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Abstract
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OBJECTIVES Most existing studies of sentencing focus on the decision of whether or not to incarcerate a convicted individual. However, many cases do not involve incarceration, and there is a considerable amount of freedom to mete out alternative sanctions as part of a sentencing decision. This study addresses this gap in the literature by investigating the patterns of sentences among those individuals not sentenced to prison.
METHODS This study used latent class analysis (LCA) to identify unobserved "classes" based on packages of sentences, which may involve both jail and non-incarceration alternative sanctions imposed on defendants not sentenced to prison. This study also demonstrates the potential use of LCA by (1) comparing and contrasting the results of LCA and the sentence severity scale approach, and (2) investigating how class membership could be explained by legal and extralegal characteristics of the cases.
RESULTS LCA identified four latent classes among individuals who were not sentenced to prison, two of which could not be differentiated by the severity scale approach. Regression models demonstrated that legal variables were better at explaining the incarceration decision, whereas extralegal variables were better at explaining some of the margins between the classes.
CONCLUSION Compared with the incarceration models and severity scales, LCA revealed variations that could not be detected by either of the approaches. LCA also has the potential to be a helpful tool in future analysis of sentencing decisions utilizing large-scale, administrative datasets.
Language: en |