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Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods emonstrates advanced quantitative techniques for language assessment. The volume takes an interdisciplinary approach and taps into expertise from language assessment, data mining, and psychometrics. The techniques covered include Structural Equation Modeling, Data Mining, Multidimensional Psychometrics and Multilevel Data Analysis.Volume II is distinct among available books in language assessment, as it engages the readers in both theory and application of the methods and introduces relevant techniques for theory construction and validation. This book is highly recommended to graduate students and researchers who are searching for innovative and rigorous approaches and methods to achieve excellence in their dissertations and research. It is also a valuable source for academics who teach quantitative approaches in language assessment and data analysis courses. Advanced item response theory (IRT) models in language assessment Applying the mixed Rasch model in assessing reading comprehension Multidimensional Rasch models in first language listening tests The log-linear cognitive diagnosis modeling (LCDM) in second language listening assessment Application of a hierarchical diagnostic classification model in assessing reading comprehension Advanced statistical methods in language assessment Structural equation modeling to predict performance in English proficiency tests Student growth percentiles in the formative assessment of English language proficiency Multilevel modeling to examine sources of variability in second language test scores Longitudinal multilevel modeling to examine changes in second language test scores Nature-inspired data-mining methods in language assessment Classification and regression trees in predicting listening item difficulty Evolutionary algorithm-based symbolic regression to determine the relationship of reading and lexicogrammatical knowledge
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Aryadoust V. Quantitative Data Analysis for Language Assessment. Vol I. 2019.pdf
