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International large-scale assessments (ILSAs) play an important role ineducational research and policy making. They collect valuable data on educationquality and performance development across many education systems, givingcountries the opportunity to share techniques, organizational structures, andpolicies that have proven efficient and successful. To gain insights from ILSAdata, we identify non-cognitive variables associated with students' academicperformance. This problem has three analytical challenges: 1) academicperformance is measured by cognitive items under a matrix sampling design; 2)there are many missing values in the non-cognitive variables; and 3) multiplecomparisons due to a large number of non-cognitive variables. We consider anapplication to the Programme for International Student Assessment (PISA),aiming to identify non-cognitive variables associated with students'performance in science. We formulate it as a variable selection problem under ageneral latent variable model framework and further propose a knockoff methodthat conducts variable selection with a controlled error rate for falseselections.

Variable Selection in Latent Regression IRT Models via Knockoffs: An Application to International Large-scale Assessment in Education