Hate Groups and Muslim Population Changes in the Fifty States: Does the Presence of Muslims Encourage Hate Group Formation?

2018 ◽  
Vol 25 (2) ◽  
pp. 317-332
Author(s):  
Daniel Hummel
Keyword(s):  
Negative Binomial ◽  
Group Formation ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Group Status ◽  
Hate Groups ◽  
Muslim Population ◽  
Binomial Regression ◽  
Positive Correlations

The size of the Muslim population in the United States is growing, while the number of hate groups also continues to increase across the states. Based on the social dominance theory and group threat theory, there may be a link between these two dynamics as social dominators become concerned about their group status, i.e. white Christian males. This potential relationship is explored in this article. Although there were significant positive correlations between the number of hate groups and the size of the Muslim population, a panelled negative binomial regression with a number of relevant control variables found that there was not a significant relationship between these variables at the 0.05 probability level. The article further explores these findings and future research in this area.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

Psychological distress among Bhutanese refugees living in the northeast region of the United States

2021 ◽  
Author(s):  
Ramraj Gautam ◽  
Jason Rydberg ◽  
Ivy Ho ◽  
Bhola Siwakoti ◽  
William Chadbourne ◽  
...  
Keyword(s):  
Mental Health ◽  
United States ◽  
Psychological Distress ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Future Research ◽  
Related Factors ◽  
Bhutanese Refugees ◽  
The Impact

Abstract The purpose of this study was to examine predictors of psychological distress among adult Bhutanese refugees living in the United States. We recruited 376 adult Bhutanese refugees living in the northeast US region, the majority of whom were employed, married men in their 40 s who were US citizens. Using Bayesian Negative Binomial Regression modelling, we examined the impact of sociodemographic measures, health status and refugee integration measures on psychological distress outcomes. The most common predictors of depression, stress and anxiety were social connection deficits and self-reported health conditions. Other associated factors included: health access deficits, number of years living in the US, paid employment, citizenship and living in more than one US city. The findings of our study revealed that mental health outcomes in this subset of the population of Bhutanese refugees are impacted by a variety of social and health related factors. Public policy makers and practitioners need to recognize the complex issues affecting mental health of Bhutanese refugees. Advocacy for expanded culturally congruent policies and evidence based mental health services are recommended. Future research needs to examine culturally relevant concepts and measures related to mental health and integration in this population.

Property Crime Clearance in Small Jurisdictions

2018 ◽  
Vol 43 (4) ◽  
pp. 477-493 ◽  
Author(s):  
Jeffrey J. Roth
Keyword(s):  
Negative Binomial ◽  
Property Crime ◽  
Negative Binomial Regression ◽  
The United States ◽  
Police Departments ◽  
Clearance Rates ◽  
Residential Instability ◽  
Large Cities ◽  
Binomial Regression ◽  
Crime Clearance

Research on the factors that influence crime clearance rates has primarily studied violent crimes in large cities. However, property crimes are among the most commonly occurring and least frequently cleared offenses, and the majority of police departments in the United States serve small jurisdictions. Thus, this study undertook an examination of the predictors of clearance rates for burglary, larceny, and vehicle theft in a sample of agencies serving populations of 50,000 people or fewer. Independent variables included both policing factors (e.g., workload, funding, broken windows arrests) and social disorganization indicators (e.g., residential instability, poverty). Negative binomial regression analyses revealed variation in the significance of the predictors across the three crimes. Additionally, many predictors found to be influential in prior work were insignificant in this study, which suggests differences in the nature of crime clearance between large cities and smaller jurisdictions and a need for further research in this area.

scholarly journals Application of Discrete Regression Models for Analyzing K-8 Students Nonchronic Absenteeism in the United States

2021 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Khairul Islam ◽  
Tanweer J. Shapla
Keyword(s):  
United States ◽  
Regression Models ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
The United States ◽  
Chronic Absenteeism ◽  
Factors Affecting ◽  
National Crisis ◽  
Binomial Regression

Absenteeism is a national crisis in the United States, and must be addressed adequately at the early stages or at its onset, to prevent consequential disaster and burden due to absenteeism. A pervasive and persuasive nonchronic absenteeism results in chronic absenteeism, and causes severe damage to students’ life, schools and societies. While a good number of articles address various issues relating to chronic absenteeism, no evidence of research exists investigating nonchronic absenteeism. The aim of this article is to investigate factors affecting nonchronic absenteeism in K-8 students in the United States by applying discrete regression models. Initially, we investigate K-8 students nonchronic absenteeism discrepancies due to socio-demographic and parental involvement factors via descriptive analysis and then employ Poisson and negative binomial regression models for exploring significant factors of K-8 nonchronic absenteeism. The findings of this study will be of great use to stakeholders in developing appropriate incentive measures for reducing nonchronic absenteeism early and thereby reducing chronic absenteeism.

