No abstract

Students who exhibit emotional and behavioral disorders (EBD) typically have high frequencies of disruptive and noncompliant behavior including physical and verbal aggression (VA). Physical aggression attracts great concern from school professionals yet VA is often overlooked, despite being a highly pervasive and harmful social act. We surveyed 279 first to 12th grade teachers of students with EBD to assess their perceptions about the harmfulness of VA, students’ intent to harm, their concern about the frequency and/or intensity of VA, and concern about types of verbally aggressive messages. We investigated if these perceptions differed when teachers considered students with EBD compared with typical peers and if special education certification related to responsiveness to VA. The majority of teachers reported that VA was either somewhat or very harmful and perceived students with EBD to be just kidding around and not intending to hurt others when perpetrating VA. Compared with noncertified colleagues, certified teachers reported more concern about VA, more intent to harm when students with EBD exhibit VA, and they were more likely than their noncertified counterparts to report the use of a structured intervention/curriculum to reduce VA. We discuss implications for special education teacher preparation and offer suggestions for further research.

The semi-generalized partial credit model (Semi-GPCM) has been proposed as a unidimensional modeling method for handling not applicable scale responses and neutral scale responses, and it has been suggested that the model may be of use in handling missing data in scale items. The purpose of this study is to evaluate the ability of the unidimensional Semi-GPCM to aid in the recovery of person parameters from item response data in the presence of item-level missingness, and to compare the performance of the model with two other proposed methods for handling such missingness: a multidimensional modeling approach for missingness and full information maximum likelihood estimation. The results indicate that the Semi-GPCM performs acceptably in an absolute sense when less than 30% of the item data is missing but does not outperform the other two methods under any particular conditions. We conclude with a discussion about when practitioners may or may not want to use the Semi-GPCM to recover person parameters from item response data with missingness.