The purpose of this study was to empirically evaluate the impact of loglinear presmoothing accuracy on equating bias and variability across chained and post-stratification equating methods, kernel and percentile-rank continuization methods, and sample sizes. The results of evaluating presmoothing on equating accuracy generally agreed with those of previous presmoothing studies, suggesting that less parameterized presmoothing models are more biased and less variable than highly parameterized presmoothing models and raw data. Estimates of standard errors of equating were most accurate when based on large sample sizes and score-level data that were not sparse. The accuracy of standard error estimates was not influenced by the correctness of the presmoothing model. The accuracy of estimates of the standard errors of equating differences was also evaluated. The study concludes with some detailed comparisons of how the kernel and traditional equipercentile continuization methods interacted with data that were presmoothed to different degrees.