Modeling Recidivism within the Study of Offender Reentry

2016 ◽  
Vol 44 (1) ◽  
pp. 85-102 ◽  
Author(s):  
Thomas J. Mowen ◽  
Scott E. Culhane
Keyword(s):  
Generalized Linear Model ◽  
Research Question ◽  
Violent Offender ◽  
Linear Modeling ◽  
Offender Reentry ◽  
Hierarchical Generalized Linear Model ◽  
Generalized Linear Modeling ◽  
Hierarchical Generalized Linear Modeling ◽  
Lagged Dependent Variable ◽  
Statistical Approaches

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.

Understanding School Principal Attrition and Mobility Through Hierarchical Generalized Linear Modeling

2019 ◽  
pp. 089590481985782
Author(s):  
Abebayehu Aemero Tekleselassie ◽  
Jaehwa Choi
Keyword(s):  
Parental Involvement ◽  
School Principals ◽  
Support Staff ◽  
School Principal ◽  
Linear Modeling ◽  
Teacher Incentives ◽  
Generalized Linear Modeling ◽  
Hierarchical Generalized Linear Modeling ◽  
Growing Body ◽  
Principal Attrition

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.

scholarly journals Statistical Downscaling to Predict Monthly Rainfall Using Generalized Linear Model with Gamma Distribution

2016 ◽  
Vol 24 (2) ◽  
pp. 215 ◽  
Author(s):  
Agus M Soleh
Keyword(s):  
Linear Model ◽  
Gamma Distribution ◽  
Statistical Downscaling ◽  
Generalized Linear Model ◽  
Principal Component Regression ◽  
Principal Component ◽  
Linear Modeling ◽  
Rainfall Prediction ◽  
Generalized Linear Modeling ◽  
Significant Difference

Statistical Downscaling (SDS) models might involve ill-conditioned covariates (large dimension and high correlation/multicollinear). This problem could be solved by a variable selection technique using L1 regularization/LASSO or a dimension reduction approach using principal component analysis (PCA). In this paper, both methods were applied to generalized linear modeling with gamma distribution and compared to predict rainfall models at 11 rain posts in Indramayu. More over, generalized linear model with gamma distribution was used to obtain non-negative rainfall prediction and compared with principal component regression (PCR). Two types of ill-conditioned data with different characteristics (CMIP5 and GPCP version 2.2) were used as covariates in SDS modeling. The results show that three methods (PCR, Gamma-PC, and Gamma-L1) did not demonstrate significant difference in term of Root Mean Square Error (RMSE) after addition of dummy variables (month) in the models. However, a generalized linear modeling with gamma distribution could be considered as the best methods since it provided non-negative rainfall predictions.

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

2020 ◽  
pp. 001112872097744
Author(s):  
Benjamin Meade ◽  
Gabriela Wasileski ◽  
Alisha Hunter
Keyword(s):  
Representative Sample ◽  
Sexual Victimization ◽  
Linear Modeling ◽  
Generalized Linear Modeling ◽  
Hierarchical Generalized Linear Modeling ◽  
Nationally Representative ◽  
State Prisons ◽  
Research And Policy

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.

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

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Jing Zhao ◽  
Xiaojing Zou ◽  
Wenpeng Shang
Keyword(s):  
Preservice Teachers ◽  
Self Efficacy ◽  
Validity Study ◽  
Linear Modeling ◽  
Teachers Perceptions ◽  
Context Variable ◽  
Generalized Linear Modeling ◽  
Sense Of Efficacy ◽  
Hierarchical Generalized Linear Modeling ◽  
Item Functioning

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.

City Children’s Nature Knowledge and Contact: It Is Not Just About Biodiversity Provision

2017 ◽  
Vol 50 (10) ◽  
pp. 1145-1171 ◽  
Author(s):  
Claire Freeman ◽  
Aviva Stein ◽  
Kathryn Hand ◽  
Yolanda van Heezik
Keyword(s):  
New Zealand ◽  
Environmental Factors ◽  
Ethnic Group ◽  
Social Factors ◽  
Green Spaces ◽  
Social Connections ◽  
Relative Importance ◽  
Linear Modeling ◽  
Generalized Linear Modeling ◽  
Nature Contact

Much attention has been directed at the perceived decline in city children’s contact with nature. We used a child-centric approach to assess neighborhood nature knowledge in 187 children aged 9 to 11 years, from different socioeconomic and ethnic groups in three New Zealand cities. We evaluated the relative importance of social (independence, gender, social connections, deprivation, age) and environmental factors (biodiversity) in explaining variation in knowledge at a scale relevant to each child’s independent movements. Our biodiversity evaluation reflected the natural dimensions of the habitats where children interacted with nature. Generalized linear modeling identified ethnicity as having the strongest association with nature knowledge. Within each ethnic group, social factors were most important (independence, social connections, deprivation) except for Pākehā/NZ European children, where local biodiversity was most important. Enhancing biodiversity values of private green spaces (yards) would be effective in facilitating opportunities to experience nature, which is fundamental to supporting nature contact.

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