Election Fraud
A Latent Class Framework for Digit-Based Tests
Bibliographic Data
| ID | 6332001 |
|---|---|
| Authors | Juraj Medzihorsky (0000-0003-1042-378X, corresponding author) |
| Year | 2015 |
| Volume | 23 |
| Issue | 4 |
| Pages | 506-517 |
| Publication date | 2015-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Political Analysis (JOURNAL) |
| Journal identifiers | ISSN: 1047-1987 • E-ISSN: 1476-4989 |
| Publisher | Cambridge University Press (CUP) (PUBLISHER) |
| DOI | 10.1093/pan/mpv021 |
| OpenAlex | W2232224798 |
| Language | EN |
| Citations received | 7 |
| References cited | 34 |
Digit-based election forensics (DBEF) typically relies on null hypothesis significance testing, with undesirable effects on substantive conclusions. This article proposes an alternative 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 numerals that needs to be either 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 fraudulence. Both alternatives are special cases of measures of model fit—theπ∗ mixture index of fit and the Δ dissimilarity index, respectively. Furthermore, independently of the latent class framework, the distributional assumptions of DBEF can be relaxed in some contexts. Independently or jointly, the latent class framework and the relaxed distributional assumptions allow us to dissect the observed distributions using models more flexible than those of existing DBEF. Reanalysis of Beber and Scacco's (2012) data shows that the approach can lead to new substantive conclusions
Class (philosophy · Data mining · Econometrics · Fraction (chemistry · Index (typography · Latent class model · Machine learning · Null (SQL · Numeral system · Benford’s Law and Fraud Detection · Computer Science · Digital Media Forensic Detection · Imbalanced Data Classification Techniques · Mathematics · Artificial Intelligence
Do dictators signal strength with electoral fraud
Signaling Democratic Progress through Electoral System Reform in Post-Communist States
With a little help from the crowd
Election fraud, digit tests and how humans fabricate vote counts - An experimental approach
Fabricating votes for Putin
Detecting Election Fraud from Irregularities in Vote-Share Distributions
Conceptual and Measurement Issues in Assessing Democratic Backsliding
Comment on “Benford's Law and the Detection of Election Fraud”
A Procedure for Estimating Intrasubject Behavior Consistency
Advancing Electoral Integrity
A New Index of Structure for the Analysis of Models for Mobility Tables and Other Cross-Classifications
What the Numbers Say
Benford's Law and the Detection of Election Fraud
Fraudulent Democracy? An Analysis of Argentina'sInfamous DecadeUsing Supervised Machine Learning
A systematic approach to study electoral fraud
Searching for electoral irregularities in an established democracy
| Citations per year | 0.78 |
|---|---|
| Citation span | 2017 - 2025 (9) |
| Highly cited | No |