A Class of Multidimensional Latent Class IRT Models for Ordinal Polytomous Item Responses

We propose a class of multidimensional Item Response Theory models for polytomously-scored items with ordinal response categories. This class extends an existing class of multidimensional models for dichotomously-scored items in which the latent abilities are represented by a random vector assumed to have a discrete distribution, with support points corresponding to different latent classes in the population. In the proposed approach, we allow for different parameterizations for the conditional distribution of the response variables given the latent traits, which depend on the type of link function and the constraints imposed on the item parameters. Moreover, we suggest a strategy for model selection that is based on a series of steps consisting of selecting specific features, such as the dimension of the model (number of latent traits), the number of latent classes, and the specific parameterization. In order to illustrate the proposed approach, we analyze a dataset from a study on anxiety and depression on a sample of oncological patients.     

Some simulations and applications of forecasting long-memory time-series models

In this paper, we show some results of forecasting based on the ARFIMA(p,d,q) and ARIMA(p,d,q) models. We show, by simulation, that the technique of forecasting of the ARIMA(p,d,q) model can also be used when d is fractional, i.e., for the ARFIMA(p,d,q) model. We also conduct a simulation study to compare the two estimators of d obtained through regression methods. They are used in the hypothesis test to decide whether or not the series has long memory property and are compared on the basis of their k-step ahead forecast errors. The properties of long-memory models are also investigated using an actual set of data.     

Explaining Gender Inequalities That Follow Couple Migration

By exploiting the unique social and economic differences between East and West Germany, the authors investigated how macro‐level opportunities interact with couple‐level decision making to explain gender differences in the determinants and economic outcomes of household migration. By incorporating regional socioeconomic conditions into household bargaining theory, 4 hypotheses for each region were derived. The hypotheses were tested using cross‐classified multilevel regressions and the German Socio‐Economic Panel (1992–2012) combined with regional economic indicators. First, gender‐specific determinants of couples' West–West (i.e., within West Germany) and East‐to‐West migration were analyzed; second, subsequent economic consequences were investigated by comparing couples with singles. The results confirm that gender differences in macro‐conditions can impose decision logics that seemingly contradict the initial power relation within couples. Despite more traditional gender arrangements in West Germany, well‐educated partnered women earn significant absolute and relative income gains from migration; their egalitarian East German counterparts suffer significant losses compared with single women and East German men.     

Dropout of Hospital Volunteers in Italy

Voluntary work in Charities is a constantly growing reality: in particular, in the last few years, voluntary work in hospitals has become of great importance, marking a substantial increase of commitment in social issues. However, at the same time, volunteering Charities have been plagued by the increase of the phenomenon of dropout, that is, volunteers abandoning the Charitable Association they’re working in, often after a relatively short period of activity. In this study, we attempted to verify if and how active and dropped out volunteers perceive their own activity as a source of anxiety and stress. Our data show that both active and dropped out volunteers show high levels of burnout; in fact, dropped out volunteers show higher levels of burnout, especially of emotional exhaustion, in comparison with active volunteers. Subsequently, we identified the factors potentially assuming the role of mediators or moderators in the direct relation involving the status of voluntary work activity and the levels of anxiety and burnout. Our study demonstrates that coping strategies and the relationship established with hospital workers can be considered good moderators.     

A clustering method for global optimization based on thekth nearest neighbour

In this paper we describe a stochastic method for global optimization based on a uniform sampling in the search domain. After a reduction of the sample, computing the distance between the remaining points and using the distribution of the kth nearest neighbour enables clusters of points to be built up, hopefully fitting the regions of attraction of significant local optima; from each of these a local search is started. The properties of the method are analysed, and detailed computational results on standard test functions are provided.     

Forthcoming in Sociological Forum

Forthcoming in Sociological Forum     

A comment on “A theoretical basis for perturbation methods” by Krishnamurty Muralidhar and Rathindra Sarathy

Statistics and Computing -     

Optimal sequential Bayesian analysis for degradation tests

Degradation tests are especially difficult to conduct for items with high reliability. Test costs, caused mainly by prolonged item duration and item destruction costs, establish the necessity of sequential degradation test designs. We propose a methodology that sequentially selects the optimal observation times to measure the degradation, using a convenient rule that maximizes the inference precision and minimizes test costs. In particular our objective is to estimate a quantile of the time to failure distribution, where the degradation process is modelled as a linear model using Bayesian inference. The proposed sequential analysis is based on an index that measures the expected discrepancy between the estimated quantile and its corresponding prediction, using Monte Carlo methods. The procedure was successfully implemented for simulated and real data.     

Determinants of Joblessness During the Economic Crisis: Impact of Criminality in the Italian Labour Market

After nearly 20 years without any significant changes, the indicators of the European Regional Development Fund (ERDF) have undergone a major overhaul in the current 2014–2020 programming period. This paper reviews the characteristics of the ERDF indicator system evolution and contextualises the changes. Based on applying a logic model and practical examples, we argue that in spite of the good justification for changes, the new ERDF intervention logic and indicator system did not solve some of the well-known problems and brought new challenges for the ERDF-supported programmes. The main challenges that still remain to be solved are: differing indicator concepts between the European Union funds; inconsistency of the common output indicators; difficulties in establishing a programme’s contribution to results; persistent problems in determining the target values for results; and broad result indicators with indirect link to interventions.