Winner of the 2017 AERA Division D Significant Contribution to Educational Measurement and Research Methodology Award!
Technological and statistical advances, along with a strong interest in gathering more information about the state of our educational systems, have made it possible to assess more students, in more countries, more often, and in more subject domains. The Handbook of International Large-Scale Assessment: Background, Technical Issues, and Methods of Data Analysis brings together recognized scholars in the field of ILSA, behavioral statistics, and policy to develop a detailed guide that goes beyond database user manuals.
After highlighting the importance of ILSA data to policy and research, the book reviews methodological aspects and features of the studies based on operational considerations, analytics, and reporting. The book then describes methods of interest to advanced graduate students, researchers, and policy analysts who have a good grounding in quantitative methods, but who are not necessarily quantitative methodologists. In addition, it provides a detailed exposition of the technical details behind these assessments, including the test design, the sampling framework, and estimation methods, with a focus on how these issues impact analysis choices.
Policy and Research Relevance of International Large-Scale Assessment Data A Brief Introduction to Modern International Large-Scale Assessment David Rutkowski, Leslie Rutkowski, and Matthias von Davier International Large-Scale Assessments: From Research to Policy Hans Wagemaker The Impact of International Studies of Academic Achievement on Policy and Research Stephen P. Heyneman and Bommi Lee Analytic Processes and Technical Issues Around International Large-Scale Assessment Data Assessment Design for International Large-Scale Assessments Leslie Rutkowski, Eugene Gonzalez, Matthias von Davier, and Yan Zhou Modeling Country-Specific Differential Item Functioning Cees Glas and Khurrem Jehangir Sampling, Weighting, and Variance Estimation in International Large-Scale Assessments Keith Rust Analytics in International Large-Scale Assessments: Item Response Theory and Population Models Matthias von Davier and Sandip Sinharay Imputing Proficiency Data under Planned Missingness in Population Models Matthias von Davier Population Model Size, Bias, and Variance in Educational Survey Assessments Andreas Oranje and Lei Ye Linking Scales in International Large-Scale Assessments John Mazzeo and Matthias von Davier Design Considerations for the Program for International Student Assessment Jonathan P. Weeks, Matthias von Davier, and Kentaro Yamamoto Innovative Questionnaire Assessment Methods to Increase Cross-Country Comparability Patrick C. Kyllonen and Jonas Bertling Relationship between Computer Use and Educational Achievement Martin Senkbeil and Joerg Wittwer Context Questionnaire Scales in TIMSS and PIRLS 2011 Michael O. Martin, Ina V. S. Mullis, Alka Arora and Corinna Preuschoff Motivation and Engagement in Science Around the Globe: Testing Measurement Invariance with Multigroup Structural Equation Models across 57 Countries Using PISA 2006 Benjamin Nagengast and Herbert W. Marsh Contextual Indicators in Adult Literacy Studies: The Case of PIAAC Jim Allen and Rolf van der Velden Advanced Analytic Methods for Analyzing International Large-Scale Assessment Data Incorporating Sampling Weights into Single and Multilevel Analyses Laura M. Stapleton Multilevel Analysis of Assessment Data Jee-Seon Kim, Carolyn J. Anderson, and Bryan Keller Using Structural Equation Models to Analyze ILSA Data Leslie Rutkowski and Yan Zhou Efficient Handling of Predictors and Outcomes Having Missing Values Yongyun Shin Multilevel Modeling of Categorical Response Variables Carolyn J. Anderson, Jee-Seon Kim, and Bryan Keller Causal Inference and Comparative Analysis with Large-Scale Assessment Data Joseph P. Robinson Analyzing International Large-Scale Assessment Data within a Bayesian Framework David Kaplan and Soojin Park A General Psychometric Approach for Educational Survey Assessments: Flexible Statistical Models and Efficient Estimation Methods Frank Rijmen, Minjeong Jeon, Matthias von Davier, and Sophia Rabe-Hesketh