This study compared three methods of detecting test item bias, the chi-square approach, the transformed item difficulties approach, and the Linn-Harnish three-parameter item response approach which is the only Item Response Theory (IRT) method that can be utilized with minority samples relatively small in size. The items on two tests which measured writing and reading skills were examined for evidence of sex and ethnic bias. Eight sets of samples, four from each test, were randomly selected from the population (N=7287) of sixth, seventh, and eighth grade students enrolled in a large, urban school district in the southwestern United States. Each set of samples, male/female, White/Hispanic, White/Black, and White/White, contained 800 examinees in the majority group and 200 in the minority group. In an attempt to control differences in ability that may have existed between the various population groups, examinees with scores greater or less than two standard deviations from their group's mean were eliminated. Ethnic samples contained equal numbers of each sex. The White/White sets of samples were utilized to provide baseline bias estimates because the tests could not logically be biased against these groups. Bias indices were then calculated for each set of samples with each of the three methods. Findings of this study indicate that the percent agreement between the Linn-Harnish IRT method and the chisquare and transformed difficulties methods is similar to that found in previous studies comparing the latter approaches with other IRT methods requiring large minority samples. Therefore, it appears that the Linn-Harnish IRT approach can be used in lieu of other more restrictive IRT methods. Ethnic bias appears to exist in the two tests as measured by the large mean bias indices for the White/Hispanic and White/Black samples. Little sex bias was found as evidenced by the low mean bias indices of the male/ female samples and the fact that the male/female mean bias indices were lower than those of the White/White in 33% of the samples.