Identification of Critical Intersection Angle through Crash Modification Functions

2019 ◽  
Vol 2673 (2) ◽  
pp. 531-543 ◽  
Author(s):  
Wesley Kumfer ◽  
David Harkey ◽  
Bo Lan ◽  
Raghavan Srinivasan ◽  
Daniel Carter ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Skew Angle ◽  
Intersection Angle ◽  
Crash Data ◽  
Highway Safety Manual ◽  
Intersection Safety ◽  
Binomial Regression ◽  
Safety Effect

A significant portion of both fatal and total crashes occurs at intersections in the United States. Skew angle may be a significant contributor to these crashes. This paper examines the effects of intersection angle on intersection safety performance. With seven years of crash data from Minnesota and five years of crash data from Ohio, random forest regression data mining and negative binomial regression models were developed to estimate crash modification functions at three-leg and four-leg stop-controlled intersections with two-lane and multilane major legs. Where possible, the results were compared between the two states and used to develop average crash modification function curves. This study shows that over half of the intersection types experience the highest number of predicted crashes when the intersection angle between roadway legs is between 50 degrees and 65 degrees. These results have practical implications for engineers and safety professionals. First, the crash modification function curves supplement and revise the guidance for intersection angle in the Highway Safety Manual and Policy on Geometric Design of Highways and Streets. Second, the functions offer new guidance to agencies planning intersection improvements. Third, the crash modification functions can be used to determine the safety effect of changes in intersection angle.

The Cycle of Violence Revisited: Childhood Victimization, Resilience, and Future Violence

2016 ◽  
Vol 34 (6) ◽  
pp. 1261-1286 ◽  
Author(s):  
Kevin A. Wright ◽  
Jillian J. Turanovic ◽  
Eryn N. O’Neal ◽  
Stephanie J. Morse ◽  
Evan T. Booth
Keyword(s):  
Protective Factors ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
Cycle Of Violence ◽  
Childhood Victimization ◽  
Adult Health ◽  
Violent Offending ◽  
Binomial Regression ◽  
The Individual

The individual and social protective factors that help break the cycle of violence are examined. Specifically, this study investigates (a) the individual and social protective factors that reduce violent offending among previously victimized children, and (b) whether certain protective factors are more or less important depending on the type and frequency of childhood victimization experienced. Data on young adults from Wave III of the National Longitudinal Study of Adolescent to Adult Health are used ( N = 13,116). Negative binomial regression models are estimated to examine the protective factors that promote resiliency to violent offending among individuals who reported being physically and sexually victimized as children. Results indicate that a number of individual and social protective factors reduce violent offending in young adulthood. With a few exceptions, these factors are specific to the type, frequency, and comorbidity of abuse experienced. The results suggest a number of promising approaches to break the cycle of violence among previously victimized children. Future research should move beyond explaining the cycle of violence to examine how the cycle may be broken.

Longitudinal Analysis of Light Rail and Streetcar Safety in the United States

2020 ◽  
Vol 2674 (9) ◽  
pp. 83-95
Author(s):  
Abubakr Ziedan ◽  
Candace Brakewood
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Descriptive Statistics ◽  
Light Rail ◽  
Right Of Way ◽  
Binomial Regression ◽  
Using Data ◽  
American Cities

Many American cities have launched or expanded light rail or streetcar services recently, which has resulted in a 61% increase in light rail and streetcar revenue miles nationwide during the period 2006–2016. Moreover, light rail and streetcars exhibit higher fatality rates per passenger mile traveled compared with other transit modes. In light of these trends, this study explores light rail and streetcar collisions, injuries, and fatalities using data obtained from the National Transit Database. This study applies a two-part methodology. In the first part, descriptive statistics are calculated for light rail and streetcar collisions, injuries, and fatalities, and a comparative analysis of light rail and streetcars is performed. In the second part, multilevel negative binomial regression models are used to analyze light rail and streetcar collisions and injuries. Three key findings have emerged from this study. First, the results generally align with findings from prior studies that show the majority of light rail and streetcar collisions occur in mixed right-of-way or near at-grade crossings. Second, this analysis revealed an issue predominantly at stations: 42% of light rail injuries were people waiting or leaving. Third, suicide was the leading cause of light rail fatalities, which represents 28% of all light rail fatalities. The implications of this study are important for cities that currently operate these modes or are planning to introduce new light rail or streetcar service to improve safety.

