This paper formulates a metatheoretical framework for latent variable modeling. It does so by spelling out the difference between observed and latent variables. This difference is argued to be purely epistemic in nature: We treat a variable as "observed" when the inference from data structure to variable structure can be made with certainty and as "latent" when this inference is prone to error. This difference in epistemic accessibility is argued to be directly related to the data-generating process, i.e., the process that produces the concrete data patterns on which statistical analyses are executed. For a variable to count as observed through a set of data patterns, the relation between variable structure and data structure should be (a) deterministic, (b) causally isolated, and (c) of equivalent cardinality. When any of these requirements is violated, (part of) the variable structure should be considered latent. It is argued that, on these criteria, observed variables are rare to nonexistent in psychology; hence, psychological variables should be considered latent until proven observed. (Contains 1 figure and 2 footnotes.)