Karen Nylund-Gibson of UC Santa Barbara’s Gevirtz School is one of a select group of faculty chosen to attend the AERA Faculty Institute for the Teaching of Statistics with Large-Scale Date Sets, June 15-17, at Stanford University. The Faculty Institute’s goal is to help develop a critical mass of U.S. education researchers at doctoral granting institutions using large-scale federal data sets, especially those sponsored by the National Center for Education Statistics (NCES), the National Science Foundation (NSF), and other federal agencies. These data sets, which are often longitudinal and nationally representative, offer an excellent opportunity for students and early career scholars to conduct research and learn advanced quantitative methods with high quality policy-relevant data. Secondary data analysis of federal data sets provides one of the most opportune and cost-effective ways of generating knowledge and contributing to policy deliberations based on large numbers of individuals and observations.
“I feel that these large scale education data set are an under utilized resources for students and faculty to be able to explore research ideas,” Nylund-Gibsno says. “I also want to learn how to more easily integrate a data set or two into my teaching so it demystifies these data sets for students.”
The AERA Faculty Institute will focus on how to incorporate secondary data analysis, especially the use of large-scale federal data sets, into doctoral-level teaching of statistics or methodology courses. The Faculty Institute will focus on how to use large-scale data sets for teaching concepts central to basic statistics courses. The concepts to be covered include: estimation, standard errors and sampling distributions; reliability; longitudinal analysis; and treatment of missing data. During the training, participants will develop teaching modules based on these topics appropriate for incorporation into their courses. Participants should come away with the knowledge and resources necessary to teach the statistical methods learned in the training to doctoral-level students at their home institutions.
Karen Nylund-Gibson is an Assistant Professor in the UCSB Department of Education. She received a B.S. in Mathematics from Sonoma State University and a M.S. in Survey Research and Statistics from University of Nebraska, Lincoln. She received her Ph.D. in Advanced Quantitative Methods from the UCLA’s School of Education in 2007. After completion of her doctoral work, Nylund-Gibson spent one year as a postdoctoral fellow on an NIH/NIMH Prevention Science Training Grant through the Prevention Research Center at the Johns Hopkins Bloomberg School of Public Health. Her primary area of research is in the development and application of quantitative methods used in social science. She has particular expertise in latent variable modeling, structural equation modeling, longitudinal analysis, and finite mixture models. Dr. Nylund-Gibson is active in the American Educational Research Association and is a founding member of the Prevention Science Methodology Group II, a group of early career prevention researchers. In additional to her methodological work, Dr. Nylund-Gibson collaborates with colleagues in the fields of Education, Psychology, Public Health, and Environmental Science.
[Karen Nylund-Gibson is available for interviews; contact George Yatchisin at 805 893 5789]
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