RESUMEN
We used data on 3,139 female social network friendship dyads from 3 waves of the National Longitudinal Study of Adolescent to Adult Health (wave I: 1994-1995; wave II: 1996; and wave IV: 2007-2008) to assess whether friends' reports of experiencing sexual violence (SV) and friends' substance use risk scores predicted whether adolescents and young adults would experience SV themselves. We also used longitudinal analyses to test the associations of combined wave-I and -II risk factors with wave-IV reports of SV and of combined wave-I and -II SV with network connectivity at wave II. After adjustment for a participant's substance use risk score, each 1-point increase in a friend's substance use risk score increased a respondent's odds of experiencing SV by 1.19 (95% confidence interval: 1.03, 1.36). Having a friend who reported SV increased a respondent's odds of reporting SV by 1.95 (95% confidence interval: 1.25, 3.07), although not after we included school-level fixed effects. Having a friend who experienced SV in adolescence did however increase the respondent's odds of reporting SV as a young adult by 1.54 (95% confidence interval: 1.00, 2.37). Respondents who reported SV by wave II had less network connectedness at wave II. Experiences of SV and substance use within adolescent girls' friendship networks are linked to risk for SV into young adulthood, which suggests that network-focused SV prevention and intervention approaches may be warranted.
Asunto(s)
Delitos Sexuales , Apoyo Social , Adolescente , Coito , Femenino , Encuestas Epidemiológicas , Humanos , Estudios Longitudinales , Factores de Riesgo , Delitos Sexuales/etnología , Delitos Sexuales/psicología , Delitos Sexuales/estadística & datos numéricos , Trastornos Relacionados con Sustancias/epidemiología , Trastornos Relacionados con Sustancias/psicología , Adulto JovenRESUMEN
Underreporting is a well-known issue in crash frequency research. However, statistical methods that can account for underreporting have received little attention in the published literature. This paper compares results from underreporting models to models that account for unobserved heterogeneity. The difference in the elasticities between the negative binomial underreporting model and random parameters negative binomial models, which accounts for unobserved heterogeneity in crash frequency models, are used as the basis for comparison. The paper also includes a comparison of the predicted number of unreported PDO crashes based on the negative binomial underreporting model with crashes that were reported to police but were not considered reportable to PennDOT to assess the ability of the underreporting models to predict non-reportable crashes. The data used in this study included 21,340 segments of two-lane rural highways that are owned and maintained by PennDOT. Reported accident frequencies over an eight year period (2005-2012) were included in the sample, producing a total of 170,468 segment-years of data. The results indicate that if a variable impacts both the true accident frequency and the probability of accidents being reported, statistical modeling methods that ignore underreporting produce biased regression coefficients. The magnitude of the bias in the present study (based on elasticities) ranged from 0.00-16.79%. If the variable affects the true accident frequency, but not the probability of accidents being reported, the results from the negative binomial underreporting models are consistent with analysis methods that do not account for underreporting.
Asunto(s)
Accidentes de Tránsito/estadística & datos numéricos , Sesgo , Planificación Ambiental , Proyectos de Investigación/estadística & datos numéricos , Investigación/estadística & datos numéricos , Seguridad/estadística & datos numéricos , Estudios Transversales , Humanos , Modelos Estadísticos , Modelos Teóricos , Población Rural/estadística & datos numéricos , Estadística como Asunto , Heridas y Lesiones/epidemiologíaRESUMEN
We introduce and make publicly available a large corpus of digitized primary source human rights documents which are published annually by monitoring agencies that include Amnesty International, Human Rights Watch, the Lawyers Committee for Human Rights, and the United States Department of State. In addition to the digitized text, we also make available and describe document-term matrices, which are datasets that systematically organize the word counts from each unique document by each unique term within the corpus of human rights documents. To contextualize the importance of this corpus, we describe the development of coding procedures in the human rights community and several existing categorical indicators that have been created by human coding of the human rights documents contained in the corpus. We then discuss how the new human rights corpus and the existing human rights datasets can be used with a variety of statistical analyses and machine learning algorithms to help scholars understand how human rights practices and reporting have evolved over time. We close with a discussion of our plans for dataset maintenance, updating, and availability.
Asunto(s)
Algoritmos , Recolección de Datos/métodos , Bases de Datos Bibliográficas , Derechos Humanos , Procesamiento de Lenguaje Natural , Publicaciones Periódicas como Asunto , Documentación , HumanosRESUMEN
Researchers often face the problem of needing to protect the privacy of subjects while also needing to integrate data that contains personal information from diverse data sources. The advent of computational social science and the enormous amount of data about people that is being collected makes protecting the privacy of research subjects ever more important. However, strict privacy procedures can hinder the process of joining diverse sources of data that contain information about specific individual behaviors. In this paper we present a procedure to keep information about specific individuals from being "leaked" or shared in either direction between two sources of data without need of a trusted third party. To achieve this goal, we randomly assign individuals to anonymous groups before combining the anonymized information between the two sources of data. We refer to this method as the Yahtzee procedure, and show that it performs as predicted by theoretical analysis when we apply it to data from Facebook and public voter records.
Asunto(s)
Confidencialidad , Modelos Estadísticos , Privacidad , Sujetos de Investigación , Algoritmos , Recolección de Datos , Humanos , Reproducibilidad de los ResultadosRESUMEN
Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (specifically, Facebook interactions) to successfully identify real-world strong ties. Ground truth was established by asking users themselves to name their closest friends in real life. We found the frequency of online interaction was diagnostic of strong ties, and interaction frequency was much more useful diagnostically than were attributes of the user or the user's friends. More private communications (messages) were not necessarily more informative than public communications (comments, wall posts, and other interactions).
Asunto(s)
Amigos , Relaciones Interpersonales , Red Social , Adolescente , Adulto , Femenino , Humanos , Internet , Masculino , Persona de Mediana Edad , Curva ROC , Análisis de Regresión , Reproducibilidad de los Resultados , Conducta Social , Máquina de Vectores de Soporte , Adulto JovenRESUMEN
Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users' friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between 'close friends' who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.