Studying Special Education Teachers in State Longitudinal Data Systems

Abstract

A growing body of research uses state longitudinal data systems (SLDS) to study the special education teacher (SET) workforce in specific states, but these studies make different decisions about how to identify SETs within SLDS and link them to school and district data. We therefore used SLDS from three states—Massachusetts, Pennsylvania, and Washington—to explore the implications of these decisions for analyses of the SET workforce. While some methods used to identify SETs in prior literature (e.g., based on classroom assignments or position funding sources) produce estimates of the size of the SET workforce that are comparable to state-reported IDEA 618 data, using teacher certification to identify SETs substantially overestimates the size of the SET workforce in these states and can lead to misleading conclusions about turnover in these states. Other data decisions (e.g., how to handle multiple school assignments) have fewer implications for these analyses.