Multivariate Data Exploration with Stata Evaluation and Wish

Multivariate Data Exploration with Stata: Evaluation and Wish List Stephen Soldz Boston Graduate School of Psychoanalysis ssoldz@bgsp. edu

Principal Components Analysis Purpose: Data exploration and data reduction Available in Stata Issues/Limitations • Base ado (pca) • Built-in (factor, pcf) • score will produce component scores • • pca just a wrapper for (now undocumented) pc option to factor, which user cannot access and modify Confusing documentation on difference between pca and factor, pcf (i. e. , scaling of eigenvectors) Does not directly allow pca of correlation/ covariance matrix – must use corr 2 data, introducing error Does not allow rotate to “protect” user – seems

Exploratory Factor Analysis Purpose: Data exploration and data reduction Available in Stata • Built-in factor allows principal factors (with and without iteration of communalities), maximum likelihood • Built-in rotate allows varimax (with and without Horst correction) and promax Issues/Limitations § factor, pfi (prinipal factors with iteration) does not allow specification of number of times to iterate – this directly conflicts with Gorsuch (1983) recommendation that communalities be iterated only 3 -4 times § As factor built-in, users cannot modify or build on it rotate options very limited (only varimax and promax) and users cannot modify, though they could access eigenvectors (matix_get) and write their own

Exploratory Factor Analysis, Continued Available in Stata • Built-in factor allows principal factors (with and without iteration of communalities), maximum likelihood • Built-in rotate allows varimax (with and without Horst correction) and promax Issues/Limitations § rotate not well documented, so not clear if one could, e. g. , rotate canonical correlations as suggested by Cliff & Krus (1976).

Correspondence Analysis Purpose: Data exploration and reduction of categorical data • • Available in Stata Issues/Limitations User-written coranal (correspondence analysis) User-written mca (multiple correspondence analysis) • Graphics broken in Stata 8 • Statalist question as to whether mca is producing correct output • Few variations implemented

Optimal Scaling Purpose: Data exploration, reduction, and transformation Available in Stata • None (that I’m aware of) Issues/Limitations

Multidimensional Scaling Purpose: Data exploration Available in Stata • None (that I’m aware of) Issues/Limitations

Conclusion • Stata is weak in”multivariate exploratory data analysis” procedures. • Many existing procedures are inflexible and not extensible, or user-contributed and not currently maintained. • Stata lags behind SPSS, SAS, S-Plus, and R in this area.