Exploratory graphs are a crucial element of statistical analysis and models that help us identify and interpret data patterns. As an example of exploratory graphs, multidimensional scaling (MDS) allows the visualization of large and complex datasets in a reduced dimensional space, while preserving pairwise differences and similarities between data points. Co-plot analysis extends the capabilities of traditional MDS and enables a multifaceted view of relationships between variables from different datasets. To develop a co-plot graph, the analyst starts out by standardizing the dataset and by generating a median absolute deviation correlation coefficient (MADCC) that subjects the dataset to MDS-embedding and helps to determine how the dataset fits the surface. This study demonstrates the use of non-metric multidimensional scaling (NMDS) and robust multidimensional scaling (RMDS) techniques to visualize dissimilarities and distances of the observations in a reduced dimensional space. The study also explains the use of shepard graphs as a second technique to visualize categorical variables in a reduced-dimensional space, with points closer together indicating higher similarity. Shepard graphs can assess the goodness of fit, which is indicated by the strength of a positive linear relationship. To demonstrate the application of co-plot analysis, two scenarios are used in the study. Scenario one graphically displays and compares MBA Programs in the U.S. Scenario two presents how co-plot analysis can be applied to describe and compare the patterns of ecological communities and species across multiple sites and environmental contexts. This study highlights the benefits of co-plot graphs in general as well as three different techniques in particular: Shepard analysis, NMDS, and RMDS techniques. Keywords: Multidimensional scaling (MDS), Co-plot, Data visualization

Recommended Citation: Poosapati P., Shi Y., Gebauer J., Song Y., (2023). Applications of Multidimensional Scaling with Co-Plot Analysis. UNCW MS CSIS Proceedings. V. 17 , N. 15 .