Multiple murder and criminal careers: A latent class analysis of multiple homicide offenders

PurposeTo construct an empirically rigorous typology of multiple homicide offenders (MHOs).MethodThe current study conducted latent class analysis of the official records of 160 MHOs sampled from eight states to evaluate their criminal careers.ResultsA 3-class solution best fit the data (?2LL = ?1123.61, Bayesian Information Criterion (BIC) = 2648.15, df = 81, L² = 1179.77). Class 1 (n = 64, class assignment probability = .999) was the low-offending group marked by little criminal record and delayed arrest onset. Class 2 (n = 51, class assignment probability = .957) was the severe group that represents the most violent and habitual criminals. Class 3 (n = 45, class assignment probability = .959) was the moderate group whose offending careers were similar to Class 2.ConclusionA sustained criminal career with involvement in versatile forms of crime was observed for two of three classes of MHOs. Linkages to extant typologies and recommendations for additional research that incorporates clinical constructs are proffered.     

Relevance analysis using revision identifier in MS word

Electronic documents often contain personal or confidential information, which can be used as valuable evidence in criminal investigations. In the digital investigation, special techniques are required for grouping and screening electronic documents, because it is challenging to analyze relationships between numerous documents in storage devices manually. To this end, although techniques such as keyword search, similarity search, topic modeling, metadata analysis, and document clustering are continually being studied, there are still limitations for revealing the relevance of documents. Specifically, metadata used in previous research are not always values present in the documents, and clustering methods with specific keywords may be incomplete because text‐based contents (including metadata) can be easily modified or deleted by users. In this work, we propose a novel method to efficiently group Microsoft Office Word 2007+ (MS Word) files by using revision identifier (RSID). Through a thorough understanding of the RSID, examiners can predict organizations to which a specific user belongs, and further, it is likely to discover unexpected interpersonal relationships. An experiment with a public dataset (GovDocs) provides that it is possible to categorize documents more effectively by combining our proposal with previously studied methods. Furthermore, we introduce a new document tracking method to understand the editing history and movement of a file, and then demonstrate its usefulness through an experiment with documents from a real case.     

Understanding research methods,limitations, and applications of drug data collected by the National Forensic Laboratory Information System (NFLIS-Drug)

The National Forensic Laboratory Information System (NFLIS) is a drug surveillance program of the US Drug Enforcement Administration that systematically collects data on drugs that are seized by law enforcement and submitted to and analyzed by the Nation's forensic laboratories (NFLIS-Drug). NFLIS-Drug data are increasingly used in predictive modeling and drug surveillance to examine drug availability patterns. Given the complexity of the data and data collection, there are some common methodological pitfalls that we highlight with the aim of helping researchers avoid these concerns. The analysis done for this Technical Note is based on a review of the scientific literature that includes 428 unique, refereed article citations in 182 distinct journals published between January 1, 2005, and April 30, 2021. Each article was analyzed according to how NFLIS-Drug data were mentioned and whether NFLIS-Drug data were included. A sample of 37 articles was studied in-depth, and data issues were summarized. Using examples from the literature, this Technical Note highlights eight broad concerns that have important implications for the proper applications, interpretations, and limitations of NFLIS-Drug data with suggestions for improving research methods and accurate reporting of forensic drug data. NFLIS-Drug data are timely and provide key information to inform drug use trends across the United States; however, our present analysis shows that NFLIS-Drug data are misunderstood and represented in the literature. In addition to highlighting these issues, DEA has created several resources to assist NFLIS data users and researchers, which are summarized in the discussion.     

A forensic examination of four popular cross‐platform file‐sharing apps with Wi‐Fi P2P

File‐sharing apps with Wi‐Fi hotspot or Wi‐Fi Direct functions become more popular. They can work on multiple platforms and allow users to transfer files in a concealed manner. However, when criminals use these apps in illegal activities, it becomes an important issue for investigators to find digital evidence on multiple platforms. At present, there are few studies on this topic, and most of them are limited to the single platform problem. In this paper, we propose a forensic examination method for four popular cross‐platform file‐sharing apps with Wi‐Fi hotspot and Wi‐Fi Direct functions: Zapya, SHAREit, Xender, and Feem. We use 22 static and live forensic tools for 11 platforms to acquire, analyze, and classify the forensic artifacts. In our experiments, we find many useful forensic artifacts and classify them into six categories. The experimental results can support law enforcement investigations of digital evidence and provide information for future studies on other cross‐platform file‐sharing apps.     

