Objectives

Longitudinal data offer many advantages to criminological research yet suffer from attrition, namely in the form of sample selection bias. Attrition may undermine reaching valid inferences by introducing systematic differences between the retained and attrited samples. We explored (1) if attrition biases correlates of recidivism, (2) the magnitude of bias, and (3) how well methods of correction account for such bias.

Methods

Using data from the LoneStar Project, a representative longitudinal sample of reentering men in Texas, we examined correlates of recidivism using official measures of recidivism under four sample conditions: full sample, listwise deleted sample, multiply imputed sample, and two-stage corrected sample. We compare and contrast the results regressing rearrest on a range of covariates derived from a pre-release baseline interview across the four sample conditions.

Results

Attrition bias was present in 44% of variables and null hypothesis significance tests differed for the correlates of recidivism in the full and retained samples. The bias was substantial, altering effect sizes for recidivism by a factor as large as 1.6. Neither the Heckman correction nor multiple imputation adequately corrected for bias. Instead, results from listwise deletion most closely mirrored the results of the full sample with 89% concordance.

Conclusions

It is vital that researchers examine attrition-based selection bias and recognize the implications it has on their data when generating evidence of theoretical, policy, or practical significance. We outline best practices for examining the magnitude of attrition and analyzing longitudinal data affected by sample selection.

中文翻译：

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非随机研究耗损：从监狱再入纵向研究中评估校正技术和偏差的大小

目标

纵向数据为犯罪学研究提供了许多优势，但遭受了损耗，即样本选择偏差的形式。损耗可能会通过在保留样本和损耗样本之间引入系统差异来破坏达成有效推论。我们探讨了（1）损耗偏倚是否与累犯相关，（2）偏倚的幅度，以及（3）校正方法对这种偏倚的解释程度。

方法

使用来自LoneStar项目的数据，该数据是得克萨斯州重返男性的代表性纵向样本，我们使用正式的累犯措施在四个样本条件下检查了累犯的相关性：完整样本，逐项删除样本，多次推算样本和两阶段校正样本。我们比较和对比了在四个样本条件下从预发布基线访谈得出的一系列协变量中回归靠背的结果。

结果

在44％的变量中存在损耗偏倚，并且对于全部样本和保留样本中的累犯相关性，零假设显着性检验也有所不同。偏差很大，将累犯的效应大小改变了1.6倍。Heckman校正或多重插补均不能充分校正偏差。取而代之的是，按列表删除的结果与89％一致性的完整样本的结果最为相似。

结论

研究人员在产生理论，政策或实践意义的证据时，研究基于损耗的选择偏差并认识到其对数据的影响至关重要。我们概述了检查损耗量和分析受样本选择影响的纵向数据的最佳实践。