56.043 - Mining Large-Scale Data Sets for Different Purposes: National and International Examples

Session Type: Paper Session

Abstract

In this session, researchers will present four papers that illustrate how data from large-scale datasets from the U.S. and England may be used for different purposes. Researchers use these data to answer basic questions about student outcomes at different levels of education, to examine the impacts of national education policy on school staff, and to assess whether several measures of student attitudes and behaviors are equally valid when used with different racial/ethnic groups. The papers make use of a variety of analytic approaches, including linear and nonlinear regression, exploratory and confirmatory factor analysis, latent class analysis, and comparative interrupted time series methods. Datasets from the National Center for Education Statistics and from the National Centre for Social Research are used.

Sub Unit

• SIG-Advanced Studies of National Databases

Chair

• Kimberly Howard Robinson, W.K. Kellogg Foundation

Papers

• Examining the Cultural Sensitivity of Scales Measuring the Sociocultural Adaptation of Ethnically Diverse Adolescents in England - Anica G. Bowe, Oakland University

• Relationships Between Family Risks and Children's Academic Gains From Kindergarten to Third Grade - Amy H. Rathbun, American Institutes for Research

• Unpacking the M in STEM: A Latent Class Analysis of STEM Graduates on High School Mathematics Attitudes - Yihan Zhao, Teachers College, Columbia University; Alex J. Bowers, Teachers College, Columbia University

• The Impacts of No Child Left Behind on Principals' and Teachers' Decision Making: A Longitudinal Study - Jiangang Xia, University of Nebraska - Lincoln; Jianping Shen, Western Michigan University; Xingyuan Gao, Western Michigan University; Houston Lester, University of Nebraska - Lincoln

Discussant

• John Poggio, The University of Kansas