@article{ref1,
title="Quantifying the likelihood of false positives: using sensitivity analysis to bound statistical inference",
journal="Journal of quantitative criminology",
year="2019",
author="Thomas, Kyle J. and McGloin, Jean Marie and Sullivan, Christopher J.",
volume="35",
number="4",
pages="631-662",
abstract="OBJECTIVECriminologists 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.<br><br>METHODSData 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.<br><br>RESULTSUnobserved 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.<br><br>CONCLUSIONQuestions 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.<p /> <p>Language: en</p>",
language="en",
issn="0748-4518",
doi="10.1007/s10940-018-9385-x",
url="http://dx.doi.org/10.1007/s10940-018-9385-x"
}