A multiple inflated negative binomial hurdle regression model: analysis of the Italians’ tourism behaviour during the Great Recession

Little research has explored whether social policies aimed at lessening economic hardship affect the prevalence of bullying, particularly after the Great Recession. This article investigates how the strains of neighborhood and cumulative disadvantage are associated with racial differences in bullying, and we consider whether social program participation—enlistment in needs-based social programs to attenuate poverty and disadvantage—upends race-based differences in bullying. Using probit, negative binomial, and propensity score matching methods, we show that adolescents who experience any markers of disadvantage are more likely to bully others, with Black and Hispanic adolescents being more likely to engage in bullying than Whites. Importantly, matched estimates reveal that participation in needs-based social programs eliminates racial differences in bullying.

Download Full-text

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052224 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2224 ◽

Cited By ~ 1

Author(s):

Kyung Im Kang ◽

Kyonghwa Kang ◽

Chanhee Kim

Keyword(s):

Risk Factors ◽

Middle School ◽

Middle School Students ◽

Regression Model ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Model Analysis ◽

Negative Binomial Regression Model ◽

School Students ◽

Binomial Regression

This cross-sectional descriptive study identified risk factors and predictors related to the perpetration of and potential for cyberbullying among adolescents, respectively. The analysis included a zero-inflated negative binomial regression model. Data were assessed from 2590 middle-school student panels obtained during the first wave of the Korean Child and Youth Panel Survey 2018. Of these respondents, 63.7% said they had not experienced the perpetration of cyberbullying. However, a subsequent count model analysis showed that several factors were significantly associated with cyberbullying, including offline delinquency, aggression, smartphone dependency, and smartphone usage on weekends (either 1–3 h or over 3 h). A logit model analysis also showed several predictive factors that increased the likelihood of cyberbullying, including gender (boys), offline delinquency, aggression, smartphone usage during weekdays (1–3 h), computer usage during weekends (1–3 h), and negative parenting. These identified risks and predictors should be useful for interventions designed to prevent the perpetration of cyberbullying among middle school students.

Download Full-text

Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins

Indian Journal of Animal Research ◽

10.18805/ijar.bf-1415 ◽

2022 ◽

Author(s):

Y. Gevrekçi ◽

Ö.İ. Güneri ◽

Ç. Takma ◽

A. Yeşilova

Keyword(s):

Regression Model ◽

Count Data ◽

Negative Binomial ◽

Information Criteria ◽

Parameter Estimates ◽

Count Data Models ◽

Factors Affecting ◽

Independent Variables ◽

Gender Parity ◽

Hurdle Regression

Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p less than 0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.

Download Full-text

A comparison of negative financial events experienced by carers and non-carers following onset of the great recession

International Journal of Care and Caring ◽

10.1332/239788221x16215259065673 ◽

2021 ◽

Author(s):

Kylie Meyer ◽

Zachary Gassoumis ◽

Kathleen Wilber

Keyword(s):

Great Recession ◽

Job Loss ◽

Negative Binomial ◽

Negative Binomial Regression ◽

The Great Recession ◽

Binomial Regression ◽

Financial Events

This study compares carers and non-carers as regards experiences of harmful financial events during and immediately after the Great Recession. Carer status was associated with experiencing more negative financial events since the Great Recession began, even after controlling for covariates in a negative binomial regression. Carers had higher odds of reporting: job loss; moving in with family and friends to save money; and selling possessions to make ends meet. Compared to non-carers, carers were more likely to experience adverse financial events during and following the Great Recession.

Download Full-text