Replication Data for: Election Fraud: A Latent Class Framework for Digit-Based Tests
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'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 distributioninto 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∗ mixtureindex of fit and the ∆ dissimilarity index, respectively. Furthermore, independentlyof the latent class framework, the distributional assumptions of digit-based electionforensics can be relaxed in some contexts. Independently or joint' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["election forensics, latent class analysis, the mixture index of fit"]} <|eot_id|>
 Here is the Description of a dataset. Description:'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 distributioninto 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∗ mixtureindex of fit and the ∆ dissimilarity index, respectively. Furthermore, independentlyof the latent class framework, the distributional assumptions of digit-based electionforensics can be relaxed in some contexts. Independently or joint' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
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 distributioninto 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∗ mixtureindex of fit and the ∆ dissimilarity index, respectively. Furthermore, independentlyof the latent class framework, the distributional assumptions of digit-based electionforensics can be relaxed in some contexts. Independently or joint
election forensics, latent class analysis, the mixture index of fit