Using Multidimensional Scaling and Unfolding to Explore Teacher Perceptual Biases for Selected Special Education Referral Categories

Abstract

Multidimensional scaling (POLYCON) and unfolding (PREFMAP) algorithms developed by Young (24) and Carroll (3), respectively, were used to explore teacher perceptions of special education labels (e.g., emotional disturbance) in terms of a reference set of 28 student behaviors (e.g., withdrawn, short attention span) across two context variables: student gender and student ethnicity. The resulting two-dimensional scaling solution revealed teacher perceptual distinctions between student acting-out and passive behaviors, and between student intellectual impairment and specific behavior problems. In addition, scaling analyses also suggested teacher perceptual biases that required both males and minority (i.e., black) students to behave more extremely before being seen by teachers as having either learning or emotional problems.