Validation of the 4‐aminophenol color test for the differentiation of marijuana‐type and hemp‐type cannabis

The analysis of cannabis plant material submitted to seized‐drug laboratories was significantly affected by the signing of the Agricultural Improvement Act of 2018, which defined hemp and removed it from the definition of marijuana in the Controlled Substances Act. As a result, field law enforcement personnel and forensic laboratories now are in need of implementing new protocols that can distinguish between marijuana‐type and hemp‐type cannabis. Colorimetric tests provide a cost‐effective and efficient manner to presumptively identify materials prior to submission to a laboratory for analysis. This work presents the validation of the 4‐aminophenol (4‐AP) color test and demonstrates its utility for discriminating between marijuana‐type and hemp‐type cannabis (i.e., typification). Validation studies included the testing of numerous cannabinoid reference materials, household herbs, previously characterized cannabis plant samples, and real‐case samples. The 4‐AP test reliably produces a pink result when the level of Δ⁹‐tetrahydrocannabinol (THC) is approximately three times lower than the level of cannabidiol (CBD). A blue result is generated when the level of THC is approximately three times higher than that of CBD. Inconclusive results are observed when the levels of THC and CBD are within a factor of three from each other, demonstrating the limitations of the test under those scenarios.     

Fraud in For-Profit Higher Education

The rising prominence of for-profit higher education in recent years has been impressive, as these organizations have come to play a more prominent role in American higher education. Nonetheless, for-profits continue to be troubled by allegations of fraud and other questionable practices, usually involving federal student-aid and the recruitment of students, which have triggered new regulations by the Department of Education. This article examines the criminogenic nature of the behavior in the for-profit sector, both from an historical and a more contemporary perspective. The analysis indicates that market structures and government policies play crucial roles in facilitating crime in these organizations and that such behavior is likely to continue despite tougher regulations, since the same elements will continue to influence for-profit colleges.     

Evidence of a gene × environment interaction between perceived prejudice and MAOA genotype in the prediction of criminal arrests

Purpose

The current study builds on a large body of research that has revealed that both genetic and environmental factors contribute to the development of antisocial behaviors. While a number of studies have indicated that stressful environments interact with specific genetic polymorphisms to create antisocial phenotypes, studies have not yet examined whether perceived prejudice and specific genetic polymorphisms combine together to predict criminal arrests over the life course.

Methods

The current study builds on the existing gene × environment literature by using data from the National Longitudinal Study of Adolescent Health (Add Health) to examine the effects of MAOA and perceived prejudice on the probability of being arrested.

Results

The results of the multivariate models reveal a statistically significant gene × environment interaction between MAOA and perceived prejudice in the prediction of arrest for males.

Conclusions

The results indicate that the presence of both perceived prejudice and MAOA increase the likelihood of being arrested. The implications of these results are discussed and limitations are noted.     

Characteristics of Abstainers from Substance Use and Antisocial in the United States

PURPOSE: Whether lifetime abstainer's antisocial behavior are maladjusted or well-adjusted is unresolved. The aim of this study was to compare abstainers (defined as persons with no lifetime use of alcohol and other drugs and non-engagement in antisocial or delinquent behavior) with non-abstainers across a range of sociodemographic and mental health characteristics in the United States. METHODS: Data were derived from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative sample of U.S. adults. Structured psychiatric interviews (N = 43,093) using the Alcohol Use Disorder and Associated Disabilities Interview Schedule - DSM-IV version (AUDADIS-IV) were completed by trained lay interviewers between 2001 and 2002. RESULTS: The prevalence of abstaining was 11%. Abstainers were significantly more likely to be female, Asian and African-American, born outside the U.S., and less likely to be unemployed. Multivariate logistic regression analyses revealed that abstainers were significantly less likely to evidence lifetime mood, anxiety, or personality disorder compared to non-abstainers. CONCLUSIONS: Findings indicate that abstainers are not maladapted and are comparatively more functional than non-abstainers.     

Criminal Specialization Revisited: A Simultaneous Quantile Regression Approach

Whether criminals are specialized or versatile in their offending is a long-standing research area that has been recently revitalized by a paradigm that recognizes that both specialization and versatility characterize offending careers. Based on data from an enriched sample of 500 adult habitual criminals, the current study introduces a measure of relative specialization—the offense specialization coefficient—and a novel analytical technique called simultaneous quantile regression to further the study of specialization. Although offenders committed a mix of offenses, there was considerable and at times pronounced evidence of specialization. Age, sex, and arrest onset had differential predictive validity of specialization for eight crimes at the 75th and 95th quantiles. Implications and suggestions for future research are offered.