Applied researchers, our noses deep in the data sources we rely on, are keenly aware of errors and inconsistencies we encounter. Though aware of the potential problems (e.g., attenuation bias) that can result from measurement error, we often plow on with conventional estimation strategies, explicitly or implicitly making convenient assumptions about the nature of the measurement error and hoping for the best. The staffing allocation methods we study represent a different degree of measurement error because the available staffing data (outside of California OSHPD data) are at the hospital level and ad hoc rules are used to allocate staff to the inpatient setting. In this paper, we use data from California where in-patient acute care staffing is recorded directly and estimate the measurement error from applying various staffing allocation methods. We find that the measurement error in allocated staffing levels is large enough to induce significant bias. For example, in our benchmark simple regression model the adjusted patient days method has measurement error large enough to cause the expected coefficient estimate for the effect of nurse staffing on quality of care to be 30-32% too small (and this attenuation bias becomes worse in a multiple regression model). Fortunately, there are easy to implement alternate estimation methods that can be applied to overcome this bias. Instrumental variable estimation may be applied, using the revenue proportion staffing measure as an instrument for staffing measured by adjusted patient days or administrative hours (but some caution is warranted in using administrative hours as an instrument for adjusted patient days or vice-versa). Alternatively, the California OSHDP data may be treated as validation data to obtain predicted staffing levels which may be used as the measure of staffing without the resulting estimates suffering from attenuation bias.