The Effects of Victimization Prior to Prison on Victimization, Misconduct, and Sanction Severity During Incarceration

Numerous studies have examined the correlation between physical and/or sexual victimization and offending and re-victimization later in life. However, fewer studies have explored how such victimization affects the adjustment of those incarcerated and the sanctioning decisions of correctional personnel. Using a nationally representative sample of inmates in state prisons, this study utilized hierarchical generalized linear modeling to examine whether victimization prior to incarceration was associated with the likelihood of victimization, misconduct, and sanctioning severity for misconduct during incarceration. Our results confirmed findings of previous research in regards to the victimization and offending/re-victimization relationship. In addition, we discovered that victimization prior to prison is associated with harsher disciplinary sanctioning in prison. Implications of our findings for research and policy are discussed.

Understanding School Principal Attrition and Mobility Through Hierarchical Generalized Linear Modeling

Despite a growing body of turnover literature, much remains unknown about the factors predicting career transitional behaviors of school principals. To bridge this gap, we examined variations in principal, school, and district characteristics influencing administrator leaver and mover behaviors, using Hierarchical Generalized Linear Modeling. Our findings revealed that class size, support staff, parental involvement, teacher incentives, unionization, and many other district-level policies offset turnover, thereby contributing to retention. Furthermore, predictors of principal mover behavior differed from those of principal leaver behavior, suggesting that different forms of exit paths may need different policy tools to improve retention.

Using Hierarchical Generalized Linear Modeling to Examine Contextual Differential Item Functioning: A Validity Study of Teachers’ Sense of Efficacy Scale

The purpose of the study was to further investigate the validity of the instrument used for collecting preservice teachers’ perceptions of self-efficacy adapting the three-level hierarchical generalized linear modeling (HGLM) model. To serve the purpose, the study used data collected by the research team which elicited preservices teachers’ self-efficacy beliefs using Teachers’ Sense of Efficacy Scale (TSES). Hierarchical generalized linear modeling (HGLM) were used to analyze the data. Results of the HGLM analyses (at level-two) showed that one item in the scale displayed gender DIF. Another item became DIF item when the context variable was added to the level-two model. However, the effect of the context on the DIF item was not big.

Modeling Recidivism within the Study of Offender Reentry

Although there are multiple statistical approaches used in understanding reentry, there is little consensus on the benefits and limitations of some of the more popular techniques as they relate to each other. Here, two common methods, lagged dependent variable modeling and hierarchical generalized linear modeling, are contrasted. To examine how particular modeling strategies may lead to different understandings of recidivism within reentry, we use data from the Serious and Violent Offender Reentry Initiative (SVORI; N = 1,697) to provide an example of the two statistical approaches and discuss the benefits and limitations of each strategy. While researchers will need to make important decisions about which strategy best addresses their research question, results of our analyses show that in dealing with reentry data across more than two waves, a hierarchical generalized linear model is often the preferred approach.