The author improves on the measurement of U.S. state public opinion by (1) applying previous methods used only on cross-sectional data to create dynamic measures of state public opinion and (2) providing a systematic comparison of the performance of the various methodological approaches on these dynamic measures. The author shows that scholars can use multilevel regression, imputation, and poststratification (MRP) coupled with a simple moving average to measure state public opinion over time. Compared to aggregation, the MRP approach has less error and is more reliable, particularly for the less populated states. The author shows the applicability of the MRP approach by measuring and validating state partisanship and state ideology over time. The validated measures are available for public use. Armed with a method to measure state public opinion over time, scholars can begin to more fully understand the dynamic relationship between public opinion and policy in the U.S. states.