Objectives

Evaluate the impact of missing data on observed racial disparities in the likelihood of an incarceration sentence, given that complete case analysis in the common analytic approach used in criminological research.

Methods

Using a simulation study with data based on cases sentenced in the Court of Common Pleas in Pennsylvania between 2010 and 2019, we assess the differences in the likelihood of incarceration between similarly situated White and Black defendants based on varying sample sizes and patterns of missing data.

Results

Complete case analysis (CCA) of incomplete data can fail to provide unbiased estimates of the race effect, even with less than 10% of cases missing. The degree of bias introduced depends on the amount, pattern, assumptions, and treatment of missing data. Multiple imputation provides an established, valid methodology for the unbiased estimation of race effects when data are missing at random, and this holds across sample sizes and number of imputations.

Conclusions

The existence and magnitude of race effects on the likelihood of an incarceration sentence can vary greatly based on the degree, pattern, assumptions, and treatment of missing data. Limitations include that missing data mechanisms cannot be truly known outside of a data simulation. Future sentencing research should prioritize the identification, treatment, and reporting of missing data prior to isolating race effects, in line with calls from the field for more open science practices. Sensitivity analyses should also be prioritized.

中文翻译：

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现在您看到了，现在您没有：模拟和说明在估计量刑中的种族影响时处理不完整数据的重要性

目标

鉴于犯罪学研究中使用的常见分析方法中的完整案例分析，评估缺失数据对观察到的监禁判决可能性中的种族差异的影响。

方法

我们使用基于 2010 年至 2019 年宾夕法尼亚州民事法庭判决的案件数据进行的模拟研究，根据不同的样本量和缺失数据模式，评估了处境相似的白人和黑人被告被监禁的可能性的差异。

结果

对不完整数据进行完整案例分析 (CCA) 可能无法提供对种族效应的公正估计，即使丢失的案例少于 10%。引入的偏差程度取决于缺失数据的数量、模式、假设和处理。多重插补提供了一种既定的、有效的方法，用于在数据随机丢失时对种族效应进行无偏估计，并且这适用于样本量和插补数量。

结论

根据缺失数据的程度、模式、假设和处理方式，种族对监禁可能性的影响的存在和程度可能会有很大差异。局限性包括在数据模拟之外无法真正了解丢失的数据机制。未来的量刑研究应在隔离种族影响之前优先考虑缺失数据的识别、处理和报告，这符合该领域对更开放的科学实践的呼吁。敏感性分析也应优先考虑。