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429 – Data Challenges in Business and Economics
A Non-Negative Matrix Factorization Analysis of a Multiple-Choice Education Test
Kumer Das
Lamar University
Jay Powell
Better Schooling Systems
Myron Katzoff
Center for Disease Control
Stanley Young
National Institute of Statistical Sciences
Educational questionnaires serve a number of purposes, one of which is to better understand the performance of students so that education delivery can be improved. Principal components analysis, PCA, is often used to help examine a large number of variables to better understand the relationships among them and to summarize into a relatively few ``score" the information in the data set. Singular value decomposition, SVD, can also be employed to reduce the size of a big data matrix. Our idea is to contrast a relatively new matrix factorization method, non-negative matrix factorization, NMF, with PCA and SVD with the goal of pointing to individualized education. We show that NMF offers interpretive advantages.