Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations
Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations

Summary/Abstract: Two-predictor suppression situations continue to produce uninterpretable conditionsin linear regression. In an attempt to address the theoretical complexities related tosuppression situations, the current study introduces two different versions of a softwarecalled suppression simulator (Supsim): a) the command-line Python package, and b) theweb-based JavaScript tool, both of which are able to simulate numerous random twopredictor models (RTMs). RTMs are randomly generated, normally distributed data vectorsx1, x2, and y simulated in such a way that regressing y on both x1 and x2 results in theoccurrence of numerous suppression and non-suppression situations. The web-basedSupsim requires no coding skills and additionally, it provides users with 3D scatterplots ofthe simulated RTMs. This study shows that comparing 3D scatterplots of differentsuppression and non-suppression situations provides important new insights into theunderlying mechanisms of two-predictor suppression situations. An important focus is onthe comparison of 3D scatterplots of certain enhancement situations called Hamilton'sextreme example with those of redundancy situations. Such a comparison suggests that thebasic mathematical concepts of two-predictor suppression situations need to bereconsidered with regard to the important issue of the statistical control function.

• Details
• Contents