This study examined the effect of baseline estimation on the quality of trend estimates derived from Curriculum Based Measurement of Oral Reading (CBM-R) progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for schedules ranging from 6-20 weeks for datasets with high and low levels of residual variance (poor and good quality datasets respectively). Three observations per day for the first three days of data collection were generated for baseline estimation. As few as one and as many as nine observations were used to calculate baseline. The number of weeks of progress monitoring and the quality of the dataset were highly influential on the reliability, validity, and precision of simulated growth estimates. Results supported the use of using the median of three observations collected on the first day to estimate baseline, particularly when the first observation of that day systematically underestimated student performance. Collecting a large number of observations to estimate baseline does not appear to improve the quality of CBM-R growth estimates.