Two-dimensional euclidean planes and additive trees are two of the most common representations of proximity data for multidimensional scaling. Guidelines for comparing these representations and discovering properties that could help identify which representation is more appropriate for a given data set are presented. (Author/JKS)

A Monte Carlo study investigated the ability of the ALSCAL multidimensional scaling program to recover true structure inherent in simulated proximity measures when data were missing. The program worked well with up to 60 percent missing data as long as sample size was large and random error was low. (Author/JKS)

Coordinate graphs of time-series data have been significant in the history of statistical graphing and in recent school mathematics curricula. A survey task to construct a graph to represent data about temperature change over time was administered to 133 students in Grades 3, 5, 7, and 9. Four response levels described the degree to which students…