Motives for Filing a False Allegation of Rape

The list of motives by Kanin (1994) is the most cited list of motives to file a false allegation of rape. Kanin posited that complainants file a false allegation out of revenge, to produce an alibi or to get sympathy. A new list of motives is proposed in which gain is the predominant factor. In the proposed list, complainants file a false allegation out of material gain, emotional gain, or a disturbed mental state. The list can be subdivided into eight different categories: material gain, alibi, revenge, sympathy, attention, a disturbed mental state, relabeling, or regret. To test the validity of the list, a sample of 57 proven false allegations were studied at and provided by the National Unit of the Dutch National Police (NU). The complete files were studied to ensure correct classification by the NU and to identify the motives of the complainants. The results support the overall validity of the list. Complainants were primarily motivated by emotional gain. Most false allegations were used to cover up other behavior such as adultery or skipping school. Some complainants, however, reported more than one motive. A large proportion, 20% of complainants, said that they did not know why they filed a false allegation. The results confirm the complexity of motivations for filing false allegations and the difficulties associated with archival studies. In conclusion, the list of Kanin is, based on the current results, valid but insufficient to explain all the different motives of complainants to file a false allegation.     

Individually-matched etiologic studies: classical estimators made new again

With greater access to regression-based methods for confounder control, the etiologic study with individual matching, analyzed by classical (calculator) methods, lost favor in recent decades. This design was costly, and the data sometimes mis-analyzed. Now, with Big Data, individual matching becomes an economical option. To many, however, conditional logistic regression, commonly used to estimate the incidence density ratio parameter, is somewhat of a black box whose output is not easily checked. An epidemiologist-statistician pair recently proposed a new estimator that is easily applied to data from individually-matched series with a 2:1 ratio (and no other confounding variables) using just a hand calculator or spreadsheet. Surprisingly—or possibly not—they overlooked classical estimators developed in earlier decades. This prompts me to re-introduce some of these, to highlight their considerable flexibility and ease of use, and to update them. Nowadays, for any matching ratio (M:1), the Maximum Likelihood result can be easily computed from data gathered under the matched design in two different ways, each using just the summary data. One is via any binomial regression program that allows offsets, applied to just M ‘rows’ of data. The other is by hand! The aim of this note is not to save on computation; instead, it is to make connections between classical and regression-based methods, to promote terminology that reflects the concepts and structure of the etiologic study, and to focus attention on what parameter is being estimated.     

Patients agressés en milieu psychiatrique : revue de la littérature et implications cliniques

Background

The psychiatric ward is a place where all forms of violence are treated. Occasionally, this violence involves acts of aggression between patients in emergency psychiatric units or hospital wards. Such events can lead to the development or worsening of posttraumatic stress disorder.