Journal of Data Science

Abstract: This paper introduces a visualization technique, SEER, devel oped for policy makers and researchers to graphically analyze and explore massive amounts of categorical data collected in longitudinal surveys. This technique (a) produces panels of graphs for multiple group analysis, where the groups do not have to be mutually exclusive, (b) profiles change pat terns observed in longitudinal data, and (c) clusters data into groups to enable policy makers or researchers to observe the factors associated with the changing patterns. This paper also includes the hash function, of the SEER method, expressed in matrix notation for it to be implemented across computer packages. The SEER technique is illustrated by using a national survey, the Survey of Doctorate Recipients (SDR), administered by the Na tional Science Foundation (NSF). Occupational changes and career paths for a panel sample of 14,901 doctorate recipients are profiled and discussed. Results indicated that doctorate recipients in some science and engineering fields are roughly two times more likely to work in an occupation when it is the discipline in which they received their doctorates.