Item parameter estimation using an item response theory (IRT) model with fixed ability estimates is useful in equating with small samples on anchor items. The current study explores the impact of three ability estimation methods (weighted likelihood estimation [WLE], maximum a posteriori [MAP], and posterior ability distribution estimation [PST]) and three types of priors set for slopes (true lognormal prior, alternative lognormal prior less informative than the true prior, and no prior) on the item parameter estimations of the two-parameter logistic (2PL) model under different conditions with varying slope mean, slope standard deviation, and data noise in a simulation study. The model is also applied to a real dataset, and the results from the three ability estimation methods and three priors on slopes are compared. The MAP ability estimation with true prior on slopes is recommended as the best choice, followed by the WLE ability estimation with less informative prior on slopes. In practice, it is recommended to use the MAP ability estimation and empirical lognormal prior on slopes and normal prior on intercept derived from a historical dataset (e.g., item bank). If a testing program prefers the WLE ability estimation, then couple it with a less informative prior on slopes than the empirical one from historical data.