The recent COVID-19 pandemic and related social distancing measures have significantly changed worldwide employment conditions. In developed economies, institutions and organizations, both public and private, are called upon to reflect on new organizational models of work and human resource management, which - in fact - should offer workers sufficient flexibility in adapting their work schedules remotely to their personal (and family) needs. This study aims to explore, within a Job Demands-Resources framework, whether and to what extent job demands (workload and social isolation), organizational job resources (perceived organizational support), and personal resources (self-efficacy, vision about the future and commitment to organizational change) have affected workers’ quality of life during the pandemic, taking into account the potential mediating role of job satisfaction and perceived stress. Using data from a sample of 293 workers, we estimate measurement and structural models, according to the Item Response Theory and the Path analysis frameworks, which allow us to operationalize the latent traits and study the complex structure of relationships between the latent dimensions. We inserted in the model as control variables, the socio-economic and demographic characteristics of the respondents, with particular emphasis on gender differences and the presence and age of children. The study offers insights into the relationship between remote work and quality of life, and the need to rethink human resource management policies considering the opportunities and critical issues highlighted by working full-time remotely.
This paper aims to investigate the relationship between Italian teachers' well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers' well-being, we investigate several aspects related to the teachers' working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers' well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers' positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers' job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher's well-being. Findings and implications are discussed.
A central question for education authorities has become ''which factors make a territory attractive for tertiary students?'' Tertiary education is recognised as one of the most important assets for the development of a territory, thus students' mobility becomes a brain drain issue whenever there are prevalent areas that attract students from other territories. In this paper, we try to identify the most important factors that could affect student mobility in Italy. In doing that we analyse students' flows across competing territorial areas which supply tertiary education programs. We will consider a wide range of determinants related to the socio-economic characteristics of the areas as well as resources of the universities in the territories in terms of variety and quantity of the degree programs there available, financial endowments provided by Central Government, and services available to students. The Bradley-Terry modelling approach based on pair comparisons has been adopted to define the attractiveness of competing territories and assess how much the detected divergences can be attributed to factors directly related to the considered characteristics of the universities in the territory and how much is ascribable to inherent characteristics of the areas where the universities are located such as the labour market conditions. Furthermore, the adopted approach allows us to consider uncertainty in defining territorial attractiveness and making comparisons. In this way, we would like to provide some evidences to assess if the rules currently used by the Central Government to finance public universities on the basis of their capabilities to attract students really reward the efforts made by the university system in the area to improve their standard of quality or, on the contrary, reward the territorial features.
A critical issue in analyzing multi-item scales is missing data treatment. Previous studies on this topic in the framework of item response theory have shown that imputation procedures are in general associated with more accurate estimates of item location and discrimination parameters under several missing data generating mechanisms. This paper proposes a model-based multiple imputation procedure for multiple categorical items (dichotomous, multinomial or Likert-type) which relies on the results of latent class analysis to impute missing item responses. The effectiveness of the proposed technique is assessed in the estimation of item response theory parameters using a range of ad hoc measures. The accuracy of the method is assessed with respect to other single and multiple imputation procedures, under different missing data generating mechanisms and different rate of missingness (5% to 30%). The simulation results indicate that the proposed technique performs satisfactorily under all conditions and has the greatest potential with severe rates of missingness and under non ignorable missing data mechanisms. The method was implemented in R code with a function that calls scripts from a latent class analysis routine
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