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• W2784849335 abstract "Explore the typical “packages” of probation conditions and assess general and interactive racial and ethnic disparities in assignment to these “packages”. Latent class analysis (LCA) is applied as an exploratory modeling technique to uncover the nature of heterogeneity in probation conditions using novel and detailed data for over 2100 adults convicted of a felony in two urban counties. Class enumeration procedures use the Bayesian Information Criterion and correct for potential violations of conditional independence assumption. Model verification is assessed using mean posterior probabilities, confidence intervals for class differentiability, split sample checks and multiple iterations with random starting points. The influence of race and ethnicity, along with other legal and extralegal factors on assignment of probation condition “packages” identified is estimated using multinomial regression. Meaningful and discrete classes that ranged in their likely combinations, type, and number of conditions are identified by LCA with high assignment accuracy. Results from regression models suggest race and ethnicity play a role in assignment of probation conditions. Young black offenders and black drug offenders are particularly more likely to receive a wider range of and more restrictive conditions. LCA has the potential of classifying the multiple components of probation sentences without masking the heterogeneous nature of the conditions and imposing classification systems. Heterogeneity in the assignment of probation conditions represents a source of racial and ethnic sentencing disparities." @default.
• W2784849335 created "2018-02-02" @default.
• W2784849335 creator A5075166723 @default.
• W2784849335 date "2018-12-14" @default.
• W2784849335 modified "2023-09-26" @default.
• W2784849335 title "Investigating the Assignment of Probation Conditions: Heterogeneity and the Role of Race and Ethnicity" @default.
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• W2784849335 doi "https://doi.org/10.1007/s10940-018-9400-2" @default.
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