Poisson-Based Regression Analysis of Aggregate Crime Rates

By D. Osgood. 2000

1,221 citations recorded by the source corpus.

Subfields: Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9a1d54cc2567e4e8dea83388762125e69919b04c

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Citations in the corpus

Re‐Establishing the Social‐Class Order: Restorative Reactions against High‐Achieving, Low‐SES Pupils (2017, methodology): “To account for the high incidence of zero counts in these measures (i.e., no wrong answers; Osgood, 2000), we analyzed the data with Poisson regres-”
The Roles of Police Officers in Schools (2018, methodology): “…1; T2 ¼ Time 2. aUnweighted sample sizes are rounded to the nearest 10 to comply with Institution of Education Sciences requirements for restricted use data. bFollowing Osgood (2000) and Na and Gottfredson (2011), the coefficients for enrollment need to be transformed because they are elasticities.”
Overdispersion and Poisson Regression (2008, background): “The negative binomial is proposed as a means to correct for this problem, and some say that it automatically does so (Osgood, 2000).”
Friendship Networks and Delinquency: The Relative Nature of Peer Delinquency (2002, methodology): “Designed for dependent variables that are counts of events, negative binomial models utilize a distribution that characterizes the probability of observing any discrete number of events given an underlying mean count of events (Osgood, 1999).”
Ideology, Social Threat, and the Death Sentence: Capital Sentences across Time and Space (2004, methodology): “Osgood (2000) provides a lucid discussion of the many advantages of analyzing counts rather than rates when a dependent variable is unusual.”
Aggressive Policing and the Educational Performance of Minority Youth (2018, methodology): “To model the number of police stops, arrests and crime incidents, we use negative binomial regressions, which are a common approach in research on crime (Osgood 2000).”
Reducing Juvenile Crime: Conferencing versus Court (2002, methodology): “The negative binomial form of the Poisson regression was chosen to analyse the number of reappearances per year as the Poisson distribution is most appropriate for analysis of rare events and the negative binomial form corrects for the overdispersion usually found when Poisson models are fitted to c”
Autocorrelation and cross-correlation in time series of homicide and attempted homicide (2014, background): “For example, it is possible to statistically measure the connection between unemployment and crime [1–3], the correlation between firearms and homicides [4], make a descriptive study of homicides considering author and victim [5], evaluate crime rates through probabilistic models [6], perform a temp”
The Ties That Bind (2014, methodology): “Thus, the distributional characteristics (mean = 0.53; variance = 0.74) of the dependent variable followed a Poisson distribution of event counts (Osgood, 2000).”