Fearful and Distracted in School: Predicting Bullying among Youths

Steven Lawrence Brewer; Hannah Meckley-Brewer; Philip M. Stinson

doi:10.1093/cs/cdx021

Fearful and Distracted in School: Predicting Bullying among Youths

Steven Lawrence Brewer, Hannah Meckley-Brewer, Philip M. Stinson

Penn State Shenango

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Bullying and aggression in schools can have a traumatic and lasting effect on the well-being of children and youths. Using data from the 2013 National Crime Victimization Survey's School Crime Supplement, this study uses a chi-square automatic interaction detection (CHAID) decision tree and logistic regression models to identify factors that increase bullying in schools. Being distracted and fear of being attacked were among the top statistically significant variables, when using both methodologies. Avoiding online activities and knowing someone who brought a gun to school were top predictors using logistic regression. Being involved in a fight and seeing hate-related words or symbols were additional influential predictors in a CHAID decision tree model. Identifying factors that increase likelihood for bullying in schools assists practitioners in implementing programs and policies to improve school climate and reduce youth bullying.

Original language	English (US)
Pages (from-to)	219-226
Number of pages	8
Journal	Children and Schools
Volume	39
Issue number	4
DOIs	https://doi.org/10.1093/cs/cdx021
State	Published - Oct 1 2017

All Science Journal Classification (ASJC) codes

Health(social science)
Education

Access to Document

10.1093/cs/cdx021

Cite this

@article{9dd2b08168904972a5ab4014470a7f63,

title = "Fearful and Distracted in School: Predicting Bullying among Youths",

abstract = "Bullying and aggression in schools can have a traumatic and lasting effect on the well-being of children and youths. Using data from the 2013 National Crime Victimization Survey's School Crime Supplement, this study uses a chi-square automatic interaction detection (CHAID) decision tree and logistic regression models to identify factors that increase bullying in schools. Being distracted and fear of being attacked were among the top statistically significant variables, when using both methodologies. Avoiding online activities and knowing someone who brought a gun to school were top predictors using logistic regression. Being involved in a fight and seeing hate-related words or symbols were additional influential predictors in a CHAID decision tree model. Identifying factors that increase likelihood for bullying in schools assists practitioners in implementing programs and policies to improve school climate and reduce youth bullying.",

author = "Brewer, {Steven Lawrence} and Hannah Meckley-Brewer and Stinson, {Philip M.}",

note = "Publisher Copyright: {\textcopyright} 2017 National Association of Social Workers.",

year = "2017",

month = oct,

day = "1",

doi = "10.1093/cs/cdx021",

language = "English (US)",

volume = "39",

pages = "219--226",

journal = "Children and Schools",

issn = "1532-8759",

publisher = "National Association of Social Workers",

number = "4",

}

TY - JOUR

T1 - Fearful and Distracted in School

T2 - Predicting Bullying among Youths

AU - Brewer, Steven Lawrence

AU - Meckley-Brewer, Hannah

AU - Stinson, Philip M.

PY - 2017/10/1

Y1 - 2017/10/1

N2 - Bullying and aggression in schools can have a traumatic and lasting effect on the well-being of children and youths. Using data from the 2013 National Crime Victimization Survey's School Crime Supplement, this study uses a chi-square automatic interaction detection (CHAID) decision tree and logistic regression models to identify factors that increase bullying in schools. Being distracted and fear of being attacked were among the top statistically significant variables, when using both methodologies. Avoiding online activities and knowing someone who brought a gun to school were top predictors using logistic regression. Being involved in a fight and seeing hate-related words or symbols were additional influential predictors in a CHAID decision tree model. Identifying factors that increase likelihood for bullying in schools assists practitioners in implementing programs and policies to improve school climate and reduce youth bullying.

AB - Bullying and aggression in schools can have a traumatic and lasting effect on the well-being of children and youths. Using data from the 2013 National Crime Victimization Survey's School Crime Supplement, this study uses a chi-square automatic interaction detection (CHAID) decision tree and logistic regression models to identify factors that increase bullying in schools. Being distracted and fear of being attacked were among the top statistically significant variables, when using both methodologies. Avoiding online activities and knowing someone who brought a gun to school were top predictors using logistic regression. Being involved in a fight and seeing hate-related words or symbols were additional influential predictors in a CHAID decision tree model. Identifying factors that increase likelihood for bullying in schools assists practitioners in implementing programs and policies to improve school climate and reduce youth bullying.

UR - http://www.scopus.com/inward/record.url?scp=85030769034&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85030769034&partnerID=8YFLogxK

U2 - 10.1093/cs/cdx021

DO - 10.1093/cs/cdx021

M3 - Article

AN - SCOPUS:85030769034

SN - 1532-8759

VL - 39

SP - 219

EP - 226

JO - Children and Schools

JF - Children and Schools

IS - 4

ER -

Fearful and Distracted in School: Predicting Bullying among Youths

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this