1st Edition

Quantitative Data Analysis for Language Assessment Volume II Advanced Methods

Edited By Vahid Aryadoust, Michelle Raquel Copyright 2020
    260 Pages
    by Routledge

    260 Pages
    by Routledge

    Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods demonstrates 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.

    List of Figures
    List of Tables

    Part I. Advanced item response theory (IRT) models in language assessment
    1. Mixed Rasch modeling in assessing reading comprehension (Purya Baghaei, Christoph J. Kemper, Samuel Greif & Monique Reichert)
    2. Multidimensional Rasch models in first language listening tests (Christian Spoden & Jens Fleischer)
    3. The Log-Linear Cognitive Diagnosis Modeling (LCDM) in second language listening assessment (Tugba Elif Toprak, Vahid Aryadoust & Christine Goh)
    4. Hierarchical Diagnostic Classification Models in assessing reading comprehension (Hamdollah Ravand)

    Part II. Advanced statistical methods in language assessment
    5. Structural equation modeling in language assessment (Xuelian Zhu, Michelle Raquel & Vahid Aryadoust)
    6. Growth modelling using growth percentiles for longitudinal studies (Husein Taherbahi & Daeryong Seo)
    7. Multilevel modeling to examine sources of Variability in Second Language Test Scores (Yo In’nami & Khaled Barkaoui)
    8. Longitudinal Multilevel modeling to examine changes in second language test scores (Khaled Barkaoui & Yo In’nami)

    Part III. Nature-inspired data mining methods in language assessment
    9. Classification and Regression Trees in predicting listening item difficulty (Vahid Aryadoust & Christine Goh)
    10. Evolutionary Algorithm-Based Symbolic Regression to determine the relationship of reading and lexico-grammatical knowledge (Vahid Aryadoust)



    Vahid Aryadoust is assistant professor of language assessment literacy at the National Institute of Education of Nanyang Technological University, Singapore. He has led a number of language assessment research projects funded by, for example, the Ministry of Education (Singapore), Michigan Language Assessment (USA), Pearson Education (UK), and Paragon Testing Enterprises (Canada) and has published his research in Language Testing, Language Assessment Quarterly, Assessing Writing, Educational Assessment, Educational Psychology, and Computer Assisted Language Learning. He has also (co)authored a number of book chapters and books that have been published by Routledge, Cambridge University Press, Springer, Cambridge Scholar Publishing, Wiley Blackwell, and so on. He is a member of the advisory board of multiple international journals including Language Testing (Sage), Language Assessment Quarterly (Taylor & Francis), Educational Assessment (Taylor & Francis), Educational Psychology (Taylor & Francis), and Asia Pacific Journal of Education (Taylor & Francis). In addition, he has been awarded the Intercontinental Academia Fellowship (2018–2019), which is an advanced research program launched by the University-Based Institutes for Advanced Studies. Vahid’s areas of interest include theory-building and quantitative data analysis in language assessment, neuroimaging in language comprehension, and eye tracking research.

    Michelle Raquel is a senior lecturer at the Centre of Applied English Studies, University of Hong Kong, where she teaches language testing and assessment to postgraduate students. She has worked in several tertiary institutions in Hong Kong as an assessment developer and has either led or been part of a group that designed and administered large-scale diagnostic and language proficiency assessments such as Hong Kong Institute of Education’s Tertiary English Language Test (TELT), Hong Kong University of Science and Technology’s English Language Proficiency Assessment (ELPA), and Diagnostic English Language Tracking Assessment (DELTA), a government-funded inter-institutional project tasked to develop a computer-based academic English diagnostic test. She specializes in data analysis, specifically Rasch measurement, and has published several articles in international journals on this topic as well as on, academic English diagnostic assessment, English as a second language (ESL) testing of reading and writing, dynamic assessment of second language dramatiskills, and English for specific purposes (ESP) testing.