THE DETECTION AND CORRECTION OF BIAS IN STUDENT-RATINGS OF INSTRUCTION

With the use of surveys of instructional effectiveness that use Likert rating scales, bias is a potential threat to the validity of interpre tations. Simple summation of ratings or the use of larger samples are not methods for removing bias. In this study, a new model for scaling ratings is examined. The method both identifies and corrects for bias. Working with a database of student ratings of college instruction, th e model was tested in terms of a variety of criteria. Results indicate d that bias was detected and that it was large enough to warrant our c oncern. The statistical corrections were significant both in terms of order and magnitude of class means. Implications for future studies in clude the specification of more potential sources of bias, the interac tion of some of these factors, and the development of more systematic evidence supporting the need to be attentive to bias. The many-faceted Rasch model used in this study needs more evaluation before we are co nvinced of its utility to study and correct for bias, but preliminary evidence is encouraging. Recommendations were offered for a theoretica l rationale for studying bias in student ratings of instructional effe ctiveness and a program of research leading to the use of this model f or reporting results for use in improving instruction and for promotio n, tenure, and merit decisions.