College women and the occurrence of unwanted sexual advances in public drinking settings

Using existing data from the 1999 Harvard School of Public Health College Alcohol Study, this study analyzes the social predictors of unwanted sexual advances experienced by college women and where this type of victimization occurs. Routine activities theory informed the analysis. While attendance and increased alcohol consumption at bars have a significant effect on experiences of unwanted sexual advances, attendance at parties, attendance at drink promotions, and participation in drinking games does not. Furthermore, while less satisfaction with education, having more sexual partners and friends that binge drink, and having more alcohol-related problems increase the likelihood of experiencing unwanted sexual advances, so does drinking less in general, having higher educated parents, and having a higher grade point average. These findings show that the predictors of unwanted sexual advances may differ somewhat from those that predict more serious forms of sexual victimization. Implications for future research and policy are discussed.     

A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon''s rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.     

Describing poverty in an ultraperipheral region: The case of the Canary Islands

Based on data from the Survey on Social Conditions in the Canary Islands of the Instituto Canario de Estadística, this study analyzes poverty in Canary households by island of residence, age, employment and the educational level of the main provider. In addition to accepted objective measures, we also estimate poverty using subjective methods, in which the poverty line is defined or obtained by the opinion of household members of their own level of poverty.     

Pivotal Quantities Based on Sequential Data: A Bootstrap Approach

A bootstrap algorithm is provided for obtaining a confidence interval for the mean of a probability distribution when sequential data are considered. For this kind of data the empirical distribution can be biased but its bias is bounded by the coefficient of variation of the stopping rule associated with the sequential procedure. When using this distribution for resampling the validity of the bootstrap approach is established by means of a series expansion of the corresponding pivotal quantity. A simulation study is carried out using Wang and Tsiatis type tests and considering the normal and exponential distributions to generate the data. This study confirms that for moderate coefficients of variation of the stopping rule, the bootstrap method allows adequate confidence intervals for the parameters to be obtained, whichever is the distribution of data.     

A local-influence-based diagnostic approach to a speeded item response theory model

Summary.  An item response theory model for dealing with omitted responses in a test is proposed. In this model formulation, non-response does not only depend on an examinee's ability and on item difficulty, but additionally also on 'test speededness'. Using a local-influence-based diagnostic approach, the sensitivity of the model regarding assumptions concerning the drop-out mechanism is explored. The methodology proposed is applied to the Chilean Sistema de Medición de la Calidad de la Educación mathematics test case-study.     

The labour division of Italian couples after a birth: assessing the effect of unobserved heterogeneity

The results of previous studies on the effect of childbearing on both parents’ paid and unpaid work suffer from the difficulty related to the specification of latent variables that influence the relationship between reproductive behaviour of the couple and working activity. The aim of this study is to estimate the effect of transition to parenthood on the partners’ division of labour by accounting for latent variables, such as the bargaining process between partners and endogenous fertility decisions. In particular, this is the first time that our specific estimation strategy has been applied to determine the impact of childbearing on the division of household labour between partners taking into account the reverse causality effect between labour and fertility decisions. We use longitudinal data on married or cohabiting Italian couples provided by the Italian panel survey of the Generation and Gender Program. Our results are consistent with previous studies, and show that the birth of a child affects a woman’s unpaid labour strongly and positively and her paid labour negatively, while men’s work is mostly unaffected. Moreover, when partners hold traditional attitudes regarding gender roles and the family, the female partner is even more strongly affected by childbearing in terms of total amount of labour (paid plus unpaid). However, our sensitivity analysis shows that these results appear only when our correction strategy for the misspecification of latent variables is applied.