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Article
Quantifying the Likelihood of False Positives: Using Sensitivity Analysis to Bound Statistical Inference
Journal of Quantitative Criminology (2018)
  • Kyle J. Thomas, University of Missouri–St. Louis
  • Jean Marie McGloin, University of Maryland, College Park
  • Christopher J. Sullivan, University of Cincinnati
Abstract
Objective
Criminologists have long questioned how fragile our statistical inferences are to unobserved bias when testing criminological theories. This study demonstrates that sensitivity analyses offer a statistical approach to help assess such concerns with two empirical examples—delinquent peer influence and school commitment.
Methods
Data from the Gang Resistance Education and Training are used with models that: (1) account for theoretically-relevant controls; (2) incorporate lagged dependent variables and; (3) account for fixed-effects. We use generalized sensitivity analysis (Harada in ISA: Stata module to perform Imbens’ (2003) sensitivity analysis, 2012; Imbens in Am Econ Rev 93(2):126–132, 2003) to estimate the size of unobserved heterogeneity necessary to render delinquent peer influence and school commitment statistically non-significant and substantively weak and compare these estimates to covariates in order to gauge the likely existence of such bias.
Results
Unobserved bias would need to be unreasonably large to render the peer effect statistically non-significant for violence and substance use, though less so to reduce it to a weak effect. The observed effect of school commitment on delinquency is much more fragile to unobserved heterogeneity.
Conclusion
Questions over the sensitivity of inferences plague criminology. This paper demonstrates the utility of sensitivity analysis for criminological theory testing in determining the robustness of estimated effects.
Publication Date
June 22, 2018
DOI
10.1007/s10940-018-9385-x
Citation Information
Kyle J. Thomas, Jean Marie McGloin and Christopher J. Sullivan. "Quantifying the Likelihood of False Positives: Using Sensitivity Analysis to Bound Statistical Inference" Journal of Quantitative Criminology (2018) p. 1 - 32
Available at: http://works.bepress.com/kyle-thomas/5/