Intergenerational Programs Review: Study Design and Characteristics of Intervention,Outcomes, and Effectiveness

There is a growing interest in intergenerational connectivity through intergenerational programs. In this work, a review of intergenerational programs was performed, with focus on the program design and objectives as well as in their outcomes.

We used a systematic review method in which we screened 3,796 articles. After analyzing titles, abstracts, and full paper analysis, 16 articles were retained. Each study was reviewed, and data were extracted related to target population, study design, characteristics of intervention, outcomes, and effectiveness.

Intergenerational programs included educational programs and art, Information technology development, cultural heritage, health education, and therapeutic activities. Most of the programs collected both quantitative and/or qualitative data. Seven studies collected data in the beginning and at the end of the program. Significant diversity in sample size and intervention length was found. Measurement of outcomes was performed in both young and/or elderly group of participants. Programs impact evaluation varied between studies, including validated scales, interviews, observation, focus groups, and conversation analysis, narratives, videotaped sessions, and field notes.

Our study highlighted the diversity in the design of studies and in the program’s effectiveness evaluation. More randomized design studies are required to support researchers and practitioners in the development of future intergenerational programs.     

Robust analysis of bibliometric data

This work stems from the idea of describing the scientific productivity of Italian statisticians. There are several problems that must be addressed in achieving this goal: What data should be used? Have the data been cleaned? What techniques can be used? We propose the use of multiple sources and multiple metrics to get a complete information base. We check the correctness of the data using multivariate outlier identification techniques. We appropriately transform the data. We apply robust clustering to verify the existence of homogeneous groups. We suggest the use of forward search to establish a ranking among scholars. The proposed methodology, which, in this case, allowed us to group scholars into four homogeneous groups and sort them according to multidimensional data, can be applied to other similar applications in bibliometrics.     

Influence Measures in Quantile Regression Models

In this article, we use the asymmetric Laplace distribution to define a new method to determine the influence of a certain observation in the fit of quantile regression models. Our measure is based on the likelihood displacement function and we propose two types of measures in order to determine influential observations in a set of conditional quantiles conjointly or in each conditional quantile of interest. We verify the validity of our average measure in a simulated data set as well in an illustrative example with data about air pollution.     

Generalized bartlett correction

This paper provides a general method of modifying a statistic of interest in such a way that the distribution of the modified statistic can be approximated by an arbitrary reference distribution to an order of accuracy of O(n ^-1/2) or even O(n ^-1). The reference distribution is usually the asymptotic distribution of the original statistic. We prove that the multiplication of the statistic by a suitable stochastic correction improves the asymptotic approximation to its distribution. This paper extends the results of the closely related paper by Cordeiro and Ferrari (1991) to cope with several other statistical tests. The resulting expression for the adjustment factor requires knowledge of the Edgeworth-type expansion to order O(n^-1) for the distribution of the unmodified statistic. In practice its functional form involves some derivatives of the reference distribution. Certain difference between the cumulants of appropriate order in n of the unmodified statistic and those of its first-order approximation, and the unmodified statistic itself. Some applications are discussed.     

A Review of Some Modern Approaches to the Problem of Trend Extraction

This article presents a review of some modern approaches to trend extraction for one-dimensional time series, which is one of the major tasks of time series analysis. The trend of a time series is usually defined as a smooth additive component which contains information about the time series global change, and we discuss this and other definitions of the trend. We do not aim to review all the novel approaches, but rather to observe the problem from different viewpoints and from different areas of expertise. The article contributes to understanding the concept of a trend and the problem of its extraction. We present an overview of advantages and disadvantages of the approaches under consideration, which are: the model-based approach (MBA), nonparametric linear filtering, singular spectrum analysis (SSA), and wavelets. The MBA assumes the specification of a stochastic time series model, which is usually either an autoregressive integrated moving average (ARIMA) model or a state space model. The nonparametric filtering methods do not require specification of model and are popular because of their simplicity in application. We discuss the Henderson, LOESS, and Hodrick–Prescott filters and their versions derived by exploiting the Reproducing Kernel Hilbert Space methodology. In addition to these prominent approaches, we consider SSA and wavelet methods. SSA is widespread in the geosciences; its algorithm is similar to that of principal components analysis, but SSA is applied to time series. Wavelet methods are the de facto standard for denoising in signal procession, and recent works revealed their potential in trend analysis.     

