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A Comparison of Three Types of Item Analysis in Test Development Using Classical and Latent Trait Methods

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Test Data Engineering

processing test data, including the latest machine learning techniques: classical test theory (CTT), item response theory (IRT), latent class;

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An Introduction to Latent Class Analysis

latent classes, a latent class model approach for analyzing learning structures, the latent Markov analysis for longitudinal data, and path;

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Principles and Methods of Test Construction

the underlying theory when designing tests, such as factor analysis and item response theory. The second section looks at item format and test;

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Statistical Approaches To Measurement Invariance

introductions to classical test theory, factor analysis, and item response theory, and the controversies associated with each, being provided;

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Statistical Approaches To Measurement Invariance

introductions to classical test theory, factor analysis, and item response theory, and the controversies associated with each, being provided;

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Modern Methods for Business Research

generalizability theory, latent trait and latent class models, and multi-faceted Rasch modeling. The chapters on decision analysis feature applied location;

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Fundamentals Of Item Response Theory

Using familiar concepts from classical measurement methods and basic statistics, Hambleton and colleagues introduce the basics of item;

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Modern Methods for Business Research

generalizability theory, latent trait and latent class models, and multi-faceted Rasch modeling. The chapters on decision analysis feature applied location;

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Multimedia In Education

interfaces and also evaluate the effectiveness of our model. Methods for estimating difficulty levels of mathematical item types using Item Response;

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Statistical Analysis of Questionnaires

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data;

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Statistical Analysis of Questionnaires

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data;

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Principles of Test Theories

provides a comprehensive survey of reliability, validity, and item analysis from the perspectives of classical true-score model, generalizability;

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Principles of Test Theories

provides a comprehensive survey of reliability, validity, and item analysis from the perspectives of classical true-score model, generalizability;

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Bayesian Psychometric Modeling

analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples;

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Bayesian Psychometric Modeling

theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated;

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Advanced Methods in Automatic Item Generation

of tests and test items. This comprehensive analysis offers thorough coverage of the theoretical foundations and concepts that define AIG, as well;

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Advanced Methods in Automatic Item Generation

of tests and test items. This comprehensive analysis offers thorough coverage of the theoretical foundations and concepts that define AIG, as well;

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A Course in Item Response Theory and Modeling with Stata

approach to IRT and IRM that is based on their connections to classical test theory, (nonlinear) factor analysis, generalized linear modeling, and;

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Item Response Theory

chapters discussing parameter estimation with multiple groups, parameter estimation for a test with mixed item types, and Markov chain Monte Carlo;

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Introduction to Nonparametric Item Response Theory

to students and practitioners with a modest background in classical test theory and item response theory. It also provides an excellent;

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Handbook of Item Response Theory

major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT;

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Structural Equation Modeling (SEM)

Bayesian approach based on SEM; an examination of predictors and outcomes related to school climate using latent class analysis and the testing;

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Handbook of Item Response Theory: Three Volume Set

major item response models, classical and modern statistical tools used in item response theory (IRT), and major areas of applications of IRT;

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Structural Equation Modeling

variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal;

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Latent Variable Modeling with R

uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis;

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Latent Variable Modeling with R

uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis;

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