What steps can be taken to reduce the risk of bias in testing?
Since test results continue to remain widely used in making critical decisions on students, test developers, as well as experts, have come up with some strategies tailored to reduce the risk, if not remove, test bias as well as unfairness. Among the representative examples of these strategies include coming up with efforts to Strive for diversity in test-development staffing while training test developers as well as scorers remaining vigilant of the potential for, linguistic, cultural and socioeconomic bias. Secondly, bias in tests can be reduced by having test materials getting reviewed by experts trained in identification of cultural bias and by representatives of culturally as well as linguistically diverse subgroups. Actors can also reduce the risks of bias by ensuring that norming processes and sample sizes used in developing norm-referenced tests remain inclusive of various student subgroups and broad enough to have a representative sample (Camilli & Shepard, 1994). Those in charge of testing can also reduce the risk of bias in testing by eliminating items which produce the significant racial as well as cultural performance gaps, and choosing items with the smallest gaps. This strategy is called “the golden rule. Bias can similarly be reduced through screening for and eliminating items, references as well as terms which prove offensive to particular groups. Initiatives such as translating tests into a test taker’s native language or utilizing interpreters to translate test items can also be the best strategy to reduce test bias. Those with the responsibility of testing can also include more performance-based items to lessen the role that language, as well as word-choice, plays in the test performance. Finally, making use of multiple assessment measures to determine academic success and progress, and averting the use of test scores while excluding other information to come up with critical decisions on students’ can also reduce the risk of bias in testing.
Camilli, G., & Shepard, L. A. (1994): Methods for identifying biased test items. Thousand Oaks [u.a.: Sage.