This study compared the accuracies of four differential item functioning (DIF) estimation methods, where each method makes use of only one of the following: raw data, logistic regression, loglinear models, or kernel smoothing. The major focus was on the estimation strategies' potential for estimating score-level, conditional DIF. A secondary focus was on assessing the accuracy of strategies' overall DIF effect sizes and statistical significance tests. A real data simulation was used to evaluate the estimation strategies with 6 items representing DIF and No DIF situations, and with 4 sample size combinations for the reference and focal group data. Results showed that the logistic regression estimation strategy was the most highly recommended strategy in terms of the bias and variability of its estimates and the power of its statistical significance test. The loglinear models strategy had flexibility advantages, but these advantages only offset the greater variability of its estimates and its reduced statistical power when sample sizes were large. The kernel smoothing estimation strategy was the least accurate of the considered strategies due to estimation problems when the reference and focal groups differed in overall ability. Appended are: (1) Differential Item Functioning (DIF) Estimates Using Raw Data; (2) Differential Item Functioning (DIF) Estimates Using Logistic Regression; (3) Differential Item Functioning (DIF) Estimates Using Loglinear Models; and (4) Differential Item Functioning (DIF) Estimates Using Kernel Smoothing. (Contains 12 figures and 10 tables.)