Covariater

Covariater is a neologism rarely used in formal statistics. In informal use, it denotes a variable, factor, or agent that induces or reflects covariance among other variables in a dataset. Unlike covariate, which is a defined observable variable included in analytical models to account for potential association with the outcome, a covariater is typically described as the source or mechanism that creates the observed covariation, or as an entity that covaries with multiple variables across observations.

Usage and examples of the term are limited and mostly anecdotal. In a data simulation context, a covariater may represent a latent construct or process whose values influence several measured variables, thereby generating a correlation structure among them. In observational research, some discussions might refer to an unmeasured covariater or underlying process as a driver of spurious or confounded correlations, though this role is more commonly described using established terms such as confounder or latent variable.

Etymology and reception: The word combines covary with the agentive suffix -er, forming a label for “one who covaries.” It is not part of standard statistical nomenclature, and its use is largely informal or speculative. For formal writing, authors are advised to use established terms—such as covariate, latent variable, or confounder—depending on the precise role in the analysis.