Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.

A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters.

The impacts of ignoring individual mobility across clusters in estimating a piecewise growth model.

A three-level piecewise growth model (3L-PGM) can be used to break up nonlinear growth into multiple components, providing the opportunity to examine potential sources of variation in individual and contextual growth within different segments of the model. The conventional 3L-PGM assumes that the data are strictly hierarchical in nature, where measurement occasions (level 1) are nested within individuals (level 2) who are members of a single cluster (level 3). However, in longitudinal research, it is sometimes difficult for data structures to remain purely clustered during a study, such as when some students change classrooms or schools over time.

A new way for handling mobility in longitudinal data.

In the social sciences, applied researchers often face a statistical dilemma when multilevel data is structured such that lower-level units are not purely clustered within higher-level units. To aid applied researchers in appropriately analyzing such data structures, this study proposes a multiple membership growth curve model (MM-GCM). The MM-GCM offers some advantages to other similar modeling approaches, including greater flexibility in modeling the intercept at the time-point most desired for interpretation.

Evaluation of a Noyce program: Development of teacher leaders in STEM education.

The development of teacher leaders in science, technology, engineering, and mathematics has become a focus as demand grows on the national scale to improve student learning in these disciplines. As teachers' role in leadership continues to be redefined, research and professional development in teacher leadership will continue to evolve. Given the lack of a clear conceptualization of teacher leadership in the empirical literature, there is a clear methodological challenge for evaluators who are charged with assessing the impact of teacher leadership professional development programs.