Correction for rain attenuation is an important data quality issue when using data collected by radars operating at attenuating wavelengths, specifically C and X bands. Such issues are especially important for quantitative use of the data, such as rainfall estimation, where a 3dB error in reflectivity factor can result in more than 60% error in the rainfall estimate. In this work, the errors from several different attenuation correction techniques are examined. To test the corrections, simulated time-series dual-polarization radar data are used. The basis for the simulations is the use of a discretized radar pulse, where each pulse element generates the appropriately calculated stochastic value to give realistic radar time series data. In addition to providing for a sufficient number of elements to generate statistically meaningful data, this discretized pulse model also enables the simulation of spatial sampling aspects of the radar beam, allowing for differential attenuation and phase shift across the radar beam. These simulated data are used to quantify the performance of several rain attenuation correction algorithms: linear ΦDP, ZPHI, and Self-Consistent, as well as a modified version of the Self-Consistent algorithm. Using the simulated data and respective truth fields, the performance of the algorithms is examined in detail across a variety of scattering and microphysics configurations, to study the impact of the assumptions made on the quality of algorithm performance. A wide array of radar spatial sampling strategies are also examined to identify the impacts on algorithm performance.