When a survey is offered in more than one mode of administration, the potential for bias attributable to the mode may threaten the cross-mode exchangeability of responses or comparability of results. We demonstrate the utility of Item Response Theory (IRT) in quantifying the presence of mode effects, providing insight into the nature of the effects, and adjusting cross-mode results without post-hoc adjustment. Such IRT applications are of interest when the survey instrument informs an underlying latent trait. We present a Bayesian hierarchical IRT model that can accommodate multiple modes of survey administration and provide cluster-level parameter estimates of the latent trait when observed groupings of respondents are of interest. We illustrate the model with data from a randomized mode experiment in which responding subjects within each of 45 evaluated institutions were randomly assigned to one of four response modes: mail, phone, IVR, and a mixed mode of mail with phone follow-up. Results indicate presence of form-wide mode of administration effects that differ across response categories and the underutilization of an interior response category in certain modes.