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Applications of New Analysis Methods to Large-Scale Assessments
Fri, April 22, 8:00 to 9:30am PDT (8:00 to 9:30am PDT), Manchester Grand Hyatt, Floor: 3rd Level, Seaport Tower, Hillcrest CDSession Type: Paper Session
Abstract
New analysis methods including Elastic Net, Lasso regression, random forest, latent profile analysis, and multinomial logistic regression are applied to large-scale assessments including PIRLS, PISA, and PIAAC, to answer different substantive research questions.
Sub Unit
Chairs
Papers
Predicting Reading Self-Concept for English Learners on 2018 PISA Reading - Onur Ramazan, Washington State University; Shenghai Dai, Washington State University; Robert William Danielson, Washington State University - Spokane; Tao Hao, East China Normal University; Yuliya Ardasheva, Washington State University - Tri Cities
Identifying Factors Affecting PISA 2018 Digital Reading Literacy via Machine Learning Algorithms - Hao-Yue Jin, University of Alberta; Lydia Marion González-Esparza, University of Alberta; Chang Lu, Shanghai Jiao Tong University; Maria Cutumisu, McGill University
A Modeling Approach to Identify Academically Resilient Students: Evidence From PIRLS 2016 - Stefan Johansson, University of Gothenburg; Kajsa Yang Hansen, University of Gothenburg; Cecilia Thorsen, University West
Do Immigrants Experience Labor Market Mismatch? New Evidence From the U.S. Program for the International Assessment of Adult Competencies - Margarita Pivovarova, Arizona State University; Jeanne M. Powers, Arizona State University