algorithm -- 31. Estimating CDMs using MCMC.

This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification. The handbook also offers applications and special topics and practical guidelines how to plan and conduct research studies with the help of DCMs. Commonly used models in educational measurement and psychometrics typically assume a single latent trait or at best a small number of latent variables that are aimed at describing individual differences in observed behavior. While this allows simple rankings of test takers along one or a few dimensions, it does not provide a detailed picture of strengths and weaknesses when assessing complex cognitive skills. DCMs, on the other hand, allow the evaluation of test taker performance relative to a potentially large number of skill domains. Most diagnostic models provide a binary mastery/non-mastery classification for each of the assumed test taker attributes representing these skill domains. Attribute profiles can be used for formative decisions as well as for summative purposes, for example in a multiple cut-off procedure that requires mastery on at least a certain subset of skills. The number of DCMs discussed in the literature and applied to a variety of assessment data has been increasing over the past decades, and their appeal to researchers and practitioners alike continues to grow. These models have been used in English language assessment, international large scale assessments, and for feedback for practice exams in preparation of college admission testing, just to name a few. Nowadays, technology-based assessments provide increasingly rich data on a multitude of skills and allow collection of data with respect to multiple types of behaviors. Diagnostic models can be understood as an ideal match for these types of data collections to provide more in-depth information about test taker skills and behavioral tendencies.

ISBN: 9783030055844$q(electronic bk.)

Standard No.: 10.1007/978-3-030-05584-4doiSubjects--Topical Terms:

446959
Educational tests and measurements
--Handbooks, manuals, etc.

LC Class. No.: LB3051 / .H363 2019

Dewey Class. No.: 371.26

Handbook of diagnostic classification modelsmodels and model extensions, applications, software packages /
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505 0 $a 1. Introduction: From Latent Class Analysis to DINA and Beyond -- PART 1: Approaches to Cognitive Diagnosis -- 2. Nonparametric Item Response Theory and Mokken Scale Analysis, with Relations to Latent Class Models and Cognitive Diagnostic Models -- 3. The Reparameterized Unified Model System: A Diagnostic Assessment Modeling Approach -- 4.Bayesian Networks -- 5. Nonparametric Classification Models -- 6. General Diagnostic Model (GDM) -- 7. Generalized Deterministic Inputs, Noisy "and" Gate Model (G-DINA) -- 8. Loglinear Cognitive Diagnostic Model (LCDM) -- 9. Diagnostic Modeling of Skill Hierarchies and Cognitive Process with MLTM-D -- 10. Explanatory Diagnostic Models -- 11. Insights from Reparametrized DINA and Beyond -- PART 2: Special Topics -- 12. Q Matrix Learning via Latent Variable Selection and Identifiability -- 13. Global Model and Item-level Fit Indices -- 14.Exploratory Data Analysis and Cognitive Diagnostic Model -- 15. CDM-CAT -- 16. Identifiability and Cognitive Diagnostic Model -- 17. Classification Consistency and Reliability -- 18.Differential Item Functioning in CDM -- 19. Parameter Invariance and Skill Attribute Continuity in DCMs: Bifactor MIRT as an Appealing and Related Alternative -- PART 3: Applications -- 20.Application of CDMs to Process Data Analysis -- 21. Application of CDMs to Learning Systems -- 22. CDMs in Vocational Education -- 23. Analyzing Large Scale Assessment Data with Diagnostic Models -- 24. Reduced Reparameterized Unified Model Applied to Learning Spatial Reasoning Skills -- 25. How to Conduct a Study with Diagnostic Models -- PART 4: Software, Data, and Tools -- 26. The R package CDM for Diagnostic Modeling -- 27. Diagnostic Classification Modeling with flexMIRT -- 28. Using Mplus to Estimate the Log-Linear Cognitive Diagnosis Model -- 29. The GDINA R-package -- 30. GDM software mdltm including Parallel EM algorithm -- 31. Estimating CDMs using MCMC.
520 $a This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification. The handbook also offers applications and special topics and practical guidelines how to plan and conduct research studies with the help of DCMs. Commonly used models in educational measurement and psychometrics typically assume a single latent trait or at best a small number of latent variables that are aimed at describing individual differences in observed behavior. While this allows simple rankings of test takers along one or a few dimensions, it does not provide a detailed picture of strengths and weaknesses when assessing complex cognitive skills. DCMs, on the other hand, allow the evaluation of test taker performance relative to a potentially large number of skill domains. Most diagnostic models provide a binary mastery/non-mastery classification for each of the assumed test taker attributes representing these skill domains. Attribute profiles can be used for formative decisions as well as for summative purposes, for example in a multiple cut-off procedure that requires mastery on at least a certain subset of skills. The number of DCMs discussed in the literature and applied to a variety of assessment data has been increasing over the past decades, and their appeal to researchers and practitioners alike continues to grow. These models have been used in English language assessment, international large scale assessments, and for feedback for practice exams in preparation of college admission testing, just to name a few. Nowadays, technology-based assessments provide increasingly rich data on a multitude of skills and allow collection of data with respect to multiple types of behaviors. Diagnostic models can be understood as an ideal match for these types of data collections to provide more in-depth information about test taker skills and behavioral tendencies.
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