No abstract

Today, the traditional role of teachers is challenged, and teaching practice requires less time for face-to-face instruction, a unique position that does not impose responsibilities on one person (teacher or student) and creates a common teaching and learning space in which new knowledge is co-created and socially developed. This change requires teachers’ high professionalism and leadership skills, which is the key component of a successful educational process. Therefore, it is important for researchers, school principals, and teachers to understand better the predictive factors of teachers’ leadership, which should be developed, nurtured, and sustained. This study addresses the teachers’ leadership regarding their attitudes toward themselves, attitudes towards the school, teachers’ activeness, and stress experienced at school. The study involved 418 teachers from five regions in Lithuania. The findings indicate that the four analyzed factors, influencing teacher leadership are strongly interrelated. Moreover, the research results reveal determinants surrounding the factors of interest, which leads to a more complex understanding of underlying reasons and problems related to practicing teachers’ leadership at school.

Temporal network data often encode time-stamped interaction events between senders and receivers, such as co-authoring a scientific article or sending an email. A number of relational event frameworks have been proposed to address specific issues raised by modelling time-stamped data with complex temporal and spatial dependencies. These models attempt to quantify how individuals' behaviour, external factors and interaction with other individuals change the network structure over time. It is often of interest to determine whether changes in the network can be attributed to endogenous mechanisms reflecting natural relational tendencies, such as reciprocity or triadic effects, with the latter thought to represent the inherent complexity of human interaction.The propensity to form (or receive) ties can also be related to individual actors' attributes. Nodal heterogeneity in the network is often modelled by including actor-specific or dyadic covariates, such as age, gender, shared neighbourhood, etc. However, capturing personality traits such as popularity or expansiveness is difficult, if not impossible. A failure to account for unobserved heterogeneity may confound the substantive effect of key variables of interest. This research shows how node level popularity in terms of sender and receiver effects may mask ghost triadic effects. These results suggest that unobserved nodal heterogeneity plays a substantial role in REM estimation procedure and influences the conclusions drawn from real-world networks.