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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.

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