EDA
(Exploratory Data Analysis)

Topics

In this module, we look at methods of analyzing and visualizing correlation matrices, including multidimensional scaling, treating correlations as networks, working with generalized proximity matrices, using principal components analysis, and cluster analysis.

We will be using the ucinet software for this class.

Readings

• Kruskal and Wish. Multidimensional Scaling

• DeJordy, R., Borgatti, S. P., Roussin, C., & Halgin, D. S. (2007). Visualizing proximity data. Field Methods, 19(3), 239-263. 

• Darlington on factor analysis

• Hierarchical cluster analysis

• Eigenstructures and factor analysis (This replaces Exploratory factor analysis which is nice-looking but not as good)

• Multidimensional scaling

• Correspondence analysis

  • illustration 

Datasets

• Holidays.##h

• Holidays.##d

• HOlidays.xls

• Greenacre Doctorates.xlsx

  • Greenacre_Doctorates.##h

  • Greenacre_Doctorates.##d

• Marks.xlsx

  • marks.##h

  • marks.##d

• occup.xlsx

• animal traits 

Class notes

• Notes | Eda