Citation

Huang FL, Cornell DG. J. School Violence 2012; 11(3): 187-206.

Copyright

(Copyright © 2012, Informa - Taylor and Francis Group)

DOI

10.1080/15388220.2012.682010

PMID

unavailable

Abstract

School violence research is often concerned with infrequently occurring events such as counts of the number of bullying incidents or fights a student may experience. Analyzing count data using ordinary least squares regression may produce improbable predicted values, and as a result of regression assumption violations, result in higher Type I errors. Count data are optimally analyzed using Poisson-based regression techniques such as Poisson or negative binomial regression. We apply these techniques to an example study of bullying in a statewide sample of 290 high schools and explain how Poisson-based analyses, although less familiar to many researchers, can produce findings that are more accurate and reliable, and are easier to interpret in real-world contexts.

Language: en

Keywords

bullying; count data; negative binomial regression; Poisson regression; school violence data; zero-inflated models