Composite indicator scores and 90 percent confidence intervals of well-being inequality in selected countries, 1820-2000

This paper explores the differences in well-being across Slovenia as well-being is one of the key social goals of modern progress. Well-being is defined by objective social, economic, demographic and environmental indicators, which reflect different dimensions of well-being. Overall well-being of municipalities was evaluated by two approaches. First, a composite indicator of well-being was calculated with the application of principal component method for 68 indicators. Second, cluster analysis was applied to further explore regional differences in well-being. The results show prevailing higher level of well-being in the west, while lower well-being is observed in the east of Slovenia.

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Assessing the Equitable and Sustainable Well-Being of the Italian Provinces

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488516400031 ◽

2016 ◽

Vol 24 (Suppl. 1) ◽

pp. 39-62 ◽

Cited By ~ 10

Author(s):

Francesco Maria Chelli ◽

Mariateresa Ciommi ◽

Alessandra Emili ◽

Chiara Gigliarano ◽

Stefania Taralli

Keyword(s):

Factor Analysis ◽

National Level ◽

Well Being ◽

Composite Indicator ◽

National Council ◽

Factor Scores ◽

Italian Provinces ◽

Being Moved ◽

Italian Province ◽

Italian National Institute

In recent years there has been an increasing interest in the measurement of well-being of individuals and societies. Influenced by the “beyond GDP” initiative, in 2012 the Italian National Institute of Statistics (ISTAT) and the National Council for Economics and Labour launched the Equitable and Sustainable Well-being (BES, from the Italian acronym of “Benessere Equo e Sostenibile”) project, a set of 134 indicators aimed at capturing the Italian well-being. Lately, the debate on how to measure the well-being moved from the national level to the local one. Following this new trend, ISTAT introduced a set of 88 indicators for the local well-being (at NUTS3 level), the so called “Provinces’ BES”. Based on this project, aim of the paper is to provide an exploratory analysis for detecting groups of Italian provinces that share similar well-being profiles. In particular, we first apply a factor analysis with the aim to reduce the high number of indicators and, grounded on these results, we then create groups of the Italian provinces, applying the cluster analysis, in order to find similarity among them. Finally, based on the result of the factor analysis, for each domain and for each Italian province, we construct a composite indicator that is a linear combination of the estimated factor scores, with weights based on the Gini index of concentration.

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Incremental Criterion Prediction of Personality Facets over Factors: Obtaining Unbiased Estimates and Confidence Intervals

10.31219/osf.io/g8kbj ◽

2017 ◽

Author(s):

Jeromy Anglim ◽

Sharon L. Grant

Keyword(s):

Confidence Intervals ◽

Regression Models ◽

Factor Model ◽

Five Factor Model ◽

Well Being ◽

R Package ◽

Alternative Methods ◽

Personality Facets ◽

Unbiased Estimates ◽

Level Analysis

Many researchers have argued that higher order models of personality such as the Five Factor Model are insufficient, and that facet-level analysis is required to better understand criteria such as well-being, job performance, and personality disorders. However, common methods in the extant literature used to estimate the incremental prediction of facets over factors have several shortcomings. This paper delineates these shortcomings by evaluating alternative methods using statistical theory, simulation, and an empirical example. We recommend using differences between Olkin-Pratt adjusted r-squared for factor versus facet regression models to estimate the incremental prediction of facets and present a method for obtaining confidence intervals for such estimates using double adjusted-r-squared bootstrapping. We also provide an R package that implements the proposed methods.

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