Abstract
Area-based prevention studies often produce results that can be represented in a 2-by-2 table of counts. For example, a table may show the crime counts during a 12-month period prior to the intervention compared to a 12-month period during the intervention for a treatment and control area or areas. Studies of this type have used either Cohen’s d or the odds ratio as an effect size index. The former is unsuitable and the latter is a misnomer when used on data of this type. Based on the quasi-Poisson regression model, an incident rate ratio and relative incident rate ratio effect size and associated overdispersion parameter are developed and advocated as the preferred effect size for count-based outcomes in impact evaluations and meta-analyses of such studies.
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References
Agresti A (2002) Categorical data analysis, 2nd edn. Wiley, New York
Al-Khasawneh MF (2010) Estimating the negative binomial dispersion parameter. Asian J Math Stat 3:1–15
Ariel B, Farrar WA, Sutherland A (2015) The effect of police body-worn cameras on use of force and citizens’ complaints against the police: a randomized controlled trial. J Quant Criminol 31(3):509–535
Ariel B, Sutherland A, Henstock D, Young J, Drover P, Sykes J, Megicks S, Henderson R (2016) Report: Increases in police use of force in the presence of body-worn cameras are driven by officer discretion: a protocol-based subgroup analysis of ten randomized experiments. J Exp Criminol 12(3):453–463
Berk R, MacDonald JM (2008) Overdispersion and Poisson regression. J Quant Criminol 24(3):269–284
Braga AA, Weisburd DL (2012) The effects of “pulling levers” focused deterrence strategies on crime. Campbell Syst Rev 8(1):1–90
Braga AA, Papachristos AV, Hureau DM (2014) The effects of hot spots policing on crime: an updated systematic review and meta-analysis. Justice Q 31(4):633–663
Braga AA, Sousa WH, Coldren JR Jr, Rodriguez D (2018) The effects of body-worn cameras on police activity and police–citizen encounters: randomized controlled trial. J Crim Law Criminol 108:511
Farrington DP, Welsh BC (2006) Improved street lighting. In: Farrington DP, Welsh BC (eds) Preventing crime. Springer, New York, pp 209–224
Farrington DP, Welsh B (2013) Measuring effect size in meta-analysis, with special reference to area-based crime prevention programmes and the effects of closed circuit television on crime. In: Kuhn A, Schwarzenegger C, Margot P, Donatsch A, Aebi M, Jositsch D (eds) Criminology, criminal policy and criminal law from an international perspective. Stampfli, Berne, pp 75–89
Farrington DP, Gill M, Waples SJ, Argomaniz J (2007) The effects of closed-circuit television on crime: meta-analysis of an English national quasi-experimental multi-site evaluation. J Exp Criminol 3(1):21–38
Gardner W, Mulvey EP, Shaw EC (1995) Regression analyses of counts and rates: Poisson, overdispersed Poisson, and negative binomial models. Psychol Bull 118(3):392
Gu K, Ng HKT, Tang ML, Schucany WR (2008) Testing the ratio of two Poisson rates. Biometr J J Math Methods Biosci 50(2):283–298
Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, Welch V (2019) Cochrane handbook for systematic reviews of interventions (version 6.0; updated July 2019 ed.). Cochrane. www.training.cochrane.org/handbook. Accessed 22 Apr 2020
Lum C, Koper C, Wilsond DB, Stoltz M, Goodier M, Eggins E, Higginson A, Mazerolle L (2020) Body-worn cameras’ effects on police officers and citizen behavior: a systematic review. Campbell Syst Rev 16(3):e1112. https://doi.org/10.1002/cl2.1112
Ng HKT, Tang M-L (2005) Testing the equality of two Poisson means using the rate ratio. Stat Med 24(6):955–965
Osgood DW (2000) Poisson-based regression analysis of aggregate crime rates. J Quant Criminol 16(1):21–43
Rodríguez G (2013) Models for count data with overdispersion. Add. WWS 509 Notes, 672
Ver Hoef JM, Boveng PL (2007) Quasi-Poisson versus negative binomial regression: how should we model overdispersed count data? Ecology 88(11):2766–2772
Weisburd D, Green L (1995) Policing drug hot spots: the Jersey City drug market analysis experiment. Justice Q 12(4):711–735
Welsh BC, Farrington DP (2009) Public area CCTV and crime prevention: an updated systematic review and meta-analysis. Justice Q 26(4):716–745
Wooldridge JM (2010) Econometric analysis of cross section and panel data. MIT Press, Cambridge
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Wilson, D.B. The Relative Incident Rate Ratio Effect Size for Count-Based Impact Evaluations: When an Odds Ratio is Not an Odds Ratio. J Quant Criminol 38, 323–341 (2022). https://doi.org/10.1007/s10940-021-09494-w
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DOI: https://doi.org/10.1007/s10940-021-09494-w