Quantitative Data Analysis for Language Assessment Volume II: Advanced Methods

✍ Scribed by Vahid Aryadoust (editor), Michelle Raquel (editor)

• Publisher: Routledge
• Year: 2019
• Tongue: English
• Leaves: 260
• Edition: 1°
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Table of contents
List of figures
List of tables
List of volume II contributors
Preface
Introduction
SECTION I: Advanced item response theory (IRT) models in language assessment
1.
Applying the mixed Rasch model in assessing reading comprehension
2.
Multidimensional Rasch models in first language listening tests
3.
The log-linear cognitive diagnosis modeling (LCDM) in second language listening assessment
4.
Application of a hierarchical diagnostic classification model in assessing reading comprehension
SECTION II:
Advanced statistical methods in language assessment
5.
Structural equation modeling to predict performance in English proficiency tests
6.
Student growth percentiles in the formative assessment of English language proficiency
7.
Multilevel modeling to examine sources of variability in second language test scores
8.
Longitudinal multilevel modeling to examine changes in second language test scores
SECTION III:
Nature-inspired data-mining methods in language assessment
9.
Classification and regression trees in predicting listening item difficulty
10.
Evolutionary algorithm-based symbolic regression to determine the relationship of reading and lexicogrammatical knowledge
Index