This study examines the relationship between sample size and item parameter estimation precision when utilizing the one-parameter model. Item parameter estimates are examined relative to "true" values by evaluating the decline in root mean squared deviation (RMSD) and the number of outliers as sample size increases. This occurs across three conditions. The first condition utilizes one test level of 40 items without missing data. The second incorporates missing data and the third utilizes a vertical scale across three levels. When test levels were calibrated individually, the majority of RMSD and outlier-reduction improvement was achieved once a sample size of 400 examinees had been reached. Incremental increases to sample size beyond a threshold of 500 examinees added little gain to estimation precision. With the vertical-scaling condition, the majority of RMSD and outlier-reduction improvement was seemingly reached with a sample size of approximately 300 examinees per level with 500-600 examinees per common item linking set. However, when compared to conditions one and two, a similar level of estimation precision was not reached until a sample size of approximately 400-450 examinees per each of the three levels. Supplemental tables are appended.