Replication Data for: Election Fraud: A Latent Class Framework for Digit-Based Tests

Digit-based election forensics typically relies on null hypothesis significance testing, with undesirable effects on substantive conclusions. This paper proposes an alterna- tive free of this problem. It rests on decomposing the observed numeral distribution into the ‘no fraud’ and ‘fraud’ latent classes, by finding the smallest fraction of nu- merals that either needs to be removed or reallocated to achieve a perfect fit of the ‘no fraud’ model. The size of this fraction can be interpreted as a measure of fraud- ulence. Both alternatives are special cases of measures of model fit–the π∗ mixture index of fit and the ∆ dissimilarity index, respectively. Furthermore, independently of the latent class framework, the distributional assumptions of digit-based election forensics can be relaxed in some contexts. Independently or jointly, the latent class framework and the relaxed distributional assumptions allow to dissect the observed distributions using models more flexible than those of existing digit-based election forensics. Reanalysis of Beber and Scacco’s (2012) data shows that the approach can lead to new substantive conclusions.基于数字的选举舞弊取证（digit-based election forensics）通常依赖原假设显著性检验（null hypothesis significance testing），但其会对实质性结论产生不利影响。本文提出了一种可规避该问题的替代方法：将观测到的数字分布分解为“无舞弊”与“有舞弊”两个潜在类别（latent classes），通过找出需移除或重新分配的最小比例数字，使“无舞弊”模型达到完美拟合（perfect fit）。该比例的大小可作为衡量舞弊程度的指标。两种替代方案分别对应模型拟合测度的两类特殊情形：π∗混合拟合指数（mixture index of fit）与∆差异指数（dissimilarity index）。此外，在部分场景中，无需依赖潜在类别框架（latent class framework），即可放宽基于数字的选举舞弊取证的分布假设。无论是单独使用还是结合使用潜在类别框架与放宽后的分布假设，均可采用比现有基于数字的选举舞弊取证方法更灵活的模型，对观测分布进行拆解分析。对贝伯与斯卡科（2012）的数据集进行的重新分析表明，该方法可得出全新的实质性结论。