Self-Reported Inmate Conduct

2016 ◽  
Vol 97 (1) ◽  
pp. 79-93
Author(s):  
Lisa Degiorgio Worthy
Keyword(s):  
Negative Binomial ◽  
Violent Behavior ◽  
Area Under The Curve ◽  
Negative Binomial Regression ◽  
The United States ◽  
Female Inmates ◽  
Operating Characteristics ◽  
Dynamic Factors ◽  
Binomial Regression ◽  
Improved Model

This article applies Walters’s criteria of dynamic factors in prison assessment to assess the predictive capabilities of the Prison Inmate Inventory (PII) in a sample of offenders from a southeastern state in the United States. Nested negative binomial regression was used, as well as ROC/AUC (receiver operating characteristics/area under the curve) to evaluate accuracy. The results show that beyond the effects of demographic and static variables, dynamic factors incrementally improved model fit and estimated counts of female inmates, non-White inmates, and inmates reporting more disciplinary actions and violent behavior. All PII scales demonstrated accuracy above .50; however, only two were within the range considered acceptable for criminal justice instruments. Practical implications have been discussed.

Homeland Violence and Diaspora Insecurity: An Analysis of Israel and American Jewry

2019 ◽  
Vol 13 (1) ◽  
pp. 1-27
Author(s):  
Ayal K. Feinberg
Keyword(s):  
United States ◽  
Fixed Effects ◽  
Hate Crime ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
The United States ◽  
Hate Crimes ◽  
Federal Bureau Of Investigation ◽  
American Jewry ◽  
Binomial Regression

AbstractJews and Jewish institutions have suffered the majority of reported religion-motivated hate crimes in the United States for nearly two decades. According to the Federal Bureau of Investigation (FBI), in 2014 the 609 reported anti-Semitic incidents made up 59% of all religious bias hate crimes alone. Rates of reported anti-Semitic hate crimes vary considerably over the course of a year. Yet, little scholarly attention has been given to what factors cause reported anti-Semitic hate crimes to fluctuate so substantially in the United States. This paper hypothesizes that violent Israeli military engagements are critical in explaining weekly surges of reported anti-Semitic hate crimes. Utilizing FBI hate crime data from 2001 to 2014 and fixed effects negative binomial regression models, consistent findings underscore that violent Israeli military engagements significantly increase the likelihood of a state reporting anti-Semitic hate crime. Most dramatically, their occurrence increases the likelihood of reported hate crime intimidating individuals or characterized as violent by nearly 35%. This paper underscores that homeland perpetrated violence can directly impact the security of diaspora communities.

Juvenile Institutional Misconduct: Examining the Role of Educational Attainment and Academic Achievement

2020 ◽  
Vol 66 (5) ◽  
pp. 663-686
Author(s):  
Ronda Shepherd Engstrom ◽  
Daniel Scott
Keyword(s):  
High School ◽  
Academic Achievement ◽  
Juvenile Offenders ◽  
Educational Attainment ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Future Research ◽  
Institutional Misconduct ◽  
Binomial Regression ◽  
Educational Variables

Identifying risk factors of institutional misconduct among juvenile offenders is important in curbing costs and consequences of these infractions. Research shows low educational attainment and poor academic achievement are consistently related to delinquency and crime. Yet, no research has examined whether these educational variables are associated with institutional misconduct among juveniles. Official records and interviews yielded information on the educational characteristics and institutional misconduct of 349 juvenile prisoners in a California facility. Negative binomial regression analysis was employed to examine hypothesized relationships. Results indicate that graduating high school significantly predicts institutional misconduct. Implications for juvenile corrections policies, including the importance of assisting juvenile detainees in obtaining their high school diploma, and directions for future research are provided.

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