Multilevel modeling offers researchers a rich array of tools that can be used for a variety of purposes, such as analyzing specific institutional issues, looking for macro-level trends, and helping to shape and inform educational policy. One of the more complex multilevel modeling tools available to institutional researchers is cross-classified random effect modeling (CCREM). CCREM is similar to other types of multilevel models in which people or other items of interest are nested within various categories. However, CCREM is appropriately used when the groupings of the data are not strictly hierarchical. This article provides one example of how CCREM can be used to assess faculty gender pay differentials in higher education and how those results can be used to help inform policy at the institutional level. The study presented examines the faculty gender pay gap both overall and by principal activity and describes institutional and disciplinary effects on base faculty salary. There are, however, many other powerful ways in which CCREM could be used to examine gender pay equity and help inform institutional policy. Some possibilities include: (1) estimating variance in salary between institutions, between disciplines, and between faculty; (2) estimating gender pay differentials by discipline and institution type; and (3) identifying disciplinary or institutional characteristics associated with faculty salary. (Contains 4 tables and 2 figures.)