Regression Methods for Combining Multiple Classifiers

As no single classification method outperforms other classification methods under all circumstances, decision-makers may solve a classification problem using several classification methods and examine their performance for classification purposes in the learning set. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single classification method is the best to predict the classification of new observations is still not clear, especially when some methods offer similar classification performance in the learning set. In this article we present various regression and classical methods, which combine several classification methods to predict the classification of new observations. The quality of the combined classifiers is examined on some real data. Nonparametric regression is the best method of combining classifiers.     

Aging and Gender Health Gap: A Multilevel Analysis for 17 European Countries

Social Indicators Research - The European population is aging and their declining capacity makes older Europeans more dependent on the availability of care. Male and female health needs at older...     

Welfare State Development in Latin America and the Caribbean (1970s–2000s): Multidimensional Welfare Index,Its Methodology and Results

There is an ongoing debate between focalization and universalization on welfare policies as the best way to develop the welfare state in Latin America and the Caribbean. However, there is a need to develop a measure that exhibits the multidimensional nature of the welfare state, instead of focusing on the social spending dimension. Segura-Ubiergo (The political economy of the welfare state in Latin America: globalization, democracy and development. Cambridge University Press, New York, 2007) constructed a welfare effort index (WEI) to facilitate understand the relative degrees of welfare state development among Latin American countries. The WEI focuses mainly on social spending and ignores the other dimensions of welfare. Based on a comparative analysis of 17 Latin American countries and following the methodology of Segura-Ubiergo, a new index that aims at enriching the WEI was constructed. The new index is multidimensional in that it has eight indicators relating to three dimensions of welfare: social spending, coverage of welfare programs and outcome of welfare institutions. Principal component analysis was used for reducing the indicators into three indexes that represent three proposed dimensions of welfare. The combination of these three indexes gives the multidimensional welfare index. The results of the index account for more than 75 % of the data variance.     

Ensuring Parliamentary Gender Equality Through a New Zipper Method: An Application to Finland

Gender equality constitutes a crucial objective for a successful societal development. Although research has found that the differences are shrinking, parliamentary composition is not gender balanced in most areas of the world. Henceforth, recent literature has undertaken important efforts aimed at developing different initiatives to promote parity in democratic parliaments, especially through the use of quota systems. Prior initiatives, although leading to an improvement of gender parity, do not manage to ensure an optimal gender-egalitarian parliamentary composition. Thus, this paper presents a method to organize closed and blocked lists of candidates that guarantees the achievement of gender-balanced representation in parliamentary elections. Specifically, parity is sought globally, in each party and also in each electoral constituency. Furthermore, the method is applied to the elections held over the last two decades in Finland. Results reveal that parity in gender representation increases at global, party, and constituency levels throughout all the parliamentary elections in Finland, even approaching optimal numerical parity (i.e. 50 % for each gender) in most of the cases.     

Materialism in Pedagogy Students in Chile

Many studies have used Richins and Dawson’s (J Consum Res 19: 303–316, 1992) Material Values Scale (MVS), applying it to different types of populations that exhibit a particular psychometric behavior, and showing little stability in their factorial structure. In the present study, 1,070 pedagogy students from the northern, central and southern regions of Chile answered the MVS. This sample was randomly divided in two. Using the first sub-sample (N = 539), an exploratory factorial analysis was carried out, from which a structure of nine items was grouped into two factors called “Social Success” and “Personal Happiness”, which presented adequate reliability. Later, with the second sub-sample (N = 531), the factorial structure indicated above was put to the test through a confirmatory factorial analysis. The data from the model show that the scale contains 8 items in total, grouped into two dimensions. The factorial loads are significant at the level of 1 %, which indicates that the 2-factor structure can be confirmed. Finally—using the proposed structure—the presence of the students’ material values was evaluated.