The purpose of this study is to explore whether latent variable models may be used to detect individual variability in the interpretation of vague quantifiers of behavioral frequency. Specifically, differential interpretation is modeled as a continuous latent interpretation factor (via a random intercept item factor model) and categorical latent interpretation classes (via a factor mixture model). Using data from an experiment embedded in the 2006 National Survey of Student Engagement (NSSE), the current study finds that differential interpretation may be best represented as variability between latent interpretation classes. These analyses illustrate how a researcher might use certain latent variable models to extract a methodological artifact—differential interpretation—from measurement models intended to be purely substantive.