Italian Teachers' Well-Being Within the High School Context: Evidence From a Large Scale Survey.

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.

Introduction to bifactor polytomous item response theory analysis.

A bifactor item response theory model can be used to aid in the interpretation of the dimensionality of a multifaceted questionnaire that assumes continuous latent variables underlying the propensity to respond to items. This model can be used to describe the locations of people on a general continuous latent variable as well as on continuous orthogonal specific traits that characterize responses to groups of items. The bifactor graded response (bifac-GR) model is presented in contrast to a correlated traits (or multidimensional GR model) and unidimensional GR model. Bifac-GR model specification, assumptions, estimation, and interpretation are demonstrated with a reanalysis of data (Campbell, 2008) on the Shared Activities Questionnaire. We also show the importance of marginalizing the slopes for interpretation purposes and we extend the concept to the interpretation of the information function. To go along with the illustrative example analyses, we have made available supplementary files that include command file (syntax) examples and outputs from flexMIRT, IRTPRO, R, Mplus, and STATA. Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.jsp.2016.11.001. Data needed to reproduce analyses in this article are available as supplemental materials (online only) in the Appendix of this article.