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.
Highlights
Data visualization is a type of graphical tools used for understanding abstract relationships among variables
Graphical methods compensate for the limits of traditional statistical techniques by displaying massive data points in one or multiple graphs such that its global patterns can be comprehended while several levels of detail can be revealed (Tukey, 1993; Tufte, 1983; Wilkinson, 1999)
The above SEER plot indicates that the Survey of Doctorate Recipients (SDR) sample has approximately 1,500 doctorate recipients who were ever employed in the computer and mathematical sciences discipline between the 1993 and 1999 survey years
Summary
Data visualization is a type of graphical tools used for understanding abstract relationships among variables. Graphical methods compensate for the limits of traditional statistical techniques by displaying massive data points in one or multiple graphs such that its global patterns can be comprehended while several levels of detail can be revealed (Tukey, 1993; Tufte, 1983; Wilkinson, 1999). Displaying categorical data remains a challenge, especially for data with many categories. This is because, as is discussed in research by Blasius (1998), Hofmann (2000), and Friendly (1992, 2000, and 2002), each category in categorical data can be represented as a dimension and, frequently, high-dimensional data can be hard to depict on paper or on computer screens. We first discuss the specific challenges faced by survey analysts in displaying multidimensional data. We propose a method followed by an illustrated example using an occupational survey
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
