{"id":1034,"date":"2023-12-09T15:42:24","date_gmt":"2023-12-09T23:42:24","guid":{"rendered":"https:\/\/labs.wsu.edu\/lprc\/?page_id=1034"},"modified":"2026-09-10T13:27:29","modified_gmt":"2026-09-10T20:27:29","slug":"lsd","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/lprc\/labs\/lsd\/","title":{"rendered":"LSD Lab"},"content":{"rendered":"
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Large-Scale Data Lab<\/h1>\n\n\n
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The\u00a0Large-Scale Data Laboratory (LSD Lab)\u00a0<\/strong>is a research and methodological resource within the\u00a0Learning and Performance Research Center (LPRC)<\/a> at Washington State University\u2019s College of Education, Sport, and Human Sciences. Established in May 2019 with support from the LPRC, the WSU College of Education High-Risk\/High-Reward Grant, and the WSU New Faculty Seed Grant, the LSD Lab provides infrastructure, methodological expertise, and collaborative support for research using complex educational, psychological, and other large-scale datasets. The lab is currently directed by Dr. Shenghai Dai<\/a>.<\/p>\n\n\n\n

The LSD Lab supports research involving large-scale assessments and surveys, educational measurement and psychometrics, complex and longitudinal data, advanced quantitative and computational methods, and program evaluation<\/strong>. The lab provides a secure research environment for working with confidential and restricted-use datasets, including data from the National Assessment of Educational Progress (NAEP)<\/strong> and other large-scale assessment and survey programs. In addition to secure data infrastructure, the lab provides access to powerful computing resources and statistical software for managing, processing, and analyzing large, complex datasets.<\/p>\n\n\n\n

Through the LPRC, the LSD Lab provides a collaborative environment for faculty and student research, interdisciplinary collaboration, methodological development, and research training<\/strong>. Its work spans assessment and survey methodology, measurement and psychometrics, advanced statistical and longitudinal analysis, machine learning and data science, instrument development and validation, and statistical software development.<\/p>\n\n\n\n