Original Article
Information Visualization (2005) 4, 276–289. doi:10.1057/palgrave.ivs.9500105
Visualizing large-scale data in educational, behavioral, psychometrical and social sciences: utilities and design patterns of the SEER computational and graphical statistics
This research was supported in part by the Law School Admission Council (LSAC), the American Statistical Association (ASA), the University of Pittsburgh, the American College Testing (ACT, Inc.), the Educational Testing Service (ETS), the National Science Foundation (NSF), the Accu Measurement and Testing (AMT), and the National Center of Educational Statistics (NCES). The research findings and methodology do not necessarily reflect the opinions, the policies, or the operational procedures of these institutions. Some part of the research was conducted while the corresponding author was a Senior Research Fellow with NCES; a Research Fellow with NSF; and a Professor at the University of Pittsburgh.
Christopher W T Chiu1, Peter Pashley1, Marilyn Seastrom2 and Peggy Carr2
- 1Law School Admission Council (LSAC), Newtown, PA, USA
- 2National Center of Educational Statistics (NCES), Washington, D.C., USA.
Correspondence: Christopher WT Chiu, LSAC, 662 Penn Street, Newtown, PA, USA. Tel: +215 968 1279
Received 26 January 2005; Revised 21 July 2005; Accepted 24 August 2005.
Abstract
This paper introduces a graphical method SEE Repeated-measure data (SEER) to visually analyze data commonly collected in large-scale surveys, market research, biostatistics, and educational and psychological measurement. Many researchers in these disciplines encounter large amounts of data. Examples include the Law School Admission Test (LSAT) repeater scores, career paths of students graduated from college, essays scores in the writing assessments of the National Assessment of Educational Progress (NAEP), and scores derived from different test equating methods in the discipline of psychometrics. Efficiency, ease-of-interpretations, applicability, user interactions are challenges due to the graphical complexity in visualizing large-scale data sets. To overcome these challenges, the author expands a systematic data-visualization technique, called SEER. The SEER technique was originally designed to depict career paths and occupational stability for professionals in the science and engineering discipline. In this paper, the author summarizes this example and highlights its applications in legal education, psychometrics, and other related areas. The author also, (a) expands this technique to examine repeat test takers' scores, (b) illustrates how to monitor inter-rater consistency for essay scoring and for depicting multi-faceted data that involve human judgments, and (c) demonstrates how to investigate differences of test equating and scaling methodology using the SEER method. The broader impacts and design patterns of the SEER method are discussed.
Keywords:
Computational statistics, data visualization, large-scale data, research methodology, SEER, social and behavioral sciences
