Using National Data Sets to Explore Postsecondary Pathways and Outcomes

Session Type: Paper Session

Abstract

The four papers in this session illustrate how data from U.S. large-scale postsecondary datasets may be used for different purposes. Researchers use these data to explore relationships between STEM coursework rigor and student decisions to change career objectives; to measure within- and out-state outmigration patterns of postsecondary students and their relation to financial and academic outcomes; to compare matching methods used to evaluate community college outcomes of high school career/technical education completers; and to examine educational attainment of English Language Learners from different backgrounds. The papers use a variety of analytic approaches, including multivariate and logistic regression, propensity score modeling, and coarsened exact matching. Datasets from the National Center for Education Statistics, the U.S. Census, and other sources are used.

Sub Unit

• SIG-Advanced Studies of National Databases

Chair

• Mehmet Ozturk, College of the Sequoias

Papers

• STEM Pipeline Attrition During Early College: The Role of More Rigorous Standards in Freshman STEM Courses - Gregory J. Palardy, University of California - Riverside; Meaghan Beth McMurran, University of California - Riverside

• (Re)Defining Student Out-Migration in the American Higher Education System - Manuel S. Gonzalez Canche, University of Pennsylvania

• Comparing Propensity Score Matching and Coarsened Exact Matching: An Example Case Using Community College Outcomes - Eric Lichtenberger, Illinois Board of Higher Education; Cecile Dietrich, Radford University

• Exploring the Factors Associated With ELLs' Educational Attainment - Myley Dang, Mathematica Policy Research, Inc

Discussant

• James A. Griffin, National Institute of Child Health and Human Development