Gender Roles and Internalized Sexual Stigma in Gay and Lesbian Persons: A Quadratic Relation

Purpose: No previous research has deeply investigated the association between gay men and lesbians’ adherence to gender roles and their internalized sexual stigma. This study suggests a potential non-linear correlation between these factors. Methods: Seventy-five gay men and 70 lesbians responded to self-report questionnaires. Hierarchical multiple regression analyses were conducted to investigate linear, quadratic, and exponential models and to determine which model best explained the correlation. Results: The quadratic model best explained the correlation, showing no gender differences. Conclusion: Adherence to gender roles constitutes a relevant factor in predicting internalized sexual stigma.     

Small area estimation: the EBLUP estimator based on spatially correlated random area effects

This paper deals with small area indirect estimators under area level random effect models when only area level data are available and the random effects are correlated. The performance of the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP) is explored with a Monte Carlo simulation study on lattice data and it is applied to the results of the sample survey on Life Conditions in Tuscany (Italy). The mean squared error (MSE) problem is discussed illustrating the MSE estimator in comparison with the MSE of the empirical sampling distribution of SEBLUP estimator. A clear tendency in our empirical findings is that the introduction of spatially correlated random area effects reduce both the variance and the bias of the EBLUP estimator. Despite some residual bias, the coverage rate of our confidence intervals comes close to a nominal 95%.     

Studying the relationship between anxiety and school achievement: evidence from PISA data

Statistical Methods & Applications - Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test...     

Asymptotic Properties and Variance Estimators of the M-quantile Regression Coefficients Estimators

M-quantile regression is defined as a “quantile-like” generalization of robust regression based on influence functions. This article outlines asymptotic properties for the M-quantile regression coefficients estimators in the case of i.i.d. data with stochastic regressors, paying attention to adjustments due to the first-step scale estimation. A variance estimator of the M-quantile regression coefficients based on the sandwich approach is proposed. Empirical results show that this estimator appears to perform well under different simulated scenarios. The sandwich estimator is applied in the small area estimation context for the estimation of the mean squared error of an estimator for the small area means. The results obtained improve previous findings, especially in the case of heteroskedastic data.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Shaping Dimensions of Urban Complexity: The Role of Economic Structure and Socio-Demographic Local Contexts

Social Indicators Research - Diversification in urban functions—a key component of urban complexity—was analysed using Pielou’s evenness indexes for 12 socioeconomic dimensions...     

Sexual Prejudice in Sport Scale: A New Measure

ABSTRACT

This study describes the process of developing and validating the Sexual Prejudice in Sport Scale (SPSS), which is a multidimensional instrument developed to assess attitudes toward lesbians and gay men (LG) in sports. The authors conducted two studies: first, to establish the factor structure of the SPSS on 297 heterosexual athletes; and, second, to test the reliability and validity of the resulting 19-item scale on a sample of 311 heterosexual and 160 LG athletes. Exploratory factor analysis of an initial item pool yielded three factors: open rejection, which assesses the blatant prejudice expressed toward LG people; denial of visibility, which evaluates attitude toward the coming out of LG people; and gendering performance, which corresponds to gender stereotypes about performance/skills of LG people. A confirmatory factor analysis was conducted to assess the stability of the SPSS. The authors documented internal consistency, test-retest stability, and convergent/divergent validity. Implications and directions for future research are discussed.     

Local stationarity in small area estimation models

Small area estimators are often based on linear mixed models under the assumption that relationships among variables are stationary across the area of interest (Fay–Herriot models). This hypothesis is patently violated when the population is divided into heterogeneous latent subgroups. In this paper we propose a local Fay–Herriot model assisted by a Simulated Annealing algorithm to identify the latent subgroups of small areas. The value minimized through the Simulated Annealing algorithm is the sum of the estimated mean squared error (MSE) of the small area estimates. The technique is employed for small area estimates of erosion on agricultural land within the Rathbun Lake Watershed (IA, USA). The results are promising and show that introducing local stationarity in a small area model may lead to useful improvements in the performance of the estimators.     

A Long Cycle in Industrial Relations,or: Regulation Theory and Political Economy

ABSTRACT: The main purpose of this paper is to test the Regulation Theory (la Théorie de la Régulation) ‘ on a subject — the long cycle of rigidity/flexibility in labour markets and industrial relations we have experienced between the late sixties and now — that should be one of the central concerns of such a theory, being linked to one of its basic concepts: the Rapport Salarial. Does the theory of regulation help us (more than rival theoretical frameworks do) in explaining why we underwent a rigidity/flexibility cycle? And in explaining why this cycle took such different forms in different countries? In order to achieve this purpose, the author first distinguishes several meanings, or dimensions, or rigidity/flexibility (money and real wage, numerical, functional, intensive, dualistic, geographical) and goes to some length in analysing why comparisons in this field between national industrial relations systems and labour market arrangements are rather difficult to perform. Having done that, the author argues that regulation theory is of little help in understanding our diachronic question — why we went through a flexibility cycle — because such a theory does not make any strong bet on the causes of change in the institutional arrangements of labour markets and industrial relations. On the synchronic question — why the forms of the cycle have been so different cross-nationally — the conclusion is that Regulation Theory can be useful as a focusing device, as an ideal-type, but that a lot of nationally-specific materials have to be added in order to obtain adequate answers. The paper ends up with a methodological discussion on the statute of regulation theory. It is argued that it is neither theory nor history: it is a series of ideal-types of system-integration, connected by ex-post, historical linkages.     

Identification of the mental workload of public employees in the administrative sector at a public university

In the workplace there are risk factors that can overwhelm the health of the worker causing physical and psychological disorders. Among these disorders, is occupational stress. Thus, this research proposes to measure the mental workload of job stress from the identification of demand, control and social support. It is an observational cross-sectional epidemiological character, performed with the Rectory office workers at the State University of West Paraná (Unioeste). Data collection was due to the application of the "Job Stress Scale" (JSS) and in its reduced version adapted to Portuguese. The survey sample consisted of 44 individuals. Statistical analysis was correlated inversely with medium intensity (r = -0.404) between age and social support. For males was found between control and age (r = 0.603). According to the data obtained, we conclude that most workers are exposed to passive work, leading to loss of skills or disinterest in the job. This study highlights the importance of research in the area and the need for new actions that change the conditions of stress at work.