Box–Cox t random intercept model for estimating usual nutrient intake distributions

The issue of estimating usual nutrient intake distributions and prevalence of inadequate nutrient intakes is of interest in nutrition studies. Box–Cox transformations coupled with the normal distribution are usually employed for modeling nutrient intake data. When the data present highly asymmetric distribution or include outliers, this approach may lead to implausible estimates. Additionally, it does not allow interpretation of the parameters in terms of characteristics of the original data and requires back transformation of the transformed data to the original scale. This paper proposes an alternative approach for estimating usual nutrient intake distributions and prevalence of inadequate nutrient intakes through a Box–Cox t model with random intercept. The proposed model is flexible enough for modeling highly asymmetric data even when outliers are present. Unlike the usual approach, the proposed model does not require a transformation of the data. A simulation study suggests that the Box–Cox t model with random intercept estimates the usual intake distribution satisfactorily, and that it should be preferable to the usual approach particularly in cases of highly asymmetric heavy-tailed data. In applications to data sets on intake of 19 micronutrients, the Box–Cox t models provided better fit than its competitors in most of the cases.     

Explaining Legislators’ Actions on Behalf of Women in the Parliamentary Party Group: The Role of Attitudes,Resources, and Opportunities

Scholars of women’s substantive representation have often looked for an empirical link between legislators’ sex and their actions on behalf of women. However, to understand “how women’s substantive representation occurs” (Celis et al. 2008), studies also need to consider differences that exist among women and include men’s activities in the analyses. This contribution presents several sources that explain women’s as well as men’s agenda-setting behavior on behalf of women in 11 European democracies. The sources relate to legislators’ feminist awareness, gender-related resources, and the incentives they receive from the party-political context.     

Gambling Disorder: Exploring Pre-treatment and In-treatment Dropout Predictors. A UK Study

The aim of this study was to identify predictors of treatment dropout in a sample of gamblers attending a specialist clinic for gambling disorder. We analysed data on 846 treatment-seeking pathological gamblers. Firstly, we investigated differences in socio-demographic and clinical variables between treatment completers and pre-treatment dropouts, as well as between treatment completers and during-treatment dropouts. Subsequently, variables were entered into a multinomial logistic regression model to identify significant predictors of pre-treatment and in-treatment dropout. Overall, 44.8% of clients did not complete the treatment: 27.4% dropped out before starting it, while 17.4% dropped out during the treatment. Younger age and use of drugs were associated with pre-treatment dropout, while family history of gambling disorder, a lower PGSI score, and being a smoker were related with in-treatment dropout. Our findings suggest that pre-treatment dropouts differ from in-treatment dropouts, and, thus, further research will benefit from considering these groups separately. In addition, this newly gained knowledge will also be helpful in increasing treatment retention in specific subgroups of problem gamblers.     

Relational Resources of Individuals Living in Couple: Evidence from an Italian Survey

The need for support becomes stronger in situations of pressure, uncertainty and overload caused by unfavorable economic, demographic or social circumstances. Especially in countries—such as Italy—where an adequate welfare system is lacking, the individual’s social space can represent a resilience (anti-frailty) tool through the activation of a support network. While the literature has mainly analyzed the support that some vulnerable categories (e.g., elderly and youths) receive from their family, we focus on individuals living in Italy in the first stages of their family life, with the aim of describing their support network. We construct the potential support ego-centered (PSE) network—at partner and couple level—of individuals living in couple using data from the survey “Family and Social Subjects” carried out in Italy in 2009 by the Italian National Statistical Institute. Furthermore, we compare the network typologies detected using two alternative clustering techniques with the objective of finding the partners’ and couples’ network types and verifying whether traditional strong support received by the family persists in Italy and/or whether new kinds of support networks are emerging. Several PSE network typologies, ranging from empty to comprehensive networks, were determined with a fair match between the two procedures. Analysis revealed the importance of friends and neighbors, especially in the North of Italy, to the support of partners and couple as a whole.     

Representativeness in panel surveys

ABSTRACT

A panel is a set of units recruited and used in successive surveys. When the sample unit is the household, so-called R-indicators together with the comparison of distributions of certain variables to those known in the total population help to measure the representativeness of the panel. The method is applied to Understanding Society, a U.K. household longitudinal study. At each wave, under- and over-represented groups of individuals are identified. This allows the implementation of better survey designs and procedures to reduce the bias of nonresponse.