Sexual Functioning in Spanish and Portuguese Young Adults: Initial Validation of the Arizona Sexual Experience Scale

Objectives: The authors conducted an initial validation of the Arizona Sexual Experience Scale (ASEX) and examined the sexual function of Spanish and Portuguese young adults. Methods: Participants were 523 Spanish and 595 Portuguese heterosexual young adults who completed a background questionnaire, the ASEX, and measures of sexual sensation seeking, excitation, and inhibition. Results: The ASEX showed good reliability and validity. Participants reported good sexual function. The most prevalent difficulties in men were related to desire and orgasm, and to orgasm and satisfaction in women. Conclusions: This is a psychometrically sound instrument to assess sexual functioning in Spanish and Portuguese young adults.     

Approximate confidence intervals for the weighted kappa coefficient of a binary diagnostic test subject to a case–control design

Case–control design to assess the accuracy of a binary diagnostic test (BDT) is very frequent in clinical practice. This design consists of applying the diagnostic test to all of the individuals in a sample of those who have the disease and in another sample of those who do not have the disease. The sensitivity of the diagnostic test is estimated from the case sample and the specificity is estimated from the control sample. Another parameter which is used to assess the performance of a BDT is the weighted kappa coefficient. The weighted kappa coefficient depends on the sensitivity and specificity of the diagnostic test, on the disease prevalence and on the weighting index. In this article, confidence intervals are studied for the weighted kappa coefficient subject to a case–control design and a method is proposed to calculate the sample sizes to estimate this parameter. The results obtained were applied to a real example.     

Monitoring the parameter vector of regression models with time-to-event response in phase II processes

In most of the existing specialized literature, monitoring regression models are a special case of profile monitoring. However, not every regression model always represents appropriately a profile data structure. This is clearly the case of the Weibull regression model (WRM) with common shape parameter γ. Even though it might be thought that existing methodologies (especially likelihood-ratio (LRT)-based methods) for monitoring generalized linear profiles can also be successfully applied to monitoring regression models with time-to-event response, it will be shown in this paper that those methodologies work fairly acceptable just for data structures with 1000 observations at least approximately. It was found out that some corrections, often referred to as Bartlett's adjustments, are needed to be implemented in order to improve the accuracy of using the asymptotic distributional properties of the LRT statistic for carrying out the monitoring of WRM with relatively small and moderate dimensions of the available datasets. Simulation studies suggest that the use of the aforementioned corrections make the resulting charts work quite acceptable when available data structures contain 30 observations at least. Detection abilities of the proposed schemes improve as dataset dimension increases.     

Flexible objective Bayesian linear regression with applications in survival analysis

We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general non-informative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [16]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.     

Computing A-optimal and E-optimal designs for regression models via semidefinite programming

In semidefinite programming (SDP), we minimize a linear objective function subject to a linear matrix being positive semidefinite. A powerful program, SeDuMi, has been developed in MATLAB to solve SDP problems. In this article, we show in detail how to formulate A-optimal and E-optimal design problems as SDP problems and solve them by SeDuMi. This technique can be used to construct approximate A-optimal and E-optimal designs for all linear and nonlinear regression models with discrete design spaces. In addition, the results on discrete design spaces provide useful guidance for finding optimal designs on any continuous design space, and a convergence result is derived. Moreover, restrictions in the designs can be easily incorporated in the SDP problems and solved by SeDuMi. Several representative examples and one MATLAB program are given.     

Layered adaptive importance sampling

Monte Carlo methods represent the de facto standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples. The performance of any such method is strictly related to the specification of the proposal distribution, such that unfortunate choices easily wreak havoc on the resulting estimators. In this work, we introduce a layered (i.e., hierarchical) procedure to generate samples employed within a Monte Carlo scheme. This approach ensures that an appropriate equivalent proposal density is always obtained automatically (thus eliminating the risk of a catastrophic performance), although at the expense of a moderate increase in the complexity. Furthermore, we provide a general unified importance sampling (IS) framework, where multiple proposal densities are employed and several IS schemes are introduced by applying the so-called deterministic mixture approach. Finally, given these schemes, we also propose a novel class of adaptive importance samplers using a population of proposals, where the adaptation is driven by independent parallel or interacting Markov chain Monte Carlo (MCMC) chains. The resulting algorithms efficiently combine the benefits of both IS and MCMC methods.     

Weighted simulated integrated conditional moment tests for parametric conditional distributions of stationary time series processes

In this article, we propose a weighted simulated integrated conditional moment (WSICM) test of the validity of parametric specifications of conditional distribution models for stationary time series data, by combining the weighted integrated conditional moment (ICM) test of Bierens (1984 Bierens, H. J. (1984). Model specification testing of time series regressions. Journal of Econometrics 26:323–353.[Crossref], [Web of Science ®] , [Google Scholar]) for time series regression models with the simulated ICM test of Bierens and Wang (2012 Bierens, H. J., Wang, L. (2012). Integrated conditional moment tests for parametric conditional distributions. Econometric Theory 28:328–362.[Crossref], [Web of Science ®] , [Google Scholar]) of conditional distribution models for cross-section data. To the best of our knowledge, no other consistent test for parametric conditional time series distributions has been proposed yet in the literature, despite consistency claims made by some authors.     

The role of empathy in training social work students in motivational interviewing

Motivational interviewing (MI), an evidence-based approach with empathy as a key principle, effectively addresses client concerns found in the child welfare population. Training social workers in MI, and to be empathic, would increase the likelihood of better service delivery. Live supervision (LS) is a form of training that provides real-time feedback by clinical supervisors. This randomized comparison trial compared the effectiveness of LS or Teaching as Usual (TAU) on empathy in 54 MSW and BSW social work students. TAU involved students receiving online modules and assigned readings. Data were collected at baseline, after the interventions, and at five months follow-up. Differences in perceived empathy and empathic behaviors were measured by the Motivational Interviewing Treatment Integrity (MITI) and Toronto Empathy Questionnaire (TEQ). The study also assessed whether demographic variables or perceived empathy (TEQ) predicted empathic behaviors (MITI). Results indicate that, while both groups improved on empathy as measured by the MITI and TEQ, the LS group demonstrated more improvement. Demographic variables had no impact on empathy. The TEQ and MITI also did not demonstrate predictability suggesting the complexity of measuring empathy. The implications for social work education and future training are discussed.     

Randomized controlled trial of the Resilience and Coping Intervention (RCI) with undergraduate university students

Objective: The purpose of this pilot study was to evaluate the Resilience and Coping Intervention (RCI) with college students. Participants: College students (aged 18–23) from a large Midwest US university who volunteered for a randomized controlled trial during the 2015 spring semester. Methods: College students were randomly assigned to an intervention (n = 64) or a control (n = 65) group. Intervention participants received three 45-minute RCI sessions over subsequent weeks. All participants completed pre- and post-intervention assessments at the beginning of Week 1 and end of Week 3. Student resilience, coping, hope, stress, depression, and anxiety were assessed. Results. RCI participants reported significantly more hope and less stress and depression from Week 1 to Week 3 compared with control participants. Results for resilience also approached statistical significance. Effect sizes were small to moderate. Conclusions: This study found preliminary evidence that RCI is an effective resilience intervention for use with college students.