smartcor: Intelligent Correlation Method Selection for Mixed Variable Types

Abstract

Pearson correlation is the default measure of association in most statistical software, yet it is only appropriate for pairs of continuous variables with a linear relationship. When variables are binary, ordinal, or categorical, specialized methods (e.g., point-biserial, polychoric, tetrachoric, and Cramér's~$V$) may be more appropriate, but practitioners rarely know which to select. The \textbf{smartcor} package for \textbf{R} (and its companion \textbf{pysmartcor} package for \textbf{Python}) automatically detects variable types, selects the statistically appropriate correlation method for each pair, and explains its reasoning. The package supports 14~correlation and association methods covering all 10~variable-type pair combinations, and distinguishes true correlation (for ordinal and continuous pairs) from statistical association (for nominal categorical pairs). Monte~Carlo simulations validate the selection logic, and a case study with General Social Survey data demonstrates substantive differences between naive and type-aware correlation analysis.

38 pages, 7 figures